Journal of Operations Management 20 (2002) 747–767
From supply to demand chain management:
efficiency and customer satisfaction
Jussi Heikkilä∗
Helsinki University of Technology, P.O. Box 9555, FIN-02015 Hut, Finland
Abstract
How do companies in the fast-growing industries achieve good customer satisfaction together with efficiency in supply
chain management (SCM)? This inductive case study of six customer cases of Nokia Networks, one of the leading providers
of mobile telecommunication technology, led to propositions exploring that question. Good relationship between the customer
and the supplier contributes to reliable information flows, and reliable demand information flows in turn contribute to high
efficiency—these are well-researched issues also in other industry environments. But in a fast-growing systems business such
as mobile telecommunications industry, the supplier needs to be able to adapt its offering to a wide variety of customer situations
and needs. Understanding the customer’s situation and need together with the right offering contributes to good co-operation
in improving the joint demand chain, which further leads to superior demand chain efficiency and high customer satisfaction.
© 2002 Elsevier Science B.V. All rights reserved.
Keywords: Marketing/operations interface; Logistics/distribution; Time-based competition; Case study research
1. Introduction
One of the main challenges of today’s manufacturing is to be both efficient and contribute to high
effectiveness, i.e. customer satisfaction. Information
is increasingly available through e-business, customer
relationship management (CRM) and supply chain
management (SCM) solutions, making it—at least in
theory—possible to serve customers individually with
customized bundles of goods and services. However,
going too far in customization would ruin efficiency.
On the other hand, too rigid an approach to SCM
would risk customer satisfaction.
How to find a good balance between good customer satisfaction and supply chain efficiency? Our
answer is to start from understanding the situation
and need in distinct customer segments—which is not
∗ Tel.: +358-50-376-1090; fax: +358-9-451-3665.
E-mail address: jussi.heikkila@hut.fi (J. Heikkilä).
normally the starting point for operations managers
to begin their improvement efforts. The next step is to
develop manageable number of alternative modular
service offerings to be adapted to individual customer situations and needs. The final step is to take
the relationship characteristics into consideration and
develop a joint improvement agenda together with
the customer to develop optimum operative efficiency
within the constraints set by the objectives important
for the customer; and if the joint improvement agenda
is implemented in good co-operation, high customer
satisfaction will follow.
Nokia Networks, one of the leading technology
vendors for mobile telecommunications networks, has
recently experienced all this. The background of this
paper is a business situation in which Nokia Networks
implemented a demand chain efficiency improvement
project with several of their customers. The company
delivers equipment for their customers’ mobile cellular
telecommunications networks. The cellular network
0272-6963/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved.
PII: S 0 2 7 2 - 6 9 6 3 ( 0 2 ) 0 0 0 3 8 - 4
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J. Heikkilä / Journal of Operations Management 20 (2002) 747–767
consists of switches, base station controllers and base
transceiver stations (BTS) (more commonly known as
base stations). Base stations are delivered in thousands
to a telecommunications operator’s network. There
are some hundreds of telecommunications operators
as customers for all technology vendors globally.
Nokia Networks implemented a demand chain improvement program called Handshake with several
customers. The central elements of the Handshake
program were funnel forecasting with the expectation
that the customer could systematically improve its
planning accuracy over time, removing inventories
between the base station manufacturing plant and the
customer’s network building operation, and assembling the final base station configurations in the plant
for direct delivery to final destinations. The global
supply chain performance targets set by Nokia Networks in 1997 (in relative terms for confidentiality
reasons) are given in Table 1.
The results of the efficiency improvement projects
initiated by Nokia were mixed: success in some of
them and failure in others. The following quotes
illustrate the difference in customers’ reactions to
Nokia’s proposed Handshake program. A successful
Alpha case as described by Alpha’s Section Manager
responsible for their network building with Nokia:
The initial reaction for Nokia’s proposal was a great
surprise. Nokia had always been very strict about
the fixed 4 months lead-time for units. We were like
hit by a log when (Nokia’s new Head of Logistics)
came and said that the lead-time could be shortened
from 4 months to 10 days and configurations defined only in the call-off. The flexibility has grown
considerably. Earlier we were suffering from lack
of material and had to live with that. Now the problem has disappeared. Removal of our warehouse has
also been a big achievement. The right material is
directly moved to the right destination, there are no
more problems of having wrong materials in warehouses. On the other hand, we have to do more work
in forecasting and planning the configurations.
