Analysing and Improving Manufacturing Operations using Production and Inventory Control SIMulator (PICSIM)

Transcription

1 LINKÖPING INSTITUTE OF TECHNOLOGY Department of Mangement and Engineering TPMM06 Analysing and Improving Manufacturing Operations Helene Lidestam March 2015 Analysing and Improving Manufacturing Operations using Production and Inventory Control SIMulator (PICSIM) A written report, together with the self evaluation form, should be handed in to Helene Lidestam (helene.lidestam@liu.se) no later than 1 st of June at 5.00 pm. Do not forget to include group name, names and personal codes of the participants in the group. The used program for this project, PICSIM, cannot be run on an Apple computer. Objective This project will deal with simulating production and inventory control techniques. The simulator allows for simulating MRP (material requirements planning), re-order point systems and cyclic planning. The goal of the simulation project is to optimise the manufacturing planning approaches and make recommendations based on analysis of the impact of different factors on the performance of manufacturing operations. Further more, when using a planning and control system, the user must choose lot sizes, lead times and safety mechanisms with consideration given to the manufacturing environment of the firm. This choice is in practice quite difficult, with respect to interactions among items and constraining capacity. For analysing and improving manufacturing operations, a thorough understanding of the relationships bwteen these control parameters and the system performance is of the utmost importance. This project is suited for students with an interest in manufacturing planning and control. The objectives of this project are: to give a deeper understanding for decision making problems of this kind, to illustrate how alternate planning and control systems can be analyzed through simulation, to give a deeper understanding for the interrelationships among parameters, to analyse manufacturing operations through studying relationships between different variables, to analyse the impact of different factors such as demand variations and load, to study development of manufacturing operations through for example set-up time reduction. Introduction A manufacturing firm is reviewing one of its manufacturing units. The preliminary findings include a combination of poor delivery service (too many shortages) and unnecessarily large inventories (and the corresponding capital tied up in materials). In order to analyze the production situation the firm has modelled its operations in a simulation model. They have simulated the ways that planning and control is carried out today, i.e. in terms of a re-order point (ROP) system using lot sizes according to the economic order quantity (EOQ) formulae.

2 Safety stocks are dimensioned to last for two weeks of expected demand. Lead times are based on the simplistic rule of thumb: one operation per week. The result of this simulation is shown in appendix 1. This result corresponds in all aspects to the experiences of the current production situation. They now want to find improvements in the planning and control procedure, in such a way that shortages are reduced and the capital tied up in inventories is reduced. The project is therefore concerned with the analysis of the current production situation and to suggest better parameter values for the planning and control system for the manufacturing unit. The company is considering the use of MRP and cyclic planning and would like you to evaluate both of these planning and control systems relative the ROP system in use. Also, they want to increase their understanding of interrelationships and would therefore like to get a number of operating characteristics curves. Market and products The firm manufactures and markets three end products, called A1, A2 and A3. The market for these products is stable. It is expected that the same kind of demand pattern will hold for the some period in time into the future. After careful analysis, it has been found that the demand per product per week is normal distributed, with means and standard deviations according to table 1. The demand between products and between weeks is independent. Since the forecast of the demand is the same as the average demand, the standard deviation of demand can be seen as the standard deviation of forecast error. Measurements have shown that the lead time, of all end products and items, have a standard deviation of 1.0 weeks. Item Mean Standard deviation A A A Table 1. Means and standard deviations for weekly demand. Product and production data The firm manufactures all items in the same production unit. The bills of material are shown in figure 1 (one item A4 consists of e.g. two items A8). In total, there are nine items, of which A1-A5 are manufactured inhouse whereas A6-A9 are purchased from outside suppliers. A1 A2 A3 A4 (2) A8 (2) A7 A5 (2) A9 (3) A6 (2) A8 (2) A6 Figure 1. Bills of materials. 2

