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Facilities planning and production management


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Facilities planning and production management

  1. 1. FACILITIES PLANNING AND PRODUCTION MANAGEMENT Final Report: TOWER OF HANOI Linnaeus University School of Engineering 1SE007 – Facilities Planning and Production Management Authors: Examiners: Boris Batljan Anders Ingwald Fatih Topaloglu Anna Glarner Nikolaos Georgadakis Farvid Mojtaba Serkan Alan
  2. 2. Acknowledgement We would like to thank our teacher/tutors Anders Ingwald, Anna Glarner and Farvid Mojtaba who have helped and supported us when make this report. Also we want to thank for the lectures and necessary information.
  3. 3. Table of Contents 1. Introduction.................................................................................................................. 4 1.1 Task.................................................................................................................................... 4 1.2 Account for Assumptions..................................................................................................... 4 2. Theory .......................................................................................................................... 5 3. Empirical Findings ......................................................................................................... 8 3.1 Task 1: Business Strategy........................................................................................................... 8 3.2 Task 2: Routing/List of Operations ............................................................................................ 9 3.3 Task 3: Facility Layout.............................................................................................................. 13 3.4 Task 4: Material Handling........................................................................................................ 15 3.5 Task 5: Safety Stock and Economic Order Quantity................................................................ 16 3.6 Task 6: Demand and Forecast ................................................................................................. 17 4. Results and conclusions............................................................................................... 19 5. References.................................................................................................................. 19 Appendix........................................................................................................................ 21
  4. 4. 1. Introduction Here are we going to present our task and assumptions. 1.1 Task We have six different tasks for this report. Tasks are showed at below with short descriptions. 1. Business Strategy: We will define business goals, who is our customer and where should the facility be layout. 2. Routing/List of Operations : Which way we choose for production, our production numbers and calculations. 3. Facility Layout : How our facility layout, how we choose that alternative and desicion process will be presented. 4. Material Handling : How our material transfer between stations and reasons will be presented. 5. Safety Stock and Economic Order Quantity : With holding and setup we calculate optimal production. 6. Demand and Forecast : Acording to previous years data, we will calculate demand and forecast for 2011. 1.2 Account for Assumptions  Assume Operation Times to Produce a Game Figure 1: Time Calculation for Parts We assume production times. For brass, we have four different diameters. The table above shows average brass produces time.  Working Time We have 7.5 hours working time and half an hour lunch break per a day. We assume that we will work 20 days a month. When we calculate work days per month we consider weekends, religion holidays, national holidays. It means we have 27,000 seconds per a day; 540,000 seconds per a month working time.  In task 5 annual demand is used as 72000 but normally it is 71811. It helps to calculation. Name of Part Time for Produce One We Need for One Game Time to Produce One Game Brass 10 seconds 4 40 Peg 8 seconds 3 24 Base 70 seconds 1 70
  5. 5. 2. Theory Here are we going to present the theory we used. 2.1 Relationship Chart According to Chien (2004) is Muther‟s view and method regarding systematic layout planning (SLP), not only a proven tool in providing layout design guidelines, the method is used over the whole world among enterprises and in the academic world. Figure 2. Example of an Activity Relationship Chart When starting with the SLP procedure and improving the Activity Relationship Chart does the process start with making the relationship between each activity on the chart more comparable. The activity relationship is used to decide the relationship score and diagram between each activity and is an important indicator in decision-making. Figure 2 shows an example of an activity relationship chart, the chart usually looks like the one in figure 2. 2.2 Material Requirements Planning (MRPI) According to Hill (2005) is MRP a system that determines the final services and products (depends on what kind of products and the amount) that a company will produce in the future; the system does also specify the necessary inputs to meet that demand. MRP is also used to manage the capacity needs in a company and also the material needs. For example is the demand for engines, tires and brakes linked to the demand of vehicles. To determine the number of engines, tires and brakes we need to determine the number for vehicles. When we have the number of vehicles can we calculate the requirements for all dependent items.
