Enhancing the Sustainability of Retail Supply Chains Through an Integrated Industry 4.0 Model

Abstract

Supply chain management in the retail sector faces numerous internal and external challenges, increasing the need to incorporate environmental and social indicators into performance evaluation. The aim of this study is to identify these challenges and propose solutions that reduce the negative environmental impact of retail companies and enhance their social responsibility by leveraging Industry 4.0 technologies. A review of the scientific literature reveals a lack of research focused on improving supply chain sustainability in the retail sector. Therefore, an expert study was conducted to identify five key barriers hindering the effective integration of digital innovations (e.g., Internet of Things, artificial intelligence) into retail logistics operations. Based on the insights from this study, an integrated model for enhancing supply chain sustainability was developed, grounded in circular economy principles and advanced Industry 4.0 technologies. This model supports retail companies in increasing supply chain resilience and sustainability by outlining measures ranging from problem diagnostics and data management to the implementation of automated solutions and the strengthening of personnel competencies. The application of the model aims to reduce environmental pollution, improve resource efficiency, and promote socially responsible practices throughout the supply chain.

1. Introduction

Supply chain management in the retail sector is facing increasingly complex challenges driven by globalization, technological changes, consumer expectations, and sustainability requirements [1]. Traditional logistics models focused on cost reduction are increasingly inadequate to meet modern business and environmental needs [2]. Therefore, growing attention is being paid to the integration of Industry 4.0 technologies to enhance supply chain efficiency, resilience, and sustainability [3]. Industry 4.0 solutions such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, and big data analytics (BDA) enable real-time monitoring of supply flows, route optimization, and demand forecasting [4,5]. However, the implementation of these technologies in the retail sector faces specific challenges—from infrastructure deficiencies to a lack of employee competencies [6,7]. However, no retail company has yet implemented all Industry 4.0 technologies under circular economic conditions to achieve comprehensive supply chain resilience and sustainability. In summary, Industry 4.0 (I4.0) technologies have great potential to transform supply chains into more sustainable systems, but there is a lack of research and models that synergize technological advancement with social responsibility, circular economy principles, and sustainability. This article presents a two-stage survey. First, challenges hindering I4.0 adoption in retail supply chains were identified from the literature to facilitate the creation of a resilient supply chain. Second, an expert panel selected and ranked these challenges by importance.

Existing research often focuses on individual technologies, but the scientific novelty of this research is that it takes a comprehensive approach, examining various challenges of I4.0 implementation in the retail sector, rather than individual I4.0 technologies. This study also identifies challenges that have a significant impact on the implementation of I4.0. In addition, the research encourages the identification, assessment, and hierarchical classification of I4.0 implementation challenges related to supply chain resilience according to their significance.

Research aim: To investigate the challenges hindering the smooth integration of I4.0 into retail supply chains to reduce environmental impact and enhance social responsibility.

Research object: Solving supply chain process management problems in retail companies using I4.0 technologies to reduce environmental impact and enhance social responsibility.

Research objectives: (1) identify supply chain process management problems in retail companies; (2) identify challenges hindering smooth I4.0 integration into retail supply chains; (3) conduct a qualitative expert survey to assess the importance/weight of challenges affecting I4.0 implementation using a multi-criteria decision-making method (MCDM); (4) summarize and process the collected data to develop a sequence of actions and a model for addressing I4.0 implementation challenges in retail supply chains.