A less successful Beta case as described by Nokia’s
Quality and Processes Manager for Beta:
In our first discussion with Beta Contract Manager
concerning the Handshake project he felt that we
were just trying to save our own costs by taking
down the country warehouse. He asked how much
price reduction would he get? . . . When Nokia
started piloting the Handshake project with Beta, the
lead-time was reduced from 9 to 4 weeks. Several
meetings were organized to explain the new model
and the advantages that it would give to Beta, and
also to get Beta’s acceptance to the model. Nokia’s
country organization and Beta set up joint development work-groups. The work-groups visited both
companies, and they met monthly for 1.5 years.
However, it took months before the work-groups
started really working. . . . Beta did not change its
practice of ordering. They demanded immediate reduction of lead-time without starting forecasting.
Handshake was a perfect fit for some of the customers, whereas in other cases there was a serious
misfit between the support that the customer needed
from their supplier partner and the improvement program elements. The resultant question for Nokia was
how to tailor their demand chain improvement program according to the distinct needs and characteristics of specific customer segments. The demand chain
architecture must be robust—in order to apply different demand chains in different customer situations.
1.1. Objective, research question and unit of analysis
Table 1
Nokia Networks’ global supply chain performance improvement
targets set in 1997 (in relative terms)
Targets
1997
Reduction
in 1998 (%)
Reduction
in 1999 (%)
Inventory reduction
Order fulfillment
lead-time
Non-perfect order
fulfillment
100
100
16
79
34
82
100
33
64
The objective of our research was to increase understanding of factors contributing to well-performing
demand chains in the mobile cellular networks industry. The aim was through case study research to find
new perspectives for the demand chain structure and
for the industrial customer–supplier relationships, and
how they influence the demand chain performance in
a young, fast-growing industry. The research question
was as follows:
J. Heikkilä / Journal of Operations Management 20 (2002) 747–767
What is the architecture of a well-performing demand chain in a young, fast-growing industry, selling systems with varying hardware and software
content to industrial customers?
The main research question was further divided
into questions of information and material flows,
customer–supplier relationships, and demand chain
performance. The unit of analysis was a demand
chain for building cellular networks, consisting of
a customer (telecommunications operator), the technology supplier’s organizational units responsible for
serving the customer in network building, and the
factory assembling and delivering base stations to the
customer’s network.
1.2. Supply/demand chain management
The SCM concept extends the view of operations
from a single business unit or a company to the whole
supply chain. Essentially, SCM is a set of practices
aimed at managing and co-ordinating the supply chain
from raw material suppliers to the ultimate customer.
The objective of SCM is to improve the entire process
rather than focusing on local optimization of particular
business units.
A number of researchers suggest that better performance can be achieved by consolidating customer
and supplier bases, removing unnecessary steps in the
chain, speeding up information and material flows,
and creating long-term partnerships with major customers and suppliers to leverage the capabilities of
several companies in the chain. Previous management theory in the area of SCM can be broadly
divided into two main categories. The first category is studies of primarily the chain structure (e.g.
Forrester, 1958, 1961; Burbidge, 1961; Sharman,
1984; Sterman, 1989; Towill et al., 1992; Lee and
Billington, 1992; Lee et al., 1997a,b; Holmström,
1994, 1995; Fisher, 1997). The second group is primarily about industrial networks and the relationships
between organizations in the chain (e.g. Williamson,
1985; Heide and John, 1990; Mohr and Spekman,
1994; Hakansson and Snehota, 1995; Kumar et al.,
1995; Dyer, 1996a,b,c, 1997; Monczka et al., 1998).
Some scholars suggest using the term demand chain
management instead of SCM (Vollmann et al., 1995,
1997, 2000; Vollmann and Cordon, 1998). This puts
749
emphasis on the needs of the marketplace and designing the chain to satisfy these needs, instead of starting
with the supplier/manufacturer and working forward.
