Next Article in Journal
NARX Neural Network for Safe Human–Robot Collaboration Using Only Joint Position Sensor
Previous Article in Journal
Design and Evaluation of an Integrated Autonomous Control Method for Automobile Terminals
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Logistics
Volume 6
Issue 4
10.3390/logistics6040074
logistics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Application of Optimization Techniques in the Dairy Supply Chain: A Systematic Review

by
Mohit Malik
1,
Vijay Kumar Gahlawat
1,*,
Rahul S Mor
2,*,
Vijay Dahiya
3 and
Mukheshwar Yadav
4
1
Department of Basic and Applied Sciences, National Institute of Food Technology Entrepreneurship and Management, Kundli, Sonepat 131028, India
2
Department of Food Engineering, National Institute of Food Technology Entrepreneurship and Management, Kundli, Sonepat 131028, India
3
Department of Business Administration, Maharaja Surajmal Institute, C-4, Janakpuri, New Delhi 110058, India
4
Department of Mechanical Engineering, CUIET, Chitkara University, Rajpura 140401, India
*
Authors to whom correspondence should be addressed.
Logistics 2022, 6(4), 74; https://doi.org/10.3390/logistics6040074
Submission received: 30 June 2022 / Revised: 7 October 2022 / Accepted: 11 October 2022 / Published: 17 October 2022
(This article belongs to the Section Artificial Intelligence, Logistics Analytics, and Automation)
Browse Figures
Review Reports Versions Notes

Abstract

:
Background: The global dairy market is experiencing a massive transition as dairy farming has recently undergone modernization. As a result, the dairy industry needs to improve its operational efficiencies by implementing effective optimization techniques. Conventional and emerging optimization techniques have already gained momentum in the dairy industry. This study’s objective was to explore the optimization techniques developed for or implemented in the dairy supply chain (DSC) and to investigate how these techniques can improve the DSC. Methods: A systematic review approach based on PRISMA guidelines were adopted to conduct this review. The authors used descriptive statistics for statistical analysis. Results: Modernization has led the dairy industry to improve its operational efficiencies by implementing the most effective optimization techniques. Researchers have used mathematical modeling-based methods and are shifting to artificial intelligence (AI) and machine learning (ML) -based approaches in the DSC. The mathematical modeling-based techniques remain dominant (56% of articles), but AI and ML-based techniques are gaining traction (used in around 44% of articles). Conclusions: The review findings show insight into the benefits and implications of optimization techniques in the DSC. This research shows how optimization techniques are associated with every phase of the DSC and how new technologies have affected the supply chain.

1. Introduction

The dairy industry worldwide is now undergoing a significant transformation. United Nations Food and Agriculture Organization dairy price index demonstrates costs 26% below the maximum in February 2014 [1]. Trade sanctions against Russia and the removal of “milk quotas” inside the EU have led to a time of oversupply and minimum pricing for milk products from China [2]. Despite this, the dairy industry is growing and is expected to reach 177 million tons of powdered milk by 2025 at an annual growth rate of 1.8%. Increasing urbanization and rising prosperity in developing economies are responsible for this rise [3]. Dairy producers in Europe have utilized intervention stocks to protect themselves against lower worldwide prices. India is the top milk producer in the world, making up 23% of the world’s milk. The compound annual growth in milk production in the country is about 6.2%. In 2020–2021, milk production reached 209.96 million tons, up from 146.31 million tons in 2014–2015 [4]. The top five milk-producing states are Uttar Pradesh (14.9%), Rajasthan (14.6%), Madhya Pradesh (8.6%), Gujarat (7.6%), and Andhra Pradesh (7%) [5]. It was announced in June 2020 by the Indian government and the Department of Animal Husbandry and Dairying that there would be a $2.1 billion infrastructure development fund. An interest subsidy scheme to help private and small businesses invest in dairy and livestock feed plants, which in turn are predicted to generate 3.5 million jobs [6]. The Ministry of Food Processing Industries has issued detailed operational scheme guidelines and started an internet-based platform for the ‘Production Linked Incentive Scheme for Food Processing Industry’, which has an expenditure of $1.4 billion. This money is meant to help make Indian food manufacturing companies that are world-class and help Indian brands of food products be more prevalent in international markets [7].
Strategies to improve and strengthen milk process optimization are essential in the dairy industry. The fast degradation of milk ingredients makes dairy manufacturers think cautiously about how they aim and manufacture their goods. The business model is to peek at the individuals working for the corporation [8]. They want to cut down on time and raw materials thrown away, unnecessary expenses, constraints, and errors while still making a high-quality product. In the last few years, many people have used machine learning algorithms on dairy farms and talked about how to predict many different things [9]. This article aims to find, evaluate, and synthesize the articles discussing how optimization techniques can be used to optimize the DSC. As part of artificial intelligence (AI), machine learning utilizes complicated algorithms to handle difficult situations that can’t be solved with traditional methods. In dairy, machine learning is already used in many ways [10]. The dairy supply chain consists of the production and planning of milk procurement, processing, inventory, marketing, distribution, and consumer end [11]. In the DSC, optimization techniques such as mathematical modeling and AI and ML-based techniques are utilized in most phases. For example, it can detect lameness and predict the calving time by looking at motion information and action sensors. The data shows early mastitis from milking robots [12]. When implementing machine learning in the dairy industry, there isn’t a good picture of the algorithms utilized, the issues solved, and the complexities that come with it [11]. The authors address optimization techniques’ role in the DSC by looking at the scientific literature. The review focuses on exploring the application of optimization techniques, including ML and AI-based techniques developed for or implemented in the DSC. The authors try to investigate how these techniques can improve dairy.
This review is structured as follows; Section 2 presents the review of literature, and Section 3 describes the review methodology used to find, select, and investigate journal articles for inclusion in the study. Section 4 discusses potential dairy industry areas where optimization techniques were applied based on a review of the included articles and interpretation and discussion, along with challenges and future directions for optimizing techniques. Finally, Section 5 summarizes the findings and concludes the review.

