Determining optimal process mean and quality improvement in a profit-maximization supply chain model. Issue 2 (4th March 2019)

• Record Type: Journal Article
• Authors: Chuang, Cheng-Ju; Wu, Chien-Wei
• Is Part Of: Quality technology & quantitative management. Volume 16:Issue 2(2019)
• Page Start: 154
• Page End: 169
• Publication Date: 2019-03-04
• Subjects: Supply chain -- quality loss -- process mean -- quality improvement -- asymmetric tolerance -- shortage; Quality control -- Periodicals; Quality control -- Statistical methods -- Periodicals; Industrial management -- Periodicals; Industrial management; Management -- Research -- Methodology -- Periodicals; Qualitative research -- Periodicals; Management; Quality control; Quality control -- Statistical methods; Periodicals; 658.00721
• DOI: 10.1080/16843703.2017.1389124
• Languages: English
• ISSNs: 1684-3703

Abstract

Abstract: Traditional view of quality has proved to underestimate quality-related cost, and the concept of quadratic quality loss proposed by Dr Taguchi is a more realistic function for measuring quality. To minimize quality loss, reducing the variation in quality using quality improvement is an effective method. Besides, for the asymmetric tolerance commonly designed in production processes, the optimal process mean setting is required to be considered. Recently, the cooperation in supply chain has attracted increasing attention as effective means of increasing competitiveness, which can reduce more cost than independent EOQ or EPQ model. This paper proposes an integrated supplier–retailer supply chain model considering an optimal process mean setting and quality improvement with an asymmetric tolerance design and allowable shortage, the objective is to maximize the supply chain's total profit by determining the supplier's optimal process mean and investment amount, the retailer's optimal number of shipments, order and maximal backorder quantities. An algorithm is established to solve the optimal solution, and numerical examples are presented to illustrate the solution results. The results show that the proposed model has the best comparative performance, and the sensitivity analyses can help supply chain managers make decisions regarding adjustments in quality management and production rates.