Abstract
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.
SOURCE:
http://www.technologyevaluation.com/research/articles/service-supply-chain-strategies-to-increase-corporate-profitability-17349/
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.
SOURCE:
http://www.technologyevaluation.com/research/articles/service-supply-chain-strategies-to-increase-corporate-profitability-17349/
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