2. Literature Review

Shine & Murphy [13] presented a systematic review of machine learning-based optimization techniques for dairy farms and explained how to use these techniques for various dairy farm tasks. It provides an in-depth study of the literature on optimization models for production scheduling for food production. Jouzdani & Govindan [14] discussed the sustainability aspects of the DSC in an optimization context and suggested changes from the policymakers’ side to optimize the supply chain efficiently. Jachimczyk et al. [15] show the IoT evolution briefly by offering the complete architecture of how devices communicate to cloud networks to manage data. Correia et al. [16] developed a distribution model to optimize the distribution process in the DSC using the structural equation modeling technique. Jouzdani et al. [17] proposed a model to optimize the system’s total costs and resolve location selection problems while keeping traffic conditions in mind. Bilgen & Çelebi [18] proposed an integrated production and distribution model to optimize each product’s process.
Piramuthu & Zhou [19] presented a model to optimize the inventory for perishable products, and Gopinath et al. [20] described the optimization for lipase production. Many articles presented an integrated modeling approach to optimize decision support systems, cost, environmental impacts, and routing problems [21,22,23,24,25,26,27,28]. Sel & Bilgen [29] review existing problems and their respective solutions to the dairy industry’s quantitative modelling. Li et al. [30] overview how to optimize the complete supply chain via the traceability model and describe the total cost and profit optimization of the entire supply chain. Schuck et al. [31] review recent optimization techniques based on modeling and simulation methods. Satya & Chimakurthi [32] explores livestock production optimization and reduction of physical labor. Sel et al. [33] briefly explain the optimization of quality decay and makespan of perishable products to manage the inventory in a better way. Nemati et al. [34] explore the benefits of modeling in. operations planning and [35,36] work on the optimization of network cost and CO2 emission. The researchers try to optimize the distribution process and scheduling process to predict the quantity of milk, protein, fat content, and feed intake of dairy animals [37,38]. Mor et al. [39] present a structured review of DSC practices and indicate that technological advancements can help to achieve a milestone in the direction of quality, food safety, and economic growth of the dairy sector.
Prediction of energy cost [40] and optimization of distribution along with uncertainty were explored [41]. Akbar et al. [42] examined the innovative techniques for farming and production, [43] including animal health and productivity as the main parameters for their study. Feil et al. [44] reviewed sustainability factors by indicating the major problems in an environmental, social, and economic context and also suggested solutions related to sustainable practices. In the literature review, the main application of optimization is related to cost, profit, routing problems, location problems, and decision-making. Mathematical modeling approaches like mixed integer linear programming, non-linear programming, heuristic methods, AI- and ML-based algorithms like genetic algorithms, simulation modeling, etc., were used by many authors.

3. Review Methodology

A systematic literature review was carried out to achieve the study goals using the “Preferred Reporting Items for Systematic Reviews and Meta-Analysis” (PRISMA) methodology, as mentioned by [45]. Very few SLR techniques in the supply chain context are focused on best practices and the unique characteristics of conducting research in the supply chain domain [46]. A systematic review is an evidence-based review of the scientific literature. By summarizing and synthesizing the available literature results, it may identify the latest condition of understanding on the study issue and aid in collecting relevant studies. Authors are motivated to conduct SLR in DSC because AI and ML research rapidly expand, especially in supply chains. This technique is relatively new in the DSC. The authors adopted a systematic review process as described by [47]. This review was conducted in five steps: developing the research question, search method, data selection, statistical analysis, and discussion [48].

3.1. Review Question

The study’s objectives necessitated the development of research questions that would shed light on the advantages of optimization techniques in the DSC. This study area focuses on the most cutting-edge DSC techniques. The following research questions were prepared: RQ 1. Which optimization techniques are commonly used in the DSC? RQ 2. Which aspects of DSC have been studied more previously? and RQ 3. How do the Al- and ML-based optimization techniques help to improve the DSC?

3.2. Search Method

We devised a method for locating relevant documents. Machine learning, artificial intelligence, and DSC were the critical elements of the search string based on the study subject. Based on three themes, we came up with three subcategories: Supply chain, machine learning, artificial intelligence, and the dairy sector round out the list. Authors only used peer-reviewed articles from reliable sources to ensure the quality of their findings. Scopus and Web of Science are two important databases for peer-reviewed multidisciplinary research papers. The authors used the Scopus and Web of Science databases to compile relevant research. Data selection is based on a set of inclusion criteria. To collect relevant articles, authors used Boolean operators OR, AND along with the following keywords (“dairy industry” OR “dairy supply chain” OR “dairy”) AND (“optimization” OR “optimization techniques” OR “Artificial intelligence” OR “machine learning” OR “Mathematical Modelling”). This evaluation only included research articles from 2013 to mid-2022 since the concepts of ML, and AI were first proposed in 1952 and 1943, respectively. This evaluation needed only peer-reviewed English-language publications to ensure validity and high output standards. We searched the database using the filter function: language, year of publication, type of publication, and area of research. Table 1 lists the inclusion/exclusion parameters used to choose the articles for this review.

3.3. Data Selection

After scanning both databases, we found 1516 entries from Scopus and 1758 documents from Web of Science. For this review, we utilized a five-phase review process to locate the research that should be considered for inclusion. Following a search, the identification, screening, and inclusion process was carried out. From two databases, 3274 search results were discovered. 31 articles from other sources, such as back references, websites, books, and manuals, were also included. After removing 1123 duplicate entries, 2182 documents remained for further screening. Table 1’s inclusion criteria were employed in the screening step to identify relevant research. During the screening process, there were two stages. There was a preliminary phase in which the abstracts and key phrases were examined. Following the first round of screening, 200 papers were selected for consideration in the second round of the selection procedure.
Following that, the full-text research papers were evaluated for eligibility. Specific reasons were given for excluding 124 articles. Several reports spoke about ML and AI but didn’t use them in their study. For data extraction and analysis, 45 full-text publications were chosen. We also exclude the 31 articles from books, webpages, and manuals. After screening, the articles’ fundamental features were summarized. The essential qualities demonstrate the advancement of the associated academic subject. Figure 1 depicts a flowchart for a systematic review based on PRISMA guidelines. The total number of articles published in each journal is shown in Figure 2. Only journals with several publications greater than or equal to 2 were considered in Figure 2. Figure 3 shows the year-by-year breakdown of publications. From 2013 to mid-2022, there was an upward trend in the number of published articles. Table A1 in the Appendix presents all the articles included for review.

3.4. Statistical Analysis

The data from each article were analyzed using the research questions as a guide. Basic features, including the number of papers in each journal and the year of publication, were retrieved and interpreted. Each article’s optimization methods were studied to address the research question. The advantages and disadvantages of technology & ML, and AI were examined for the second question. Section 3 contains the results of this literature review. The findings are based on a content analysis of a small number of publications from the literature, considering the conclusions of the content analysis’s results.
Figure 3 indicates the growing interest in supply chain optimization based on artificial intelligence and machine learning techniques may cause more yearly publications.

4. Discussion

Based on the outcomes of the systematic review and content analysis, this part discusses the analysis of selected articles. Table 2 summarizes the optimization techniques used in different areas of the DSC.
The authors highlighted the optimization techniques used in various DSC areas in Table 2. The categories for optimization techniques are as follows: part 1 includes all techniques based on mathematical modeling, and part 2 consists of all techniques based on artificial intelligence, machine learning, and IoT. Table 2 demonstrates that researchers are now using AI and ML-based algorithms for supply chain optimization. However, only 15% of studies address the entire supply chain optimization, leaving a gap in the field. Most studies, nearly 67%, deal with production and planning optimization, and only 29% are reported on inventory management.

