I am the Vice President of Supply Chain Services at ARC Advisory Group, a leading industry analyst and technology consulting company. I engage in quantitative and qualitative research on supply chain management technologies, best practices, and emerging trends.
One-number plans are bad plans. A one-number forecast, for example, looks across the range of demand that might emerge in a period and decides to create a forecast that is mid-way between the most and the least demand that is apt to emerge. A probabilistic approach to planning would be better, but our supply chain tools have not supported this approach well.
This is similar to the problems companies face in supply chain planning, where they have to deal with variable lead times, inventory levels, demand, and information lags. Trump Ally Warned Ex-President About Keeping Classified Docs—Over A Year Before Indictment, FBI Filing Says What an individual plans to do when they arrive at their destination is another factor. Do they need to get right to a conference, or is the schedule looser? Further, how many flights are there per day? Would it be possible to get another flight in two hours if they miss the first?
John Galt is using a Markov Decision Process to quantify the value of supply chain processes and power a more comprehensive model of real-world supply chain probabilities in their Atlas Planning Platform. For example, in a trip to the airport, there is a chain of events that determines whether a person will arrive on time. Each event is a “state.” I slept in late, or I didn’t. The Uber arrived on time, or it didn’t. There is a probability attached to each state.
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