Service Supply Chain Strategies to Increase Corporate Profitability

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The last decade has witnessed a substantial shift in emphasis on the part of many OEM manufacturers, from a focus on the products they produce to a concentration on their customers and the value that their customers derive from ownership and use of these products after the initial product sale. The importance of service is made clear in a recent AMR survey1 of manufacturing companies which revealed that service represents 24 percent of their revenue and 45 percent of their profit contribution. With only 20 percent of IT spend allocated to service, there is indication of value in increasing corporate attention to the service area.

With an increasing awareness of the strategic value of service, companies are beginning to focus on their service supply chains, which can be defined as the network of resources that includes the appropriate service parts, customer engineers, and infrastructure for material movement and storage, repair, transportation, information systems, and communication.

This shift toward a service-centric strategy represents an important aspect of firms' efforts toward enhancing overall revenue and profitability, customer acquisition and retention, and competitive differentiation.

In this paper, we describe the unique challenges of the service supply chain, and a framework for understanding the service management decision hierarchy. Most importantly, we highlight the dramatic value proposition available to companies that deploy advanced service strategies and decision-support tools to address these challenges. Brief case studies from leading service organizations Cisco and KLA-Tencor describe examples of successful deployments of service supply chain strategies that leverage this approach.

[1] J. Bijesse, M. McCluskey and L. Sodano, "Service Lifecycle Management (Part 1): The Approaches and Technologies to Build Sustainable Competitive Advantage for Service," AMR Research Report, August, 2002.

Introduction: Service Supply Chain Challenges

The mechanisms required to design, produce, and deliver service products in a cost-effective and competitive manner are quite different than those used to manufacture goods and to procure direct materials. Significant assets must be dedicated toward service delivery. The task of effectively deploying these assets across a wide network of locations and fulfilling demand which is driven by infrequent service events is a daunting one. Figure 1 shows a representation of a typical multi-echelon service supply chain network, and the resulting material flows required to supply material and to fulfill demand.

As a specific example of a service supply chain, consider Cisco Systems Global Product Services, which manages a complex supply chain consisting of the following elements:

  • Over 10 million service contracts defined in terms of specific customer performance targets (i.e., high priority with 2—4 hour response time guarantee, 8—12 hour response time and next business day response time), with thousands of service contract transactions per day.
  • 3—5 echelons consisting of nearly 750 stocking locations (including a central depot, regional warehouses, local warehouses, and forward locations positioned at or near major customer sites) required to position inventory close to the customer to support rapid response.
  • Hundreds of supported products that are mission critical to customers (e.g., net servers, communication systems), with more than 100,000 part numbers supported throughout the service supply chain. Most of these parts have very infrequent demand, with global demand rates of fewer than ten hits a year not uncommon.

Figure 1. Multi-Echelon Service Supply Chain Material Flows

While not all manufacturers face this level of complexity, it is not surprising that performance metrics are vastly different from the production supply chain, as indicated by a recent Wharton benchmark study that showed that inventory turns of one to two are common for providers of same-day service agreements2, even for manufacturers whose production supply chains show turns of fifty to one hundred.

[2] Morris Cohen and Vipul Agrawal, "After-Sales Service Supply Chains: A Benchmark Update of the North American Computer Industry," Fishman-Davidson Center for Service and Operations Management, The Wharton School of the University of Pennsylvania (August 1999).

Risk-Management Framework for Decision Making

Given the complexity of the service management problem, it is appropriate to decompose it into a collection of interrelated decision problems. Figure 2 illustrates the levels of managerial decision making that we have observed in many service supply chain environments. Each of the following components corresponds to a different period of the planning horizon, over which managerial trade-offs and objectives must be considered as the relevant decisions are made.

Budget Planning is in the longest decision timeframe, with a planning horizon typically measured in months or years, where decisions that determine specification of the overall service strategy are made. Such decisions can include design of the products being supported, the design of the "service products" that are offered to customers in the after-sales market, and the design of the infrastructure used to deliver these service products.

Strategy Planning decisions are made in shorter timeframes, typically weeks and months. At this level, management is concerned with the forecasting and strategic positioning of its material and human resources in anticipation of the need to meet customer service demands in a manner consistent with the response, and cost entitlements as set out in the warranty and service agreements. These strategic resource deployment decisions give rise to a challenging optimization problem that must be solved periodically if the service strategy is to be implemented in a cost-effective manner.

Tactics Planning decisions are made at a nearer-in planning horizon (weeks, days, or hours), and include the redeployment decisions that are associated with repositioning resources within relevant lead times to meet the service objectives and resource levels defined in the strategic plan. This includes generation of orders for service parts allocation (from a central to field location in the network), replenishment (from the network to external sources of supply for repair and new buy), and transshipment (across parallel nodes in the network).