4.1. Technological Innovations in the Dairy Supply Chain

This section focuses on the many DSC support solutions that use ML and AI. The conventional DSC is being changed or optimized using various techniques [13]. According to the study, there have been 14 disruptive innovations in the DSC. Multiple stages, activities, and supply chain procedures have been made more efficient via these technologies [28,62]. There were 16 physical and computerized innovations that the writers of the publications we were involved in examined, as indicated in Table 3. These technologies include simulations and AI, as well as ML and IoT. These include RFID, IoT, blockchain, cloud technology, data analytics, and remote sensing.

4.1.1. Supportive Technologies in the Dairy Supply Chain

  • Automatic calf feeders are designed to supply nutrients to calves in exact doses throughout the day, resulting in a healthier and more productive life for the calves. It can control the maturation of the calves. It gives dairy cows the freedom to eat whenever they like [50,53]. Calves’ development and health may be tracked in real-time, which benefits farmers.
  • Milk yield recording systems provide information about individual animals. You can see how much milk is produced each time a cow is milked and the daily trends in milk production. Farmers may use this early detection information to discover changes in the animals’ health and food consumption routines [43]. As a result, it aids caregivers in identifying problems that could otherwise go undetected and implementing solutions.
  • Automated milking systems reduce the time and effort required to milk cows. Regarding milking, the cows can make their schedule and explore the refuge without human interference [63]. Computer-aided equipment detects the bovine, cleans the udder, softly collects the milk, and ultimately releases the animal after complete milking.
  • Rumination activity collars and sensors aid in the early identification of behavioral and activity abnormalities that indicate the onset of any illness or infection. It is possible to follow certain bovine activities, such as the animal’s readiness to breed or enter labor, using devices such as pedometers [31]. This allows the farmer to give timely help.
  • Blockchain Technology shows a dairy farm’s ability to produce milk. The technology is an electronic ledger with unique QR codes on the dairy label. These codes may provide information on the cow feed, possible treatment administration, raising procedure, slaughter event, time and place of milk production, processing processes, added ingredients, product release date, etc. [64]. Consumers may scan the tags with their cell phones to follow the whole milk manufacturing process from start to finish. This method attempts to eliminate the market for fake milk products.
  • AI Machine learning employs optical and motion sensors to analyze milk quality, fat and protein levels, and the reproductive health of cows. These devices may accurately transmit a cow’s walking, drinking, eating, laying down, or contemplating. Health concerns like mastitis or lameness may be detected 24 h before they become serious [65]. These sensors can detect sick animals, allowing farmers to treat them quickly and prevent disease transmission.

4.1.2. Supportive Technologies Implemented in Different Phases of the Dairy Supply Chain

Herd Management Software

Herd management technology is transforming the dairy sector. Transparency and data analysis are the cornerstones of good herd management software [66]. The top solutions on the market extract and show real-time data in an easy-to-use style. To get actual ROI, you need to gather meaningful data and use it wisely. SCR by Allflex livestock intelligence is the most acceptable method for gaining these advantages: data collection and analysis for advanced cattle monitoring systems; heat detection and health monitoring in real-time; milk measuring technology that makes milking more accessible and more efficient; daily dashboards with rapid drill-down access to management tools, including reports, graphs, analytics, task lists, and lifetime cow card history.

Farm Management Software

Given the varying farm characteristics, software assistance is required to simplify and automate these activities [67]. Using Agrivi’s farm management system, users may plan, monitor, and analyze all agricultural operations, including weather monitoring, financial data and documentation management, human resource management, and inventory management.

Food Safety Software

Standardized safety procedures encourage openness and retain accurate records at every supply chain and production stage. CONTROL-PRO is the most excellent option for food safety in dairy production. As a result, users are more equipped to deal with food safety issues. It has dynamic features, such as making a floor layout with key control points and additional preventative control points; finding trends and places in the food safety strategy; interdepartmental and management communication; automating alerts; setting controls; easy access to all samples, testing, and correcting data; validating results; easy report generation and data analysis; early identification and response based on data component monitoring, trends and quick access to records reducing the audit preparation burden.

Supplier Management Software

From record-keeping and communication to audit planning and assessment, robust software can help you manage supplier relationships. Food LogiQ is the leading solution in this area, allowing customers to create a community of supply chain partners, address problems directly with suppliers, and achieve full chain traceability [40,68]. Features include easy-to-use supplier documentation and template procedures to verify and consolidate needed data; dashboards to quickly identify vendors with expired documents, failed audits, or high-quality problems and process credit requests, reduce supplier interactions, and catch quality complaints.
Table
Table 3. Technologies utilized in DSC.
Table 3. Technologies utilized in DSC.
ProcessesTechniquesApplicationReference
Livestock ManagementSmart Monitoring, Smartphone application, RFID, ANN, Clustering, Deep LearningGrassland monitoring, animal welfare, livestock production, and decision-making based on accurate data and evidence[32,42,43,50,51,52,53]
Procurement and TransportationSimulation, IoT
Blockchain, ANN, Genetic Algorithm		Quality and safety management, Route planning, Cost optimization[10,12,33,55]
Planning and ProductionML such as Bayesian network clustering, genetic algorithm, Forecasting,
ANN, Decision tree, Regression, SVM		Cost and profit Optimization in the production system to reduce setup time and better demand sensing[20,22,49]
Inventory ManagementSmart Phone application, genetic algorithmTo predict daily demand and deal with inventory-related problems[17,19,24,28]
Dairy managementSimulation,
Data analytics,
robust and heuristic optimization		To achieve sustainability goals, to manage the data better[9,21,26,36]
DistributionBlockchain, robust optimization, regression, clusteringTo predict future demand[18,25,41,59,61]
Entire supply chainSimulation, Cost optimization, Environmental impacts, Transportation optimizationTo maintain transparency and visibility[13,23,29,30,31,60]

