Optimal Lot Sizing, Process Quality Improvement and Setup Cost Reduction

Abstract

This paper seeks to demonstrate that lower setup costs can benefit production systems by improving quality control. It does so by introducing a simple model that captures a significant relationship between quality and lot size: while producing a lot, the process can go "out of control" with a given probability each time it produces another item. Once out of control, the process produces defective units throughout its production of the current lot. The system incurs an extra cost for rework and related operations for each defective piece that it produces. Thus, there is an incentive to produce smaller lots, and have a smaller fraction of defective units. The paper also introduces three options for investing in quality improvements: (i) reducing the probability that the process moves out of control (which yields fewer defects, larger lot sizes, fewer setups, and larger holding costs); (ii) reducing setup costs (which yields smaller lot sizes, lower holding costs, and fewer defects); and (iii) simultaneously using the two previous options. By assuming a specific form of the investment cost function for each option, we explicitly obtain the optimal investment strategy. We also briefly discuss the sensitivity of these solutions to changes in underlying parameter values. A numerical example illustrates the results.

Mitigating Supply Risk: Dual Sourcing or Process Improvement?

“…The first stream primarily focuses on finding the optimal control policies to improve the production process while minimizing operating cost, e.g., Porteus (1986), Fine andPorteus (1989), Marcellus andDada (1991), Dada and Marcellus (1994), Chand et al (1996), where process improvement is typically measured in effective capacity (Spence and Porteus 1987), amount of defects (Marcellus and Dada 1991), or general cost of failures (Chand et al 1996). The second stream of the literature focuses on the interaction of process improvement with the firm's knowledge creation and learning curve, e.g., Fine (1986), Zangwill and Kantor (1998), Carrillo and Gaimon (2000), Terwiesch and Bohn (2001), Carrillo and Gaimon (2004).…”

“…We adopted a uniform distribution U (0, b) for the capacity loss. Drawing from the process improvement literature, e.g., Porteus (1986), we used a log function for reliability improvement, i.e., a(z) = a 0 + log(1 + z).…”

Ordering Policy for Imperfect Quality Itemsets Using Cross Selling Effects

“…The quantity received should be subject to 100% inspection in almost all types of concern and its role becomes more prominent when items are imperfect in nature. By considering this fact, researchers devoted a great amount of effort to develop EPQ/EOQ models for defective items ( [1]- [5]). In 2000, Salameh and Manuscript received August 14, 2013; revised October 20, 2013 The authors are with the Department of Computer Science Engineering, Amity School of Engineering and Technology, Bijwasan, New Delhi -110061, India (e-mail: mmittal@amity.edu, mittal_mandeep@yahoo.com).…”

Credit Financing in Economic Ordering Policies for Defective Items with Order Overlapping

“…By considering this fact, researchers devoted a great amount of effort to develop EPQ/EOQ models for defective items (Porteus [1], Rosenblatt and Lee [2], Lee and Rosenblatt [3], Schwaller [4], Zhang and Gerchak [5]). In 2000, Salameh and Jaber [6] extended the traditional EPQ/EOQ model for the imperfect quality items.…”