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Forward-thinking companies understand that a reliance on analytics presents the only scalable approach to analyzing and gaining insights from deluge of big data. Much like a grandmaster in chess, they must become expert in looking at different patterns within their supply chain. A chess grandmaster employs a set of actionable strategies to win matches, which adjust real-time depending on the moves selected by their opponents. In a similar fashion companies must establish a set of protocol strategies which can be effectively deployed on a real-time basis as the pieces on our board change on a daily and weekly basis as a result of supply conditions, consumer decisions, available tradeoffs, or some relevant combination of factors.
The grandmaster does not make ad hoc decisions in the moment. Rather he or she deploys a move within a much larger context. Similarly, as companies seek to understand how their outcomes are related to all the actions and decisions of their trading partners and consumers, they must consider protocol strategies that relate to the entire end-to-end continuum. In previous posts I’ve called this capability “big visibility”.
An important point to remember is that relying on human intuition or manual analysis cannot support the kind of fact-based, fast, profitable decision making that is required by big visibility. Advanced insights of this type are driven by a combination of both human and machine interaction. Combining human insights with statistical/mathematical approaches yields better predictions than either is capable of producing on their own. A good example of this is weather forecasting where the amount of data and computer power brought to bear on the problem is huge, yet the human element still adds value (I’m borrowing this example from Nate Silver’s excellent book).
Reaching the holy grail of big visibility will not be easy. It requires a wide range of data from across the internal supply chain, the trading partner network, and from macroecomomic conditions. Current supply chain analytics tools are nowhere close to delivering these kinds of advanced analytics. They struggle with capturing, housing, and analyzing data, much less recognizing demand and supply patterns.
Even more discouraging, Gartner predicted that many of the analytics-based supply chain decision support tools will likely become obsolete due to their inability to deal with big data, conduct analysis within the required time cycle for the decision, and automate decision making.
So how can companies achieve big visibility? I’ll tell you how in future posts, but in the meantime, I suggest you read the new whitepaper, “Turning Big Data into Big Visibility”.
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