CONSTRUCTION MATERIAL PRICE PREDICTION USING MACHINE LEARNING FOR ECONOMIC ORDER QUANTITY MODEL OF RESOURCE REQUIREMENT PLANNING

Abstract

Various studies show that the major cause of delay and cost overrun in developing and underdeveloped countries is inflation or fluctuation of construction material prices. Construction materials price fluctuations do not have only a direct effect on project performance. It can cause a shortage of materials in projects; so the effect is multiplied in line with the idleness of equipment and manpower. Construction material cost is the major component of construction project costs, so optimization of procurement costs can decrease project costs. So, this study proposes a hybrid machine learning model for constructing material prediction and economical procurement of construction materials to assist the smooth flow of the construction process. To show how the proposed model helps in predicting construction material prices and decreasing project costs, the model was tested in Cement material in the Ethiopian construction industry. The cement price prediction models developed are Multi-Layer Perceptron (MLP) and Long Short Term Memory (LSTM) using Python programming language. From historical indicators data including Sep-2010 to May-2022, the Net Foreign Asset of Ethiopia is identified as the main indicator of cement price fluctuation. The MLP model has better performance than the LSTM model in cement price prediction. The three and seven-month lag prediction by the MLP model on the test set has a mean absolute percentage error of 16.01 and 13.67 % but LSTM has 14.9 and 24.8 % respectively. For Procurement optimization, the Deep Q Network model (DQN) was developed. Using MLP cement price prediction, actual price, and data from the project as input, the comparison of DQN with the mathematical EOQ model shows a cost reduction of 76.8%. The price prediction model is helpful toowners and contractors to know the cost of construction in advance and the procurement model helps contractors to avoid extra costs due to procurement and shortage costs by optimizing the procurement process considering price fluctuation, construction material requirement plan, warehouse size, holding, ordering and shortage costs