Two Decades of Advancements in Cold Supply Chain Logistics for Reducing Food Waste: A Review with Focus on the Meat Industry

Abstract

The current study focuses on the critical role of efficient cold supply chain logistics (CSCL) within the beef meat supply chain (SC), ensuring the timely delivery of premium products. Despite its significance, substantial food loss and waste (FLW) in CSCL pose multifaceted challenges across economic, social, and environmental dimensions. This comprehensive literature review aims to identify state-of-the-art CSCL for reducing food waste, key research themes, and their potential roles in red meat waste reduction, as well as identify future research directions. It explores four pivotal themes—management, sustainability, network design (ND), and new information technologies (IT)—each contributing uniquely to mitigating FLW. A review of papers published in the last two decades reveals management as the predominant theme, followed by sustainability, ND, and IT. The study underscores the interconnectedness of these themes and highlights gaps in current research, particularly the need for multi-objective optimisation models. These models should integrate uncertainties, sustainability dimensions, and technological advancements, while aligning strategic, tactical, and operational decisions to enhance CSCL sustainability and reduce FLW in the beef meat industry. This review informs stakeholders—researchers, policymakers, practitioners, the government, and the public—about emerging trends and opportunities in addressing food waste, thereby fostering more efficient and sustainable CSCL practices.

1. Introduction

Transporting food relies heavily on cold supply chain logistics (CSCL) to ensure timely delivery while preserving quality [1,2]. Unlike durable goods, food is perishable and susceptible to quality deterioration due to environmental factors. In Australia, the food cold chain handled 23 million tons, valued at AUD 42 billion in 2018 [3]. Future projections anticipate a substantial increase in food production and transportation over the next two decades. Globally, the meat industry holds significant value, estimated at USD 250 billion [4,5], with Australia ranking among the top meat producers and exporters [6,7]. Despite ranking third in production volume, the meat industry leads in production value (Figure 1a,b), contributing 46.09% of total food production value and playing a crucial role in the country’s economy [3,6]. Furthermore, beef dominates both in volume and value among Australian meat products. Nevertheless, it also has the third place in terms of loss and waste of food products (Figure 1c).

Figure 1a–c depict the shares of production volumes, production values, and FLW in CSCL for various foodstuffs reliant on CSCL year-round. Figure 1a clearly shows that meat ranks first in terms of production quantity percentage among all food products. Figure 1b indicates that the production cost per unit (value) is highest for meat compared to other products, suggesting that the wastage of one unit of meat results in a significantly greater monetary loss compared to other food items. Lastly, Figure 1c demonstrates that meat, after fruits and vegetables combined, is wasted more than any other food product across CSCL systems. By connecting these aspects and noting that beef constitutes the largest portion (48.83%) of the main types of meat, including beef, sheep, poultry, and pork [3], it becomes clear that beef wastage is a critical issue compared to other foods, emphasising the need for comprehensive review studies to reduce CSCL beef loss and waste.

The beef meat industry in Australia holds significant value, but approximately 30% of food waste occurs within CSCL during transportation [8]. Globally, food loss and waste (FLW) account for one-third of total food production annually, prompting the Australian government to implement the National Food Waste Strategy aiming to halve food waste by 2030 [9,10]. Addressing FLW is crucial due to the rising global population and food trade demand, offering social, financial, and environmental benefits [11,12]. Reducing FLW translates to increased profits for logistics companies and improved customer satisfaction, public health, and equity [13,14,15,16,17]. Moreover, it alleviates environmental burdens by conserving resources such as water, land, and energy, thereby reducing emissions [18,19]. While various studies explore FLW in food supply chains (FSCs), few focus specifically on CSCL for the meat industry and logistics perspective, indicating a need for more comprehensive research [9,20,21,22,23,24,25,26,27,28]. Recent studies have diversified topics concerning FLW, emphasising the necessity of comprehensive literature examination to understand its operational aspects [29,30,31,32].

Despite extensive research, a comprehensive analysis of sustainable cold chain logistics for the beef meat industry is lacking. While some logistics improvements have been suggested, there is a notable oversight in integrating management, network design (ND), information technology (IT), and sustainability. The valuable nature of beef meat, combined with significant associated loss and waste, has intensified pressure on meat CSCL to address inefficiencies. This highlights the need to explore state-of-the-art CSCL for the beef industry to mitigate FLW. Addressing this gap will assist decision-makers in enhancing the sustainability of beef CSCL, reducing FLW, and maintaining industry competitiveness.

Objective and Scope of Study

This study aims to identify state-of-the-art CSCL for reducing food waste, key research themes, and their potential roles in red meat waste reduction, as well as identify future research directions. This study is concerned with the food loss and waste issue across cold chain logistics in the beef industry. The food products in this context are beef and beef products, which are considered “food products” and align with the goal of reducing “food loss and waste” in the beef industry. Specifically, the study investigates the following research question and corresponding objectives:

• What is the current state of the art on beef CSCL in terms of management, sustainability, network design, and the use of information technologies for red meat waste reduction?

The study’s objective is defined as follows to answer the research question:

• To provide an overview of the current state of the art and to identify the gaps and contemporary challenges to red meat waste reduction;
• To identify key research themes and their potential role and associated elements in mitigating red meat waste reduction, especially across the beef CSCL systems;
• To pinpoint the directions in each theme that warrant further research advancement.

The study analyses two decades of research on beef and red meat CSCL, reviewing 300 academic articles in management, sustainability, ND, and IT. The study presents a thorough overview of contemporary literature on reducing FLW in beef CSCL and identifies promising research areas. It also offers practical insights into current practices for reducing FLW and the challenges faced in making meat CSCL systems more sustainable. The research advocates for an integrated approach involving various stakeholders to improve sustainability across the entire supply chain. These respond to the literature’s call for an integrated approach spanning the entire SC and involving various stakeholders [33].

Section 2 outlines the methodology. Section 3 comprises six parts. The first part, Section 3.1, presents evolution of the literature and provides descriptive results. The second part, Section 3.2, introduces the identified themes and subjects. In Section 3.3, Section 3.4, Section 3.5 and Section 3.6, the study examines and categorises articles based on these four identified themes. Section 4 provides an in-depth discussion of the research themes and explores future research avenues. Section 5 offers concluding remarks and practical implications, and it marks the research limitations.

2. Materials and Methods

The study’s methodology, presented in Figure 2, involves three phases. The first phase, known as the research strategy phase, outlines our approach for identifying relevant research from databases. This was accomplished through the definition of five key elements. The first element involved defining research questions and objectives, which necessitated an initial literature review to craft meaningful research questions (RQs) and objectives (ROs), ensuring the research’s significance by addressing existing gaps. The second element involved defining the research construct, which adopted a structured literature review approach (SLR). Although similar to a systematic literature review, this approach does not strictly follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Nevertheless, the SLR method aims to organise and present existing literature in a structured way and is recommended in high-quality studies. Overall, the adopted methodology aligns with recommendations from scholars [34,35]. The third key element involved identifying keywords and research strings. This was accomplished by reviewing the keywords from relevant studies, consulting with librarians, utilising Boolean operators to construct research strings—combinations of keywords and testing the capability of strings in identifying studies focused on reducing FLW in CSCL for the beef meat industry. The fourth element involved deciding which databases needed to be searched. While Scopus is commonly used as the sole source in similar studies, we chose to cross-check with Web of Science and Google Scholar to ensure the comprehensiveness of our database search. The last key element in this phase involved defining the appropriate timeframe. After an initial search with the defined research stream in Scopus without applying timeframe limits and analysing the results obtained, the research timeframe was narrowed to two decades starting from 2003. The decision was made due to a significant increase in publications post-2003.

The second phase, the conducting phase, focused on material collection through the literature retrieval and selection process. This included five critical steps in the research process aimed at gathering and evaluating relevant information for the study. The first step involved comprehensive database retrieval. This was accomplished through searching relevant databases, using appropriate keywords and search strings (combinations of keywords using Boolean operators), and employing the snowballing strategy. The second step involved literature selection, which means evaluating and selecting the most pertinent studies. This required assessing the relevance and quality of the retrieved literature based on criteria such as the study’s focus, methodology, and findings by assessing titles, keywords, and abstracts. The third step focused on document refinement. This involved selecting and applying inclusion and exclusion criteria, including the following: Year: 2003 to 2023; Subject area: engineering, energy, business, management, computer, decision science, economy, mathematics; Document type: article; Language: English; Keywords: all; Source type: journal; Country: all; Access: all. The fourth step, duplication filtering, involved identifying and removing duplicate entries from search results. This was accomplished by cross-checking identified materials from different databases and eliminating repetitive documents. The last step in this phase, fully reading these identified materials, ensured their usefulness and alignment with our research objectives, facilitating the answering of the research questions and objectives.

The final phase, the reporting phase, consisted of three sub-processes: (1) descriptive data analysis of the data obtained from the previous phase, (2) identification of the main research themes, and (3) an in-depth discussion on identified themes. Descriptive data analysis aimed at identifying and summarising the main patterns and trends in the data as presented in Section 3.2 and providing a clear overview of studies through identifying the researchers, the timing of the research, and the aimed and main findings obtained from conducting the Ws analysis (Who, When, What). This information is reflected in Appendix A tables. As presented in Figure 2, the first sub-process of this phase addresses the RO1. The second sub-process, identifying main research themes, involved Keyword Co-Occurrence Analysis (VKCA) and content analysis of extracted materials, including categorising elements of the content, identifying patterns, themes, and relationships, and presenting the findings in a structured manner. This study utilised VOS software (v1.6.19) for visual presentations of the themes’ chronological evolution, trends, and density. This information was used to identify the potential role of each theme and its associated elements in beef and red meat waste reduction across the CSCL systems, addressing RO2. The final sub-process in the reporting phase aimed to provide a comprehensive discussion by integrating information from all previous stages to identify the current state of the art on beef CSCL in terms of management, sustainability, network design, and the use of information technologies for beef and red meat waste reduction. Additionally, it aimed to identify gaps, suggest future directions, and develop a conceptual framework that illustrates the main themes, their roles in reducing FLW in CSCL for the beef meat industry, and their interdependencies, addressing the RQ and RO3. These aspects, along with the research limitations reflected in the study conclusion, ensure the comprehensiveness of this study and its contribution to understanding the state of the art. Subsequent subsections elaborate on methodology details in Phases 2 and 3, which are essential for achieving research objectives.

2.1. Literature Retrieval and Selection

To reach the research objectives, a comprehensive literature review of relevant investigations in reducing FLW across CSCL for beef meat was undertaken. A comprehensive search strategy encompassing both general Internet and targeted database search was implemented for the review. Boolean searches of databases were conducted to obtain literature from those electronic databases (Scopus, Web of Science, and Google Scholar). The keywords used for the search were “food loss*”, “food wast* “, “wast* of food”, “cold chain*”, “cold chain logistics”, “supply chain*”, “cold-chain logistics”, “food cold chains”, “cold chain transport*”, “refrigerated transport*”, “supply-chain*”, “logistic*”, “meat*”, “beef meat” in the available titles, abstracts, or keywords. There was no restriction on the geographical places or year of relevant literature selected. Only publications in English were considered in this review as they are widely accessible to the researchers. The search resulted in a total of 300 papers, which were taken into consideration in the present research. The selection of a paper for review was based on whether the paper developed or examined assessments/methods with respect to the four main identified themes, including management, sustainability, ND, and IT to minimise FLW.

2.2. Extracting the Research Themes

Keyword Co-Occurrence Analysis (VKCA) is a method used to delineate research themes and visualise outcomes, as showcased by Ali and Golgeci in 2019 [36]. They highlighted its objectivity and algorithmic nature in identifying key phrases, forming thematic clusters, and guiding future research directions. The current study, conducted in 2023, employed VKCA through [37]. Figure 3 depicts the outcomes of conducting our author-generated keywords analysis, presenting the keywords with the highest frequency in the selected articles. The network of keywords formed clusters based on a threshold of at least five occurrences, represented by four colours denoting primary research themes: yellow for management, green for IT, red for ND, and purple for sustainability. The yellow area includes keywords such as SC management, quality control, and performance. The green area includes the Internet of Things, traceability system, transparency, and blockchain. The red area includes vehicle routing, location, costs, and optimisation. Finally, the purple area contains keywords such as environmental impact, sustainability, and greenhouse gases. These themes align with management, IT, ND, and sustainability, validating their relevance and importance while studies aim to deal with complex issues in CSCL systems like FLW reduction. Despite the emergence of four distinct research themes, their common boundaries are evident. In Figure 3, blue dots, scattered across theme boundaries, suggest partial interrelatedness and interaction. For example, the appearance of Efficiency, one of the blue dots, in the common boundaries of themes indicates it is a linkage point of themes and that each of them mutually affects and is being affected via efficiency. Figure 4 illustrates the chronological emergence of themes from 2016 to 2023 (the most recent trends), with the Management theme (dark blue) being the earliest and transitioning to Sustainability, information technology, and optimisation (yellow). Notably, blockchain (light yellow) emerges as a dominant micro-scale interest among new technologies. Figure 5 depicts study density per theme, showing management as the most studied, followed by ND optimisation, sustainability, and information technology, indicating the interest and focus levels of publications. Additionally, the interactions between themes underscore their interconnectedness, emphasising the complexity of research themes and the significance of considering their trade-offs and simultaneous effects.

3. Results

3.1. The Literature Review Identified Themes and Subjects

As described in Section 2.2, extracting the research themes, this study used the VKCA method to delineate and visualise research themes, as presented in Figure 3. In this process, the keywords with the highest frequency in the selected articles were extracted. The network of keywords formed clusters, represented by four colours, denoting the primary research themes. Their frequency and content aligned with management, sustainability, ND, and IT.

The literature review was organised into four sections aligned with the themes identified in the current research to address the diverse subjects identified. Figure 6 depicts these themes and their corresponding subjects. Initially exploring management, the subsequent sections delve into sustainability, ND, and IT applications, providing insights into their effectiveness in reducing FLW and enhancing sustainability performance in the CSCL for food and meat products.

Table A1. Overview of literature on the management theme.

