Optimizing sustainability in allium crop supply chains: a framework for enhancing efficiency and circular economy practices in India

The allium supply chain in India is faced with a structural crisis that has been typified by inefficiencies, the lack of infrastructure, and economic devastation. Although India is the second largest onion producer in the world, and the volume of onions grown is 23.3 million tonnes annually, post-harvest losses are estimated to be 30–35% of total production, which is equivalent to USD 1.8 billion of economic losses annually (Shekhar et al., 2023; Chauhan, 2020). These losses are attributable to lack of unity in supply chains, insufficiency of cold storage facilities to meet the demand, and ineffective logistics network to maintain the quality of products at farm-to-consumer levels (Gulati et al., 2024; Haque et al., 2023). The impacts go beyond the economic measures. The socio-political instability is prompted by price volatility in allium markets – in 2019, retail onion prices increased by 800% in four months and led to civil conflicts and emergency government action (Shankar et al., 2023; Dey, 2022). The disproportionately affected 145 million households of smallholder farmers who rely on allium to secure livelihoods (Kumar and Pant, 2023). As an example, many curry dishes include onions and garlic as an ingredient, and pickling may include shallots and garlic to enhance the taste (Fenwick et al., 1985a, b, c; Ahmed et al., 2025; Moldovan et al., 2022). Allium crop supply chain organization is critical to food security and welfare of the society. Allium crop supply chain is a big contributor to the local economies but specifically in East Asia where nations such as China control production and export (Park, 2012).

The supply chain management is the one that contributes directly to the food security as it helps to improve the planning, organization, and distribution of the allium crops in order to guarantee the stable availability in the market (Kamaruddin and Hamizar, 2022). This is due to several factors, which include, economic factors and public health. With a focus on economic sustainability, great attention should be paid to an efficient food logistics supply chain because through its core food commodities are supplied on a stable basis hence reducing disruptions that otherwise would have contributed to the instability of prices of such foods. This is because of the emergence of consumer concerns around food safety and quality and the increasing pressures from big supermarkets, department stores, and hypermarkets for standardized and quality products (Condratchi, 2014). The adoption of technologies in logistics enhances operational efficiency and promotes socially responsible practices, aligning with environmental, social, and governance considerations (Kresnanto et al., 2021; Sun et al., 2024; Yan et al., 2021) explained that sustainable supply chains in agri-foods can reduce waste and losses using logistics. It is good to note that these strategies should be targeted at preventing waste at the transport level while at the same time; being done with minimal costs and effects on the surrounding environment. The possibilities of using the Internet of Things (IoTs) are also illustrated to enhance resource efficiency in food supply chains (Jagtap and Rahimifard, 2019). It has also complicated the supply of alliums, onions, garlic and other similar species to the market since the demand of the product rises as market demands and trends vary. Alliums are influenced by climate change that causes ambiguity and quandaries in the course of farming. Increased temperatures and decreased water supply can have a disastrous impact on allium crops, which reduces their yields (Khar et al., 2020; Bukharov et al., 2023). Other external environmental conditions that can affect the quality of crops are changes in temperature, precipitation, and extreme weather conditions (storms) (Porter et al., 2014; Bukharov et al., 2023). The modern allium supply chains are facing compounding weaknesses due to climate variability, as the climate models suggest that the traditional growing areas will fall by 8–15% in productivity (Khar et al., 2020; Duque-Acevedo et al., 2023). To ensure that the manufacture of allium remains a viable venture in the future, learning and dealing with the effects of climate change is necessary. Allium crops are vulnerable to disruptions throughout the entire supply chain; the disruptions may emanate through transport issues, global politics and pandemics. Allium crops have not been an exception since the outbreak of COVID-19 has triggered the phenomenon of food insecurity (Cristiano, 2021; Panossian et al., 2021; Matthan, 2025). The development of technology application and innovation in agriculture activities is immensely essential in addressing the challenges that come along the production of allium crops. The situation with allium crops can be improved with the breeding and cultivation activity, as well as with the emergence of new technologies of storage, which will allow adapting to the new conditions and position regarding the market needs and environmental requirements (Taylor, 2018). Allium crops therefore experience supply chain issues and challenges that are technical, financial, environmental, legal and sociocultural in nature. Supply disruption, lack of flexibility, logistics inadequacy, and poor integration are some of the practices that impact supply chain management (Kandil et al., 2024). Structural issues that complicate these vulnerabilities are: lack of flexibility-stiff procurement contracts that do not respond to yield variations (Duque-Acevedo et al., 2023), lack of logistics-inadequate refrigerated transportation that cannot meet lower demand (Shekhar et al., 2023), integration factors-inaccurate information asymmetry among supply chain players that cause coordination breakdown and lack of traceability-no quality assurance system The interrelated vulnerabilities require the provision of overall optimization models and no single interventions (Haque et al., 2023; Darmawan et al., 2018). Food safety management is one of the other issues where the practice of tactics and targets are also required or needs inclusion in the existing research where traceability is considered the most studied aspect (Machado Nardi et al., 2020). The integrity of the supply chain information is crucial in ensuring quality and food provenance, but it comes with some barriers such as the Governance structures, quality, safety and hygiene information and exchange (Trienekens et al., 2012). The problems of combating food losses at the producer level include low technological advancement, new market demands and laws, and collaboration barriers, and the effects of climate change (Despoudi, 2021). The present food chain of allium crops, likewise, encounters various problems, for instance, safety and adulteration hazards, food waste, and insecurity (Chen et al., 2021). In Figure 1, several news articles highlight the market inequalities happening for the allium crops. These challenges create more such needs as the establishment of sustainable and resilient food systems not only within but within the context of disruptions including the COVID-19 pandemic. These include robotics, machine learning and nanotechnology among others are being viewed as the solutions to these (Mcclements et al., 2021). Despite extensive agricultural supply chain literature, three critical gaps persist in addressing perishable crop optimization. First, crop-specificity gap: Generic agricultural supply chain models inadequately account for allium-specific characteristics—prolonged storability creating strategic inventory decisions, dual-channel demand, and sulphur compound volatility affecting quality degradation (Nguyen and Chen, 2022; Staff and Mustafee, 2025). Second, methodological limitations: Prior multi-criteria decision-making (MCDM) applications in agriculture lack large-scale stakeholder validation (n < 100), robustness testing, and alternative solution comparisons (Atlı, 2024). Third, contextual void: International optimization studies overlook India's unique institutional environment—fragmented smallholder farming, Agricultural Produce Market Committee (APMC) mandi-based marketing systems, and infrastructure deficits (Shekhar et al., 2023).

This research paper fills these gaps by following the systematic research with two research questions interrelated:

The question of the original supply chain that deals with allium crops – onions, garlic and other related crops – being optimally developed to cope with the increased consumer demand for the food commodity, together with other factors such as increased market demand, external factors such as competitor pressure, fluctuating consumer preferences among others is a matter of importance to discuss. To realize efficiency, vulnerability and sustainability in the growing process and distribution of allium crops, an appropriate supply chain is crucial. A focused supply chain management is a necessity for allium crops including onions, garlic and related crops because it reflects the dynamic nature of supply chain that has several social issues, increasing demand, and market volatility and other factors. The use of alliums crops in meal preparation and preparation in global markets as shown by (Fenwick et al., 1985a, b, c) requires a supply system that enhances a continuing provision of bulbs to fit the increasing market. The more effective management of the supply chain, improvements in the logistics as well as intermediation of stakeholders that may support a given supply chain the better availability of supply chain solutions to recurring problems regarding the production and consumption of allium crops (Yeshiwas et al., 2024). Management of the value chain is very effective in addressing wastage and loss associated with allium crops. Minimizing post-harvest losses which are achieved through a well-organized supply chain to enhance better stockist; timely transport facilities and storage hence increase the ratio of the harvested food getting into the consumer's plate rather than wasting (ReFED, 2016). This focus is on quality and safety standards, and an optimized supply chain ensures that the production process is under keen observation at each segment of manufacturing and delivery. Another problem that is addressed by an optimized supply chain is another problem of an optimal supply chain practice that is scalability within the framework of the fluctuating market conditions (Belhadi et al., 2024). Using such a supply chain it is also easier that allium crops can maintain the necessary competitiveness in the market and continuously adjust to the changing needs of clients and the world trade. The given study attempts to solve the problems by filling in the gaps in research.

To address these questions, the three research objectives that we are seeking are:

In an attempt to meet the objectives and answer the research questions of our study on the supply chain of allium crops, we were able to identify the relevant parameters in the existing literature in a systematic manner. An interview questionnaire was designed and given to the key officials representing the selected APMC Mandi (Market). The received data, such as the completed questionnaires and the results of the interviews, were analysed carefully with the help of the mixture of the observation and the qualitative research methodologies (Kristensen and Mosgaard, 2020).

After the introduction part the research paper is structured with the headings as the theoretical background of the study, methodology, analysis, results of the study, findings, discussion, conclusion, research implication, limitations, and future research scope.

In this section, we review the relevant literature on the food supply chain of alliums crops in India later, we present and discuss the theoretical frameworks to examine the feasibility of the study.

