Purchasing, production, and sales strategies for a production system with limited capacity, fluctuating sales and purchasing prices

Abstract

In many industries, the revenue and cost structures of manufacturers are directly affected by the volatility of purchasing and sales prices in the markets. We analyze the purchasing, production, and sales policies for a continuous-review discrete material flow production/inventory system with fluctuating and correlated purchasing and sales prices, exponentially distributed raw material and demand inter-arrival times, and processing time. The sales and purchasing prices are driven by the random environmental changes that evolve according to a discrete state space continuous-time Markov process. We model the system as an infinite-horizon Markov decision process under the average reward criterion and prove that the optimal purchasing, production, and sales strategies are state-dependent threshold policies. We propose a linear programming formulation to compute the optimal threshold levels. We examine the effects of the sales price variation, purchasing price variation, correlation between sales and purchasing prices, customer arrival rate and limited inventory capacities on the system performance measures, through a range of numerical experiments. We also examine under which circumstances the use of the optimal policy notably improves the system profit compared to the use of the buy low and sell high naive policy. We show that using the optimal purchasing, production, and sales policies allow manufacturers to improve their profits when the purchasing and sales prices fluctuate.

Keywords:

• Random environment
• price fluctuation
• Markov decision process
• linear programming

Additional information

Notes on contributors

Oktay Karabağ

Oktay Karabağ is a post-doctoral researcher at Eindhoven University of Technology and is working on joint university–industry research projects, namely, integrated maintenance and service logistics concepts for pro-active service logistics for capital goods–the next steps (ProSeLoNext). He received a B.S. degree in statistics from Ege University. He received a master’s degree in intelligent production systems engineering from Izmir University of Economics and a Ph.D. degree in industrial engineering and operations management from Koç University. His current research interests include stochastic modeling, design and control of manufacturing systems, maintenance optimization, and cooperation in supply chain management.

Bariş Tan

Bariş Tan is a professor of operations management and industrial engineering at Koç University, Istanbul, Turkey. He received a B.S. degree in electrical and electronics engineering from Boğaziçi University. He received a master’s degree in industrial and systems engineering, a manufacturing systems engineering degree, and a Ph.D. degree in operations research from the University of Florida. His main expertise is in the design and control of production systems, analytical and numerical modeling techniques, supply chain management, and operations research. His current research interests are in stochastic modeling of operations, performance evaluation and control of manufacturing systems, and cooperation in supply chain management.