Predictive Analytics For A Modern Digital Supply Chain

Technology is rapidly changing how companies operate. Manufacturing, Commerce and many other industries of our economy require new skills and a new level of integration and automation to quickly react to demand changes as the market is becoming more and more dynamic and volatile.
This forces businesses to design a responsive and flexible supply chains, reduce the WIP (Work In Progress) and minimize their operational costs. But most important, a modern business must take data-driven decisions with real-time visibility into its processes.
All over the world, every day, a great amount of data is generated by companies, social networks, and information platforms and new emerging technologies are mature enough to convert data assets into valuable actionable insights. Data-driven models can support decisions and strategies, they can be used to optimize the production cycle, determine the minimal inventory of products to satisfy the forthcoming demand, the demand trends according to a specific geographical context, and much more.

Manufacturing companies need to address properly their Supply Chain strategies in order to optimize the resource, maximize the revenue and meet the demand requests coming from the market.
On the other hand, e-Commerce stores competitiveness is based on minimizing store costs and having short and predictable lead time in order to be always ready-to-sell. Stockouts events not only mean lost sales, but they frequently mean lost customers.
Furthermore, if an on-demand sales supply model is adopted and no storing is made, minimizing the delivery time from suppliers to customers becomes vital for e-Commerce stores.

Deciding the quantity of demand to be satisfied for a certain product is one of the hardest part of the decision process: traditionally based on human experience, this process will hardly scale on a large catalog of products, which eventually sells over a set of market regions and/or customers, without some kind of automation. Common approaches make it impractical and error prone due to the human judgment. Furthermore, manual forecasting will not likely take in account linear or non-linear trends over time, seasonality factors and even exogenous variables that impact on the forecasting quality (promotions, competition, holidays, weather, etc.).

New, smart, highly connected, automated, advanced Predictive Analytics technologies are the new era for Supply Chain operations: digital technologies make more efficient networked supply models come true. The modern digital supply networks are powered by Data Analytics and Machine Learning, which match the expectations
for deriving a higher level of business value and return on companies’ digital high-tech investments.

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