In Indian culture, alliums (onions, garlic, and other genera) are important products and are part of our heritage. The earliest documentation of alliums is from ancient texts like Charaka Samhita (Fenwick et al., 1985a, b, c). Economically, allium agriculture is not just cultural; it is also a very important aspect of the Indian economy. Currently, India is the second largest producer of onions in the world with an annual production of 23.3 million tonnes and the fourth largest producer of garlic at 3.2 million tonnes. Alliums are produced on approximately 1.5 million hectares of land (Ochar and Kim, 2023) in India, where alliums create 12–15 million value chain actors (many being small holder farmers (86% of whom have less than 2 hectares), traders, processors, and retailers), and create annual revenues of more than ₹45,000 crores (USD 5.4 billion). Unfortunately, the allium sector is underutilized because of inefficiencies throughout the supply chain. Significant amounts of is lost during the post-harvest phase (30–35% of production), with losses throughout the supply chain being as follows: losses during the storage phase (15–18%); losses during transportation (8–10%) and losses during retail distribution (5–7%) (Rahal, 2024; Zhang and Mohammad, 2024). This supply chain inefficiency necessitates that the supply chain be systematically improved to resolve the unique characteristics of the allium supply chain. The allium crops have been a part of medicinal and culinary practices, where the Ayurvedic system of Indian medicine adds to the curative powers of the food ingredient. The alliums that are commonly used in Indian kitchen are many, that is as result of agro biodiversity of the country. Onion (Allium cepa), garlic (Allium sativum) and shallots (Allium cepa var aggregatum) are common and used as flavour accompaniments of many foods (Fenwick et al., 1985a, b, c). It is the speciality of each of these varieties to impart specific flavours and fragrances to form the basic taste palette of the regions' produce. Scallions (Allium fistulosum) and chives (Allium schoenoprasum) are often used in cookery to enhance the flavours of foods and giving them the desired fresh finish. Allium species, encompassing a variety including “Allium cepa, Allium Chinese, Allium hookeri, Allium macranthum, Allium prattii, Allium rubellum, Allium sativum, Allium tuberosum, and Allium wallichii,” are indispensable elements of Indian cuisine. Allium are used as spices, condiments and vegetables and are very crucial components in the cuisine front since they add flavour and enhance product variety to food preparations.

Allium biodiversity conservation and the livelihoods of smallholder farmers are in jeopardy due to two factors: the growing food demand, and unsustainable production practices (Ayam, 2011; Ochar and Kim, 2023). In addition to agronomic interventions, there is a need to systematically optimize all levels of the supply chain—this requires optimizing post-harvest storage facilities and reducing transport costs through more efficient routing of goods. In order to coordinate the activities of each stakeholder within the supply chain, systems must be in place to provide digital tracking of products (Rahal, 2024). The interventions being made at the supply chain level will directly relate to the MCDM frameworks used in this research study, as they pertain to the allium sector in India from an agricultural economics, operations research, and sustainable food system perspective.

The specific food web in the framework of the Indian food web is not a simple linear way or a much generalized system which is evident from the study of Prakash (2018). The subject matter of this topic is diverse and consists of procurement; food processing; innovation; traceability; safety; environment and sustainability; food policy; quality; health; consumer behaviour; and packaging. However, the Indian food processing sector faces certain problems in their operations in particular supply chain management according to Dharni and Sharma (2015). Some of these are: There is a need for better logistics and management, information technology and cold chain properly equipped. According to Parwez (2014), there is need to work on the supply chain with an aim of correcting the challenges facing agriculture in India including poor infrastructure and high rates of food wastage. He also clarified how its actualization in a sector could be done through investment in equipment of cold chain systems, post-harvest technologies, food processing plants as well as establishment of the food retailing sub-sector. There is also the issue of food wastage which is also on the rise in India and a massive effect to health of the environment and the economy (Kharola et al., 2022). There are activities that are most important in a food supply chain, and it is necessary to endeavour most on them to avoid food waste. Awareness, training and skill development of growers is the most significant criteria of waste minimization according to the study conducted by Sharma et al. (2021). Sustainable and effective chains play a very important role in the provision of food and being safe for human consumption (Mastos and Gotzamani, 2022). Blockchain technology is suggested to improve supply chains related to dairy products in India in terms of the level of transparency, sustainability, and accountability (Khanna et al., 2022). The issues such as post-harvest losses and low income of the farming community of India are another factor the agriculture sector in the country (Vats et al., 2019). Many programs for controlling of food wastage should therefore centre on how the connection between producers and industries shall be strengthened and made more efficient and productive in the food chains. Prior research has been conducted on several food supply chains, concentrating the so-called “perishable” food products that must have their supply chains managed in a way to avoid post-harvest losses. Li et al. (2021) and Qiu et al. (2020) have extended this in developing a model for cost, time, and quality in the food supply chain. Authors have identified the fresh-food supply chain characteristics as perishable food product and reported studies on the trade-off between logistic costs and perishable food product quality and a called for a methodology for improving supply chain performance and hence provided a hybrid model for the purpose. According to Despoudi (2019) this goal was underlined as ordinary, and specifically mentioning the role of optimization to decrease food losses. Mirabelli and Solina (2022) took time to provide additional insights into short life products supply chain positioning and elaborated on integrated decision-making and noted that the supply chain area has gained much attention and needs more research to be conducted.

Recent advances in perishable food supply chain optimization emphasize integration of digital technologies—IoT sensors, blockchain traceability, and AI-driven demand forecasting—to address quality degradation, shelf-life constraints, and real-time decision-making challenges (El Mane et al., 2024; Tang et al., 2024). El Mane et al. (2024) developed a blockchain-enabled Java smart contract framework integrated with IoT for agricultural traceability, demonstrating 95% reduction in food fraud incidents and 28% improvement in supply chain transparency through immutable record-keeping across four layers: producer, processor, distributor, and retailer. Similarly, Tang et al. (2024) assessed the utilization of blockchain technology in conjunction with the IoTs (IoT) to increase the efficiency of African agricultural supply chains. Their study showed that by utilizing temperature-sensitive smart contracts and automated alerts, farmers were able to reduce their post-harvest spoilage rates by between 18% and 22% for certain types of perishable vegetables through the dynamic adjustment of pricing. However, despite this success, they found that developing regions like India still pose significant challenges to the successful implementation of this technology. For example, only 22% percent of APMC marketplace locations have reliable Internet service; 78% percent of farmers in India do not own a smartphone; and the cost of setting up the necessary physical infrastructure (₹15 to 25 Lakhs) is more than most farmers can access through financing. Therefore, the authors concluded that a hybrid optimization framework that combines traditional relational coordination methods with selective deployment of technological innovations best addresses the existing gaps in the technology-institutional frameworks. The present study will demonstrate how the analytical hierarchy process (AHP) can be used flexibly to create optimized solutions that can be tailored to the stakeholder capacities.

Optimization of supply chain has been influenced by several factors, including risk and uncertainty, environmental sustainability, and strategic consumer behaviours (Choi et al., 2016; Timsina et al., 2016; Abrha et al., 2020). Optimizing vehicle routing and loading operations has been studied alongside the selection of suppliers that best serve each order allocation in real problems, considering different economic, environmental, and social aspects (Vega-Mejia et al., 2019), where the supplier's location over time may influence lead times between plants and enterprise responsiveness to customer orders at high or low scale capacity.

Taleizadeh et al. (2020) initiated the study of coordinating vendor-managed inventory systems when the storage capacity (SC) is limited and where there is partial backordering in the case of unpredictable demand. Their research as well as other studies make it clear that optimizing supply chains is a very complex process. It is important to consider several factors to achieve maximum efficiency, sustainability and resilience throughout the entire allium food supply chain. This method uses a combination of integration of production, transportation, and distribution where these three Factors work together optimally, thus keeping costs under control, yet competitive prices and not compromising quality (Chopra et al., 2019). Companies that operate within fluctuating price environments, where changes in price directly affect profit margins, must use strategic pricing and improve Procurement Negotiations as this is essential for maintaining stable profitability during times of price fluctuation (Gaudenzi et al., 2021). Even though Supply Chain has always suffered tremendous challenges from Risk and Uncertainty, the current Climate Change Trends, and Fluctuating Markets Impact Emerging Issues to be More Important Today than Ever Before. Therefore, during times of uncertainty, Strong Mitigation Plans and Robust Scenario Planning Frameworks are critical to being able to bounce back Resiliently. Onions are perishable; therefore, the SC in the supply chain Distribution process is critical to Onions. Additionally, Intelligent Distribution Hubs would likely provide for both Cost-efficient and environmentally sustainable benefits of Improving Cold Chain Management by Mitigating Waste. Therefore, To Comply with All Nations and Reduce the Risks of Food Safety Challenges Evolving from Non-Compliance, Companies must develop regulations that protect onions Supply Chain Integrity by Reducing Liabilities and Risks to Consumers.

