Lot Sizing and Dynamic Pricing with Random Yield and Different Qualities

Lot Sizing and Dynamic Pricing with Random Yield and Different Qualities

Guo Li (School of Management and Economics, Beijing Institute of Technology, Beijing, China), Tao Gao (North China Electric Power University, China), Zhaohua Wang (School of Management and Economics, Beijing Institute of Technology, Beijing, China) and Shihua Ma (School of Management, Huazhong University of Science and Technology, Wuhan, China)
DOI: 10.4018/japuc.2012070106


The literature under random component yield has focused on coordination of supply chain at the determined price, where decision maker chooses its optimal production quantities. The authors consider a centralized system when the price is not determined under both random yield and demand. Type A with perfect quality and type B with imperfect quality are produced due to the random yield. They prove the unique concavity of expected profit in centralized system at determined price. Then dynamic pricing is considered and algorithm is put forward for dynamic pricing. Errors can be sufficiently small as long as some parameters can be set suitably. Apart from lot sizing and dynamic pricing, the authors also provide qualitative insights based on numerical illustration of centralized and decentralized solutions.
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1. Introduction

The intense competition in the semiconductor and electronics industry poses a great challenge for manufacturers to reduce cost. Many manufacturers try to reduce sales’ representatives and adopt the direct shipping, such as dell. For the manufacturers, the customers’ demand is stochastic and price sensitive (e.g., purchasing laptops). So the manufacturers have to control the order quantity and pricing dynamically to get maximum profit and incur minimum cost.

In recent years, the uncertainty of supply chain increased significantly due to influence of natural disasters, strikes, terrorist attacks and political instability and other factors. Supply chain risk management has attracted interest from both researchers and practitioners in operations management. Chopra and Sodhi (2004) provided a diverse set of supply disruption examples. Various operational tools that deal with supply disruptions have been studied: multisourcing (e.g., Anupindi & Akella, 1993; Wang et al., 2011; Babich et al., 2007), alternative supply sources and backup production options (e.g., Serel et al., 2001; Kouvelis & Milner, 2002; Babich, 2006), flexibility (e.g., Van Mieghem, 2003; Tomlin & Wang, 2005), and supplier selection (e.g., Deng & Elmaghra, 2005). For a recent review of supply-risk literature, see Tang (2006). Generally, after investigation of 800 companies’ disruption cases, Hendricks and Singhal (2003, 2005a, 2005b) find that firms that experienced supply glitches suffer from declining operational performance and eroding shareholder value (e.g., the abnormal return on stock of such firms is negative 40% over three years).

The issue of linking risk assessment with risk mitigation for low-probability high-consequence events such as disruptions of supplies is discussed by Kleindorfer and Saad (2005), where a set of 10 principles is formulated for specifying sources of risk, assessment and mitigation of risk.

In addition to high-impact, low-likelihood disruption risks, supply chains are also vulnerable to high-likelihood, low-impact Operational risks (e.g., Oke & Gopalakrishnan, 2010) that may arise from problems in supply and production process. Though the production is strictly controlled, yield of the components can be uncertain due to the characteristics of process engineering or uncontrolled operations (Maddah et al., 2009; Gurmani, 2005). For example, in the LCD manufacturing industry, it is quite common to get production yield of less than 50%. So in these industries, the manufacturers have to face the random yields besides random demand.

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