A mathematical model formulation to design a traditional Indian agri-fresh food supply chain: a case study problem

A farmer-centric agricultural decision support system for market dynamics in a volatile agricultural supply chain

Purpose

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

Design/methodology/approach

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Findings

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

Practical implications

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Originality/value

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

Modeling traceability in food supply chain

Purpose

Currently, digital technology has been proposed as a new archetype for developing an effective traceability system in the perishable food supply chain (FSC). Implementation of such a system needs significant investment and the burden lies with the members of the supply chain. The purpose of this paper is to examine the impact on the profit of the supply chain members due to the implementation of an effective traceability system with such a large investment. The study also tries to explore the impact of the implementation of such a system by coordination among the members through a cost-sharing mechanism.

Design/methodology/approach

A two-level supply chain that comprises a supplier and retailer is analyzed using a game-theoretic approach. The mathematical models are developed considering the scenario for an individual, centralized and both members invest using a cost-sharing mechanism. For each of the models, the impact of product selling price, information sensing price and quality improvement level on profit is analyzed through numerical analysis.

Findings

The study reveals that consumer involvement can be a strong motivation for the supply chain members to initiate investment in the traceability system. Further, from an investment perspective cost-sharing model is beneficial compared to the individual investment-bearing model. This mechanism can coordinate as well as benefit the FSC members. However, the model is less beneficial to the centralized model from profit and quality improvement levels.

Practical implications

Food wastage can be less from supplier and retailer perspectives. Moreover, consumers can purchase food items only after verifying their shipping conditions. Consequently the food safety scandals can be reduced remarkably.

Originality/value

Digital technology adoption in the perishable FSC is still considered emerging. The present study helps organizations to implement a traceability system in the perishable FSC through consumer involvement and a cost-sharing mechanism.

In-transit interventions using real-time data in Australian berry supply chains

Purpose

The objective of this paper is to contribute to Australian berry supply chains with a relevant identification regarding the possible data driven interventions that stakeholders can take while the berries are in transit.

Design/methodology/approach

An exploratory series of semi-structured interviews was conducted through six Australian experts in the industry with more than 20 years of experience in Australian berry supply chains and the Australian perishable food industry, to identify key possible in-transit interventions that could be implemented in the Australian berry industry.

Findings

The analysis of the interviews revealed a total of 18 possible in-transit interventions. An important finding is that in-transit interventions are made possible by the use of real-time data gathered through IoT devices such as Active Radio Frequency Identification, Time and Temperature Indicators interacting with Wireless Sensor Networks. Another key finding is that Australian berry growers and retailers do possess the technologies and the resources necessary to make in-transit interventions possible, however they have yet applied these technologies to operational decision-making and interventions based on the product, rather focussing on supply chain transactions and events.

Research limitations/implications

Since the research focusses on an Australian context, its findings may or may not be applicable to other countries. The research is exploratory in nature, and its findings should be verified by future research, in particular to test whether the in-transit interventions proposed here can be implemented in a cost-efficient way.

Originality/value

To the authors' knowledge, this publication is the first known academic article to provide a clear understanding of the Australian berry industry from a supply chain and logistics perspective, and the first to explore possible data driven in-transit interventions in perishable food supply chains.

Using blockchain technology to drive operational excellence in perishable food supply chains during outbreaks

Purpose

This paper aims to explore the potential of blockchain technology (BT) to support the operational excellence in perishable food supply chain (PFSC) during outbreaks, by doing use-case analysis.

Design/methodology/approach

A systematic literature review is performed to determine the dimensions of operational excellence in the food supply chain (FSC), then a single use-case analysis is conducted to explore the potential of blockchain in order to achieve operational excellence for PFSC during the pandemics by applying context, interventions, mechanism and outcomes (CIMO) logic.

Findings

The findings of this study reveal that blockchain capabilities such as immutability and transparency, visibility, traceability, integration and interoperability, disintermediation and decentralisation, smart contracts and consensus mechanism provide better sustainable operational excellence outcomes for PFSCs to be more responsive, flexible, efficient and collaborative to cope with the impacts of COVID-19.

Research limitations/implications

This research employs only one real case with multiple PFSC participants. Statistical generalisation is not possible at this stage of the research. However, the findings are not restricted to this single use-case.

Practical implications

This study provides a research direction to explore the potential of BT to achieve operational excellence in the PFSC during outbreaks and generates prescriptive knowledge for better managerial decision-making across the PFSC during outbreaks.

Originality/value

This research conducts semi-structured interviews with different participants in one blockchain ecosystem to understand multiple participants' perspectives of operational excellence within PFSC.

Measuring agri-food supply chain performance: insights from the Peruvian kiwicha industry

Purpose

Agri-food firms face many challenges when assessing and managing their performance. The purpose of this research is to determine important factors for an integrated agri-food supply chain performance measurement system.

