Data Analytics for Decision-Making in Evaluating the Top-Performing Product and Developing Sales Forecasting Model in an Oil Service Company

Abstract

This study addresses the strategic challenges faced by a company specialising in the manufacture of oil and gas equipment. Following organisational restructuring, which involved the dissolution of one business unit and the creation of another, the company is navigating complexities in product focus and manpower allocation within the Asia-Pacific region. The research problem centres on identifying the top-performing product, determining potential countries for establishing a support base facility based on sales performance, and developing a method for forecasting future sales. The research involved retrieving and pre-processing historical sales data, then performing a thorough descriptive and predictive analysis. The data was partitioned into training and testing sets to facilitate predictive analytics. Several predictive models were developed and tested, including neural networks, linear regression, gradient-boosted trees, random forests, and ARIMA methods. Tableau Public was utilised for descriptive analytics, whereas RapidMiner Studio was employed for predictive analytics. The study’s results, derived through both descriptive and predictive analytic methods, reveal critical insights. The Blowout Preventer (BOP) emerged as the top-performing product in the Asia-Pacific region. In terms of establishing support base facilities, Malaysia was identified as the ideal location for the BOP, while Indonesia was found suitable for the Manifold product group. Furthermore, the Random Forest model was determined to be the most effective for forecasting future sales. These findings provide strategic guidance for the company in product focus, regional expansion, and resource allocation, contributing significantly to the company’s decision-making process in a competitive industry.