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Abstract

The digitalization of the food supply chain (FSC) is reshaping food production, potentially increasing productivity and reducing food waste. However, it is still being determined whether implementing digital twin technologies in FSC can also prevent or reduce food loss and waste. This research work identified and analyzed the enablers of digital twins in implementing FSC. Very few studies have analyzed the role of digitalization in FSC, and no study was found in the open literature that has analyzed the enablers of the digital FSC to prevent food waste. This investigation employed a novel gray influence analysis (GINA) methodology for the analysis of causal relationships between factors. Using the GINA method, responses may be aggregated cumulatively without information loss. Consequently, the primary advantage of the GINA method is its capacity to execute a causal analysis model while accommodating a vast amount of data. The study has identified 17 enablers that can improve the sustainability of the FSC through the implementation of digital twin technology. After analysis, enablers visibility (ENB5) and collaboration (ENB16) received the first and second positions based on the total influence score. The digitalization of the FSC promotes better collaboration between stakeholders, enhances traceability and visibility, encourages high-quality products, and reduces food waste. This research study is helpful for the researchers, practitioners, and managers working in the field of digitalization of FSC.

Keywords

Food supply chain food waste sustainability assessment gray influence analysis industry 4.0

Introduction

Digital twin technology (DTT) can play a crucial role in supply chain management (SCM) by increasing operational efficiency, decreasing costs, and enhancing decision-making processes (He & Bai, 2021; S. Singh et al., 2024b). A digital twin (DT) is a digital representation of a physical asset or a system, such as a manufacturing facility or a product (Ivanov & Dolgui, 2021; Kamble et al., 2022).

The utilization of a DT in SCM can enable the generation of a digital replica of the entire supply chain (SC), beginning with the procurement of raw materials, through to the delivery of completed goods to consumers (Hautala-Kankaanpää, 2022). In order to optimize the performance of their supply chains, businesses can benefit from creating a DT of their SC (Annosi et al., 2021a; Cimino et al., 2019; Jain et al., 2024; Yontar, 2023) in order to better comprehend how the various components of the SCs collaborate with one another. For instance, a digital equivalent might be used to model the impact of fluctuating product demand on a company’s SC (Rana & Daultani, 2022). By running simulations, a company can anticipate and prevent SC interruptions and capacity restrictions (He & Bai, 2021; Ivanov et al., 2019; Paul et al., 2023; Scholten & Schilder, 2015) by boosting production capacity or switching to different suppliers. He and Bai (2021) noted that DTT can be used to track and monitor the operation of specific pieces of manufacturing and delivery machinery in real time (Patidar et al., 2023). Overall, the use of DTT in SCM can aid businesses in reducing costs, enhancing efficiency, and improving decision-making processes (Anifa et al., 2024; Beata et al., 2023).

The incorporation of digital agricultural technologies into the agricultural sector provides farmers and food processors with the opportunity to leverage new and integrated technologies to enhance food production, boost food safety, minimize food wastage, and increase profitability (Annosi et al., 2021; Benyam et al., 2021; Pylianidis et al., 2021). There are also many claims that the digitalization of food supply chain (FSC) will have positive socioeconomic effects, particularly in terms of improving the employment opportunities available to farmers and those in professions connected to agriculture (Annosi et al., 2021). Recent technological advancements in FSC have been recognized as potential possibilities to improve resource use efficiency (Benyam et al., 2021; Burgos & Ivanov, 2021; Defraeye et al., 2021; Strotmann et al., 2022; Zscheischler et al., 2022). Technology advancements like digitalization give farms a chance to cut out the intermediaries and make direct connections with restaurants, retailers, and customers, reducing the likelihood of perishable goods being wasted after harvest (Burgos & Ivanov, 2021). The enhancement of the value chain gives farmers the ability to generate high-quality agricultural products. The use of digital SC practices assists in conserving energy and other resources by minimizing wastage at every stage of the supply-chain process (S. Kumar et al., 2021).

Technology like DT’s built-in tracking aids food manufacturers in isolating, verifying, and identifying potential contamination sources. By increasing visibility, DT has the potential to improve the food SC and contribute significantly to the cause of assuring the safety of the food supply. Having a complete history of where each product has been helps SC see the big picture, detect and prevent potential quality issues, and quickly identify and remove problematic items from the supply chain.

Along with providing a visual representation of SCs and the dangers they pose, DTs also provide an analysis of supplier performance and risk and the ability to predict disruptions in SC operations (Lotfi et al., 2021; Perno et al., 2022). They can also help you plan and analyze alternative paths, complete with arrival time estimates. During incidents, digital twins analyze key performance indicators to determine inventory, service level, financial parameters, and demand, as well as to compute the effect of the disruption (Lioutas et al., 2021; Zhao et al., 2019).

Increased transparency and trustworthiness of data on food product manufacturing, processing, and distribution can be achieved with the help of DT (Defraeye et al., 2021). Using blockchain technology, DT can make it easier to create a public and immutable record of all FSC transactions that cannot be tampered with. This can be useful for identifying where an item came from, how it was made, and whether there were any problems during production or distribution (Astill et al., 2019; Gupta et al., 2021). DT can also make it easier for IoT devices to be used to track and monitor food items as they make their way through the supply chain. Real-time information on environmental elements, such as temperature and humidity, that might affect the quality of culinary goods is now available, thanks to sensors and other monitoring systems (Zscheischler et al., 2022). Big data analytics tools can be used to process and analyze FSC data with the help of DT. Decisions about food safety, quality control, and logistics can benefit from this information.

DT can also facilitate the use of digital records to store and manage FSC information. This can aid in reducing the risk of fraud or error associated with paper-based records. Overall, DT can contribute to the improvement of the FSC’s traceability by enhancing the transparency, accuracy, and efficacy of data collection and analysis (Pandey et al., 2022). This can enhance food safety, reduce food waste, and increase consumer confidence in food products.

Much research has been conducted into food, specifically relating to questions concerning its quality, safety, cold chain facilities, logistics, food waste (C. Li et al., 2022; Gokarn & Choudhary, 2021), and risk mitigation. However, very few studies have analyzed the role of digitalization in FSC, and no study in the open literature appears to have analyzed the enablers of digital FSC to prevent food waste. This research study has used a novel methodology, a gray influence analysis, to identify the influence of one factor over the other. Food is one of the most widely used commodities globally, and thus, such a comprehensive approach is needed to understand its complex implications. Despite the widespread recognition of the necessity to decrease food waste in the FSC, many nations around the globe have yet to achieve success in employing digitalization in FSC. The effectiveness of digital FSC management has not been adequately emphasized by researchers in terms of understanding the role of various enablers.

The objective of this research is to recognize and analyze the enablers that influence the digital FSC and the food waste associated with it. In this regard, the aims of this research are:

RO1: What are the crucial enabling factors of the implementation of DTs in the FSC that prevent food waste?

RO2: Which identified enablers have higher influence than others to facilitate food waste reduction?

The subsequent sections of this research study have been organized accordingly. The second section offers an analysis of extant literature pertaining to the FSC and its associated food waste as well as enablers for lowering food waste in the FSC. The third section analyzes the steps used in gray influence analysis, and the fourth section reports on the real case study application of the proposed model. The fifth section discusses results of the research model applied to the case study, and the fifth section offers managerial implications of the study. Finally, the seventh section summarizes contributions of the study, including the best practices for successful FSC management through digitalization, which is expected to result in a decrease in food waste. The last section also provides limitations and the basis for future research possibilities.

Literature Review

The literature review is divided into three sub-sections: extant literature related to DTs and its application in various areas, DTs and FSCs, and research framework.

Digital Twin and Its Application

The notion of DT was initially presented by Dr. Michael Grieves in a lecture in 2002 (Grieves & Vickers, 2016). Numerous definitions of DT have been suggested in academic literature, leading to a lack of consensus on the concept despite the growing body of research and publications on DT (Perno et al., 2022). Multiple definitions have been found in the literature, each differing significantly in terms of the technologies, features, and scope attributed to a DT. Overall, “DT can be defined as a virtual representation of physical assets that utilizes data and simulators to enable real-time prediction, optimization, monitoring, control, and better decision making” (Rasheed et al., 2020). In addition, DTT can be used to monitor and trace the performance of individual SC components in real time, including the performance of manufacturing equipment and the transportation of products (He & Bai, 2021; Zheng et al., 2019). Generally, the use of DTT in supply chain management can aid businesses in reducing costs, enhancing efficiency, and improving decision-making processes (Moshood et al., 2021). Table 1 provides a chronological summary of some extant literature related to DTs and its application in various areas of management.

