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Performance and Uptime Analysis for Production Lines – UA02-02

Sponsor:

ConAgra Foods

Research Team:

Earnest W. Fant, Bob J. Narcum, Larry Roberts, Erhan Kutanoglu, Mike Tracy, Terry R. Collins, Fernando Tovia

Universities Involved:

University of Arkansas

Start Date:

08/20/01

End Date:

10/31/02

Summary:

This project performs a performanace and uptime analysis for prodction lines at a food processing plant in three phases: Phase I: Performance Analysis in Production Lines
Phase II:Sanitation Procedures and Scheduling
Phase III: Implementation
Select multiple control measures and operating parameters will be used to evaluate, analyze and optimize the processing performance at a food processing plant. We will focus on two selected production lines for testing and evaluation. An operational analysis will be conducted to identify the best- and worst- case scenarios for the selected production lines. After comparing the actual performance to the best-case and worst-case scenarios, opportunities for improvement will be identified. Estimating the effects of reducing the variability in production, better scheduling and line balancing methods, analyzing the relationship between the set production line speed and performance measures such as downtime and overall throughput will be the first steps of this project. Alternative trade-offs will further refine a performance tool that can help benchmarking the performance over time. Recommendations will be made to the company for improvement of the selected production lines and opportunities will be presented for the performance of other production lines to be evaluated using the performance model.
Tangible Benefits:
30% reduction of sanitation cycle time
Time reduction in processing the rework
Elimination of sanitors idle time
Reduction in 2245 man-hours per year
Intangible Benefits:
The proper use of the 1.5 extra hour per day by maintenance and production departments could result in a 2% increase in the Uptime%.
Allow more time to maintenance department to perform preventive and predictive maintenance