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Machine learning algorithms can evaluate historical sales patterns and predict future trends in demand planning. The first step is collecting data about past sales, like product category, purchase frequency, quantities sold, seasonality, discount, and more. This essential data is then churned into an algorithm to develop models to identify sales patterns over time. Once these patterns are successfully identified, they can be utilized to create precise forecasts for future trends.
Machine learning algorithms use data to understand how trends and patterns work for the datasets. The algorithms will generate a practical model based on this relevant data. The more data injected into the datasets over time, the more precise the forecasting model becomes. Advanced deep AI and ML in the supply chain may require up to four times as much data as conventional forecasting methods.
Machine learning in supply chain forecasting is used along with traditional demand forecasting methods such as exponential smoothing, naïve forecasting, and moving average approximation to enhance accuracy and reliability when forecasting future trends. It can also provide effective pricing and inventory management insights by analyzing customer behavior across various locations and channels. By leveraging these valuable insights, organizations can make informed decisions regarding inventory movements or handling promotions and discounts.
