Demand Forecasting on Sales at Grocery Stores using a Data Science Approach
Keywords:
ARIMA, Data Science, Demand Forecasting, Exponential Smoothing, VUCAAbstract
This study tries to present the application of predictive analysis to forecast sales transaction data for the next month. The analysis results are presented in the form of graphs and tables to provide a more comprehensive picture of each product's sales patterns and trends. Based on the graph presented, it can be seen that some types of merchandise do not experience sales activity in certain months. This can affect forecasting results, as analytical models cannot capture unstable or consistent sales patterns for those products. Fluctuations and instability in sales data can cause difficulties for predictive models in producing accurate forecasts. From the observation results, the ARIMA and Exponential Smoothing models have shown relatively good accuracy with a MAPE value of 23.646% in rice merchandise. All merchandise shows quite good results, but the accuracy value reaches 58.509% in sugar merchandise. This is because sugar sales data shows higher fluctuations and is unstable, making it more difficult for predictive models to produce accurate forecasts. Economic conditions, consumer trends, and market competition can affect sugar sales patterns and cause difficulties in modelling.
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