Simul8 in Supply Chain

pic PLYMOUTH BUSINESS SCHOOL COURSEWORK COVERSHEET GROUP WORK NAMES and NUMBERS of students in the group (2 Students) 1. El-Iraki, Youssef (10448517) 2. Badr, Noureldin (10445226) MODULE CODE MBM5204 MODULE NAME Logistics, append Chains, Systems and Methods Lecturer Professor Dongping Song DEADLINE 11th February 2013 WORD COUNT 1,657 By submitting this piece of assessment the group confirms that all the work is thoroughly and adequately endorse and referenced, and has been completed in accordance with the University and Programme Regulations.Table of Contents 1. 0 Introduction3 2. 0 Current pretending Model3 2. 1 Clock Options3 2. 2 The warm-up flow3 2. 3 Results compendium dot4 2. 4 The number of trials used4 2. 5 Results psychoanalysis4 3. 0Pooling Resources5 3. 1 The impact of pooling resources5 3. 2 Comparison between initial fashion sticker and pooled model6 4. 0Usefulness of Simulation Model in Business Context6 4. 1 Simulation and decisiveness make6 4. 2 Researcher R ecommendation8 5. 0 Bibliography9 6. 0 Appendices11 1. 0 IntroductionSimulation 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 devil stores and four drivers can be pooled to compare the results and the influence of the bestow kitchen stove performance, in order to give an optimal supply-production-distribution system decisions. The researchers used SIMUL8 program to fulfil the simulations and d novel the predictable models needed. . 0 Current Simulation Model 2. 1 Clock Options The business is working daily from Monday till Friday by which the shifts are kickoff from 900 till 1700 (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 fo r manufacturing models, because there is no work-in-progress in such industries at the beginning of the plow (Concannon et al. , 2007).Robinson (2007) stated that there are various methods to mould 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 genuinelyistic 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 pic 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 week s as statistically n ? 30, it is important to use large sample size to be more than accurate and it is prerequisite to produce results among variables that are heartly different (Freeman, et al. , 2010). 2. 4 The number of trials usedAfter running the simulation model, it was important to generate the results required to help the company analyse the produce 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) illustrates the results that conducted after running the simulation of model 1. The average time in system is 110 hours due to many reasons in the supply chain which affects production plan that get to poor delivery performance.Although the main objective of any manufacturer is to decrease lead-time in order to satisfy customer and grasp better delivery performance. Drivers perf ormances are 91% and they are considered as an important resource to deliver finished goods to end customers at the chasten time. The waiting ploughshare of the available vehicles is set to be 2% which cannot be considered as a factor that hinder the efficiency of customer delivery. However, the working percentage of vehicles can be enormous factor that affect customer delivery.As shown in appendix (2), vehicles are only operating at a 79% of its total working ability. Since the warehouses hold finished goods and is considered to be an inventory, therefore it is crucial to minimise the capacity of the warehouses to achieve greater financial success. Appendix (2) shows that the average queue size of both warehouses is nearly 16 units whereas the maximum capacity of the warehouses is 50 units, thus the capacity of the warehouses are goodly used. Average queue time of the available warehouses is some other factor that must be taken in the prior considerations.An average of 34 hours is spend to deliver orders from warehouses to customers and this can be nearly 30% of the completely time spent in system. The rule of thumb declares that once the goods are manufactured, it must be delivered as quickly as possible to reduce retention costs and to satisfy customers. Pooling Resources 3. 1 The impact of pooling resources Pooling resources is a possible method to improve service performance without adding any other resources. Pooling help to reduce the variability of data collection, however pooling of customers adds variability to the system and no efficiency will be gained (Vanberkel et al. 2010). Furthermore, it helps to reduce the average queue time in system for the products it is optimal to schedule the shortest job first and to give priority to short jobs (Downey, n. d. ). Thus, it can reduce inventory holding period and costs. This method used in the model is called FIFO (first-in first-out). 