Abstract
This chapter introduces the reader to Supply Chain Network Design (SCND) models that include the Cost Of Quality (COQ) among the relevant costs. In contrast to earlier models, the COQ is computed internally as a function of decisions taken as part of the design of the supply chain. Earlier models assume exogenously given COQ functions. Background information is provided on previous COQ modeling and on supply chain network design models. The authors’ COQ modeling is described in detail as is the SCND model that incorporates COQ. The COQ modeling includes prevention, appraisal, and both internal and external failure costs. Solution methods based on metaheuristics such as simulated annealing and the genetic algorithm are provided, including details on parameter tuning and computational testing. A genetic algorithm was found to yield the best results, followed by the simulated annealing approach. Topics for further research are provided as well as an extensive list of references for further reading.
Top1. Introduction
Cost Of Quality (COQ), or quality cost, represents a powerful measurement system that translates the implications of poor quality, the activities of a quality program, and quality improvement efforts into a monetary language for managers. COQ is a language that every stakeholder can understand; it affects operating costs, profitability, and consumer needs (Srivastava, 2008). Although COQ has been applied mostly within enterprises, it is crucial to extend COQ as an external measure and integrate these costs into Supply Chain (SC) modelling.
This chapter shows how to model a supply chain design problem that incorporates the COQ (SC-COQ model). It is a strategic-level model that internally computes the Cost of Quality in a single-product, multi-stage, serial Supply Chain Network Design (SCND) problem. The model selects from among several potential suppliers, several manufacturing plants, and several potential retailers to generate a logistic route that maximizes profit while attaining a required quality level. Two heuristic procedures are presented that can be used to solve the resulting nonlinear mixed-integer optimization problem. The heuristics are based on Simulated Annealing (SA) and the Genetic Algorithm (GA). The procedures followed for tuning the heuristics’ parameters is documented for readers wishing to implement similar procedures.
The chapter is organized as follows. The background section covers the concept of the supply chain, supply chain management, supply chain network design, the concept of Cost of Quality, the modelling of the Cost of Quality, a review on strategic SCND problems, and concludes a discussion of the motivation and contributions of the proposed model for supply chain design including the COQ. In section 3, the supply chain design model including COQ is presented. Computational experiments and results are presented in sections 4 and 5, respectively. A discussion of the impact of the COQ on supply chain design is provided in section 6, followed by suggestions for future research and conclusions.
Key Terms in this Chapter
Cost of Quality: A classification of costs associated with achieving a given level of quality. These costs are usually classified as prevention, appraisal, and failure costs.
Genetic Algorithm: A metaheuristic that explores a solution space via adaptive search procedures based on principles derived from natural evolution and genetics. Solutions are typically coded as strings of binary digits called chromosomes.
Metaheuristics: Heuristic procedures that can be adapted to a wide variety of problems as opposed to ad hoc heuristics that often apply only to one particular problem. They can often be used to address problems that cannot be tackled through traditional optimization theory. Metaheuristics iteratively aim to improve a candidate solution with respect to a given measure of quality (fitness); some metaheuristics use stochastic search methodologies.
Supply Chain Network Design: A strategic level decision process that seeks to select the best combination of a set of facilities to achieve an efficient and effective management of the supply chain.
Simulated Annealing: A metaheuristic based on a local search that escapes local optima to search new regions by being able to accept inferior solutions by means of a probabilistic process.
Heuristic Procedure: A procedure for solving optimization problems that does not guarantee finding an optimal solution. This often permits finding good solutions to larger problems than would be possible or practical to solve to optimality. Usually, heuristic procedures are used when exhaustive enumeration and/or optimal solutions are impractical (a considerable amount of time is needed to reach an acceptable solution).
Supply Chain: An integrated collection of various business entities, such as suppliers, manufacturing plants, retailers, distribution centers, among others, working together to acquire and transform raw materials and deliver value added products to customers.