April 1996 Volume 23 Number 2
Shape Up, Ship Out
How a team of OR analysts redefined manufacturing strategy for the IBM
PC Company in Europe, and saved the computer giant $40 million per year
in distribution costs.
By Gerald Feigin, Chae An, Daniel Connors and Ian Crawford
In early 1993, the European arm of the IBM PC Company faced a number of
challenges that posed serious threats to its ability to remain a major player
in the competitive European personal computer market. Against a backdrop
of continuing world-wide recession, IBM PC Company Europe faced unrelenting
pressure from increasingly agile and aggressive competitors which were eroding
IBM's market share. Pressure came in the form of frequent price cuts, rapid
customer order response times, and a steady arrival of new products and
features. Poor forecasting only added to the problem, leading to critical
shortages of popular products and excess supplies of others.
At the same time, IBM's new CEO, Louis Gerstner, reacting to record corporate
losses, made clear the importance of reducing operational costs and inventory
throughout the corporation. Under particular scrutiny was the largest of
IBM's personal computer manufacturing plants, a sprawling 1.3 million square
foot factory located in Greenock, 30 miles west of Glasgow in the heart
of Scotland's famed Silicon Glen. The plant, comprised of manufacturing,
warehouse and staging areas, had produced approximately 1.2 million personal
computers the previous year and was responsible for supplying the bulk of
IBM's personal computers to Europe, the Middle East and Africa.
Recognizing the urgency of the situation, as well as the difficulties in
finding the right balance between cutting costs and maintaining customer
responsiveness, the IBM PC Company management enlisted a team of operations
research analysts and management science experts from IBM's T.J. Watson
Research Center in Yorktown Heights, N.Y., to help identify, analyze and
recommend the most cost-effective changes for IBM Europe's PC manufacturing
and distribution operations. Working closely with the PC Company executives
responsible for manufacturing and distribution strategy in Europe, the team
(which included three of the four co-authors of this article) developed
a supply chain simulation model designed to quantitatively assess the impact
of various operational strategies on cost and customer service levels.
With this model, the team was able to compare different manufacturing execution
strategies, examine the effect of different planning and forecasting methods,
and identify lower cost distribution policies. Ultimately, the analysis
led to significant changes in both manufacturing and distribution, including
the adoption of a build to order (BTO) manufacturing strategy, a direct-ship
distribution process that by-passed costly country distribution centers,
and the rejection of a cost-inefficient idea that had been gaining currency
among IBM executives -- the introduction of a new late-customization assembly
plant on the European continent.
The first step in the analysis was to develop a detailed understanding of
manufacturing and distribution as it was currently practiced in the PC company.
We needed to understand all of the relevant processes in place before developing
a model. These included planning processes, manufacturing execution processes,
and finished goods distribution processes. Based on this first stage of
analysis, the team identified key areas it believed to have a major impact
on operational costs and customer responsiveness. In the process of understanding
the existing business processes, we generated many ideas on how to improve
them. Some of these ideas were original; others were borrowed from operations
research and management science literature.
We next constructed a detailed simulation model (see
accompanying story on the Supply Chain Simulation Model) of the relevant
business processes so that we would have a platform for evaluating proposed
changes and directly assessing their effect on costs and service levels.
We first validated the simulation model against the existing business policies
and then explored alternative policies. We viewed the simulation model as
a vehicle for convincing both ourselves and PC company management that proposed
changes in operational policies were appropriate.
Demand and Supply Planning Activities
The PC Company's planning activities begin with the demand planning process,
which attempts to generate an unbiased forecast of expected shipments of
each product in each time period (typically monthly or quarterly). The forecasts
are based on historical sales information, product announcements, market
and economic conditions, competition, etc. The demand forecast is then translated
into two types of detailed plans via a material requirements planning (MRP)
system: the production requirements at Greenock, called the "base plan,"
and the component requirements on the parts suppliers through a bill of
A large number of parameters govern this translation including lead times,
frozen zones (period in which previous orders cannot be modified), minimum
thresholds for order quantities and changes in orders, and safety stock
levels. These parameters are set either by specific part numbers or according
to part groupings based on annual usage. One of the supply planning activities
is to assess the capability of the suppliers to produce a feasible production
and procurement plan. A typical planning cycle used to take at least one
We recognized that supply planning was critical in determining Greenock's
parts inventory and its responsiveness to customer orders. We decided to
analyze several changes to the supply planning process that we believed
might improve customer service levels and reduce inventory holding costs.