Table 1.

Key Literature Related to DT and Its Application in Various Areas.

Source	Objective	Application Area	Method	Findings
S. Kumar et al. (2025)	Identified the supply chain 5.0 adoption challenges.	Manufacturing	Gray influence analysis	Digital infrastructure emerged as the most influential challenge.
G. Singh et al. (2024)	Investigated the role of DT in enhancement of resilience and sustainability in MSC.	Manufacturing	Structural equation modeling	DT enhances the visibility and coordination among the players and hence increases the resilience and sustainability in the manufacturing supply chains.
Li et al. (2023)	Identified the impact of DTs on firm resilience.	Manufacturing SC	Structural equation modeling	Extent and intensity of DT deployment both improve supply chain coordination; supply chain coordination only moderates the connection between DT deployment intensity and firm resilience.
Yazdani et al. (2022)	Measured the resiliency of food supply chain.	Food industry	BWM and fuzzy MARCOS	Natural disasters and weaknesses in water systems are two major risk factors that have the greatest weight coefficients.
Kamble et al. (2022)	Analyzed the DT’s role in sustainable manufacturing SC.	Manufacturing SC	Literature review	Internet of Things (IoT), cloud computing, and blockchain have elevated the supply chain’s potential for DT uses.
Bhandal et al. (2022)	Analyzed the DTT’s role in supply chain and operation management.	Supply chain	Bibliometric analysis	Four value clusters and one enabler cluster have been identified. Units within each value cluster address how DTs can be used to enhance supply chain operations on two different levels: the business process level and the capability level.
Gupta et al. (2022)	Investigated the barriers in the digitalization of logistics SC for resilience during pandemic.	Logistics SC	Bayesian BWM	As per findings, “high cost of Investment, lack of monetary resources, inadequate internet connectivity, lack of IT Infrastructure, and unclear economic benefit of digital investment are the top five barriers to implementing innovative digitalization technologies in developing countries like India, during a pandemic situation.”
Ambrogio et al. (2022)	Examined the comprehensive effects of COVID-19 on the workforce and the resilience of the supply chain.	Manufacturing SC	Experimental analysis	It is suggested that forward-thinking manufacturing companies should utilize a disruptive event such as a pandemic as a platform for digital and technological innovation in the workplace, which is guided by the ethos of harmonic digital innovation, where the needs and priorities of people are given paramount importance.
Perno et al. (2022)	Studied the extant literature on digital technologies with an emphasis on the various barriers and facilitators.	Process industry	Literature review	This article provides a valuable addition to the extant literature on digital transformation by analyzing the various DT components and providing conceptual models that explore the factors that facilitate or impede DT implementation and the connections between them.
Kazancoglu et al. (2022)	Analyzed the capacity of global supply chains (GSCs) to demonstrate resilience in the face of pandemics such as COVID-19, to mitigate the impact of disruption.	Global SC	Structural equation modeling	It is revealed that, “Supply chain agility is found to be directly affected by supply chain flexibility. Moreover, supply chain flexibility and supply chain agility directly affect the responsiveness of the global supply chain.”
Benzidia et al. (2021)	Investigated how customers regard the part of blockchain technology in investigating and leveraging innovation management in the industry 4.0 era.	Manufacturing SC	Structural equation modeling	The unique findings stated that, “blockchain technology mediation is dominant in exploiting the internal capabilities and benefits buyers’ innovation orientation.”
Mubarik et al. (2021)	Assessed the influence of supply chain mapping on the supply chain visibility and resilience of a business.	Electrical and electronics manufacturing	Structural equation modeling	The results of the research provide strong proof that companies should implement a supply chain mapping approach to enhance the visibility and resilience of the supply chain.
Rasheed et al. (2020)	DTs and its application in various areas, current challenges, and enablers.	Various areas	Literature review	Studied the current challenges, values, and enabling technologies of DTs.

Digital Twins and Food Supply Chain

The use of enabling technologies of Industry 4.0, traceability of foods technology, and information and communication technology tools that can improve resource efficiency in food operations are a few examples of digital solutions in agriculture (Benyam et al., 2021). Aside from other applications, digital technologies have been used in food production to handle the efficient use of water and applications requiring variable or exact inputs (Pandey et al., 2022). Much research has been conducted into FSC and questions concerning its quality, safety, cold chain facilities, logistics, and risk mitigation. Yet a very few studies have analyzed the role of digitalization in FSC, and no study has analyzed the enablers of digital FSC to prevent food waste. Overall, DT can contribute to the improvement of the FSC’s traceability by enhancing the transparency, accuracy, and efficacy of data collection and analysis (Pandey et al., 2022). This can enhance food safety, reduce food waste, and increase consumer confidence in food products. Some important studies related to FSC and the role of digitalization in managing the efficiency and productivity of FSC are discussed in Table 2.

Table 2.

Some Extant Literature Related to DTs and Food Supply Chain.

Source	Objective	Findings
A. Kumar et al. (2025)	Developed a framework for supplier selection in food SC for reducing food waste.	Energy and resource efficiency identified as critical factors for sustainable supplier selection.
G. Singh et al. (2023)	Investigated the role of DT in enhancement of resilience and sustainability in FSC.	DT enhances the visibility and coordination among the players and hence increases the resilience and sustainability in FSCs.
Purcell & Neubauer (2023)	Assessed the impact of DTs in agriculture.	“Existing trends and unresolved questions, with the intention of raising comprehension and consciousness of the DT and its possibilities are discussed.”
S. Kumar et al. (2022)	Formulated a theoretical model to mitigate the barriers associated with the adoption of Blockchain technology-IoT in FSC.	Absence of government regulation and the insufficient skill level of workers are significantly preventing the acceptance of Blockchain technology-IoT.
Shoji et al. (2022)	Evaluated the efficacy of various strategies in terms of their potential to maintain the freshness of produce during the cold chain process.	Exploiting the potential of this digital twin to analyze multiple scenarios, we can more economically and effectively quantify the effects of cold chain optimization during the design phase.
A. Kumar et al. (2022)	Studied the adoption barriers for Industry 4.0 in FSC.	“Technological immaturity, High investment, Lack of awareness and customer acceptance and technological limitations, and lack of eco-innovation are identified as the most prominent barriers for adoption of I4.0 in Sustainable FSC.”
Zscheischler et al. (2022)	Identified the risks and vulnerabilities in the adoption of digitalization in agriculture.	Results indicated that “despite the potential of digitalization to provide more environmentally friendly and resource-efficient agricultural production, some key stakeholders expressed a variety of perceived risks. The risks were anticipated to be the result of unanticipated detrimental and unpredictable consequences on agro-ecological and social systems.”
Burgos & Ivanov (2021)	Studied the FSC resilience and impact of digitalization.	The computational analysis demonstrates that “the resilience of food retail Supply Chains (SCs) during the period of upheaval is contingent upon the severity of the pandemic and the associated governmental lockdowns and restrictions, the inventory-ordering dynamics of the SC, and customer behavior.”
Benyam et al. (2021)	Analyzed the role of DT in food waste prevention: opportunities and barriers.	The review results illustrate that prohibitive investment expenditures and the disparity between technology users hinder the widespread adoption of digital agricultural technologies.
de Souza et al. ( 2021)	Examined the ways in which supermarkets can decrease food wastage through the implementation of digital innovations.	The results of this analysis illustrate the potential for digital technologies to support the marketing departments of supermarkets in achieving corporate sustainability objectives, while also generating positive outcomes for both consumers and social welfare.
Annosi et al. (2021)	Examined the role of digitalization in FSC to prevent the food waste.	By analyzing the impact of digital technologies on supply chain design and corporate procedures, and the institutional factors that both inhibit and promote the diffusion of digital food supply chains, we can gain valuable insights into the topic.
Lioutas et al. (2021)	Analyzed the role of digitalization on agriculture.	Found novel pathways for the digital agricultural revolution, so as to enhance food production without causing detriment to society.
Defraeye et al. (2021)	Explored the key benefits of utilizing DTs in the supply chain of fresh horticultural produce and how they can be beneficial to the food industry in the near future.	The twins are able to identify and anticipate potential supply chain problems which could lead to a reduction in food quality and associated waste of food.
Torky & Hassanein (2020)	Provided significance of combining blockchain and IoT in precision agriculture.	Some of the security and privacy obstacles and open issues hindering the development of Blockchain-IoT systems in precision agriculture are discussed.