2. Literature Analysis

Supply chains are becoming increasingly vulnerable due to pandemics, geopolitical conflicts, and climate change [8]. Research shows that sustainability and resilience goals can be aligned if integrated through digital innovations [9]. It is also emphasized that supply chain sustainability depends on the ability to adapt to unforeseen circumstances [2]. Applications of I4.0 technologies (robotics, augmented reality, blockchain) in the meat supply chain highlight their role in ensuring food safety, transparency, and waste reduction [10]. Tang et al. [11] demonstrate how AI models (e.g., XGBoost) can be used for inventory forecasting in cross-border e-commerce, reducing the bullwhip effect and improving supply chain resilience. I4.0 technologies enable process automation, improved data transparency, and real-time decision-making [12,13]. Swink et al. [14] identify types of supply chain visibility (SCV) and their relationship with contextual factors. AI helps forecast demand and optimize routes, while IoT devices enable tracking of goods throughout the supply chain [4,15,16]. Shakur et al. discuss how to address challenges for smoothing the supply chain (SC) and logistics operations, and one solution is to adopt I4.0-based technologies in the FMCG business processes [5]. Pessot et al. [17] propose a systemic model showing how I4.0 technologies (AI, IoT, blockchain) can help supply chains adapt to megatrends—urbanization, climate change, geopolitical shifts—highlighting five key supply chain capabilities needed in the modern context. Circular economic principles—reuse, recycling, waste reduction—are becoming increasingly relevant in the retail sector [18]. Digital technologies enable more efficient management of returns, waste, and product life cycles [13,19]. It is also emphasized that circular economy models must be integrated into the entire supply chain strategy [20]. Calandra et al. [21] show how blockchain can be used to develop sustainable business models (SBM). Analyzing 20 cases, they found that blockchain helps reduce supply chain costs, increase transparency, and monitor environmental impact—especially useful for achieving the UN Sustainable Development Goals (SDGs). Supplier selection and evaluation based on sustainability criteria are essential for long-term collaboration [22,23]. It is important to consider not only supplier potential but also their willingness to cooperate on sustainability. Risk management is also highlighted as crucial in supplier coordination [24]. Aman et al. [25] explore how sustainability KPIs can be integrated into risk management processes, showing that both strategic and operational KPIs help manage risks in BoP contexts, distinguishing between preventive and reactive risk management methods. In the retail sector, digitalization often focuses on e-commerce, with less attention to supply chain digitalization [26,27]. Research indicates the need to strengthen employee competencies, invest in infrastructure, and develop digital maturity models [3,7]. Liu et al. [28] propose a supply chain coordination mechanism based on CSR cost-sharing, showing that CSR investments can increase overall profitability if properly distributed among partners—especially relevant in retail-dominated chains. Oliveira-Dias et al. [29] propose the ‘Agile Supply Chain 4.0’ model, categorizing I4.0 technologies into mature and emerging, and showing how they accelerate physical, informational, financial, and service flows in the supply chain. Smart warehouses based on I4.0 principles reduce errors, increase efficiency, and ensure transparency [15], but require not only technological but also organizational changes. Moreover, automation must not violate workers’ rights and social standards [26]. Holmström et al. [30] analyze how digital encapsulation allows each product to have a unique digital representation, facilitating individualized production and distribution—transforming traditional economies of scale and enabling decentralized decision-making. Qi et al. [31] review data-driven retail solutions, focusing on inventory management where machine learning improves demand forecasting and order optimization, reducing surplus and boosting efficiency. Literature analysis reveals that I4.0 technologies are key to achieving supply chain sustainability. Research shows that digitalization, IoT, AI, blockchain, and data analytics not only improve efficiency but also address environmental and social challenges. These technologies ensure transparency and traceability, while CSR integration fosters partner collaboration. KPI systems help assess sustainability progress even in high-risk environments.

The presented model focuses on the sustainability of retail supply chains by integrating I4.0 technologies and circular economy principles. The structure consists of three levels: diagnostics, data management, and automation. It also includes a component focused on developing personnel competencies. The model stands out for its practical approach, targeting the identification and overcoming of challenges in the retail sector. A comparison with several other models is included in the

Table 1. Table of model comparison.

Model	Orientation	Methodology	Uniqueness	Comparison with the Research Model
A Framework for Sustainable Manufacturing: Integrating Industry 4.0 Technologies with Industry 5.0 Values [32]	Sustainable manufacturing, Industry 4.0 and 5.0 integration	Analytical framework, synthesis of technological and ethical aspects	Combining technological solutions with social values	Different orientation–focus on manufacturing, not retail
Benchmarking Sustainability on an Industrial Case Within Industry 4.0 Paradigm: Advantages of Involving Exergetic Analysis in Life Cycle Thinking [33]	Industrial case analysis, energy efficiency	Life cycle assessment, exergetic analysis	Technical approach to energy and resource efficiency	Different methodology–technical assessment instead of practical solution presentation
Driving sustainable supply chain performance through digital transformation the role of information exchange and responsiveness [34]	Sustainable supply chain, digital transformation	Analysis of data exchange and responsiveness capabilities	Focus on the impact of digital technologies on sustainability	Digital transformation is examined more broadly, lacks specific sector application
Linking the digital and sustainable transformation with supply chain practices [35]	Supply chain practices, digital and sustainable transformation	Literature review, analysis of best practices	Conceptualization of the link between digitization and sustainability	Theoretical model, lacking specific sector application
Revitalizing the circular economy: An exploration of e-waste recycling approaches in a technological epoch [36]	Circular economy, e-waste recycling	Analysis of technological recycling methods	Focus on waste management technologies	Different thematic area–focused on waste recycling, not supply chain sustainability

.