Design/methodology/approach

This research uses the Peruvian kiwicha supply chain as a meaningful context to examine critical factors affecting agri-food supply chain performance. The research uses interpretative structural modelling (ISM) with fuzzy MICMAC methods to suggest a hierarchical performance measurement model.

Findings

The resulting kiwicha supply chain performance management model provides insights for managers and academic theory regarding managing competing priorities within the agri-food supply chain.

Originality/value

The model developed in this research has been validated by cooperative kiwicha associations based in Puno, Peru, and further refined by experts. Moreover, the results obtained through ISM and fuzzy MICMAC methods could help decision-makers from any agri-food supply chain focus on achieving high operational performance by integrating key performance measurement factors.

Distribution planning problem of a supply chain of perishable products under disruptions and demand stochasticity

Purpose

The emergence of risk in today's business environment is affecting every managerial decision, majorly due to globalization, disruptions, poor infrastructure, forecasting errors and different uncertainties. The impact of such disruptive events is significantly high for perishable items due to their susceptibility toward economic loss. This paper aims to design and address an operational planning problem of a perishable food supply chain (SC).

Design/methodology/approach

The proposed model considers the simultaneous effect of disruption, random demand and deterioration of food items on business objectives under constrained conditions. The study describes this situation using a mixed-integer nonlinear program with a piecewise approximation algorithm. The proposed algorithm is easy to implement and competitive to handle stationary as well as nonstationary random variables in place of scenario techniques. The mathematical model includes a real-life case study from a kiwi fruit distribution industry.

Findings

The study quantifies the performance of SC in terms of SC cost and fill rate. Additionally, it investigates the effects of disruption due to suppliers, transport losses, product perishability and demand stochasticity. The model incorporates an incentive-based strategy to provide cost-cutting in the existing business plan considering the effect of deterioration. The study performs sensitivity analysis to show various “what-if” situations and derives implications for managerial insights.

Originality/value

The study contributes to the scant literature of quantitative modeling of food SC. The research work is original as it integrates a stochastic (uncertain) nature of SC simultaneously coupled with the effect of disruption, transport losses and product perishability. It incorporates proactive planning strategies to minimize the disruption impact and the concept of incremental quantity discounts on lot sizes at a destination node.

An efficient solution method for an agri-fresh food supply chain: hybridization of Lagrangian relaxation and genetic algorithm

In the traditional agri-fresh food supply chain (AFSC), geographically dispersed small farmers transport their products individually to the market for sale. This leads to a higher transportation cost, which is the primary cause of farmers' low profitability. This paper formulates a traditional product movement problem in AFSC. First, the aggregate product movement model is combined with the vehicle routing model to redesign an existing AFSC (the ETKA Company; the most extensive domestic agri-fresh food supply chain in Iran) based on the available data. For the four-echelon, multi-period supply chain under investigation, a mixed integer linear programming (MILP) model is developed for the location-inventory-routing problem of perishable products via considering the clustering of farmers to minimize the total distribution cost. Considering the complexity of the problem, an efficient and effective "matheuristic" is introduced based on hybridizing the Lagrangian relaxation and genetic algorithm (GA). The solution obtained by the proposed "matheuristic" algorithm is robust and efficient in comparison with an exact solver, GA, and the Lagrangian relaxation approach individually. The comparison analysis reveals that the location-inventory-routing model is efficient, leading to a reduction in total distribution cost by 33% compared to the existing supply chain. Finally, the findings encourage further development and application of the proposed "matheuristic" to solve other complicated location-inventory-routing problems heuristically.

Postharvest supply chain losses: a state-of-the-art literature review and bibliometric analysis

Purpose

The literature review of post-harvest supply chain (PHSC) losses is carried out and analyzed in this paper followed by bibliometric analysis of the literature.

Design/methodology/approach

The literature survey is performed across various dimensions such as PHSC losses, PHSC risks and PHSC sustainability (waste management and waste reduction). One hundred thirty research articles during the period of 1989–2020 were considered for the review.

Findings

The PHSC losses have been identified in this literature survey. The calculation and mitigation strategies stated by various researchers in the literature are addressed. The important loss mitigation dynamics are also presented to reduce the PHSC losses and to improve food availability.

Research limitations/implications

The major focus is given on the PHSC of agriculture produces. However, research articles from fish and meat supply chain are excluded as they follow a different perishability curve.

Practical implications

The current work will add value to the agriculture supply chain literature, provide a platform for PHSC losses and provide assistance/guideline toward loss calculation, loss mitigation, improved rural employability, improved rural entrepreneurship and improved revenue generation.

Social implications

The performed research will assist the researchers, entrepreneurs and farmers to understand the current scenario of food wastage at different stages of the supply chain better. It will provide the guidelines for calculation and mitigation of various stated PHSC losses. This study will be helpful to enhance food availability and food security in post-coronavirus crisis.

Originality/value

The paper explores and highlights PHSC loss calculations and mitigation strategies to identify the postharvest loss situation and better utilization of fresh produces.