Articles

Comparative Analysis of Demand Forecasting Methods to Optimize Supply Chain Efficiency in PharmaHealth Group

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PharmaHealth Group encounters significant challenges in its supply chain distribution due to the pharmaceutical industry’s demand for rapid responsiveness and the high risk of demand fluctuations, particularly during events like the COVID-19 pandemic. Additional complexities include the short shelf-life of pharmaceutical products and extensive quality control processes mandated by strict regulations. This study compares advanced demand forecasting methods to address these issues and optimize supply chain efficiency.

The research examines three forecasting techniques: Holt-Winters, ARIMA (Autoregressive Integrated Moving Average), and the hybrid ARHOW (ARIMA & Holt-Winters additive) model. The Holt-Winters method, effective for time series data with trends and seasonal patterns, improves supply chain management but has limitations in inventory forecasting. ARIMA, known for its simplicity and effectiveness in capturing trends and seasonality, faces challenges with non-linear data and the need for stationarity. The hybrid ARHOW model combines the strengths of both Holt-Winters and ARIMA, offering enhanced forecasting accuracy and efficiency.

By analyzing these methods, the study highlights the potential of hybrid approaches like ARHOW to address PharmaHealth Group’s unique supply chain challenges, leading to improved inventory management and overall supply chain performance.

Inventory Management with EOQ Model for Telecommunication Tower Accessories (Study Case at BMTec)

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BMTec is a telecommunication infrastructure manufacturer based in West Java, Indonesia. They produce tower accessories made of steel materials with various fabrication processes. The ineffectiveness of their current inventory management system has driven them to stockpile raw materials, resulting in reduced material quality in the form of corrosion and obsolescence. This brings up the question regarding how to improve the inventory management in the company, then drives the research to compare the effectiveness of BMTec’s current inventory management with the new-preferred inventory model. Before analyzing the mentioned issue, the primary data is gathered, accompanied by semi-structured interviews for the additional empirical data. An ABC classification is used to distinguish the essential items, which results in the telecommunication tower harmonica fence as the most crucial product in BMTec. Demand forecasting is applied based on the pattern of historical demand data. The Holt-Winters method was chosen due to the ability to adjust the seasonality and trend factor, although the forecasting inaccuracy reached 4.7 (MAD) and 78.4% (MAPE). By comparing the inventory costs over the current company’s method and EOQ model, analysis shows that with the EOQ model, BMTec could save their inventory cost 2022 up to 68%. The discussion in this paper begins with the background of the research’s underlying issues, followed by a literature review to update the scientific development for related studies. The third section conveys how the flow of this research is conducted, then discusses the findings or results from the analysis of collected data and chosen methods.