Original equipment manufacturers (OEMs) know all too well that any failure to support their customers during crucial periods of equipment breakdown can seriously damage reputation and destroy any potential for future sales. OEMs that sell industrial equipment to corporate customers appreciate this troublesome fact better than most. These businesses often face what is known as the long-tail effect, which describes having a large proportion of spare parts that are sold infrequently. Keeping this long tail under control is key to effective management in the spares distribution environment.
Challenges of the long tail
OEMs commit to ensuring uninterrupted service, which implies that these service firms are duty-bound to stock even items that fail infrequently. However, keeping large numbers of slow-moving stock keeping units (SKUs) increases cash tied up in inventory. Further, as new models are released, the number of spare-part SKUs also increases. Consequently, an increasingly large range of spares need to be stocked.
To resolve this issue, many businesses focus on lead-time reduction at all nodes in their service distribution networks. The assumption is that, if lead times are reduced, then the minimum inventory needed (also called the norm) will be reduced at various nodes. When this happens, many professionals hope they can stock an increased range of spares, leading to improved service.
At first glance, this assumption seems correct. Thus, many procurement and distribution experts work to reduce lead times or redesign their distribution networks in order to minimize transit times from one node to another. However, even if lead times are reduced, significant inventory reduction may be impossible.
Norm and lead time
The norm for an item stocked in a make-to-availability environment is determined by the peak demand within the replenishment lead time. Comparing demand within multiple lead time buckets in the past and then identifying the peak sales figure attained within any one of the buckets provides 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 over the past year would be compared. Thereafter, the maximum demand in a 15-day bucket is designated as the norm for the item.
Norm equals peak demand during a reliable replenishment period factored for variability and reliable replenishment time. Based on the formula, norm and replenishment lead time are directly related, as depicted by the straight-line graph in Figure 1.
But be warned: While this may be the case sometimes, it is not always so. For instance, Vector Consulting Group recently conducted a detailed study of both slow- and fast-moving parts at a large material handling equipment company in India. Figure 2 shows the annual trend of norm computation with increasing lead times for a slow-moving spare part. This part was sold only nine times in the last year, about one sale every 40 days on average.
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, changing in value only when moving from one interval of lead time to the next.
In the case of a fast mover that has sold daily for the past year, the graph of norm and lead time looks similar to Figure 1. For fast movers, any reduction in lead time results in a reduction in norm. Therefore, decreasing lead time can be the right action for firms selling fast-moving goods. However, an initiative to broadly reduce lead times of items irrespective of their demand profiles may not be as successful in reducing inventory levels.
So, what is the best approach for slow-moving parts? Consider an item with a simplified demand pattern, as depicted in Figure 3. Suppose the item has sold four times in the past month. Its minimum interval between two consecutive sales, as seen from the demand table, is five days. Let’s assume for simplicity’s sake that the demand pattern of the item is of a repetitive nature each month: The first sale is on 10th, the second is on the 15th, the third is on the 25th, and the fourth is on the 30th.
Considering Figure 4, if this item were to be kept as made-to-availability and its norm computed based on the past month’s sales data, the minimum achievable norm remains at seven units if the lead time is five days or less. Thereafter, it increases as a stepped function as seen earlier in Figure 2.
This pattern develops because the norm for any make-to-availability item is decided by comparing sales within multiple lead-time buckets in the past and adopting the peak sale as the norm. For slow-moving items, a typical lead-time bucket comprises multiple days of zero sales and a few days of actual sales. As lead-time increases, the bucket size considered for determining the norm increases. However, for slow-moving items, a rise in bucket size does not always result in a rise in consumption within the bucket because additional days in a bucket may not mean additional sales.
Figure 5 shows a slow-moving item during a demand duration of 15 days. Here, 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 seven units. Only on the 15th day does another sale event get added inside the bucket, increasing the sale within the bucket. For a slow mover, the sale within a lead-time bucket depends on the number of sale events that get covered within a bucket. The greater the interval between any two sales events, the larger the lead-time bucket size required.
According to the demand profile in Figure 4, five days is the minimum duration between any sale over a 30-day timeframe. Therefore, a lead-time bucket of five days or less can accommodate only one sale event or none (every sale event being a sale of seven units) throughout the month. For lead-time bucket sizes between one and five days, the peak consumption within the lead time remains equivalent to a single sale event, which is seven units. The minimum norm possible for this item therefore is seven units and is achieved when the replenishment lead time is equal to or less than the minimum duration between consecutive sales — five days in this case. This is defined as the minimum time between sales. Based on their minimum time between sales and the minimum norm values achievable, these parts can be designated as base norm items. The only way in which the norms for such items can be reduced further is through the reduction of their minimum time between sales below the current lead times. This could happen through demand-generation activities, for example.
Because demand profiles and replenishment lead times vary by locations in the distribution network, an item that is base norm at one location might not be base norm somewhere else. Therefore, service leaders must make sure the base norm item can be fulfilled, even if the stock is farther away at a different location, before the minimum time between sales is exceeded.
For instance, if a base norm item with minimum time between sales of 30 days is stocked at a dealer location, with transit lead times to the location equal to 10 days, then any further reduction in transit lead times to less than 10 days will not result in any norm reduction for the item at the dealer. Conversely, any increase in transit times from 10 days up to the limit of 30 days will not result in the item’s inventory increase at the dealer locations. Consequently, such an item can be supplied directly from the central warehouse, although it is farther away, provided the transit lead time for the item remains less than 30 days.
If the base norm items were replenished directly from aggregated points, then no stock for such items will be required in the intermediate nodes of the distribution network. This reduces 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 in order to improve overall customer service.
Fine-tuning base norm parts
In any distribution network, as items are stocked farther away from the central warehouse, they tend to become progressively slower moving. This deterioration in movement is more pronounced in OEM service organizations — particularly those that are affected by long tails. Such items are likely to become base norm.
When customers want their parts quickly, decision-makers might stock more base norm items at the point of sale. In such cases, companies tend to replenish all SKUs from the immediately preceding nodes (regional warehouses) in the distribution network. Such parts sit unsold for long periods of time before getting consumed and then get replenished quickly, resulting in poor inventory turns for those parts. Therefore, an item identified as base norm at any node in the distribution network should not be restocked immediately. Rather, it should be maintained at a more aggregate location.
Experts like to build sophisticated distribution networks with many intermediate nodes, but this often adds unnecessary complexity. Instead, OEM leaders should evaluate their stock and base norm items regularly, enabling a better inventory profile for the company and its customers. This simplicity drives the rigorous examination and re-examination of past assumptions.
Vivek Chopra is a consultant with Vector Consulting Group and has more than 13 years of experience helping businesses achieve supply chain excellence. He has served clients in the pharmaceutical, industrial products, consumer products, high-tech, paper, infrastructure and facility management industries. Chopra may be contacted at email@example.com.