4.2. Strategic Outcomes

Strategic outcomes are the expected advantages and implications of applying these techniques to DSCs. The Dairy Supply Chain’s predicted benefits and consequences are discussed below. The strategic outcomes have three dimensions: economic, environmental, and social. The strategic outcomes lower costs, improve efficiency, boost transparency, and minimize complexity. For example, RFID technologies help the dairy business by providing managers with real-time data to make better decisions [69]. Reduced greenhouse gas emissions and optimized energy use are critical environmental objectives. One study found that integrating technology into the DSC increased employment possibilities and reduced unlawful behavior. Cost reduction: The result might be a reduction in expenses for all parties involved. An IoT-enabled integrated system may cut dairy processing expenses. Researchers employed simulation to lower supply chain expenses [64]. A simulation-based methodology was designed to discover the best inventory strategy for the dairy business.
Efficiency: Supportive technologies have improved the efficiency of the DSC. Technology might improve dairy efficiency. RFID-based prototypes and tracking systems can boost economic efficiency. They deliver real-time data to stakeholders/managers to enhance supply chain management and operations. Transporting dairy products may be more efficient by adopting innovative technologies and limiting considerable truck weight [70,71]. Increase transparency: The DSC environment becomes more transparent for safe transactions between stakeholders. A traceability system based on RFID and blockchain helps collect data from farm to fork. This system aimed to earn consumer confidence by providing transparent supply chain information. Economic and technological advancement brought an untrustworthy environment. Transparency may be improved by combining numerous technologies and integrated systems [72,73]—complexity reduction: Less complicated than the usual DSC. Two studies focused on leveraging technology to simplify the DSC. An artificial neural network is easier and faster for researchers in the dairy industry simulations. The outcome reveals that the ML-based strategy facilitates decision-making [74]. However, few researchers have employed ML in the dairy business.
Legislative approval may be difficult because certain nations’ governments and policies limit certain technologies. Governments did not provide any beneficial guidelines for the use of blockchain technology. Cybersecurity is a concern. The supply chain collects massive data from many digital devices. Various privacy and cyber and data security challenges arise due to the DSC’s high degree of openness and interoperability [75]. Enhanced security, including anti-virus technology and software, is essential for expansion.

4.3. Future Research Directions

Even though various optimization techniques have addressed a wide range of problems over the past years in DSC, many issues still need to be resolved. This section describes the areas that can be addressed in the future. The increasing applications of emerging techniques based on AI and ML offer numerous opportunities for research into these gaps in DSC. Almost every study was focused on logistics process optimization, such as inventory, warehouse and transportation management [76]. Based on the findings, only 15% of studies addressed complete supply chain optimization. But with rising consumer demands, the industry requires an integrated optimization framework. Although segmented optimization is effective but rapid growth in demands necessarily involves the implementation of appropriate infrastructure. The DSC has not implemented AI- and ML-based techniques properly. Future studies might benefit the whole supply chain and create more jobs with technological implementation [42].
In addition, these technologies promote real-time data, interoperability, and integration amongst all partners in the supply chain. Supportive technologies facilitate the DSC. For the DSC, ML and new technologies are complicated, but proper implementation can improve supply chain operations [31]. However, certain technologies are seldom employed in practice or economically, particularly by SMEs. The key issue is adopting and adapting new technology. One factor for restricted technology adoption is high implementation costs. Substantial investment challenges still exist in the DSC in digital technologies, the latest equipment, network infrastructure, and decision support systems. Most infrastructural facilities and cutting-edge technologies require government R&D funding. Future technological advancements may reduce the cost of adopting new technologies and discovering less costly materials [64]. Governments and organizations must assist dairy sector technology research and development to preserve assets, risk management and evaluation. Based on the findings and analysis, these are some key points that can be the area of research in the future:
  • Integrated supply chain optimization;
  • Real-time optimization for the routing problem;
  • Real-time optimization for waste management in the supply chain;
  • Real-time monitoring for information flow in the supply chain;
  • Integrated optimization system based on AI and ML for the entire supply chain;
  • Development of integrated frameworks for complete supply chain optimization;
  • Implementation of optimizations models based on ML algorithms;
  • The challenges and benefits of implementing optimization techniques can be explored;
  • Factors affecting the adoption of digital techniques for DSC can be an area of research in future;
  • Blockchain and IoT sensor-based real-time optimization in DSC.
Findings indicate that technological advancement is necessary for DSC, especially in real-time monitoring and integrated supply chain optimization.

5. Conclusions

Enhanced transparency, reduced cost, complexity and energy usage, and enhanced efficiency and employment possibilities are some implications of developing techniques based on ML and AI in the DSC. This review demonstrates that the potential of optimization techniques still needs a focus in the DSC. A brief overview of optimization techniques and how these techniques can enhance operations in the DSC is new to dairy. As per Table 2, optimization techniques are applied most frequently in the DSC production and planning phases, approx. 67% of articles, followed by distribution at 38%, processing at 33%, and inventory management at 29%. Also, around 15% of the articles covered the entire supply chain optimization. Also, mathematical modeling-based theoretical techniques are still dominant in the DSC compared to AI and ML-based algorithms, with 56% of studies reporting such techniques and 44% reporting those based on AI and ML. Thus, this review will help stakeholders understand current practices, their application in the DSC, and the advantages of ML and AI for the DSC. This review has some limitations too. The search criteria excluded some research in other languages. Also, some themes may be explored, including identifying system and network infrastructure risks in the DSC, Assessing ML and AI’s suitability for the dairy industry on a small scale, and challenges to new technology adoption.

Author Contributions

Conceptualization, Formal analysis, and Writing—initial draft preparation and editing: M.M.; Review and Supervision: V.K.G., R.S.M., V.D. and M.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

