Studying Material Inventory Management for Sock Production Factory

Transcription

1 Studying Inventory Management for Sock Production Factory Pattanapong Ariyasit*, Nattaphon Supawatcharaphorn** Industrial Engineering Department, Faculty of Engineering, Sripatum University Abstract Studying Purposes of the inventory managed inventory in the raw materials warehouse of socks manufacturing and reduced the amount of wastes resulting from inventory management. That's the cost of production has led to revisions in the amount of wastes. From data collection of the system management show low quality of raw materials in manufacturing and inventory management does not suitable for production system. Methodology of this study from the technical products segment ABC of cord material. A selected group of raw materials from 3 items and the forecast demand for raw material in 1 period and pre-test data by Mean Square Error (MSE) analysis for the most economical size order (Economic Order Quantity) to the safety stock. Inventory Cost occur at the level of service 80% 85% 90% and the study found that the total cost incurred on the education system, inventory office inventory is lower than the original. From the test results show that in real systems. Keywords: warehouse Forecasting Economical Order quantity point Safety stock Deterministic 1. Introduction Important of inventory control management must take into account the business. Because of the inventory is a list of turnover asset as business to be in production or sales to be conducted smoothly. Inventory control, sufficient to amount to not much or too little will result in lower costs. The operational issue that is important to find the problems of stock control and inventory may have a product that is unnecessarily and cause expenses such as excessively high costs of storage, unnecessary products. The problem of education can provide raw materials production line machinery in textile socks (BRAVO) and plans to control the management of raw material inventory system 2. Methodology 2.1 The raw material inventory classification by ABC techniques. Yarn in inventories of raw materials from all 13 types, Inventory control of costs operated a minimum of inventory. Classification of materials with significant value as ABC groups of inventory turnover in the year. The share of inventory into 3 categories, Category A group is the value of inventory turnover in the year as the most valued, B group is medium value in the year and C group is value minimum in the year A group of inventory is approximately 5-10 percent of all items of inventory. However, a maximum value of approximately percent of the inventory value. Type B is about percent of the inventory list but the entire inventory is valued at approximately 15 percent of total inventory. Type C is the most amounts of inventory remains at approximately percent of all items of inventory. That estimated value as only 5-10 percent of the value of inventory. 2.2 Demand Forecasting The objective of the forecast to find the demand of raw materials after the data to the classification of raw materials inventory using ABC technique and select the appropriate forecasting technique by using MAPE values (Mean Absolute Percent Error) is a precise measure. The forecast for the lowest MAPE value is using time series analysis techniques to forecast model (winter s method) 1

2 2.3 Acidification of demand forecast After forecasting that fit each type of raw materials. Then checked the accuracy of forecast models by a plot fragments left value (Residual Errors) compared with time series. Residual Errors be left between the actual values (Actual) and the forecast (Forecast), hypothesis testing the adequacy of the forecast model. That are checked independent by plot of Residual Errors values from forecasting in ACF graph (Autocorrelation Function) and PACF (Partial Autocorrelation Function) and continue measure to forecast demand for raw materials. ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function) is used choose a value back. Both equations show relationship between the current values with the past value. Relationship measure the absolute value close to 1 as the current value that is dependent with significant historical value. Relationship may be in the same direction or change. If the value close to 0 indicates that is independent with different periods of little or no concern. 2.4 Deterministic of Inventory System The inventory model finds a size that is used for analysis in order to save each time. Economic order quantity is chose as a model of inventory under certain circumstances (Deterministic Demand). The presumptions that know needs occur in the entire time. Models adopted for the purchase of inventory at the economical. The order of the inventory increase is the same volume every time, the quantity is the economic cost saving unit is below the system so that better management of inventory must be about the most appropriate level of inventory. SS = Zσ L (2) Z = Standard Normal Distribution σ L = Demand at Lead Time 2.6 Calculating of point The new order (Reorder ) is responsible for providing information about the order that is the time to come out of command. may set a new level of new orders (Reorder ) is to set levels of inventory order should be issued so the level of new orders is dependent on the two variables are the rate used and the time period to prevent a shortage of inventory. ROP = SS+ (D L) (3) SS = Safety Stock D = Demand L = Lead Time Start Survey Objective Sample Forecasting (winter) MAPE test ACF, PACF No Q = (1) P = Ordering Cost D = Demand H = Holding Cost (Unit/Yr.) 2.5 Calculating of Safety Stock Uncertainties of used rates and lead time are important to make an inventory of storage volumes greater than demand average for the regular Part of the inventory increase is calculated the safety stock to prevent a shortage of inventory that may occur without hope before EOQ Kolmogorov-Sminov Compair Conclusion End Figure 2.1 Inventory s Flow Chart 2.2 Sample Preparation 2.1 Classification ABC techniques 2

