Real-time capacity requirement planning for make-to-order manufacturing with variable time-window orders

Authors:
Yi-Feng Hung;Chuan-Che Huang;Ying Yeh
Affiliations:
Department of Industrial Engineering and Engineering Management, National Tsing Hua University, 101 Kuang Fu Road, Sec. 2, Hsinchu 30010, Taiwan, ROC;Department of Industrial Engineering and Engineering Management, National Tsing Hua University, 101 Kuang Fu Road, Sec. 2, Hsinchu 30010, Taiwan, ROC;Department of Industrial Engineering and Engineering Management, National Tsing Hua University, 101 Kuang Fu Road, Sec. 2, Hsinchu 30010, Taiwan, ROC
Venue:
Computers and Industrial Engineering
Year:
2013

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Abstract

In a make-to-stock (MTS) manufacturing environment using material requirement planning (MRP), checking the capacity feasibility of a master production schedule (MPS) requires capacity requirement planning (CRP) that can be easily calculated. The time window of an order is the time interval from its ready date to its due date. In a make-to-order (MTO) manufacturing environment, the CRP method checks whether a set of orders with different time windows can be scheduled for timely completion. This corporate-level CRP problem has long perplexed MTO contract manufacturers, such as those in the fashion industry. This study therefore develops an efficient and effective CRP approach that considers orders with variable time windows. Real-time capacity feasibility can be checked on both the corporate planning and detailed operational scheduling levels by applying the preemptive earliest due date (PEDD) rule to a single machine problem. This simple and efficient dispatching rule can assess the impact on capacity consumption each time an inquiry order is received or select a set of pre-prioritized orders that can be feasibly scheduled. The efficiency of a supply chain network is affected by its overall lead time, which includes time spent on order processing, manufacturing, and transportation. The proposed approach significantly reduces the order processing time and enhances supply chain efficiency.