3. 2 Comparison between initial model and pooled model 1- There are dramatic changes after pooling warehouses, the queuing time dropped from 34 hours to 15 hours composition queue size decreased from16 units to 15 units.As a result the average time in system declined from 110 hours to 88 hours, thus it can lead to better customer service, saving storage costs and save time as well. 2- After pooling the drivers, it has influenced the waiting times of the vehicles to increase slightly from 2% to 2. 4%. While drivers function has improved significantly to rise from 91% to 93%, therefore drivers after pooling can respond quickly and flexibly to customers. Usefulness of Simulation Model in Business Context 4. 1 Simulation and decision makingThe simulation model can help the real-world companies to provide efficient production and distribution systems as stated by Tunali et al. (2011). SIMUL8 has become the preferred tool as it brings solutions for production planning and scheduling to thousands of engineers that cast complex supply chains and dis tribution systems such as Chrysler, GM, Ford etc. (SIMUL8, 2013). SIMUL8 is easy to use and support numerous critical decisions making every yr because it enables to grow accurate and flexible output more rapidly.Moreover, it helps bridging the ERP gap by creating brisk and feasible production plans (Concannon et al. , 2003). Analysis and assessment of business processes development of what if scenarios and export to murder platforms, such as workflow management and ERP systems are the key advantages of simulation modelling, because it enables the integration of these functions easily and more accurately (Verma et al. , n. d. ). As a result, decision making can be easily overtaken and this is the reason why thousands of companies use simulation modelling to optimise their supply-production-distribution systems.Chrysler saved $5 Million by using Simul8 software system which helped them to identify the best performance and bottleneck lines, thus it assisted them to slow it down. Simul8 also reduced the manpower on these assembly lines which have saved $ 600,000 per year as labour costs. On the other hand, the researchers could not identify the best performance and bottleneck lines because it needs Simul8 professional software which is used in real world companies and the need of historical data is crucial to be more realistic when identifying the bottlenecks in the supply-production-distribution systems (Simul8, 2013).The researchers used Simul8 education software in this case and they effect out after pooling warehouses and drivers, significant results are achieved such as reducing inventory (from 16. 7 units to 15. 5 units) and the time of finished goods spent in the warehouse was also decreased significantly (34. 5 hours to 15. 9 hours). As a result, the lead-time dropped from 110 hours to 88 hours. Furthermore, drivers economic consumption increased from 91. 1% to 93. 6% after pooling the resources (drivers).Thus, the business could react more respons ively to customers and achieve enormous financial success because of their drivers flexibility (Velverde et al. , 2000). According to McLean and Leong, simulation models can help manufacturing and operational departments to determine which new technologies need to be used, organise labour shifts and materials management required for each production stage and modelling of supplier relationships (McLean and Leong, 2001). Table (1) Usefulness of Simulation Model Usefulness of the Simulation Model Business Context Current Simulation Model Support the operation of supply chain through what-if A trial of approximately 3,000 runs were conducted to compare the available manufacturing model results Perform capacity planning analysis usable capacities for warehouses in the initial model were set to 50 each, but it was planned to pool both warehouses together to have a capacity of 100 which delivers enormous results.Maximum batches for trucks and availability% of drivers were set which helped for planning the distribution process of the model Establish the required resources for production and material Determine and manage the required raw materials needed for assembling the product handling (How many raw materials needed from each supplier) it can also be set on which statistical distribution used to supply these values materials as the simulation runs mightiness to evaluate overall firm performance Every stage of the production and distribution process are evaluated such as working%, waiting%, utilisation of drivers, queue sizes, queue times, etc. As a result, this can elp evaluate the performance of the company and assist the top management in taking the in good order decisions 4. 