The APMC Mandi system in India is intended to provide farmers with a minimum support price and curb the abuse of farmers. Still, it is also creating a major structural inefficiency, thereby preventing the optimal flow of allium to its the consumer. The state laws regulating these APMC Mandis inhibit direct transactions between farmers and processors/exporters by mandating that all agricultural products be routed through Mandis that involve three to five intermediaries and require them to charge 25%–40% margins cumulatively before reaching retail price (Maharajan, 2024). The fragmented nature of these supply chains leads to asymmetrical information between farmers and retailers, wherein retailers neither know where the product came from nor have real-time pricing available to assist them with their purchasing decision. Consequently, this results in excess production for farmers in that they must sell their product at distress prices and retailers experiencing shortages that drive prices higher. The new established policies of allowing e-NAM (National Agriculture Marketing) and contract farming were designed with the intent of reducing intermediation along the supply chain (Venkatesh et al., 2021). However, both methods of reducing intermediaries have seen limited adoption (only 18% of Indian farmers use e-NAM platforms, while fewer than 5% of farmers are engaged in contract farming for allium crops) due to the difficulty of enforcing regulations and a general scepticism regarding these types of commercial practices amongst small farmers. When comparing the allium supply chains between India and China, significant institutional differences exist. In China, cooperatives are the primary method for establishing direct links between farmers and retailers, as they facilitate the establishment of 62% of all fresh vegetable transactions and have co-regulatory partnerships, in which both government and private entities work together to develop product quality standards and waste reduction practices (Roosen et al., 2015; Kumar and Pant, 2023). India's reliance on institutional constraints related to the APMC context requires that any model of optimizing the allium supply chain incorporates and recognizes the various layers of intermediation, limited digital infrastructure and fragmented stakeholder networks. The framework for optimization was incorporated into the AHP design used in this study through the inclusion of the voice of each stakeholder group (farmer, trader, processor and retailer).

Ecommerce growth is the third aspect of supply chain integration (SCI) associated with alliums. There are various ways to examine the impact of ecommerce growth as a means of extending market access and removing barriers to entry by adopting technological advancements and changing consumer buying habits through online outlets (Ivanov and Dolgui, 2021). Figure 2 illustrates the hierarchy established by the decision-making processes of supply chain managers regarding environmental sustainability. Consumer expectations have also been enhanced — there is a growing expectation that businesses be held accountable for their back-end services and have responsible business practices. Additionally, the globalization of markets and geopolitical events are causing a shift in the way supply chain managers think and operate due to challenges associated with sourcing strategies, trade regulations, and overall market dynamics. As we look at potential future scenarios that could affect our supply chain globally, it is essential to develop strategies to deal with uncertainties and to create a resilient supply chain capable of adapting to those surprises (Hou et al., 2024). To aid in this endeavour, Table 1 demonstrates a theoretical-based classification of eight important variables that impact allium's supply chain optimization, based on systematic literature reviews and verified through consultation with 320 actors involved in the supply chain from 12 APMC mandis. These variables have been grouped into six meta-categories representing economic viability, risk management, infrastructure, governance, digital integration, sustainability, and external environment, demonstrating the multi-dimensional complexity of agricultural supply chains.

MCDM methodologies have undergone significant theoretical and computational advancements in recent years, particularly through the integration of fuzzy set theory extensions to address uncertainty inherent in agricultural supply chain decisions. The growth of the new MCDM methods arose due to an increasing awareness that real-world, practical decision-making in agriculture entails imprecision, vagueness, and subjective expert judgement of the decisions-making process and that those conditions cannot be captured well using traditional methodologies (Alamoodi et al., 2024). Therefore, there is a clear evolution within the field of new fuzzy MCDM frameworks that assist with the type of complex agricultural experiences (i.e. Agriculture 4.0) requiring MCDM (i.e. decision support system (DSS)) combining multiple criteria from various stakeholders or clients concurrently under uncertainty, such as environmental sustainability, economic viability, technology readiness, and stakeholders' preferences (Alamoodi et al., 2024; Widayat et al., 2024). To further assist researchers and practitioners in applying fuzzy MCDM methods in Agriculture 4.0 contexts, new types of fuzzy numbers have been introduced to accommodate the needs of modern agricultural DSS. Historically, traditional fuzzy number types included only triangular fuzzy numbers, however, recent advances have developed more complex fuzzy frameworks, including spherical fuzzy numbers (Ünver and Aydoğan, 2025), Fermatean fuzzy numbers (Aydoğan and Ozkir, 2024), hyperbolic fuzzy numbers, and q-rung orthopair fuzzy numbers to allow greater representational flexibility and enhance decision-making accuracy (Afrasiabi et al., 2022). A unique fuzzy MCDM method created specifically for an agriculture 4.0 context, the Hy-FWZIC-CODAS, has been introduced by Alamoodi et al. (2024) and was tested on 40 DSS platforms with 95% accuracy when compared to the AHP methodologies commonly used; thus providing a proof-of-concept for the considerable improvement provided by fuzzy methodologies when compared to AHP in supporting the agricultural revolution. Widayat et al. (2024) conducted a review of the literature that evaluated the implementation of fuzzy logic within smart farming, stating that current fuzzy logic applications in smart farming showed better decision-making precision than comparable crisp methodologies, particularly in the areas of irrigation scheduling, detection of common agricultural diseases, and supply chain optimization under climate variability.

A variety of hybrid fuzzy MCDM frameworks have been rapidly adopted within the agricultural supply chain domain, with the aim of leveraging the complementing strengths and compensating for the individual weaknesses of each of the multiple methods through their hybridization. Rouyendegh and Savalan (2022) developed a hybrid MCDM approach using fuzzy theory, which integrates the strengths of the fuzzy AHP (AHP-F) methodology for determining the weight of each criterion by combining it with fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank alternatives in the case study of agricultural production analysis in the context of Turkey. The use of the hybrid approach is evidenced by its superior consistency (i.e. consistency ratio (CR) = 0.018) compared with either the AHP-F or fuzzy TOPSIS methodologies when employed separately. This trend toward hybridization reflects the completed acknowledgement that in order to make informed decisions about agriculture, both the hierarchical decomposition (i.e. AHP methodology) and distance-based alternative ranking (i.e. TOPSIS methodology) must be taken into account to effectively represent the complexities inherent in agricultural decisions (Paul et al., 2021). Recent developments in hybrid fuzzy MCDM methodologies illustrate the need for the inclusion of a sustainability dimension (i.e. economic; environmental; social) within the MCDM frameworks that are being developed for the agri-food sector. Joshi et al. (2023) identified 25 factors in their analysis of the factors influencing the adoption of sustainable agribusiness in India using a fuzzy Delphi methodology, including a lack of capability to adopt sustainable practices and inconsistencies in government policy as the two leading constraints to adopting sustainable business practices. In addition to their work, Sonar et al. (2025) used fuzzy Delphi and Wings methodologies to identify causal relationships among 11 barriers to sustainable agri-supply chains in India and concluded that both the lack of access to information and post-harvest mismanagement contribute to the root causes of these barriers, with subsequent impacts felt throughout the supply chain network. The findings from both Joshi et al. (2023) and Sonar et al. (2025) show that MCDM in India's agricultural sector must be adapted to fit the specific characteristics of that market instead of simply importing generic frameworks developed in the West and applying them to India's agricultural sector, which is dominated by smallholder farmers and has various unique infrastructure and institutional challenges. Despite the broad use of the Analytic Hierarchy Process (AHP) since Saaty's seminal work in 1980, the AHP continues to face established shortcomings such as sensitivity to rank reversals, restrictions on the range of scale values, and an inability to adequately manage uncertainty during pairwise comparisons, which fuzzy extensions of the process have attempted to resolve. Therefore, various AHP-F methods have been introduced, each having benefits for different types of decision-making. For example, triangular fuzzy AHP, spherical fuzzy AHP (SF-AHP), and neutrosophic fuzzy AHP have been used, offering benefits for various applications. Atlı (2024), used AHP-F to select sustainable suppliers for Turkish agricultural supply chains, finding a CR of 0.024. However, an in-depth evaluation shows that challenges remain, such as (1) the exponential increase of challenges associated with calculations due to the large number of criteria; manual calculations become impractical if there are more than 10 criteria, (2) the lack of standardization of fuzzy pairwise comparisons among AHP-F methods, and (3) compatibility issues of fuzzy MCDM methods (such as TGFS, VIšekriterijumsko KOmpromisno Rangiranje (VIKOR), etc. with other methods), relating to the use of fuzzy numbers. For example, robustness testing should be a requirement of any method for validating AHP-F rankings. Alamoodi et al., in 2024, performed a sensitivity analysis for the purpose of demonstrating that the optimal rankings among alternatives of AHP-F will remain stable for weight variations of ±15%, which is considered stable across agricultural materials. Widayat et al., in 2024, used bootstrap resampling methods to evaluate the statistical stability of fuzzy MCDM rankings. For agricultural products where uncertainty exists, Widayat et al. (2024) recommended using 1,000 or more iterations for this purpose. Integrating circular economy (CE) principles and sustainability metrics into an MCDM framework is an emerging research area, especially within agricultural supply chains aligning with the United Nations Sustainable Development Goals (SDGs) and climate mitigation goals. Srhir et al. (2023) created a holistic framework linking Industry 4.0 technologies with achieving SDGs through optimizing supply chain processes based on Supply Chain Operations Reference (SCOR) models; demonstrating how using technologies to create traceability, reduce waste, and increase efficiency of resource use impacts 12 of the 17 SDGs directly. Their derived framework applied to Agri-food supply chains indicated that Blockchain integration reduced food waste by 23–28% contributing to achieving SDG 12 (Responsible Consumption and Production) whereas IoT enabled Cold Chain Management reduced post-harvest losses by 18–22% thereby aiding in achieving SDG 2 (Zero Hunger) (Srhir et al., 2023). Arcese et al. (2023) enhanced methodologies utilized for assessing sustainability by integrating environmental life cycle assessment, social life cycle assessment (S-LCA), and life cycle costing within a MCDM framework for Agricultural and Food Sectors. Their meta-analysis of European Agri-Food supply chains highlights critical deficiencies including: (1) indicators for S-LCA are less developed than those for environmental metrics; (2) Trade-Offs between dimensions of sustainability (i.e. Organic Farming has higher land use than conventional, but lower chemical inputs) need explicit MCDM reconciliation; (3) Selection of context-specific indicators greatly affects the ranking of Sustainability (Arcese et al., 2023). It is evident from these works that clear justification of criteria selection and weighting must take place in any agricultural MCDM application, however, most existing literature fails to address this.

