Arena Simulation Software - Case Study Example

Arena Simulation Software Introduction Arena is the world’s leading simulation software, which has been used successfully by organizations over the world to increase productivity and efficiency of their businesses. It provides the ability to simulate within an easy-to-use modeling environment. The animated Arena simulation model allows making changes or “test driving” the solution before applying it. Operational strategies could be compared, and optimal solutions could be implemented with confidence as their performance is known. It allows for communication with management and shop-floor personnel as its function is known, and the implications of specific variations (Advantage Software Limited, 2008). Arena simulation software could be used to demonstrate, measure, and predict system strategies for effective, efficient and optimized performance. The software helps analyze “what-if” business ideas, rules and strategies before live implementation without disruptions in service. Arena helps improve the life cycle of business performance. The software allows the user develop simple, well-animated, high-level models including advanced modeling and analysis. Statistical design and analysis of simulation experiments have been integrated with the modeling (Rockwell Automation, 2009). An Arena window allows opening a model and interaction with components of the window. Applications of Arena include manufacturing; scheduling and inventory; staffing and personal-service operations such as banks, fast food, theme parks; distribution and logistics; health care; emergency and operating rooms; computer systems; telecommunications; military; and public policy. Types of simulation include static and dynamic; the role of time in the model; continuous-change and discrete-change; change in state continuous or at discrete points in time; domestic and stochastic; and inclusion of uncertainty. The majority of operational models include dynamic, discrete-change, and stochastic. The hierarchical structure of allows multiple levels of modeling, allows mixing different modeling levels together, and start high and go lower as needed. Also, Arena allows ease-of-use advantages without sacrificing modeling flexibility. Advantages of simulation include flexibility to model applications as they are; allows for uncertainty, nonstationarity in modeling; allows to for accommodation of advances in computing; improves cost rations; and includes advances in simulation. Arena allows modeling of complex and messy situations; uncertain scenarios; system variability and model validity. Approximately 75% computing power is used to simulate various scenarios; and there are dedicated machines for real-time shop-floor control. The GUIs are simpler and easier to use; is not restricted to modeling constructs; and has statistical design and analysis capabilities. The disadvantages of simulation include approximations or estimates but no exact answers; and random output from stochastic simulations. Machine roundoff could result in bound errors, and standard statistical methods do not work always (Farquardt, 2007). Statement of the Problem Scenario A manufacturing company has 24 workers equally divided into two teams including assemblers and inspectors. The operating time has been arranged in two-shift for 8 hours each and 12 workers in each shift. The assemblers show full attendance and were doing two jobs; assembling and disassembling the failures (expected at 10%). Inspectors, on the other hand, were in charge of the inspection job, and their attendance record stated that 9 out of 12 inspectors showed up. The manpower available had half an hour break between four hours in each shift. The management aimed for production amount to be produced early, which let workers pretend they were busy. The company management has noticed that profit recently decreased. One more important issue was that workers were paid for their free time, which meant that labour cost was high while the benefit gained remained low. A recommendation popped up recently that suggested rearranging operation time to be on a single shift basis. However, the system should allow meeting the management requirements at the lowest cost and highest benefit. Conditions to be assured were 300 items as a targeted production quantity at £10/hour as a labour cost. A consultant has been hired for to examine the viability of the system that satisfied those conditions and requirement by application of a suitable technique to simulate the current scenario and adjust it until reaching the most effective layout. This examination should be done without affecting the current situation in reality. Approach Taken The most effective simulation software for manufacturing systems is Arena3®, which are widely used through the following steps: 1. Simulate the system of current scenario; 2. Suggest adjustments to enhance the performance of the first model; 3. Keep adjusting the model until reaching the perfect system layout; 4. Write down outcomes of each tryout; 5. Evaluate outcomes to decide if the tryout is the one; and 6. Evaluate all tryout outcomes and suggest final recommendations. The above steps would be followed on the first model for the current scenario. Current Scenario Simulation Data given for the current scenario are: Operation Schedule Number of Workers Labour Cost/hour Arrangement Weekly Period Break time Assemblers Inspectors £ 2 shift (8 hours) 5 days (4800 min) 30 min 12 9 210 The current layout of the system is shown below: The above layout began with the assembly job. Produced items passed to the inspection area, where failures, 10%, were sent back to assemblers for reworking and the fine items, 90%, were sent out of the manufacturing area. According to the amount of work involved, the number of assemblers should be higher than number of inspectors. This was because assemblers, performed two functions; assembling and disassembling. To reveal outcomes of the current scenario, simulation model runs and have been shown below: Outcomes: Production Time Required Failures 300 3600 32 The aimed quantity could be established with 32 failures. The time needed for the operation to finish is 3600 min while the allowable time is 4800 min. The difference is the time workers pretend being busy. The management paid the cost of this time with no production gained. To understand it more, the cost of this period of time could be determined as follows: 4800 min – 3600 min = 1200 min → (1200/60) x £10 = £200/week (loss) So, minimizing this cost would be an important issue to be considered. In the tryouts, the operation schedule and number of workers would be adjusted. Tryout 1: In the first tryout, the suggestion provided by the management has been applied: Operation Schedule Number of Workers Labour Cost/hour Arrangement Weekly Period Break time Assemblers Inspectors £ 1 shift (8 hours) 5 days (2400 min) 30 min 12 9 210 Outcomes: Production Time Required Failures 300 1920 28 The system could produce the aimed quantity a period that is 480 min shorter than allowable time. Paying a labour cost for this difference is still considered a loss (£80). Moreover, production quantity could be achieved in a significantly short time. This led to the fact that number of workers was high. So, lesser number of workers would be employed in the next tryout. Tryout 2: In this tryout, lesser workers have been employed: Operation Schedule Number of Workers Labour Cost/hour Arrangement Weekly Period Break time Assemblers Inspectors £ 1 shift (8 hours) 5 days (2400 min) 30 min 12 6 180 Outcomes: Production Time Required Failures 300 1920 31 No difference could be noticed especially in the time required to complete producing the aimed quantity. This indicated that number of assemblers should go down. So, lesser number of assemblers would be used in the next tryout. Tryout 3: In this tryout, lesser number of assemblers have been employed: Operation Schedule Number of Workers Labour Cost/hour Arrangement Weekly Period Break time Assemblers Inspectors £ 1 shift (8 hours) 5 days (2400 min) 30 min 9 6 150 Outcomes: Production Time Required Failures 306 2400 20 A big change was observed. The total allowable time was utilised to produce 6 items more than the aimed quantity. In the way to find out the most effective layout, the number of assemblers and inspectors would be decreased until the least possible number was attained. Tryout 4: In this tryout, lesser workers have been employed: Operation Schedule Number of Workers Labour Cost/hour Arrangement Weekly Period Break time Assemblers Inspectors £ 1 shift (8 hours) 5 days (2400 min) 30 min 8 4 120 Outcomes: Production Time Required Failures 272 2400 20 With the number of workers used, only 272 items could be produced. This showed that the number of assemblers could not be less than 9. Now, the number of inspectors would be varied. Tryout 5: In this tryout, lesser workers have been employed: Operation Schedule Number of Workers Labour Cost/hour Arrangement Weekly Period Break time Assemblers Inspectors £ 1 shift (8 hours) 5 days (2400 min) 30 min 9 3 120 Outcomes: Production Time Required Failures 306 2400 23 As per data above, the layout displayed a good performance. The aimed production quantity was met utilizing all the allowable time with quite good labour cost. For more cost saving and since number of assemblers could not be less than 9, the number of inspectors would be reduced further. Tryout 6: For the maximum cost saving, the system would have lesser number of inspectors: Operation Schedule Number of Workers Labour Cost/hour Arrangement Weekly Period Break time Assemblers Inspectors £ 1 shift (8 hours) 5 days (2400 min) 30 min 9 2 110 Outcomes: As per the dialog box above, data of layout caused an error. This indicated that further lessening of inspectors was not possible. So, number of inspectors should remain 3 or higher. Tryouts Evaluation, Analysis & Recommendation Tryout Operation Schedule Number of Workers Labour Cost Quantities Arrangement Weekly time Break time Assemblers Inspectors £/hour Pass Fail Current Layout 2 shift 5 days 30 min 12 12 240 300 32 (8 hours) (4800 min) Tryout 1 1 shift 5 days 30 min 12 9 210 300 28 (8 hours) (2400 min) Tryout 2 1 shift 5 days 30 min 12 6 180 300 31 (8 hours) (2400 min) Tryout 3 1 shift 5 days 30 min 9 6 150 306 20 (8 hours) (2400 min) Tryout 4 1 shift 5 days 30 min 8 4 120 272 20 (8 hours) (2400 min) Tryout 5 1 shift 5 days 30 min 9 3 120 306 23 (8 hours) (2400 min) Tryout 6 1 shift 5 days 30 min 9 2 110 Err Err (8 hours) (2400 min) The table above indicates a statistical way to evaluate the performance of each of the tryout against the current scenario and the layout suggested by the management. Figures, e.g. time, number of workers, labour cost etc., revealed from each tryout represent its performance indicator. The most satisfactory figures are those resulted from Tryout 5, taking into consideration that the system successfully produced 306 items with 23 failures applying the schedule recommended and during the time allowed by management. It is recommend that the layout and data applied in Tryout 5 should be the new system layout that would satisfy the management and help recovering existing issues. Performance Enhancement To build up a wiser decision, the current scenario has been compared with the recommended tryout. Tryout Operation Schedule Number of Workers Labour Cost Quantities Arrangement Weekly time Break time Assemblers Inspectors £/hour Pass Fail Current Layout 2 shift 5 days 30 min 12 12 240 300 32 (8 hours) (4800 min) Tryout 5 1 shift 5 days 30 min 9 3 120 306 23 (8 hours) (2400 min) The above table indicates that a huge enhancement can be accomplished on application of Tryout 5. For example, number of workers reduced from 24 to 12 (50%), which led to 50% savings in labour cost. Moreover, number of failures decreased from 32 to 23, which indicated saving in operating expenses (materials, equipments, power, labour etc.) Conclusion The application of simulation in manufacturing systems has shown a precise way to indicate performance of several types of industrial systems. The process to simulate includes a variety of steps summarized as: Creating a simulation model for the existing; Examining modifications within the first model; and Evaluating and justifying outcomes. This multi-step technique was the way used to provide a general description of manufacturing systems arrangement, possible modifications evaluated and decision made about which modification would advance the system performance. References Advantage Software Limited. (2008). Rockwell Software: Arena Simulation Modeling. Available: http://www.advantages.co.nz/arena.asp. Last accessed 29 November 2009. Farquardt, B. (2007). What is Simulation? Available: http://nsl.pusan.ac.kr/lecture/MAutomation/simulation01.pdf. Last accessed 23 November 2009. Rockwell Automation. (2009). Arena Simulation Software. Available: http://www.arenasimulation.com/arena_Home.aspx. Last accessed 23 November 2009. Read More