The first approach focused on modifying the way in which safety stock levels
were set. In Greenock's MRP system, uncertainty in demands were dealt with
by adjusting the product demands to be above their average values by a certain
fixed percentage (a common practice among production planners).
We believed that significant reduction in inventory would be possible by
implementing an algorithm to plan the requirements for parts that achieves
a specified service level for products while attempting to minimize the
safety-stocks of parts required. The algorithm, developed by operations
research analysts at IBM, exploits commonality of parts as well as differences
in price and lead time reliability between suppliers. While benefits of
component commonality in reducing overall inventory are well known, this
algorithm, referred to here as Flex Planning , was among the first to
be developed that addressed industrial size problems.
The second modification of the supply planning process focused on the issue
of constrained supply. When not enough parts are available from suppliers
to build the forecasted demand, an allocation of the constrained parts to
products must be performed. This allocation process is usually performed
in an ad hoc manner because MRP systems assume the supply of parts to be
unconstrained. In our analysis, we wanted to assess the value of performing
this allocation in an optimal way, by solving an optimization problem. To
do this, we utilized the Production Resource Manager (PRM) , also developed
by OR practitioners at IBM, which can perform this optimization based on
many different business objectives, including product priority, fair allocation
and revenue maximization.
The results of our simulation studies indicated that the Flex Planning method
for safety stock planning required less inventory compared with the existing
MRP-based method to achieve the same service level (see Figure 1). As for
the use of the Production Resource Manager to handle constrained parts,
our analysis indicated that it is marginally useful when there are normal,
minor shortages; but when there are significant parts shortages, its use
becomes critical. Today, Greenock is one of several IBM sites deploying
PRM-based applications for planning processes.
Greenock operated in a Build-To-Plan (BTP) mode in which it built products
according to the base plan (or forecast), pushing finished products into
warehouses and country distribution centers. There were several perceived
advantages of the BTP strategy. For products having large and stable demands,
manufacturing production schedules for these products could be implemented
so as to use manufacturing resources efficiently. Provided that the right
product was on hand in the finished goods warehouse, IBM could quickly respond
to a customer order by drawing upon that finished goods inventory. The major
drawback of the BTP strategy was that it provided no flexibility for meeting
actual customer orders. If the forecasts and subsequent base plans were
not accurate (as was often the case), manufacturing would end up building
the wrong products, thus tying up assets in finished goods that were not
wanted by customers.
We believed that IBM would benefit from a more responsive manufacturing
strategy. We developed two alternative approaches which we compared to BTP
via simulation: Build- To-Order (BTO) and Late-Customization (LC). We defined
BTO to mean that a product is built from its subassemblies only after an
order for that product has been received. Under the LC strategy, products
are built from shells. A shell is a partially built PC that consists of
such sub-assemblies as a power supply, a frame, planars or motherboards,
and minimal memory and disk storage. There may be several types of shells
depending on the final configurations of the finished products. The shells
are built according to the base plan (which in this case specifies the demand
for shells) and are stocked at the manufacturing warehouse. Final assembly
for products using the LC strategy is triggered by firm orders.
The major advantage of the BTO strategy is that assets are not tied up in
unwanted finished goods; PCs are built only to real orders. The BTO strategy
can exploit the commonality of components that may exist across the different
products. Rather than committing a common critical component to a product
whose demand has been forecasted, as in the BTP case, the BTO strategy does
not commit critical components until the order is firm. The major drawback
to BTO is its effect on manufacturing efficiency and serviceability. With
no finished goods inventory to draw upon under the BTO strategy, a product
must be built from its subassemblies and tested after the order has been
For customer orders requiring rapid delivery times, it may not be possible
to build, test and ship a product to a customer within the requested time.