3 Processing times, setup times and capacity for the five work centers of the production unit are shown in tables 2 and 3. There are 50 working weeks in a year. A1 A2 A3 A4 A5 P P P P P Table 2. Processing times per unit (hours); P=planning group; A=item. Planning group Setup time (hours) P P P P P Capacity (hours/week) Table 3. Setup times and capacities for work centers W1-W5. The production is flow oriented is such a way that operations in planning groups (work centers) with lower numbers always are processed before operations in planning groups having higher numbers. Thus, P1 is always the gateway work center, i.e. the first in an operations sequence. When an end product is demanded but cannot be delivered, a back order is taken and the product is delivered as soon as finished products are available. There is no need for spare parts. The values of the items are given in table 4. Item Value A A2 500 A3 940 A4 370 A5 210 A6 70 A7 390 A8 80 A9 100 Table 4. Item values in SEK (Swedish Crowns) per unit. The delivery lead times for purchased components A6-A9 are three weeks. Historically, suppliers have been very accurate and no variation in lead times has been detected. Simulation of ROP, MRP and cyclic planning The manufacturing, planning and control of the production unit has been modelled in a simulation model. This model is used to study how different parameter values affect the total relevant costs. 3

4 The basic structure for the planning and control simulator is MRP. With this structure ROP can be described, such that order quantities are calculated for each item individually and independently of the other items. Then, the planning logic of the simulator will trigger orders when the re-order point is passed with respect to the inventory levels. Also, cyclic planning can be emulated, by focusing on a constraining resource and synchronizing the order quantities for the item at this resource, using a common cycle approach or a basic period approach. In the beginning of every week a complete MRP regeneration is run. The net requirements are calculated based on demand forecasts (related to average demand) and the inventory situation before any withdrawals from stock. The result of the MRP is of course dependent upon the choice of parameter values. MRP use item lead times for lead time offsetting between different levels in the bills of materials (BOM). The need date for an item at the next lower BOM level is calculated as the need date for the parent item minus the lead time of the parent item. The ordering time for purchase components is the need date minus the item lead time. For example, if the lead time is 5 weeks, the items will arrive two weeks prior to the need date, since the delivery lead time from the supplier is three weeks. Orders that are to be started in a given week are released immediately, provided that all components are available. Orders with an earlier planned start date are initiated first. If two items have the same planned start date they are released in the order of item number, i.e. A1 is released first. The planned start date is obtained as the need date for the item according to MRP minus the planned production time for the item. The parameter planned production time is used for the simulation model to know when to release the first operation for an item. When an order is released, it is placed in the queue of its first work center. The queue principle is the well-known FIFO, i.e. first-in, first-out. As soon as an order is finished in one work center it is immediately moved to the next work center according to its operations sequence. The internal transportation times are negligible. In order to obtain stability in the simulation results, one simulation run is for three years. The first year is used for initiation to obtain a steady state. Only the results for the last two years are registered. The final simulation result in terms of annual costs and production statistics are calculated as an average over these two years. This fact should not be interpreted such that the parameter values must be valid for a two-year period. It is only used as a means to indicate that the parameter values will be valid as long as the demand and production data is valid. Parameters The parameters that can be varied are given in the appendix 2, which is the parameter data entry table. Please note the units of each respective parameter. The lead times which can be specified in terms of days, i.e. in steps of 0.2 weeks (since there are 5 working days per week). Ordering quantity fro each item, in terms of unit. Safety stock level for each item, in terms of unit. The choice of parameter values should thus be seen as a control mechanisms in order to obtain the planning and control of manufacturing that is desirable. 4

5 A large part of the production costs are fixed and independent upon the choice of planning and control parameters. However, to determine the order quantity, setup/ordering cost and inventory holding cost could be considered. In total, the ordering costs for manufacturing (setup costs) of items A1-A5 is 200 SEK plus 400 SEK per hour of capacity for setups and for purchasing of items A6-A9 is 400 SEK per order. By calculation of the holding cost, the company uses the calculated holding cost rate (20%) multiplied by the value of each item. With those costs, the company now has the following control parameters regarding the ordering quantity: Item Setup/ordering cost Economic order quantity A A A A A A A A A Table 5. Ordering costs and ordering quantities. Perfomance measures There are two performance measures. The first one is the service level of the finished goods and the second one is the total inventory holding cost including raw materials, work-inprocess and the finished goods. The object of this simulation study is to find a good balance between these two performance measures. Your final results should be better than: Inventory holding costs maximun and Service level (total) 95 % minimun. Running the simulator The steps for running PICSIM are the following: 1. Copy all files from floppy disk into your hard disk under the same directory. 2. Start the Microsoft Excel. 3. Click File and then Open in the Microsoft Excel main menu. 4. Open the file Picsim2015-ENG.xls and enable the macros. 5. Go to sheet1, modify parameters and click the Create Input File button. 6. Click Run Picsim2000 button. Or alternatively you may run piscim2000.exe either in Dos environment or by double clicking the icon of this program. 7. Turn to sheet2. Click Simulation Results button. 8. To make more than one run, repeat steps 5-7. Sheet1: Input information for current run. Sheet2: Results for current run. Sheet3: Results for all previous runs. When you open the Picsim2015-ENG.xls, it is recommended that you open it from the Microsoft Excel. After Step 5, you should automatically create a Pi.dat file under the same 5