Some important key enabling factors of the implementation of digital twins in the FSC that prevent food waste are shown in Table 3.

Table 3.

Enablers of DT in FSC.

Enablers (Code)	Description	References
Weather forecasting (ENB1)	DT can enhance agricultural weather forecasting by providing farmers and other stakeholders with more precise, timely, and localized weather data. This can improve agricultural yields, reduce the risk of crop loss due to adverse weather, and enhance the overall sustainability of agriculture.	Lezoche et al. (2020); Zhao et al. (2020)
Quality (ENB2)	The technological enhancement of the value chain gives farmers the ability to generate high-quality food products.	S. Kumar et al. (2022); Singh, Daultani, Rajesh, et al. (2022)
Food security (ENB3)	DT contributes to ending hunger, bolstering food security, boosting farmer earnings, and ensuring agriculture’s long-term viability.	Torky & Hassanein (2020); Zscheischler et al. (2022)
Logistics management (ENB4)	DT enhances the visibility of logistic data such as inventory level, service level, capacity, temperature of containers, stock level, and transportation data.	Defraeye et al. (2021); Singh, Daultani, Rajesh, et al. (2022)
Visibility (ENB5)	By leveraging digital technologies such as IoT sensors, artificial intelligence, machine learning, and blockchain, companies can gain real-time insights into their supply chain operations, including inventory levels, shipping times, and product quality.	Benyam et al. (2021); Salah et al. (2019); Singh & Sahu (2019)
Cold chain (ENB6)	DT can help to enhance the cold chain facilities in FSC by enhancing temperature monitoring and control’s visibility, precision, and efficiency. Digitalization in cold storages extend food shelf- life and reduces food waste.	Shoji et al. (2022); Singh, Daultani & Sahu (2022)
Privacy (ENB7)	The adoption of smart contracts, the detection and prevention of counterfeiting, and the tracking and tracing of food products can all benefit greatly from digitalization advancements.	Torky & Hassanein (2020); Zhao et al. (2019)
Environment (ENB8)	DT can aid in enhancing food supply chain sustainability, reducing waste, and mitigating environmental impact.	Kabadurmus et al. (2022); Lioutas et al. (2021)
Information sharing (ENB9)	DT helps in bridging the gap between supply and demand management by proper information sharing and visibility.	Benyam et al., (2021); Zheng et al. (2021)
Real-time monitoring (ENB10)	DT can have a significant impact on real-time monitoring of the food supply chain by enhancing the visibility and precision of food product tracking as they travel through the supply chain.	Burgos & Ivanov (2021); Mithun Ali et al. (2019)
Flexibility (ENB11)	By providing stakeholders with real-time visibility and control over supply chain operations, DT can play a crucial role in enhancing the flexibility of the food supply chain.	Amindoust (2018); Yazdani et al. (2022)
Food processing (ENB12)	Digital agricultural technologies are using new and integrated technologies to help farmers and food processors to increase food production, improve food safety, reduce food waste, and remain profitable.	Benyam et al. (2021); Singh, Daultani & Sahu (2022); Singh, Daultani, Rajesh, et al. (2022)
Risk assessment (ENB13)	By using sensors and other data collection techniques, digital twins can provide real-time information about the status of the food supply chain. This information can be used to identify potential risks as they arise, such as delays in transportation or spoilage of food products.	Lioutas et al. (2021); Zscheischler et al. (2022); Purcell & Neubauer (2023)
Traceability (ENB14)	DT can have a significant impact on the traceability of the food supply chain by improving the visibility and accuracy of information related to the production, processing, and distribution of food products.	Astill et al. (2019); Gopi et al. (2019); Parashar et al. (2020); Gupta & Shanskar (2024)
Governance (ENB15)	The ability of digitalization to advance the interests of farmers and food growers is hampered by variables such as trade relations, customs, and governance problems in food production.	Benyam et al. (2021); Al Ketbi et al. (2021); Mahmoudi et al. (2022)
Collaboration (ENB16)	DT can enhance collaboration within the food supply chain by enhancing the visibility, transparency, and efficiency of information sharing and stakeholder coordination.	Annosi et al. (2021); Parashar et al. (2020); Scholten & Schilder (2015)
Safety (ENB17)	DT have a significant impact on food safety by increasing the transparency, accuracy, and efficacy of data collection and analysis pertaining to food safety and quality control.	Pandey et al. (2022); Vivaldini (2021); Zhao et al. (2019)

Methodology

GINA is a novel and distinct method of analyzing influence that we employ in our research. This methodology is applicable to a variety of studies that rely on surveys or significant numbers of expert responses. GINA is based on gray theory (Julong, 1989) and input–output models (Li & Liu, 2008), which seek to recognize and quantify how various factors or characteristics are interconnected. In the field of SC management, researchers frequently employ causal modeling techniques such as ISM and DEMATEL to assess the correlations between multiple characteristics and influencing factors (Dash et al., 2022). However, these approaches usually rely on a limited group of experts who measure alternatives on a crisp value or a gray/fuzzy scale (Golinska et al., 2015; Rajesh, 2023). This earlier method reduces the overall data collected and increases the amount of information lost during the compilation process, limiting the study’s potential impact. This study uses the GINA methodology, which has the potential to overcome limitations in standard causal modeling approaches such as ISM and DEMATEL (Chouhan et al., 2025). Data loss during answer consolidation can be kept to a minimum with the help of the GINA technique. Furthermore, this method is recommended to include many answers in any influence analysis/causal modeling situation to improve the accuracy of the results. The direct influence coefficient, the complete influence coefficient, the gray responsibility and influence coefficient, and total influence coefficient (TIC) are the four impact measures used in the GINA method S. Singh et al., 2024a). The TIC assigns a numerical value to how important a given part of the system is, considering its dependence on and control over other factors. By comparing TIC values, managers and practitioners can measure an enabler’s significance in the system.

Next, we will go over the steps required by the GINA methodology, which can be used to examine the enablers of DT in FSC to prevent food waste. The GINA methodology, introduced by Rajesh (2023) for the goal of influence research, consists of 11 stages. Detailed explanations of each stage are as follows:

Step 1: Enabler identification for the research study: GINA’s first step is to find system-influencing enablers. Delphi, literature study, and expert opinions can identify enablers.

Step 2: Evaluating the influence relationship among enablers: This involves identifying the relationships between different factors by collecting expert opinions or conducting surveys. The strength of each factor’s direct influence on others is measured using a 5-point Likert scale with linguistic terms ranging from very high to very low.

Step 3: Cleaning and sorting of the responses: It is important to clean and sort the survey data to ensure the accuracy and reliability of the GINA model’s output. This can be done manually or using tools by carefully examining each response and removing any unreliable or biased ones to minimize errors that may accumulate.

Step 4: Converting the linguistic values into gray numbers: To convert the linguistic labels in the survey results into a numerical gray score for analysis, a conversion table can be utilized. The survey’s analysis of influence relations can be represented by the influence matrix, denoted as ⊗α_ij, where i and j refer to the enablers being analyzed. The lower and upper bounds of the influence matrix can be expressed as $\underline{\otimes}$ α_ij and $\bar{\otimes}$ α_ij, respectively. It can be written as follows:

\otimes a_{i j} = (\underline{\otimes} a_{i j}, \bar{\otimes} a_{i j})

(1)

Now, the influence matrix will be written as follows:

A (\otimes) = {[\otimes a_{i j}]}_{n \times n} = [\begin{matrix} \otimes a_{11} & \dots & \otimes a_{1 n} \\ ⋮ & ⋱ & ⋮ \\ \otimes a_{n 1} & \dots & \otimes a_{n n} \end{matrix}]

(2)

where i, j = 1,2,3, …, n indicates the influence of the ith factor over the jth factor, taken in gray values.