3. Methodology

3.1. Research Plan and Data Collection

Based on the analysis of the scientific literature, nine challenges were identified that hinder the implementation of I4.0 in retail supply chains (high investment and resource requirements; incompatible technological infrastructure; unclear value chain; shortage of highly skilled personnel; ineffective management of technological transitions; complexity of database system management; resistance to change; lack of government support and regulation; unclear return on digitalization). Respondents were selected from professionals actively engaged in retail supply chain operations. Experts were chosen for their experience and knowledge in supply chain, operations, logistics, procurement, and quality management, with at least seven years of professional experience. The experts were selected using purposive sampling, which is suitable for selecting individuals for specific evaluations to achieve the research objective. The survey was conducted both digitally and in person. Initially, 13 retail sector experts were approached, and 11 (84.62%) agreed to participate. These experts were surveyed to identify and summarize key elements for further research. It should be noted that it is difficult in a small country such as Lithuania to find competent experts who work in the retail supply chain, have experience in implementing I4.0, and are willing to participate in the research.

Experts answered binary questions (“yes” or “no”) to evaluate the relevance of each challenge. A “no” response was scored as 0, and a “yes” as 1. The arithmetic meaning of each task was calculated, and tasks with a mean score above 0.5 were selected for further analysis. After confirming the final nine challenges, the same 11 experts were invited to participate in the next phase of the research. For convenience, the survey was conducted online using standardized forms.

3.2. Analysis of Multi-Criteria Decision-Making Methods

Difficulties often arise because quantitative models require assistance in collecting the necessary data, especially in large industries. Therefore, established MCDM tools such as DEMATEL, NSGA-II, Bayesian BWM, or MILP models are typically used in studies to quantify complex decision-making scenarios [5]. In this research, Kendall’s rank correlation method was applied to retail supply chains to determine the degree of consistency among expert opinions, which is very important when making complex decisions with limited data.

According to Zavadskas [37] and Sivilevičius [38], the essence of expert evaluation methods lies in the rational organization of expert assessments using quantitative opinion evaluation and result processing. When conducting expert surveys, it is essential to choose an appropriate method, such as questionnaires, interviews, brainstorming, Delphi method, or discussions [39]. To assess the impact of challenges on I4.0 implementation in retail supply chains, a questionnaire-based survey of expert groups was conducted.

Once the survey results were obtained, data were processed using multi-criteria evaluation methods. The initial data consisted of numerical values representing expert preferences and their justification. The goal of processing was to obtain summarized data and new information hidden in expert evaluations. Based on the results, problem-solving strategies were formulated [38].

As noted by Simanavičienė [40], multi-criteria decision-making (MCDM) methods are used to find optimal solutions and are divided into multi-objective and multi-goal methods. Although many widely accepted MCDM methods exist, none are perfect, and the field is still evolving.

To determine the consistency of expert opinions regarding the impact of challenges on implementation of I4.0, Kendall’s rank correlation method was used; ref. [41]. Rankings were transformed into linear weights using the average rank transformation into weight (ARTIW) method [42].

The determination of the impact of challenges on the implementation of I4.0 in the supply chain of the retail sector is carried out based on the ARTIW method application algorithm. The calculations then follow the methodology presented by Sivilevičius [38].

An expert group consisting of n experts quantitatively evaluates m objects (quality indicators). The evaluations R_ij (i = 1, 2, …, n; j = 1, 2, …, m) form a table (matrix) with n rows and m columns. Experts may assess the expected value R_ij in different ways. However, only the ranking of expert indicators is suitable for calculating the concordance coefficient [43].

Once all experts have assigned ranks to the criteria, the consistency of their opinions is determined by calculating Kendall’s rank concordance coefficient. The idea behind Kendall’s concordance coefficient is related to the sum of ranks R_j for each j-th criterion across all experts:

$$R_j={\sum }_{i=1}^n{R}_{ij} \left(j = 1, 2, \dots , m\right),$$

(1)

More precisely, it is associated with the sum of squared deviations S of the values R_j from the overall average $\overline{R}$:

$$S={\sum }_{j=1}^m{\left(R_j-\overline{R}\right)}^2,$$

(2)

where R_j—the sum of ranks assigned to the j-th criterion; (j = 1, 2, …, m); $\overline{R}$—the average rank of each criterion; n—the number of experts in the group (i = 1, 2, …, n); m—the number of criteria (j = 1, 2, …, m).