This research aims to fill critical gaps in the current literature about agricultural MCDM specifically. These gaps are addressed in the following three ways: (1) Crop-specificity gap: Existing agricultural MCDM frameworks have developed general agricultural models that do not consider crop-specific characteristics. For example, alliums are perishable crops. Their unique features, such as long shelf life (6–8 months) for strategic inventory management; dual-channel demand for fresh produce and processed goods; and volatility of sulphur-containing compounds that negatively affect quality through post-harvest handling, create a need for MCDMs specifically developed for alliums as there are no current methodologies available to do so in the literature. (2) Methodological Validity Gap: Although fuzzy MCDMs have provided a theoretical framework for agricultural applications, they lack evidence supporting their use as valid methodologies when agricultural applications have been conducted on a large scale (i.e. whereby fewer than 100 stakeholders participated in 78% of studies); robustness testing has only been conducted for 42% of the articles reviewed through the sensitivity analysis; and only 65% of studies reviewed using the fuzzy MCDM method have conducted comparative validation of alternative MCDM methodologies (Paul et al., 2021; Sonar et al., 2025). (3) Contextual Adaptation Gap: Internationally developed MCDM frameworks do not adequately reflect the particularities of India's agricultural institutional environment, such as smallholder fragmentation [86% of all farmers own less than 2 hectares of land], agricultural product markets based on APMC Mandis, infrastructure deficits [inadequate cold SC of 65%], and complex regulations governing agricultural production and marketing. Although Joshi et al. (2023) and Sonar et al. (2025) identified barriers to the development of India-specific MCDM frameworks and provided some supporting evidence for the need for MCDM for specific crop supply chains in India, they did not operationalize these barriers into validated MCDM frameworks. The research described here builds upon previous work by bringing together several things: (1) A new validated crop-specific MCDM approach was developed. This incorporates theories of resource-based view (RBV) into the supply chain for allium as a crop; (2) A large-scale stakeholder validation (n = 320) and a thoroughly tested robust process using bootstrap analysis, alternative methods to triangulate results and sensitivity analysis across 20 different variables were conducted; and (3) In the context of India's APMC institutional environment, this research shows pathways identified in a recent article from the Journal of Cleaner Production for achieving CE and SDGs (SDG) (Srhir et al., 2023).

The integration of fuzzy set theory with MCDM Techniques provides a significant advancement to help resolve the uncertainty and imprecision inherent in agricultural supply chain decision-making (Atlı, 2024; Keshavarz-Ghorabaee et al., 2025). Although Traditional AHP provides an effective means to evaluate choices, it does so by assuming that comparisons will be made precisely. This is unlikely to occur in the agricultural environment, where variability and uncertainty are common factors, such as with variability in weather patterns related to periods of time; volatility in prices from the Marketplace, as well as the difference in judgement by an expert (Mahmoudi et al., 2025). The Fuzzy MCDM Methods help alleviate such limitations by providing a representation of both linguistic variables and vague preferences through triangular, trapezoidal, and spherical fuzzy numbers (Dutta and Borah, 2021).

New research shows how using AHP-F is more efficient than using crisp AHP when selecting agricultural suppliers to meet sustainability objectives. Atlı (2024) took both AHP-F and fuzzy ARAS and created a method for selecting sustainable fertilizer suppliers for Turkey's agriculture supply chains, consistently demonstrating consistency ratios of less than 0.05, while also providing for the uncertainty of experts' opinions using triangular fuzzy numbers. In similar research, Gupta et al. (2023) developed a framework called Delphi-Fuzzy AHP to optimize supply chains involving perishable goods. Their study showed how the use of fuzzy methods delivered a variance reduction of 35%–42% from what had been achieved through normal AHP when analysing priority cold storage infrastructure for handling perishable goods. The combined use of AHP-F and TOPSIS has provided an excellent alternative for choosing green suppliers in the agri-food sector (Ekşi et al., 2020), with studies showing that supplier performance metrics have been improved by 18%–25% when fuzzy methods provided for the true uncertainty related to environmental criteria. Beyond using AHP-F as a stand-alone method, hybrid models that combine different fuzzy MCDM approaches have been introduced to the area of sustainable agriculture. Yildirim et al. (2025) implemented the use of Spherical Fuzzy Decision Support systems to evaluate the sustainability of agri-food supply chains. The authors showed that their approach provided better performance (CR = 0.018) than traditional fuzzy methods. Jain et al. (2023) also demonstrated that hybridization of AHP-F and Fuzzy VIKOR provided for the identification of the most effective technology acceptance model for the adoption of Industry 4.0 in agriculture, with the enhancement of fuzzy hybridization leading to an increase of 32% in ranking robustness due to high levels of uncertainty. Mahmoudi et al. (2025) utilized hybrid AHP-F-TOPSIS models to evaluate the sustainability of cropping systems across different agricultural landscapes, with their research findings indicating that the fuzzy method allows for a better understanding of the heterogeneity of stakeholder preferences; this is an important aspect of evaluating systems that are predominantly smallholder-based, such as the Agricultural Producer Marketing Corporations (APMCs) of India.

The traditional MCDM approach does not adequately deal with the uncertainty arising from the perishability of goods; however, in agricultural cases of perishable goods Z-number (the combination of fuzzy number and reliability measures) has proven to increase green supplier selection efficiency (Puška et al., 2022). The combination of Fuzzy logarithmic method of additive weights and Fuzzy Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) is an effective way of addressing the complexities in Agriculture Supply Chain Management arising from seasonal production, diminishing quality of farm products from storage, and fluctuations in quality (Puška et al., 2022). Allium crops have a shelf life of only 6–8 months so the problems of perishability and uncertainty need to be addressed soon. The integration of fuzzy MCDM and data-driven approaches represents a new field of research. The introduction of artificial intelligence (AI) to fuzzy MCDM supports real-time optimization of agricultural supply chain management by using predictive analytics, IoT sensor data, and machine learning-based forecasting of demand (Zhang and Li, 2024). Evidence from research has demonstrated that AI-enhanced fuzzy MCDM can reduce expected post-harvest loss by 22–30% by dynamically adjusting thresholds based on monitoring real-time environmental conditions such as temperature, humidity, and transport (Kumar et al., 2016). The application of fuzzy analytic hierarchy process to cold chain logistics of Fresh Agricultural Products indicates that cost weight of 45.5% is dominant and the application of the defuzzification process has addressed the uncertainties of operations (Liu et al., 2024).

Although MCDM methods aid in making operational decisions, there must be a solid foundation of theory behind them so that what you learn from them can be applied to other contexts (Whetten, 1989). In this case, we will use two major theories related to the study of the allium supply chain: RBV theory (Barney, 1991) and SCI theory, both of which have been adapted for the Pursuable Nature of Perishable Agricultural Products.

RBV posits that competitive advantage comes from heterogeneous distribution, value, rarity, difficulty of imitation, and non-substitutability (VRIN) attributes of resources (Barney, 1991). In the allium supply chain context, VRIN attributes include: (1) Cold Storage Infrastructure. Currently in India, cold storage meets only 35% of demand (Shekhar et al., 2023), (2) Market Intelligence Systems for predicting price fluctuations, (3) Relationship with APMC Mandis to reduce transaction costs and (4) Technology capabilities (e.g. E-commerce platform and Traceability) to improve profitability of transactions. The RBV perspective assumes that all resources are simultaneously optimized. However, stakeholder input indicates that smallholder allium farmers are experiencing constrained resource hierarchies. The basic conditions required to achieve economic survival (cost-effectiveness and commodity margin (CM)) must be satisfied prior to investing in sustainability or innovation. The agricultural RBV research should develop a new theoretical framework for the hierarchy of resources in agriculture based on the findings from this research, and therefore this study uses AHP hierarchical analysis for this investigation.

According to Flynn et al. (2010), SCI Theory posits that by providing greater coordination of upstream and downstream activities, sharing information across the supply chain, and collaborating to develop plans, the supply chain can perform better by providing greater certainty. However, the unique storability characteristics of allium, at 6–8 months compared to 24–48 h of storability for most other leafy vegetables considered conventionally perishable, will create unique opportunities for strategic inventory storage in comparison to a conventional perishable supply chain model. The latest extensions to the SCI theory are primarily focused on incorporating advanced digital technology, including crypto, blockchain, the IoTs (IoT), and AI, however, the applicability to smallholder farmers in India may decrease due to a number of limitations, including: 78% of farmers in India do not have access to a smartphone, or use a smartphone (Joshi et al., 2023). The lack of access to these technologies highlights the importance of having hybrid SCI models that leverage both traditional and modern technology-based coordination approaches.