Scholar, Ref.	Year	Subject	Objectives I	II	Methodology	Industry (Product)	Measures to Reduce FLW
Gunasekaran et al. [39]	2008	Logistics management	To improve the responsiveness of SCs	To increase the competitiveness of SCs	Group Process and Analytical Hierarchy Process	Multi-industry	-
Dabbene et al. [40]	2008	Food logistics management	To minimise logistic costs	To maintain food product quality	Stochastic optimisation	Fresh food	-
Lipinski et al. [42]	2013	Food logistics management	To minimise the costs associated with food waste	To reduce food waste	Qualitative analysis	Food products	Proposing appropriate strategies
van der Vorst et al. [43]	2011	Food logistics management	To improve the competitiveness level, maintaining the quality of products	To improve efficiency and reduce food waste levels	Qualitative analysis	Agrifood products	The development of a diagnostic instrument for quality-controlled logistics
Soysal et al. [45]	2012	Sustainable logistics management	To enhance the level of sustainability and efficiency in food supply chains	To reduce FLW levels	Qualitative analysis	Food supply chains	The analysis of existing quantitative models, contributing to their development
Bettley and Burnley [48]	2008	Sustainable logistics management (SLM)	To improving environmental and social sustainability	To reduce costs and food waste	Qualitative analysis	Multi-industry	application of a closed-loop supply chain concept to incorporate sustainability into operational strategies and practices
Zokaei and Simons, [322]	2006	SML, Collaboration, Regulation, Cost, Inventory, Waste reduction, Information sharing,	To introduce the food value chain analysis (FVCA) methodology for improving consumer focus in the agri-food sector	To present how the FVCA method enabled practitioners to identify the misalignments of both product attributes and supply chain activities with consumer needs	Statistical analysis/FVCA	Red meat	Suggesting the application of FVCA can improve the overall efficiency and reduce the waste level
Cox et al. [323]	2007	SML, Cost, Decision-making, Risks, Waste reduction, Sustainability	To demonstrate the proactive alignment of sourcing with marketing and branding strategies in the red meat industry	To showcase how this alignment can contribute to competitive advantage in the food industry	Qualitative	Beef and Red meat	Emphasising the role of the lean approach, identifying waste hotspots, and collaboration in reducing food loss and waste
Jie and Gengatharen, [324]	2019	SML, Regulation, Collaboration, Cost, Inventory, Waste reduction, Info. Sharing, IT, Sustainability, Sco	To empirically investigate the adoption of supply chain management practices on small and medium enterprises in the Australian food retail sector	To analyse the structure of food and beverage distribution in the Australian retail market	Statistical analysis	Food/Beef Meat Industry	Adopting lean thinking and improving information sharing in the supply chains
Knoll et al. [325]	2017	SML, Collaboration, Regulation, Cost, Inventory, Decision-making, Risks, Information sharing, Deficiencies, Network design	To characterise the supply chain structure	To identify its major fragilities	Qualitative	Beef meat	-
Schilling-Vacaflor, A., [326]	2021	Regulation, Sustainability	To analyse the institutional design of supply chain regulations	To integrate human rights and environmental concerns into these regulations	Qualitative	Beef and Soy Industries	-
Knoll et al. [327]	2018	Regulation, Collaboration, Cost, Risks, Deficiencies, Decision-making, Sustainability, Information sharing	To analyse the information flow within the Sino-Brazilian beef trade, considering the opportunities presented by the Chinese beef market and the vulnerabilities in the supply chain	To investigate the challenges and opportunities in the information exchange process between China and Brazil within the beef trade sector	Mixed method	Beef Industry	-
E-Fatima et al. [328]	2022	Regulation, Risks, Safety, Collaboration, Business model, Packing, information sharing	To critically examine the potential barriers to the implementation and adoption of Robotic Process Automation in beef supply chains	To investigate the financial risks and barriers to the adoption of RPA in beef supply chains	Mixed method	Beef supply chain	-
Jedermann et al. [52]	2014	Regulations and Food Safety	To reduce food loss and waste	To improve traceability	Qualitative analysis	Meat and Food products	Proposing appropriate strategies to improve quality monitoring
Kayikci et al. [51]	2018	Regulations, Sustainability, Waste reduction	To minimise food waste by investigating the role of regulations	To improve sustainability, social and environmental benefits	Grey prediction method	Red meat	Proposing circular and central slaughterhouse model and emphasising efficiency of regulations based on circular economy comparing with the linear economy model
Storer et al. [329]	2014	Regulation, Collaboration, Cost, Inventory, Decision-making, Risks, IT, Sustainability	To examine how forming strategic supply chain relationships and developing strategic supply chain capability influences beneficial supply chain outcomes	To understand the factors influencing the utilisation of industry-led innovation in the form of electronic business solutions	Mixed methods	Beef supply chain	-
Liljestrand, K., [63]	2017	Collaboration, FLW, Information sharing	To analyse sustainability practices adopted in collaboration, including vertical collaboration in the food supply chain	To explore the role of collaboration in tackling food loss and waste	Qualitative analysis	Meat and Food products	Investigating how Food Policy can foster collaborations to reduce FLW
Mangla et al. [330]	2021	Collaboration, food safety and traceability	To enhance food safety and traceability levels through collaboration lens	To examine traceability dimensions and decrease information hiding	Qualitative analysis	Meat and Food products	Offering a framework for collaboration role in reducing info hiding and FLW in the circular economy
Liljestrand, K. [63]	2017	Collaboration, FLW, Information sharing	To investigate the role of logistics management and relevant solutions in reducing FLW	To explore the role of collaboration in food supply chains	Qualitative analysis	Meat and Food products	Examining the role of collaborative forecasting in reducing food waste
Esmizadeh et al. [79]	2021	Cost and Network design	To investigate the relations among cost, freshness, travel time, and Hub facilities vs Distribution centres	To investigate the product perishability effect in the distribution phase under hierarchical hub network design	Deterministic optimisation	Meat and food products	-
Cristóbal et al. [304]	2018	Cost, FLW and Sustainability	To consider the cost factor in the planning to reduce FLW	To develop a method to reduce costs and FLW environmental effects and improve the sustainability level	Mixed method	Meat and Food products	Proposing novel methods and programmes for cost effective and sustainable FLW management
Esmizadeh et al. [79]	2021	Cost and Network design	To investigate the relations among cost, freshness, travel time, and Hub facilities vs Distribution centres	To investigate the product perishability effect in the distribution phase under hierarchical hub network design	Deterministic optimisation	Meat and food products	-
Faisal. M. N., [331]	2015	Cost, Risks, Regulations, Deficiencies, Collaboration, Decision-making, IT, Information sharing	To identify variables that act as inhibitors to transparency in a red meat supply chain	To contribute to making the supply chain more transparent	Mixed method	Red meat	-
Shanoyan et al. [332]	2019	Cost, Risks, Information sharing	To analyse the incentive structures at the producer–processor interface within the beef supply chain in Brazil	To assess the dynamics and effectiveness of incentive mechanisms between producers and processors in the Brazilian beef supply chain	Qualitative	Beef Industry	-
Nakandala et al. [333]	2016	Cost, Sustainability	To minimise transportation costs and CO2 emissions	To maximise product freshness and quality	Stochastic optimisation	Meat and food products	-
Ge et al. [334]	2022	Cost, Decision-making,	To develop an optimal network model for the beef supply chain in the Northeastern US	To optimize the operations within this supply chain	Mathematical modelling	Beef meat	-
Hsiao et al. [335]	2017	Cost, Inventory, Network design	To maximise distribution efficiency and customer satisfaction	ZTo minimise the quality drop of perishable food products/meat	Deterministic optimisation	Meat products	-
Shanoyan et al. [332]	2019	Cost, Risks, Information sharing	To analyse the incentive structures at the producer–processor interface within the beef supply chain in Brazil	To assess the dynamics and effectiveness of incentive mechanisms between producers and processors in the Brazilian beef supply chain	Qualitative	Beef Industry	-
Magalhães et al. [85]	2020	Inventory and FW	To identify FLW causes in the beef supply chain in Brazil and explore the role of inventory management strategies and demand forecasting in FLW issue	To investigate their interconnections	Mixed method	Beef meat industry	Providing a theoretical basis to implement appropriate FLW mitigation strategies
Jedermann et al. [52]	2014	Inventory and Food Safety	To reduce food loss and waste	To improve traceability	Qualitative analysis	Meat and Food products	Proposing appropriate strategies to improve quality monitoring
Meksavang et al. [336]	2019	Inventory, Cost, Decision-making, Information sharing, Sustainability	To develop an extended picture fuzzy VIKOR approach for sustainable supplier management	To apply the developed approach in the beef industry for sustainable supplier management	Mixed methods	Beef meat	-
Herron et al. [89]	2022	Inventory and Sustainability	To identify the minimum shelf life required to prevent food waste and develop FEFO models	To identify the risk of food products reaching the bacterial danger zone	Deterministic optimisation	Meat products	Building a decision-making model and incorporating quality and microbiological data
Rahbari et al. [95]	2021	Decision-making and Network design	To minimise distribution cost, variable cost	To reduce inventory costs, the total cost	Deterministic optimisation	Red meat	-
Taylor D.H., [337]	2006	Decision-making, Cost Risks, Inventory, Waste Reduction, Deficiencies, Sustainability, Env.	To examine the adoption and implementation of lean thinking in food supply chains, particularly in the UK pork sector	To assess the environmental and economic impact of lean practices in the agri-food supply chain	Qualitative	Red meat	Suggesting the combination of Value Chain Analysis and Lean principles
Erol and Saghaian, [338]	2022	Risks, Cost, Regulation	To investigate the dynamics of price adjustment in the US beef sector during the COVID-19 pandemic	To analyse the impact of the pandemic on price adjustments within the US beef sector	Mixed method	Beef Industry	-
Galuchi et al. [339]	2019	Risks, Regulations, Sustainability, Soc., Env.	To identify the main sources of reputational risks in Brazilian Amazon beef supply chains	To analyse the actions taken by slaughterhouses to manage these risks	Mixed method	Beef supply chain	Mitigating risks
Silvestre et al. [340]	2018	Risks, Collaboration, Regulation, Management, Sustainability	To examine the challenges associated with sustainable supply chain management	To propose strategies for addressing identified challenges	Qualitative	Beef Industry	-
Bogataj et al. [125]	2020	Risks, Cost, Sustainability, Inventory	To maximise the profit	To improve sustainability performance	Mixed method	Beef industry	Incorporating the remaining shelf life in the decision-making process
Nguyen et al. [126]	2023	Risks, Waste reduction, Sustainability, Cost, Inventory	To improve the operational efficiency	To reduce carbon footprint and food waste	Statistical analysis	Beef industry	Identifying the root causes of waste and proposing a framework composed of autonomous agents to minimise waste
Amani and Sarkodie, [129]	2022	Risks, Information technologies, Sustainability	To minimise overall cost and waste	To improve the sustainability performance	Stochastic optimisation	Meat products	Incorporating artificial intelligence in the management context
Klein et al. [130]	2014	Risks, Information Technologies	To analyse the use of mobile technology for management and risk control	To identify drivers and barriers to mobile technology adoption in risk reduction	-	Beef meat	Introducing a framework that connects the challenges associated with the utilisation of mobile technology in SCM and risk control
Gholami-Zanjani et al. [138]	2021	Risk, ND, Inventory, Wastage Hot Spots, Sustainability	To reduce the risk effect and improve the resiliency against disruptions	To minimise environmental implications	Stochastic optimisation	Meat products	-
Buisman et al. [87]	2019	Waste reduction	To reduce food loss and waste at the retailer level	To improve food safety level and maximise the profit	Stochastic optimisation	Meat and Food products	Employing a dynamically adjustable expiration date strategy and discounting policy
Verghese et al. [109]	2015	Waste reduction, Information Technologies and Sustainability	To reduce food waste in food supply chains and relevant costs	To improve the sustainability performance	Qualitative analysis	Meat and Food products	Applying of information technologies and improved packaging
Jedermann et al. [52]	2014	Waste reduction	To reduce food loss and waste	To improve traceability	Qualitative analysis	Meat and Food products	Introducing some initiatives and waste reduction action plans
Mohebi and Marquez, [131]	2015	Waste reduction and Information Technologies	To improve the customer satisfaction and the quality of food products	To reduce food waste and loss	Qualitative analysis	Meat products	Proposing strategies and technologies for meat quality monitoring during the transport and storage phases
Kowalski et al. [133]	2021	Waste reduction and Information Technologies	To reduce food waste	To create a zero-waste solution for handling dangerous meat waste	Mixed method	Meat products	Recovering meat waste and transforming it into raw, useful materials
Beheshti et al. [135]	2022	Waste reduction, Network design, and Information Technologies	To reduce food waste by optimising the initial rental capacity and pre-equipped capacity required for the maximisation of profit	To optimise CLSCs and to improve cooperation level among supply chain stakeholders	Stochastic optimisation	Meat products	Applying optimisation across reverse logistics and closed-loop supply chains
Albrecht et al. [140]	2020	Waste reduction, IT, Decision-making, Inventory	To examine the effectiveness of sourcing strategy in reducing food loss and waste and product quality	To validate the applicability of the TTI monitoring system for meat products	Mixed method	Meat products	Applying of new information technologies in order to monitor the quality of products
Eriksson et al. [341]	2014	Waste reduction and Sustainability	To compare the wastage of organic and conventional meats	To compare the wastage of organic and conventional food products	Mixed method	Meat and perishable food products	Providing hints to reduce the amount of food loss and waste based on research findings
Accorsi et al. [342]	2019	Waste reduction, Decision support, Sustainability (Eco., Soc., Env.)	To address sustainability and environmental concerns related to meat production and distribution	To maximise the profit	Deterministic optimisation	Beef and meat products	Providing a decision-support model for the optimal allocation flows across the supply chain and a system of valorisation for the network
Jo et al. [146]	2015	Information technologies, Sustainability	To reduce food loss and waste levels, improve food traceability and sustainability	To minimise CO2 emissions	Mixed method	Beef meat products	Incorporating blockchain technology
Ersoy et al. [343]	2022	Information technologies, Sustainability, Food loss and Waste	To improve collaboration among multi-tier suppliers through knowledge transfer and to provide green growth in the industry	To improve traceability in the circular economy context through information technology innovations	Statistical analysis	Meat products	Suggesting a validated conceptual framework expressing the role of information technologies in information sharing
Kler et al. [147]	2022	Information technologies, Sustainability	To minimise transport CO2 emission level and food waste level	To improve traceability and demand monitoring levels	Data Analytics	Meat products	Employing information technologies (IoT) and utilising data analytics for optimising the performance
Singh et al. [118]	2018	IT, Information sharing, Waste reduction, Decision-making, and Packing	To explore the application of social media data analytics in enhancing supply chain management within the food industry	To investigate how social media data analytics can be utilised to improve decision-making processes and operational efficiency	Mixed method	Beef and food supply chain	Highlighting the role of content analysis of Twitter data obtained from beef supply chains and retailers
Martinez et al. [23]	2007	Deficiencies, Regulation, Cost, Inventory	To improve food safety	To lower regulatory cost	Statistical analysis	Meat and food products	-
Kayikci et al. [51]	2018	Deficiencies, Regulations, Waste reduction, Sustainability	To minimise food waste by investigating the role of regulations	To improve sustainability, social and environmental benefits	Grey prediction method	Red meat	Proposing circular and central slaughterhouse model and emphasising efficiency of regulations based on circular economy comparing with the linear economy model
Nychas et al. [151]	2008	Deficiencies, Waste reduction, Information Technologies	To characterise the microbial spoilage of meat samples during distribution	To assess the factors contributing to meat spoilage	Mixed method	Meat products	Identifying and discussing factors contributing to meat spoilage
Sander et al. [161]	2018	Deficiencies, Risks, Information Technologies	To investigate meat traceability by outlining the different aspects of transparency	To understand the perspectives of various stakeholders regarding BCT	Qualitative analysis	Meat products	-

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Table A2. Overview of literature on the sustainability theme.