  6. 6. 2.3 Master Production Schedule (MPS) Master production schedule focuses on to produce certain quantities of services or products in a particular time periods. To do this, does it take statements of demand (forecast sales and known orders) and test those against statements of capacity and resources for the same period(s). The result would be an anticipated schedule of finished services and products. This schedule has a key role in the control system that leads to an agreement between marketing and operations on what a company shall produce. The requirements are inventory records, the quantity and timing of current operations schedules, outstanding purchase orders, up-to-date bills of materials that reflect changes and clear information about the customers requirements (the existing customers), current orders and sales forecasts. The master production schedule is based mostly on forecast in the later periods of the planning horizon. (Hill, 2005) 2.4 Material Handling System Equation According to Tompkins (2010) are they‟re some material handling system designs, and the material handling system equation is one of them. The handling system is used to identify opportunities for improvement. It gives framework to identify solutions to material handling problems. What defines what type of material has been moved, where and when identifies the time and place requirements, who and how tells the material handling methods. Whit these questions shall the system lead to a recommended system. The material handling system equation is given by: Materials + Moves + Methods = Recommended System Figure 3. Material Handling System Equation
  7. 7. 2.5 Simple/Weighted Moving Average and Exponential Smoothing Inman (2006) explains that a simple moving average takes a predetermined number of periods, sums their actual demand, then the sums is divided by the number of periods to reach a forecast. For each period, the latest period gets added and the oldest period of data drops off. A good example could be if we use actual demand with example numbers; 45 in January, 60 in February and 72 in March would give the result of: 45 + 60 + 72 = 177/3 = 59 If there would be interesting to get the forecast for May, we would drop January‟s demand from the equation and add the demand from April. A weighted moving average takes the predetermined weight to each month of past data, then shall the past data from each period be summed, and at least be divided by the total of the weights. The results are later on summed to achieve a weighted forecast. Generally can it be said that when the older the data is, the smaller is the weight, and the more recent the data is, the higher will the weight be. If we say that we use demand examples with a weighted average using weights of .4, .3, .2 and .1, would the forecast fore June be: 60(.1) + 72(.2) + 58(.3) + 40(.4) = 53.8 Exponential smoothing is about taking the precious period‟s forecast and adjusts it by a predetermined smoothing constant, ά (called alpha) multiplied by the difference in the previous forecast and the demand that actually occurred during the previously forecasted period (called forecast error). The formula of exponential smoothing is: New forecast = previous forecast + alpha (actual demand – previous forecast) F = F + ά(A-F) Exponential smoothing requires an amount of past data and a beginning or initial forecast. It does also require the forecaster to begin the forecast in a past period and to continuously work forward to the period for which a current forecast is needed. Initial forecast can be an actual forecast from a previous period, actual demand from a previous period, or it can also be estimated by averaging all or part of the past data. The accuracy of the initial forecast will not be critical if someone is using large amounts of data, just because exponential smoothing is self-correcting. If there are enough periods of old data, will the exponential smoothing eventually make enough correction to compensate for a reasonably inaccurate initial forecast. Using for example an initial forecast of 50, and an alpha of .7 for February will the forecast for February be: New forecast (February) = 50 + .7(45-50) = 41.5 New forecast (March) = 41.5 + .7(60-41.5) = 54.45 This process will continue until the forecaster reaches the desired period. (Inman, 2006)
  8. 8. 2.6 Economic Order Quantity (EOQ) According to Hill (2005) is there one fundamental decision in the inventory management, which can be very hard to answer. The question is how much a company should order, in the context of what quantity will result in the lowest total cost. The economic order quantity (EOQ) and the economic batch quantity (EBQ)/economic lot size (ELS) models are linked to the question: “how much shall for example a company order to minimize the total cost of holding inventory?” The formulas for each are given below: - EOQ = 2zCs/cC - EBQ/ELS = 2zCs/cC * p/p-d Variables:  z = total annual usage  Cs = cost of placing an order  c = unit cost of the item  C = carrying cost rate per year  p = production provisioning rate (units) per day  d = demand rate (units) per day It is important to note that these models make the following assumptions: - The rate of demand is constant - Costs remain fixed - Operations capacity and inventory holdings are unlimited 3. Empirical Findings 3.1 Task 1: Business Strategy Business Goals Our company is customer oriented. We try to be always in time for our deliveries and never let our customers down .The quality of our products is high but without exaggerations to the price or the appearance. Also, as a profit organization, we are looking forward to increase our earnings year by year but not in the expense of our clients. On the contrary it is our constant effort to lower the production cost by improving the facility‟s quality rate. That is the reason that the production line is equipped with new and up to date machinery. Customers Since we are a factory, it is preferred for us to sell our products at wholesale. We ship the products to retailers all over the world, toy stores, hobby centers and also supermarkets on selected countries trying to make our product available for a big number of people. The target group varies from country to country but in general are people interested in puzzles and mathematical games.
