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Spares distribution- When the long tail wags the dog


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

But while this may be the case often, this is not always so! Let’s examine whether the reality conforms to our intuition. For this analysis we at Vector consulting group conducted a detailed study of both slow moving and fast moving parts of a large material handling Equipment Company in India.

Depicted below is a graph (Figure 2) showing the annual trend of Norm computation with increasing lead times for a slow moving spare part of this service firm.  This slow moving spare part has been sold only 9 times in the last one year, translating to a sale once every 40 days on an average (365/9).


Source – Research @ Vector Consulting Group

Figure 2: Norm vs Replenishment time – Real time data of Slow moving spare Part

This graph (Figure 2) is quite different from expected and indicates that the Norm, when computed for such slow moving parts is a stepped function of lead-times. In other words, the Norm remains constant within a certain interval of lead-time and changes in value only when moving from one interval of the lead-time to the next.

In the case of a fast-mover which has sold daily for the past one year, the graph of Norm vs. the lead-time for such an item looks like figure 3:


Source – Research @ Vector Consulting Group

This graph conforms exactly to the initial intuition about the relationship between Norm and lead-time. Therefore, for fast movers, any reduction in lead-time does result in a reduction in Norm and that this can be the right action to be taken for firms selling fast moving goods like soaps and chocolate. But an initiative to broadly reduce lead times of items in a spare parts scenario irrespective of its demand profile may not be as successful in reducing inventory levels!

Norm behavior in a Slow moving parts environment

It is clear that we need to delve deeper into the reasons for the Norm graph looking and behaving the way it does for environments with slow moving parts. For this let’s examine an item with a simplified demand pattern as depicted in the table 1. Suppose the item has sold 4 times in the past month, the first sale being on 10th, the second sale after an interval of 5 days (on the 15th), the third sale after an interval of another 10 days (on 25th) and the fourth sale after another 5 days, on the 30th of the month. The minimum interval between 2 consecutive sales, as seen from the demand table is 5 days. Let’s assume for simplicity’s sake that the demand pattern of the item is of repetitive nature, month on month.


The tabular representation of the Norm vs Lead Time graph is depicted in Table 2. Therefore if this item were to be kept under MTA (Made to Availability) and its norm computed based on past month’s sales data ,it can be seen that the minimum achievable norm remains at 7 units if the lead-time is 5 days or less and thereafter increases as a stepped function (Figure 4) as seen earlier.


This pattern is because norm for any item under MTA is decided by comparing sale within multiple lead-time buckets in the past and adopting the peak sale as norm. For slow moving items, a typical lead-time bucket comprises of multiple days of zero sales and a few days where sales would have happened. As the lead-time considered increases, the bucket size considered for determining the norm increases. However, for slow moving items an increase in bucket size does not always result in an increase in consumption within the bucket, since additional days in a bucket may have generated no additional sales.

An illustration is provided in figure 5 for a slow moving item during demand duration of 15 days. As can be seen, an increase in lead-time bucket size from 10 days to 14 days only adds multiple days of zero sales and does not result in an increase in total sales within the bucket, which remains fixed at 7 units. Only on the 15th day does another sale event get added inside the bucket and so the sale within the bucket increases.

Therefore it can be said that for a slow mover, the sale within a lead-time bucket depends on the number of sale events that get covered within a bucket. Greater is the interval between any two sales events, larger the lead-time bucket size required to count both of them.


According to the demand profile in Table 2, the minimum duration between any consecutive sale events in the entire duration of 30 days considered is 5 days. Therefore, a lead-time bucket of 5 days or less can accommodate only one sale event or none (every sale event being a sale of 7 units) throughout the month. For lead-time bucket sizes between 1 and 5 days, the peak consumption within the lead-time remains equivalent to a single sale event, which is 7 units. The minimum norm possible for this item is 7 units and is achieved when the replenishment lead time is equal to or less than the minimum duration between consecutive sales (5 days in this case). This minimum time between two consecutive sales of an item as seen historically can be defined as Minimum Time between Sales (MTBS).  And such items, which have reached the minimum norm values achievable, based on their demand profile (MTBS) and current lead-times, can be called Base Norm items. The only way in which the norms for such items can be reduced further is through reduction of their MTBS below the current lead times for example, by employing demand generation activities.


But since demand profiles (which determine the MTBS) and replenishment lead times of stocked items vary based on their location in the distribution network, an item which is a Base Norm item in one location of the distribution network need not be a Base Norm at another location. Therefore, a Base Norm item stocked at a given location in the distribution network should not be replenished from the immediately previous location/node, as long as the increased replenishment lead time due to it being replenished from a more aggregated point, lying farther away, does not exceed its Minimum Time Between Sales (MTBS) at the location.

For example:

  • If a Base Norm item with MTBS of 30 days is stocked at a dealer location, with transit lead times to the location being 10 days ..
  • then any further reduction in transit lead times to less than 10 days will notresult in any norm reduction for the item at the dealers.
  • Conversely, any increase in transit times from 10 days up to the upper limit of 30 days will not result in norm and inventory increase at the dealer locations for such item.
  • Therefore, such an item can be supplied directly from the central warehouse which is farther away, provided the transit lead time for the item from the CWH remains below 30 days.
  • If the Base Norm items were to be replenished directly from aggregated points, then no stock for such items will be required in the intermediate nodes in the distribution network, thereby reducing the overall inventory in the supply chain with no impact on serviceability.

The cash released through inventory reduction at the intermediate nodes can be used to add more spare part SKUs under MTA purview in the interest of customer serviceability.

How to treat Base Norm items

In any distribution network, as items are stocked farther away from the central warehouse, they tend to become progressively slower in movement from their stocking points.  For example, a part moving every day from central stocking location may move once every 10 days from the branches and once every 30 days from a dealer stocking point. This deterioration in movement is more pronounced in OEMs service organizations, particularly those that are affected by the long tail. Such items are likely to become Base Norm items for their locations.

In many cases, owing to extremely low customer tolerance times especially in spares environment, it may be required to hold a considerable number of such Base norm items at the POS (point-of-sale) in the dealer locations. In such cases, most firms, as a matter of long standing policy tend to replenish all SKUs from the immediately preceding nodes (like Regional Warehouses) in the distribution network. Such items lie unsold for long periods of time before getting consumed and get replenished in comparatively short time after consumption and so the inventory turns for such items are very poor. Therefore, ideally an item identified as Base Norm at any node in the distribution network should not be stocked as an MTA at the node. It should be maintained at a more aggregate location/node where it does not qualify as Base Norm- this aggregate node should be the location from where the movement of the item is faster than the lead-time to deliver the item to that node (in other words, the MTBS at the aggregate location should be lower than the replenishment lead-time to the node). As has been explained, replenishing such Base norm items from such aggregated locations, which are farther away will not result in any increase in norm for such items at the POS locations. On the contrary, this will help get rid of the unnecessary inventory held at the regional warehouse for such items.


As can be seen clearly, our inclination to build sophisticated distribution networks with large number of intermediate nodes often only adds to the complexity and takes away from our ability to provide superior service to our customers. Organizations have to evaluate their stock on an on-going basis, and any past decision to stock Base Norm items at multiple stocking points in the distribution network should be reevaluated and opportunities for removing the intermediate stocking nodes identified. The cash released thus can be used for stocking other slow moving SKUs that were hitherto not stocked.

Inherent simplicity is a hallmark of Theory of Constraints. Our belief in this simplicity drives us to rigorously examine and re-examine our assumptions that guided our past actions. As Eli Goldratt rightly said, ‘Even sky is not the limit’.

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