Step 5: Aggregating the influence in gray scores: The survey results are aggregated in gray scales to ensure that all valid responses are included in the GINA analysis and to minimize data loss. This is achieved by applying the gray addition operator (Julong, 1989) to the influence responses in gray numbers. Assuming that there are n valid replies from the survey, the aggregated influence relation matrix can be produced and expressed as follows:

\sum_{l = 1}^{n} \otimes a_{i j}^{l} = (\sum_{l = 1}^{n} \underline{\otimes} a_{i j}^{l}, \sum_{l = 1}^{n} \bar{\otimes} a_{i j}^{l})

(3)

where l is an index varying from 1,2,3, …, n.

Step 6: Whitenizing of gray values into crisp scores: In this step,

the aggregated grey influence values are whitened using three different models. The critical model considers the lower limit of the aggregate influences as a crisp score for whitening. In the ideal model, the upper limit values of the aggregate influences are used to convert the grey values into crisp scores. (Rajesh, 2023)

The typical model, which is the third model, calculates the whitenized crisp values by taking the average of the upper and lower limit values.

Step 7: Evaluate the direct influence coefficient: In this step,

the coefficient of direct influence will be calculated. Based on the critical model, the ideal model, and the typical model, the study found three distinct values (y = 1, 2, and 3) for the direct influence coefficient. The evaluation of the direct influence coefficient is used to describe these crisp numbers. (Rajesh, 2023)

x_{i j}^{y} = \frac{a_{i j}^{y}}{a_{j}^{y}}; i, j = 1, 2, 3, \dots, n; y = 1, 2, 3

(4)

The matrices denoting values of are denoted as follows:

X^{y} = {[x_{i j}^{y}]}_{n \times n} = [\begin{matrix} x_{11}^{y} & \dots & x_{1 n}^{y} \\ ⋮ & ⋱ & ⋮ \\ x_{n 1}^{y} & \dots & x_{n n}^{y} \end{matrix}]; y = 1, 2, 3

(5)

Step 8: Determining the complete influence matrix: The complete influence coefficient matrix can be determined by using the following equation:

Z^{y} = ({(I - X^{y})}^{- 1} - I)

(6)

Here, I means the identity matrix of the same order as that of Z^y, and it can be written as follows:

Z^{y} = {[z_{i j}^{y}]}_{n \times n} = [\begin{matrix} z_{11}^{y} & \dots & z_{1 n}^{y} \\ ⋮ & ⋱ & ⋮ \\ z_{n 1}^{y} & \dots & z_{n n}^{y} \end{matrix}]; y = 1, 2, 3

(7)

Step 9: Evaluating the gray responsibility coefficient and influence coefficients: These values are evaluated by utilizing the following equation:

s_{i}^{y} = \frac{\sum_{j = 1}^{n} z_{i j}^{y}}{\frac{1}{n} \sum_{i = 1}^{n} \sum_{j = 1}^{n} z_{i j}^{y}}; y = 1, 2, 3

(8)

$s_{i}^{y}$ is the gray responsibility coefficient of the ith factor.

q_{j}^{y} = \frac{\sum_{i = 1}^{n} z_{i j}^{y}}{\frac{1}{n} \sum_{i = 1}^{n} \sum_{j = 1}^{n} z_{i j}^{y}}; y = 1, 2, 3

(9)

$q_{j}^{y}$ is the gray influence coefficient of the jth factor.

Step 10: Evaluating the gray importance coefficient: It is obtained by utilizing the following equation:

t_{i j}^{y} = s_{i}^{y} + q_{j}^{y} \forall i = j; y 1, 2, 3

(10)

Step 11: Identification of most influential factors and analyses: By analyzing the $t_{i j}^{p}$ value, we can determine the factor that has the greatest impact on the critical model, ideal model, and typical model. The average of $t_{i j}^{p}$ can be obtained by examining these models using the following formula:

t_{i j} = \frac{(t_{i j}^{1} + (4 \times t_{i j}^{2}) + t_{i j}^{3})}{l}

(11)

where l is typically an arbitrary number between 1 and 6. The value of t_ij can then be used to identify the most influential factor, which adheres to the Pareto principle, indicating that the top 20% of factors can be considered the most influential out of a collection of selected factors found during the research.

Case Study Application of the Proposed Model

To examine the relationships between significant enablers of digitalized FSC, we conducted a survey-based study, using input from academic professionals, businesspersons, and faculty members. Based on a review of existing literature and expert opinions, we identified 17 factors and used a Likert scale to rate the level of influence for each factor, with 1 representing very low influence and 5 indicating high influence. Only individuals with a PhD in management or related field and knowledge of food industry were eligible to participate. As a result, a small sample of eligible responses was chosen at random, with 27 out of 36 responses deemed useful for the analysis. Compared to other causal modeling techniques such as ISM/DEMATEL, which typically use 4–6 responses and average the results for analysis, the GINA technique allows for the use of more responses. This approach helps to avoid data loss during the averaging process and ensures that all responses are considered during aggregate operations. A detailed step-by-step procedure for implementing the research methodology is discussed below.

Using Steps 1–6, the initial data were sorted and purified. Next, the responses were converted into gray scales using Table 4 and then compiled using Equations (2) and (3). Table A1 was then used to compute the critical, ideal, and most typical whitenized values of the aggregated responses in gray scales, which were employed for further analysis. The most typical values of the aggregated data for the impact of factors may be utilized to demonstrate the relative degree of influence that each factor has when compared to the other factors.

Table 4.

Linguistic Terminology and Its Associated Gray Scale.aw

Rating	Linguistics Terms	Gray Values
1	Very low	(1, 3)
2	Low	(2, 5)
3	Medium	(4, 7)
4	High	(6, 9)
5	Very high	(8, 10)

In Steps 7 and 8, we derived the direct coefficient matrices for the critical, ideal, and most typical whitenized models using Equations (4) and (5), which can be found in Table A2. Next, we created the complete influence coefficient matrices using Equations (6) and (7), which are displayed in Table 5.

Table 5.

Complete Influence Coefficient Matrices (Critical, Ideal, and Most Typical Matrices).