The overall average $\overline{R}$ is calculated using Formula (3):

$$\overline{R}={\displaystyle \frac{\sum _{j=1}^m{R}_j}{m}}={\displaystyle \frac{\sum _{j=1}^m\sum _{i=1}^n{R}_{ij}}{m}}={\displaystyle \frac{n\left(m+1\right)}{2}},$$

(3)

where R_ij—the rank assigned by the i-th expert to the j-th criterion.

If S is the actual sum of squares calculated using Formula (2), then the concordance coefficient W, assuming no tied ranks, is defined as the ratio between the obtained S and the corresponding maximum possible value S_max [44]:

$$w={\displaystyle \frac{12\cdot S}{n^2\cdot m(m^2-1)}}={\displaystyle \frac{12\cdot \mathrm{S}}{n^2\cdot (m^3-m)}},$$

(4)

where S—the sum of squared deviations from the average rank.

The sum of squared deviations S of ranks R_ij from the average rank can be conveniently calculated using Formula (5):

$$S={{\sum }_{j=1}^m\left[{\sum }_{i=1}^n{R}_{ij}-{\displaystyle \frac{1}{2}}\cdot n(m+1)\right]}^2.$$

(5)

The concordance coefficient can be applied in practice if its threshold value is determined, indicating when expert evaluations can be considered consistent. Kendall proved that if the number of criteria m > 7, the significance of the concordance coefficient can be assessed using the ${\chi }^2$(chi-square) Pearson criterion [45]:

$${\chi }^2=n(m-1)\cdot W={\displaystyle \frac{12\cdot S}{n\cdot m(m+1)}}.$$

(6)

The random variable follows a ${\chi }^2$ distribution with v = m − 1 degrees of freedom. Based on the chosen significance level α (typically 0.05 or 0.01 in practice) from table of the ${\chi }^2$ distribution, with v = m − 1 degrees of freedom, the critical value is determined ${\chi }_{kr}^2={\chi }_{v,\alpha }^2$. If the calculated ${\chi }^2$ value from Formula (6) exceeds ${\chi }_{kr}^2$—the expert opinions are considered consistent [45].

When the number of compared indicators (objects) m ranges from 3 to 7, the χ² distribution should be applied cautiously, as the critical value χ²_kr may exceed the calculated value even if the level of agreement among experts is sufficient. In such cases, probabilistic tables of the concordance coefficient or critical S value tables for 3 < m < 7 may be used [43].

The minimum value of the concordance coefficient W_min, below which it cannot be claimed that all n experts’ opinions on the quality of the object composed of m compared criteria are consistent at the specified significance level α and degrees of freedom v = m − 1, can be calculated using Formula (7):

$$W_{\mathrm{m}\mathrm{i}\mathrm{n}}={\displaystyle \frac{{\chi }_{v,\alpha }^2}{n(m-1)}},$$

(7)

where ${\chi }_{v,\alpha }^2$—the critical Pearson statistic value found by Sivilevičius [38], using the degrees of freedom and significance level α (${\chi }_{kr}^2$ = ${\chi }_{v,\alpha }^2$)

If expert opinions are consistent, the value of the concordance coefficient W is close to one; if evaluations differ significantly, W is closer to zero.

In practice, it is often more convenient to use significance indicators whose optimal value is the highest possible [37].

When the quality of an object is evaluated using an additive mathematical model, which calculates a composite quality indicator allowing the object’s quality to be expressed by a single value and compared with similar objects, it is more appropriate to use importance indicators Q_j rather than average ranks $\overline{R_j}$ which do not reflect how much more important one rank is over another.

The normalized importance of the quality criteria of the object (with their sum equal to one) can be determined by calculating the importance indicator Q_j for each criterion, as proposed by Sivilevičius [38], using Formula (8):

$${\overline{Q}}_j={\displaystyle \frac{(m+1)-{\overline{R}}_j}{\sum _{j=1}^m{\overline{R}}_j}},$$

(8)

where m—the number of criteria (indicators) reflecting the object’s quality (properties); $\overline{R_j}$—the average rank of the j-th criterion, calculated using Formula (3).

The importance indicators Q_j calculated in this way are highest for the most important criterion. Thus, the importance indicator Q_j allows not only the identification of which criterion is more important (as shown by average ranks $\overline{R_j}$), but also how many times one criterion is more important than another.

3.3. Determining the Consistency of Expert Group Opinions and the Importance of Challenges Affecting the Implementation of I4.0 in the Retail Sector Supply Chain

Based on the description of challenges affecting the implementation of I4.0 in the retail sector supply chain, a questionnaire was developed to evaluate these challenges.

