Spares distribution- When the Long Tail wags the dog

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Hell hath no fury like a customer scorned! All OEMs know that any failure to support their customers during crucial periods of equipment breakdown can have an enormous impact on the OEM’s reputation and future sales. OEMs that sell industrial equipment to corporate customers possibly know this better than most but the service firms of these OEMs experience an enormous challenge called the “Long tail effect” due to low volumes of commissioned equipment in the market. For them, a large proportion of their spare parts sell infrequently and managing this long tail is key to managing the spares distribution environment.

The challenge is that:

  • A commitment to ensure uninterrupted service implies that any OEM service firm is duty-bound to stock even items that fail infrequently. On the other hand, stocking large numbers ofslow moving SKUs increases cash tied up in inventory.
  • With new models being launched with increasing frequency, the number of spare part SKUs that have to be serviced increases. Consequently, an increasingly large range of spares needs to be stocked.

To resolve this conflict, many organizations tend to focus on lead-time reduction at all nodes in the service distribution network. The assumption is that if lead times are reduced, the Norms (minimum inventory) to be held at various nodes for every SKU will reduce, enabling stocking of an increased range of spares for the same working capital leading to improved service.

At first glance, this assumption seems correct and so the procurement and distribution professionals of such service organizations are always trying to reduce lead-times or redesign the distribution network to minimize transit times from one node to another. However it has been seen that even if they succeed in reducing lead times, significant inventory reduction is not always achieved.

Norm and its relationship with lead-time

The Norm for an item stocked in a Make to Availability (MTA) environment is determined by the peak demand within the replenishment lead-time.  Comparing demand within multiple lead-time buckets in the past and identifying the peak sales figure attained within any one of the buckets gives us the Norm. For example, if reliable lead-time of an item is 15 days, then sales figures within successive lead-time buckets of 15 days in the past year are compared. Thereafter, the maximum demand in a 15-day bucket among them is designated as the Norm for the item. Therefore:

Norm= peak demand during a reliable replenishment period factored for variability x reliable replenishment time 

Based on the above formula, norm and replenishment lead-time are apparently directly related as depicted by this straight-line graph depicted in Figure 1:
Figure 1: Norm vs Lead time- The Expected Curve

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