Scholar, Ref.	Year	Subject	Objectives I	II	Methodology	Industry (Product)	Measures to Reduce FLW
Mahbubi and Uchiyama, [344]	2020	Eco, Soc., Evn., Management, Collaboration, IT, Information sharing	To identify the Indonesian halal beef supply chain’s basic system	To assess the sustainability level in the Indonesian halal beef supply chain	Life cycle assessment	Beef Industry	Identifying waste in different actors’ sections
Bragaglio et al. [345]	2018	Env., Management, Inventory, Decision-making	To assess and compare the environmental impacts of different beef production systems in Italy	To provide a comprehensive analysis of the environmental implications	Life cycle assessment	Beef Industry	-
Zeidan et al. [346]	2020	Env., Management, Collaboration, Cost	To develop an existence inductive theory	To study coordination failures in sustainable beef production	Qualitative	Beef Industry	-
Santos and Costa, [347]	2018	Env., Packing, Management, Cost, Regulations	To assess the role of large slaughterhouses in promoting sustainable intensification of cattle ranching in the Amazon and the Cerrado	To evaluate the environmental and social impacts of large slaughterhouses	Statistical Analysis	Beef Industry	-
E-Fatima et al. [348]	2023	Business model, Packing, Eco., Socio., Env., Management, Waste reduction	To investigate the financial risks and barriers in the adoption of robotic process automation (RPA) in the beef supply chains	To examine the potential influence of RPA on sustainability in the beef industry	Simulation	Beef Industry	Adopting Robotic Process Automation
Huerta et al. [349]	2015	Env., Packing, Waste Management, Waste	To assess the environmental impact of beef production in Mexico	To conduct a life cycle assessment of the beef production process	Life cycle assessment	Beef Industry	Suggesting utilising generated organic waste to produce usable energy
Cox et al. [350]	2007	Env., Business model, Packing, Management, Waste reduction, Information sharing, Cost, Risk	To explore the creation of sustainable strategies within red meat supply chains	To investigate the development of sustainable practices and strategies in the context of red meat supply chains	Qualitative	Red meat Industry	Proposing the adoption of lean strategies in the red meat supply chain industry
Teresa et al. [351]	2018	Eco., Env., Business model, Management, Deficiencies, Regulation, Collaboration, Cost	To provide current perspectives on cooperation among Irish beef farmers	To explore the future prospects of cooperation within the context of new producer organisation legislation	Qualitative	Beef Industry	Highlighting the role of legislation in the joint management of waste
Kyayesimira et al. [352]	2019	Eco., Waste hotspots, Management, Regulations	To identify and analyse the causes of losses at various post-harvest handling points along the beef value chain in Uganda	To estimate the economic losses incurred due to those factors	Statistical analysis	Beef Industry	Providing insights into potential improvements in the beef value chain management
Ranaei et al. [353]	2021	Env., Eco., Wastage hotspots Management, deficiencies, Waste reduction, Regulation, Collaboration	To identify the causes of meat waste and meat value chain losses in Iran	To propose solutions to reduce meat value chain losses	Qualitative	Meat/Red Meat Industry	Identifying the causes and hotspots of wastage points and proposing solutions
Wiedemann et al. [354]	2015	Env., Eco., Waste hotspots, Manag., Inventory	To assess the environmental impacts and resource use associated with meat export	To determine the environmental footprint	Life Cycle Assessment	Red meat Industry	Providing insights into potential improvements
Pinto et al. [167]	2022	Sustainability (Eco., Evo., Soc.) Management	To explore the sustainable management and utilisation of animal by-products and food waste in the meat industry	To analyse the food loss and waste valorisation of animal by-products	Mixed method	Meat products and industry	Employing the CE concept in the context of the meat supply chain suggested the development of effective integrated logistics for wasted product collection
Chen et al. [195]	2021	Sustainability (Env.) and Management	To identify existing similarities among animal-based supply chains	To measure the reduction effect of interventions applied	Mixed method	Beef meat and food products	Applying the food waste reduction scenario known to be effective in emission reduction
Martínez and Poveda, [355]	2022	Sustainability (Env.), Management	To minimise environmental impacts by exploring refrigeration system characteristics	To develop refrigeration systems-based policies for improving food quality	Mixed method	Meat and food products	-
Peters et al. [202]	2010	Sustainability (Env.), Wastage hotspots	To assess the environmental impacts of red meat in a lifecycle scope	To compare the findings with similar cases across the world	Life Cycle Impact Assessment	Beef meat and red meat	-
Soysal et al. [251]	2014	Sustainability (Env.), Wastage hotspots, Network Design	To minimise inventory and transportation costs	To minimise CO2 emissions	Deterministic optimisation	Beef meat	-
Mohebalizadehgashti et al. [252]	2020	Sustainability (Env.), Wastage hotspots, Network Design	To maximise facility capacity, minimise total cost	To minimise CO2 emissions	Deterministic optimisation	Meat products	-
Fattahi et al. [356]	2013	Sustainability (Env.), Packing, Management	To develop a model for measuring the performance of meat SC	To analyse the operational efficiency of meat SC	Mixed method	Meat products	-
Florindo et al. [357]	2018	Sustainability (Env.), Wastage hotspots, Management	To reduce carbon footprint	To evaluate performance	Mixed method	Beef meat	-
Diaz et al. [358]	2021	Sustainability (Env.), Wastage hotspots	To conduct a lifecycle-based study to find the impact of energy efficiency measures	To evaluate environmental impacts and to optimise the energy performance	Life Cycle Impact Assessment	Beef meat	Reconversing of Energy from Food Waste through Anaerobic Processes
Schmidt et al. [359]	2022	Sustainability (Env.), Wastage hotspots, Management, Information Technologies	To optimise the supply chain by considering food traceability, economic, and environmental issues	To reduce the impact and cost of recalls in case of food safety issues	Deterministic optimisation	Meat products	-
Mohammed and Wang, [169]	2017	Sustainability (Eco.) Management, Decision-making, Network design	To minimise total cost, To maximise delivery rate	To minimise CO2 emissions and distribution time	Stochastic optimisation	Meat products	-
Asem-Hiablie et al. [203]	2019	Sustainability (Env.), energy consumption, greenhouse gas	To quantify the sustainability impacts associated with beef products	To identify opportunities for reducing its environmental impacts	Life cycle assessment	Beef industry	-
Bottani et al. [206]	2019	Sustainability (Eco., and Env.), Packaging, Waste management	To conduct an economic assessment of various reverse logistics scenarios for food waste recovery	To perform an environmental assessment for them	Life cycle assessment	Meat and food industry	Examining and employing different reverse logistics scenarios
Kayikci et al. [51]	2018	Sustainability (Eco., Soc., Env.) Management, Regulations, Waste reduction	To minimise food waste by investigating the role of regulations	To improve sustainability, social and environmental benefits	Grey prediction method	Red meat	Proposing circular and central slaughterhouse model and emphasising efficiency of regulations based on circular economy comparing with the linear economy model
Tsakiridis et al. [204]	2020	Sustainability (Env.), Information technologies	To compare the economic and environmental impact of aquatic and livestock products	To employ environmental impacts into the Bio-Economy model	Life cycle assessment	Beef and meat products	-
Jo et al. [146]	2015	Sustainability (Eco. and Env.), Management, Cost, Food Safety, Risks, Information Technologies	To reduce food loss and waste levels, improve food traceability and sustainability	To minimise CO2 emissions	Mixed method	Beef meat products	Incorporating blockchain technology
Jeswani et al. [224]	2021	Sustainability (Env.), Waste management	To assess the extent of food waste generation in the UK	To evaluate its environmental impacts	Life cycle assessment	Meat products	Quantifying the extent of FW and impact assessment
Accorsi et al. [210]	2020	Sustainability (Eco. and Env.), Waste Management, Decision-making, Network design (LIP)	To reduce waste and enhance sustainability performance	To assess the economic and environmental implications of the proposed FSC	Deterministic optimisation	Meat and food industry	Designing a closed-loop packaging network
Chen et al. [195]	2021	Sustainability (Env.) and Waste Management	To identify the environmental commonality among selected FSCs	To measure the reduction effect of novel interventions for market characteristics	Life cycle assessment	Beef meat and food products	Confirming the efficiency of food waste management and reduction scenario
Sgarbossa et al. [175]	2017	Sustainability (Eco., Evo., Soc.) Network design	To develop a sustainable model for CLSC	To incorporate all three dimensions of sustainability	Deterministic optimisation	Meat products	Converting food waste into an output of a new supply chain
Zhang et al. [176]	2022	Sustainability (Eco. and Env.), Packaging, Network design	To maximise total profit	To minimise environmental impact, carbon emissions	Stochastic optimisation	Meat and food products	Using Returnable transport items instead of one-way packaging
Irani and Sharif., [223]	2016	Sustainability (Soc.) Management, IT	To explore sustainable food security futures	To provide perspectives on FW and IT across the food supply chain	Qualitative analysis	Meat and food products	Discussing potential strategies for waste reduction
Martindale et al. [180]	2020	Sustainability (Eco. and Env.), Management, food safety, IT (BCT)	To develop CE theory application in FSCs by employing a large geographical database	To test the data platforms for improving sustainability	Mixed method	Meat and food products	-
Mundler, and Laughrea, [187]	2016	Sustainability (Eco., Env., Soc.)	To evaluate short food supply chains’ contributions to the territorial development	To characterise their economic, social, and environmental benefits	Mixed method	Meat and food products	-
Vittersø et al. [189]	2019	Sustainability (Eco., Env., Soc.)	To explore the contributions of short food supply chains to sustainability	To understand its impact on all sustainability dimensions	Mixed method	Meat and food products	-
Bernardi and Tirabeni, [193]	2018	Sustainability (Eco., Env., Soc.)	To explore alternative food networks as sustainable business models	To explore the potentiality of the sustainable business model proposed	Mixed method	Meat and food products	Emphasising the role of accurate demand forecast
Bonou et al. [197]	2020	Sustainability (Env.)	To evaluate the environmental impact of using six different cooling technologies	To conduct a comparative study of pork supply chain efficiency	Life cycle assessment	Pork products	-
Apaiah et al. [201]	2006	Sustainability (Env.), Energy consumption	To examine and measure the environmental sustainability of food supply chains using exergy analysis	To identify improvement areas to diminish their environmental implications	Exergy analysis	Meat products	-
Peters et al. [202]	2010	Sustainability (Env.), energy consumption, greenhouse gas	To assess greenhouse gas emissions and energy use levels of red meat products in Australia	To compare its environmental impacts with other countries	Life cycle assessment	Red meat products	-
Farooque et al. [21]	2019	Sustainability (Env., and Eco.) Management, Regulation, Collaboration	To identify barriers to employing the circular economy concept in food supply chains	To analyse the relationship of identified barriers	Mixed method	Food products	Employing the CE concept in the context of the food supply chain
Kaipia et al. [93]	2013	Sustainability (Eco. and Env.) Management, Inventory, Information Technologies	To improve sustainability performance via information sharing	To reduce FLW level	Qualitative analysis	Food products	Incorporating demand and shelf-life data information sharing effect
Majewski et al. [191]	2020	Sustainability (Env.) and Waste management	To determine the environmental impact of short and longfood supply chains	To compare the environmental sustainability of short and long-food supply chains	Life cycle assessment	Food products	-
Rijpkema et al. [139]	2014	Sustainability (Eco. and Env.) Management, Waste reduction, Information Technologies	To create effective sourcing strategies for supply chains dealing with perishable products	To provide a method to reduce food waste and loss amounts	Simulation model	Food products	Proposing effective sourcing strategies

,

Table A3. Overview of the literature on network design theme.

Scholar, Ref.	Year	Modelling Stages: Single or Multi	Solving Approach	Objectives I	II/III	Model Type	Supply Chain Industry (Product)	Main Attributes
Domingues Zucchi et al. [360]	2011	M	Metaheuristic/GA and CPLEX	To minimise the cost of facility installation	To minimise costs for sea and road transportation	MIP	Beef meat	LP
Soysal et al. [251]	2014	S	ε-constraint method	To minimise inventory and transportation cost	To minimise CO2 emissions	LP	Beef meat	PIAP
Rahbari et al. [95]	2021	M	GAMS	To minimise total cost	To minimise inventory, transport, storage costs	MIP	Red meat	PLIRP
Rahbari et al. [249]	2020	S	GAMS	To minimise total cost		MIP	Red meat	PLIRP
Neves-Moreira et al. [245]	2019	S	Metaheuristic	To minimise routing cost	To minimise inventory holding cost	MIP	Meat	PRP
Mohammadi et al. [241]	2023	S	Pre-emptive fuzzy goal programming	To maximise total profit	To minimise adverse environmental impacts	MINLP	Meat/Perishable food products	LIP
Mohebalizadehgashti et al. [252]	2020	S	ε-constraint method	To maximise facility capacity, minimise total cost	To minimise CO2 emissions	MILP	Meat	LAP
Mohammed and Wang, [247]	2017a	S	LINGO	To minimise total cost	To minimise number of vehicles/delivery time	MOPP	Meat	LRP
Mohammed and Wang, [169]	2017b	S	LINGO	To minimise otal cost, to maximise delivery rate	To minimise CO2 emissions and distribution time	FMOP	Meat	LRP
Gholami Zanjani et al. [138]	2021	M	Metaheuristic	To improve the resilience and sustainability	To minimise inventory holding cost	MP	Meat	IP
Tarantilis and Kiranoudis, [242]	2002	S	Metaheuristic	To minimise total cost	To maximise the efficiency of distribution	OMDVRP	Meat	LRP
Dorcheh and Rahbari, [361]	2023	M	GAMS	To minimise total cost	To minimise CO2 emissions	MP	Meat/Poultry	IRP
Al Theeb et al. [74]	2020	M	Heuristic CPLEX	To minimise total cost, holding costs, and penalty cost	To maximise the efficiency of transport and distribution phase	MILP	Meat/Perishable food products	IRP
Moreno et al. [231]	2020	S	Metaheuristic/hybrid approach	To maximise the profit	To minimise the costs, delivery times	MIP	Meat	LRP
Javanmard et al. [362]	2014	S	Metaheuristic/Imperialist competitive algorithm	To minimise inventory holding cost	To minimise total cost	NS	Food and Meat	IRP
Ge et al. [334]	2022	S	Heuristic algorithm	To develop an optimal network model for the beef supply chain in the Northeastern US	To optimize the operations within this supply chain	MILP	Beef meat	LRP
Hsiao et al. [335]	2017	S	Metaheuristic/GA	To maximise distribution efficiency and customer satisfaction	To minimise the quality drop of perishable food products/meat	MILP *	Meat/Perishable food products	LRP
Govindan et al. [246]	2014	M	Metaheuristic/MHPV	To minimise carbon footprint	To minimise of the cost of greenhouse gas emissions	MOMIP *	Perishable food products	LRP
Zhang et al. [243]	2003	S	Metaheuristic	To minimise cost, food safety risks	To maximise the distribution efficiency	MP *	Perishable food products	LRP
Wang and Ying, [244]	2012	S	Heuristic, Lagrange slack algorithm	To maximise the delivery efficiency	To minimise the total costs	MINLP *	Perishable food products	LRP
Liu et al. [234]	2021	S	YALMIP toolbox	To minimise cost and carbon emission	To maximise product freshness	MP/MINLP	Perishable food products	LIRP
Dia et al. [363]	2018	S	Metaheuristic/GA	To minimise total cost	To reduce greenhouse gas emissions/maximise facility capacity	MINLP	Perishable food products	LIP
Saragih et al. [364]	2019	S	Simulated annealing	To fix warehouse cost	To minimise nventory cost, holding cost, and total cost	MINLP	Food products	LIRP
Biuki et al. [230]	2020	M	GA and PSO	To incorporate the three dimensions of sustainability	To minimise total cost, maximise facility capacity	MIP *	Perishable products	LIRP
Hiassat et al. [365]	2017	S	Genetic algorithm	To implement facility and inventory storage cost	To minimise routing cost	MIP	Perishable products	LIRP
Le et al. [366]	2013	S	Heuristic- Column generation	To minimise transport cost	To minimise inventory cost	MP	Perishable products	IRP
Wang et al. [367]	2016	S	Two-phase Heuristic and Genetic algorithm	To minimise total cost	To maximise the freshness of product quality	MP	Perishable food products	RP
Rafie-Majd et al. [368]	2018	S	Lagrangian relaxation/GAMS	To minimise total cost	To minimise product wastage	MINLP *	Perishable products	LIRP