	ENB1	ENB2	ENB3	ENB4	ENB5	ENB6	ENB7	ENB8	ENB9	ENB10	ENB11	ENB12	ENB13	ENB14	ENB15	ENB16	ENB17	Sum
ENB1	134.59	135.10	134.86	134.82	135.11	134.97	134.74	135.04	135.07	134.70	135.25	134.98	134.56	135.01	135.16	134.95	135.12	2,294.04
ENB2	220.22	220.96	220.50	220.52	221.03	220.84	220.53	220.87	220.80	220.39	221.27	220.79	220.13	220.85	221.01	220.76	221.03	3,752.50
ENB3	205.59	206.37	205.86	205.96	206.33	206.20	205.80	206.19	206.21	205.77	206.60	206.14	205.52	206.15	206.33	206.11	206.32	3,503.45
ENB4	219.25	220.06	219.54	219.54	220.05	219.83	219.46	219.87	219.83	219.37	220.30	219.82	219.16	219.89	220.03	219.89	220.02	3,735.91
ENB5	272.87	273.87	273.28	273.32	273.84	273.65	273.12	273.81	273.70	273.11	274.21	273.56	272.76	273.65	273.88	273.64	273.91	4,650.17
ENB6	230.50	231.32	230.88	230.84	231.30	231.07	230.71	231.17	231.12	230.67	231.60	231.09	230.47	231.13	231.31	231.09	231.36	3,927.64
ENB7	182.99	183.68	183.32	183.32	183.68	183.53	183.18	183.55	183.52	183.20	183.87	183.50	182.96	183.51	183.69	183.54	183.68	3,118.69
ENB8	214.43	215.23	214.72	214.78	215.20	215.03	214.66	215.07	215.07	214.64	215.47	215.02	214.34	215.13	215.27	214.98	215.20	3,654.23
ENB9	235.66	236.58	236.01	236.10	236.59	236.36	235.90	236.45	236.33	235.85	236.84	236.31	235.60	236.39	236.61	236.27	236.60	4,016.45
ENB10	244.59	245.49	244.99	244.99	245.51	245.30	244.84	245.37	245.27	244.75	245.74	245.21	244.59	245.29	245.49	245.25	245.56	4,168.25
ENB11	261.74	262.66	262.08	262.12	262.72	262.44	262.05	262.58	262.54	261.90	262.94	262.38	261.61	262.50	262.73	262.38	262.69	4,460.06
ENB12	216.38	217.18	216.71	216.81	217.23	217.02	216.63	217.14	217.06	216.57	217.46	216.94	216.38	217.04	217.28	217.01	217.18	3,688.02
ENB13	247.57	248.49	247.95	248.00	248.53	248.24	247.82	248.31	248.27	247.78	248.79	248.23	247.46	248.32	248.53	248.20	248.50	4,219.00
ENB14	261.32	262.29	261.76	261.76	262.25	262.01	261.64	262.15	262.10	261.50	262.61	262.02	261.23	262.05	262.37	262.01	262.31	4,453.38
ENB15	191.78	192.45	192.08	192.08	192.44	192.25	191.93	192.31	192.34	191.88	192.68	192.26	191.68	192.35	192.45	192.24	192.43	3,267.63
ENB16	267.53	268.51	267.98	267.98	268.53	268.27	267.80	268.31	268.33	267.69	268.84	268.24	267.51	268.33	268.60	268.18	268.49	4,559.13
ENB17	221.14	221.94	221.42	221.42	221.96	221.70	221.31	221.78	221.73	221.30	222.22	221.64	221.06	221.79	221.97	221.64	221.89	3,767.92
Sum	3,828.17	3,842.19	3,833.92	3,834.36	3,842.30	3,838.71	3,832.10	3,839.96	3,839.30	3,831.06	3,846.72	3,838.14	3,827.03	3,839.36	3,842.71	3,838.14	3,842.27	6,5236.45
ENB1	–140.79	–141.06	–140.31	–140.37	–140.81	–140.53	–140.78	–141.08	–141.28	–140.94	–140.94	–140.75	–141.06	–140.68	–140.80	–140.61	–141.21	–2,394.01
ENB2	–186.91	–187.34	–186.37	–186.39	–186.98	–186.56	–186.88	–187.35	–187.68	–187.12	–187.11	–186.91	–187.29	–186.80	–187.01	–186.69	–187.50	–3,178.87
ENB3	–176.67	–177.02	–176.16	–176.13	–176.75	–176.33	–176.68	–177.10	–177.35	–176.87	–176.85	–176.66	–177.02	–176.59	–176.78	–176.46	–177.26	–3,004.69
ENB4	–182.97	–183.35	–182.43	–182.48	–183.04	–182.65	–182.99	–183.43	–183.73	–183.21	–183.17	–182.97	–183.35	–182.86	–183.09	–182.72	–183.58	–3,112.00
ENB5	–208.93	–209.36	–208.28	–208.31	–209.04	–208.54	–208.96	–209.36	–209.73	–209.15	–209.14	–208.94	–209.36	–208.81	–209.03	–208.64	–209.58	–3,553.16
ENB6	–190.87	–191.27	–190.26	–190.32	–190.97	–190.56	–190.88	–191.32	–191.64	–191.09	–191.07	–190.86	–191.22	–190.77	–190.98	–190.63	–191.47	–3,246.16
ENB7	–170.41	–170.74	–169.84	–169.88	–170.46	–170.07	–170.41	–170.80	–171.07	–170.56	–170.58	–170.38	–170.72	–170.31	–170.47	–170.15	–170.94	–2,897.78
ENB8	–183.49	–183.86	–182.95	–182.95	–183.57	–183.15	–183.49	–183.93	–184.20	–183.68	–183.69	–183.48	–183.87	–183.34	–183.55	–183.27	–184.10	–3,120.57
ENB9	–193.55	–193.91	–192.94	–192.94	–193.58	–193.16	–193.55	–193.95	–194.31	–193.76	–193.72	–193.52	–193.91	–193.39	–193.59	–193.31	–194.12	–3,291.20
ENB10	–199.30	–199.70	–198.65	–198.70	–199.36	–198.91	–199.30	–199.73	–200.09	–199.55	–199.54	–199.30	–199.64	–199.18	–199.39	–199.03	–199.90	–3,389.26
ENB11	–209.06	–209.51	–208.44	–208.46	–209.12	–208.69	–209.05	–209.52	–209.86	–209.32	–209.33	–209.07	–209.49	–208.93	–209.14	–208.82	–209.73	–3,555.55
ENB12	–182.61	–182.97	–182.04	–182.02	–182.63	–182.24	–182.58	–182.96	–183.29	–182.80	–182.77	–182.61	–182.92	–182.47	–182.63	–182.34	–183.19	–3,105.05
ENB13	–202.76	–203.16	–202.12	–202.14	–202.80	–202.39	–202.76	–203.24	–203.56	–202.98	–202.96	–202.74	–203.18	–202.61	–202.82	–202.51	–203.39	–3,448.11
ENB14	–208.32	–208.74	–207.65	–207.69	–208.42	–207.96	–208.30	–208.78	–209.12	–208.57	–208.52	–208.30	–208.73	–208.21	–208.37	–208.05	–208.97	–3,542.69
ENB15	–175.01	–175.38	–174.45	–174.50	–175.09	–174.72	–175.04	–175.44	–175.69	–175.23	–175.20	–175.01	–175.37	–174.88	–175.12	–174.81	–175.60	–2,976.54
ENB16	–210.04	–210.48	–209.37	–209.42	–210.11	–209.66	–210.05	–210.55	–210.85	–210.32	–210.26	–210.03	–210.42	–209.90	–210.11	–209.82	–210.73	–3,572.13
ENB17	–183.39	–183.77	–182.85	–182.90	–183.45	–183.08	–183.43	–183.82	–184.13	–183.61	–183.58	–183.43	–183.75	–183.27	–183.46	–183.21	–184.02	–3,119.17
Sum	–3,205.09	–3,211.58	–3,195.11	–3,195.61	–3,206.17	–3,199.22	–3,205.13	–3,212.33	–3,217.57	–3,208.75	–3,208.44	–3,204.96	–3211.29	–3203.00	–3206.34	–3201.07	–3215.28	–54506.93
ENB1	–210.39	–209.82	–210.53	–210.79	–210.78	–210.19	–210.51	–210.35	–210.96	–209.55	–210.26	–210.14	–209.95	–210.58	–209.99	–210.99	–211.15	–3,576.93
ENB2	–300.89	–300.15	–301.22	–301.52	–301.50	–300.61	–301.04	–300.92	–301.87	–299.71	–300.72	–300.60	–300.30	–301.22	–300.42	–301.79	–302.03	–5,116.50
ENB3	–283.88	–283.12	–284.19	–284.41	–284.46	–283.60	–284.07	–283.92	–284.75	–282.76	–283.71	–283.59	–283.32	–284.22	–283.45	–284.73	–284.99	–4,827.16
ENB4	–296.43	–295.65	–296.75	–297.07	–297.03	–296.17	–296.64	–296.48	–297.39	–295.30	–296.27	–296.14	–295.85	–296.75	–295.98	–297.26	–297.58	–5,040.76
ENB5	–348.86	–347.94	–349.19	–349.53	–349.59	–348.51	–349.12	–348.80	–349.90	–347.47	–348.64	–348.53	–348.18	–349.25	–348.29	–349.84	–350.15	–5,931.78
ENB6	–309.71	–308.91	–309.99	–310.34	–310.37	–309.48	–309.94	–309.75	–310.70	–308.50	–309.54	–309.41	–309.06	–310.08	–309.25	–310.63	–310.88	–5,266.54
ENB7	–265.28	–264.57	–265.49	–265.77	–265.80	–265.01	–265.46	–265.29	–266.10	–264.19	–265.13	–264.99	–264.72	–265.58	–264.83	–266.01	–266.27	–4,510.51
ENB8	–294.87	–294.08	–295.18	–295.44	–295.47	–294.59	–295.06	–294.90	–295.77	–293.68	–294.70	–294.56	–294.29	–295.14	–294.35	–295.74	–296.01	–5,013.81
ENB9	–315.61	–314.74	–315.91	–316.19	–316.20	–315.28	–315.82	–315.57	–316.60	–314.36	–315.40	–315.27	–314.96	–315.92	–315.04	–316.54	–316.75	–5,366.17
ENB10	–325.39	–324.54	–325.68	–326.02	–326.03	–325.07	–325.61	–325.38	–326.42	–324.15	–325.24	–325.08	–324.69	–325.76	–324.87	–326.33	–326.58	–5,532.85
ENB11	–344.01	–343.14	–344.38	–344.71	–344.69	–343.71	–344.22	–344.00	–345.05	–342.70	–343.87	–343.70	–343.35	–344.38	–343.44	–345.05	–345.32	–5,849.69
ENB12	–294.36	–293.57	–294.63	–294.87	–294.90	–294.04	–294.52	–294.29	–295.22	–293.18	–294.15	–294.07	–293.71	–294.65	–293.80	–295.17	–295.47	–5,004.61
ENB13	–330.71	–329.83	–331.02	–331.34	–331.34	–330.41	–330.93	–330.74	–331.75	–329.40	–330.50	–330.37	–330.07	–331.04	–330.14	–331.69	–331.95	–5,623.24
ENB14	–343.56	–342.65	–343.85	–344.22	–344.28	–343.27	–343.76	–343.55	–344.60	–342.23	–343.34	–343.21	–342.88	–343.96	–342.95	–344.56	–344.84	–5,841.73
ENB15	–275.93	–275.23	–276.19	–276.48	–276.52	–275.72	–276.16	–275.98	–276.78	–274.89	–275.79	–275.67	–275.40	–276.22	–275.52	–276.77	–277.03	–4,692.30
ENB16	–348.44	–347.53	–348.74	–349.11	–349.13	–348.12	–348.68	–348.49	–349.51	–347.12	–348.23	–348.09	–347.70	–348.81	–347.83	–349.50	–349.78	–5,924.81
ENB17	–297.53	–296.76	–297.86	–298.18	–298.13	–297.29	–297.78	–297.57	–298.49	–296.38	–297.35	–297.29	–296.94	–297.85	–297.04	–298.46	–298.71	–5059.60
Sum	–5,185.8629	–5,172.2358	–5,190.79	–5,196.00	–5,196.20	–5,181.07	–5,189.31	–5,185.98	–5,201.86	–5,165.59	–5,182.82	–5,180.73	–5,175.36	–5,191.41	–5,177.19	–5,201.07	–5205.50	–8,8178.98

