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20 Things To Know About Artificial Intelligence For Supply Chain Management

I think his article provides a good premise for a more focused article on the topic of as it applies to supply chain management.

    1. Any device that can perceive its environment and takes actions that maximize its chance of success at some goal is engaged in some form of (). is a loosely defined term that can refer to several technologies. But operations researchers will tell you there is a tendency to refer to algorithms, embedded in a technology, that are less mature as , whereas other branches of math and statistics that are mature tend not to be labeled as even though they fit the definition.
    2. In the supply chain realm, is where most of the activity has been focused. Adeel Najmi, chief product officer at Symphony RetailAI, has a definition of I like. “Learning occurs when a machine takes the output, observes the accuracy of the output, and updates its own model so that better outputs will occur. Any machine that does this is using . It does not matter if data science methods are used or not. It does not matter if neural networks or some other form of supervised or technique is being used. It’s important not to get bogged down on the specific technique. What matter is if the machine is itself capable of learning and improving with experience.”
    3. When you look at this way, is supply chain management is nothing new. Machine learning has been used to improve demand forecasting since the early 2000s. Demand planning applications rely on a series of algorithms to take historical shipment data and turn it into a forecast. One algorithm works better for promotions, another for end of life products and so forth. The machine looks at the forecast, compares it to actual shipments, and suggests when it may be time to move from one algorithm to a different one for a certain stock keeping unit or product family.
    4. Over time, many more data inputs have been introduced into the demand planning process, and many companies are doing far more forecasts. For example, instead of just doing a monthly forecast in the eastern half of the country, some companies are doing forecasts at the product/store level at daily, weekly, monthly and longer time frames. For a product being forecast daily at the store level, it may be that algorithms applied to the point of sale data stream have the most predictive power. Forecasting that same product at the warehouse level on a monthly basis, an algorithm applied to warehouse shipment history and warehouse ordering patterns has more predictive power. […]

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