Manufacturing efficiency may also be affected by a BTO strategy if large
setup times are incurred switching from the building of one product to a
different product. Finally, order volatility, also known as order skew,
may adversely affect the performance and utilization of a BTO manufacturing
line. It is common for order volumes to surge at the end of month, the end
of the quarter and the end of the year.
The LC approach is a compromise between BTO and BTP. Its advantages include
a possible smaller final assembly and test time compared to the BTO approach,
and a more efficient use of manufacturing capacity since the shells are
built to a plan. It may also provide a less costly means to meet service
requirements in outlying regions. On the other hand, the LC approach has
its disadvantages. Shells are built to a base plan so assets may be tied
up in certain types of shells which are not needed. Also, the multiple-stage
build process may incur additional handling and storage costs, and may expose
the products to more handling damages.
Our analysis revealed that under the then current business environment,
the BTO strategy achieves the same level of service with significantly less
inventory in the supply chain than both the LC and BTP strategies (see
Figure 1). Based on this analysis, we strongly recommended a shift to
a BTO strategy -- a recommendation that was accepted and implemented. To
handle seasonal demands that exceed Greenock's capacity, IBM has contracted
with a local vendor to perform some assembly operations when needed. After
the initial implementation of the BTO strategy, Greenock adopted a hybrid
BTO/BTP process in which standard off-the-shelf products are built to plan.
This modification allowed Greenock to be more responsive to high-order skew.
It is worth noting that a BTO strategy is not necessarily the best for all
manufacturing environments. Also, the choice of manufacturing strategy --
BTP, LC or BTO -- does not lessen the importance of forecasting and part
planning. In an industry in which the procurement lead times for critical
components are typically several weeks or months, and product life cycles
on the order of several months, good procurement planning is crucial for
the success of manufacturing performance. The BTO, LC and BTP strategies
all fail if the right components are not available when needed.
Finished Goods Distribution
Prior to 1993, IBM's PC distribution network in Europe consisted of IBM-managed
country distribution centers and transshipment points. The distribution
centers served as warehouses and staging areas for configuring orders. Customers,
consisting mostly of independently owned dealerships and retail outlets,
placed orders which were filled by the distribution center in the country
from which the order originated. A typical order consisted of some number
of system units, together with monitors, country-specific keyboards, cables
and documentation in specified languages. Other peripheral devices such
as printers and storage backup devices might be included in orders as well.
Once an order was configured, it would be shipped from the country distribution
center to the customer. The distribution centers would receive replenishments
from IBM Greenock. These shipments usually proceeded by truck from Greenock
to a nearby port, then by ship to one of 13 transshipment points located
throughout Europe, and then by truck to the country distribution centers.
Several concerns were raised about the existing distribution process. First,
service levels -- measured as the fraction of on-time order shipments --
were low. Distribution centers often did not have the right finished goods
on hand to fill customer orders and thus had to wait for shipments from
Greenock. Shipment delays, especially at transshipment points, were not
uncommon. Second, the costs of the distribution network were high. Freight
rates that were negotiated were not necessarily the best, the operational
costs of the transshipment points and country distribution centers were
high, and significant inventory was being held by the distribution centers.
Third, IBM believed that increasing competition would necessitate meeting
service requirements that could not be met at a reasonable cost given the
As a result, IBM was seriously considering the creation of a final assembly
or late-customization plant -- located closer to primary European markets
-- which would perform such functions as adding additional cards or hard
disks to the system units, loading language specific software, and bundling
peripheral devices with the system units. The plant would handle customer
orders with stringent service requirements that could not be met at a reasonable
cost directly from Greenock. In addition, a reduction in the number of transshipment
points and distribution centers in Europe was expected to significantly
reduce distribution costs and delivery times. Finally, questions were raised
about whether order consolidation could be done more efficiently in Greenock
and shipped directly to dealers (or even the end customers), rather than
at the distribution centers; and whether the shipping of orders to dealers
would be better managed by IBM or by a commercial shipping vendor.
