Optimization of Inventory Ordering Decision in Retail Business using Exponential Smoothing Approach and Decision Support System

In the context of a challenging retail business, optimizing inventory ordering decisions is crucial to maintain product availability and avoid excessive storage costs. Decision Support System (DSS) approach with the application of exponential smoothing method has emerged as an effective solution to integrate data analysis and more precise decision making. This abstract discusses how exponential smoothing is used in optimizing inventory ordering decisions in retail businesses. We explain the concept of exponential smoothing as a forecasting technique that integrates historical data and future predictions. We also analyze the steps of implementing exponential smoothing in DSS, including smoothing parameters, initialization of initial levels, and forecast calculation. The benefits and challenges in the use of exponential smoothing are discussed in the context of inventory optimization and ordering decision making. The results show that exponential smoothing can provide forecasts that are more adaptive and responsive to changes in demand, with the potential to improve operational efficiency and customer satisfaction. Nonetheless, an understanding of the product characteristics and limitations of the method needs to be considered. This research illustrates how the use of exponential smoothing in DSS can provide valuable guidance for retailers in optimizing inventory and making inventory decisions.