This research explores the integration of RBV, SCI, and CE theories through the use of a hierarchy (AHP). In the AHP hierarchy, Level 1's criteria represent the three categories of resources in the RBV and Level 2's sub-criteria represent the five SCI Coordination Mechanisms, while Alternatives represent different CE maturity levels. The AHP Pairwise Comparison method accommodates stakeholder heterogeneity while validating theoretical consistency through use of Consistency Ratios (CR < 0.10).

This paper identifies major research gaps concerning the operations of food supply chains, which provides the basis of its contribution toward improving the body of knowledge on this topic.

1. There is a critical need for improved techniques for optimizing narrow margins of harvested goods in perishable food supply chains (Osman et al., 2023).

2. True sustainability in the food sector requires an integrative approach for assessing the economic, environmental and social dimensions of an agri-food supply chain (Oglethorpe, 2010). Though much has been written about sustainability, models which objectively and quantitatively consider these three pillars of sustainability concurrently have yet to be developed in the context of a particular agricultural crop production system.

3. Research focused on developing techniques for the simultaneous optimization of planting and harvesting schedules and production planning in agri-food supply chains has been severely limited (Taşkıner and Bilgen, 2021). For the alliums industry, the timing and coordination of planting, harvesting and processing can dramatically impact the production of high-quality, high-yielding alliums at minimum costs.

4. Pérez et al.,.(2017) report that there is a general lack of research regarding sustainability issues in the downstream (i.e. post-harvest) stages of agri-food supply chains. For the alliums industry, opportunities for valorizing by-products, maximizing energy-efficient processing, and minimizing waste in late-stage processing represent an area of significant potential exploration.

A thorough comprehension of the alliums supply chain was achieved through the use of both qualitative and quantitative data. To this end, primary data was obtained from the use of a pre-created survey that was distributed to all the stakeholders located within the alliums supply chain. The data collected through the survey will provide a better understanding of the operations, issues, and opportunities surrounding the alliums supply chain from the perspective of farmers, distributors, and retailers (Smith et al., 2021; Creswell and Creswell, 2017). Secondary data sources were also used in the study in order to gain further context and history regarding this particular supply chain. This secondary research included both previously published articles and governmental publications providing insight into the historical, economic, and regulatory aspects of the alliums supply chain (Yin, 2018; Creswell and Creswell, 2017). The use of both primary interviews and secondary literature combined in a mixed-methods approach has been established as the best way to approach the optimization of the alliums supply chain and to identify the best way to improve the efficiency of the alliums supply chain based upon the input of the stakeholders.

This research considers the Analytic Hierarchy Process (AHP) as the main MCDM tool based on a comparison of five key factors against the other MCDM methods: theoretically appropriated to the optimization of supply chain in a hierarchical order; allowed for the consideration of multiple interests; makes possible the validation of consistency; computationally easy when dealing with large sample sizes (n = 320); and interpretable or able to easily implement for use by the practitioner (Saaty, 1980; Ishizaka and Labib, 2011; Khan et al., 2023). The matrix below provides a side by side view of AHP against some other leading MCDM techniques—Fuzzy AHP, TOPSIS, VIKOR, and PROMETHEE—using the five factors previously described, and based on a number of the most recent applications to agricultural supply chains (Khan et al., 2023; Burak et al., 2022; Mukhametzyanov and Pamucar, 2018).

The advantages of the AHP are as follows: (1) AHP can decompose a problem into an expected hierarchy of theory/categories of resource; (2) AHP has an established method for validating consistency ratios (CRs) mathematically; (3) AHP is computationally tractable because it only requires solving N*N eigenvalue systems while AHP-F requires computing triangular fuzzy relationship arithmetic (this increases computational complexities for n > 10); (4) The process of making pairwise comparisons using AHP better reflects how people think than the absolute scales used by the TOPSIS and VIKOR methods as such, therefore reducing the cognitive burden on respondents as shown in Table 2.

While AHP-F represents the best opportunity for improving the representation of linguistic uncertainty, it was rejected in this research due to problems such as: Complexities of validating consistency between judgements; there are no established acceptability thresholds for the Fuzzy CR method, so that the defined acceptable ranges (between 0.05 and 0.15) vary by research, making this method not reproducible (Mehrparvar et al., 2024); Sensitivity to the methods of defuzzification (centroid, alpha-cut, geometric means) lead to varying weights from 15 to 22% for the same fuzzy PCMs, which allows for a degree of arbitrariness in methodology; Stakeholders experienced a cognitive burden with the semantics of triangular fuzzy numbers during trials (respondent sample n = 15) — stakeholders were confused by how to interpret fuzzy scales in contrast with crisp (i.e. traditional) Saaty scales. Although TOPSIS and VIKOR were computationally efficient, they were excluded because: there is no method to validate consistency of comparison between judgements; there is no way to mathematically identify contradictions in judgements, so that inconsistencies in results would arise (Mukhametzyanov and Pamucar, 2018); perturbations of ±5% in the decision matrix lead to rank reversals for 34% of the applications of TOPSIS compared to 8% for AHP with a CR of CR < 0.05, thereby creating rank instability between the methods (Mukhametzyanov and Pamucar, 2018); Criteria applied in the TOPSIS and VIKOR methods only allowed for flat (i.e. unstructured) applications of multi-level RBV constructs and therefore disallowed any theoretical integration of these RBV constructs with the theoretical constructs being researched (Ishizaka and Labib, 2011); and Rankings generated by TOPSIS are vulnerable to the selection of alternative combinations of criteria (Mukhametzyanov and Pamucar, 2018). The preference function requirements for PROMETHEE introduced unnecessary assumptions of permutations (i.e. indifference thresholds, preference thresholds, and veto thresholds) that were not based on empirical research in the context of allium supply chain applications, while AHP rank scores are 92% stable under the effects of ±10% perturbations of judgement (CR < 0.05), compared to 76% for TOPSIS and 68% for PROMETHEE (Mukhametzyanov and Pamucar, 2018; Khan et al., 2023).

According to recent benchmarks on the optimization of Agricultural Supply Chains, this method is consistent with the research of Khan et al. (2023) who used AHP to do risk assessment on the Livestock Supply Chains amongst stakeholders (n = 225) and had a CR of 0.034 with validated stakeholder rankings and likewise by Ayyildiz and Taskin (2022), who used SF-AHP-VIKOR to assess Food Supply Chain Sustainability.

The Analytic Hierarchy Process (AHP) operationalizes MCDM through five sequential computational steps: hierarchical decomposition, pairwise comparison matrix (PCM) construction, eigenvalue-based priority derivation, consistency validation, and hierarchical synthesis (Saaty, 1980; Ishizaka and Labib, 2011).

• Step 1: Hierarchical Structure Construction

The decision hierarchy aligns with the RBV framework, consists of four levels:

1. Level 0 (Goal): Optimization of the allium supply chain

2. Level 1 (Main Criteria): Eight primary criteria (Cost Efficiency, CM, Risk and Uncertainty Management, SC, regulatory compliance (RC), E-commerce growth (ECG), Environmental Sustainability, Globalization and Geopolitical Factors)

3. Level 2 (Sub-criteria): Eleven operational sub-factors (Production, Demand, Political Stability, Trade Barriers, Weather/Climate, Price Volatility, Financial Risk, etc.)

4. Level 3 (Alternatives):

   • E-commerce Synergy Strategy

   • Sustainable Harvest Initiative

   • Tech-Opti Chain Solution

5. Step 2: PCM Construction

For n criteria, decision-makers construct an (n \times n) reciprocal matrix

where:

Elements (a_ij) represent the relative importance of criterion i over j, measured using 9-point scale (Saaty, 1980). For the eight primary criteria, the final PCM was generated using geometric mean aggregation of 320 respondents' judgements (Ishizaka and Labib, 2011), provided in Table 3.

• Step 3: Priority Vector Derivation via Eigenvalue Method

For a consistent matrix:

The principal eigenvector w yields the priority weights. Saaty (1980) established that $λmax$ = n, only for perfectly consistent matrices; real-world matrices satisfy $λmax$ ≥ n.

The normalized priority vector is:

where v is the eigenvector associated with $λmax$⁠. Computational Implementation (Khan et al., 2023):

1. Compute (⁠$λmax$⁠) from (\det (\ $λ$ I – A) = 0)

2. Solve ((A- $λmax$ I) $v$ = 0)

3. Normalize eigenvector entries

For the 8 × 8 PCM (Table 6):

1. Priority vector:

• Step 4: Consistency index (CI) and CR

The CI is:

For this study:

The CR is:

Using random index (RI) = 1.40 for n = 8 (Saaty, 2010):

The computed CR value of 0.0243 is clearly lower than the 0.10 limit, meaning there is a high level of consistency (97.6%) with the ranking between each pair of comparisons, thus leading to a high level of reliability for the decisions made. There are many acceptable ranges as reported in the literature for Agricultural MCDM (Khan et al., 2023; Ayyildiz and Taskin, 2022), thereby supporting the use of the methodology used for making these decisions.

• Step 5: Hierarchical Synthesis

Global priorities of alternatives are computed via weighted aggregation:

where:

1. $GPk$⁠: Global priority (GP) of alternative k

2. $ωi$⁠: Weight of criterion i

3. $LPik$⁠: Local priority of alternative under criterion i

4. m: number of criteria

The Tech-Opti Chain Solution has the highest GP score of 0.336 and is the first alternative. The second choice is the Sustainable Harvest Initiative with a GP Score of 0.318. The third option is the E-commerce Synergy Strategy, having a GP score of 0.277. The GP score difference of 0.018 between the Tech-Opti Chain Solution and the Sustainable Harvest Initiative (5.4% difference) indicates only a moderate level of preference. This narrow difference warrants further examination of the position of these rankings using bootstrap resampling and sensitivity analysis as, according to Khan et al. (2023), it is an established methodology for determining the reliability of ordinal rankings.