  9. 9. Location of the customer Although our industry is capable of delivering all over the world, we focus on countries that have normal or high living standards. Also, one of the regions that the company pays more attention is west Europe and France in particular. That is happening because of the popularity the game has there since its inventor of was of French nationality Facility Location After consideration, it was decided that best location for a company of this kind is best to be in central Europe. France and Germany represent the prerequisites we have set. Stability in the economic life in addition to the low tax rates for new or young factories were the main concerns. The proximity to the target countries also affected our decision. Moreover, rent costs, experienced labor in factories and easier delivering throughout Europe was also considered. Specifics about the factory‟s location - A non-urban area  low rents, plenty of space - Close to highway  raw material/finished products deliveries - Preferably close to a port  shipping with containers 3.2 Task 2: Routing/List of Operations Machine Choosing: Name Cost per Hour Cost per Month Alternative 1 103 Vertical Machining Centre 150 56,320 104 Assembly Table 20 106 CNC Auto Lathe with Bar Feed 140 2x Operators 21x2 Alternative 2 101 Band Saw Machine 20 56,480 102 CNC Auto Lathe 100 103 Vertical Machining Centre 150 104 Assembly Table 20 3x Operators 21x3 Alternative 3 103 Vertical Machining Centre 150 82,080 104 Assembly Table 20 2x 106 CNC Auto Lathe with Bar Feed 140x2 3x Operators 21x3 Figure 4: Machine Alternatives First we compare alternative 1 and alternative 2. Both alternatives satisfy our demand and operation costs are almost same. We choose alternative 1 because alternative 1 is more automatic and less human dependent. Human work can be wrong or more possibility to make
  10. 10. fault. On the other hand automatic systems are more trustable and less possibility to make some faults. Then we think we can use 3 machines for 3 parts. On that way our production will be so fast because we produce all parts all the time and do not lose time for setup machines. But when we compare our production, numbers between produced parts are too far from each other and we do not need that production. Also our production is too much that our demand. So we do not have to that much operation cost because we do not need it. Calculate Working Time Spend Time Description Time per Month (Seconds) Brass Setup time 147,600 Mini Setup Time 5,400 Peg Setup Time 19,200 Base No Setup Time 0 Figure 5: Setup Time for Machines Name of Part Days a Month Seconds a Month Brass 12 304,200 Peg 8 196,800 Base 20 540,000 Figure 6: Clear Work Time In production planning, we have 106 CNC Auto Lathe with Bar Feeder which we are planning to produce brass and peg. The machine produces brass 2 days, after one day peg, then 2 days Brass and one day peg. And we have 103 Vertical Machining Centre which we produce just base part. Calculate Production Name of Part Average Deviation for Planned Production Time Total Planned Production Time per Month (Seconds) Clear Work Time per Month (Seconds) Needed Time to Produce for one Game (Seconds) Real Capacit y per Month (Items) Avera ge Scrap Rate Approved Product Capacity per Month (Items) Base 1.18 540,000 457,627 70 6,536 5.58% 6,157 Peg 0.94 196,800 209,361 24 8,723 1.34% 7,715 Brass ø40 1.05 90,000 85,714 12 7,142 0.74% 7,089 Brass ø30 1.02 80,200 78,627 11 7,148 1.02% 7,075 Brass ø25 1.02 67,000 65,686 9 7,298 0.78% 7,241 Brass ø20 1.14 67,000 58,772 8 7,347 0.66% 7,299 Figure 7: Number of Parts We Produce When we calculate “Average Deviation for Planned Production Time” we tool average of previous five years, then we divided “Average Deviation for Planned Production Time” with “Total Planned Production Time per Month” and got “Clear Work time per Month”. For calculate “Real Capacity per Month” we divided “Clear Work Time per Month” with “Needed Time to Produce for one Game”. We took average of previous five years scrap rate to calculate “Average Scrap Rate”. At the end, to find “Approved Product Capacity per Month” we subtract “Average Scrap Rate” from “Real Capacity per Month”.
