ENBIS-8 in Athens

21 – 25 September 2008 Abstract submission: 14 March – 11 August 2008

Design of experiments in simulation with application to services

23 September 2008, 12:40 – 13:00


Submitted by
Shuki Dror
Shuki Dror Tamar Gadrich Maya Kaner
ORT Braude College
The role of experimental design is to improve performance by focusing on design details rather than on trial-and-error approaches. Design of experiments enables the analyst to define each system components and its levels thus obviating the need to decide arbitrarily regarding different components or factors (Sanshez, 2006).

Several applications of design of experiments in simulation have been presented by different researchers. However most of the applications focus on production systems that incorporate a few process factors such as variability in the demand, processing time variability and work-in-process (WIP) (Yucesan and Groote, 2000)

Service processes include a wide range of variegated factors such as activities, persons, tangibles and intangibles (Kaner and Karni, 2007). The factors and their interactions have to be incorporated into the design. We propose to integrate design of experiments in simulation for design of new and improvement of the existed service processes.

Generally, designing simulation experiments comprises three steps (Kleijnen at el, 2005): 1) developing a basic understanding of the problem for simulation settings; 2) finding robust, not necessarily optimal decisions; 3) comparing robust decisions for choosing the best alternative. We apply these steps for service processes. We assert that there is a set of generic factors (i.e., customer arrival rate, numbers and skills of the servers/workers etc.); generic performance measures (i.e., customer waiting time, number of customer complaints etc.) and generic possible interactions between the factors (i.e., a steady customer waiting time could depend on the number of workers; this could be changed if the customer arrival rate rises above a specific threshold). Our methodology enables the designer to define the simulation settings for a specific new process using generic collection of the service factors, their interactions and service performance measures. Business process improvement practices (Mansar and Reijers, 2007) such as activity resequencing, triage, parallelism, employee empowerment, flexible performer assignment are incorporated in our methodology and allow the designer to improve the existed service process. In addition our framework assists the designer to distinguish the statistically significant factors and interactions for finding and comparing robust decisions.

In summary, this paper describes a methodology, based on design of experiments and simulation, which enables a designer of service processes to cope with various factors and their statistically significant interactions. We illustrate the application of the methodology for design of order handling process in service system.

1. Kaner, M., Karni, R., 2007, Design of service systems using a knowledge-based approach. Knowledge and Process Management, 14(4): 260-274.
2. Kleijnen, J.P.C., Sanchez, S.M., Lucas, T.M., Cioppa, T.M., 2005. A User’s Guide to the Brave New World of Designing Simulation Experiments. INFORMS Journal on Computing 17(3): 263-289.
3. Mansar, L. M., Reijers, H.A., 2007. Best practices in business process redesign: use and impact. Business Process Management 13(2): 193-213.
4. Yucesan, E., de Groote, X., 2000. Lead times, order release mechanisms, and customer service. European Journal of Operational Research 120:118-130.
5. Sanchez, S.M., 2006. Work Smarter, Not Harder: Guidelines for Designing Simulation Experiments. In Proceedings of the 2006 Winter Simulation Conference, eds. Perrone, L.F., Wieland, F.P., Liu, J., Lawson, B.G., Nicol, D.M., Fujimoto, R.M., 47-57.

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