Improving the management of manufacturing assets across large-scale networks of suppliers in the plastic industry

Beckman Coulter, one of Danaher's operating companies, provides diagnostic equipment and consumables to the health industry. A variety of plastic manufacturing methods are used to make instrument parts and consumables worldwide via third-party manufacturers who operate assets owned by the original brand. Worldwide, more than 200 of these manufacturers operate more than 2000 tools owned by Beckman Coulter. The lack of a centralized visibility of the real-time condition of these tools promotes a faulty maintenance plan that causes unexpected failures, reduces its productivity, and stimulates a "fire-fighting" environment within the engineering team. The motivation for this research is to contribute to protect the company's revenue stream by improving the efficiency of the manufacturing assets, which will ultimately improve the on-time deliveries and reduce procurement and operational costs. The thesis proposes that those objectives can be achieved through an efficient and effective system to track the current condition of manufacturing assets (primarily tooling) designed for the complex network of part manufacturers. The system provides reliable and dynamic information about the progress of the tools' life-cycle, record maintenance and failure events, monitors the OEE, and collects relevant data to enable a predictive model for future failures. The research starts investigating root causes for low effectiveness, through the analysis of the current state, and evaluates alternatives to track assets' condition and life-cycle across the complex and large supplier network. The selected alternative is to use the parts receipts, currently available through the company's ERP, as a proxy for the tools' shot-count. This indicator is used as the cornerstone for the Manufacturing Assets Management System, which acts as a single-reference point database and interface to visualize the assets' life-cycle, interdependencies with other elements in the network, condition, and effectiveness. It is also a depository for maintenance and failure data which could enable predictive maintenance. It is designed to scale up and to be useful for any internal and external manufacturing assets. Lastly, the thesis analyzes the ideal conditions and characteristics that the system would require to achieve Industry 4.0 standards, exploring and proposing the most effective technologies that are viable to be implemented in a large, commoditized, supplier-based, manufacturing network, to enable more advanced predictive analytics designed to improve OEE.

Department dc:contributor.department

Leaders for Global Operations Program at MIT

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Mufarech Rey, Álvaro

Advisor dc:contributor.advisor

• Roy Welsch and David Hardt.

• Sloan School of Management.
• Mechanical Engineering.
• Leaders for Global Operations Program.

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/1721.1/117966

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/117966