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.

Business Escalation Strategy Using Time Series Forecasting for Hotel X in Yogyakarta

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Yogyakarta is one of the cities attracting significant foreign and local tourist attention with its beautiful city, culture, education, and traditional cuisine which prospered the hospitality industry in the city. However, since the pandemic Covid-19 hit the global economy in 2020, Hotel X Yogyakarta was also affected. The total occupancy room per year fell ten times lower than usual during the pandemic which jeopardized the business stability. Starting from preliminary interviews and SWOT analysis, this research aims to figure out what strategy needs to be implemented in Hotel X Yogyakarta to escalate the business and stabilize the occupancy room by using a quantitative methodology from a marketing and business analytic perspective. All data provided in this research is based on the internal data and information from the hotel, systematically calculated with Time Series Forecasting Theory using ARMA and ARIMA to provide a comprehensive forecasting result for business escalation strategy that is proposed to be implemented in Hotel X Yogyakarta.