Service Supply Chain Strategies to Increase Corporate Profitability
The last decade has witnessed a substantial shift in emphasis on the part of many original equipment manufacturers (OEMs) 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 that revealed that service represents 24 percent of their revenue and 45 percent of their profit contribution. With only 20 percent of information technology (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 direct their 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.
We will discuss the unique challenges of the service supply chain, a framework for understanding the service management decision hierarchy. Most importantly, we highlight the dramatic value proposition available to companies who deploy advanced service strategies and decision support tools to address these challenges.
Service Supply Chain Challenges
The mechanisms required to design, produce, and deliver service products in a cost-effective and competitive manner are quite different from those used to manufacture goods and to procure direct materials, and significant assets must be dedicated toward service delivery.
Consider as an example Cisco Systems Global Product Services, which manages a complex supply chain consisting of the following elements:
- More than 10 million service contracts defined in terms of specific customer performance targets (i.e., high priority with twoto four-hour response time guarantee, eight- to 12-hour response time, and next business day response time), with thousands of service contract transactions per day.
- Three to five 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 10 hits a year not uncommon.
While not all manufacturers face this level of complexity, the tasks of effectively deploying assets across a wide network of locations and fulfilling demand driven by infrequent service events are daunting. Figure 1 shows a representation of a typical multi-echelon service supply chain network, and the resulting materials flows required to supply material and to fulfill demand.
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 one or two inventory turns are common for providers of same-day service agreement, even for manufacturers whose production supply chains show 50 to 100 turns.2
Risk-Management Framework
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 at 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 aftersales 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 includes 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).
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 serviceevent- based demands for resources have been made (e.g., parts 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.
Driving 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.
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 B toward a more efficient frontier. As companies have increased service levels in moving to point C, 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., enterprise resource planning systems [ERP] and distribution requirements planning [DRP]), which attempt to match service supply to demand by assigning enabling resources to specific service products in a static and separable fashion.
Successful Implementation
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. Successful implementation strategies should reduce the risk of implementation and deliver rapid time-to-value.
In successful implementations with customers across a variety of industries, it has been repeatedly proven that implementation of dynamic service supply chain planning in traditional planning environments shifts the efficient frontier (as demonstrated in the movement from point C to point D in Figure 2), resulting in 10 to 30 percent reductions in inventory at the same service levels.
Cisco Systems is one of several companies that has effectively transitioned their service supply chain utilizing MCAs Service Planning and Optimization (SPO) dynamic sparing capability to replace their legacy "static sparing" functionality. In a five-month worldwide deployment, Cisco rolled out MCAs SPO to more than 1,000 users. They achieved a 21 percent reduction in inventory levels, and a service level increase from 94 percent to 97 percent. Not only did Cisco move their efficient frontier downward, they are also now able to negotiate the curve more effectively by simulating the cost/service tradeoffs as they develop new service offerings.
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 of dollars in revenue. With a challenging environment 75 percent of their supported parts have one demand or less globally per 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 response to functional requirements, KLA-Tencor determined it 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 vendors ability to model business environment and data; and
- Understanding of implementation risk pre-contract signing.
KLA-Tencor selected MCA based on superior performance in the evaluation, and was able to immediately implement MCAs SPO suite of products for strategic and tactical planning of the service supply chain. In just two months after contract signing, KLA-Tencor achieved a positive return on investment through an implementation in a hosted environment, and subsequently rolled it out to 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.
Rapid Deployment of Service Product Strategy Through Outsourcing
Tellabs, a leading provider of bandwidth management solutions 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 stocking of planning and stocking parts themselves. Driven by competitive and market pressure, Tellabs made a decision to offer same-day service contracts to their customer, requiring positioning of service parts across a network of strategic parts centers located close to their customer base.
To quickly succeed in the strategic spares management arena, it was vital for Tellabs to build a strategic parts infrastructure and provide parts in expedited timeframes while providing its customers the highest level of customer service. Tellabs selected DHL Logistics to provide the warehousing, transportation, and execution of service parts logistics. Tellabs had a successful implementation of SAP ERP, but found that its planning approach was not appropriate for managing service planning, 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, Tellabs 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 more than 60 percent. Through intelligent outsourcing and effective deployment, Tellabs was able to deliver a much needed service to its customers far more rapidly than had they managed the transformation with internal resources and systems.
Conclusion
Increasing corporate realization of the value of service has focused attention on an area that managers of service supply chains have always recognized is a high-stakes gamble requiring decision making in a complex and risky environment. An advanced and dynamic service management approach is a way to introduce the concepts of flexibility and planned responsiveness formally into the area of service delivery, allowing service managers to better manage in this environment that is far different than the traditional production supply chain.
This dynamic service management approach can deliver significant financial benefit to service organizations and help them achieve the goal of supply chain flexibility. Service organizations cannot afford to neglect the potential to deliver business value in todayfs hypercompetitive, customer-centric world where service is often the key competitive differentiator.
Endnotes
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.
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.