  11. 11. Calculate Cost Name Cost per Hour Working Hours per Month 103 Vertical Machining Centre 150 160 106 CNC Auto Lathe with Bar Feeder 140 160 104 Assembly Table 20 160 2 Operators 21x2 160 Total Operating Cost: 56,320 Figure 8: Operating Cost Name of Part Real Capacity per Month Length (Meters) Price per Meter (Pound) Cost Base 6,536 0.102 6 4,000 Peg 26,169 0.05 1.8 2,355 Brass ø40 7,142 0.005 170 6,071 Brass ø30 7,148 0.005 140 5,004 Brass ø25 7,298 0.005 78 2,846 Brass ø20 7,347 0.005 50 1,837 Total Cost for Row Material 22,113 Figure 9: Raw Material Cost Calculate How Many Games We Can Sell According to previous years‟ quality rate, we calculate weighted moving average for quality. Our weighted moving average for quality is %98.77. Our lowest production is base. So when we calculate how many games we will produce per a month, we took base production number as game production number which is 6,157. Our game production per year is 12x6,157=73,884. For find how many games we can produce to sell per year, we multiply how many games produced with weighted moving average for quality which is 73,884x0.9877=72,975 Our capacity for produce games to sell is 72,975 games per year List of Operation for Peg Part name: Peg Prep. by: Group 62 Part no: AI1003 Date: 05-03-2013 Amount: 3,271 / Day Op # Description Work site Setup time (min) Op. time (sec) Cost (£) 0 Feeding 106 - 3 0.12 1 Screw Cutting 106 30 min / 3 35.12 2 Cutting 106 1 day 2 35.08
  12. 12. List of Operation for Base Part name: Base Prep. by: Group 62 Part no: AI1002 Date: 05-03-2013 Amount: 6,536 / Month Op # Description Work site Setup time (min) Op. Time (sec) Cost (£) 0 Put the Parts 103 - 7 0.29 1 Site Cutting 103 - 10 0.42 2 Face Cutting 103 - 20 0.84 3 Frame Cutting 103 - 10 0.42 4 Drill 103 - 8 0.33 5 Screw Cutting 103 - 8 0.33 6 Take the parts 103 - 7 0.29 List of Operation for Brass Part name: Brass Pieces Prep. by: Group 62 Part no: AI1004-1007 Date: 05-03-2013 Amount: 4,822 / 2 Days Op # Description Work site Setup time (min) Op. Time (sec) Cost (£) 0 Feeding 106 - 2 0.08 1 Drilling 106 30 min / 2 21.08 2 Cutting 20 106 2 days 4 21.16 3 Cutting 25 106 5 5 21.19 4 Cutting 30 106 5 7 21.27 5 Cutting 40 106 5 8 21.31
  13. 13. 3.3 Task 3: Facility Layout While designing the facility layout we tried to be as simple as possible. We considered about different difficulties that may present on the production and tried to prevent them. Since the machines 103 and 106 are the only ones to use raw materials (the first levels of our bill of material) we placed them next to the storage of raw materials. Also, knowing that the brass bars are difficult to handle (even 6 meters long) we placed the 106 in such way that the feeding is easy and simple. The storage of finished products was designed as a simple square room next to the last production station. Shelves are being used and the capacity is big enough to store a lot more products than our safety stock. The handling of the products is easy and carts are used to move objects around the facility. But for safety reasons, the machinery on our blueprints is designed bigger than the reality (example: 103- in reality 5mX4m, on blueprints 6mx5m) . This idea was approved among other reasons because movement around the factory must be easy