* Modelling: LP—Linear Programming, MILP—Mixed Integer Linear Programming, MIP—Mixed Integer Programming, MINLP—Mixed Integer Non-Linear Programming, MP—Mathematical Programming.

and

Table A4. Overview of literature on the information technology theme.

Scholar, Ref.	Year	Subject	Objectives I	II	Methodology	Industry (Product)	Measures to Reduce FLW
Singh et al. [267]	2018	Information technologies, Sustainability, Regulations, Management	To measure greenhouse emission levels and select green suppliers with top-quality products	To reduce carbon footprint and environmental implications	Mixed method	Beef supply chain	-
Singh et al. [369]	2015	Information technologies, Sus. (Env.), Inventory, Collaboration, Management	To reduce carbon footprint and carbon emissions	To propose an integrated system for beef supply chain via the application of IT	Simulation	Beef supply chain	-
Juan et al. [7]	2014	Information technologies, Management, Inventory, Collaboration, Management	To explore the role of supply chain practices, strategic alliance, customer focus, and information sharing on food quality	To explore the role of lean system and cooperation, trust, commitment, and information quality on food quality	Statistical analysis	Beef supply chain	By application of IT and Lean system strategy
Zhang et al. [370]	2020	Information technologies, Management, Inventory, Food quality and safety	To develop a performance-driven conceptual framework regarding product quality information in supply chains	To enhance the understanding of the impact of product quality information on performance	Statistical analysis	Red meat supply chain	-
Cao et al. [371]	2021	IT, Blockchain, Management, Regulation, Collaboration, Risks, Cost, Waste reduction	To enhance consumer trust in the beef supply chain traceability through the implementation of a blockchain-based human–machine reconciliation mechanism	To investigate the role of blockchain technology in improving transparency and trust within the beef supply chain	Mixed method	Beef products	By applying new information technologies
Kassahun et al. [372]	2016	IT and ICTs	To provide a systematic approach for designing and implementing chain-wide transparency systems	To design and implement a transparency system/software for beef supply chains	Simulation	Beef meat Industry	By improving the traceability
Ribeiro et al. [373]	2011	IT and ICTs	To present and discuss the application of RFID technology in Brazilian harvest facilities	To analyse the benefits and challenges of implementing RFID	Qualitative	Beef Industry	-
Jo et al. [146]	2015	IT (BCT) Sustainability (Eco. and Env.), Management, Cost, Food safety, Risks	To reduce food loss and waste levels, improve food traceability and sustainability	To minimise CO2 emissions	Mixed method	Beef meat products	By incorporating blockchain technology
Rejeb, A., [227]	2018	IT (IoT, BCT), Management, risks, food safety	To propose a traceability system for the Halal meat supply chain	To mitigate the centralised, opaque issues and the lack of transparency in traceability systems	Mixed method	Beef meat and meat products	-
Cao et al. [374]	2022	IT and blockchain, Management, Collaboration, Risk, Cost, Sustainability	To propose a blockchain-based multisignature approach for supply chain governance	To present a specific use case from the Australian beef industry	A novel blockchain-based multi-signature approach	Beef Industry	-
Kuffi et al. [291]	2016	Digital 3D geometry scanning	To develop a CFD model to predict the changes in temperature and pH distribution of a beef carcass during chilling	To improve the performance of industrial cooling of large beef carcasses	Simulations	Beef meat products	-
Powell et al. [289]	2022	Information technologies, (IoT and BCT)	To examine the link between IoT and BCT in FSC for traceability improvement	To propose solutions for data integrity and trust in the BCT and IoT-enabled food SCs	Mixed method	Beef meat products	-
Jedermann et al. [52]	2014	Management, Regulations and Food Safety, FW, Information sharing, RFID	To reduce food loss and waste	To improve traceability	Qualitative analysis	Meat and Food products	By proposing appropriate strategies to improve quality monitoring
Liljestrand, K., [63]	2017	Collaboration, FLW, Information sharing	To analyse sustainability practices adopted in collaboration, including vertical collaboration in the food supply chain	To explore the role of collaboration in tackling food loss and waste	Qualitative analysis	Meat and Food products	By investigating how Food Policy can foster collaborations to reduce FLW
Liljestrand, K., [63]	2017	Collaboration, FLW, Information sharing	To analyse sustainability practices adopted in collaboration, including vertical collaboration in the food supply chain	To explore the role of collaboration in tackling food loss and waste	Qualitative analysis	Meat and Food products	By investigating how Food Policy can foster collaborations to reduce FLW
Harvey, J. et al. [228]	2020	IT and ICTs, Sustainability (Env. and Sco.), waste reduction, Management, decision-making	To conduct social network analysis of food sharing, redistribution, and waste reduction	To reduce food waste via information sharing and IT application	Mixed method	Food products	By examining the potential of social media applications in reducing food waste through sharing and redistribution
Rijpkema et al. [139]	2014	IT (Sharing), Sustainability Management, Waste reduction	To create effective sourcing strategies for SCs dealing with perishable products	To provide a method to reduce food waste and loss amounts	Simulation model	Food products	By proposing effective sourcing strategies
Wu, and Hsiao., [375]	2021	Information technologies, Management, Inventory, Food quality and safety, Risks	To identify and evaluate high-risk factors	To mitigate risks and food safety accidents	Mixed method	Food supply chain	By reducing food quality and safety risks and employing improvement plans
Kaipia et al. [93]	2013	IT (Sharing), Sustainability (Eco. and Env.) Management, Inventory	To improve sustainability performance via information sharing	To reduce FLW level	Qualitative analysis	Food products	By incorporating demand and shelf-life data information sharing effect
Mishra, N., and Singh, A., [229]	2018	IT and ICTs, Sustainability (Env.), waste reduction, Management, decision-making	To utilise Twitter data for waste minimisation in the beef supply chain	To contribute to the reduction in food waste	Mixed method	Food products	By offering insights into potential strategies for reducing food waste via social media and IT
Parashar et al. [18]	2020	Information sharing (IT), Sustainability (Env.), FW Management (regulation, inventory, risks)	To model the enablers of the food supply chain and improve its sustainability performance	To address the reducing carbon footprints in the food supply chains	Mixed method	Food products	By facilitating the strategic decision-making regarding reducing food waste
Tseng et al. [58]	2022	Regulations, Sustainability, Information technologies, (IoT and BCT)	To conduct a data-driven comparison of halal and non-halal sustainable food supply chains	To explore the role of regulations and standards in ensuring the compliance of food products with Halal requirements and FW reduction	Mixed method	Food products	By highlighting the role of legislation in reducing food waste and promoting sustainable food management
Mejjaouli, and Babiceanu, [101]	2018	Information technologies (RFID-WSN), Management, Decision-making	To optimise logistics decisions based on actual transportation conditions and delivery locations	To develop a logistics decision model via an IT application	Stochastic optimisation	Food products	-
Wu et al. [292]	2019	IT (Information exchange), Sustainability (Eco., and Env.)	To analyse the trade-offs between maintaining fruit quality and reducing environmental impacts	To combine virtual cold chains with life cycle assessment to provide a holistic approach for evaluating the environmental trade-offs	Mixed method	Food/fruit products	By suggesting a more sustainability-driven cold chain scenario

in Appendix A show the overview of the literature on the themes of management, sustainability, ND and IT, respectively. In the following subsections, we describe the key findings related to these four themes.

3.2. The Literature’s Evolution and Descriptive Results

This section outlines the evolution of literature and a descriptive analysis of the publication distribution for four research themes over 32 years (1990–2022). However, the focus of this study lies in the last 20 years (2003–2023, Phase 2), reflecting significant advancements during this period. Figure 7, Figure 8, Figure 9 and Figure 10 illustrate yearly article counts, annual publication growth, and theme trends, offering insight into the temporal evolution of research themes.

The analysis revealed three distinct periods in annual article distribution. Initially (1990–2004), research interest was low, averaging nearly seven articles yearly. The second phase (2005–2015) witnessed growth, with an annual mean of 33 articles. While management, sustainability, and ND garnered similar attention, interest in information technologies lagged. However, interest spiked post-2015, indicating it as the next emerging theme, likely surpassing others in the coming decades. The third and most prolific period began in 2016, averaging 80 articles yearly, peaking in 2022. These trends underscore the importance of the given themes and the growing demand within the food and meat CSCL industry for innovative solutions to enhance efficiency and tackle FLW.

3.3. Management

This section reviews and details the management theme and its key associated subjects. Section 3.4, Section 3.5 and Section 3.6 follow the same logical sequence, aligned with the overview of themes and key subjects presented in Figure 6. This overview of themes and corresponding subjects is based on the analysis of keywords and the VKCA method discussed in Section 2.2 and Section 3.1.

3.3.1. Logistics Management and Chronological Evolution

Logistics Management (LM) has evolved significantly in the last two decades, initially focusing on cost minimisation and system responsiveness [38,39]. Consumer demand for food quality has driven the need for tailored approaches in Food Logistics Management (FLM), addressing concerns like food safety and perishability [40,41]. This shift broadened LM objectives to include food quality and waste reduction [42,43,44,45]. Increasingly, transparency and traceability are prioritised in FSCL, aligning with rising sustainability awareness and customer demand for eco-conscious practices [46,47]. This evolution toward sustainable FLM (SFLM) aims to address economic, social, and environmental impacts while enhancing product excellence and reducing FLW [48]. In this context, new information technologies are pivotal, as studies show their integration enhances LM efficiency and is influenced by management approaches, guiding future research directions.

3.3.2. Management and Regulations

One of the key aspects of the management theme refers to efforts to reduce FLW, which are centred on legislations, regulations, and industry practices within FCSCL. Heightened consumer awareness of food quality and safety [49] has spurred global legislative actions, such as the European Parliament Regulation (2002), FSMA in the US (2011), and regulatory measures in India (2017) and Canada (2019) [50]. Industry compliance with these standards not only meets consumer demands for quality but also reduces FLW in CSCL and meat SCs [41,51,52]. However, concerns persist regarding enforcement effectiveness and the lack of industry norms, especially in developing countries [41,53,54]. Addressing gaps in traceability implementation, training levels, and safety guidelines is crucial [55,56,57,58]. Future research should focus on incorporating food quality into operational practices and decision-making processes to optimise FSCs and logistics systems. Notably, few studies, such as [59,60,61], have integrated food quality with cost management, stockout prevention, and FLW reduction, highlighting the need for further exploration in this area.

3.3.3. Management and Collaboration

Collaboration among stakeholders not only reduces logistics costs and emissions but also aids in minimising FLW [62]. Studies emphasise the importance of enhanced collaboration for data accuracy and improved coordination to address FLW challenges [30,42,63,64,65]. Wang (2016) [66] notes collaborative resource sharing reduces operational expenses in CSCLs. Strategic collaboration between SC partners optimises delivery times. Fostering cooperation and consensus among stakeholders enhances equipment utilisation [67]. Stakeholder coordination is crucial for sustainability within food SCs [68,69]. Government collaboration is highlighted by Weng et al. (2015) to harmonise industrial structures [67]. Game theory is often used to address cooperation challenges in FLW reduction, emphasising inter-organisational relationship improvements, motivational involvement, data exchanges, and technology sharing [30]. It typically treats the FLW level as a factor or a limitation [70,71], for instance. Understanding these linkages aids in prospect research for effective FLW reduction strategies.

3.3.4. Management and Costs

Several studies highlight the significant costs faced by CSCLs [72,73,74]. These costs encompass investments in cold storage, refrigerated transportation [72,75], cooling infrastructure, including refrigerated vehicles and warehouses [76], refrigeration [66], and information technology [73,74]. Effectively addressing FLW relies heavily on investing in facility modifications within SCs and logistics systems, particularly in emerging economies. Strategies to minimise associated costs for necessary facility adjustments, coupled with collaboration among peers and stakeholders, are essential [77]. Reducing overall logistics expenses can offset facility costs, allowing for quality enhancement and FLW reduction. Vehicle routing optimisation is a key approach, lowering transportation and energy costs while minimising FLW through reduced travel times [14,78,79,80]. Management intertwines with ND and optimisation, while concepts like Freight Villages foster horizontal collaboration, utilising shared logistics infrastructure for sustainability benefits [81,82,83]. Future studies should develop models capable of concurrently analysing these multi-dimensional trade-offs.