Figure 2. Interactive Decision Hierarchy

It is important to note that all of the resource decisions described in Budget Planning, Strategy Planning, and Tactics Planning must be made prior to the occurrence of a particular service event whose fulfillment will require use of those resources. Hence these decisions are based on estimates of future resource requirements along with visibility of all of the events that affect supply and demand of such resources that have occurred throughout the service supply chain prior to the occurrence of the service event in question.

Given the random nature of service events, it is clear that demand uncertainty cannot be eliminated through forecasting, and hence, trade-offs must be evaluated on the basis of future risk assessments captured by estimates of the demand probability distribution relevant to specific customer products and locations at particular future points in time. The decisions made at all pre-event planning levels, (Budget, Strategy and Tactics), thus constitute an exercise in risk management.

Event Management is the "last mile" of decision making in the planning horizon hierarchy which concerns fulfillment after service event-based demands for resources have been made (e.g. part failure). This is where the service product is actually "produced" to meet the goals of customers. Intelligent decision making here can improve the performance of the system by allowing managers to make the best use of current and projected resource deployments throughout the service supply chain. This framework has a global perspective which has implications for the organization, tools, and processes to effectively deliver a service strategy

Risk Management Solutions to Drive the Efficient Frontier

Balancing the trade-offs among revenue, cost, and service is challenging because of escalating service expectations, complexity of the service supply chain, and, as mentioned before, the high degree of uncertainty associated with service events. The results of the planning decisions are best expressed using the concept of an efficient frontier curve as shown in figure 3. This demonstrates that, in general, the greater the promised level of service performance, the larger the required investment in such assets, which increases the total costs incurred by the service provider. Note that the curve rises steeply; the costs increase disproportionately as the promised service performance level increases.

Figure 3. The Service Supply Chain Efficient Frontier

Over the past decade, firms have made great progress in implementing transaction disciplines and traditional service supply chain systems, moving them from point A to point B towards a more efficient frontier. As companies have increased service levels by moving from point B to point C along the efficient frontier, they have found further progress difficult, limited by traditional modes of planning. These traditional modes of planning found in first-generation service supply chain systems are inspired by manufacturing and finished-product distribution thinking (e.g., ERP and DRP), which attempt to match service supply to demand by assigning enabling resources to specific service products in a static and separable fashion.

After years of research and development of solutions for the service supply chain with organizations such as IBM, General Motors, and the U.S. Navy, and after observing that no existing commercial software solutions addressed the risk management nature inherent in the service supply chain, MCA Solutions developed the Service Planning and Optimization (SPO) suite of products for strategic and tactical planning of the service supply chain. In successful implementations with customers across a variety of industries, it has been repeatedly proven that implementation of SPO's dynamic planning capability in traditional planning environments shift the efficient frontier as demonstrated in the movement from point C to point D in figure 3, resulting in 10 percent to 30 percent reductions in inventory at the same service levels.

These dramatic performance improvements are enabled by the capability demonstrated in figure 4, which is a comparison of traditional planning approaches for the service supply chain to MCA's Risk Management Approach.

Area Traditional Planning Approach SPO Dynamic Planning Approach
Forecasting Production-based forecasting from historical demand that doesn’t recognize probabilistic nature of demand A proprietary composite forecasting methodology that combines time series demand history with causal factor projections to generate item location-specific estimates of usage probability distributions
Positioning Each part location and inventory echelon planned in isolation or in planning groups without considering multi-echelon and system interactions State-of-the-art multi-echelon optimization based on rapid solution algorithms and a robust model/system architecture that can be applied across a wide range of industries and company contexts
Tactical Planning Deterministic DRP type logic using discrete forecasts not suited to intermittent demand environment and characterized by unplanned, reactive expediting Risk-based decision making that incorporates the probability of stockout in all order generation and deployment activities, integrating strategic and tactical planning
Fulfillment & Service Fulfillment to traditional fill rate metrics. Fulfillment strategy not tied to asset management strategy Differentiated service-level commitments enabled by strategic positioning of inventory, including availability-based planning that maximizes product uptime for budget constrained multi-echelon, multi-indenture, multi-period environments

Approach for the Service Supply Chain

Cisco Systems, described earlier, is one of several companies that has effectively transitioned its service supply chain utilizing MCA's SPO dynamic sparing capability to replace its legacy "static sparing" functionality. In a five-month worldwide implementation of MCA's SPO, rolled out to over 1,000 users, Cisco achieved a 21 percent reduction in inventory levels, and a service level increase to 97 percent from 94 percent. Not only did Cisco move its efficient frontier downward, it is also now able to negotiate the curve more effectively by simulating the cost and service trade-offs as it develops new service offerings.

