Optimizing inventories and standardizing planning procedure in a multipart manufacturing system

This work illustrates how to apply a multi-echelon periodic review base-stock model to a real manufacturing company, Waters Corporation, for their major product Analytical Columns. At Waters, the Analytical Column supply chain ranges from raw materials to the final delivery to customers, and covers US, Europe, and Asia. The goal of this work is to find the best locations to hold safety stock along the supply chain so as to minimize the inventory holding cost for the whole company. To do this analysis, a 5 stage multi-echelon supply chain model is constructed. All the stage costs are measured and standardized based on data from the "SAP" system. To estimate the demand variability, we utilize an adjustment method that accounts for the aggregate bias in the forecast. The final optimal solution will reduce the safety stock level by 67% for the supply chain. We also find a nearoptimal solution that is easier to implement; this solution would reduce the safety stocks by 59%. Finally, we argue that the implementation of this model and its assumed operating policies can improve internal communications within the company, leading to better integration across operating units..

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Hua, Yu, M. Eng. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Stephen C. Graves.

• Mechanical Engineering.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/107546

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/107546