for the workers and contact with dangerous machine should be avoided. The area covered was increased by nearly 25% (160m2 198m2 ) but the extra cost for renting a bigger place was considered an investment on safety. Production The production is close to straight-line production prototype but there are some differences. Also there is no storage between the stations and the handing is easy because of the resistance of the product (no special handling needed ex. Temperature or fragility ) Relationship Chart Figure 10. Relationship Chart As mentioned before, it is of big importance that 103 and 106 are close to the raw material storage and also near to the assembly station that is the next step of the production. In addition, the finished product storage must be close to the assembly area to minimize the transporting time. 1.Raw Material Storage 2.Lathe 106 3.Vertical 103 4.Assembly 104 5.Finished Product Storage Proximity needed Proximity is unimportant
  14. 14. Relationship diagram Figure 11. Relationship Diagram Space Relationship Diagram Figure 12. Space Relationship Diagram Layouts Figure 12. Layouts The second layout is both more space efficient and production assisting. Raw Material R.M Storage 106 103 Assembly 1 2 3 4 Storage 5 4mX8m 1. 5mX9m 2. 3X3 4. 5mX6m 3. 5mX5m 5.
  15. 15. 3.4 Task 4: Material Handling When my group and me thinking about material handling, we took some decisions. Everyone can use it easily because we do not have someone who responsible for material handling, operators do that. It is not necessary to be sensitive because we will not carry fragile parts. And of course we have to choose cheapest alternative. According these requirements, we decide to use push/pull hand control wheeled steel cars. We planned that in every station has cars. We locate two cars for every station. With that, operator take row material from car and when it is finished put part to another car. And also we have one extra car. When operator moves the parts to next station, he/she replace car‟s location with free car. With that production will not be stop. Also we want to show our material handling system with “Material Handling Systems Equation” Why? We have to transfer parts between stations. What? Row material, machined part and finished product Where? Between stations and storages When? When part‟s operation finish in that station How? With push/pull hand control wheeled steel cars Who? Operators Which? Hand control wheeled steel cars by operators Figure 13. Material Handling
  16. 16. 3.5 Task 5: Safety Stock and Economic Order Quantity EOQ is mainly describe the relationship between ordering cost, holding cost and the order quantity. Thanks to EOQ, total cost can be minimized by the optimum batch size. Formulations which are used in task 5 are presented below. Formulations The general Q* formulation is include A= ordering cost, h= holding cost, D= annual demand 𝐻𝑜𝑙𝑑𝑖𝑛𝑔 𝐶𝑜𝑠𝑡 = (𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡𝑠 + 𝑅𝑎𝑤 𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝐶𝑜𝑠𝑡𝑠)/2 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡 = 𝑀𝑎𝑐𝑕𝑖𝑛𝑒 𝐶𝑜𝑠𝑡 + 𝑂𝑝𝑒𝑟𝑎𝑡𝑜𝑟 𝑐𝑜𝑠𝑡 𝑂𝑟𝑑𝑒𝑟𝑖𝑛𝑔 𝐶𝑜𝑠𝑡 = 𝑆𝑒𝑡𝑢𝑝 𝑡𝑖𝑚𝑒 ∗ 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑚𝑎𝑐𝑕𝑖𝑛𝑒(𝑕𝑜𝑢𝑟) base holding cost operation cost raw material cost