A stratified random sampling method was conducted across 12 Agricultural Produce Market Committees (APMCs) from 3 Indian states (Maharashtra, Karnataka, and Madhya Pradesh), which combined to represent 66% of total allium production in India (Government of India, 2024). To calculate the minimum sample size needed for this study, Cochran's (1977) formula was used for finite populations:

According to Cochran's (1977) sample size determination formula, the following study parameters were considered: a 95% confidence level (Z = 1.96) and a maximum percent proportion (0.5) of population variability, with a ± 5% margin of error (e = 0.05). For the finite population of 1,847 registered allium Supply Chain Stakeholders in the 12 sampled APMCs (N = 1,847), the sample size was adjusted based on the finite population correction. Thus, the final calculation calculated a minimum sample size needed of approximately 320 respondents for adequate statistical power and representativeness in this study as shown in Table 3.

A valid structured questionnaire based on the research of Creswell and Creswell (2017) was used through a four-stage validation process. First, an extensive literature review of the optimization of perishable supply chains (Li et al., 2021; Khan et al., 2023) gave rise to an initial pool of 47 items mapped to the eight factors (domains) identified in the systematic review. Next, to establish content validity, eight experts (three supply chain scholars, three agricultural economists and two APMC practitioners) participated in a three-round Delphi process. Panel members used a 75% consensus threshold to reduce the number of questionnaire items to 32. The third stage consisted of a pilot test with 15 people, made up of 6 farmers, 5 traders and 4 processors, who participated in cognitive interview, which examined clarity, understanding and time to completion (<30 min). The results from the pilot test showed that the questionnaire had very high internal consistency with a Cronbach's alpha score of 0.87. Finally, a PCM was developed as per the analytic hierarchy process (AHP) as defined by Saaty (1980). Respondents compared 28 primary criteria, 55 sub-criteria and 24 alternatives, resulting in a total of 107 pairwise comparisons. To avoid cognitive overload, the PCM tasks were divided into four sections, with scheduled breaks for each section, as previously suggested by Ishizaka and Labib (2011). The four-stage validation process provided a foundation for developing a valid and reliable questionnaire, as well as establishing its suitability for decision modelling in perishable supply chain applications.

To evaluate the allium supply chain with AHP, it was first necessary to identify multiple criteria against which the allium supply chain was being evaluated. Then, for each level of the hierarchy, a pairwise comparison was done; that is, each element was compared to every other element in the same level of the hierarchy to determine which element is most important with respect to the specified criteria. The results of the AHP analysis are illustrated in Figure 3; that is, a hierarchy of factors from the AHP analysis is depicted in Figure 3. Saaty (1980) introduced a fundamental scale ranging from 1 to 9 for comparing two elements, as outlined in Table 5. The criteria weights, which result from pairwise comparisons, indicate how critical each criterion is when making decisions. The criteria weights obtained from the AHP are summarized in Table 4.

The CI is an important statistic used in the analytic hierarchy process (AHP), which is a decision-making framework for dealing with complex decisions that require multiple criteria. The CI helps decision-makers evaluate whether their estimates are consistent because they compare two things at a time to show how much one criterion or option is better than another. Strict adherence to transitivity property is therefore essential since this guarantees the validity of AHP procedure. The CI measures PCM consistency and helps decision-makers point out and adjust anything that may seem contradictory or inconsistent. By calculating CI through decision-making processes, decision-makers are able to enhance the quality and strength of their decisions. When people know their own preferences, they can make better decisions.

To illustrate how the CI formula applies to analytic hierarchy process:

1. $λmax is the maximum eigenvalue of the pairwise comparison matrix.$

2. n is the number of criteria or alternatives being compared.

A CI of 0.0340 has been obtained in the Analytic Hierarchy Process (AHP) for the PCM, indicating an elevated level of consistency in the judgements made by decision-makers regarding the relative importance of criteria or alternatives. A low CI value (i.e. less than 0.1) indicates that the AHP matrix is highly consistent with the Transitivity property, thereby providing strong, clear, and dependable Judgements. The findings from this study show that AHP results in the formation of an excellent overall basis for the foundations of an informed Decision-Making Process through the use of careful consideration and accurate measurement of the Relative Importance of each of the Criteria used to make Decisions regarding the Relative Importance of each Criterion as it relates to the relative importance of the other Criteria. The AHP method used in this study strengthens this Overall Validity and improves the reliability and credibility of the Derived Priorities and associated Informed Decision-Making (see Table 5).

The CR can be used to measure consistency in a decision-maker's judgements when comparing different criteria or alternatives using the AHP process. The CR is calculated by dividing the CI by the RI. The CI has been established for different sizes of matrices and is shown in Table 6. The CR serves as a normalized measure of how reliable the decision-maker's judgement is, ensuring that the relative importance assigned to various elements maintains a logical and transitive relationship. A CR value of approximately 0.1 or lower indicates a consistent set of judgements.

After the calculation of CI, the next step is to check the CR, which can be calculated using the formula,

1. CI is the CI.

2. RI, in this case of 8x8 matrix is 1.40 (Saaty, 2010)

In this study, the AHP results show a CR calculated to be equal to 0.0243. To calculate the CR, the CI was divided by the RI to get the CR of 0.0243. As CR is significantly less than the typical threshold of 0.1, it shows that the pairwise comparisons made by the decision-makers in this study are very consistent with one another. In addition, as the CI is lower than would be expected from a random selection of pairwise comparisons, the low CR indicates greater consistency than what would be expected from chance. Therefore, the results from this study support the overall reliability and validity of the decision-making process based on the criteria used in the AHP methodology, and the prioritization of criteria and subsequent decisions derived through this methodology are very reliable (see Table 7)

Strategic decisions, especially through the Analytic Hierarchy Process (AHP), are influenced heavily by attributes given to alternatives. Our research has taken care to name these attributes in a way that relates to what stakeholders, farmers and/or experts input have shared on what each alternative strategy represents. In particular, the attributes that underpin the E-commerce Synergy Strategy have been strategically selected based on ECG and RC, as this strategy emphasizes the preferences of stakeholders wanting to engage with digital market trends; however, at the same time, ensuring that they remain in compliance with all applicable regulations. The Sustainable Harvest Initiative attributes include Environmental Sustainability, SC, Globalization and Geopolitical Factors; these attributes reflect the interests of stakeholders and farmers that want to implement sustainable practices, have adequate storage, and think globally. The Tech-Opti Chain Solution attributes highlight Cost Efficiency, Commodities Margin, and Risk/Uncertainty Management; these attributes align with experts' input indicating that the development of efficient cost structures, financial viability, and effective risk management activities are critical elements of the strategic decision-making process. Figure 4 illustrates how the AHP computational methodology identifies Tech-Opti Chain Solution being the best and most highly-prioritized option with an overall priority score of 0.336, consistently demonstrating it to be superior across all of the criteria used for assessment. The Sustainable Harvest Initiative is prioritized second with a score of 0.318, and the E-Commerce Synergy Strategy ranks third with a score of 0.277 (See Table 8). These priority scores provide insight into how the alternatives compare within the attribute spectrum described, and allow us to identify some of the relative strengths/weaknesses of the alternatives.

Tech-Opti Chain Solutions (GP = 0.336) dominate in performance, thanks to the relationship of cost efficiencies (weight = 0.285), commodity margins (0.147), and risk management (0.168) to R.B.V. predictions. Cost and risk mitigation capabilities are core strategic resources for agri-food companies operating in the volatile economy of the agri-food industry (Barney, 1991; Zhu et al., 2018). The outcome is consistent with studies that found that companies that focus on providing operational efficiencies and financial resilience outperform their competition in the emerging economies' agri-food sector in terms of post-harvest loss and profit margin reductions (Paul et al., 2021; Govindan et al., 2015). An R.B.V. explanation for this result suggests that Tech-Opti's focus on production efficiencies, demand forecasting and hedging of financial risk leads to the presence of VRIN (Valuable, Rare, Inimitable, Non-Substitutable) resource configurations that other companies cannot replicate easily because of the large investments necessary in cold storage infrastructure, market intelligence systems, and hedging instruments (Barney, 1991). Additionally, Sustainable Harvest Initiative's (GP = 0.318) close second place position is attributed to environmental sustainability (0.061), SC (0.168) and geopolitical resilience (0.053) as it contradicts the traditional belief that sustainability is a secondary consideration in cost-sensitive agricultural markets. The near parity of the Sustainable Harvest Initiative and the Tech-Opti Chain Solutions demonstrates the validity of recent theories in the CE and resilience literature; investments in cold storage infrastructure, low-carbon practices, and diversified supply chains will provide sustainable, long-term competitive advantages by reducing vulnerability to climate shocks, trade disruptions, and regulatory changes (Belhadi et al., 2024; Yuan et al., 2024).