Steps 9–11 involve obtaining the overall (total) influence coefficients using Equations (8)–(11), as shown in Table 6, based on the GINA analysis. The resulting values are used to determine the most influencing enablers in the implementation of DT in the FSC. By applying the Pareto principle to the top 20% of the factors, the five most significant factors were identified as visibility (ENB5), collaboration (ENB16), flexibility (ENB 11), traceability (ENB14), and risk assessment (ENB13). These five factors have a greater influence compared to other factors. All these factors enhance the resilience of the SC and hence increase the sustainability of the FSC by reducing food waste. The ranking of these factors in terms of total influence is as follows: visibility (Rank 1), collaboration (Rank 2), flexibility (Rank 3), traceability (Rank 4), and risk assessment (Rank 5). A detailed analysis of all the factors is presented in the next section.

Table 6.

Gray Responsibility, Influence, and the Total Influence Coefficient.

	ENB1	ENB2	ENB3	ENB4	ENB5	ENB6	ENB7	ENB8	ENB9	ENB10	ENB11	ENB12	ENB13	ENB14	ENB15	ENB16	ENB17
Critical: Gray inf. coeff.	0.0587	0.0589	0.0588	0.0588	0.0589	0.0588	0.0587	0.0589	0.0589	0.0587	0.059	0.0588	0.0587	0.0589	0.0589	0.0588	0.0589
Critical: Gray resp. coeff.	0.0352	0.0575	0.0537	0.0573	0.0713	0.0602	0.0478	0.056	0.0616	0.0639	0.0684	0.0565	0.0647	0.0683	0.0501	0.0699	0.0578
Critical: Sum (GIC, GRC)	0.0939	0.1164	0.1125	0.1161	0.1302	0.119	0.1065	0.1149	0.1205	0.1226	0.1274	0.1153	0.1234	0.1272	0.109	0.1287	0.1167
Ideal: Gray inf. coeff.	0.0588	0.0589	0.0586	0.0586	0.0588	0.0587	0.0588	0.0589	0.059	0.0589	0.0589	0.0588	0.0589	0.0588	0.0588	0.0587	0.059
Ideal: Gray resp. coeff.	0.0439	0.0583	0.0551	0.0571	0.0652	0.0596	0.0532	0.0573	0.0604	0.0622	0.0652	0.057	0.0633	0.065	0.0546	0.0655	0.0572
Ideal: Sum (GIC, GRC)	0.1027	0.1172	0.1137	0.1157	0.124	0.1183	0.112	0.1162	0.1194	0.1211	0.1241	0.1158	0.1222	0.1238	0.1134	0.1242	0.1162
Typical: Gray inf. coeff.	0.0588	0.0587	0.0589	0.0589	0.0589	0.0588	0.0588	0.0588	0.059	0.0586	0.0588	0.0588	0.0587	0.0589	0.0587	0.059	0.059
Typical: Gray resp. coeff.	0.0406	0.058	0.0547	0.0572	0.0673	0.0597	0.0512	0.0569	0.0609	0.0627	0.0663	0.0568	0.0638	0.0662	0.0532	0.0672	0.0574
Typical: Sum (GIC, GRC)	0.0994	0.1167	0.1136	0.1161	0.1262	0.1185	0.11	0.1157	0.1199	0.1213	0.1251	0.1156	0.1225	0.1251	0.1119	0.1262	0.1164
Aggregate: Gray inf. coeff.	0.0588	0.0588	0.0588	0.0588	0.0589	0.0588	0.0588	0.0588	0.059	0.0587	0.0589	0.0588	0.0587	0.0589	0.0588	0.0589	0.059
Aggregate: Gray resp. coeff.	0.0403	0.058	0.0546	0.0572	0.0676	0.0598	0.051	0.0568	0.0609	0.0628	0.0665	0.0568	0.0639	0.0664	0.0529	0.0674	0.0574
Total influence	0.099	0.1167	0.1134	0.116	0.1265	0.1186	0.1098	0.1157	0.1199	0.1215	0.1253	0.1156	0.1226	0.1252	0.1117	0.1263	0.1164
Rank	17	9	14	11	1	8	16	12	7	6	3	13	5	4	15	2	10

Results and Discussions

This research work has identified and analyzed the enablers in the implementation of DTs in the FSC. Seventeen enablers were identified in the application of DT in FSC through literature review, which fulfils RO1. In order to determine which enabler has more influence than the others, this research used a novel approach called GINA, which accomplishes second research objective (RO2). Table 6 provides information on gray influence coefficients (GIC), gray responsibility coefficients (GRC), and overall (total) influence scores of the 17 enablers of digitally operated FSC that were considered in the study. The rank of all the enablers is also given in Table 6. Figure 1 visually displays GIC, GRC and the sum of GIC and GRC of critical, ideal, and typical models and compares them with aggregate GIC, aggregate GRC, and total influence.

Figure 1.

Total Influence and Responsibility coefficient of Ideal, Critical, and Typical Model.

The variation pattern of the three models is slightly different than the aggregate variation pattern. Visibility (ENB5) and collaboration (ENB16) received the first and second positions, respectively, based on the total influence score. The key enabling factors driving the adoption of DTT in the FSC are highlighted in recent research. The implementation of DT in the FSC can enhance the sustainability of the system by improving visibility and tracking processes throughout the product life cycle (Qader et al., 2022). The use of DTT can significantly benefit a company’s SC visibility and capacity to remain sustainable and competitive in the market.