Using the supply chain simulation, we were able to create a detailed model
of the distribution process that enabled us to test various distribution
strategies and to compare their performance in terms of total distribution
costs and the achieved service level. We used the simulation to evaluate
two different distribution strategies for the European market. In Strategy
1, customer orders are consolidated in Greenock and shipped directly to
dealers via IBM's network of transshipment points. Orders would be shipped
in bulk to these transshipment points and then shipped individually to the
final customers. In Strategy 2, order consolidation occurs at two locations:
in Greenock and at a proposed late-customization plant located on the European
In our analysis of Strategy 1, we considered several cases in which we varied
the number of transshipment points in Europe (See Figure 2). A rough cut
location optimization analysis followed by a detailed simulation revealed
that the least expensive distribution strategy involved the use of three
transshipment points located at strategic ports in Europe. When we evaluated
cases with as many as 13 transshipment points, we found that the volume
of orders shipped to any one site was not sufficient to permit frequent
shipments from Greenock. Instead, orders would be held at the shipping dock
until a container was full enough to justify a shipment. Thus, significant
delays were incurred and inventory holding costs were large.
The costs of operating the distribution network with three transshipment
points were significantly smaller due primarily to lower operational costs
and inventory holding costs. Also, we observed, somewhat unexpectedly, that
service levels improved slightly when we reduced the number of transshipment
points because of the increased frequency of shipments to each of the locations.
When we further reduced the number of transshipment points to fewer than
three, overall costs began to increase mainly because of the large inland
freight charges incurred when shipping orders from the transshipment points
to the customers.
In our evaluation of Strategy 2, we were able to clearly quantify the tradeoff
in service level and costs associated with introducing a late-customization
plant in Europe (see Figure 3). In particular, the simulation results indicated
that introducing the plant only made sense if more than 25 percent of all
orders required fulfillment in under two days. Because the market demand
for such stringent service was much smaller than 25 percent, we concluded
that the introduction of an additional plant was not warranted.
As a result of our analysis, we recommended that IBM direct ship orders
from Greenock to customers, bypassing country distribution centers. The
estimated savings in distribution related costs was approximately $40 million
per year. By avoiding the country distribution centers and effectively pooling
inventory in one location, IBM would improve its customer service levels
and, at the same time, decrease finished goods inventory.
In addition to the insight provided by our analysis, IBM benefited from
our work in another significant way: By providing an objective means to
quantitatively assess the impact of different decisions, we helped to defuse
a politically sensitive and emotionally clouded decision process. As a result,
potentially harmful but politically easy decisions were avoided and good,
but less popular, decisions were easier to make. While the analysis we performed
and the recommendations we made have been instrumental in making IBM's manufacturing
and distribution operations more competitive, the external factors that
led to IBM's problems -- stiff competition, short product life cycles and
volatile customer demand -- are still very much present in today's market.
As the battle to increase market share -- and the efforts to streamline
operations, reduce costs and improve customer service -- continues today
and into the future, operations research and management science professionals
in IBM will play an important role in introducing new and innovative operational
methodologies and assessing the impact of policy changes on overall business
1. R. Srinivasan, R. Jayaraman, R. Roundy and S. Tayur, "Procurement
of Common Components in a Stochastic Environment," IBM Research Report,
RC-18580, 12/1992, 45 pages.
2. B. Dietrich, D. Connors, T. Ervolina, J.P. Fasano, G. Lin, R. Srinivasan,
R. Wittrock, R. Jayaraman, "Production and Procurement Planning Under
Resource Availability Constraints and Demand Variability," IBM Research
Report, RC-19948, 2/1995, 40 pages.
Gerald Feigin, Chae An and Daniel Connors are Research Staff Members
at the IBM T.J. Watson Research Center in Yorktown Heights, N.Y. They and
their colleagues in the Production, Distribution and Transportation Research
Department work on applied operations research problems. Ian Crawford is
the Director of Manufacturing and Distribution for the IBM PC Company Europe.
He is also the plant manager of the IBM Greenock PC plant. The authors acknowledge
the help of Steve Buckley, Ranga Jayaraman, Tony Levas, Nitin Nayak, Raja
Petrakian and Ramesh Srinivasan from IBM Research, and Alan Peat and Ian
Baillie from IBM Greenock.
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