setup cost EOQ 1,89 3,16 0,612 0 0 brass 20 holding cost operation cost raw material cost setup cost EOQ 1,44 0,38 2,5 11,67 1080 brass 25 holding cost operation cost raw material cost setup cost EOQ 2,16 0,42 3,9 11,67 882 brass 30 holding cost operation cost raw material cost setup cost EOQ 3,75 0,50 7 11,67 669 brass 40 holding cost operation cost raw material cost setup cost EOQ 4,52 0,54 8,5 11,67 610 pegs holding cost operation cost raw material cost setup cost EOQ 0,51 0,38 0,63 11,67 1823 Figure 14: EOQ Calculation Calculation example of Brass 20 Operation Cost= (8/60)*(140/60)+(21/60)*0,2=0,38 8 second is operation time over 60 to find minute. 140 is cost of machine per hour. 21 is operator cost per hour and multiple with 0,2 which is operator requirement. Raw material Cost= (5/100)*50=2,5 5mm shows thickness of brass and 50 is raw material cost for per meter. Holding cost= (operation cost + raw material cost)/2 =1,44 Ordering cost= 5*(140/60)=11,67 5 shows setup time and 140 is operation time of machine.
  17. 17. 𝐸𝑂𝑄 = 2∗𝐴∗𝐷 𝑕 , 𝐸𝑂𝑄 = 2∗11,67∗72000 1,44 = 1080 Safety Stock In this project safety stock added to amount of production. Safety stock is prevent to company shortage stock by maintain the product. Stock out problem can affect the company more than keep stock. So safety stock which another name is buffer stock should be used to meet customer needs. Also sometimes forecasting doesn‟t find certain number of sales. In this situation safety stock help to meet customer needs too. In this project safety stock is choose as a 5% of 6000. Company have 300 units of finished product each month. This number can meet the fluctuation of forecast or demand. 3.6 Task 6: Demand and Forecast Company‟s last two years sales data presented in table 3. Thanks to these data next year forecast can be done. Simple moving average, weighted moving average, regression and exponential smoothing methods used for the 2011„s forecast. The 2009 and 2010 sales data represented below. 1 2 3 4 5 6 7 8 9 10 11 12 2009 5805 6061 5888 5944 5845 5822 5992 6079 5892 6141 5873 5892 2010 5810 5882 5771 6184 6167 6075 6046 6136 5967 6075 6024 5954 SMA is mainly unweighted mean of previous “n” data. In this part of report during the forecasting n is equal to 5. In table 9 data looks varying slowly so n used like 5. If between two months these sales amount were changing much, varying would be more and in this case n had to be smaller number. There is a calculation example of SMA for January of 2011. 𝑺𝑴𝑨 = 𝟔𝟏𝟑𝟔+𝟓𝟗𝟔𝟕+𝟔𝟎𝟕𝟓+𝟔𝟎𝟐𝟒+𝟓𝟗𝟓𝟒 𝟓 = 𝟔𝟎𝟑𝟏 Weighted moving average method is another method which is used for forecasting. This method is also mean of previous “n” data but the difference is that previous first data weighted more than others. In this chapter this weight like 0,4 for first previous month sales data, 0,3 for second , 0,2 for third and 0,1 for fourth data. It means that first previous month sales data affect the forecast more than others because of the weight coefficient. There is a calculation example for January of 2011. 𝑾𝑴𝑨 = 𝟎, 𝟒 ∗ 𝟓𝟗𝟓𝟒 + 𝟎, 𝟑 ∗ 𝟔𝟎𝟐𝟒 + 𝟎, 𝟐 ∗ 𝟔𝟎𝟕𝟓 + 𝟎, 𝟏 ∗ 𝟓𝟗𝟔𝟕 = 𝟔𝟎𝟎𝟏 Regression is method to find a relationship among the data to forecast. It is nonlinear regression and based on dependent parameter and independent variables. For the regression method Minitab 14 which is statistical calculation software is used. The model show that basic parameter is 5910 and each month increase the forecast by the 9,49* ( month) 𝑭 = 𝟓𝟗𝟏𝟎 + 𝟗, 𝟒𝟗 ∗ 𝒎𝒐𝒏𝒕𝒉 So forecast for January of 2011 is like: 𝑭 = 𝟓𝟗𝟏𝟎 + 𝟗, 𝟒𝟗 ∗ 𝟏 = 5919