Additionally, incorporating cyber-physical systems in the food industry can lead to efficient collaborations and operation of jobs, resulting in food waste reduction and greater flexibility in natural resource availability and environmental costs. To build a coordinated SC through information exchange and collective decision-making, businesses can leverage DTs (L. Li et al., 2022). The digitalization of the FSC promotes better coordination between stakeholders, enhances traceability and visibility, encourages high-quality products, and reduces food waste.

Similarly, Flexibility (ENB 11), Traceability (ENB14) and Risk assessment (ENB13) received third, fourth, and fifth positions, respectively, based on total influence score. The FSC can react quickly to changes in supply and demand because of digital technologies. Digital tools can help the SC adapt to changes in the environment by making working processes more efficient and flexible (G. Singh et al., 2023). Additionally, DT can have a significant impact on the traceability of the FSC by improving the visibility and accuracy of information related to the production, processing, and distribution of food products (Mubarik et al., 2021). Also, the digital food delivery chain might be able to handle risks well. Overproducing food or ingredients in the food business to cover for unclear demands further down the line can increase the risk of food waste.

Real-time monitoring (ENB10) and information sharing (ENB9) got ranked sixth and seventh, respectively. The use of DTT can have a big effect on how information is shared and monitored in real time along the FSC. By making a digital copy of real-world assets and processes, DTs can give real-time data and insights about how equipment and inventory are working (Cimino et al., 2019). This can make it easier to find problems early and act on them quickly, which makes SC processes more efficient and effective. In addition, DTs can facilitate communication along the FSC, facilitating teamwork and decision-making. Overall, DTT can strengthen the transparency, resilience, and sustainability of the FSC. Both cold chain performance (ENB6) and quality performance (ENB2) came in at eighth and ninth positions, respectively. There can be significant effects on cold chain infrastructure and product quality from the implementation of digital technologies. The temperature and humidity conditions in the cold SC may be carefully monitored and managed in real time, thanks to the use of digital sensors and monitoring systems. Thus, the quality of food goods is maintained and not diminished while they travel through the SC. The efficiency and effectiveness of cold chain facilities can also be improved with the use of digital technologies. Predictive maintenance using digital tools, for instance, can lessen the likelihood of equipment failures and outages in cold storage facilities. Also, digital systems can help with automating inventory management, which helps cut down on mistakes and makes sure perishable goods are used up before they go bad.

Digital technologies can help the FSC become more sustainable by increasing the efficiency and quality of cold chain facilities, which in turn helps reduce food waste and increases food safety. By increasing end-to-end visibility, encouraging SC collaborations, facilitating efficient inventory strategies, enabling capacity planning, bolstering cold chain and logistics capabilities, and enhancing traceability, the use of DTT in the FSC can increase resilience (Kabadurmus et al., 2022). The early detection of food shortages, along with enhanced planning, decision-making, and risk management made possible by digital supply networks, will increase food security around the world.

Managerial Implications

Several managerial insights that can aid in the reduction of food spoilage can be gained through the implementation of DTT in the FSC. DTT can provide SC administrators with real-time visibility. This enables them to keep tabs on their goods all the way through manufacturing and delivery. This can help pinpoint any problems or inefficiencies that are leading to wasted food. Managers can use DTT’s historical data analysis and pattern recognition with predictive analytics capabilities. They can then take preventative actions against food waste by anticipating demand and SC problems.

Using DTT, managers may determine the most effective transit routes and the best time to harvest commodities, thereby optimizing SCs. This has the potential to increase earnings while decreasing food waste. Managers can lessen their activities’ negative effect on the environment with the use of DTT. They can, for instance, reduce emissions and improve their sustainability by keeping tabs on the carbon impact of their SCs. In order to improve sustainability and cut down on food waste, managers can use data from DTs to guide their decisions.

There are a variety of ways in which DTT can aid in the fight against food waste along the FSC. The entire food distribution network may be tracked in real time with the use of DTT. This allows farmers, manufacturers, and retailers to monitor their products all the way through the distribution chain. This will aid in locating any bottlenecks or defects that could result in wasted food.

Companies in the food industry can prevent food waste from occurring in the first place by using DTT to foresee shifts in demand and the SC. Food producers, for instance, can adjust production levels or look for new markets if a DT predicts that there will soon be an oversupply of a certain food item. The use of DTT has helped the food industry determine the most efficient routes to move equipment and when to harvest crops. As a result, less food will be wasted, and more money will go to the farmers.

The FSC can also benefit from DT’s ability to improve traceability. This will aid food manufacturers in tracing the origin of any contamination or spoilage, allowing them to promptly address the issue and prevent further food waste.

Digital technologies can help the FSC become more sustainable by increasing the efficiency and quality of cold chain facilities, which in turn helps reduce food waste and increases food safety. Overall, DTT can help cut down on food waste by making it easier to see what is going on, making processes run more smoothly, and getting rid of flaws in the FSC.

Conclusion

This study has developed a decision-making framework for managing FSC systems. A case study is presented to illustrate how enablers can play an important role in reducing the food waste in digitally operated FSCs. Digital FSC (DFSC) can reduce food waste and enhance the resilience of the SC. The study has identified 17 enablers that can improve the sustainability of the FSC through the implementation of DTT.

In order to determine what causes what, this study used the cutting-edge GINA approach. It was simple for researchers to determine which variables had the most impact on a selected research issue. The GINA technique allows for progressive aggregation of replies with no loss of information. The ability to run a causal analysis model while storing massive amounts of data is the primary benefit of the GINA approach. According to the results, the two enablers with the highest combined influence are visibility (ENB5) and collaboration (ENB16).

Risks in the SC, such as product recalls, food fraud, and SC interruptions, can be identified and mitigated with the help of DTT. Managers can use these data to make educated decisions and take precautions to lessen the blow of these threats.

The study has some limitations that need to be addressed. First, the sample size of 27 responses may not be enough to provide reliable results, and future research could consider a larger sample size. Second, more research is required to explore the application of DTT in the FSC. This research offers an in-depth analysis of how digital solutions have been implemented throughout the FSC to lessen food waste. The use of DTTs in the management of real-world SCs, such as those for food products or fresh produce (fruits and veggies), could be studied in the future. It is possible that future studies could center on creating software to help put the methodology into practice and reduce the amount of computational work required.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Appendix A

Table A2.

Direct Influence Coefficient Matrices (Critical, Ideal, and Most Typical Model).

