Niels Van Hove, Principal Consultant at Crimson & Co ANZ, discusses Big data in Foresight Magazine.

Every so many years, a new supply chain terminology takes the front page and dominates the conversation in magazines and conferences. In the last decade or two we’ve seen JIT (Just in Time), TQM (Total Quality Management), 6 Sigma, S&OP (Sales & Operations Planning), Lean, Agile, Demand-Driven Supply Chains…to name just a few.

Often it is for good reasons that these concepts draw attention, as they have proven to provide value in certain companies or industries. What frequently happens next is that the marketing machines of commercial expert analysts, research institutes, consultancies, and IT vendors run overtime to capitalize on them. Usually the result is that the supply-chain concept is generalised as a solution for everything, accompanied by a simplified interpretation of the complexity involved in implementation. Risks and costs of implementation are hardly ever mentioned, only the benefits. On top of this, a sense of urgency and fear of being left behind is created, often pressuring companies to decide rapidly and adopt the new concept.

Big data has been one of the concepts to dominate the supply-chain scene for a while. There is no denying that we’re getting more connected, we consume more and more data, and we produce exponentially more data, but like any concept, big data is not a holy grail for every supply chain. One of the premises Shaun Snapp’s article examines in this issue of Foresight is that big data will be used to switch forecasting from the product and more towards the customer. This might be true in some cases, but in many supply-chain cases it will not.


Some industries are more supply driven rather than demand driven. We’ve recently seen the oil industry creating a glut in oil, whilst knowing that oil demand is hardly increasing. Similarly, in the mining industry, production often continues to pay off fixed asset costs, even when demand stagnates or drops. While these industries might use big data for exploration or other purposes, they are unlikely to use it for customer forecasts. Supply-driven industries focus less on the customer and so hardly require a customer-driven forecast. On top of this, in some industries, the ability to respond to customer demand is limited. In the agricultural industry, connected technology that produces masses of data is used for pest control. However, a tomato plant will produce tomatoes for nine months, regardless of customer demand. This limits the need for big data or customer based forecasting.

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