3 The volume of material collected at each flow in the year to distinguish the importance of raw materials used by value criteria. Table 2.1: Demand usage of inventory Price Demand Value Usage CM32/ CA32/1 CM40/2 CM20/1 TC34/ Results and Discussions 3.1 Classification ABC techniques Classification of inventory 79.2% criterion in determining the specific types of raw material inventory has A groups as total of 7 show on the table. Table 3.1: Classification of A group % Usage Total Value Type CM32/1 CA32/1 CM40/2 CM20/1 TC34/ % A 3.2 Raw material demand Forecasting This study is to analyze and manage raw material inventory of future events. There is a need to analyze data from the volume of potential material in the past year which found that detailed information is normal distribution. Is under the hypothesis that a fixed rate. And allow goods to be wanting. The terms of the models used for the purchase of economic order quantity and then selected appropriate models for forecasting. MINITAB program use the cost analysis, using MAPE (Mean Absolute Percent Error) is a precise measure for the lowest MAPE values. Table 3.2: The terms of the models used and MAPE value testing MAPE Parameter Forecasting CM 32/ CA 32/ CM 40/ CM 20/ TC 34/1 4.5 α=0.9, =0.6, β=0.1 α=0.9, =0.9, β=0.1 α=0.9, =0.9, β=0.2 α=0.3, =0.6, β=0.1 α=0.6, =0.6, β=0.1 α=0.9, =0.9, β=0.1 α=0.6, =0.9, β= Validation demand forecasting After the forecasting appropriated for each material, Hypothesis testing the accuracy of forecast models. The distribution of the models forecast is independently when used to forecast demand for raw materials. Plot of values from left in the forecast ACF c hart (Autocorrelation Function) and PACF (Partial Autocorrelation Function) found that all of raw materials can be tested through hypothesis Because the value in the control limit. It can provide results to seem similar trends in the value of that portion left to the unusual. Sample ACF and PACF graphs of the raw material for thread type CM 60 / 2. Figure 3.1 ACF and PACF graphs of CM 60 / 2 3

4 3.4 Economic order quantity and total cost of inventory Inventory model with demand for its products know the exact value and demand is constant (kilograms per year) find the volume of economic orders quantity. Total inventory costs show summary table to have the raw material costs in the purchase and storage order economical order quantity. Table 3.3: The raw material costs in the purchase and storage order economical order quantity Demand Purchase Cost Holding EOQ CM 32/ CA 32/ CM 40/ CM 20/ TC 34/ point and safety stock Safety stock be checked the detailed requirements of raw materials; The forecast cost of the year analysis using the Kolmogorov Smirnov test to determine the requirements of raw materials are explained as normal all Items can be checked through Normality Test. It can be to calculate smart safety stock and the new ordering point, which are defined at different levels of service and lead time define 7 days. Chart shows the cost of raw materials thread way Kolmogorov - Smirnov Normality Testing. Figure 3.2 graph of Kolmogorov - Smirnov Normality Testing From figure 4.2 the 95% confidence H 0 is supposed to have normal distribution H1 is supposed to have other distribution Results from computing statistical evaluation by MINITAB found that the P-Value more than 0.05 indicates that the value does not fall in the critical area and accept H 0 rejected H 1 that is detailed normal distribution with average is and standard deviation is and distribution coefficient is Table 3.4: 95% services level of inventory 95% Safety Stock Old New CM32/ CA32/ CM40/ CM20/ TC 34/ % services level that cost of inventory equal 310, bath representing 37% of the capital stock at a store dropped Table 3.5: Inventory cost of 95% services level Cost CM32/1 15, CA32/ % CM40/ CM20/ TC 34/ Total Cost 310, Table 3.6: 90% services level of inventory Safety Stock Old New 90% CM32/ CA32/ CM40/

5 Safety Stock CM 0/ TC 34/ % services level that cost of inventory equal 345, bath representing 42% of the capital stock at a store Table 3.7: Inventory cost of 90% services level level Cost CM32/1 19, CA32/ % CM40/ CM 0/ TC 34/ Total Cost 345, Table 3.8: 85% services level of inventory 85% Safety Stock Old New CM32/ CA32/ CM40/ CM20/ TC 34/ % services level that cost of inventory equal 334, bath representing 44% of the capital stock at a store Table 3.9: Inventory cost of 85% services level Cost CM32/1 22, CA32/ % CM40/ CM20/ TC 34/ Total Cost 367, Table 3.10: 80% services level of inventory Safety Stock Old New 80% CM32/ CA32/ CM40/ CM20/ TC 34/ % services level that cost of inventory equal 386, bath representing 47% of the capital stock at a store Table 3.11: Inventory cost of 80% services level Cost CM32/1 25, CA32/ % CM40/ CM20/ TC 34/ Total Cost 386, Conclusion The study found that costs used the inventory system will be lower than when does not using the inventory management because of the raw materials used. Allows manufacturers purchase raw materials that frequency should be much a time for ordering Raw s should be stored and backed up so much volume will cost occurring low, that get lower production costs from manufacturing. 5. Acknowledgement Upon the completion of this study, I would like to express gratitude and sincere appreciation to all of factory and for their valuable suggestions and all data from foreman, throughout the completion of this paper. I am grateful to Sripatum University regards their fully support throughout financial support of this study. 5

6 Special thank are also extended to my dear family to supports, always powerful and encouragement during the studying. 6. References [1] Siriporn tankwiboonpanich Improvement of Raw Inventory Control: A Case Study of Coil Manufacturing Industry. Master Thesis, King Mongkut s University of technology of north Bangkok. [2] Apasiri Gerasrikulton Kamol Anyamaneetakul and member Raw Inventory Control in Rubber and Part Factory. Project, Srinakharinwirot University. [3] Piyaporn Keatikachonpan Inventory development of Pudsakorn group. Project, King Mongkut s Institute of technology Ladkrabang. [4] Sutee Tongloun Inventory Management to Reduce the Ordering Cost. Project, Kasembundit University. [5] Norapon Wongsurawat Inventory model for wholesale business: case study. Project, Khonkaen University. [6] Tananya Wasusee Forecasting and total production planning, Case study; katisod production. project, King Mongkut s Institute of technology Ladkrabang. 6