We acknowledge the Department of Basic and Applied Science and Department of Food Engineering, NIFTEM Kundli, for supporting review and analytical work.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. List of articles included for systematic review.
Table A1. List of articles included for systematic review.
No.Title
1Dynamic dairy facility location and supply chain planning under traffic congestion and demand uncertainty: A case study of Tehran
2Integrated production scheduling and distribution planning in dairy supply chain by hybrid modelling
3RFID and perishable inventory management with shelf-space and freshness dependent demand
4Strategies to characterize fungal lipases for applications in medicine and dairy industry
5Decision Support System, Based on the Paradigm of the Petri Nets, for the Design and Operation of a Dairy Plant
6Multi-bucket optimization for integrated planning and scheduling in the perishable dairy supply chain
7Quantitative models for supply chain management within dairy industry: A review and discussion
8A food traceability framework for dairy and other low-margin products
9Possibilistic programming approach for production and distribution problem in milk supply chain
10Recent advances in spray drying relevant to the dairy industry: A comprehensive critical review
11Implementation of Artificial Intelligence Policy in the Field of Livestock and Dairy Farm
12Integrated production and distribution scheduling with a perishable product
13Order Picking Process in Warehouse: Case Study of Dairy Industry in Croatia
14Planning and scheduling of the make-and-pack dairy production under lifetime uncertainty
15The effect of Sales and Operations Planning (S&OP) on supply chain’s total performance: A case study in an Iranian dairy company
16A comprehensive model of demand prediction based on hybrid artificial intelligence and metaheuristic algorithms: A case study in dairy industry
17A bi-objective multi-echelon supply chain model with Pareto optimal points evaluation for perishable products under uncertainty
18An integrated two-layer network model for designing a resilient, green, closed loop supply chain of perishable products under disruption
19Heuristic method for robust optimization model for green closed-loop supply chain network design of perishable goods
20Optimal Production Scheduling in the Dairy Industries
21Proposing a new model for location—Routing problem of perishable raw material suppliers with using meta-heuristic algorithms
22A stochastic approach for integrated production and distribution planning in dairy supply chains
23An intelligent Edge-IoT platform for monitoring livestock and crops in a dairy farming scenario
24Artificial intelligence applied to a robotic dairy farm to model milk productivity and quality based on cow data and daily environmental parameters
25Energy cost assessment of a dairy industry wastewater treatment plant
26IoT for Development of Smart Dairy Farming
27Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle
28Optimization of Sampling for Monitoring Chemicals in the Food Supply Chain Using a Risk-Based Approach: The Case of Aflatoxins and Dioxins in the Dutch Dairy Chain
29Robust design and planning for a multi-mode multi-product supply network: A dairy industry case study
30The design and planning of an integrated supply chain for perishable products under uncertainties: A case study in milk industry
31Towards combining data prediction and internet of things to manage milk production on dairy cows
32A comparison of analytical test methods in dairy processing
33A robust multi-objective optimization model for inventory and production management with environmental and social consideration: A real case of dairy industry
34Biometric physiological responses from dairy cows measured by visible remote sensing are good predictors of milk productivity and quality through artificial intelligence
35Future of dairy farming from the Dairy Brain perspective: Data integration, analytics, and applications
36Implementation of technical and technological progress in dairy production
37IoT-based Dairy Supply Chain—An Ontological Approach
38Milk reception in a time-efficient manner: A case from the dairy processing plant
39Multi-Product Multi Echelon Measurements of Perishable Supply Chain: Fuzzy Non-Linear Programming Approach
40Robustness within the optimal economic poly generation system for a dairy industry
41Sustainable closed-loop supply chain for dairy industry with robust and heuristic optimization
42Environmental friendly route design for a milk collection problem: The case of an Indian dairy
43Optimization of resource flows across the whole supply chain. Application to a case study in the dairy industry
44Over 20 years of machine learning applications on dairy farms: A comprehensive mapping study
45The role of livestock feed fertilization as an improvement of sustainability in the dairy sector