3.3.5. Management and Inventory

Improper inventory management increases microbial hazards, foodborne illnesses, and FLW [84,85]. Inadequate temperature and hygiene control accelerate perishable product deterioration, often falling below acceptable quality standards [85]. Temperature fluctuations during transportation and storage worsen this [48,63], while CSCL ruptures harm food quality and increase losses [52,86]. Overstocking exacerbates FLW due to inaccurate demand predictions and poor coordination [85,87], stemming from imprecise predictions regarding demand, inadequate coordination, and a deficit in information sharing across stages. These factors, coupled with concerns for food quality and safety, amplify FLW across CSCL, directly and indirectly affecting customer satisfaction [88]. Various management measures aim to reduce FLW in inventory. The first-expired-first-out (FEFO) approach aligns product shelf life with transportation time to minimise waste [52,89]. FIFO ensures the earliest received products are sold first based on storage time, while least shelf life, first out (LSFO) prioritises products with minimum shelf life for sale, mitigating quality fluctuations during delivery [52]. Dynamic first-expired-first-out (FEFO) allows flexible delivery allocations based on shelf-life deviations, emphasising prioritisation of products with the least remaining shelf life [89,90]. Wireless sensor nodes enable shelf-life strategies, with programs enhancing information sharing to reduce FLW [91,92]. The effect evaluation for such vertical information exchange shows that based on an improved circulation of information, the amounts of FLW could be largely reduced [92,93]. Despite progress, the connection between shelf life and SC structure needs more research attention [88,89]. Nevertheless, the connection between shelf life and SC structure requires further research attention, emphasising the importance of integrating shelf-life considerations into CSCL to further mitigate FLW.

3.3.6. Management and Decision-Making

The management of meat and food CSCL systems involves critical decision-making processes across various aspects, including supplier selection [94,95,96], facility location [97,98], transportation mode [99,100], routing [61,101,102], energy mode and consumption [103,104], capacity [95,105], quality [60,61,106], and packaging [107,108,109]. These decisions directly influence minimising FLW, maximising profitability, and enhancing sustainability, underscoring the need to explore drivers, impediments, and benchmarks for strategic, tactical, and operational planning and the performance metric in meat and food CSCL [56,110,111]. Addressing decision-making complexities requires Multi-Criteria Decision-Making techniques (MCDM) and Decision Support Systems (DSS) [95,112]. Methods like Analytical Hierarchy Process (AHP), Interpretive Structural Modelling (ISM), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Elimination and Choice Expressing Reality (ELECTRE), and Decision-Making Trial and Evaluation Laboratory (DEMATEL) are prevalent [112,113,114]. Nevertheless, conventional MCDM approaches often struggle in dynamic environments, prompting the integration of fuzzy logic for effective problem-solving [112,115]. Hence, decision structures of the existing meat CSCL systems need to be re-examined through a sustainability lens and by developing models capable of considering the dynamics of these environments. Assessing their impacts on the performance is an imperative aspect of this analysis.

3.3.7. Management and Risks

While risks are inevitable in SCs, food SCs, due to perishability attributes, are more vulnerable to risks, amplifying FLW levels [116,117,118]. These risks typically fall into disruption and operational categories, stemming from human-made or natural disasters and day-to-day uncertainties [119]. Effective risk assessment and management are crucial for countering risks and negative impacts. They aid in tackling FLW by empowering enterprises to evaluate the repercussions of risk and waste reduction strategies, and they facilitate the screening of resilient food safety approaches [120], enhancing sustainability performance [121]. Contemporary literature delineates three streams in SC risk management. One advocates systematic risk mitigation to enhance global FSCs performance [122,123,124]. Another delves into SC architecture and planning [125,126,127,128], while the third focuses on new information technologies and artificial intelligence incorporation [125,129,130]. Nevertheless, these studies often overlook the intricate risks in meat CSCL and the role of risk assessment frameworks in optimising resource utilisation and reducing FLW. Addressing these gaps is crucial for future research to enhance the resilience and efficiency of meat CSCL, particularly in the beef industry.

3.3.8. Management and Waste Reduction

In food and meat CSCL management, waste management is pivotal, employing two primary approaches: Prevention and Reduction. Prevention involves averting FLW through CSCL, technological solutions, proper packing, and food surplus donation [52,109,131,132]. Reduction focuses on managing FLW post-occurrence, employing methods like composting, converting waste into animal feed or fuel (FW valorisation), or landfilling [109,133,134,135]. Contemporary literature presents mainly two streams of FLW studies. One stream, exemplified by studies [12,136,137], analyses socio-economic, demographic, and behavioural factors impacting FLW at the household and consumer level. Another stream addresses FLW sources in FSCs and strategies for prevention and reduction. Waste management in FCSCL intersects with inventory management, ND optimisation, and IT application. Gholami-Zanjani et al. (2021) [138] and Buisman et al. (2017) [87] applied discounting strategies based on the age of products and considered dynamic shelf life to reduce FLW at the retail level. Practices like first-expired-first-out in inventory management [52] and cost incorporation in procurement strategies [139] significantly lower FLW. Effective CSCL and time–temperature management are crucial across FSCs [140,141]. Research also underscores the role of information dissemination, awareness campaigns, and education programs in FW management [142,143,144,145]. Future studies should integrate these insights into models and strategies, focusing on optimising CSCL systems, especially within the beef meat industry.

3.3.9. Management and Information

The circulation of information at horizontal and vertical levels is pivotal in CSCL. Information technologies and digital tools enable planning, sharing, and synchronising inventory levels based on demand forecasts and real-time data [146], thereby enhancing inventory management accuracy and efficiency, consequently reducing FLW. Real-time cargo visibility technologies aid in locating and monitoring perishable product characteristics, facilitating the adoption of optimal intervention strategies like rerouting deliveries to minimise FLW in the distribution phase and to improve FCSCL performance [147]. Finally, information technology ensures the integrity of FCSCL by facilitating seamless information flow among stakeholders [148,149], significantly impacting food safety and security. These efforts reflect the interconnectedness of IT and management themes. The integration of information technologies improves the management of CSCL systems. As these systems have become more complex in recent years, they now involve more data-driven decisions. Conversely, in the management theme, adopting technologies is considered a strategic decision aligned with the SFLM approach, which enhances the efficiency and sustainability of CSCL systems [93]. Despite notable benefits, the adoption of information technologies in FCSCL lags [150]. Understanding the challenges faced by logistics professionals is crucial to boost their implementation, improve efficiency, and mitigate FLW.

3.3.10. Management and Cold Chain Deficiencies

The last identified subject in the management theme refers to FCSCL deficiencies impacting FLW. The literature review reveals several aspects involved in deficiencies in meat and food CSCL. We classified them into three groups. At a strategic level, some of the most common CSCL deficiencies include the lack of cold storage [151,152,153], processing facility [9,13], integration [154,155], cooperation [13,156], lack of proper regulation [154,157], standardisation [158], lack of funds [21], poor logistics ND [21,158], poor logistic infrastructure [152,153], high expenses and costs [154,159]. Tactical level deficiencies include improper handling [154,160], lack of modern methods in handling, processing, and packing phases [51,152], lack of proper temperature monitoring and traceability [161,162], lack of cold chain expertise [23,154,155]. Operational level deficiencies include the shortage of refrigerated trucks and vehicles [153,158], lack of equipment problems [163], poor road infrastructure [9,13], and lack of IT-skilled labourers [21,157].

3.4. Sustainability

The sustainability concept itself is not a new research area; however, enhancing sustainability in FSCs and CSCL is a pressing issue [32]. Incorporating the sustainability concept into meat CSCL can improve the CSCL sustainability performance by comparing and aligning the economic, social, and environmental objectives with sustainability dimensions [164,165]. Further, the sustainability concept ties closely with FLW reduction [166,167], meeting rising customer demands for the inclusion of sustainable practices [168]. The subsequent section delves into these interconnected dynamics.

3.4.1. Sustainability and Closed-Loop SCs (CLSCs)

CLSCs integrate forward and reverse logistics, enhancing waste product utilisation and mitigating CSCLs’ economic, social, and environmental impacts [169,170]. In this realm, some studies delved into specific domains of the FSCs; Ko and Evans (2007) [171] and Mosallanezhad et al. (2023) [172] model third-party logistics and sea food-driven CLSC networks. Accorsi et al. (2017) [173] present a traceability system for a dairy SC, enhancing competitiveness and efficiency through technology and monitoring. Alinezhad et al. (2022) [174] design a sustainability-driven CLSC network, optimising economic and environmental factors for sustainable management. Sgarbossa et al. (2017) [175] explore proactive sustainable strategies and practices for managing SFSCs, highlighting the importance of CLSC models for environmental responsibility. Zhang et al. (2022) [176] optimise food CLSCs with reusable transport items, reducing logistic expenses and carbon emissions. Bogataj et al. (2020) [125] focus on mitigating risks and income improvement through enhanced planning and control in meat SCs. In contrast, Amin’s (2020) [177], MahmoumGonbadi’s (2021) [178], and Tavana’s (2022) [179] studies provide a comprehensive review of CLSCs. Nevertheless, the CLSC domain lacks studies on the utilisation of FW in meat CSCLs, which can include the conversion of FW as valuable input for other chains. To fill this gap, developing comprehensive models is required that account for the products wasted or rejected by customers and the sustainability considerations in beef meat CSCL. Further, to leverage the benefits of CLSC, the implementation must be facile and inexpensive, leading to cost-effective, efficient, and sustainable systems.

3.4.2. Sustainability and Business Models

Sustainable business models play a crucial role in reducing FLW in CLSC systems. Conventional CSCL faces sustainability challenges, spurring a demand for improved models. The circular economy (CE) offers a solution, addressing sustainability issues in traditional SCs by enhancing resource efficiency [51,180]. Studies in this realm predominantly centre on applying a CLSC approach, emphasising the comprehensive utilisation of raw materials through reuse, waste reduction, recycling, and the second-time manufacturing processes [181]. In this approach, FW is recognised as a valuable resource viable for reuse and recycling, contributing to nutrient and energy valorisation [175] or as an ingredient for animal feed and natural fertilisers [182]. To promote CE principles in SFCSCL, addressing barriers [21,183] and critical factors [184] is crucial. Challenges unique to the beef meat and food industry, like difficulty in directly transforming end products back into raw materials [32], highlight the need for incorporating CE concepts into distribution planning.

Alternative food SC networks (AFSC) are also gaining momentum among SFCSCL studies, acknowledged for their unique traits like relocalisation, straight distribution, enhanced connectivity, and superior quality [185,186,187]. Short FSCs are frequently discussed in the literature [188,189,190,191], reflecting their prevalence. Wiskerke et al. (2007) [185] discussed technological and institutional forces shaping the structure of processes and practices, improving sustainability performance for a pork SC. Collison et al. (2007) [192] and De Bernardi (2018) [193] delved into challenges faced by AFSCs, particularly regarding fresh products, advocating for technology adoption, information sharing and digitalisation to enhance connectivity and sustainability in food logistics, reducing FLW and emissions. Despite their advantages, AFSCs present challenges due to their stochastic nature and weak links between production and consumption ends, complicating measurements of local food consumption. Further, import reliance contradicts AFSC’s goals of promoting local food systems. These issues create a notable literature gap in understanding how to model production and distribution processes in AFSC, particularly CSCL, for beef meat products.

3.4.3. Sustainability and Wastage Hotspots

Identifying and addressing wastage and unsustainability hotspots in FSCs is crucial for investigating their impact on FLW [194,195,196]. This entails assessing FSCL stages, methodologies, practices, technologies, and alternatives across the system [197]. Moreover, the utilisation of impact assessment is crucial in the process of making decisions based on sustainability considerations [198]. The literature introduces a range of impact assessment tools tailored to this objective. The most prominent and accepted approaches include Mass Flow Analysis (MFA) [199,200], exergy analysis [201], Life Cycle Assessment (LCA) [202,203], economic input–output LCA [204], and water footprint assessment [205]. While the production stage often garners attention for environmental effects [195], the distribution stage and targeted markets (local, regional, international) require deeper exploration. In this realm, limited attention has been assigned to exploring the environmental effects of CSCL, particularly concerning FLW [157,206]. Some studies, like [51,207], conducted impact assessment regarding different FLW prevention and management interventions. Lipińska et al. (2019) [208] examined how enhancing logistics systems contributes to the enhanced sustainability performance of the FSC systems. Nevertheless, unlike economic performance, quantifying social and environmental performance remains challenging [209]. To comprehensively address meat and FCSCL impacts in the transportation and distribution phase, upcoming studies should integrate social, environmental, and financial factors for optimised logistics.

3.4.4. Sustainability and Packing

Sustainability efforts to reduce FLW also encompass packaging concerns, notably meat and transport packing, which contribute 15% of municipal solid waste and are on the rise [210]. While reusable packaging holds environmental promise [211], it practically requires additional logistical efforts such as collection, cleaning, and recycling [210,212]. This urges a need to scrutinise the effects brought about by these logistical processes [213]. Conversely, proper meat/food product packaging can decrease FLW in FCSCL [214,215]. Meat packaging acts as a protective barrier, preventing direct exposure to external elements, minimising secondary contamination risk, and maintaining meat quality by creating a confined environment and curbing decay [216]. Currently, three predominant packing methods aid in reducing FLW in CSCL systems. Active packaging employs substances to preserve meat quality and extend shelf life [217]. Intelligent packaging not only provides meat quality information but also integrates with technology for enhanced safety, quality, traceability, and decision support in FCSCL [218,219], minimising FLW [218,220]. Green packaging emphasises eco-friendliness and sustainability, using non-toxic, natural-driven components and degradable materials to minimise environmental impact while ensuring meat quality and boosting sales efficiency [221]. Despite the benefits, studies should focus on designing and optimising CSCL by incorporating packing and associated economic, social, and environmental sustainability considerations. Hence, the exploration of low-carbon CSCL stands out as a crucial avenue for future research.

3.4.5. Sustainability and Information Flow

Data sharing and accessibility can enhance sustainability performance and lead to FLW reduction [93,222]. Understanding the information flow and its determinants is essential for designing interventions to mitigate FLW and associated food security risks [223]. Two perspectives emerge in this context: one highlights the lack of comprehensive data on FLW in FSCs, hindering effective reduction strategies. This underscores the importance of obtaining data on FLW extent, origins, and implications across economic, social, and environmental scales [224,225]. The other perspective emphasises the need to comprehend dynamics within meat and food CSCL phases to reduce FLW effectively, necessitating evaluation of information across the entire system beyond individual stages. The rapid evolution of the Internet, information technologies, and electronic devices presents viable solutions for FLW mitigation, facilitating efficient information sharing and traceability throughout supply chain stages [146].

