Service Supply Chain Strategies to Increase Corporate Profitability

by Morris Cohen

June 14, 2004

An advanced and dynamic service management approach can introduce the concepts of flexibility and planned responsiveness formally into the area of service delivery.

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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 MCA’s Service Planning and Optimization
(SPO) dynamic sparing capability to replace their legacy “static sparing” functionality.
In a five-month worldwide deployment, Cisco rolled out MCA’s 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 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 MCA’s 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.


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 todayfs hypercompetitive, customer-centric world
where service is often the key competitive differentiator.


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.

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