References

  1. Akanksha, E.; Debnath, A.; Dey, B. Extensive Review of Cloud Based Internet of Things Architecture and Current Trends. In Proceedings of the 6th International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 20–22 January 2021. [Google Scholar] [CrossRef]
  2. Davis, C.G.; Hahn, W. Assessing the Status of the Global Dairy Trade. Int. Food Agribus. Manag. Rev. 2016, 19. Available online: https://www.ifama.org/resources/Documents/v19ib/0_EdIntro.pdf (accessed on 16 May 2022).
  3. Houhou, R.; Bocklitz, T. Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data. Anal. Sci. Adv. 2021, 2, 128–141. [Google Scholar] [CrossRef]
  4. Dwivedi, S. Economic Survey Says per Capita Milk Availability Increased to 427 Grams/Day. Krishi Jagran, 1 February 2022. Available online: https://krishijagran.com/agriculture-world/economic-survey-says-per-capita-milk-availability-increased-to-427-gramsday/(accessed on 17 May 2022).
  5. Angad Singh Punia, Dairy. Invest India. Available online: https://www.investindia.gov.in/sector/food-processing/dairy (accessed on 17 May 2022).
  6. Mor, R.S.; Bhardwaj, A.; Singh, S.; Khan, S.A.R. Modelling the distribution performance in dairy industry: A predictive analysis. Logforum 2021, 17, 425–440. [Google Scholar] [CrossRef]
  7. Indian Brand Equity Foundation. Indian Food Processing. Ministry of Food Processing Industries (MoFPI). December 2020. Available online: https://www.ibef.org/industry/indian-food-industry.aspx (accessed on 17 May 2022).
  8. Ramírez-Faz, J.; Fernández-Ahumada, L.M.; Fernández-Ahumada, E.; López-Luque, R. Monitoring of temperature in retail refrigerated cabinets applying iot over open-source hardware and software. Sensors 2020, 20, 846. [Google Scholar] [CrossRef] [Green Version]
  9. Georgiadis, G.P.; Kopanos, G.M.; Karkaris, A.; Ksafopoulos, H.; Georgiadis, M.C. Optimal Production Scheduling in the Dairy Industries. Ind. Eng. Chem. Res. 2019, 58, 6537–6550. [Google Scholar] [CrossRef]
  10. Khademi, H. A comprehensive model of demand prediction based on hybrid artificial intelligence and metaheuristic algorithms: A case study in dairy industry. J. Ind. Syst. Eng. 2018, 11, 190–203. Available online: http://www.jise.ir/article_76524.html (accessed on 16 May 2022).
  11. Jukan, A.; Carpio, F.; Masip, X.; Ferrer, A.J.; Kemper, N.; Stetina, B.U. Fog-to-Cloud Computing for Farming: Low-Cost Technologies, Data Exchange, and Animal Welfare. Computer 2019, 52, 41–51. [Google Scholar] [CrossRef] [Green Version]
  12. Fuentes, S.; Viejo, C.G.; Cullen, B.; Tongson, E.; Chauhan, S.S.; Dunshea, F.R. Artificial intelligence applied to a robotic dairy farm to model milk productivity and quality based on cow data and daily environmental parameters. Sensors 2020, 20, 2975. [Google Scholar] [CrossRef]
  13. Shine, P.; Murphy, M.D. Over 20 years of machine learning applications on dairy farms: A comprehensive mapping study. Sensors 2022, 22, 52. [Google Scholar] [CrossRef]
  14. Jouzdani, J.; Govindan, K. On the sustainable perishable food supply chain network design: A dairy products case to achieve sustainable development goals. J. Clean. Prod. 2021, 278, 123060. [Google Scholar] [CrossRef]
  15. Jachimczyk, B.; Tkaczyk, R.; Piotrowski, T.; Johansson, S.; Kulesza, W.J. IoT-based dairy supply chain—An ontological approach. Elektron. Elektrotechn. 2021, 27, 71–83. [Google Scholar] [CrossRef]
  16. Correia, V.H.L.; de Abreu, R.P.; Carvalho, M. Robustness within the optimal economic polygeneration system for a dairy industry. J. Clean. Prod. 2021, 314, 127976. [Google Scholar] [CrossRef]
  17. Jouzdani, J.; Sadjadi, S.J.; Fathian, M. Dynamic dairy facility location and supply chain planning under traffic congestion and demand uncertainty: A case study of Tehran. Appl. Math. Model. 2013, 37, 8467–8483. [Google Scholar] [CrossRef]
  18. Bilgen, B.; Çelebi, Y. Integrated production scheduling and distribution planning in dairy supply chain by hybrid modelling. Ann. Oper. Res. 2013, 211, 55–82. [Google Scholar] [CrossRef]
  19. Piramuthu, S.; Zhou, W. RFID and perishable inventory management with shelf-space and freshness dependent demand. Int. J. Prod. Econ. 2013, 144, 635–640. [Google Scholar] [CrossRef]
  20. Gopinath, S.C.B.; Anbu, P.; Lakshmipriya, T.; Hilda, A. Strategies to characterize fungal lipases for applications in medicine and dairy industry. Biomed. Res. Int. 2013, 2013, 154549. [Google Scholar] [CrossRef] [Green Version]
  21. Latorre-Biel, J.I.; Jiménez-Macías, E.; Blanco-Fernández, J.; Martínez-Cámara, E.; Sáenz-Díez, J.C.; Pérez-Parte, M. Decision Support System, Based on the Paradigm of the Petri Nets, for the Design and Operation of a Dairy Plant. Int. J. Food Eng. 2015, 11, 767–776. [Google Scholar] [CrossRef]
  22. Sel, C.; Bilgen, B.; Bloemhof-Ruwaard, J.M.; van der Vorst, J.G.A.J. Multi-bucket optimization for integrated planning and scheduling in the perishable dairy supply chain. Comput. Chem. Eng. 2015, 77, 59–73. [Google Scholar] [CrossRef]
  23. Touil, A.; Echchtabii, A.; Charkaoui, A. Possibilistic programming approach for production and distribution problem in milk supply chain. In Proceedings of the 2016 3rd International Conference on Logistics Operations Management (GOL), Fez, Morocco, 23–25 May 2016; pp. 1–8. [Google Scholar] [CrossRef]
  24. Habazin, J.; Glasnović, A.; Bajor, I. Order picking process in warehouse: Case study of dairy industry in Croatia. Promet-Traffic Transp. 2017, 29, 57–65. [Google Scholar] [CrossRef] [Green Version]
  25. Devapriya, P.; Ferrell, W.; Geismar, N. Integrated production and distribution scheduling with a perishable product. Eur. J. Oper. Res. 2017, 259, 906–916. [Google Scholar] [CrossRef]
  26. Yavari, M.; Geraeli, M. Heuristic method for robust optimization model for green closed-loop supply chain network design of perishable goods. J. Clean. Prod. 2019, 226, 282–305. [Google Scholar] [CrossRef]
  27. Yaghoubi, A.; Akrami, F. Proposing a new model for location—Routing problem of perishable raw material suppliers with using meta-heuristic algorithms. Heliyon 2019, 5, e03020. [Google Scholar] [CrossRef] [Green Version]
  28. Dutta, P.; Shrivastava, H. The design and planning of an integrated supply chain for perishable products under uncertainties: A case study in milk industry. J. Model. Manag. 2020, 15, 1301–1337. [Google Scholar] [CrossRef]
  29. Sel, Ç.; Bilgen, B. Quantitative models for supply chain management within dairy industry: A review and discussion. Eur. J. Ind. Eng. 2015, 9, 561–594. [Google Scholar] [CrossRef]
  30. Li, H.; Zhang, B.; Zhang, L.; Xue, Y.; He, M.; Ren, C. A food traceability framework for dairy and other low-margin products. IBM J. Res. Dev. 2016, 60, 10:1–10:8. [Google Scholar] [CrossRef]
