Customer loyalty is the main focus of every service provider, including the testing laboratory services provider. The proper management of the work-in-progress (WIP) level is critical to the success of the services of the laboratory. WIP management can regulate the material flow, make full use of bottleneck capacity, and act as a valuable parameter.
Hierarchical Timed Colored Petri Net-based approach is used in this study to model, simulate, and analyze the performance at one of the testing institutions in Indonesia. This study examines the problem of WIP management in the testing system. The simulation study, which aims to find out the bottleneck stations, is conducted based on the procedure and the data given from the testing laboratory. Then by using Priority Queueing based on Shortest Processing Time of the item Flow (SFPT), the simulation is conducted again to find out the impact of proposed strategies on WIP performance.
Based on the performance analysis, this study shows that the priority rule in firing using SFPT can reduce the maximum WIP of the system. This firing priority has a relatively small impact on the WIP in the single item station, but about one-third of the maximum WIP in the most complex station is decreased.

Customer loyalty is the main focus of every service provider, including the testing laboratory services provider. The proper management of the work-in-progress (WIP) level is critical to the success of the services of the laboratory. WIP management can regulate the material flow, make full use of bottleneck capacity, and act as a valuable parameter.
Hierarchical Timed Colored Petri Net-based approach is used in this study to model, simulate, and analyze the performance at one of the testing institutions in Indonesia. This study examines the problem of WIP management in the testing system. The simulation study, which aims to find out the bottleneck stations, is conducted based on the procedure and the data given from the testing laboratory. Then by using Priority Queueing based on Shortest Processing Time of the item Flow (SFPT), the simulation is conducted again to find out the impact of proposed strategies on WIP performance.
Based on the performance analysis, this study shows that the priority rule in firing using SFPT can reduce the maximum WIP of the system. This firing priority has a relatively small impact on the WIP in the single item station, but about one-third of the maximum WIP in the most complex station is decreased.

Aalst, W. M. P. v. d., Stahl, C. & Westergaard, M., (2013). Strategies for Modeling Complex Processes using Colored Petri Nets. Transactions on Petri Nets and Other Models of Concurrency VII. Lecture Notes in Computer Science, Vol. 7480, p. 6- 55.
Aziz, M.H., Bohez, E. L.J., Pisuchpen, Roongrat., & Parnichkun, Manukid. (2013) Petri Net model of repetitive push manufacturing with Polca to minimise value-added WIP, International Journal of Production Research, 51:15, 4464-4483
Bai, D., & Zhang. Z.. (2014). Asymptotic optimality of shortest processing time-based algorithms for flow shop and open shop problems with nonlinear objective functions, Engineering Optimization. Vol. 46 (12), p. 1709-1728
Camurri A, Franchi P, Gandolfo F, Zaccaria R (1993) Petri net-based process scheduling: a model of the control system of flexible manufacturing systems. J Intell Robot Syst 8:99–123
Hatano I., Yamagata K., & Tamura H. (1991). Modeling and on-line scheduling of flexible manufacturing systems using stochastic Petri nets. IEEE Trans Softw Eng 17(2):126–133
Hu, G.H., Wong, Y.S., Loh, H.T.. (1995). An FMS scheduling and control decision support system based on generalised stochastic Petri nets. Int J Adv Manuf Technol, 10:52–58.
Jensen, K., Christensen, S., Kristensen, L. M. & Westergaard, M., (2010). CPN Tools 4.0. http://cpntools.org/documentation/tasks/performance/start [Accessed 20 July 2020].
Gradišar, D., & Mušič, G. (2007). Production-process modelling based on production- management data: A Petri-net approach. International Journal of Computer Integrated Manufacturing, Vol. 20(8), p. 794–810.
Levalle, Rodrigo Reyes., Scavarda, Manuel., & Nof, Shimon Y.. (2013) Collaborative production line control: Minimisation of throughput variability and WIP, International Journal of Production Research, 51:23-24, 7289-7307.
52
Lin, Y. H., & Lee, C. E.. (2001). Total standard WIP estimation method for wafer fabrication. European Journal of Operational Research, Vol. 131(1), p. 78–94.
Mazzuto, G., Bevilacqua, M., & Ciarapica, F. E. (2012). Supply chain modelling and managing, using timed coloured Petri nets: A case study. International Journal of Production Research, Vol. 50 (16), p. 4718–4733.
Miller, D.J. (1990). Simulation of a semiconductor manufacturing line. Communications of the ACM Vol. 33 (10), p. 99-108.
Netjes, M., Aalst, W. M. v. d., & Reijers, H. A., (2009). Analysis of resource-constrained processes with Colored Petri Nets. Sixth Workshop and Tutorial on Practical Use of Coloured Petri Nets and the CPN Tools, p. 251-265.
Pashazadeh, S., (2011). Modeling a resource management method using hierarchical colored petri net. International eConference on Computer and Knowledge Engineering (ICCKE), p. 34-39.
Raju, K.R. & Chetty, O.V.K. (1993). Priority nets for scheduling flexible manufacturing systems. J Manuf Syst 12(4):326–340.
Roelofse, Frederick. (2007) An Exploratory Study into the Multidimensional Nature of Quality in Analytical Laboratories: Managerial Implications, Quality Management Journal, 14:3, 7-14.
Tyagi, Rajesh Kumar., Varma, Nikhil., & Vidyarthi, Navneet. (2013) An Integrated Framework for Service Quality: SQBOK Perspective, Quality Management Journal, 20:2, 34-47.
Wu, Y. et al., 2009. Solving Resource-constrained Multiple Project Scheduling Problem Using Timed Colored Petri Nets. Journal of Shanghai Jiaotong University (Science). Vol. 14 (6), p. 713-719.
Yee, S.-T., 2005. Impact analysis of customised demand information sharing on supply chain performance. International Journal of Production Research, Vol. 43 (16), p.3353–3373.
Zhang, X., Lu, Q., & Wu, T. (2011). Petri-net based applications for supply chain management: an overview. International Journal of Production Research, 49(13), 3939-3961.