Simul8 in Supply Chain

Topics: Computer simulation, Simulation, Supply chain management Pages: 10 (2408 words) Published: April 16, 2013




NAMES and NUMBERS of students in the group (2 Students):

1. El-Iraki, Youssef (10448517)

2. Badr, Noureldin (10445226)


MODULE NAME: Logistics, Supply Chains, Systems and Methods

Lecturer: Professor Dongping Song

DEADLINE : 11th February 2013


By submitting this piece of assessment the group confirms that all the work is thoroughly and adequately acknowledge and referenced, and has been completed in accordance with the University and Programme Regulations.

Table of Contents
1.0 Introduction3

2.0 Current Simulation Model3

2.1 Clock Options3

2.2 The warm-up period3

2.3 Results collection period4

2.4 The number of trials used4

2.5 Results analysis4

3.0Pooling Resources5

3.1 The impact of pooling resources5

3.2 Comparison between initial model and pooled model6

4.0Usefulness of Simulation Model in Business Context6

4.1 Simulation and decision making6

4.2 Researcher Recommendation8

5.0 Bibliography9

6.0 Appendices:11

1.0 Introduction

Simulation is one of the three quantitative analysis solutions and it is essential in logistics decision making (Ghiani, et al., 2004). Simulation model can answer what if questions in existing system as for this case, the business needs to know and evaluate performance if two warehouses and four drivers can be pooled to compare the results and the influence of the supply chain performance, in order to give an optimal supply-production-distribution system decisions. The researchers used SIMUL8 program to run the simulations and draw the predictable models needed.

2.0 Current Simulation Model

2.1 Clock Options

The business is working daily from Monday till Friday by which the shifts are starting from 9:00 till 17:00 (8 hours/day), and the time is set up in hours to monitor the start time and the length of each day.

2.2 The warm-up period

The warm-up period is crucial when building up simulation for manufacturing models, because there is no work-in-progress in such industries at the beginning of the process (Concannon et al., 2007). Robinson (2007) stated that there are various methods to determine warm-up period in the simulation model such as the model of run-in for a warm-up period until it reaches a steady state and then the data are deleted and the model of a realistic initial condition at the start of the run. The first model was taking into consideration when determining the warm-up period and has shown that the warm-up period is 280 hours. It is worth adding a 20% to the normal warm-up period as a safety margin (SIMUL8, 2013). The table below shows the exact warm-up period after running and monitoring the simulation model.

Figure (1): Warm-up period

2.3 Results collection period

The result collection period is usually chosen to reflect an appropriate operating period. In this model the period set to 1600 hours = 40 weeks. The researchers decided to choose 40 weeks as statistically n ≥ 30, it is important to use large sample size to be more accurate and it is necessary to produce results among variables that are totally different (Freeman, et al., 2010).

2.4 The number of trials used

After running the simulation model, it was important to generate the results required to help the company analyse the output data accurately. The more trials used, the more accurate the results will be. Approximately 3000 trials for both initial and pooling models are conducted to give sufficient accurate results needed for the company.

2.5 Results analysis

Appendix (2)...

Bibliography: • Concannon, K. Elder, M. Hindle, K. Tremble, J. and Tse, S. (2007) Simulation Modeling with simul8. [online] Available at: < > [Accessed on 26th of January 2013].
• Concannon, K. H., Hunter, K. I. & Tremble, J. M. (2003) SIMUL8-Planner: Simulation-Based Planning and Scheduling. [online] Available at: < > [Accessed on 28th of January 2013].
• Freeman, J., Shoesmith, E., Anderson, D.R., Sweeney, D.J. & Williams, T.A. (2010) Statistics for business and economics. 2nd ed. Hampshire: South-Western Cengage learning.
• Ghiani, G., Laporte, G. and Musmanno, R. (2004) Introduction to logistics systems planning and control. Chichester: Wiley.
• McLean, C. and Leong, S. (2001) The Role of Simulation in Strategic Manufacturing. [online] Available at: < > [Accessed on 30th of January 2013].
• Simul8 (2013) Warm-up Time. [online] Available at: < > [Accessed on 26th of January 2013].
• Simul8. (2013) Chrysler projected to save $5 million using SIMUL8. [online] Available at: < > [Accessed on 28th of January 2013].
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