The questionnaires were distributed to a group of experts consisting of 11 individuals. The selected experts have many years of professional experience not only in the supply chains of retail sector companies but also in other enterprises whose core activities are related to trade or transportation.

The ranks assigned by the expert group to the criteria and the results of the performed calculations are presented in

Table 2. Ranks assigned by the expert group to the criteria and results of the performed calculations.

Expert Number, i = (1, 2, …, n)	Challenges Affecting the Implementation of I4.0 in the Retail Supply Chain, j = (1, 2, …, m)									Sum
	K1	K2	K3	K4	K5	K6	K7	K8	K9
E1	2	7	4	3	1	9	6	8	5	45
E2	1	6	2	5	3	9	4	8	7	45
E3	3	6	1	5	2	8	7	9	4	45
E4	2	8	3	5	1	9	4	7	6	45
E5	5	4	6	1	2	8	3	7	9	45
E6	3	6	1	5	4	7	8	9	2	45
E7	4	8	3	5	1	9	2	6	7	45
E8	5	7	4	2	1	8	3	6	9	45
E9	2	6	3	5	1	8	4	9	7	45
E10	4	6	3	1	2	9	5	8	7	45
E11	1	8	4	2	3	9	6	6	6	45
$R_j=\sum _{j=1}^n{R}_{ij}$	32	72	34	39	21	93	52	83	69	495
${\overline{R}}_i={\displaystyle \frac{\sum _{j=1}^n{R}_{ij}}{n}}$	2.9	6.5	3.1	3.5	1.9	8.5	4.7	7.5	6.3	45
$\sum _{j=1}^n{R}_{ij}-{\displaystyle \frac{n\left(m+1\right)}{2}}$	−23	17	−21	−16	−34	38	−3	28	14	0
${\left[\sum _{j=1}^n{R}_{ij}-{\displaystyle \frac{n\left(m+1\right)}{2}}\right]}^2$	529	289	441	256	1156	1444	9	784	196	5104

.

The statistical Pearson criterion is determined using Formula (6):

$${\chi }^2={\displaystyle \frac{12\cdot 5104}{11\cdot 9\cdot (9+1)}}=61.8667.$$

The critical value ${\chi }_{v,\alpha }^2$ taken from Sivilevičius, with v = 9 − 1 = 8 degrees of freedom and a significance level of α = 0.050, is [38]:

$${\chi }_{v,\alpha }^2=15.5073.$$

The empirical value ${\chi }^2$ = 61.8667 is greater than the critical value ${\chi }_{kr}^2$ = 15.5073, which means that the expert evaluations are consistent, given the degrees of freedom and significance level $({\chi }_{kr}^2={\chi }_{\upsilon ,\alpha }^2)$.

Next, the concordance coefficient W, indicating the consistency of expert opinions, is calculated using Formula (4):

$$W={\displaystyle \frac{12\cdot S}{n^2\cdot m(m^2-1)}}={\displaystyle \frac{12\cdot 5104}{{11}^2\cdot (9^3-1)}}=0.7811.$$

Using Formula (7), the minimum value W_min is calculated:

$$W_{\mathrm{m}\mathrm{i}\mathrm{n}}={\displaystyle \frac{{\chi }_{v,\alpha }^2}{n(m-1)}}={\displaystyle \frac{18.3070}{11(11-1)}}=0.1742$$

The calculated (empirical) concordance coefficient W = 0.7811 is greater than the minimum value W_min = 0.1742, and, therefore, it can be concluded that the expert opinions are consistent.

It should be noted that the most important criterion has the lowest rank. Therefore, the importance of each challenge affecting the implementation of I4.0 in the retail sector supply chain (i.e., the weights of all criteria) is determined using Formula (8).

The calculated importance indicators of the criteria are presented in

Table 3. Importance indicators of challenges affecting the implementation of I4.0 in the retail sector supply chain.

Indicator	Challenges Affecting the Implementation of I4.0 in the Retail Supply Chain, j = (1, 2, …, m)									Sum
	K1	K2	K3	K4	K5	K6	K7	K8	K9
Importance indicators, Qj	0.16	0.08	0.15	0.14	0.18	0.03	0.12	0.06	0.08	1.00

.

The resulting priority order of criteria is: K₅ > K₁ > K₃ > K₄ > K₇ > K₉ > K₂ > K₈ > K₆.

