Reduction of the Tardy Jobs in the Plastic Injection Molding Factory

Transcription

1 Reduction of the Tardy Jobs in the Plastic Injection Molding Factory Wimolphan Kongsomboon Abstract The purpose of this research to reduce the number of tardy jobs in the plastic injection molding factory by analyzing the causes of the problem. The problem of factory is caused by 1.Lacking of planning in purchasing raw materials and 2.Inappropriate production schedule management. In this research, the researcher forecasted the amount of the raw materials needed in the production by using Time-Series Forecasting method to solve the problem of raw material shortage during manufacturing process; and create computer program to help planning and production scheduling. The method of scheduling by Branch and Bound (B&B) in Heuristics was used to obtain an efficient production schedule and minimize the number of tardy jobs. After the computer program was used with the planning and scheduling in the research plant, the productivity increased significantly. The EDD (Earliest Due Date) rule was also used in the program. This rule chooses the priority of dispatching from earliest due date. The research findings can effectively reduce the number of the tardy jobs from % to 16.87% Keywords production planning and scheduling, Heuristics Method, forecasting I I. INTRODUCTION N Thailand, the industrial enterprise has grown rapidly which could cause to increase consumer competition in the industry. As a result, the business development is considered as the important aspect for consumer in order to adjust its organization to be more effectively as well as to satisfy consumer needs. Beside, the production planning and control are the most interesting business rule technique due to the mistakes might occur easily without well business planning and control, for instance, the delay of import owing to the production problem for a limited time which affecting on overtime work of employee and unstable time for delivery to the customers. The factory case is plastic injection molding factory in form of make to order, for example, some plastic part that made from car components and some part of electric appliances, technology instruments as well as basic consumer goods. Moreover, the production monthly will not be repeated many times. Also, there is loads of new order trades provided in the period of production which could cause the delay of Author is with the Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Phayathai Road, Patumwan, Bangkok (corresponding author to provide phone: ; fax: ; wimonpunky@hotmail.com). production planning, then also could not produce order in time with a monthly average of 39.14% which can be solved as to increase the productivity by providing overtime work of employee which affect to overtime pay with a monthly average of 28.70% due to the factory has not been setting the production lead time as well as has managed its raw materials appropriately. II. METHODOLOGY This research uses the prediction theory which is the theories of production planning and scheduling in order to solve the problem which can be clarified as following: 1.The theory of Time Series Hanke and Winchern [6] point out that the prediction method can be divided in many ways by observing of a periodic random variable (Time Series) which also can be shown as: 1.1 Moving Average Method the equation is shown as Ŷ t+1 = Then Ŷ t+1 is the mean of the previous data points, Y t is an observation variable at t, k is the amount of average data points. 1.2 Single Exponential Smoothing Method - the equation is shown as Ŷ t+1 = α Y t + (1-α) Ŷ t Then Ŷ t+1 is the mean of the previous data points, α is the alpha variable (0 < α< 1), Y t is a time observation variable at t, Ŷ t is the predicted value at t. 1.3 Winters Method the equation can be shown as Ŷ t+p = (L t + pt t ) S t-s+p L t = α +(1-α)( T t = β ( )T t-1 S t = γ Then Ŷ t+p is the mean of the previous data points, L t is the estimate of the level in period t+1 calculated at the end of period t, α is the alpha variable (0 < α< 1), Y t is a time observation variable at t, β is the estimated Latest Trend (0<β<1), T t is the estimate of the trend in period t, γ is the Estimated Latest Seasonal Ratio, S t is the estimate of seasonal index, p is the length of the seasonal cycle, s is the length of seasonal period. 196

