Key Success Factors for Service Parts Inventory Management
by Michael R. Blumberg, President & CEO, D.F. Blumberg Associates, Inc.
Normally, the largest investment – and the second largest operating cost (after establishing the service workforce) associated with Aftermarket Service involves the purchase and acquisition of parts and subunits and the labor costs associated with the management of the inventory, logistics pipeline, and depot repair operations, to ensure availability of parts when and where required.
Any action that can improve logistics support productivity will help to contain or reduce such sizeable expenses. The key actions involved with improvement of Logistics Support productivity begin with gaining control over the Logistics Support Pipeline. In essence, parts and materials used in an Aftermarket Service Operation should follow a unique, distinct, and closed path or "pipeline" as outlined in Figure 1.
In such a model, materials and parts are received from outside vendors, the manufacturing line (either external or internal), or depot refurbishing and repair operations. The materials are then stored in a central warehouse until they are shipped out to regional or local storage locations, and eventually placed into the hands of the service engineers at customer sites.
In examining this service logistics pipeline, it is important to recognize a number of critical parameters or factors affecting productivity. These include:
- Nearly 50% of the value of the inventory is normally found below the manned national and/or regional/local depots, usually either flowing to the service engineers (in the service engineer's trunk stocks) or flowing back to local/regional storage locations or repair depots. Service engineers may also maintain parts and subassemblies at local sites, in their lockers, or at their homes. Thus, it is absolutely essential to introduce mechanisms for direct control of the inventory at the field service engineer level.
- Approximately 80% of the value of the service pipeline is returned annually through the reverse logistics and depot repair operations. Pragmatic experience in the service industry indicates that a very significant portion of the total logistics pipeline flows through the return or reverse logistics loop to the depot refurbishment operations. This clearly indicates that it is much more efficient to make use of the return cycle (including optimized scheduling and sequencing of the depot itself) as the primary mechanism for refilling the pipeline, rather than to make use of external purchases.
- A portion of the returns to central or regional repair depots are actually good parts and units. Typically products are repaired through the process of pull and replace of modular components. If, after replacement, the whole unit is still down, a second component may then be "swapped" the replaced unit. In general, such an action would imply that the first board or subassembly pulled was actually good. Actual experience indicates that approximately 30%-40% of all returns to depots are actually good units (no trouble found).
A major step in improving logistics productivity is to formally establish computerized control over the entire logistics pipeline through direct control of the central and field manned inventories, via: a) the use of the RMA or service call closeout information to determine parts use (to better control the service engineer trunk stocks and returns), and b) the same-day delivery of parts to reduce "broken calls."
Ultimately, greater control of the Service Parts Inventory can be achieved through improved forecasting accuracy of parts flowing through the logistics pipeline. This can be achieved vis-à-vis:
- Stock control of key SKUs - Typically, a service organization controls between 20,000-40,000 Stock Keeping Units (SKUs), of which approximately 10% (i.e., 2,000-4,000) are active. In addition, the most critical SKUs used in day-to-day maintenance and repair actions are typically only 10% of the active items (in the range of 200-400). Thus, the primary focus of inventory control should be on the active and most critical items, and the transactions concerning the storage, movement, and receipt of the active SKUs (in terms of both the effective and defective stocks) should be maintained. This involves on-line updating of the inventory status at each location, including field engineer use.
- Use of advanced forecasting - Anticipating future inventory demands should also be performed, employing advanced forecasting methodology. This involves initially segmenting stock keeping units into various classes or groups. In general, our experience indicates that the cumulative stock demands will vary as a function of stage in the product life cycle. For example, the demand will be considerably higher and nonlinear (in terms of SKUs used) in support of a product in the phase-in state. Similarly, the demand would also be nonlinear and decreasing in the event of a product phase-out. Thus, the first step in improving the accuracy of forecasting is to segment the SKUs by the status of the products that they support (i.e., roll-in, roll-out, or normal operation), and then use a cumulative model, fit to a standard linear or logistics curve, expressing the general status of the product-to SKU-level demand.
- Same day delivery - As shown in Figure 2, reducing the time for parts delivery can substantially increase the return on investment for a given inventory: As delivery time is improved, the number of days of inventory required to achieve a given fill rate decreases rapidly.