Kaipia et al. (2013) [93] demonstrated that improving information exchange and adjusting material flows reduces FLW. Rijpkema et al. (2014) [139] found that considering shelf-life information in order policies effectively decreases FLW. Kamble et al. (2019) [226] and Rejeb (2018) [227] showed that RFID technologies aid in FLW reduction through better inventory management. Jo et al. (2022) [146] proposed a blockchain-based dataset to tackle FLW in meat SCs. Online and social media platforms can reduce FLW by reshaping product distribution, facilitating direct sharing, donation, or redistribution, and benefiting end-users or intermediaries [228,229]. Despite the potential of new information technologies to enhance sustainability and reduce FLW, research on their application in meat CSCL remains scarce. Future studies should address this gap by integrating different meat SCs echelons, engaging multiple stakeholders, and considering economic, social, and environmental aspects. Developing an evaluation system for sustainable meat CSCL focusing on FLW reduction through technology is crucial. This would assess how concentrating on one sustainability aspect affects others and elucidate the interactions among sustainability pillars, FLW, and related parameters.

The items described in Section 3.3 to Section 3.4 highlight the internal nexus of management and sustainability themes. The management theme affects the sustainability themes through the following: 1—the adoption of the SFLM approach; 2—inclusion of collaboration among stakeholders, which enhances equipment, facilities, and resource utilisation, thereby improving system sustainability; 3—inventory management, which can reduce FLW; 4—management decision-making processes at strategic, tactical, and operational levels; 5—risk management to improve the resiliency of CSCL systems; 6—waste reduction strategies; and t—the adoption of IT(s). Conversely, the sustainability theme impacts the management themes by offering and advising on sustainable objectives, practices and approaches, including 1—developing CLSCs; 2—implementing sustainable business models; 3—investigating of wastage hotspots; 4—introducing sustainable packing; and 5—emphasising improvements in information flow and application of data-sharing practices.

3.5. Network Design Optimisation

The third theme refers to the concept of CSCL network design and optimisation. The following section delves into this concept and scrutinises how it can play a crucial role in reducing FLW across these systems for the beef meat industry.

3.5.1. Network Design and Decision Levels

Perishable products encounter significant challenges in CSCL, including quality decline, value loss over time, and potential hazards from reduced functionality [230]. Further, expanding networks and longer distances between production and consumption sites, coupled with varying food demands, create a dynamic and time-sensitive operational environment, complicating FSC network design and management [230,231,232]. Addressing location, inventory, and routing issues in the ND process is crucial to enhance logistics efficiency across the FCSCL [233,234]. Optimising perishable FSC networks has the potential to slash business costs by 60% and improve service levels [235]. Moreover, ND problems, alongside associated decisions, constitute the primary decision-making challenges in perishable product SC management, as depicted in Figure 11. Optimising facility location involves long-term strategic decisions, while tactical decisions handle medium-term network problems such as fleet size and stock level. Operational decisions focus on short-term decisions such as inventory control across FCSCL and routing allocation for perishable food products [234].

Mathematical programming (MP) models play a crucial role in efficiently addressing logistics complexities and aiding strategic, tactical, and operational decision-making processes in SCs. MP models encompass linear programming (LP), non-linear programming (NLP), integer linear programming (ILP), integer non-linear programming (INLP), mixed-integer linear programming (MILP), mixed-integer non-linear programming (MINLP), and multi-objective programming (MOP) [236]. The distinction between multi-objective programming (MOP) and the other models mentioned lies primarily in the nature and treatment of objectives. All these models, excluding MOP, typically focus on a single objective function. These methods are designed to optimise one primary goal, though some can incorporate multiple objectives if they are aggregated into a single composite objective function or weighted sum. MOP, on the other hand, explicitly handles multiple conflicting objectives simultaneously. It aims to find a set of solutions that represents a trade-off among these objectives, where no objective can be improved without worsening another.

Model selection relies on the characteristics of variables, constraints, and the objective function crucial for achieving optimal outcomes across various applications. However, CSCL network design and optimisation are recognised as NP-hard problems, challenging to find efficient solutions for [230,237]. Addressing this issue for integrated SCND problems involves two categories: exact and non-exact methods. Exact techniques like Benders decomposition, Column generation, and Branch-and-cut precisely determine optimal outcomes [238,239]. Non-exact methods, such as heuristics and meta-heuristics, offer alternatives. Solving integrated decision-making models involves decomposing the problem into subproblems, facilitating more efficient handling using exact or heuristic methods.

3.5.2. Network Design and the Location–Inventory Problem

Keshavarzfard et al. (2023) [240] presented a model addressing location–inventory problems in SCs handling perishable items. It accounts for the impact of demand fluctuations on pricing to improve financial performance of SC. Results reveal heightened pricing reliance on customer demand, leading to reduced profits, while decreased SC dependency boosts profits. Rahbari et al. (2022) [88] proposed a two-stage meat production model, reducing costs by 2% per kg of red meat through SC dynamics optimisation, validated with GAMS. Sensitivity analysis identified increased retailer storage and carcass suppliers’ limitations as cost escalators. Mohammadi et al., 2023 [241] offered a sustainable SC model optimising multiple objectives like effective pricing and environmental impact reduction. Validation via case studies and sensitivity analyses highlighted practicality. Gholami Zanjani et al., 2021 [138] devised a stochastic meat inventory planning framework, integrating resilience strategies using Monte-Carlo analysis, showing a 6% network performance improvement, especially sensitive to throughput capacity and lead time.

3.5.3. Network Design and Routing-Inventory Problem

Some studies delved into efficient approaches for handling perishable products through effective logistics routing and SC design. Tarantilis and Kiranoudis (2002) [242] addressed fresh meat delivery through a meta-heuristic approach, optimising vehicle routing. Zhang et al. (2003) [243] proposed taboo search algorithms to enhance refrigerated food distribution in SC structures. Wang and Ying (2012) [244] developed a model considering transport modes for refrigerated food within time constraints. Al Theeb et al. (2020) [74] introduced the IVRPCSC Model, minimising expenses in the cold SC with a multi-phase strategy. Real-world implementation saw a 9.25% cost reduction. Neves-Moreira et al. (2019) [245] crafted a solution for diverse product routing and production planning, targeting cost reduction in European meat SC stores.

3.5.4. Network Design and the Location Routing Problem

Another research stream targets the location routing problem. Govindan et al. (2014) [246] aimed to enhance sustainability in FSCs. Their study introduces a multi-objective optimisation model for the two-echelon multiple-vehicle location-routing problem with time windows, integrating environmental concerns. Their approach includes a bi-objective MILP to minimise SCs costs while considering environmental impact. Mohammed and Wang, (2017a) [247] developed a cost-effective meat SC network using a multi-objective possibilistic programming model, managing conflicting objectives effectively. Their robust optimisation model handles uncertainties and complexities in meat SC network design. Moreno, Pereira, and Yushimito (2020) [248] developed a novel approach, combining K-means-based clustering with integer programming to address location-routing problem in meat distribution. Their study presents a mathematical model for commercial territory design and applies a hybrid approach to solve it.

3.5.5. Network Design and the Integrated Location–Inventory Routing Problem

Rahbari et al. (2021) [249] addressed cost reduction challenges in a red meat SC by integrating location, inventory, and routing (LIR) decisions with time windows using an MILP model within an algebraic framework. Their objective was to decrease expenses across transportation, production, meat storage, and refrigerator rentals. By applying this approach to five SC echelons and incorporating a diverse vehicle fleet, they achieved a 4.20% reduction in product cost, validated through a case study. Similarly, Liu et al. (2022) [234] conducted a study in 2022 on a FSC in China, focusing on financial cost, carbon emission reduction, and product freshness maximisation. They utilised the YALMIP toolbox to optimise their multi-objective model and highlighted the correlation between vehicle speed, expenses, and pollutant discharges. Some studies address decision-making in uncertain environments. Qu et al. (2024) [250] for instance, developed a Maximum Expert Consensus Model (MECM) with a dynamic feedback mechanism based on robust optimisation principles to manage uncertainties in consensus-reaching. Though not designed for the red meat industry, this tool is applicable in various group decision-making scenarios, including policymaking, business strategy, and complex problem-solving requiring expert opinions. However, there remains a lack of studies addressing uncertainties and realistic industry assumptions in designing meat CSCL networks.

The content described in Section 3.5.1, Section 3.5.2, Section 3.5.3, Section 3.5.4 and Section 3.5.5 demonstrates that the ND theme is interconnected with the management theme. This is due to the nature of a variety of management decisions, at strategic, tactical, and operational levels, involved in the CSCL design and optimisation process. Specifically, facility location represents a strategic management decision, while inventory and routing represent tactical and operational management decisions, respectively. Figure 11 visualises that ND theme and process can cover only one aspect, for instance, designing and optimising the location of facilities or inventory or the routing problem; two aspects, for instance, LI, LR, IR problems; or all aspects of SC decisions in the management theme by addressing LIRP, reflecting their interdependency. Furthermore, incorporating or failing to incorporate management theme decisions into the ND theme and corresponding processes has a significant impact on the comprehensiveness, quality, and accuracy of the ND theme and process and CSCL efficiency. Figure 11 visualises how the management theme and SC decisions fit into the ND theme and the corresponding problems, reflecting their interdependency. The interconnectedness of the ND theme with sustainability and IT themes is further discussed in Section 3.5.6 and Section 3.5.7.

3.5.6. Network Design and Sustainability

Our study indicates that there is a direct relationship between logistics ND and sustainability objectives. Soysal et al. (2014) [251] tackled the challenge of creating a sustainable SC for beef products, integrating environmental benchmarks. They proposed a method utilising an MOLP model to minimise stock and distribution costs while reducing carbon dioxide emissions, highlighting social implications and delivery rates. Mohammed and Wang (2017b) [169] formulated a model incorporating fuzziness to address multiple objectives in an eco-friendly meat supply chain, employing Multiple Criteria Decision-Making (MCDM) techniques. Mohebalizadehgashti et al. (2020) [252] proposed an MILP model to construct an eco-friendly network for the meat SC. This framework prioritised reducing expenses and CO₂ emissions while maximising facility operational capacity and performance. It shows the feasibility of reducing emissions without high costs, offering an innovative method to enhance SC efficiency, particularly in managing complex objectives amidst uncertainties.

The literature also underscores the importance of developing integrated systems to monitor real-time temperatures, emissions, energy footprint, and land utilisation in FSCs and meat CSCL [253,254]. Even though a decline in temperature coupled with prolonged storage duration results in escalating costs, heightened energy consumption, and increased emissions across the CSCL [255,256], its absence leads to a surge in FLW. Hence, research must concentrate on reducing the energy levels essential for running the food CSCL systems. In this context, Keizer et al. (2015) [257] demonstrated that neglecting product deterioration rates in decision-making leads to delivering substandard quality products to customers, hindering high-quality services and excessing FLW. Coelho and Laporte (2014) [258] delved into inventory routing challenges, focusing on the strategic planning of restocking and delivering perishable products. Their study formulated models and evaluated sales prioritisation strategies, highlighting the inadequacy of traditional methods for managing perishable inventory due to shorter product life. Hasani et al. (2012) [259] devised a CLSC structure equipped with both food-driven and high-tech sensors, providing a strategic framework to manage perishable goods amid uncertainty. Song and Ko (2016) [260] explored diverse vehicle fleet utilisation for transporting food, enhancing vehicle routing strategies to address perishable product challenges. Keizer et al. (2017) [261] integrated perishable item characteristics into logistics operation planning, using an innovative MILP model to optimise logistics networks for perishable products with varying decay rates.

The above content indicates that ND is interconnected with sustainability themes because the ND and optimisation process determine whether to incorporate sustainability criteria while addressing ND theme’s problems and the ways to achieve this. Refining network design approach directly impacts developing sustainable CSCL and CLSCL systems required for addressing the sustainability challenges including FLW and to minimize environmental impacts. Conversely, the sustainability theme and relevant practices can also affect the ND theme elements. Sustainable practices including waste reduction, use of sustainable packaging, and optimal route planning often involve the use of durable and scalable solutions causing the design and optimisation process of CSCL systems to be more resource-efficient, cost-effective, durable, and collaborative, enhancing their overall efficiency and effectiveness.

3.5.7. Network Design and Information Flow

The role of information in SCND is pivotal for decision-making, inventory optimisation, risk mitigation, and communication, reducing FLW. Keshavarzfard et al. (2023) [240] present a model tailored to address location–inventory problems in perishable SCs, incorporating Radio-Frequency Identification (RFID) and demand variation impacts on pieces to optimise decision-making across strategic, tactical, and operational levels, enhancing SCND efficiency and reducing FLW. Mohammed et al. (2023) [262] developed a meat SC network by incorporating a sustainable resilient trade-off framework for the facility-driven optimal order quantities. The study highlighted the role of information in developing the proposed integrated framework. Tavakoli-Moghaddam et al. (2019) [263] employed technological solutions to create eco-friendly reverse SCs for meat products, aiming to increase financial gains, customer satisfaction, and environmental sustainability. Mohammed et al. (2017) [264] developed a cost-effective decision-making algorithm for an RFID-enabled Halal meat SCND, aiming enhance traceability and minimise costs, demonstrating economic viability through innovative methodologies. The content described above indicates that the ND theme is interconnected with the IT theme because the ND phase determines which technologies are adopted, how they are upgraded, and how well these technologies are integrated, impacting real-time monitoring and data accuracy. A well-designed network can more easily incorporate these advancements, allowing for continuous improvement and adaptation. Conversely, the IT theme affects the ND theme and the effectiveness of CSCL design and optimisation process. Cold supply chain logistics relies heavily on accurate, real-time data to manage perishable goods. The application of new information, such as advanced analytics and IoT data, helps in optimising routes, managing inventory, and predicting demand more accurately.

Our review indicates that incorporating sustainability into logistics ND and optimisation can reduce FLW systemwide. Nevertheless, concurrent research on all sustainability pillars, logistics ND, and optimisation is scarce, particularly for the beef meat industry. Moreover, this study highlights a dearth of comprehensive studies on how information influences communication and coordination among network elements. Future studies should develop comprehensive, sustainable location–inventory routing models for beef logistics. These models must integrate all sustainability dimensions and reverse logistics impacts to effectively minimise FLW within the CSCL systems. Such studies should explore industry-specific challenges and opportunities, offering tailored insights into the role of information in SCND, which is crucial for mitigating FLW throughout the beef meat industry.

3.6. Information Technologies

The final theme explores the role of emerging information technologies in meat CSCL systems. Subsequently, it examines their potential to revolutionise these systems and provides insights into the current state of the art.

3.6.1. IT and Meat SC Transformation

The integration of new information technologies is poised to revolutionise meat CSCL from two perspectives. First, advancements in information technology and electronics have led to compact, efficient systems. Alfian et al. (2020) [265] recommend combining RFID tags and IoT for traceability. This fusion reduces meat spoilage during transport and enhances sustainability and traceability in logistics. Mejjaouli et al. (2018) [101] investigated perishable product logistics decision models, employing IT and temporary virtual machines in cloud-based decision-making. They adapt operations in real time to transportation conditions and geography. If food quality changes, they adjust transportation routes or sell nearby. Their research highlights significant cost reductions in optimising fresh meat shipments. The study by Jedermann et al. (2014) [52] found that enhancing information distribution and employing smart demand predictors reduce losses. Verghese et al. (2013) [266] highlighted RFID-enabled intelligent food packaging, enabling retailers to communicate with suppliers, optimising production schedules, managing inventory, and reducing FLW. Singh et al. (2018) [267] created a sustainable beef supplier selection framework using big data, cloud computing, and fuzzy techniques to assess production data and identify cost-effective premium suppliers, promoting low-carbon practices.

