Optimized material requirements planning for semiconductor manufacturing

• Published in: The Journal of the Operational Research Society Vol. 63; no. 11; pp. 1566 - 1577
• Main Authors: Milne, R J, Wang, C-T, Yen, C-K A, Fordyce, K
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 01.11.2012; Palgrave Macmillan; Palgrave Macmillan UK; Taylor & Francis Ltd
• Subjects: Algorithms; Applied sciences; Business and Management; Exact sciences and technology; General Paper; General Papers; Heuristics; Inventory control, production control. Distribution; Linear programming; Logistics; Management; Manufacturing; Manufacturing processes; Material requirements planning; material requirements planning (MRP); Materials; Mathematical programming; Multiplication & division; Operational research and scientific management; Operational research. Management science; Operations research; Operations Research/Decision Theory; practice of OR; Preventive maintenance; Product lines; production; Purchase orders; Receipts; Semiconductors; Supply chain management; Supply chains; material requirements planning (MRP); linear programming; heuristics; production; practice of OR; Material requirements planning; Priority; Linear programming; In-process inventory; Modeling; Alternative process; Production management; Microelectronic fabrication; Manufacturing process; Heuristic method; Make to stock; Resource management; Logistics; Production process; Mathematical programming
• ISSN: 0160-5682; 1476-9360
• DOI: 10.1057/jors.2012.1

This paper describes a custom operational research algorithm, which is run nightly by IBM to create a material requirements plan for its semiconductor fabrication facility in Vermont, USA. To model alternative manufacturing processes and part substitutions, this application interweaves linear programming and heuristic methods to reap the benefits of each decision technology. At each level of the bills of materials supply chain with complex decision choices to be made, parallel linear programmes are invoked and their results are fed into a material requirements planning (MRP) heuristic, which processes parts through multiple iterations. The results from processing one level of the bills of materials supply chain are exploded to create demand for the next level and the interweaving of the two decision technologies continues. The algorithm creates recommended manufacturing releases and work-in-process priorities. These outputs point out opportunities for improvement in order to satisfy all demands on time. The output can be interpreted with well-known MRP assumptions.