Abstract
A Markov chain model to analyze quality in flexible manufacturing systems with batch productions is developed in this article. The cycles when good quality and defective parts are produced are defined as the good and defective states, respectively, and transition probabilities are introduced to characterize the changes between these states. The product quality is presented as a function of these transition probabilities, and the transition that has the largest impact on quality is referred to as the quality bottleneck transition (BN-t). Analytical expressions to quantify the sensitivity of quality with respect to transition probabilities are derived, and indicators to identify the BN-t based on data collected on the factory floor are developed. Through extensive numerical experiments, it is shown that such indicators have a high accuracy in identifying the correct bottlenecks and can be used as an effective tool in quality improvement efforts. Finally, a case study at an automotive paint shop is presented to illustrate the applicability of the method. © 2013 Copyright Taylor and Francis Group, LLC.
| Original language | English |
|---|---|
| Pages (from-to) | 190-205 |
| Journal | IIE Transactions (Institute of Industrial Engineers) |
| Volume | 45 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Feb 2013 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Funding
This work is supported in part by NSF grant no. CMMI-1114263.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Research Keywords
- batch production
- bottleneck transition
- flexible manufacturing system
- indicators
- Markov chain
- Quality
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