Second, the integration of new technologies enhances traceability, transparency, and real-time temperature monitoring, which is critical for food safety. This reduces food loss, preserves product value, and improves cost-efficiency, decision-making, and customer communication [268,269]. Specifically, within the meat CSCL, traceability serves two key purposes. One is to track the physical journey of products, treating them as a typical product. The other is to monitor the quality of perishable goods in real time, offering data on product and logistics conditions to ensure safety and compliance with food standards [253] by tracking the chronological record of temperature, humidity, and diverse environmental elements [270]. The latter purpose of new IT applications has nexuses to sustainability, management, and ND optimisation principles, especially in a food safety context. Food safety aims to set a consistent criterion meeting general and specific requirements across FCSCL, involving processes influenced by societal and natural factors [57]. Hence, it serves as an indicator application of social sustainability, with implications for ecological and environmental considerations [271] supported by new IT tools and application.

Additionally, new IT integration improves FSC stakeholder collaboration and management system efficiency [18], which is crucial for ensuring food safety [58,272] by enabling critical information exchanges [273]. Further, it facilitates the implementation of management systems based on early warning and risk mitigation frameworks, supporting food safety, risk reduction, financial success, and enhanced sustainability performance. For instance, Gholami-Zanjani et al. (2021) [274] introduced a risk-analysis method utilising scenario pruning and Benders partitioning to evaluate production, storage, and environmental factors in meat SCs, enhancing safety evaluations and product quality. Nevertheless, further exploration is needed to fully understand the potential role of these factors in enhancing SFSC efficiency [275].

Lastly, new IT applications aid in collecting and monitoring a wide range of data in FSCs, including demand, market trends, temperature, and environmental factors. They facilitate CSCL ND and optimisation while considering management and sustainability considerations by investigating data for suppliers, stock levels, transport fleet and route, packaging, waste management, energy efficiency, logistics operations, environmental impacts, and FLW [52,58,276,277]. The final point emphasises the connection between new IT applications and food safety culture. SC food safety performance relies on the shared values, attitudes, and beliefs within organisations [278]. This culture is reflected in technology, management, staff, and operational environments [279]. Despite its growing importance, there is a lack of research in this area, and manufacturers are hesitant to assess their food safety culture adequately.

3.6.2. Emerging Information Technologies and Meat SCs

Numerous emerging technologies are vying to tackle the challenges in meat CLSCL. This overview details the current research status, constraints, and expected advancements in instruments (Alphanumeric codes, Bar codes, Radio Frequency Identification (RFID), Wireless Sensor Networks (WSN)) and systems, including the Internet of Things (IoT), advanced data analytics (Big data/BD), Blockchain technology (BC), and Digital Twin Technology (DT).

Technical Instruments

Alphanumeric codes, a blend of numbers and letters in various sizes, offer simplicity and affordability. Yet, their manual read-and-write process poses limitations. Challenges include poor performance, vulnerability to data corruption, undefined standards, limited connectivity, and no environmental sensing capability. These drawbacks have led to their near replacement by barcodes. Barcodes, optical and machine-readable, encode alphanumeric data through vertical bars, spaces, squares, and dots. They excel in simplicity, cost-effectiveness, and precise traceability compared to alphanumeric codes. However, they necessitate line-of-sight reading, making them ineffective for damaged labels. Scanners read them sequentially, and they lack environmental sensing capability. RFID functions by detecting labelled objects using radio waves, allowing for identification or tracking without direct line of sight. It reads and writes tags with faster data rates and larger memory compared to prior technologies, supporting reversible tags. It excels in reading multiple tags simultaneously but requires a reader for data collection and is unable to initiate communication itself. RFID lacks device cooperation and is limited to single-hop data reading. Despite benefits, it can be costly, and environmental sensing is limited. WSNs collect data from the environment, employing sensors for monitoring. They enable multi-hop networking and in-network processing, ensuring secure communication via diverse network topologies. The WSN offers extended reading ranges and integrates with Sensor–Actuator networks. Nonetheless, it is not ideal for identification and demands energy-efficient methods to maintain continuous sensing [253].

Technological Systems

IoT is an evolution of traditional Internet technology, integrating Global Positioning System (GPS), Radio Frequency Identification (RFID), Wireless Sensor Network (WSN), and data-driven sensors through the Internet infrastructure. This creates a vast network that fosters seamless communication among interconnected devices and between devices and individuals, enhancing connectivity and efficiency [280]. In meat CSCL, traditional data collection relies on barcode scanning and manual record-keeping. This method hinders efficient information gathering, exchange, and dissemination within the SC. Operators struggle to adapt to dynamic conditions like temperature fluctuations, moisture levels, and spatial changes. Improved data acquisition methods are necessary to enhance adaptability and responsiveness in the meat CSCL [281]. The integration of IoT technology significantly elevates traceability, transparency, integrity, and safety within meat CSCL networks. IoT devices gather real-time data on meat and food products, encompassing inventory status, spatial coordinates, and environmental factors such as moisture and temperature. This data transmission occurs across all CSCL nodes, facilitating seamless information exchange throughout the SC. Such interconnectedness empowers stakeholders, including distributors, producers, regulators, and consumers, with enhanced decision-making capabilities [282]. Alfian and colleagues’ study (2020) [265] indicates that by combining RFID technology with humidity and temperature sensors, the system extends beyond mere tracking, offering real-time monitoring of environmental conditions to safeguard product quality and minimise risks. Additionally, it enables instant tracking and comprehensive recording of meat specifics, serving as a proactive measure against the distribution of inferior goods, thereby enhancing consumer confidence and safety. Nevertheless, the integration of advanced data-sensing technologies incurs significant expenses in setup and energy usage. These costs escalate the overall investment in meat CSCL, prolonging economic return cycles and reducing production output. Consequently, this financial burden becomes a pivotal factor limiting the adoption of IoT technology in beef meat industry CSCL operations, hindering its widespread implementation.

Big data emerges as IoT technology integrates into meat CSCL systems, vastly expanding data volumes. While SC management traditionally relied on statistical and operational methods for supply–demand alignment, this shift brought about a significant accumulation of data. This includes product details, supplier information, retail data, consumer transactions, and third-party logistics data. The term “Big data” encompasses this comprehensive dataset, necessitating advanced analytical approaches for effective management [283]. SC analytics refers to the sophisticated application of big data analytics [284]. Efficient analysis and processing of big data could enhance meat quality and security solutions, benefiting all stakeholders via furnishing decision support for meat CSCL management [285]. Current research, exemplified by Sestino et al. (2020) [280], underscores the need to boost data analysis method functionality and efficiency by leveraging advanced technologies such as distributed computing, machine learning, deep learning, and cloud computing. These advancements aim to extract richer insights, providing technical support for advancing sustainable and efficient CSCL for the beef meat industry. Furthermore, the extensive incorporation of private and confidential data in CSCL systems poses a significant risk to stakeholders, potentially resulting in substantial losses if breached. Thus, safeguarding data privacy and security in CSCL systems is a critical focus of both ongoing and future research endeavours.

BC technology is increasingly integrated into current FCSCL systems, complementing the predominant use of RFID, WSN, GPS, and various sensing tools. These technologies actively monitor SCs in real time, ensuring traceability and food product quality oversight [286]. However, they are reliant on centralised client–server communication and face vulnerabilities to data tampering, challenging data security and accuracy. This affects meat product quality, safety, and CSCL supervision. In contrast, BC pioneers decentralised record-keeping, offering reliability, cost-effectiveness, optimal performance, traceability, and security. These traits ensure data and transaction protection, highlighting BC’s superiority in safeguarding meat SC integrity and enhancing operational efficiency [287]. Furthermore, it fosters trust by improving transaction clarity [288]. Sander et al. (2018) [161] examined traceability and transparency issues in current meat SCs, considering stakeholders’ perspectives. They found BC implementation improves both traceability and transparency, preventing issues like deception and fraud, underscoring BC’s role in addressing challenges within SCs for promoting integrity and accountability throughout the SC.

The complexities of meat CSCL, such as data diversity, loss, and sensor precision, amplify challenges in integrating BC mechanisms. Emerging strategies seek to mitigate these hurdles. Powell et. al. (2021) [289] introduced a novel BC-driven framework merging BC and IoT. This fusion creates a secure, unalterable digital ledger, revolutionising the management, distribution, and verification of data in the industry. The primary aim is to ensure beef-related data’s legitimacy and authenticity, empowering consumers to accurately verify product information. This improves SC efficiency by addressing information imbalances among stakeholders and promoting transparency and trust in transactions. Due to blockchain’s complexity, its current advancement is in the initial phases, with limitations in implementing it in meat SC and CSCL. Furthermore, balancing data privacy and transparency creates conflicts within the BC framework, affecting the traceability and security of CSCL systems. To address this, businesses, regulatory bodies, and governments must collaboratively establish legal frameworks, regulations, and policies ensuring the reasonable and secure disclosure of information. This proactive approach is vital for fostering trust and efficiency in meat CSCL operations.

DT is an innovative simulation method that creates a virtual replica of real processes. It is intricately tied to the physical world through sensor data and sophisticated analytics [290,291], enhancing the optimisation of designs and processes. The utilisation of DT technology in contemporary CSCL systems for meat products is progressively developing. This evolution targets challenges like declining meat quality, increased energy usage, and significant expenses linked to food distribution network upkeep [292]. Employing DT technology enables precise digital control of meat quality within CSCL systems by simulating and predicting environmental and temporal fluctuations. This approach provides an intelligent understanding of meat quality, incorporating data from diverse sources and scales. Hence, DT empowers decision-making processes and aids in optimal product regulation identification, fostering adaptable operational management.

In the DT realm, Computational Fluid Dynamics (CFD) is pivotal for managing meat CSCL due to its extensive usage [1]. Virtual models of actual meat products are created and tested before implementation by utilising CFD technology. These simulation models accurately replicate real-world conditions, enhancing the optimisation of the product design process [293]. Kuffi et al. (2016) [291] utilised a CFD model to investigate the impact of various cooling factors on beef carcass cooling dynamics. The analysis involved variables such as the speed of cooling, consistency of temperature, and alterations in meat quality during refrigeration. The aim was to enhance the efficiency of the cooling process by refining the operational conditions. Results outlined significant cooling rate variations and pH shifts across different carcass sections, impacting meat quality. Verboven et al. (2020) [290] utilised a multifield coupling cross-scale model with CFD DT technology, enhancing FSC analysis. Improved understanding of fluid dynamics, thermal exchange, and mechanical alterations exposes meat quality variations, optimising FSC to reduce food quality loss, cut energy usage, and boost economic benefits. Despite advancements, challenges persist in implementing DT, particularly regarding data security and management efficiency. These obstacles hinder information preservation, retrieval, exchange, and integrity. Future efforts should prioritise integrating BC with DT in meat distribution systems to bridge security and management gaps, thereby improving information security and management efficiency in meat CSCL systems.

Section 3.4 and Section 4.4 reveal that in meat CSCL, the IT and sustainability themes are closely linked in several key ways: 1—Enhanced efficiency, as IT tools boost meat CSCLs efficiency by optimising temperature control, reducing energy use, and minimising waste. This leads to a smaller carbon footprint and lower resource consumption, thereby advancing sustainability. 2—Traceability and transparency, as advanced tracking and monitoring systems provided by IT improve traceability in the cold chain. This increased transparency helps manage resources better and quickly address inefficiencies, supporting sustainable practices. 3—Energy transition, as new IT(s) facilitate the energy transition in CSCLs. Innovations like energy-efficient routing and refrigeration align with sustainability goals by cutting down energy use and greenhouse gas emissions. 4—Quality assurance, as IT(s) such as real-time monitoring, intelligent packaging, DT, and automated alerts ensure meat quality throughout the cold chain. These advancements in CSCL enhance efficiency, reduce waste, and minimise environmental impact, directly supporting sustainable practices, promoting sustainability.

4. Discussion

The current review aims to pinpoint state-of-the-art potential tools and solutions for addressing beef meat CSCL challenges to minimise FLW within the system. Findings indicate that four specific themes—management, sustainability, logistics ND and optimisation, and IT—can actively contribute to mitigating the issue of FLW within the CSCL system for the beef meat industry. Our findings interestingly suggest that these themes interact with each other, and simultaneous consideration of them poses a greater potential to address the FLW issue across these systems. While there is a significant amount of research focusing on investigating and addressing FLW across the food SCs, most of these studies have an emphasis on agricultural products. In comparison, relatively few studies have examined this issue along the food CSCL systems, particularly for the meat industry, with even fewer studies focusing specifically on beef meat. Further, most studies have focused on addressing the issue of FLW across entire SCs, while CSCL, especially CSCL, a crucial part of the food SCs, has not been sufficiently investigated in terms of (I) the effects related to the simultaneous consideration of all sustainability dimensions, (II) the effects related to the simultaneous consideration of all decision levels associated with cold chain logistics network design, (III) the mutual trade-off existing among components forming the sustainability or logistics network design concepts, (IV) the potential effect of utilising new information technology in FLW reduction, including the drives and barriers involved in their adoption, and (V) the simultaneous effect of considering all the above-mentioned factors on the levels of FLW reduction across CSCL for the beef meat industry.

4.1. Management

The first identified theme, management, plays a pivotal role in food and meat CSCL [54], evolving through phases like Traditional LM, Food LM, and Sustainable Food LM. Integrating emerging technologies could usher in an “Intelligent SFLM” phase. Despite advancements, FLW remains high due to suboptimal CSCL management [29,52,158]. This comprehensive review underscores the importance of effective management practices in addressing FLW and enhancing the sustainability of food and meat CSCL systems. These subjects encompass the impacts of diverse logistics strategies and the consequences of regulations and standards, especially mandatory legislations and guidelines [72,75], collaboration and coordination [22,66,294], information flow, SC transparency [22,27,295] and employee training [77], waste reduction, inventory, cold chain ruptures, risk management, poor temperature, hygienic controls [54,120,296], high costs, and deficiencies in CSCLs. These items were reviewed and discussed in Section 3.3.1, Section 3.3.2, Section 3.3.3, Section 3.3.4, Section 3.3.5, Section 3.3.6, Section 3.3.7, Section 3.3.8, Section 3.3.9 and Section 3.3.10. While existing literature extensively covers food SC management, available discussions on the above-mentioned subjects are relatively limited in the context of beef meat CSCL. Our research underscores the necessity of incorporating FW management strategies into beef meat CSCL network design. Waste management and recycling are crucial for sustainable FSCs [51], alongside examining practices that may potentially lead to FLW. Further, the complexity of waste management entails significant financial and resource investments, covering collection, transportation, treatment, and energy consumption. Therefore, it is imperative to consider these factors in research focused on waste management and valorisation strategies. Finally, our review reveals that prevailing strategies in FCSCL prioritise processes, institutionalisation, and profit, neglecting the importance of customer centricity. Customer value management, emphasising consumer needs and satisfaction, is crucial [297]. This approach ensures higher sales and reduces FLW, necessitating collaboration among logistics stakeholders. Thus, future CSCL management in the beef industry should shift towards customer-centric strategies to enhance overall efficiency and satisfaction.