  31. Schuck, P.; Jeantet, R.; Bhandari, B.; Chen, X.D.; Perrone, T.; de Carvalho, A.F.; Fenelon, M.; Kelly, P. Recent advances in spray drying relevant to the dairy industry: A comprehensive critical review. Dry. Technol. 2016, 34, 1773–1790. [Google Scholar] [CrossRef]
  32. Chimakurthi, V.N.S.S. Implementation of Artificial Intelligence Policy in the Field of Livestock and Dairy Farm. Am. J. Trade Policy 2019, 6, 113–118. Available online: https://ideas.repec.org/a/ris/ajotap/0051.html (accessed on 16 May 2022). [CrossRef]
  33. Sel, Ç.; Bilgen, B.; Bloemhof-Ruwaard, J. Planning and scheduling of the make-and-pack dairy production under lifetime uncertainty. Appl. Math. Model. 2017, 51, 129–144. [Google Scholar] [CrossRef]
  34. Nemati, Y.; Madhoshi, M.; Ghadikolaei, A.S. The effect of Sales and Operations Planning (S&OP) on supply chain’s total performance: A case study in an Iranian dairy company. Comput. Chem. Eng. 2017, 104, 323–338. [Google Scholar] [CrossRef]
  35. Gitinavard, H.; Shirazi, M.A.; Ghodsypour, S. A bi-objective multi-echelon supply chain model with Pareto optimal points evaluation for perishable products under uncertainty. Sci. Iran. 2019, 26, 2952–2970. [Google Scholar] [CrossRef]
  36. Yavari, M.; Zaker, H. An integrated two-layer network model for designing a resilient green-closed loop supply chain of perishable products under disruption. J. Clean. Prod. 2019, 230, 198–218. [Google Scholar] [CrossRef]
  37. Guarnaschelli, A.; Salomone, H.E.; Méndez, C.A. A stochastic approach for integrated production and distribution planning in dairy supply chains. Comput. Chem. Eng. 2020, 140, 106966. [Google Scholar] [CrossRef]
  38. Alonso, R.S.; Sittón-Candanedo, I.; García, Ó.; Prieto, J.; Rodríguez-González, S. An intelligent Edge-IoT platform for monitoring livestock and crops in a dairy farming scenario. Ad Hoc Netw. 2020, 98, 102047. [Google Scholar] [CrossRef]
  39. Mor, R.S.; Bhardwaj, A.; Singh, S. A structured-literature-review of the supply chain practices in dairy industry. J. Oper. Supply Chain. Manag. 2018, 11, 14–25. [Google Scholar] [CrossRef]
  40. Yapicioğlu, P. Energy Cost Estimation for a Dairy Wastewater Treatment Plant in Terms of Organic Load. Acad. Perspect. Procedia 2019, 2, 859–864. [Google Scholar] [CrossRef] [Green Version]
  41. Jouzdani, J.; Fathian, M.; Makui, A.; Heydari, M. Robust design and planning for a multi-mode multi-product supply network: A dairy industry case study. Oper. Res. 2020, 20, 1811–1840. [Google Scholar] [CrossRef]
  42. Akbar, M.O.; Khan, M.S.S.; Ali, M.J.; Hussain, A.; Qaiser, G.; Pasha, M.; Pasha, U.; Missen, M.S.; Akhtar, N. IoT for Development of Smart Dairy Farming. J. Food Qual. 2020, 2020, 4242805. [Google Scholar] [CrossRef]
  43. Taneja, M.; Byabazaire, J.; Jalodia, N.; Davy, A.; Olariu, C.; Malone, P. Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle. Comput. Electron. Agric. 2020, 171, 105286. [Google Scholar] [CrossRef]
  44. Feil, A.A.; Schreiber, D.; Haetinger, C.; Haberkamp, M.; Kist, J.I.; Rempel, C.; Maehler, A.E.; Gomes, M.C.; Da Silva, G.R. Sustainability in the dairy industry: A systematic literature review. Environ. Sci. Pollut. Res. 2020, 27, 33527–33542. [Google Scholar] [CrossRef]
  45. Moher, D.; Shamseer, L.; Clarke, M.; Ghersi, D.; Liberati, A.; Petticrew, M.; Shekelle, P.; Stewart, L.A. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst. Rev. 2015, 4, 1. [Google Scholar] [CrossRef]
  46. Durach, C.F.; Kembro, J.; Wieland, A. A New Paradigm for Systematic Literature Reviews in Supply Chain Management. J. Supply Chain. Manag. 2017, 53, 67–85. [Google Scholar] [CrossRef]
  47. Tranfield, D.; Denyer, D.; Smart, P. Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. Br. J. Manag. 2003, 14, 207–222. [Google Scholar] [CrossRef]
  48. Sharma, S.; Gahlawat, V.K.; Rahul, K.; Mor, R.S.; Malik, M. Sustainable Innovations in the Food Industry through Artificial Intelligence and Big Data Analytics. Logistics 2021, 5, 66. [Google Scholar] [CrossRef]
  49. Wang, Z.; van der Fels-Klerx, H.J.; Lansink, A.G.J.M.O. Optimization of Sampling for Monitoring Chemicals in the Food Supply Chain Using a Risk-Based Approach: The Case of Aflatoxins and Dioxins in the Dutch Dairy Chain. Risk Anal. 2020, 40, 2539–2560. [Google Scholar] [CrossRef]
  50. Righi, R.d.; Goldschmidt, G.; Kunst, R.; Deon, C.; da Costa, C.A. Towards combining data prediction and internet of things to manage milk production on dairy cows. Comput. Electron. Agric. 2020, 169, 105156. [Google Scholar] [CrossRef]
  51. Burke, N.; Zacharski, K.; Adley, C.C.; Southern, M. A comparison of analytical test methods in dairy processing. Food Control 2021, 121, 107637. [Google Scholar] [CrossRef]
  52. Shafiee, F.; Kazemi, A.; Jafarnejad Chaghooshi, A.; Sazvar, Z.; Amoozad Mahdiraji, H. A robust multi-objective optimization model for inventory and production management with environmental and social consideration: A real case of dairy industry. J. Clean. Prod. 2021, 294, 126230. [Google Scholar] [CrossRef]
  53. Fuentes, S.; Viejo, C.G.; Tongson, E.; Lipovetzky, N.; Dunshea, F.R. Biometric physiological responses from dairy cows measured by visible remote sensing are good predictors of milk productivity and quality through artificial intelligence. Sensors 2021, 21, 6844. [Google Scholar] [CrossRef]
  54. Cabrera, V.E.; Fadul-Pacheco, L. Future of dairy farming from the Dairy Brain perspective: Data integration, analytics, and applications. Int. Dairy J. 2021, 121, 105069. [Google Scholar] [CrossRef]
  55. Gaworski, M. Implementation of technical and technological progress in dairy production. Processes 2021, 9, 2103. [Google Scholar] [CrossRef]
  56. Zacharski, K.A.; Burke, N.; Adley, C.C.; Hogan, P.; Ryan, A.; Southern, M. Milk reception in a time-efficient manner: A case from the dairy processing plant. Food Control 2021, 124, 107939. [Google Scholar] [CrossRef]
  57. Ali, S.S.; Barman, H.; Kaur, R.; Tomaskova, H.; Roy, S.K. Multi-product multi echelon measurements of perishable supply chain: Fuzzy non-linear programming approach. Mathematics 2021, 9, 2093. [Google Scholar] [CrossRef]
  58. Gholizadeh, H.; Jahani, H.; Abareshi, A.; Goh, M. Sustainable closed-loop supply chain for dairy industry with robust and heuristic optimization. Comput. Ind. Eng. 2021, 157, 107324. [Google Scholar] [CrossRef]
  59. Jabir, E.; Panicker, V.; Sridharan, R. Environmental friendly route design for a milk collection problem: The case of an Indian dairy. Int. J. Prod. Res. 2022, 60, 912–941. [Google Scholar] [CrossRef]
