Modeling a Government-Manufacturer-Farmer game for food supply chain risk management

Abstract

Farmers may add chemical additives to crops to enhance their appearances/tastes 5 or decrease their costs, which may also increase the food demand and sales profits. Man-6 ufacturers buy products from farmers and sell them to consumers, where the government 7 benefits from tax income based on sales revenues. However, once the contaminated food 8 is consumed, customers could get sick. The government would, thus, be partially respon-9 sible for society's health risks from the chemical additives. The punishment policies are 10 set up by the government to regulate and deter farmers' and manufacturers' risky behav-11 ior, balancing tax income, punishment income, and society's health risks. Based on the 12 observation of government regulations, the farmers strategically choose the optimal level of 13 * , and manufacturers pay the appropriate price to farmers. To our knowl-14 edge, little work has studied the strategic interactions among the regulating government, 15 manufacturers, and farmers with endogenous customer demand. This paper fills this gap by 16 building a Government-Manufacturer-Farmer model with three decentralized and centralized 17 sub-models. The models are validated and illustrated through applying the 2008 Sanlu food 18 contamination data. Our results show that (a) the higher the food price is, the higher the 19 punishment is needed to deter the use of chemicals; (b) the optimal chemical level increases 20 in the payment to the farmer when it is low and decreases in the government punishment; 21 (c) the manufacturer's payment to the farmer decreases in the government punishment; (d) 22 the chemical level is significantly higher in the centralized model than in the decentralized 23 model especially when the food price and slope for sales amount are high, or the base sales 24 demand, tax rate, and chemical cost are low; and (e) the decentralized model leads to the 25 lowest chemical level at equilibrium. This paper provides some novel policy insights for food 26 supply chain risk management.27

International sourcing decisions in the wake of a food scandal

“…Coincident with the recent growth in public interest in food fraud, literature on the issue has expanded across a variety of academic disciplines (Smith, Manning and McElwee, 2017). From an economic perspective, the most relevant of this literature can be divided into three inter-related strands: (1) understanding suppliers' incentives to engage in fraud (Manning, Smith and Soon, 2016;Moyer, DeVries and Spink, 2017;Song and Zhuang, 2017),…”

“…(2) determining the economic and public health consequences of fraud (Ali Meerza and Gustafson, 2018;Barnett et al, 2016;Spink and Moyer, 2011;Yamoah and Yawson, 2014), and (3) designing optimal regulatory response (Ali Meerza, Giannakas and Yiannaka, 2018;Manning and Soon, 2014;Song and Zhuang, 2017;Spink, 2012).…”

Agricultural Risk Management Using Fuzzy TOPSIS Analytical Hierarchy Process (AHP) and Failure Mode and Effects Analysis (FMEA)

“…Yet [53] proposed a Bayesian network (BN) modelling framework in a case study of a project in agricultural development for calculating costs and benefits as per multiple causal factors encompassing the individual risk factors effects, budget deficits, and time value discount. Song and Zhuang [54] brought a game-theoretic model to study optimal risk management policy in the food supply chain. Nakandala et al [55] developed a combined model including hierarchical holographic modelling and fuzzy logic for assessing risk in food supply chains.…”

Food Network Resilience Against Natural Disasters: A Conceptual Framework

“…One of the large milk wholesaler's inventories ran out in less than a week and they were supplying packaged milk to 35% of retail market (Ellick, 2010). Concurrently, Peck (2006) argues that food contamination is what retailers and their suppliers are most afraid of because it can destroy their brand image (Song & Zhuang, 2017). As most of the retailers are present within a city, they are most vulnerable to disasters.…”