2 1.4 Quadratic Trend Analysis the equation can be analysed as Ŷ = α + bx + c X 2 a = b = c = Then Ŷ is the variable of time to be studied, x is the dependent time variable, a is the estimate of time period variable at the beginning, b is the estimate of the time level calculated at the every period of time. According to the above analyze, Holt[7] claimed that the forecasting seasonals and trends by exponentially weighted moving averages can be the effective elastic method which is suitable for making a practical use in both weighted and unweighted in order to forecast the seasonals and trends. 2. The theory of Production Scheduling The machine that is used for production scheduling is Parallel Machines with Different Speed or Uniform Parallel Machine. This machine system consists of the machine which is worked as a parallel system but all machines are different in terms of fast mode of working. Then is set as basic time working system and is also set itself as a ratio of fast mode working of machine. Moreover, is machine which is used as the estimate of time periodic variable (these sorts of machine can be including and so, the is the time that the work of is used in machine which can be referred to. The theory of Production scheduling in terms of Brand and Bound The using of ineffective Lower Bound by cutting out the Node term can reduce the answer which is in Search Space term. However, the condition can be applied for the test of problem decision in term of production scheduling which it cannot find the value effectively in advance by using Linear Program (LP) Relaxation due to the change of buying order of particular product that using to inject in each machine which has the different production rate. Therefore, to find the value of Lower Bound by defining the initial value for the first variable as well as the other variables cannot be set in Partial Assignment. After that, the algorithm will slightly increase into the particular variable. To determine the value of Lower Bound can be shown as following: 1. Node Selection: To select the Node which has the smallest value of Lower Bound in each series of tree. 2. Variable Selection: To select the variable from the different production rate of injected machine. A 1 = 1,2,3,,A m, B 1 = 1,2,3,,B m, C 1 = 1,2,3,,C m 3. Bounding Function: To define the machine for the product that does not be set which will not be count as a value of Lower Bound. 4. Fathoming: To realize the Node as Feasible Node and the product has to be determined in the proper machine. III. Analysis and Discussion 1. The study and prediction of the quantity of using raw material. Due to the sample factory has no plan for buying raw materials and the quantity for using raw materials monthly is unknown, then the exact number of the quantity of raw materials in factory cannot be defined as well as cannot be mentioned that it will be satisfy the marketing needs in the industry or enough for the next production or not. Besides, the problem has been occurred which is the raw materials are not enough when the production has been planned. As a result, it could cause the problems in terms of the delay of delivery. The researcher claimed to analyse the information of the quantity of each raw materials which is tested by using Minitab Release 17 program in order to predict the using of proper quantity of raw materials. Moreover, the researcher also defined the prediction by using Time Series which including Moving Average method, Single Exponential Smoothing method, Winter s method and Quadratic Trend Analysis. Furthermore, the forecast of raw materials in case of plastic pill is consisted of five types including ABS, PC, POM, PP and PA as shown in Figure1. Then, the result is shown only the least error which is measured by the mistaken average (MAPE) as a major consideration in Figure5. So, when the proper forecast result of each materials are found, these will be used to forecast the quantity of raw materials in the next month period which could lead to produce the great production in quantity of raw materials. ABS Moving Average Plot for ABS Index Variable Actual Fits Forecasts 95.0% PI Moving Average Length 5 Accuracy Measures MAPE MAD MSD Figure 1: the sample of the forecast result and mistaken value 197

3 According to the forecast and the comparison result of using raw materials, the result in June, 2011 can be shown in the Table 1: Table 1: the forecast result of quantity of raw materials in June, The amount of time production the factory will manage the time period in order to pack its product to be ready to delivery in a day by adding a day with time production. The formula is + 1 (3) IMT = Injection Molding Time When IMT is the Injection Molding Time (The period of time to inject the plastic) which has unit in second/piece. NM = Number of mold NP = Number of product = the number of second time 1 a day 2. the program development of time production forecast Due to the sale department has not provided enough time production which could cause in need of more time in order to be ready for the date of delivery. So, there are program provided to define the time production in order to help the sale department effectively manage and assign to deliver directly its product to customers in the promise date. Additionally, it could reduce the problem in case of the delay of delivery in the due date to the customer which the study of the plastic injection is found interesting details as following: The program studied the period of time production to inject the plastic that is used to predict the proper time production in order to let the sale department negotiates with the customers as the sample shown in Figure2. 1. Set up time the factory determines the limit time which is 60 minutes but it might be minutes if the previous plan is not effective. 2. Injection Molding Time the period of time to inject the plastic in each one and it determines the abbreviation based on the factory IMT which has unit in second/piece. The formula is (1) When CT is the standard time for using to inject the plastic in 1 time with the second/piece unit. CN = cavity number CN is the cavity number (the number of product to be gotten in plastic injection in 1 time.) 3. Processing Time the period of time to be used in production with the unit of second in time. The formula is IMT NP (2) IMT = Injection Molding Time When IMT is the Injection Molding Time (the period of time to inject the plastic) which has second/piece unit. NP = Number of product NP is the number of product which defines the abbreviation as NP piece of unit. Figure2: the program shows the period of time production 3. the computer program for production scheduling Firstly, the buying order of customers have to be put into the program, then the program will calculate the value that is used to plan the production scheduling by using the Branch and Bound method which choosing the early major order as the first input. Next, when the information has been set, the program will calculate the period of time production in each group of injection machine by the highest production rate will be firstly put into the injection machine. Lastly, the program will present the production scheduling in terms of report and graph which providing step by step in order to plan the new production scheduling of the factory as shown as: Step 1: starting by getting the buying order from customers. Step 2: Planning in injection machine or production scheduling by using B&B method. Step 3: Launching production plan based on buying order from production scheduling. 198

