Collaborative production line control: Minimisation of throughput variability and WIP

Abstract

Production research challenges over the last four decades are reviewed retrospectively. Industries can benefit from collaborative production advancements. Specifically, industries with automated, discrete production lines struggle with intrinsic process variability affected by rules that coordinate interactions among production line machines. Benefits can be gained by responsive collaboration among these machines. A given set of rules can potentially yield either an amplifying or smoothing effect on the process’s inherent variability, subject to current system conditions. A key challenge is the lack of models to dynamically select/re-select the appropriate set of rules to optimise performance.

This problem involves uncertain and dynamic constraints, raising the need for collaboration and conflict resolution mechanisms. Through collaboration and conflict resolution the production flow disruptions can be minimised; high process efficiency to meet customer demand with minimum inventory levels can be achieved. A collaborative control theory approach based on three of its principles is developed to accomplish this objective. Performance under this approach, called collaborative production line control (CPLC) is compared to common industry standard control methods. A case study shows that CPLC outperforms traditional models, achieving a statistically significant 43% reduction in throughput variability, 24% reduction in WIP, and a 0.45% increase in service level.

Keywords:

• automation
• collaborative control theory (CCT)
• production systems
• inventory control
• work in process (WIP)

Acknowledgement

This research has been developed with partial support from the Production, Robotics, and Integration Software for Manufacturing & Management (PRISM) Center at Purdue University. Collaboration with production researchers from PGRN, the PRISM Global Research Network, is also acknowledged. The authors also wish to thank the reviewers for helpful comments.

Notes

1. This section was written by Shimon Y. Nof.