2 Researcher Recommendation By using SIMUL8, the researcher suggested to add value to the company even after pooling their resources which affected in significant results. The researches created new model and recommended to add one more vehicle with the same am ount of resources that are available (drivers, warehouses) to compare with the previous results. The following table shows even more effective results as customers received their orders in less than the time spent by using only 2 vehicles.It has also shown that drivers utilisation increased significantly from 93. 2% to 97. 9% and this is due to a huge reduction in waiting times of drivers. Finally, warehouses queue size and queuing time have decreased to meet nearly the maximum efficiency by which slight amount inventory holding and very tiny amount of time is spent inside the warehouse where most of the finished goods are ready for delivery to customers once arrived. Table (2) Results of adding extra vehicle pic 5. 0 Bibliography Concannon, K. Elder, M. Hindle, K. Tremble, J. and Tse, S. (2007) Simulation Modeling with simul8. online unattached at http//www. visual8. om/wp-content/uploads/2011/03/simulation_modeling_with_simul8_web. pdf Accessed on 26th of January 2013. Concan non, K. H. , Hunter, K. I. & Tremble, J. M. (2003) SIMUL8-Planner Simulation-Based Planning and Scheduling. online Available at http//ieeexplore. ieee. org/stamp/stamp. jsp? arnumber=01261593 Accessed on 28th of January 2013. Downey, A. B. (n. d. ) Using queue time predictions for processor Allocation. online Available at http//www. cs. huji. ac. il/feit/parsched/jsspp97/p-97-2. pdf Accessed on 27th 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 http//citeseerx. ist. psu. edu/messages/downloadsexceeded. html Accessed on 30th of January 2013. Robinson, S. (2007) A statistical process control approach to selectin g a warm-up period for a discrete-event simulation. European Journal of Operational Research online, 176 (1). Available at http//ac. els-cdn. com/S0377221705005643/1-s2. 0-S0377221705005643-main. pdf? tid=65d0a6b8-6edb-11e2-94b5-00000aacb35e&acdnat=1359990116_8f49ecb58acc4020e744141def925d90 Accessed on 26th of January 2013. Simul8 (2013) Warm-up Time. online Available at http//www. simul8. com/support/help/doku. php? id=gettingstartedtechguidewarmup&do=show Accessed on 26th of January 2013. Simul8. (2013) Chrysler projected to save $5 million using SIMUL8. online Available at http//www. simul8. com/our_customers/case_studies/chrysler_line_balancing. htm Accessed on 28th of January 2013. Tunali, S. , Ozfirat, P. M. & Ay G. (2011) Simulation Modelling Practice and Theory. Setting order promising times in a supply chain network using hybrid simulation-analytical approach An industrial case study. 9, (9), p. p 1967 1982. online Available at http//ac. els-cdn. com/S1569190X11 000888/1-s2. 0-S1569190X11000888-main. pdf? _tid=9efcdf7a-6a48-11e2-a658-00000aab0f01&acdnat=1359487271_e121e9fba1ca576f0e980d12317a80a9 Accessed on 29th of January 2013. Valverde M. , Tregaskis O. & Brewster C. (2000) International Advances in Economic Research. Labor Flexibility and Firm performance. 6, (4), pp. 649-661 online. Available at http//link. springer. com. up3xt5ae3w. useaccesscontrol. com/article/10. 1007/BF02295375 Accessed on 31st of January. Vanberkel, P. T. Boucherie, R. J. Hans, E. W. Hurink, J. L. & Litvak, N. 2010) Efficiency evaluation for pooling resources in health care. OR Spectrum online, 34 (1), pp. 371-390. Available at http//doc. utwente. nl/67543/1/memo1902. pdf Accessed on 27th of January 2013. Verma, R. , Sharma, A. & Gupta, A. (n. d. ) Role of Simulation Modeling in Business Process Re-engineering. online Available at http//simvehic. com/admin/rpapers/Role%20of%20Simulation%20Modeling%20in%20Business%20Process%20Reengineering. pdf Accessed on 28th of January 2013. 6. 0 Appendices Appendix (1) Initial Simulation Model pic Appendix (2) Initial results pic Appendix (3) Pooled Simulation Model pic Appendix (4) Pooling Results pic