  18. 18. Exponential smoothing method is last method for forecasting. This method mainly weighted by a number (α) between 0 and 1 to difference of past real and forecasting data. In this part α is equal to 0,3. Deciding α is mainly most important part of this method. When α is so big it means that previous data and its forecast so important for next month forecast. 0,3 is chosen and it is not big number to affect next forecast so much. There is a calculation for January 2011.6041 is showed that 2010/12 sales forecast and 5954 is same month real data. F2011/01= 6041+0,3*(5954-6041)= 6015 During forecasting all four method was used and then average of these methods results is the forecast for 2011. Average of these four method is more useful than per method because when average of them used it is decrease the error of forecast. There is average of January 2011 forecast table. Figure 15: Forecast for 2011 MPS Week 1 2 3 4 5 6 7 8 9 10 11 12 Forecast 5992 5984 5982 5978 5977 5979 5980 5982 5984 5987 5991 5996 Available 89 186 285 300 300 300 300 300 300 300 300 300 MPS 6081 6081 6081 5993 5977 5979 5980 5982 5984 6987 5991 5996 On Hand 0 Figure 16: MPS First three months machines work full capacity to meet customer needs and make a safety stock. Safety stock is chosen as 5% of 6000 which is general number for forecast. When reach the safety stock which is fourth month in this case, manufacturing is going on just as amount of forecast. 2011 SMA WMA Regression Exponential Average January 6031 6001 5919 6015 5992 February 6002 5995 5929 6008 5984 March 6006 5984 5938 6001 5982 April 5987 5982 5948 5995 5978 May 5978 5982 5957 5990 5977 June 5982 5979 5967 5986 5979 July 5980 5978 5976 5984 5980 August 5979 5979 5986 5983 5982 September 5979 5980 5995 5982 5984 October 5980 5982 6005 5983 5987 November 5982 5984 6014 5984 5991 December 5985 5988 6024 5986 5996
  19. 19. MRP As you can see there is kind of bill of material of Tower Hanoi. In this figure req show that abbreviation of requirement. January and Fabruary of 2011 has same production amount. So these two months has same MRP. 4. Results and conclusions In this paper we consider about almost all manufacturing phase. From facility layout to scheduling all parts activities calculated or discussed. Good planning enables to companies to use minimum resources while getting the maximum benefit. The aim of this paper is to show how planned manufacturing , facility layout, decided business strategy, selected material handling also how it can be planned better and how it can be supported by the relevant other theories. This is done by solving several tasks related to facilities planning and production management. In the first task, the business strategy is explained in detail. The selection of the machines is argued in the second task. There are three alternative to produce tower of Hanoi. Alternative one which is consist of vertical machining centre, assembly table, CNC Auto lathe with bar feed is chosen because of the costs. Also in second