In 1956, considerable fluctuations in production, inventories and profit baffled managers in General Electric’s household appliance division. Despite supervisory efforts, the variations endured. Traditionally, managers blamed these types of fluctuations on external causes, like business cycles.
The 2018 holiday season is already upon us and as with years past, consumer shopping preferences have shifted in accordance with technology. Specifically, retailers should plan for: a mobile dominated shopper experience, the growing influence of social networks, and artificial intelligence-powered personalized product recommendations.
When it comes to demand forecasting, most companies have way too many forecasting mechanisms in play across their organization. Each segment of the business ends up siloed from the others, relying on its own data and analysis, which impacts both efficiency and effectiveness.
To create intelligent supply chains, ones that can adapt to changing customer demands and increase efficiency and profits, enterprises are increasingly leveraging artificial intelligence (AI) and machine learning (ML).
Most organizations do a poor job forecasting, with just one in five coming within 5% of forecasts. This statistic is staggering, and it implies that although many organizations understand the value of forecasting, the majority of them are doing it inaccurately.
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