4.2. Sustainability

Sustainability is another key focus in food and meat CSCL research, aligning with reducing FLW. Adhering to the sustainability concept, as advocated by Papargyropoulou et al. (2014) [152], emphasises prevention as the most effective strategy for reducing FW. Embracing sustainable design facilitates waste prevention and reduction in harmony with the hierarchy of waste management strategies [298]. FW is generally categorised into avoidable and unavoidable, aligning with the sustainability concept that avoidable waste should be minimised first, while unavoidable waste should be repurposed. Studies suggest methods to minimise avoidable FW, including addressing issues like transportation, packaging, and product handling [162,299,300]. Unavoidable FW, on the other hand, is rooted in inedible items or components of food products. These can also be transformed into valuable products such as animal food, enzymes for industrial processes, edible fibers, and sustainable biofuels [301,302]. Further, activities like value creation, recycling, remanufacturing, and recovery linked to the valorisation of FW have the capacity to generate job opportunities and stimulate an economic boom, known to be associated with social sustainability enhancement [303]. Another group of studies emphasises the environmental dimension, highlighting how reducing FLW minimises environmental impacts, aligning with sustainability goals [30,304]. Hence, adhering to the sustainability concept can positively contribute to FLW reduction by supporting its prevention in the first place (avoidable food loss and waste prevention) or converting the unavoidable part into valuable components. On the other hand, neglecting FLW importance adversely affects the sustainability and efficiency of FCSCLs, causing reduced profitability, depleted financial resources, a decline in customer value and repurchase intention [2,305]. Additionally, it tarnishes the reputation of logistics service providers, causing a decline in labour productivity and wages, diminishing nutritional content, and heightening concerns in food safety, security, price, accessibility, and public health [129,306]. Moreover, FLW exacerbates environmental issues by contributing to increased methane and CO₂ gas emissions, energy wastage, and non-productive resource uses [13,14,29]. These highlight a two-directional link between FLW amounts and CSCL efficiency and sustainability. Finally, recent reviews highlight a surge in the adoption of proactive sustainability measures among corporations, fostering equity in social, environmental, and economic domains [21,30,175,307]. They stress the importance of exploring diverse strategies to tackle FLW [47,308]. This underscores the need to develop comprehensive approaches to sustainable CSCL for the beef industry. Our review indicates that implementing CLSC practices and models is vital for sustainable operations and FLW reduction. Optimisation of CSCL networks and the extraction of value from product retrieval processes are key. This involves integrating forward and reverse logistics to manage, store, and transport products, packaging, ingredients, or reusable waste [309,310]. Such initiatives aim to minimise FLW and enhance sustainability across the SCs. Nevertheless, our review indicates that studies addressing all three sustainability dimensions and their connection with FLW in beef meat CSCL are notably lacking.

4.3. Network Design

Well-designed CSCLs regulate temperature, cut travel times, energy usage, and FLW, preserving 40% of the food inventory [311,312]. Nevertheless, non-optimal design can increase FLW [52], with nearly 30% occurring in CSCLs [8]. Designing logistics networks for meat products requires balancing intricate nexuses among facility location, inventory management, and vehicle routing. Logistics ND and optimisation tie to SC decisions and hence to the management theme. The literature emphasises incorporating these decisions in optimising FSCs to reduce FLW, transportation inefficiencies, emissions, and environmental impacts [106]. SC decisions are typically categorised into strategic, tactical, and operational tiers, each handling long-term, intermediate, and short-term arrangements, respectively [303]. Strategic decisions align with organisational objectives, dealing with prolonged investment-driven concerns such as facility locations, required infrastructures, technology, and supplier networks. Achieving sustainable development also stands as a key strategic objective for SCs, and an elevated degree of integration in their design and structure is crucial to attain this objective [313]. The literature advocates sustainability-driven ND and optimisation for food and meat CSCL to minimise FLW, costs, emissions, and environmental impact while enhancing food quality, transportation efficiency, and social benefits [46,106]. Tactical decisions align with strategic benchmarks, focusing on production planning and manufacturing scheduling for periods ranging from months to a year. They encompass stock handling, material movement supervision, and operational logistics. Operational decisions, preceding tactical ones, entail daily management of procurement, logistics, and distribution operations [314]. The quality of this decision is important considering the limited shelf life and dynamic and time-dependent nature of food products.

While location, routing, and inventory literature is vast, there is a gap in addressing supply uncertainties in meat CSCL design under sustainability frameworks. Realistic assumptions, especially for perishable product industries, are rarely explored. Many studies focus on one or two sustainability dimensions, overlooking its multi-dimensional nature and trade-offs. Additionally, research on food perishability often overlooks how shelf-life considerations affect sustainability pillars. Addressing these gaps requires examining logistics chains that extend across various time frames and products. This analysis should be based on practical assumptions regarding uncertainties in demand, price reductions, and backlog accumulation. Subsequent investigations should also consider uncertainties associated with facility location and the planning of CSCL networks to improve their economic and sustainability performance. Further characteristics, including the ability to adapt to demand variations, the capability to manage fluctuations in volume and scheduling, strategies for maintaining high quality, and adopting approaches centred on enhancing customer contentment, can be incorporated into CSCL ND and optimisation research.

Further, our review indicates that incorporating information technologies in ND and the optimisation process needs to be extensively explored due to its great potential to reduce FLW, fostering their effective management and sustainable operation [315]. Finally, there is a significant opportunity to derive value from FW by converting it into valuable products, yet FW recovery plants are largely overlooked when optimising meat CSCL networks. To rectify this, research should prioritise integrating essential performance agents into meat industry logistics ND. However, addressing all logistics chain aspects complicates network configuration. For example, industries struggle to align tactical inventory decisions and operational transportation variants with strategic facility location planning [316]. Hence, future studies need to develop comprehensive models to tackle these challenges, harmonising short- and medium-term interventions with long-term strategies for optimal sustainable performance in beef meat CSCL systems.

4.4. Information Technology

The integration of information technologies in food and meat CSCL holds immense potential. Maintaining an uninterrupted cold chain is crucial for ensuring meat quality and safety by regulating temperature and humidity. However, the current meat SCs often face integrity issues, leading to significant FLW and economic detriment due to prevalent chain breaks. In response, logistics firms need to find innovative ways to enhance SC sustainability performance [317,318]. The adoption of new technologies can not only potentially enhance CSCL integrity and sustainability but also reduce FLW. Information technologies enable real-time inventory management, facilitating synchronised planning and forecasting based on demand signals, whether from financial or real-time data [319], providing the instant flow of information among different parties involved in the food SCs [148,149,320]. Effective data exchange and data accessibility contribute to elevating sustainability adherence for all engaged parties [222], and efficient distribution of information and understanding the factors that impact its usage are essential for executing organisational tactics to minimise FLW [223].

Real-time cargo visibility tech and monitoring systems, along with relevant data, identify perishable product locations and characteristics, enhancing meat/food CSCL sustainability and tackling food safety and FLW concerns [321]. Reviewing the state of the art in this theme indicates, notably, four key technologies: IoT, Big Data, Blockchain, and Digital Twin, which have emerged in food and meat CSCL, each with distinct benefits and drawbacks analysed in Section Technological Systems. Despite the recognised necessity and potential advantages of utilising information technologies in addressing the intricate challenge of FLW, its application level in meat/food CSCL has been relatively slow-paced and far behind expectations [32,56,150]. This can affect not only the food safety of products but also the tendency to use information technologies and to improve the sustainability of logistics systems. Our review indicates challenges, including non-compliant data, varied standards, uneven distribution of data among involved parties, unconventional business production methods, and divergent methods for regulatory assessments within the SCs, which pose substantial hindrances to the robust and sustained application of information technologies in meat CSCL systems. Hence, these subjects should be the focus of future studies. Future research should also investigate how external surroundings influence the food safety culture within an organisation. This entails assessing the performance of firms operating in diverse food safety environments. Furthermore, it is vital to initiate a conversation concerning the interactions among information technology protocols, food safety guidelines, and regulatory structures endorsing sustainability. This should be performed with the aim to discover the means of aligning these policies with the CSCL operational perspective in the food industry. This requires investigating traceability implementation in meat and food CSCL. Understanding the potential drivers and barriers faced by logistics professionals in adopting real-time cargo visibility technologies and implementing data-sharing practices among the network stakeholders is a necessity, leading to paving its application across the logistics chains and improving their performance.

Figure 12 illustrates a conceptual framework developed based on our review findings. It suggests achieving a superior optimisation level through the effective integration of these identified key themes, which leads to elevated sustainability performance and reduced levels of FLW. This conceptual framework and correlational relationships proposed should be explored and validated from the questionnaire survey, and other appropriate methods, such as structural equation modelling, should be incorporated in future studies.

5. Conclusions

This study significantly advances the literature on CSCL for red meat and addresses the pressing issue of reducing FLW. In providing an overview of the state of the art, RO1, we found four pivotal themes on CSCL for the red meat industry, management, sustainability, network design (ND), and new information technologies (IT), each contributing uniquely to mitigating FLW. Management stood out as the predominant theme, followed by sustainability, ND, and IT.

Through methodology, we found four main research themes related to RO2, pinpointing significant areas of research, their potential role, and associated elements in mitigating red meat waste reduction across CSCL, providing insights into how the field of CSCL in the red meat industry is evolving.

With regard to RO3 identifying the directions within each theme that warrant further research advancement, our review highlights a dearth of studies on reducing FLW within food CSCL, particularly for the beef meat industry. This is concerning given beef’s high production value and susceptibility to CSCL fluctuations, underscoring the need for targeted research to mitigate FLW and optimise efficiency in these systems. Future studies need to thoroughly examine the following aspects, including (I) the direct effect and internal trade-offs related to the simultaneous consideration of all sustainability dimensions on FLW reduction, (II) the direct effects, internal trade-offs, and the simultaneous effects related to the consideration of all decision levels involved in CSCL ND, (III) the direct effect and existing potentials in the use of new ITs, the barriers and drivers for their adoption and for sharing the information, (IV) the analysis of the mutual trade-offs among the mentioned components (I, II, and III), and finally (V) the simultaneous effect of considering all these factors (I, II, III) on the amount of FLW along the CSCL systems for the beef meat while considering the time-dependent nature of meat and food products and uncertainties across these systems.

Our research introduces an innovative conceptual framework that highlights the interconnectedness of key themes and their direct impact on reducing FLW in meat CSCL systems, ensuring the study’s originality. It provides valuable insights into how these themes contribute to FLW reduction and sustainability in meat CSCL. These findings deepen understanding of each theme’s significance in mitigating FLW, benefiting researchers, practitioners, and managers in the meat and CSCL industries. Future studies should explore these aspects further, developing models to incorporate their simultaneous impact. This would aid managers and stakeholders in decision-making, enhancing sustainability performance.

To address the RQ, the study provided an in-depth discussion by integrating all key findings related to the four main identified themes and their associated key subjects. This information was obtained through the methodology process illustrated in Figure 2 and by incorporating additional insights gained from addressing the research objectives. Based on what was discussed earlier, here is the summary of the current state of the art on beef CSCL in terms of management, sustainability, network design, and the use of information technologies for red meat waste reduction:

• Management:

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  Effective management practices are crucial for addressing FLW in beef CSCL systems.

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  There is a notable transition from LM to FLM and SFLM, with the potential for emerging technologies to create an “Intelligent Sustainable Food Logistics Management” phase.

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  Suboptimal management practices continue to contribute significantly to FLW, underscoring the need for enhanced strategies and adherence to regulations and standards.
• Sustainability:

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  Sustainability in beef CSCL involves addressing social, economic, and environmental benefits.

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  Reducing FLW can lead to increased profits, improved customer satisfaction, public health, equity, and environmental conservation by minimising resource use and emissions.

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  Comprehensive research integrating all sustainability dimensions is needed to fully understand and mitigate FLW. Current efforts often address only parts of sustainability. A more holistic approach is required to balance environmental, economic, and social dimensions effectively.
• Network Design:

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  Effective network design and optimisation are pivotal in reducing FLW within beef CSCL systems.

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  There is a necessity for integrating all three levels of management decisions in the logistics network design process. Decision levels in network design must be considered to understand trade-offs among sustainability components in this process.

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  Future research should focus on integrating management decisions and network design, CSCL uncertainties, sustainability dimensions, and advanced technologies to enhance efficiency and reduce waste in beef CSCL systems.
• Information Technologies:

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  Information technologies such as Digital Twins (DTs) and Blockchain (BC) play a significant role in improving efficiency and reducing FLW in beef CSCL.

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  The integration of these technologies can enhance understanding of fluid dynamics, thermal exchange, and meat quality variations, optimising the cooling process and reducing energy usage.

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  Challenges like data security and management efficiency need to be addressed to maximise the benefits of these technologies.

The review’s limitations stem from the selected publication timeframe and the broad spectrum of topics related to FLW within CSCL systems. The classification of this vast scope is constrained to our extracted themes. Initially, our literature review focused on FLW in meat SC, particularly beef. Keywords like “beef”, “meat”, and “supply chain logistics” were used in searches across Scopus, Web of Science, and Google Scholar. However, due to limited data on FLW in beef logistics, we broadened our scope to general meat SC. This involved replacing “beef” with “meat” in the review. Despite these adjustments, the review offers valuable insights into FLW reduction in meat and beef meat CSCL.

This study seeks to elucidate the cutting-edge research themes and their prospective roles in mitigating red meat waste. The findings can have significant implications for regional and national CSCLs of meat and food, influencing global logistics and distribution operations, as well as shaping environmental policies. Furthermore, the research underscores the interdependencies among the identified themes and their collective impact on system efficiency. It suggests that during the ND process, the simultaneous effects and interactions of the four identified themes should be considered to address trade-offs effectively, leading to improved sustainability performance and efficiency. Finally, the study’s outcomes may encourage organisations to enhance the integration of all CSCL stakeholders towards a unified objective, thereby advancing the overall sustainability performance of the logistics network.