Strategy Optimization of Quality Improvement and Price Subsidy of Agri-Foods Supply Chain

Abstract

Based on the realistic concerns about the improvement of the quality of agricultural foods (agri-foods), the optimal supply quality and price subsidy strategies of producers and sellers for the two-level agricultural supply chain, composed of a producer and a seller, are studied. The differences in the quality safety, price, and market demand of agri-foods in the supply chain are compared and analyzed. The study found that the maximum profit of supply chain participants decreases with the increase of price elasticity of demand. When the quality of agri-foods is upgraded in a producer-led manner, the quality of agri-foods in the supply chain does not undergo substantial improvement, and the maximum profit of agri-foods operators is insensitive to the price elasticity of demand at this time. When the seller-led quality upgrading is launched, the maximum profit of the producer decreases with the increase of the quality elasticity of demand, the maximum profit of the seller increases with the increase of the quality elasticity of demand, and the total profit of the supply chain also increases with the increase of the quality elasticity of demand under the centralized decision situation. The quality and safety of agri-foods as well as the overall profit of the supply chain can be improved most effectively under the centralized control decision with the goal of maximizing the supply chain benefits. In terms of quality and price, quality improvement actions of agri-foods driven by supply-side producers are less effective than those driven by demand-side consumption. In addition, cost-sharing contracts can significantly improve the quality of agri-foods in the supply chain and make them more “high-quality and low-price” than before the adoption of cost-sharing contracts.

Exploring the Impact of the COVID-19 Pandemic on Firms’ Financial Performance and Cash Holding: New Evidence from China’s Agri-food Sector

“…The agri-food supply chain has its own special characteristics; that is, agri-foods of good quality do not always sell at a good price [10]. In the early stages of COVID-19, many grocery stores, restaurants, and shopping malls were closed, which led to the disruption of the demand and supply of the agri-food system in most countries [11].…”

Blockchain Adoption and Organic Subsidy in an Agricultural Supply Chain Considering Market Segmentation

“…Considering the yield uncertainty, Ye et al [28] explored three subsidy strategies regarding blockchain adoption (none, single, and both supply chains) in two competing agricultural supply chains. Xu et al [29] examined the optimal quality of supply and price subsidy strategies for the producer and the seller in a two-level agricultural supply chain. Cao et al [30] studied how the four subsidy strategies of local governments (no, only manufacturer, only retailer, both) affect optimal operational strategies in the fresh produce supply chain involving cross-regional sales.…”

Incorporating Consumers’ Low-Carbon and Freshness Preferences in Dual-Channel Agri-Foods Supply Chains: An Analysis of Decision-Making Behavior

“…2 Referring to the studies of scholars such as Lan et al [28] and Cai et al [5], it was assumed that the freshness of agri-foods θ and the level of freshness preservation effort τ is related to θ(τ) = θ 0 τ, where θ 0 ∈ [0, 1] is the initial freshness level. Referring to the studies of scholars such as Xu et al [29] and Yang et al [2], it is assumed that the freshness input cost of agricultural product producers c 1 and the level of preservation effort τ is related to c 1 (τ) = 1/2λ 1 τ 2 , where λ 1 > 0 is the coefficient of freshness preservation effort cost; the low carbon input cost of agricultural product producers c 2 and the low carbon level of agri-foods g is c 2 (g) = 1/2λ 2 g 2 where λ 2 > 0 is the low carbon effort cost coefficient. 3 Since the actual consumer demand is influenced by the freshness of the produce θ, low carbon level g and price p r and it is affected by the multiple effects of freshness, low carbon level, and price; referring to the linear inverse demand function, it is assumed that the demand for offline channel d r (θ, g, p r ) = ρa + αθ + βg − bp r and the online channel demand d e (θ, g, p r ) = (1 − ρ)a + αθ + βg − bp r , where 0 < ρ < 1 is the proportion of consumers choosing offline channels to purchase, b > 0 is the price elasticity of demand, and α > 0 is the sensitivity coefficient of consumer demand to the freshness of agri-foods, and β > 0 is the sensitivity coefficient of consumer demand to the low carbon level of agri-foods.…”