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Is your current Inventory Buffer Management system dynamic enough to reflect market reality?
Dynamic Buffer Management (DBM) is a widely used technique by TOC practitioners to ensure that product availability is protected against market demand fluctuations. Buffer size, trigger for buffer revision, frequency of buffer revision, and quantum of buffer change are the four important parameters which determine the effectiveness of DBM.
DBM practices were developed when access to market demand data was limited, computing power was moderate, and application of AI/ML was still in its infancy.
Now that we have access to faster, more frequent, and granular level demand data, computing power is higher, and AI/ML can be used effectively for short-term demand prediction, it is time to revisit our current practices of setting and revising the above four parameters to make DBM more effective.
I will delve deeper on each of these parameters in my coming posts, presenting an alternate point of view. Stay tuned…