  60. Cóccola, M.E.; Basán, N.; Méndez, C.A.; Dondo, R.G. Optimization of resource flows across the whole supply chain. Application to a case study in the dairy industry. Comput. Chem. Eng. 2022, 158, 107632. [Google Scholar] [CrossRef]
  61. Quintero-Herrera, S.; Zwolinski, P.; Evrard, D.; Cano-Gómez, J.J.; Botello-Álvarez, J.E.; Rivas-García, P. The role of livestock feed fertilization as an improvement of sustainability in the dairy sector. Sustain. Prod. Consum. 2022, 31, 448–458. [Google Scholar] [CrossRef]
  62. Brar, G.S.; Saini, G. Milk Run Logistics: Literature Review and Directions Gurinder; Newswood Ltd.: Hong Kong, China, 2011; Volume 1, Available online: http://www.iaeng.org/publication/WCE2011/WCE2011_pp797-801.pdf (accessed on 16 May 2022).
  63. Tang, Y.; Dananjayan, S.; Hou, C.; Guo, Q.; Luo, S.; He, Y. A survey on the 5G network and its impact on agriculture: Challenges and opportunities. Comput. Electron. Agric. 2021, 180, 105895. [Google Scholar] [CrossRef]
  64. Poongodi, T.; Rathee, A.; Indrakumari, R.; Suresh, P. IoT Sensing Capabilities: Sensor Deployment and Node Discovery, Wearable Sensors, Wireless Body Area Network (WBAN), Data Acquisition. In Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm; Peng, S.-L., Pal, S., Huang, L., Eds.; Springer International Publishing: Cham, Switzerland, 2020; pp. 127–151. [Google Scholar] [CrossRef]
  65. Witten, I.H.; Holmes, G.; McQueen, R.J.; Smith, L.A.; Cunningham, S.J. Practical Machine Learning and Its Application to Problems in Agriculture; Department of Computer Science, University of Waikato: Hamilton, New Zealand, 1993; Available online: https://hdl.handle.net/10289/9915 (accessed on 16 May 2022).
  66. Amiri, S.; Aghamirzaei, M.; Mostashari, P.; Sarbazi, M.; Tizchang, S.; Madahi, H. The impact of biotechnology on dairy industry. In Microbial Biotechnology in Food and Health; Elsevier: Amsterdam, The Netherlands, 2021; pp. 53–79. [Google Scholar] [CrossRef]
  67. Yildirim-Elikoglu, S.; Erdem, Y.K. Interactions between milk proteins and polyphenols: Binding mechanisms, related changes, and the future trends in the dairy industry. Food Rev. Int. 2018, 34, 665–697. [Google Scholar] [CrossRef]
  68. Badia-Melis, R.; Mishra, P.; Ruiz-García, L. Food traceability: New trends and recent advances. A review. Food Control 2015, 57, 393–401. [Google Scholar] [CrossRef]
  69. Malihi, E.; Aghdasi, M. A decision framework for optimisation of business processes aligned with business goals. Int. J. Bus. Inf. Syst. 2013, 15, 22–42. [Google Scholar] [CrossRef]
  70. Lu, S.; Xu, C.; Zhong, R.Y.; Wang, L. A RFID-enabled positioning system in automated guided vehicle for smart factories. J. Manuf. Syst. 2017, 44, 179–190. [Google Scholar] [CrossRef]
  71. Bibi, F.; Guillaume, C.; Gontard, N.; Sorli, B. A review: RFID technology having sensing aptitudes for food industry and their contribution to tracking and monitoring of food products. Trends Food Sci. Technol. 2017, 62, 91–103. [Google Scholar] [CrossRef]
  72. Astill, J.; Dara, R.A.; Campbell, M.; Farber, J.M.; Fraser, E.D.; Sharif, S.; Yada, R.Y. Transparency in food supply chains: A review of enabling technology solutions. Trends Food Sci. Technol. 2019, 91, 240–247. [Google Scholar] [CrossRef]
  73. Provenance. Blockchain: The Solution for Transparency in Product Supply Chains. 21 November 2015. Available online: https://www.provenance.org/whitepaper (accessed on 8 January 2022).
  74. Praveen, U.; Farnaz, G.; Hatim, G. Inventory management and cost reduction of supply chain processes using AI based time-series forecasting and ANN modeling. Procedia Manuf. 2019, 38, 256–263. [Google Scholar] [CrossRef]
  75. Butler, M.; Herlihy, P.; Keenan, P.B. Integrating information technology and operational research in the management of milk collection. J. Food Eng. 2005, 70, 341–349. [Google Scholar] [CrossRef]
  76. Anjomshoae, A.; Banomyong, R.; Mohammed, F.; Kunz, N. A systematic review of humanitarian supply chains performance measurement literature from 2007 to 2021. Int. J. Disaster Risk Reduct. 2022, 72, 102852. [Google Scholar] [CrossRef]
Logistics 06 00074 g001 550
Figure 1. Flowchart for review process based on PRISMA guidelines.
Figure 1. Flowchart for review process based on PRISMA guidelines.
Logistics 06 00074 g001
Logistics 06 00074 g002 550
Figure 2. The number of articles per journal (if no. of articles ≥ 2).
Figure 2. The number of articles per journal (if no. of articles ≥ 2).
Logistics 06 00074 g002
Logistics 06 00074 g003 550
Figure 3. Publications per year.
Figure 3. Publications per year.
Logistics 06 00074 g003
Table
Table 1. Inclusion/exclusion criteria.
Table 1. Inclusion/exclusion criteria.
Inclusion CriteriaExclusion Criteria
Articles published from 2013–2022Articles published before 2013 and after 2022
Published in the English languagePublished in a non-English language
Articles focused on Optimization Techniques based on Artificial Intelligence, Machine Learning, and Dairy supply chainArticles that are not relevant to review questions
Articles related to food scienceArticles related to non-food domains
Full-text articles onlyArticles that are not available in full-text form
Peer-reviewed research and review articlesConference proceedings, thesis, poster abstracts, and short communications are excluded
Table
Table 2. Optimization techniques applied in DSC.
Table 2. Optimization techniques applied in DSC.
ReferenceAreaTechnique Used
ProductionProcessingInventoryDistributionMathematical ModellingAI/ML/IoT
[9]✓ ✓
[10] ✓✓✓
[12]✓ ✓
[13]✓ ✓
[15]✓✓✓✓ ✓
[16] ✓ ✓
[17]✓ ✓ ✓
[18]✓ ✓✓✓
[19] ✓ ✓
[20] ✓ ✓
[21]✓✓✓✓ ✓
[22]✓ ✓✓
[23]✓✓✓✓✓
[24] ✓ ✓
[25]✓
[26]✓ ✓✓✓
[27] ✓ ✓
[28]✓ ✓
[29]✓ ✓
[30]✓✓✓✓ ✓
[31] ✓
[32]✓ ✓
[33]✓ ✓✓
[34] ✓✓
[35] ✓✓
[36]✓ ✓✓
[37]✓ ✓✓
[38]✓ ✓
[40] ✓ ✓
[41] ✓✓
[42]✓ ✓
[43]✓ ✓
[49]✓✓ ✓
[50]✓ ✓
[51]
[52] ✓✓ ✓✓
[53]✓ ✓
[54]✓ ✓
[55]✓
[56] ✓
[57]✓✓✓✓✓
[58]✓✓✓✓✓✓
[59] ✓ ✓
[60]✓✓✓✓✓
[61]✓ ✓
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Malik, M.; Gahlawat, V.K.; Mor, R.S.; Dahiya, V.; Yadav, M. Application of Optimization Techniques in the Dairy Supply Chain: A Systematic Review. Logistics 2022, 6, 74. https://doi.org/10.3390/logistics6040074

AMA Style

Malik M, Gahlawat VK, Mor RS, Dahiya V, Yadav M. Application of Optimization Techniques in the Dairy Supply Chain: A Systematic Review. Logistics. 2022; 6(4):74. https://doi.org/10.3390/logistics6040074

Chicago/Turabian Style

Malik, Mohit, Vijay Kumar Gahlawat, Rahul S Mor, Vijay Dahiya, and Mukheshwar Yadav. 2022. "Application of Optimization Techniques in the Dairy Supply Chain: A Systematic Review" Logistics 6, no. 4: 74. https://doi.org/10.3390/logistics6040074

Article Metrics

Back to TopTop