4 Start ร บใบค าส งซ อ ปร มาณท ต องการ ก าหนดส ง เล อก Group ของ เคร องจ กร ตามขนาดของส นค า ค านวณSafety Factor ปร มาณการผล ต = ปร มาณท ต องการ+ (Safety Factor % x ของปร มาณการผล ต) ม จ ดเร ยงงานตามก าหนดส งจากส งก อนไปส งหล ง ถ างานม ก าหนดส งเท าก น ให เล อกจากความส าค ญของล กค า เล อกเคร องจ กรท ใช เวลาการท างานน อยส ด หากเคร องจ กรใช เวลาท างานเท าก นให เล อกเคร องใดเคร องหน ง ค านวณปร มาณท จะผล ตจร ง Processing time = (Cycle time x จ านวนช นงาน) Cav Figure 4: the sample of production scheduling by using Branch and Bound method After running the program, the result will be shown as the table of production scheduling, products, the number of injection machine, the amount of hours for injection, date of injection, the final date of injection and the amount of production which will be represent by deviding into 8 tables based on each types of machine to make it easy to understand. Besides, gantt chart also is presented in order to understand clearly about time production which the sample tables of production scheduling in July are shown in Figure 5-6. เวลาเสร จส นงาน = เวลาเร มต น + Set up time + processing time เวลาล าช า = เวลาเสร จส น - ก าหนดส ง ย งม งานท ย งไม ได มอบ หมายลงเคร องฉ ด ไม ม หย ดการค นหา Figure 5: the table of the group machine production END Figure 3: the steps of production scheduling Based on Figure 3, it is applied from Branch and Bound theory which has been used with a lot of customers and products. As a result, production scheduling can be present by using the program as a tool in order to calculate and show the best the result as the sample shown in Figure 4. Figure 6: Gantt Chart of group of machine 199

5 IV. Conclusion This study shows the comparison of the number of buying order that cannot be delivered on due date. The production scheduling can be divided into two methods which are the new production scheduling method and the original production scheduling of the factory which using the same amount of buying order, the number of delayed order in both the original and new version in June to August, In Conclusion, the program shows production scheduling that is used in sample factory which the use of program produces effectively better result than the original tool. Also, it can be summarized that the delayed buying order has been reduced from 39.93% to 16.87%. References Salongkarasiri, Chompon The production planning and control. 10 th ed. Bangkok: Technology Promotion Association (Thailand-Japan). Lalitapon, Phipop The production plan and control system. 9 th ed. Technology Promotion Association (Thailand-Japan). Yommapool, Nathawara The production scheduling of unrelated parallel machine in plastic packaging industry, King Mongkut's University of Technology. Page Jatupong, Suthilak The applied principle of ordered production scheduling for planning and controlling as a case study: Automotive component factory, King Mongkut's University of Technology of North Bangkok. Sornklin, Aphichart The production scheduling management of parallel plastic injection molding machine under the different production rate. King Mongkut's University of Technology. th Hanke,J.E.and Wichern,D.W. Business Forecasting.8 ed.new Jersy:Pearson Education, Inc., Holt.,C.C Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting. 20, pages Christos Koulamas, George J. Kyparisis, 2009, "Scheduling on uniform parallel machines to minimize maximum lateness", Department of Decision Sciences and Information Systems, College of Business Administration, pp