4. Research Findings/Results

The research on challenges affecting the implementation of I4.0 in the retail supply chain found that the five most significant challenges, out of nine examined, represent 75% of the total weight. A prioritization plan was developed for these challenges, ranking them from most to least important:

• High investment and resource requirements—0.18;
• Incompatible technological infrastructure—0.16;
• Unclear value chain—0.15;
• Shortage of highly skilled personnel—0.14;
• Ineffective management of technological transitions—0.12;
• Complexity of database system management—0.08;
• Resistance to change—0.08;
• Lack of government support and regulation—0.06;
• Unclear return on digitalization—0.03.

The total weight of the challenges affecting I4.0 implementation in the retail supply chain equals one.

It has been demonstrated that multi-criteria decision-making methods can be applied to develop a solution plan for these challenges. To achieve tool synergy in addressing the identified issues, a sequence of actions is proposed, as illustrated in Figure 1.

The research revealed that the implementation of I4.0 technologies can be beneficial in supply chain and logistics processes. Using the multi-criteria decision-making (MCDM) methodology, the priorities of key challenges were identified through a survey of domain experts. The findings indicate that the most critical challenges are high investment and resource requirements, incompatible technological infrastructure, unclear value chain, shortage of highly qualified personnel, and ineffective management of technological transitions. Therefore, company managers and specialists should focus more attention on these five key challenges and develop strategies to address them.

The research findings will enable practitioners and managers to optimize the technology implementation process into an intelligent, digital system. This approach will strengthen supply chain resilience, improve decision-making efficiency, and boost productivity by addressing the identified challenges.

The research introduces a model designed to assist retail companies in the systematic implementation of I4.0 solutions, thereby enhancing the sustainability, flexibility, and adaptability of supply chains in response to both external and internal disruptions. This model is oriented toward long-term impact—not only efficiency but also environmental and social aspects, which are becoming increasingly important in today’s business environment.

5. Model for Enhancing the Sustainability of Retail Supply Chains

Based on the analysis of the scientific literature and the results of the research, a set of solutions addressing supply chain management issues in retail companies was developed and presented to the eleven experts who participated in the research. The experts confirmed the validity of the proposed measures. Based on the research results, a model for improving supply chain process management in retail companies using I4.0 technologies was developed. The model is presented in Figure 2.

The research shows that the most significant challenge related to the application of I4.0 in enhancing the resilience of the retail sector supply chain is “high investment and resource requirements (K5)”. While I4.0 technologies offer considerable potential for enhancing profitability, they remain particularly costly for small and medium-sized enterprises (SMEs) in the retail sector. The implementation of I4.0 demands significant upfront investments in acquiring advanced technologies, upgrading existing infrastructure (e.g., IoT devices, data analytics solutions), training personnel, and integrating artificial intelligence. Consequently, insufficient financial resources constitute a major barrier to the adoption of I4.0 technologies within retail SMEs, which typically operate under constrained budgets and face intense market competition. A substantial portion of their financial resources is directed toward technological enhancements, thereby limiting investment in other critical domains such as marketing, logistics, product development, and distribution. Therefore, policymakers and organizational leaders must carefully weigh the need for I4.0 investments against other strategic priorities, which introduces additional complexity. A long-term strategic plan can be developed by prioritizing investments in critical areas that require improvement. High investment costs, particularly in the retail sector, can be mitigated through sustainability accountability mechanisms. Companies that transparently disclose their sustainability activities are more likely to attract funding, become more appealing to investors and consumers, and ultimately reduce investment risks while promoting circular economy principles.

Retail companies must dedicate financial resources to acquiring technologies and training personnel to operate and manage them efficiently. To address the investment barrier, firms may initiate small-scale pilot projects that enable them to investigate and evaluate the effects of implementing I4.0 technologies. These pilot initiatives provide an opportunity to examine the functionality of advanced technologies, determine their advantages, and identify potential obstacles prior to committing to full-scale deployment. Successful pilot projects can boost confidence and justify further investments. The need for significant investment increases when most of the technological infrastructure is outdated, and due to evolving technological trends, legacy systems are rendered obsolete due to evolving technological trends. For this reason, “incompatible technological infrastructure (K1)” is identified as the second major challenge hindering the implementation of I4.0 in retail supply chains and their resilience. Technological infrastructure is the foundation of the industrial revolution, accelerating business processes by enabling inventory reduction, timely procurement, increased efficiency, and uncertainty management. A harmonized technological infrastructure allows rapid response to disruptions and market changes, thereby ensuring supply chain resilience. Retail supply chains must incorporate cloud platforms, blockchain, AI components, robotics, IoT, and other innovative technical solutions. Integrated technological solutions should be interconnected, facilitate event management, enable information exchange, support decision-making, and reduce resource consumption associated with data maintenance and synchronization. Companies may opt to outsource integration initiatives to specialized firms that possess expertise in addressing technological incompatibility. Ongoing and dependable monitoring can incorporate diverse analytical tools to evaluate the performance of current technological systems, manage data flows, and identify potential bottlenecks. Aligning technological infrastructure enables more efficient resource management, waste reduction, and the promotion of circular economy principles. Integrated systems contribute to the creation of closed material loops, enhance production efficiency, and reduce dependence on primary raw materials.

