Data Mining and Statistical Analysis for Available Budget Allocation Pre-procurement of Manufacturing Equipment
O. O. Ojo *
Department of Mechanical Engineering, Adeleke University, P.M.B. 250, Ede, Osun State, Nigeria.
B. O. Akinnuli
Department of Industrial and Production Engineering, Federal University of Technology, P.M.B. 704, Akure, Ondo State, Nigeria.
P. K. Farayibi
Department of Industrial and Production Engineering, Federal University of Technology, P.M.B. 704, Akure, Ondo State, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
In a situation where a decision maker faces problems of allotting the available budget on the strategic decisions in a manufacturing industry, data information plays an important role to maintain long run profit in the industry. Statistical analysis was incorporated to determine the correlational strength between the number of years and each of the strategic decisions, their confidence level, and the predicted values. This study identified the strategic areas of addressing the issues which are machine (), accessory (), spare part () and miscellaneous (), exploring the hidden data of the selected strategic decisions from International Brewery Plc, Ilesha and statistical analysis between the number of years and each of the selected strategic decisions. The model used in this work is simple linear regression while Statistical Analysis Software “SAS” was used for its applications. After exploring the hidden data from a case study, the suggested cost of procurement for machines, accessories, spare-parts and miscellaneous are: ₦119,975,000.00; ₦127,968,000.00; ₦134,965,000.00 and ₦33,491,500.00 respectively. From appendix, the probability of each of the strategic decision is less than 0.05 which implies that the Null-Hypothesis is rejected. The number of years has significant effect on Machines, Accessories, Spare-parts and Miscellaneous. As the number of years increases, the cost of procurement of the strategic decisions increases due to high rate of demand and consumption of their products. However, the cost of procurement may fall depending on the level of demand and maintenance culture. Besides, management of the company may ask decision maker to maintain the cost before procurement. This result may be used for further research on optimization of the available budget for equipment procurement.
Keywords: Data mining, statistical analysis, pre-procurement, budget allocation, manufacturing equipment.