INVENTORY MANAGEMENT AUTOMATION
69 each product can benefit from a different engine and can present the best forecast . ML is applied with a technique called time-series combination . For each item , all engines are run and combined . The ML process selects the best combination for the forecast time-series .
Most effective currently is in the automatic selection of ( combined ) statistical models . It is most effective because it stores the selected statistical model and the parameters of why this selection has been made . So many more statistical models can be analysed , and it improves the quality of the output forecast . And it does that every time the forecast is generated .”
INVENTORY MANAGEMENT AUTOMATION
“ The most repetitive tasks are the most applicable to RPA . Tasks such as demand prediction , inventory allocation , stock replenishment and even mark down calculations and assortment and financial plan seeding are easily completed with ML / AI-powered tools . These ML / AI use cases can be delivered with accuracy improvements of 2,000 basis
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