A compatible technological infrastructure is essential for shaping the retail sector’s value chain in the context of I4.0 implementation. Retail companies allocate substantial portions of their revenue to meet customer demands and uphold product quality. Concurrently, I4.0 technologies enable the integration of data from multiple sources throughout the value chain. One of the primary obstacles in adopting I4.0 within retail supply chains is the “Unclear value chain (K3),” which ranks as the third major challenge. By enhancing supply chain resilience, I4.0 technologies contribute to increased product value and competitive advantage in the retail market. Technologies such as IoT, RFID tags, sensors, and other smart devices support real-time data transmission, optimize vehicle routing, monitor inventory levels, and more. Furthermore, I4.0 devices and automation tools streamline retail and logistics operations, track equipment performance, and preserve product quality, thereby ensuring operational efficiency and regulatory compliance. Digital platforms and related systems promote effective communication and collaboration among supply chain stakeholders, while sensors enhance product traceability across the value chain. Consequently, companies can deliver high product value at reduced costs, boosting customer satisfaction. To address a fragmented value chain, retail firms can implement value stream mapping (VSM) with advanced technologies to restructure the entire supply chain. Enhancing supply chain transparency, diversifying technology providers, developing efficient distribution networks, optimizing vehicle routes, and fostering supplier collaboration are key strategies for structuring the retail value chain for I4.0 integration. Prominent global retailers invest in I4.0 technologies to secure a competitive edge through robust and resilient supply chains.

Transparency and structuring of the value chain promote social justice, employee engagement, and sustainable consumption. In the context of the circular economy, this enables more efficient distribution of responsibility among supply chain participants, reduces waste, and encourages innovation.

The absence of adequately trained personnel can negatively affect the structure of the value chain, potentially reducing operational effectiveness, limiting productivity, and weakening overall market competitiveness. Maintaining the retail sector’s value chain requires addressing the challenge of a ‘shortage of highly skilled employees (K4)’, identified as the fourth most critical issue in the research. The retail industry, similar to other sectors, is experiencing a deficit of qualified personnel due to the novelty of I4.0 infrastructure in this domain, which necessitates employee training in emerging technologies. Competent staff are essential for driving operational effectiveness, fostering innovation, enhancing adaptability, and improving customer experience—factors that accelerate the deployment of I4.0. Consequently, organizations should prioritize investments in workforce development and training initiatives to meet the growing need for skilled professionals capable of strengthening supply chain resilience. A well-trained workforce is a catalyst for innovation within firms. In its absence, companies may encounter difficulties in executing technological advancements, limiting their capacity to respond to challenges and capitalize on new opportunities.

The shortage of highly qualified specialists limits the ability to implement sustainable innovations and circular economic solutions. Public and private investments in training and skills development are essential for achieving long-term sustainability and competitiveness.

Technology management can accelerate the adoption of advanced I4.0 technologies and the development of skilled personnel. However, employees often lack the ability to manage new technologies, making “ineffective management of technological transition (K7)” the fifth most significant issue. To overcome these challenges, retail companies must set clear goals related to technological upgrades or changes, aligned with overall business strategy. To assess progress and identify areas for improvement, key performance indicators (KPIs) can be used.

Effective management of technological transitions helps reduce employee resistance, increase engagement, and promote sustainable behavior within the organization. This is important not only for technological advancement but also for social well-being.

Upgrading or modifying technological systems is inherently a long-term endeavor, and many executives perceive I4.0 technologies as intricate and demanding. The issue of “complexity of database system management (K9)” is recognized as the sixth major challenge in the research. Nevertheless, critical functions such as data tracking, cleansing, database administration, advanced analytics, and encryption rely heavily on robust and high-performing database systems. As such, maintaining a well-structured and efficiently managed database infrastructure is essential for extracting actionable insights, enhancing supply chain performance, and fully capitalizing on the capabilities offered by I4.0 technologies.

Complex databases, when properly managed, enable resource optimization, reduction of redundant processes, and support the circular economy. Data analysis helps identify sources of waste and efficiency gaps.