	ENB1	ENB2	ENB3	ENB4	ENB5	ENB6	ENB7	ENB8	ENB9	ENB10	ENB11	ENB12	ENB13	ENB14	ENB15	ENB16	ENB17
ENB1	0.011	0.023	0.078	0.01	0.036	0.012	0.027	0.043	0.099	0.02	0.014	0.049	0.022	0.033	0.063	0.02	0.039
ENB2	0.084	0.012	0.015	0.024	0.073	0.089	0.163	0.043	0.016	0.08	0.066	0.074	0.044	0.066	0.032	0.039	0.077
ENB3	0.061	0.088	0.015	0.097	0.036	0.094	0.053	0.023	0.083	0.08	0.073	0.076	0.043	0.017	0.016	0.039	0.019
ENB4	0.084	0.088	0.031	0.012	0.073	0.059	0.054	0.022	0.016	0.04	0.066	0.078	0.044	0.082	0.016	0.149	0.038
ENB5	0.061	0.059	0.062	0.073	0.018	0.094	0.028	0.166	0.106	0.099	0.086	0.039	0.022	0.033	0.032	0.127	0.096
ENB6	0.084	0.058	0.117	0.048	0.019	0.012	0.053	0.043	0.032	0.08	0.067	0.074	0.121	0.033	0.016	0.079	0.097
ENB7	0.022	0.047	0.078	0.06	0.036	0.058	0.027	0.023	0.032	0.099	0.026	0.059	0.043	0.017	0.032	0.098	0.039
ENB8	0.055	0.07	0.015	0.048	0.036	0.064	0.053	0.022	0.066	0.101	0.053	0.078	0.023	0.125	0.078	0.039	0.019
ENB9	0.022	0.088	0.03	0.092	0.09	0.086	0.027	0.088	0.016	0.04	0.067	0.067	0.043	0.066	0.086	0.02	0.106
ENB10	0.056	0.059	0.093	0.066	0.073	0.094	0.054	0.088	0.032	0.02	0.013	0.039	0.132	0.033	0.031	0.079	0.124
ENB11	0.089	0.047	0.03	0.048	0.109	0.064	0.135	0.12	0.124	0.04	0.013	0.049	0.043	0.082	0.079	0.039	0.077
ENB12	0.012	0.023	0.016	0.092	0.073	0.064	0.053	0.12	0.066	0.039	0.053	0.01	0.088	0.033	0.102	0.077	0.02
ENB13	0.055	0.07	0.062	0.092	0.109	0.047	0.053	0.023	0.032	0.08	0.08	0.078	0.022	0.091	0.078	0.039	0.077
ENB14	0.056	0.07	0.117	0.078	0.019	0.023	0.109	0.088	0.083	0.04	0.086	0.074	0.044	0.017	0.119	0.077	0.096
ENB15	0.089	0.047	0.093	0.066	0.036	0.023	0.028	0.022	0.082	0.039	0.053	0.063	0.043	0.099	0.016	0.039	0.02
ENB16	0.067	0.064	0.117	0.078	0.073	0.07	0.054	0.023	0.083	0.021	0.08	0.074	0.132	0.082	0.119	0.02	0.038
ENB17	0.089	0.088	0.03	0.013	0.092	0.047	0.027	0.043	0.032	0.08	0.106	0.019	0.088	0.091	0.086	0.02	0.019
ENB1	0.023	0.038	0.074	0.024	0.049	0.024	0.037	0.056	0.084	0.031	0.025	0.056	0.033	0.047	0.065	0.031	0.051
ENB2	0.074	0.023	0.027	0.04	0.068	0.076	0.11	0.056	0.028	0.072	0.065	0.068	0.054	0.065	0.046	0.052	0.072
ENB3	0.062	0.074	0.027	0.079	0.048	0.077	0.06	0.035	0.075	0.072	0.068	0.068	0.054	0.028	0.028	0.051	0.031
ENB4	0.074	0.074	0.046	0.024	0.068	0.062	0.061	0.034	0.028	0.051	0.065	0.069	0.054	0.074	0.028	0.101	0.051
ENB5	0.062	0.061	0.064	0.071	0.029	0.077	0.038	0.111	0.086	0.081	0.076	0.048	0.033	0.047	0.046	0.095	0.082
ENB6	0.074	0.06	0.089	0.055	0.03	0.023	0.06	0.056	0.046	0.072	0.066	0.068	0.09	0.046	0.028	0.072	0.082
ENB7	0.037	0.053	0.074	0.063	0.048	0.062	0.037	0.035	0.046	0.081	0.04	0.062	0.054	0.028	0.046	0.082	0.051
ENB8	0.06	0.068	0.027	0.055	0.049	0.064	0.06	0.034	0.065	0.082	0.058	0.069	0.033	0.091	0.074	0.051	0.031
ENB9	0.038	0.074	0.045	0.077	0.077	0.074	0.037	0.079	0.028	0.051	0.066	0.065	0.054	0.065	0.076	0.032	0.085
ENB10	0.06	0.061	0.082	0.065	0.068	0.077	0.061	0.079	0.046	0.031	0.025	0.048	0.098	0.047	0.046	0.072	0.095
ENB11	0.075	0.053	0.045	0.055	0.087	0.064	0.097	0.094	0.091	0.051	0.025	0.055	0.054	0.074	0.074	0.052	0.072
ENB12	0.023	0.038	0.028	0.077	0.068	0.064	0.06	0.094	0.065	0.05	0.058	0.021	0.076	0.047	0.085	0.071	0.032
ENB13	0.06	0.068	0.064	0.077	0.087	0.054	0.06	0.035	0.046	0.072	0.074	0.069	0.033	0.077	0.074	0.052	0.072
ENB14	0.06	0.068	0.089	0.073	0.03	0.038	0.086	0.079	0.075	0.051	0.076	0.068	0.054	0.028	0.09	0.071	0.082
ENB15	0.075	0.053	0.082	0.065	0.048	0.038	0.038	0.034	0.074	0.05	0.058	0.064	0.054	0.084	0.028	0.052	0.032
ENB16	0.068	0.062	0.089	0.073	0.068	0.07	0.061	0.035	0.075	0.031	0.074	0.068	0.098	0.074	0.09	0.031	0.051
ENB17	0.075	0.074	0.045	0.024	0.078	0.054	0.037	0.056	0.046	0.072	0.082	0.034	0.076	0.077	0.076	0.031	0.031
ENB1	0.018	0.032	0.075	0.019	0.044	0.019	0.034	0.051	0.089	0.027	0.021	0.053	0.029	0.042	0.064	0.027	0.047
ENB2	0.078	0.018	0.023	0.034	0.07	0.081	0.126	0.051	0.024	0.074	0.066	0.07	0.051	0.066	0.041	0.048	0.074
ENB3	0.062	0.079	0.023	0.086	0.044	0.084	0.058	0.031	0.078	0.074	0.07	0.071	0.05	0.024	0.023	0.047	0.027
ENB4	0.078	0.079	0.04	0.019	0.07	0.061	0.059	0.03	0.024	0.047	0.066	0.073	0.051	0.077	0.023	0.118	0.046
ENB5	0.062	0.06	0.063	0.072	0.025	0.084	0.035	0.13	0.093	0.087	0.079	0.045	0.029	0.042	0.041	0.106	0.087
ENB6	0.078	0.059	0.1	0.053	0.026	0.019	0.058	0.051	0.041	0.074	0.066	0.07	0.1	0.041	0.023	0.075	0.088
ENB7	0.031	0.05	0.075	0.062	0.044	0.06	0.034	0.031	0.041	0.087	0.035	0.061	0.05	0.024	0.041	0.088	0.047
ENB8	0.058	0.069	0.023	0.053	0.044	0.064	0.058	0.03	0.065	0.088	0.056	0.073	0.03	0.103	0.075	0.047	0.027
ENB9	0.031	0.079	0.04	0.083	0.082	0.079	0.034	0.082	0.024	0.047	0.066	0.065	0.05	0.066	0.08	0.028	0.092
ENB10	0.059	0.06	0.086	0.066	0.07	0.084	0.059	0.082	0.041	0.027	0.02	0.045	0.109	0.042	0.04	0.075	0.105
ENB11	0.081	0.05	0.04	0.053	0.095	0.064	0.109	0.103	0.103	0.047	0.02	0.052	0.05	0.077	0.076	0.048	0.074
ENB12	0.018	0.032	0.024	0.083	0.07	0.064	0.058	0.103	0.065	0.047	0.056	0.016	0.08	0.042	0.091	0.073	0.028
ENB13	0.058	0.069	0.063	0.083	0.095	0.051	0.058	0.031	0.041	0.074	0.076	0.073	0.029	0.082	0.075	0.048	0.074
ENB14	0.059	0.069	0.1	0.075	0.026	0.032	0.093	0.082	0.078	0.047	0.079	0.07	0.051	0.024	0.101	0.073	0.087
ENB15	0.081	0.05	0.086	0.066	0.044	0.032	0.035	0.03	0.077	0.047	0.056	0.064	0.05	0.09	0.023	0.048	0.028
ENB16	0.067	0.063	0.1	0.075	0.07	0.07	0.059	0.031	0.078	0.028	0.076	0.07	0.109	0.077	0.101	0.027	0.046
ENB17	0.081	0.079	0.04	0.02	0.083	0.051	0.034	0.051	0.041	0.074	0.091	0.028	0.08	0.082	0.08	0.027	0.027

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Optimizing Sustainability in Food Supply Chains Through Strategic Digital Twins Enablers: A Gray Influence Analysis Approach (GINA)

Abstract

Keywords

Introduction

Literature Review

Digital Twin and Its Application

Digital Twins and Food Supply Chain

Methodology

Case Study Application of the Proposed Model

Results and Discussions

Total Influence and Responsibility coefficient of Ideal, Critical, and Typical Model.

Managerial Implications

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

Appendix A

References