task all the manufacturing operation time presented. In the third task, two facility layout are suggested for the machines which are selected in the second task. Alternative 2 is selected because it has less space and better material handling function for CNC Auto lathe machine. In the fourth task, material handling system equation are identified and answered seven question and decided to material handling system. Lastly, in the fifth and sixth task calculated EOQ and decided safety stock with one year forecast. Tower of hanoi req=6081 Base Plate req=6081 set of pieces req=6081 Brass 20 req=6081 Brass 25 req=6081 Brass 30 req=6081 Brass 40 req=6081 Pegs req=18243 Figure 17: MRP
  20. 20. 5. References Inman, R. Anthony. "Forecasting." Encyclopedia of Management. Ed. Marilyn M. Helms. 5th ed. Detroit: Gale, 2006. 307-311. Gale Virtual Reference Library. Web. 5 Mar. 2013. Hill, Terry. 2005. Operations management. 2nd ed. Basingstoke: Palgrave Macmillan Tompkins, J.A., White, J.A. and Bozer, Y.A., 2010. Facilities Planning. 4th ed. Hoboken: John Wiley & Sons, Inc. Chien, T.K., 2004. An empirical study of facility layout using a modified SLP procedure. Journal of Manufacturing Technology Management, [e-journal] 15(6). Available through: The Emerald Research Register <> [Accessed 5 March 2013]
  21. 21. Appendix The game, Tower of Hanoi, consists of 1 ground plate made of aluminum, 3 play pegs made of aluminum and 4 play bricks made of brass. Figure 2 Drawings of Tower of Hanoi
  22. 22. Figure 3 Bill of Material Figure 4 Product structure Level 2 Level 1Tower of Hanoi PegsSet of PiecesBase Plate Piece 1 Piece 4Piece 3Piece 2 3 1 1 1 1 11 Level 3 Brass, SS-ISO 5170, Ø 40 x 6 Brass, SS-ISO 5170, Ø 25 x 6 Brass, SS-ISO 5170, Ø 20 x 6 Brass, SS-ISO 5170, Ø 30 x 6 Aluminium, EN6082-T6, 50 x 102 x 8 Aluminium, EN6082-T6, Ø 8 x 35
  23. 23. Table 1 Cost of material Material Standard length Cost Aluminum, EN6082-T6, 50x8 3 m 6 £/m Aluminum, EN6082-T6, Ø 8 2 m 1,8 £/m Brass SS-ISO 5170, Ø 40 4 m 170 £/m Brass SS-ISO 5170, Ø 30 4 m 140 £/m Brass SS-ISO 5170, Ø 25 6 m 78 £/m Brass SS-ISO 5170, Ø 20 6 m 50 £/m If bought cut in special length the lead-time is 2 weeks. Table 2 Production and quality rate of AI1001 Year Production Quality rate 2006 80 000 98,9% 2007 79 000 98,1% 2008 77 000 99,1% 2009 73 000 99% 2010 65 000 98,8% Table 3. During 2009 and 2010 WÄDUR had the following orders: Month Orders 2009 2010 Jan 5805 5810 Feb 6061 5882 March 5888 5771 April 5944 6184 May 5845 6167 June 5822 6075 July 5992 6046 Aug 6079 6136 Sept 5892 5967 Oct 6141 6075 Nov 5873 6024 Dec 5892 5954
  24. 24. Table 4. Average deviation from planned production time. Year AI1002 AI1003 AI1004 AI1005 AI1006 AI1007 AI 1001 2006 1,0 0,75 0,86 1,0 1,0 1,1 0,9 2007 1,3 0,75 1,0 1,1 1,0 1,1 0,9 2008 1,3 1,0 1,28 1,1 1,1 1,1 0,95 2009 1,0 1,2 0,71 1,0 1,1 1,2 0,95 2010 1,3 1,0 1,42 0,9 0,9 1,2 0,9 1 means according to plan, >1 it takes more time, <1 it requires less time Table 5 Average scrap rate Year AI1002 AI1003 AI1004 AI1005 AI1006 AI1007 AI 1001 2006 7,1 % 2,1 % 0,6 % 1,0 % 0,9 % 0,5 % 1,1 % 2007 6,0 % 1,2 % 0,7 % 1,1 % 0,9 % 0,5 % 1,9 % 2008 6,0 % 1,1 % 0,9 % 1,1 % 0,7 % 0,7 % 0,9 % 2009 3,7 % 1,4 % 0,5 % 1,0 % 0,8 % 0,7 % 1,0 % 2010 5,1 % 0,9 % 1,0 % 0,9 % 0,6 % 0,9 % 1,2 %