Smoothing the Rough Places in Supply Chain Management

Recently Software Advice posted a guest blog on supply chain planning by Chad Smith and Carol Ptak  of the Demand DrivenSupply Chain Institute.  Their agenda is to improve the efficiency and agility of supply chains by changing the way manufacturers conceive of Material Requirements Planning.  They believe that conventional ERP software is too generic to account for the complexities of managing materials and scheduling for globally distributed manufacturing operations, and they’re looking for a shift in paradigm from "push and promote" to "position and pull," in which at any given moment a multi-layer planning procedure brings demand into full alignment with material requirements.

For Smith and Ptak, the current manufacturing environment is characterized by volatility and variability, and I’d like to add to their excellent discussion the point that  there is a long-established technique for mitigating the risks posed by these forces: Monte Carlo simulation.  Predictive modeling of the risks as they occur in the manufacturing-supply cycle would be a straightforward way to aggress volatility and variability. It would be roughly parallel to the risk analyses we’ve been applying to value chains for a number of years now.  And, as my customers working in Six Sigma, Design for Six Sigma, and other process improvement efforts have discovered, good tools for doing Monte Carlo in Excel are not hard to come by.  They’re now adopting Monte Carlo simulation on a much wider scale, and there’s no doubt that this technique could also smooth out the rough places in any demand-driven planning process.

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