Demand forecasting using time series analysis and economic order quantity model for inventory control: a case study of a construction company - Pang, Hui Er (2023)
Inventory management, is the process of ensuring the right amount supply is available in a company. It helps the company to maintain inventory level and fulfill the customers’ needs and wants. But unfortunately, there are still many construction companies fail to practice a systematic inventory management process in this fast-growing industrial era. Apart from that, they are also lack of proper forecasting techniques for predicting accurate demand. Therefore, the purpose of this study is to identified a suitable inventory management model by integrating the monthly order system, Economic Order Quantity (EOQ) and forecasting techniques. This study is conducted as a case study based on a construction company located in Singapore. Numerical data from the year 2014 to year 2017 for the raw materials is collected from the company’s inventory record. The raw materials are diesel, quarry dust, concrete dan industrial gas. All the data are analysed by Microsoft Excel add-in tool (Xrealstats), QM for Windows and Microsoft Excel. The data from 2014 until 2016 is used by six main forecasting techniques and three performance measure to predict the best forecasted data for 2017. After identifying the most accurate forecasted demand quantity, it is used in the monthly order system and EOQ to compute the minimum total inventory cost. Decisions tree analysis is used to compare minimum total inventory cost in identifying the suitable inventory management model. As a final result, after analysing the minimum total inventory cost, the best suitable forecasting technique and inventory model for all the raw materials is linear regression and EOQ respectively. The EOQ and forecasting techniques proposed in this research are potential to predict the budget for the raw materials efficiently. This will enable the management of the construction company to prevent any financial issues in raw material purchasing in the future