In many cases, retail company leadership demonstrates “resistance to change (K2)” when it comes to adopting I4.0 technologies. This hesitation is often driven by factors such as regulatory frameworks, substantial financial commitments, limited technological expertise, and the influence of external disruptions. Nonetheless, senior management has the capacity—and responsibility—to actively support transformation efforts by embracing I4.0 solutions that strengthen supply chain resilience within the retail sector.

Organizational resistance to change often stems from unclear communication and cultural unpreparedness. Well-managed organizational culture and leadership can encourage employee engagement in sustainability initiatives and the implementation of circular economy models.

The challenge of “Lack of government support and regulation (K8)” is identified as the eighth key barrier to implementing I4.0. Despite this, several institutional mechanisms are available to facilitate I4.0 adoption in the retail domain, such as targeted investment schemes, initiatives led by public agencies, and strategic programs from national banks. These efforts aim to enhance customer experience, streamline operations, and strengthen decision-making processes. However, these are available to only a small portion of retail companies. The General Data Protection Regulation (GDPR) governs personal data protection, defining data subject rights, company obligations, and international data transfer rules, but its application is not uniformly enforced. Cross-border data transfers are regulated by GDPR, standard contractual clauses, and the Data Act to ensure high protection standards. Retail companies face challenges in ensuring data subject rights, data security, lawful data processing, international data transfers, and appointing data protection officers. Companies are subject to significant liabilities and fines for non-compliance, but violations by public and private entities are often treated unequally. The lack of government support hinders the development of the circular economy. Financial instruments, advisory centers, and incentives for implementing digital solutions are needed to enable retail sector companies to transition to sustainable business models.

In such cases, governments should develop appropriate standardization, legal regulation, finance, and tax programs to avoid potential issues in implementation of I4.0. Governments must recognize the hidden profitability benefits of digital infrastructure, as indicated by the fact that “unclear return on digitalization (K6)” is the least significant challenge. Although digitalization requires investment, it ultimately helps optimize resource use, reduce waste, and increase supply chain transparency. This is a crucial step toward long-term sustainability.

6. Conclusions and Recommendations

This research addresses a research gap by introducing a novel sustainability model for the retail sector. The results indicate that the proposed model helps retail companies optimize the use of materials, energy, and labor resources, while also enhancing efficiency and reducing energy consumption.

Future research directions may include analyzing the application of the model in various retail contexts, integrating sustainability initiatives, and evaluating the impact of technological transformations. The model’s contribution to sustainability is reflected in material savings, energy efficiency, and labor resource optimization, enabling retail companies not only to pursue environmental goals but also to strengthen their market competitiveness.

The analysis of the scientific literature reveals that I4.0 technologies have significant potential to transform supply chains into more sustainable systems. However, further development of models integrating technological advancement with social responsibility, circular economy principles, and sustainability is necessary.

The authors’ research identified key challenges in implementing I4.0 in the retail sector, including high investment needs, incompatible infrastructure, unclear value chains, shortage of highly skilled employees, and ineffective management of technological changes.

The results of the expert research enabled the assessment of the current situation in the retail sector and the formulation of I4.0 implementation strategies to achieve sustainability goals and improve operational efficiency.

The research also demonstrates how the application of I4.0 can stimulate sectoral activity, increase productivity, reduce economic risks, minimize defects and failures, and mitigate the environmental impact of the retail sector, confirming the hypothesis stated at the beginning of the work.

The research found that implementing I4.0 technologies can be beneficial in supply chain and logistics processes. Using a multi-criteria decision-making methodology, the priorities of key challenges were identified. A survey of relevant retail sector experts was conducted, and Kendall’s rank correlation method was used to assess the consistency of opinions.

Based on expert evaluation, the five most significant challenges (as reflected by the importance indicator) are high investment and resource requirements—0.18; incompatible technological infrastructure—0.16; unclear value chain—0.15; shortage of highly skilled employees—0.14; and ineffective management of technological transition—0.12.

Drawing upon insights from the literature and empirical research, a comprehensive model was formulated to support practitioners and managers in transitioning the technology implementation process into an intelligent and digitalized system. This model aims to enhance supply chain sustainability and resilience, improve decision-making, efficiency, and productivity, and address identified challenges while meeting customer expectations in retail companies.

Nevertheless, the study does not provide comprehensive answers to all questions. Further analysis is required, and future research is expected to deepen the understanding of the effectiveness of technology integration across different retail contexts. It will be especially critical to examine the model’s impact on long-term energy and material savings, employee skill development, and the resilience of supply chains to external factors in the retail sector.