6 directory as Picsim.xls. If you open the Picsim.xls through a short cut, e.g. clicking the file icon, you may not be able to create the input file in a proper directory. The result of a simulation run is illustrated in appendix 1. Appendix 1 depicts the current situation and the choice of parameter values (see data input). Also, some production statistics are given, that can be used for decision support for new decisions. Appendix 2 shows the input information file with the numbers for the current planning and control approach by the company. The bottom section includes the planning and control parameters that you can use to control the system, i.e. lead time, ordering quantity and safety stock. Compare your results with the initial condition of the company and discuss similarities and differences. Discuss the improvements that you have achieved. Project task The project can be divided in two tasks, task A and task B. For task A, there are 10 simulation runs, which will be ed to Lujie Chen, lujie.chen@liu.se. Lujie will carry out those simulations. The detailed schedule for the simulations can be found in the course presentation. There will be 18 scheduled time slots in total where of you should use 10. Each group s simulation results and get them back according to the schedule. Task B is conducted by each group and simulation package will be distributed after task A is finished. The project tasks are: Task A Give a suggestion for optimal planning and control for the company. The evaluation is based on a good tradeoff between the service levels and the total inventory holding costs. These control parameters can be determined by either ROP, MRP or cyclic planning systems. Give suggestions for better planning, as recommended planning ways and values of the control parameters. The proposal includes the choice of planning method, ie reorder point systems, MRP or cyclic planning. Motivate recommended values. Note that, inorder to dismiss a planning system, strong theoretical or empirical evidence is needed as motivation. Only control parameters may be changed in this task. The group will receive 10 simulation runs for this. These simulations are carried out by the course management based on group input template (10 per group; see Appendix 2). In Appendix 2 the company's current parameter values. Compare your proposal with the company's position and discuss the differences and similarities. Describe, compare and interpret the improvements you have achieved. Task B Each group will get a copy of the simulation software and be able to plan and execute your own simulation experiments. In this task the group can start from the recommended solution of task A, but you can also choose to use another, more stable solution, as a reference case (base case). Task B1 Study how a setup reduction affects delivery service, capital, parameters, etc. Refer to the direct effect of set-up time reduction as such versus the indirect effect of a subsequent adjustment of the planning parameters such as order quantity, lead time and safety stock. 6

7 Task B2 Study how different parameters are interrelated, such as cost, lead time, customer service, resource utilization (load), inventory turnover rates, demand variability etc. You can study how an input vairable affects an output variable; such as demand variability relative actual lead times. You can also study how a control parameter affects an output variable; such as order quantity relative customer service. You can further study how two output variables are interrelated; for example is there a relationship between actual lead times and customer service. In the third type of relationships you typically use a scatter diagram to study the relationship in principle, looking at the shape of the relationship and estimate regression lines or curves. Example of an Operating Characteristic Curve Avoid simulating trivial relationships, i.e. where one variable can be calculated from the oteher mathematically. Please use the insights you have from the Statistical Analysis course for the design of experiments and the calculation of statistical significance of the results. Seminars, presentation and the report In Phase I, three seminars are arranged for introducing the project, discussing the progress in the simulation study. In phase II, one seminar is arranged for discussing the study tasks and simulation results in Phase II. Finally each group shall present the simulation method and results in the final seminars (seminar 5). Afterwards, a complete report should be handed to Helene Lidestam (helene.lidestam@liu.se) not later than June 1 st, at 5.00 pm. The report must be written using the report template found on the course webpage. The report content should follow this structure. Report content ü Cover page including the group number and the group members names and info ü Table of content ü Introduction - Purpose - Method (as how you worked) - Delimitations ü Review of task A - Explain how you did each simulation run, which formulas you used and motivate! - Conclusion of task A 7

