Simulation based model for component replenishment in multi-product ATO systems with shared resources

ABSTRACT

With increasing product complexity, sophistication and customization, Assemble-to-Order (ATO) systems have gained a lot of popularity in recent years. ATO systems have the advantage of delivering customer orders at shorter leadtimes by manufacturing components to stock. However, for an on-time delivery of the final assembled product, the corresponding components must be replenished and be available when needed for assembly in a timely yet cost-effective manner. This research investigates the production and subcontracting decisions in the multi-product ATO systems. We also provide insights on resource allocation decisions among various components and how does randomness in the service times impact these decisions. Using, Monte Carlo simulation approach, we identified that when the manufacturer is cheaper the direction towards optimality is by having the threshold values kept close to the base stock level for all components.

KEYWORDS:

• Assemble-to-Order
• simulation
• subcontractor
• Threshold-based policy

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Azeem Shami

Azeem Shami received a MS degree in industrial engineering from Kansas State University in 2019. He is currently a PhD student at the same university and a strategic supply chain and industrial engineer at Foot Locker, Inc. His research interests include stochastic modeling and optimization of supply chain networks.

Ashesh Kumar Sinha

Ashesh Kumar Sinha is an assistant professor at Kansas State University. He received a bachelor's degree in industrial engineering at the Indian Institute of Technology in Kharagpur, India. He earned a master's degree in manufacturing systems engineering with a minor in computer science and a doctorate in industrial engineering at the University of Wisconsin-Madison. Before coming to K-State, he worked as an optimization engineer at Schneider. His research focuses on developing optimization models and data analytics to address key supply chain challenges at strategic, operational and tactical levels.