Agricultural Supply Chain Risk Management Under Price and Demand Uncertainty

Introduction

The economy of a country depends on agriculture as it is the primary source of food supplies. The agriculture sector in India accounts for about 17-18% of its GDP and employs approximately 50% of the country's workforce (Sunder, 2018). It is an important source for generating demand for industrial goods and services. Besides that, the rural areas are the biggest markets for low-priced, and middle-priced consumer goods, including consumer durables and rural domestic savings, are an important source of resource mobilization (Planning Commission India). The products in the agricultural supply chain have attributes such as perishability, seasonality, and bulkiness, which make the agribusiness supply chains (ASCs) more complicated. Dealing with seasonality requires the appropriate organization of the product as the supply is limited, whereas the demand is throughout the year. ASCs have longer lead times; in turn, it increases the chances of perishability. Furthermore, there is often significant time stress on post-harvest activities as most agricultural products are perishable in nature.

Risk management in the agribusiness supply chain is quite challenging because of the existence of various uncertainties (Jones et al., 2003). For example, Jones et al. (2003) studied Syngenta Seeds, Inc’s seed corn supply chain’s production planning problem. The production planning seed corn is complicated because of the biological process, during the growing season, which further depends on the weather and insect conditions. Syngenta has developed and implemented a second-chance production-planning model to mitigate the production yield uncertainty and demand uncertainty. The supply procedure is linked to biological production, which is influenced by weather changes (e.g., floods, droughts), disease, and pests. Such aspects indicate that both harvest levels and harvest times are subject to uncertainties. During the production stage, risks in food handling and storage make risk management even more complicated than other manufacturing supply chains. The situation even worse if there is uncertainty in demand and price of the product to be sold. Hence, there is also a need to build strategies for the sustainability of the agricultural supply chain (Guma et al., 2018; Anilkumar & Sridharan, 2019).

Risk management strategies can be classified into either robust or resilient (Behzadi et al., 2018). A robust supply chain is able to resist disruption, while a resilient supply chain can swiftly recover from the disruption and return back to its original form. Most quantitative works in the agricultural supply chain considered multiple-sourcing as a robust strategy to manage supply risk (Boyabatlı et al., 2017). However, in recent years, the resilient strategy is also used for managing supply risk (Christopher & Peck, 2004; 6838-131& Martel, 2012; Namdar et al., 2018). In ASC, the scope of risk management studies is limited when appropriate measures for perishability are integrated for managing harsh or rare disruptions. Perishability is often modelled as product depreciation, especially in inventory management (Thron et al., 2007). Cai & Zhou (2014) suggested a resilient strategy to reroute fruit between two markets after a disruption. They modelled perishability using an exponential decay cost, where they incorporated two shelf-life metrics, namely best-before-date and good-until-time, in a vegetable supply chain when there is a disruption in the main transportation link. Given the perishability is a critical concern in ASC risk management, there is a clear need to consider a variety of more sophisticated perishability functions for developing more realistic quantitative models. Behzadi et al. (2017) considered exponential perishability function in a two-stage stochastic programming model. They provided optimal strategy for mitigating the risks in ASC, but they didn’t consider demand and price uncertainty in their model. In the real-world scenario, agri-product have uncertainty in supply, demand and hence price. The authors (Behzadi et al., 2018) investigate market disruption risk by developing a multi-commodity multi-period model with the specific purpose of allocation flexibility in the risk-averseness scenario. However, they did not consider supply and demand-side risk together. This paper addresses the gaps by developing a model that considers both demand and price uncertainty together with supply-side risks.