Optimal Production Inventory Policies for Operations: A Case Study of PVC Pipes Production

Abstract

This study focuses on solving the problem of overstocking and under stocking of production inventory in manufacturing sector. To ensure effective management of inventory in manufacturing sector, three years production data were gathered and properly analyzed using multiple linear regression analysis and time series forecasting methods. A multiple linear regression model was developed in MINITAB software to make prediction for inventory requirements. From the result, the coefficient of determination (R2) is 1.00, the adjusted R2 is 1.00, F-distribution is 4.212 x 107 which is greater than any value in F-distribution table, and all these show a very strong relationship between the dependent variable and the independent variables. Also, a Time series analysis was done to make forecast of monthly inventory requirements for both raw materials and finished products. Trend analysis and Moving Average method were used in Time series forecasting, and lower Mean Absolute Percent Error (MAPE) and Mean Absolute Deviation (MAD) were used as criteria for selecting the method that gives the best forecast. From the results obtained, Trend analysis gave MAPE 13% and MAD 2350, while Moving Average gave MAPE 14% and MAD 2574. This work adds to growing body of literatures on data driven inventory management by utilizing historical data in customized software for generation of models that can effectively make forecast of inventory requirements in manufacturing sector. Nomenclature: a = Value of yt at t = 0; b = Trend Value; MA= Moving Average; MAD = Mean Absolute Deviation MAPE =Mean Absolute Percentage Error; N = Number of periods; t = Period Yt = Forecast for period t y = Monthly Quantity of Product Produced α=regression constant β1-βk=Coefficients of the independent variables