8 ü Task B1 - Direct effects of set-up time reduction - Indirect effects of set-up time reduction - Design of experiments - Conclusion of task B1 ü Task B2 - Interrelation between different parameters - Conclusion of task B2 ü Final conclusion of the project and managerial insights ü References Observe: You can add additional sections/subsections but all the reports should at least have the ones presented here! Note: ü Use of basic statistics is expected to highlight results (e.g. tests, regression analysis etc.) ü Uses references where appropriate ü Relate findings to covered literature ü Set up tests to verify covered literature ü For top grades, please follow the grading criteria. Format: ü Maximum 20 pages (incl. cover page, attachements, etc.) ü Font type: Calibri and Font size: 11 ü Use of vivid colors and clear legends for the charts/figures ü All tables and figures should be numbered and titled The report is used for grading the project part of the course, see grading criteria in the next section. Together with the report, a self evaluation of the report is handed in. The self evaluation contains which grade you strive for, and, connected to the criteria, a motivation on each critera for the desired grade. Use the self evaluation template for the self evaluation. Grading criteria Grade 3 requires the following: - Active participation in the project group. - Presence at the presentation (seminar 5). - For part A: o Maximum of 10 simulation runs with at least 1 result is better or as good as the requirements for the inventory holding cost and service level. o Simulation results for at least three of the planning methods that show why or why not Reorder point system, MRP, Cyclic planning and Cyclic planning with base period solution is an appropriate planning method in this project. - For Part B1 o A motivated and implemented set-up time reduction on a resource. o Discussion and analysis about the direct effects of the set-up time reduction. o Discussion and analysis about the indirect effects of the set-up time reduction. 8

9 o An improved planning situation after the set-up time reduction by a freely chosen planning method. - For part B2 o At least 5 operating characteristic curves, constructed from the simulation model, over non-trivial relations between different parameters, containing confidence intervals. o Discussion and reasoning about these five operating characteristic curves. Grade 4 requires the following (in addition to the requirements for grade 3): - A report submitted on time. - For part A: o Theoretical motivation for at least three of the planning methods that show why or why not Reorder point system, MRP, Cyclic planning and Cyclic planning with base period solution is an appropriate planning method in this project. o Motivation for choice of safety stock and/or safety lead time. - For part B1: o An improved planning situation after the set-up time reduction produced by a freely chosen planning method and by the use of experimental design, DOE. - For part B2: o Discussion and reasoning about these five operating characteristic curves clearly linked to course literature. o Motivation for choice of number of replications and random number seeds. Grade 5 requires the following (in addition to the requirements for grade 4): - For part A: o Theoretical motivation and simulation results that show why or why not Reorder point system, MRP, Cyclic planning and Cyclic planning with base period solution is an appropriate planning method in this project. o Additional planning methods are examined, with more simulations than the first 10, in order to further improve the results. - For part B1: o Discussion on advantages and disadvantages of using a theoretical planning method compared to experimental design, DOE, for improving operations. - For part B2: o Discussion and reasoning about these five operating characteristic curves clearly linked to other literature found by the project group. References 1. Anupindi, Chopra, Deshmukh, Van Mieghem & Zemel (2006), Managing Business Process Flows, Pearson Education. 2. Browne J, Harhen J and Shivnan J (1996), Production Management Systems, Addison- Wesley, Harlow, England. 3. Kivenko K (1981), Managing Work-in-Process Inventory, Marcel Dekker Inc, New York. 4. Vollmann TE, Berry WL and Whybark DC (1997), Manufacturing Planning and Control Systems, (4 th edition), Irwin, Homewood, Ill. 9

10 Appendix 1: Simulation Result for the Case Company. Simulation Results Simulation Results Group No. 1 Run No. 1 Input data Product/Item A1 A2 A3 A4 A5 A6 A7 A8 A9 Lead time Order quantity Safety stock Costs Inventory of raw materials Work in process Semi-finished and finished goods inventory Total Service levels (%) Product Product Product Overall 50.6 Statistics Stockouts of finished products Average inventory level A1-A Average actual lead time A1-A5 (weeks) Average queueing time P1-P5 (hours) Average load P1-P5 (%) Inventory turnover rates Raw material inventory Work in process 9.82 Semi-finished and finished goods inventory Total 9.62 Appendix 2: Planning Input Data Sheet for the Simulations. Input data to simulation model PICSIM TPMM06 Group no. Run no. A1 A2 A3 A4 A5 A6 A7 A8 A9 Lead time (weeks)* Order quantity (units) Safety stock (units) Please observe that the lead time is the item lead time, i.e. for A1 it is the lead time required to manufacture A1 based on that A4 and A8 are available. This can be set in steps of 0.2 weeks, i.e. in steps of full days. 10