The small difference in ranking between the first alternative and the second alternative indicates that the allium value chain in India has moved away from pure cost minimization toward hybrids (a balance of economic efficiencies and sustainability-resilience). This movement follows the traditional supply chain maturity models described in Zhu et al. (2018) and Govindan et al. (2015), in which firms begin with a “reactive cost focus” and progress toward a “proactive sustainability integration” (Stage 3). Additionally, given that the e-commerce synergy strategy ranked significantly lower (GP = 0.277) than expected, the results illustrate key theoretical observations. E-commerce is often touted as a key aspect of Industry 4.0; however, this ranking suggests that access via e-commerce does not provide a unique competitive advantage unless paired with and supplemented by investments in physical logistics, risk management, and storage (Paul et al., 2021; Bag et al., 2020). Hence, this supports theoretical observations that digital capabilities alone do not create substantial performance enhancement unless paired with solid operational foundations; this is often referred to as the phenomenon of digital-physical complementarity (Belhadi et al., 2024). With post-harvest losses ranging from 30% to 35% and price volatility approaching 800%, allium supply chains' stakeholders appear to rationally prioritize tangible infrastructure (e.g. cold storage and transport) and risk instruments (e.g. forward contracts and insurance) over intangible digital platforms, as exemplified by the bounded rationality of resource constraints (Govindan et al., 2015). The strategic selection of attributes is rooted in their strategic significance and potential impact on decision outcomes, ensuring a robust and insightful decision-making process.

Sensitivity Analysis is a key aspect of Analytic Hierarchy Process (AHP) because the sensitivity of the various drivers may influence decision outcomes in AHP. In particular, for this case study, there were 20 sensitivity driver or criteria weights which demonstrate the relative importance of the criteria or sub-criteria drivers on the prioritization of the alternatives. As such, the pairwise comparisons in the form of the judgement matrices utilized in AHP are highly significant, as any changes to the basic judgements may significantly change the overall consistency and reliability of AHP. As well as the relative strength of the judgements, sensitivity indicators such as the CI of the judgements and the resulting derived CI measurement (CR), may vary significantly from those based on unstable judgements. Conducting a complete sensitivity analysis of the variables allows for decision-makers to assess the accuracy and strength of the AHP model and enable better decision-making under uncertainty and variance of input data.

1. [Criterion: Production] → [Option: Sustainable Harvest Initiative] → [Value] [Pair Comparison Against] → [Option: Tech-Opti Chain Solution]

Decision change sensitivity refers to the extent to which alterations in input parameters, criteria weights, or judgements can influence the final decision outcome. The criterion “Production,” when assessing “Sustainable Harvest Initiative” in comparison to “Tech-Opti Chain Solution,” a 90% sensitivity implies that a relatively small adjustment, approximately 10%, in the assigned values or judgements for this specific pairwise comparison could lead to a notable change in the prioritization or ranking of the alternatives as shown in Figure 5.

1. [Criterion: Production] → [Criterion Weight] [Pair Comparison Against] → [Criterion: Demand]

The sensitivity analysis indicates that the decision outcome is highly responsive to changes in the criterion weights when comparing “Production” against “Demand.” The specified sensitivity of 83.75% implies that a modest alteration, around 16.25%, in the assigned weight of the “Production” criterion concerning the “Demand” criterion could result in a notable shift in the prioritization of alternatives or the overall decision as shown in Figure 6.

1. [Criterion: Production] → [Option: E-commerce Synergy Strategy] → [Value] [Pair Comparison Against] → [Option: Tech-Opti Chain Solution]

In the framework, where criteria and alternatives are systematically compared, the specified decision change sensitivity of 78.75% highlights the responsiveness of the decision to variations in the assigned values associated with the “Production” criterion when comparing “E-commerce Synergy Strategy” with “Tech-Opti Chain Solution.” This level of sensitivity suggests that even a modest change in the judgement or value for “E-commerce Synergy Strategy” concerning its production in comparison to “Tech-Opti Chain Solution” has a considerable influence on the overall prioritization or ranking of the alternatives as shown in Figure 7.

1. [Criterion: CM] → [Option: E-commerce Synergy Strategy] → [Value] [Pair Comparison Against] → [Option: Tech-Opti Chain Solution]

Within the AHP framework, where criteria and alternatives are systematically compared, the specified decision change sensitivity of 76.25% underscores the responsiveness of the decision to changes in the assigned values associated with the “Commodity Margin” criterion when comparing “E-commerce Synergy Strategy” with “Tech-Opti Chain Solution.” This level of sensitivity suggests that even a modest change in the judgement or value for “E-commerce Synergy Strategy” concerning its CM in comparison to “Tech-Opti Chain Solution” can exert a notable influence on the overall prioritization or ranking of the alternatives as shown in Figure 8.

Figures 5–8 present four sensitivity experiments testing how alternative rankings respond to criterion weight perturbations and pairwise comparison variations.

The findings associated with the three sets of sensitivity analyses (Production [90% sensitivity], Production-Demand Weight Comparison [83.75% sensitivity], and CM [76.25% sensitivity]) demonstrate that while changes made to any of the criteria create inconsistencies to the rankings within a controlled environment, the complete set of decision criteria maintains sufficient resilience. In fact, while there were significant changes to the rankings of the top two options (TechOpti and Sustainable Harvest) when comparing their overall sensitivity of 78%–90% to E-commerce Synergy Strategy, the E-commerce Synergy Strategy continually remained in third place. The stability of the rankings found in these studies mirrors results from Resilient Sourcing and Green Supplier Selection MCDM Studies that have shown that Analytic Hierarchy Process based rankings maintain ordinal ranking stability with ±10% to ±20% weight variation as long as the CR remains below the 0.10 threshold (Khan et al., 2023; Gupta and Barua, 2017; Govindan et al., 2015).

The high significance given by producers compared to that given by consumers (0.8375 compared to 0.6875) supports theoretical predictions from the perishable supply chain literature that production constraints and yield uncertainty are the main drivers of risk in agri-food systems. Furthermore, it supports theoretical research which quantifies the relative importance of supply (producers) versus demand (consumers). By changing the weight assigned to Producers from 0.8375 to 0.6875 gives producers approximately a 5 times greater prioritization of supply reliability in the optimization of the allium Supply Chain over the prioritization of demand management. This asymmetrical relationship is consistent with arguments in institutional economic theory that conditions affecting the supply side of agricultural markets comprise shocks that are 3–5 times greater than conditions affecting the demand side (Belhadi et al., 2024). Metrics for the robustness of this study are summarized in Table 9.

Among all of the agricultural MCDM peer-reviewed studies, this study's CR = 0.0243 ranks at the bottom (most consistent). With the use of a comprehensive validation protocol, which includes a 20-variable sensitivity analysis and establishes methodologies consistent with those used in Paul et al.'s (2021) systematic review study, this study's high sensitivity to the Production criterion indicates supply chain managers should prioritise interventions that stabilize their production systems over marketing initiatives targeting their customers as the greater variability in production is likely to impact their strategic objectives.

This prioritization of resource allocation based on these findings appears to be in direct conflict with existing agricultural development programs which typically allocate resources to marketing access and demand generation relative to stabilizing production (Belhadi et al., 2024) and suggests a reallocation of resources is necessary to achieve the optimum optimization performance. An in-depth examination of how alliums are supplied to Indian households, using MCDM methods, in particular Analytic Hierarchy Process (AHP), has resulted in obtaining a clearer picture of how we should optimize this vital element of the food production chain. To create this picture, we have assessed different established criteria such as cost per commodity, commodity margins, and environmental sustainability through pairwise comparisons, to derive the weights given to each criterion. The resulting consistency analysis of our method, the CI and CR, is indicative of a high degree of confidence and rationality in how we are making decisions. The CI is very low at 0.0340 while the CR is 0.0243, both of which are indicative of a very strong methodology, ensuring that our priorities are valid and lead to well-informed decisions. The type of sensitivity analyses of the various types of criteria weights, judgement matrices, CI and CR allow us to view how uncertainties or changes in conditions impact the decisions we make of many important criteria or alternatives, through the sensitivity the analysis describes and indicates a level of ability of the opinions and judgement systems we applied. The results of the study not only promote sustainable management of alliums for the preparation of Indian food, but provide an outline to assist management improve their supply chains to achieve maximum productivity. By carefully consideration of the weights of the criteria used to make decisions, and by utilizing the methods of the consistency measures and sensitivity analyses, we can provide a sound basis for unbiased decision-making in the Allium supply chain which presents many challenges of through intractability.

The results of this research provide valuable insight into the economic foundation of the supply chain's critical importance, with economic-related factors (cost efficiency, commodity margins, and risk management) accounting for the largest percentage of decision weight (over 60%) compared to other factors associated with competitive advantage, including digital transformation and sustainability. The results create a hierarchy for supply chain players that mirror Maslow's hierarchy of needs. Supply chain players must first establish themselves economically to attain sustainable business growth; once they have done this, they will be able to invest in advanced digital solutions and sustainability. Supply chain performance-enhancing digital innovations, such as blockchain-enabled traceability or AI for forecasting, do not enhance existing systems with unreliable physical infrastructure, unstable markets, and chronic post-harvest losses until these players have established stable operational foundations. The close proximity of the Sustainable Aggregate to the highest-ranked options represents a potentially complex sustainability paradox: while economic issues still rank highest in terms of decision weight, sustainability is now moving toward centrality on the decision weight continuum and has quickly embraced a definition of “enlightened efficiency”. In today's world of organizations and the marketplace, sustainability has become a complementary aspect of operational agility, rather than opposing efficiency. Existing research indicates that hybrid approaches should be developed to allow for the integration of both cost efficiency and the environmental stewardship of an organization, rather than strictly adhering to Porter's Differentiation or Cost Leadership framework.

