Master production schedule time interval strategies in make-to-order supply chains¶

¶ This research was partially funded by a grant from the Scholarly Research Grant Program of the College of Business Administration at the University of Tennessee.

Abstract

While the literature primarily addresses MPS design from the manufacturer's perspective, this research considers MPS policy design in a two-stage rolling schedule environment with a particular focus on the policy governing schedule flexibility in the non-frozen time interval (i.e. liquid versus slushy orders). Using computer simulation, we experimentally evaluate the impact of four MPS design factors (non-frozen interval policy, planning horizon length, frozen interval length and re-planning frequency) and four environmental factors (natural order cycle length, vendor flexibility, demand range and demand lumpiness) on MPS schedule cost and instability. The experimental design considers the often-conflicting impact of MPS policy on the channel members by capturing performance metrics at the manufacturer, vendor and system level. The research findings indicate that moving from a liquid to a slushy non-interval strategy increases the manufacturer's costs, but may result in an even greater cost reduction for the vendor resulting in lower system costs. The economic benefit of the slushy strategy is directly tied to the vendor's relative flexibility in responding to the manufacturer's orders on a lot-for-lot basis. High vendor flexibility favours the liquid strategy, while low vendor flexibility favours the slushy strategy.

¶ This research was partially funded by a grant from the Scholarly Research Grant Program of the College of Business Administration at the University of Tennessee.

Keywords:

• Materials requirement planning
• Master production scheduling
• Dynamic demand
• Simulation

Notes

¶ This research was partially funded by a grant from the Scholarly Research Grant Program of the College of Business Administration at the University of Tennessee.

† Following traditional practices in make-to-order supply chains, we assume the following. First, the lumpiness and dynamic nature of demand makes forecasting individual items highly inaccurate. Hence, the vendor does not maintain inventory in anticipation of demand. Second, the liquid or planned MRP orders are not communicated to the vendor due to the instability of their order schedules. Instead, the manufacturer shares his intermediate-term product family forecast with the vendor to enable aggregate capacity planning and ensure sufficient resources are available to meet short-term order requirements.