Abstract

An existing power system production process includes two operations and produces twelve different part types. The first operation fills WIP carts used by the second operation. A combined lean and discrete event simulation study supported by the analysis of order history information stored in a corporate information system is presented. The goal was to identify operations alternatives that could be used to reduce customer lead time from the current 3 to 3 days to 1 to 3 days. The application of lean methods included the examination of the order history data that showed that 80% of parts ship to a single primary customer and 20% to many secondary customers. The lean part of the study further concluded that the number of WIP carts should equal the number of different products, that each WIP cart should be associated with one and only one product and that each WIP cart should be should refilled daily after orders are processed. Using a discrete event simulation model, four order processing sequencing alternatives for improving on-time delivery were evaluated. The percent of orders delivered in 1 day was maximized, with the lowest variance, by sorting all orders for the primary customer first from smallest in size to largest. The orders for the same product from the remaining customers are processed immediately after the order for the same product from the primary customer. The value of the synergistic effect of combining lean tools with simulation supported by order data extracted from the corporate information system is demonstrated.

Keywords:

Discrete event simulation, lead time, lean, order data, scheduling,

References

1. Banks, J., 2000. Getting Started with Automod. Brooks-PRI Automation, Bountiful, UT.
   
2. Hopp, W.J. and M.L. Spearman, 2007. Factory Physics: Foundations of Manufacturing Management. 3rd Edn., McGraw-Hill/Irwin, New York.
   
3. Jeong, K.Y. and D.T. Phillips, 2011. Application of a concept development process to evaluate process layout designs using value stream mapping and simulation. J. Ind. Eng. Manage., 4(2): 206-230.
   CrossRef    
4. Kleinberg, K., 2000a. Simulation and Animation Technology for Health Care. Strategy and Tactics/Trends and Directions. Gartner Inc., Stamford, CT, United States of America.
   
5. Kleinberg, K., 2000b. Simulation of Emergency Rooms at VHA. Best Practices and Case Studies. Gartner Inc., Stamford, CT, United States of America.
   
6. Marvel, J.H. and C.R. Standridge, 2009. A simulation-enhanced lean design process. J. Ind. Eng. Manage., 2(1): 90-113.
   
7. Miller, G., J. Pawloski and C.R. Standridge, 2010. A case study of lean, sustainable manufacturing. J. Ind. Eng. Manage., 3(1): 11-32.
   
8. Robinson, S., Z.J. Radnor, N. Burgess and C. Worthington, 2012. SimLean: Utilising simulation in the implementation of LEAN in healthcare. Eur. J. Oper. Res., 219(1): 188-197.
   CrossRef    
9. Sargent, R.G., 2013. Verification and validation of simulation models. J. Simulat., 7: 12-14.
   CrossRef    
10. Standridge, C.R., 2013. Beyond Lead: Simulation in Practice. 2nd Edn., Retrieved from: http://scholarworks.gvsu.edu/books/6/.
    Direct Link
11. Standridge, C.R. and J.H. Marvel, 2006. Why lean needs simulation. Proceedings of the Winter Simulation Conference. Monterey, California.
    CrossRef    
12. Tapping, D., T. Luyster and T. Shuker, 2002. Value Stream Management. CRC Press, Boca Raton, FL.
    

Competing interests

The authors have no competing interests.

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The authors have no competing interests.