E-commerce Synergy Strategies have ranked third. This reflects the current realities of the digital divide and reinforces the principle of the complementarity between digital and physical systems. Digital tools enhance existing operational capabilities; they cannot compensate for structural deficiencies. Therefore, it is vital that any investments in digital interventions should be made after an established investment in core physical, financial and institutional foundations. Sensitivity analysis strongly supports the notion of production primacy. This is due to the fact that production-side elements are five times as influential as demand-side elements. For those supply chains that are characterized by high levels of uncertainty on the production side and relatively stable levels of demand, production stabilization provides the maximum value as opposed to market expansion. This creates a contradiction in the traditional market-oriented approach to developing programs, and indicates that priority should be placed on improving the seed-systems, irrigation systems, risk mitigation, and contract farming aspects of agriculture. The overall results highlight the paradox of infrastructure, which indicates that cold storage facilities serve as critical enablers that allow system-wide efficiency but are challenging to achieve. Since no single entity can overcome the deficits associated with the infrastructure necessary to support a system, coordinated models of investment—i.e. public-private partnerships, cooperatives, and blended financing—are required to harvest the advantages associated with collaboration.

The comparative evaluation of the three strategic alternatives, derived from the AHP GP scores, reveals a clear hierarchy of competitiveness and adaptability within the allium supply chain. The Tech-Opti Chain Solution secures the leading position, demonstrating the strongest alignment with high-priority criteria such as Cost Efficiency (0.285), CM enhancement (0.147), and Risk and Uncertainty Management (0.168). With 60% coverage across Tier-1 criteria, this strategy provides stakeholders—particularly small to medium processors and commission agents—with immediate and tangible improvements in operational performance, price stabilization, and financial resilience. Its emphasis on predictive analytics, production optimization, and risk-hedging instruments makes it the most pragmatic short-term choice in a supply chain characterized by high perishability and volatile market conditions. However, its comparative weakness in Environmental Sustainability (0.061) and ECG (0.077) signals a structural vulnerability; while this strategy excels under current priorities, it risks eventual obsolescence if global markets, retailers, and regulatory systems accelerate their shift toward sustainability compliance and digital trading ecosystems. This creates an inherent dependency on stable policy environments and traditional market channels—conditions that may not hold over the next decade given India's rapid digital transformation and rising sustainability mandates.

The second placed Sustainable Harvest Initiative (GP = 0.318; Normalized Score = 94.6), is clearly the top future-oriented challenger, being only 5.4% beneath the leader. Most notably, the Initiative outperformed in Environmental Sustainability (0.061), SC advancements (0.168), and resilience to Global and Geopolitical disruptions (0.053) representing 28.2% of the total available priority weight. The evident trend towards increasing stakeholder support of Resilience, Climate Adaptation, and Premium Market Access amongst Organized Retailers as well as Export-oriented Processors. The Initiative's focus on Circular Practices, Renewable-Based Cold Storage, and Complying with Internationally Accepted Sustainability Protocols positions it as a “Future-Proofing Strategy”, potentially helping increase long-term sustainable competitiveness. However, the high Capital Investment required for Cold Chain Implementation and the Certification Costs associated with Carbon Neutral initiatives present Spatial Limitations of the Initiative and thus limit the development of the Initiative via the smaller holder sector. The E-commerce Synergy Strategy, rated Third (GP = 0.277; Normalized Score = 82.4) reflecting an Performance Gap of 17.6% vs the leader; has an leading opportunity to produce a higher GP score through Digital Readiness of Farmers and ECG (0.077) and RC (0.168). At this time only 22% of mandis have Reliable Internet Connections, while nearly 78% of Farmers lack Smart Phones and Must therefore rely upon traditional paper-based systems to connect to these digital channels of distribution. As Digital Infrastructure and Farmer Literacy increase, the E-commerce Synergy Strategy will present as a Higher Potential Pathway for Disruption Innovative Processes which create Long-term Competitive Advantage. The ranking illustrates the development of a supply chain moving from the fulfilment of immediate efficiency requirements (Tech-Opti) via the emergence of sustainability pressures (Sustainable Harvest) to the eventual digital transformation (E-commerce Synergy), in order to support Investments and Policies that are appropriate to support the maturity of each of these strategies as well as the infrastructure required for their respective maturity levels.

Integrating innovative technologies and sustainability into agriculture creates social and management implications that reach well beyond decreased productivity. Socially, these technologies encourage healthier communities and environmental responsibility through the encouragement of environmentally sound practices (i.e. organic growing, less reliance on chemicals, and adopting renewable energy sources). Not only does this education help to create greater ecological balance, but it also promotes consumer confidence in the product through the demonstration of responsibility in the production process. Additionally, digital platforms are used to provide traceability and transparency within the entire supply chain, allowing for complete visibility at every level of the market and ultimately resulting in greater market confidence and increased relationships to consumers. As consumers increasingly want to support ethical and sustainable food systems, both initiatives will continue to shape how consumers perceive sustainable agriculture and provide greater societal support for sustainable agriculture practices.

The application of advanced or future technologies in any organization requires management to develop employees continually to ensure that the organization has a competent, creative and adaptable workforce to meet the demands of this transition. This transition to advanced, emerging, and disruptive technology encourages an organizational culture of shared responsibility, achieved through collaborative decision-making and engagement with all stakeholders through regular consultations across the supply chain. Additionally, managers need to be aware of the changing regulatory requirements, compliance and market dynamics occurring throughout the world in order to maintain strategic agility (or “relative flexibility”). In addition to encouraging the use of best practices and operational efficiencies, organizations that integrate sustainability into an organization will not only increase their profitability but also create a more resilient and competitive organization for the long-term. All of these elements combined will establish a firm as an industry leader that can successfully manage potential uncertainties in the future and promote more sustainable and future-ready agricultural systems.

The purpose of the study was to develop a framework to optimize the supply chains for allium crops in India by establishing a level of analytical rigour while providing practical assistance to decision-makers involved in those supply chains. It applied the Analytic Hierarchy Process (AHP) with appropriate methodologies to distinguish between different levels of cost efficiency, SC, and RC as the most significant factors influencing the strategic focus of perishable agricultural value chains. The results of the study demonstrate that, while stakeholder preferences are shaped by multiple factors, there is a clear relationship between the above-mentioned factors and stakeholder demands for long-term stability and economic efficiency as well as a reduced risk. The study adds to our understanding of the RBV by showing that the development of resources must occur sequentially and that the development of digital, sustainable, and resilient resources should be layered onto an established base of economic performance rather than viewed as equal priorities for development.

As the leading Tech-Opti Chain Solution approaches parity with the Sustainable Harvest Initiative, a tipping point in strategic evolution can be seen: Indian agrisystems are transitioning from cost-based methods to a new hybrid model where environmental stewardship will be supported by operational excellence. The close proximity of both models indicates the ongoing maturation of the agriculture sector in India and an increasing willingness to explore CE concepts, adapt to the impacts of climate change, and enhance the innovation process through collaboration among various stakeholders. In addition, the explicit mapping of each SDG (SDG) and differentiated strategies for each stakeholder adds to the global importance of the work being done in both the Tech-Opti Chain Solution and the Sustainable Harvest Initiative, establishing a platform for companies operating in other countries moving towards improved supply chain optimization. Rather than just providing descriptive information about supply chain optimization, this research presents a framework for connecting theory with practical application, and as a result, it has the potential to be a tool for companies and organizations throughout the world.

This study provides an excellent and widely applicable optimization model; however, it did so with multiple limitations. Firstly, the geographical concentration of our sampling was limited to a subset of the APMC mandis, which, while representative of that area, would not allow for straightforward extrapolations to much different agricultural geographies and/or APMC systems. Secondly, while pairwise comparisons are subjectively determined by respondents based on their perceived priorities, it is conceivable that a particular sample could be indicative of an entire agricultural system but would only be applicable to the exact producers surveyed. Thirdly, the AHP model is very rational and easy to utilize with large data sets; however, it does not reflect all of the fuzziness that exists in human judgement and future researchers should conduct studies employing hybrid fuzzy MCDM techniques and/or dynamic simulation models.

The rapid growth rate of agri-supply chain technology and the increasing connectivity of the global food system are driving the need to adapt methodology and strategy continually. Established research should continue to: (a) replicate and test the findings in different agro-ecological/institutional contexts (global comparisons); (b) identify ways in which IoT, blockchain technology machines and AI-based predictive analytic tools can be used to further optimize production and traceability; (c) develop longitudinal modelling frameworks for how a dynamic shift will eventually occur when longer-term investments into CE models and Climate-Smart Agriculture become more mainstream; and, (d) develop further the use of social LCA measurables (equity, labour rights, smallholder involvement, gender responsive) as a more holistic, three-prong approach to creating supply chain models that create a socially sustainable and environmentally sustainable supply chain. These “trends” will both verify and expand on the contributions made by this body of work to help establish and anchor the allium supply chain as a central driver of India's transformation of agriculture and also as an example of developing sustainable food systems globally.

This research was conducted in accordance with the ethical principles outlined by the Committee on Publication Ethics. All applicable ethical principles were followed during the study design, data collection and analysis.