Technology and the Service Supply Chain

Technology is opening up new opportunities for companies to set and execute their service supply chain strategies.

By Ken Ruggles -- Supply Chain Management Review, 10/1/2005

Competition continues to drive down margins on new product sales. For some industries, jet engines being just one example, achieving break even on new programs can take years because of the heavy investment in research and development and declining margins on new sales. Only lucrative replacement part sales allow suppliers to recoup costs and achieve an acceptable margin over the product's lifetime.

At the same time that companies are looking to service to bolster the bottom line, customers are demanding changes in how they purchase service. Rather than accepting financial responsibility for "break-fix" activities, customers are seeking guaranteed response times and equipment up-time as metrics from their suppliers. Customers are driving these requirements into their service level agreements (SLAs). As new goals like time and asset availability are introduced, more responsibility shifts to the service provider to predict service needs and carry the right mix of inventory to ensure compliance to the SLAs. These suppliers can capitalize on additional revenue opportunities through offering differentiated service contracts that give customers a choice of performance and price

While service accounts for 27 percent of corporate revenue, it can contribute up to 50 percent of overall profitability. This profit opportunity is driving investment in the service supply chain. Traditional supply chain planning tools fail to address service-specific trade-offs in inventory vs. service levels. Technology investments allow for the prediction and modeling of service demand across thousands of locations. Based on this demand, optimization can trade off service levels, inventory investment, and profitability to simultaneously improve service and reduce costs.

In the past, technology limitations caused companies to adopt a one-size-fits-all approach to service. Where customer service was a priority, each new contract meant customer-dedicated safety stock, which lead to overstock conditions. When cost was a priority, under-delivery was a problem as all customers line up in a first-come, first-served basis regardless of priority. Those companies that delivered high service typically did so at the cost of high inventory and exposure to product obsolescence.

With increased bundling of the core product with aftermarket service, the same pressures that have impacted margins on new product sales are now being felt on the service side as well. Providing profitable service will rely on better intelligence around service demand, a flexible supply network, and incorporation of failure data back into product improvements to improve reliability.

Forecasting service requirements can be difficult. Low and intermittent demand is common, equipment can move, and usage impacts service levels required. True service demand is difficult to track in situations where channel partners service the end customer. Support for new products is difficult when support needs and product quality are not yet known. End-of-life support typically requires predicting demand far into the future as parts go out of production even though support needs remain.

Providing differentiated service requires a view of demand at the customer or customer segment level. Today many companies only view demand by product and by shipping location. Extending demand data below part and location level allows companies to set and execute against customer-specific service level agreements. The inventory investment requirements to support service are then directly aligned to specific customer agreements. When new business is contracted, requirements are modeled by duplicating demand from similar customers, giving a valid estimate of the cost to serve the new business.

Collaboration tools facilitate the sharing of demand data and inventory positions across channel partners. Views into end user demand reduces the bullwhip effect on inventory that is prevalent among extended service supply chains. The ability to share demand data will enable concepts like vendor managed inventory (VMI) that can counter the bullwhip effect.

Forecast accuracy can suffer when general supply chain forecasting algorithms are applied to service parts. When utilizing history-based demand data, service-specific algorithms that address low-volume, intermittent demand and seasonality will improve forecast accuracy.

For long lifecycle products, tracking the install base is key to predicting repair needs. The equipment each customer has, the configuration of that product, the service demand, and contracted service level all factor into the decisions around how that customer is serviced within the overall network. To effectively handle this complexity, a customer management system must be in place to track the install base and relevant attributes around usage.

Classifying your service demand by fast movers and slow movers alone is no longer adequate for setting service level targets. The rapid introduction of new products creates a multiplier effect on the number of parts in service. One industrial equipment manufacturer experienced more than a 200 percent increase in the number of parts it serviced over a 25-year time period. The number of fast-moving parts remained relatively stable over that time frame; growth was fueled predominantly by extremely low-demand parts. To manage these low-demand parts, other attributes are tracked to aid in stocking and replenishment decisions. These attributes include information on cost, profit, size, commodity code, criticality, and product maturity. In turn, these attributes can be used as a constraint or objective when assessing the optimal mix to meet demand.

Using these product characteristics along with customer-specific SLAs, you can arrive at value-based decisions for servicing customers. Such decisions would be based, for example, on the cost to meet contractual agreements or the optimal mix to carry for a space-constrained location.

Scale is another significant service issue. The number of products, customers, and service locations all serve to make the service supply chain more complicated than the OEM supply chain. To manage this large scale, most service supply chains were split into manageable pieces. Yet this fragmentation created islands of inventory while at the same time hampering service delivery for lack of visibility across the network. The fragmentation also often resulted in separate legacy operating systems across the multiple distribution tiers (master distribution centers, regional, and forward stocking locations).

Consolidation of service networks comes in several forms. One obvious action is to address the system's fragmentation. Another is to consolidate facilities. One telecommunications company, for example, was able to eliminate more than 150 stocking locations by consolidating tiers of distribution and reallocating inventory considering the entire network.

Keep in mind, however, that consolidating across regional or business unit boundaries can run counter to local incentive programs. Inventory pooling decisions can impact local profit and loss statements. Therefore, incentives need to align with the broader business goals with regard to service. Collaboration with channel partners represents an exciting opportunity to better service the end customer. Recently, automotive OEMs and the independent dealer base have been sharing inventory data. Dealers can search other dealer inventory and the OEM's can utilize dealer inventory to fill critical shortages. The end result is better inventory turns for dealers, improved customer satisfaction, and fewer product returns to the OEM.

Service supply chains are utilizing replenishment flows similar to those seen in product supply chains. Faster-moving parts are handled in cross dock facilities. High-cost, long-leadtime items are shipped directly from the supplier. Pooling and postponement strategies are used to lower inventory positions for slow-moving, hard-to-forecast items. And finally, VMI relationships are structured to defer ownership until consumption.

These varied replenishment policies were not available previously when all parts followed the same path through the distribution network. Optimization tools are effective for making decisions around pooling and locating inventory efficiently across the network. Supply chain network tools assist in modeling different replenishment flows based on product characteristics.

When tuning up your service supply chain, look to leverage core capabilities learned from the product supply chain while respecting the unique requirements of service. Fundamental efforts around consolidating the service network provide the necessary framework for providing consistent service and optimizing inventory decisions. With that framework in place, focus on understanding demand and aligning supply through alternative replenishment strategies. By taking these steps companies can continue to expand service revenues while maintaining favorable margins.