Successful Implementation Approaches

While there is a clear opportunity to increase service performance and profitability through implementation of advanced service planning software, the business world is replete with stories of failed software implementations. In this section, we review successful implementation strategies intended to reduce the risk of implementation and to deliver rapid time to value.

KLA-Tencor: Risk Reduction through a Data-Driven Evaluation
KLA-Tencor is the world leader in yield management and process control solutions for the semiconductor manufacturing industry, and supports equipment across 400 fabs in a capital-intensive environment in which an hour of downtime can cost hundreds of thousands in revenue. With a challenging environment — 75 percent of its supported parts have one demand or less globally a year — KLA-Tencor found itself unable to meet its service commitments using its legacy software solution.

In late fall 2001, KLA-Tencor initiated an extensive evaluation of available solutions for service supply chain planning. In addition to reviewing vendors' responses to functional requirements, KLA-Tencor determined it was imperative to operationally test the solutions through a data-driven use case evaluation to provide the following analysis of solution capability:

  • Direct comparison of vendor solutions in operating environment
  • Development of a credible business case based on actual solution results
  • Understanding of vendor's ability to model business environment and data
  • Understanding of implementation risk pre-contract signing

KLA-Tencor selected MCA based on superior performance in the evaluation, and was able to immediately implement the SPO recommended target stocking levels by leveraging the model developed in the evaluation process. In just two months after contract signing, KLA-Tencor achieved a positive return on its investment through an implementation in a hosted environment, and subsequently rolled SPO out as an internally hosted solution, ultimately realizing an 18 percent improvement in local fill rates, and a 4 percent reduction in supply chain cost as percent of revenue.

Telecom Equipment Provider: Rapid Deployment of Service Product Strategy Through Outsourcing

A leading provider of bandwidth management equipment to telecom service providers such as MCI, SBC and Verizon, traditionally sold parts to customers from a central distribution center as part of a product sale, with the expectation that the customers would manage the planning and stocking of parts themselves. Driven by competitive and market pressures, this company made a decision to offer same-day service contracts to its customers, requiring positioning of service parts across a network of strategic parts centers located close to its customer base.

To quickly succeed in the strategic spares management arena, it was vital for the equipment provider to build a strategic parts infrastructure and provide parts in expedited timeframes, while providing its customers the highest level of customer service. The equipment provider selected DHL Logistics to provide the warehousing, transportation, and execution of service parts logistics. The company had a successful implementation of SAP ERP, but found that its planning approach was not appropriate for service parts management, and asked DHL to provide a service parts planning software solution. DHL teamed with MCA Solutions to provide a hosted software solution for forecasting and planning of parts in the new network.

Within two months of vendor selection, the equipment provider had deployed new strategic parts locations, implemented the logistics processes required to deliver the expanded service, and through deployment of the planning software and process, realized a service parts inventory reduction of over 60 percent. Through intelligent outsourcing and effective deployment, it was able to deliver a much-needed service to its customers more rapidly than if the company had managed the transformation with internal resources and systems.


Increasing corporate realization of the value of service has focused attention on an area that managers of service supply chains have always recognized as a high stakes gamble requiring decision making in a complex and risky environment. The advanced and dynamic service management approach that we are proposing here is a way to formally introduce the concepts of flexibility and planned responsiveness into the area of service delivery, allowing service managers to better manage in this environment, which is much different than the traditional production supply chain.

The dynamic service management approach described here can deliver significant financial benefit to service organizations and help them achieve supply chain flexibility. Service organizations cannot afford to neglect the potential to deliver business value in today's hyper-competitive, customer-centric world where service is often the key competitive differentiator.

This article is from Parallax View, ChainLink Research's on-line magazine, read by over 150,000 supply chain and IT professionals each month. Thought-provoking and actionable articles from ChainLink's analysts, top industry executives, researchers, and fellow practitioners. To view the entire magazine, click here.

About the Author

Morris Cohen is the Matsushita professor of manufacturing and logistics at the Wharton School of the University of Pennsylvania, and co-director of Wharton's Fishman-Davidson Center for Operations Management. Dr. Cohen has spent years researching, planning, and designing advanced value chain systems working with customers such as IBM, Cisco, Applied Materials, Intel, General Motors, and the U.S. Navy. In 1999, he founded MCA Solutions to bring the intellectual capital of service value chain optimization from the classroom into the technology marketplace. Dr. Cohen holds a B.S. from the University of Toronto, and an M.S. and Ph.D. from Northwestern University.

ChainLink Research is a bold new supply chain research organization dedicated to helping executives improve business performance and competitiveness.

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