- Bar coding and use of other methods for accurately identifying and improving accuracy of inventory counts - Typically, in many organizations, the transposition of SKU numbers, or misreading of these numbers, can lead to significant variation between the inventory levels, status as reported, and actual physical counts made. These types of errors can be significantly reduced by introducing a bar code on each SKU, and utilizing scanning devices at the manned stock levels, depots, and in the field. In this way, actual SKU information and data on received and shipped parts and assembles (as well as "good versus failed" units on hand) can be reported. The newer PDAs that are issued to many service engineers can be very useful in support of this task.
- Improved "just-in-time" scheduling and control of depot repair operations - one of the most efficient mechanisms for refilling the logistics pipeline is to significantly speed up the schedule and assignment of work within the repair and refurbish side of reverse logistics. Typically, as identified in Figure 3A returned stocks are selected by individual depot "bench" technicians who move the stock to their own local benches, and carry out the appropriate diagnostics and tests to complete the repairs. This material is then either immediately sent out to the field, or it goes through a limited quality assurance process (sometimes by the same bench tech). The problem with this approach is that advanced technologies for diagnostics and initial quality assurance testing are not employed. Much more important is the fact that in this approach the depot works on an inefficient "job shop" basis, with no prioritization or scheduling of workflow.
Figure 3A & B
A more efficient approach, as identified in figure 3B, is to operate the depot on a just-in-time basis. This would first involve doing a quality assurance check upon the receipt of parts, to identify and return to the field those units that are in good condition or that have no trouble found (NTF). Defective units (after passing through the quality assurance test) would then be put into the "work-in-process" inventory. Then, as parts are demanded by the logistics pipeline, the depot would pull the appropriate SKU from the work-in-process inventory and pass it through a preliminary diagnostic screen, utilizing artificial intelligence mechanisms. This diagnostic screen would then establish the specific workstations through which the SKU must proceed in order to achieve a total fix. Conveyor systems can then be used to move the parts to the individual workstations or bench technicians. Under this mechanism for sequencing assignment and scheduling of parts retrofit and repair (on a just-in-time basis), the individual workbenches are staffed by the service technicians skilled in a particular type of repair. Upon completion of the repair process, the unit is sent through quality assurance and placed back in the operation pipeline. Thus, the efficiency of the entire logistics pipeline can be improved by shifting the depot rework and repair operations from a job shop approach to just-in-time, optimally scheduled, controlled operation that utilizes basic production principles and centralizing the operation.
In summary, experience suggests that the productivity of a service organization could be improved by 10%-25%, or more, by utilizing a combination of the new technology improvements and practices we've described. In addition to the basic improvements in productivity and efficiency of existing operations, there is also a general mechanism for achieving productivity and efficiency improvement on a strategic basis. This involves the utilization of advanced strategic logistics service planning models, in order to examine the optimum tradeoff between service response to a customer (i.e., service quality) and service cost (Figure 4).
Such tradeoff analysis can be completed through the use of a simulation or "closed form" model designed to explore the tradeoffs between the manpower and logistics support levels, the customer's willingness to pay for given levels of service, service efficiency, and cost. A computational strategic planning model can be extremely useful in establishing the optimum, strategic allocation of service staff and logistics support to meet a given hardware-based customer service requirement. Thus, the keys to service productivity and quality improvement lie in the establishment of a formal, optimized business plan and model, that define: a) the role and importance of service, and b) the scope of products supported (i.e, focus on the service on one's own product or technology only). It is important to recognize that, given the array of standard and best-in-class enterprise and point solution software, systems integration becomes a vitally important issue in successfully implementing and utilizing a Service Logistics management system.
Michael R. Blumberg is a Certified Management Consultant (CMC) and President & CEO of D.F. Blumberg Associates, Inc. His firm focuses on providing strategic and tactical assistance to client organization for improving the overall profitability and quality of aftermarket service operations. Mr. Blumberg has established himself as an expert and industry authority on Reverse Logistics and Closed Loop Supply Chain Management. Mr. Blumberg also serves as a member of the Board of Advisors to the Reverse Logistics Association.