Service Parts Planning 101 - Part 2

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Here is part two of my posting on service parts planning. Check out part one here. On Friday I started explaining how in regards to service parts, supply chain planning is different from planning for manufacturing. The first functional area I covered is Master Data. Here are the differences with Demand Management and Supply Management.

Demand management

For accurate demand determination of service parts, which are those that have independent demand, several techniques and data streams may come into play. A good system should be able to aggregate demands from various locations (multi-echelon) and should also provide drill downs from the top level. Some of the demand determination techniques are outlined below:

  1. Reliability Data/Failure rates: If the total population of install base product is known and the data on failure rate or the mean time between failure is known, it can be used to calculate a baseline service parts demand plan for the service organization.  When a New Product Introduction (NPI) happens in the absence of historical data to do statistical forecasting, the failure rates are used to make the base plan. But often this data needs to be monitored, as with time, there are several product revisions and engineering changes on high failure parts; leaving a mix of parts in the install base to serviced.
  2. Statistical Techniques: Several statistical techniques may be used to determine demand pattern of service parts. Forecasting algorithms like; Weighted/Moving average, Single/Double/Adaptive smoothing, Winters/Croston forecasting algorithms may be used. The intent is to minimize forecast tracking errors and a relevant method may be picked for it. Apart from forecast, these statistical methods may also be used for calculating repair BOMS.
  3. Service Level contracts: Typically service organizations have contracts to maintain service levels for different products with customers. A higher the service level, in most cases equates to a higher investment in inventory to support the service level (In the teaser, what happens if you want to have the bulb replacement available 90 percent of the time?).
  4. Product Life Cycle Curves: Provides indication on volume ramp up/ramp down over the period of time. They act as multipliers on calculated forecast to calculate the increasing or decreasing volumes.
  5. End of Life Planning: Very typical in electronics manufacturing where a supplier declares that he is doing last production run; The 60GB/5400rpm drive is getting obsolete- so supplier informs the service organization that they want to do a last production run (the service organization should have the tools to calculate the final demand) to figure out how many it should order to honor all the open service contracts.
  6. Understanding what is in the channel:  It is critical to understand what product has made it all the way through the channel and is installed at the end user.

Supply Management

The main focus area for managing supplies for operational effectiveness in service parts planning are:

  1. Managing Returns: A service organization receives defective parts or units. These are primary source of supply post repair/refurbishment. As soon as returns are received, it typically goes through triage to determine its proper disposition. fault found, defective within OEM warranty, defective out of OEM warranty, etc. Based on the triage results, returns should be available for future fulfillment with relevant rules –like defective but within OEM warranty needs to be sent back for credit, not to be stocked, etc. There may be lead-time associated with repairs which should be taken into account.
  2. Order Priority: Service organization should always try to minimize the new buy parts. The preference should always be given to using similar repaired part, or repaired parts which are valid alternates.  So when the planning engine runs, it should be able to generate supply recommendations accordingly. New buys should happen only when repaired supplies are not available.
  3. Obsolescence Management: To decrease the risk of obsolescence, when service organization buys a part, it wants to buy the part which is very flexible and may be used as an alternate on several BOMS even if it is slightly expensive. If a purchase is done of unique component, even if it is priced less, the risk of obsolescence may erode all the cost savings.  The system should be able to run analytics on cost savings vs risk of obsolescence for better purchasing decisions.
  4. Inventory Management: Since the service organization is multi-echelon, visibility into location level inventory is very important. Inventory could be at manufacturing site, supplier, depot, third party logistics company, a service contractor’s truck, an onsite storage locker, etc. and at each level it has its cost benefit equation. Having all that information available in the system and to do cost of service/benefits analytics, can be vital in decision making.  For example,  what is the benefit of keeping inventory with Fedex/UPS  vs a local warehouse, which and how many of those sku’s will provide good balance on investment/service.

Several electronic manufacturing service (EMS) providers have started to provide after sales services as a part of their end-to-end service offerings. For them to be successful, the top four things they should focus on are:

  • Accurate Forecasting – This leads to:
    • Improved service level
    • Improved fulfillment metrics
    • Reduced liability
    • Decreased costs due to order expediting
    • Reduced excess and obsolete (E&O) inventory at end-of-life (EOL)
  • Lower transaction fees:
    • Promotes competitiveness
    • Value proposition that is passed down to customer
  • Global process with visibility throughout the network:
    • Ease of process adherence and process monitoring company-wide
    • Ease of NPI
    • Supports the communication of NPI and EOL throughout the network
    • Overall inventory reduction
  • Ability to Share Data:
    • Ability to access the EMS data
    • Allows for purchasing power for commodity items, as well as other items (E.g.:  transportation)
    • Consistent data format on all data elements allows for easier communication exchange

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