Percent Planned Complete: Development and Testing of a Simulation to Increase Reliability in Scheduling
Explore this paper's citation graph
Summary
This research develops and test a new simulation to better understand how participants perceive the impact of using PPC as a tool to measure and subsequently improve reliability in planning and demonstrates that playing the simulation led to a 718% enhanced understanding of how applying PPC to schedule planning can lead to improved reliability of performance.
- Type
- article
- Published
- 2016-01-01
- Cited by
- 2
- References
- 7
- OpenAlex
- https://openalex.org/W2524764439
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:63234512
Keywords
Reliability engineering, Scheduling (production processes), Computer science, Reliability (semiconductor), Operations management
References
- The Psychology of Management: The Function of the Mind in Determining, Teaching and Installing Methods of Least Waste
- Understanding the role of “tasks anticipated” in lookahead planning through simulation
- PARADE GAME :I MPACT OF WORK FLOW VARIABILITY ON TRADE PERFORMANCE
- THE LAST PLANNER SYSTEM OF PRODUCTION CONTROL
- Lean construction and EPC performance improvement
Cited by
Related papers
- An Effective Approach For The Maintenance Scheduling In Large Systems With Required Reliability Level: A Case Study
- Reliability improvements in aircraft maintenance planning and scheduling under uncertainty
- Generator maintenance scheduling to maximize reliability and revenue
- A robust reliability-based scheduling for the maintenance activities during planned shutdown under uncertainty of activity duration
- Energy cost based technique for maintenance scheduling of generating systems
- Optimum Consecutive Preventive Maintenance Scheduling Model Considering Reliability
- Probabilistic evaluation of the effect of maintenance parameters on reliability and cost
- EFFECTIVENESS OF IMPROVED REPAIR SCHEDULING IN THE PERFORMANCE OF BUS TRANSIT MAINTENANCE (DISCUSSION AND CLOSURE)
- Preventive Maintenance & Replacement Scheduling Model for Repairable and Maintainable Systems