Technological innovation and green growth in the Organization for Economic Cooperation and Development economies

In this paper, we use a Markov decision process (MDP) to model the joint inventory-promotion decision problem. The state variable of the MDP represents the demand state brought about by changing environmental factors as well as promotion decisions. The demand state in a period determines the distribution of the random demand in that period. Optimal inventory and promotion decision policies in the finite horizon problem are obtained via dynamic programming. Under certain conditions, we show that there is a threshold inventory level P for each demand state such that if the threshold is exceeded, then it is desirable to promote the product. For the proportional ordering cost case, the optimal inventory replenishment policy is a base-stock type policy with the optimal base-stock level dependent on the promotion decision.

A Periodic Review Inventory Model with Demand Influenced by Promotion Decisions

This paper presents a model for designing two-stage dynamic pricing strategies when the seller faces strategic consumers in the presence of a reference price effect. The consumers form the utilitie...

Dynamic pricing in the presence of reference price effect and consumer strategic behaviour

A systematic review of decision-making in remanufacturing

Elevated business uncertainties and competition over recent years have caused changes to the data-driven supply chain management of sourcing and inventories across industries. However, only large-sized enterprises have the resources to harness data for aiding their decision-making and planning. By contrast, small- and medium-sized enterprises (SMEs) commonly have limited resources and knowledge, which affects their ability to collect and utilize data. Thus, it is a challenge for them to implement advanced decision support tools to mitigate the effects of market uncertainties. This paper proposes a decision support system (DSS) for sourcing and inventory management, with the aims of helping SMEs compile and exploit data, and supporting their decisions under business ambiguities. The DSS was developed using a simulation-optimization approach by incorporating an artificial neural network and a genetic algorithm for problem representation and optimizing decision support solutions. The exploitation of observational and empirical data reduces the burden of data compilation obtained from unorganized data sources across SME operations. Further, uncertainty factors such as raw material demand, price, and supply lead time were considered. When implemented in a medium-sized food industry company, the DSS can provide decision support solutions that integrate the selection of recommended suppliers and optimal order quantities. It can also help decision-makers to shape their inventory management policies under uncertain raw material demands, while also considering service levels, sales promotions, lead times, and material availability from multiple suppliers. Consequently, implementation of the DSS helped to reduce the total purchased raw material costs by an average of 51.62% and reduced the order interval and on-hand inventory costs by an average of 54.24%.

Decision support system for adaptive sourcing and inventory management in small- and medium-sized enterprises

Green growth is becoming an important mode for the sustainable operation of economy and society in the world. All countries are developing innovative methodologies to achieve green growth. Particularly, inventory and pricing are important technologies for companies’ leadership to make decisions. The increasingly polluted environment has made sustainable utilization of resources and environmental protection more urgent, however, there is very little literature studying inventory and pricing from a green growth perspective. To fill this gap, we consider both inventory and pricing decisions for companies under different green growth mechanisms. We first consider the single period inventory model with a remanufactured supplier and the government subsidy for single and dual sourcing system, respectively, further derive the optimal order decisions for the retailer. Then, we assume the customers are environmentally sensitive and there is a green supplier in the market, examine the optimal pricing decisions for the retailer using the two-stage optimization method. We explore the value of the recycling supplier and government subsidy in the numerical study; furthermore, we examine the influences of the green degree of product on the optimal price decisions for different products. One interesting finding is that the retailer should order more green products no matter how expensive it is, as long as the customers in the market are environmentally sensitive.

Inventory control and supply chain management: A green growth perspective

Dynamic pricing with Bayesian demand learning and reference price effect

In this paper, we consider a retailer that sells a product with high storage cost over a two-period horizon. The goal is to investigate the single and combined effects of reference price and price-...

Dynamic pricing with reference price effect and price-matching policy in the presence of strategic consumers

Research on customer behavior and marketing has shown that reference prices significantly influence the customers’ purchase behaviors and retailers’ policies. In this paper, we consider a retailer selling a single product to loss-averse customers over a finite planning horizon, and we address the reference price effect on the retailer’s optimal pricing and inventory policies. The customers’ demand is determined from their purchasing utility through a Multinomial Logit (MNL) model, and the utility is contingent on the reference price and the current sales price. A generalized model is presented to jointly characterize the optimal pricing and inventory policies to maximize the retailer’s total expected profit. First, we derive the optimal myopic policy for the retailer in a single-period and reveal the sensitivity of the optimal myopic policy to the reference price. Next, we perform an equivalent transformation on the model such that the single-period revenue is jointly concave, which is critical to characterize the optimal policy in the multi-period case. Dynamic programming is then used to analyze the optimal policy for the retailer. Interestingly, we find that a reference price-dependent base-stock-list-price policy is proved to be optimal in each period.

Nenggui Zhao

Papers

Inventory control and supply chain management: A green growth perspective

Dynamic pricing with Bayesian demand learning and reference price effect

Dynamic pricing with reference price effect and price-matching policy in the presence of strategic consumers

Optimal pricing and inventory policies with reference price effect and loss-Averse customers

Pricing decisions with reference price effect and risk preference customers