Articles

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

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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.

The Role of Demand Forecasting Analysis (Case Study: Bio Farma for Papua Area)

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Bio Farma as the only vaccine manufacturer in Indonesia, divides its marketing area for vaccine distribution throughout Indonesia, represented by marketing representatives in 34 provinces in Indonesia. Segmentation based on geography for vaccine products is divided into five regions, one of the region is Papua. The forecast method used in Bio Farma for Papua Area is still manual. Marketers order vaccines from central Bio Farma and make forecasts if product stock is empty. If the product is empty, a buffer stock will be created. With the buffer stock system that has been implemented so far, there are often problems with excess product which causes the product to expire. From the data, the total loss due to overstock is Rp. 14,161,693 in 2022. If consumer demand falls short of expectations, it will definitely have an impact on the manufacturing of high inventory value, even leading to lost opportunities for sales. That is the fundamental issue with this research. The goal of this research is to identify the possible causes for the overstock and to identify a suitable solution for those issues.

There are five things consist of root causes the problem; (1) fully manual forecasting, (2) low sales forecast accuracy, (3) sales forecasting based on sales of the last one or two months, (4) lack of employee knowledge about sales forecasting, (5) significant gap between target and actual sales. The alternate strategy recommended is to provide a forecasting technique that is suitable for the company. Different forecasting techniques were selected to be compared in order to determine which may be used to improve forecasting accuracy. The forecasting techniques that are employed are the 3-month simple moving average, the 5-month simple moving average, the 3-month weighted moving average, the 5-month weighted moving average, and exponential smoothing. By using the MAD and MAPE measurement tools, exponential smoothing showed the most acceptable accuracy result.