In this research, the emphasis on the customer needs is
adopted as the starting point for supply/demand chain
management.
1.3. Mobile cellular networks demand chain
The mobile telecommunications industry in 1990s
was a fast-growing global industry. New technologies were constantly developed for cellular networks
and liberalized markets were growing at rates over
50% annually during the latter half of the 1990s.
The traditional division of companies in the demand
chain—telecommunications operators, suppliers of
telecommunications equipment and systems, and
suppliers of components and modules—changed radically. Deregulation of telecommunications markets
forced operators to focus sharply on competitive
end-user services by increasing the variety of services. New operators transferred parts of operators’
traditional activities to technology suppliers, such as
network planning and building, and even operation of
telecommunications networks. At the same time, the
suppliers were also moving up in the value chain. Specialized contract manufacturers developed increasing
capability of offering manufacturing services to allow telecommunications technology vendors—among
them Alcatel, Ericsson, Lucent, Motorola, Nokia,
Nortel, and Siemens—to concentrate on meeting the
changing needs of the telecommunications operators.
Throughout the 1990s, the leading companies providing technology for the cellular networks industry
enjoyed strong growth and good profitability. Companies offering new technologies entered a large number
of fast-growing new markets all over the world. New
supply chains were quickly built to serve a wide variety of customers.
Building a cellular network engages the customer
and the supplier in a business relationship that lasts
for several years. The cellular network consists of
switches, base station controllers and BTS. The standardization of the technology is not completely open.
It is possible to combine switches of one supplier to
base station subsystems (base station controller+BTS)
from another supplier, but it is not possible to mix
BTSs from several vendors within one base station
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J. Heikkilä / Journal of Operations Management 20 (2002) 747–767
subsystem. This means high switching costs when
changing from one supplier to another in a geographic
area. However, it happens that the technology vendor
is changed during the several years of building and expanding a mobile telecommunications network, meaning replacement of the old vendor’s technology with
that of the new one.
In our research, we concentrated on the demand
chain of BTSs, the most numerous network element
in a cellular network. The process of building a BTS
site starts from network planning. Network planning
gives the approximate locations of the BTS sites as a
basis for site acquisition and/or provides a list of the
sites readily available. Further, network planning defines how sites will be connected and related to the
base station controllers. Site acquisition locates three
alternative sites for further technical review. One is selected, resulting in negotiations for a lease agreement
with the site owner and applying for necessary permits
to build the BTS site. When the lease agreement is
signed and the necessary permits granted, construction
works would start to build the necessary foundations,
antenna masts and power supplies. After the construction work is finished, the site is ready for installation
of the BTS, antennas and other auxiliary equipment.
Either a line in a fixed network or a radio link connects
the site to base station controller. Finally, the BTS is
integrated operatively into the network.
Many of the steps in the BTS site building and installation process have high uncertainty. It is not always sure that the owner of a site will agree to lease it.
It is also uncertain if all the necessary permits will be
granted for a site, or, even if the permits are granted,
there are questions as to when. Construction and installation times also raise uncertainty. If the network is
built using leased lines from the owner of the fixed network, receiving the leased lines can also be delayed.
For these reasons, network building can be iterative in
nature. Configuration of an individual site depends on
the neighboring sites. If, for some reason, a planned
site cannot be used, it can influence the configuration
of other sites around it.
The above described complexity, combined with
very rapid and unpredictable growth in the demand
for mobile communication services makes effective
SCM a challenging task for both the customer and the
supplier. Success in network building requires close
co-operation between the two parties at several stages
of the building process. The supplier needs to be ready
to take on varying roles to deal with the customer depending on the customer’s objectives, own resources,
skills and capabilities.
2. Literature review
2.1. Supply/demand chain structure
Time-based management and the relationship between speed of operations and efficiency has been one
of the key issues in operations management literature
during the 1980s and 1990s (e.g. Stalk, 1988; Stalk
and Holt, 1990; Womack et al., 1991; The Toyota
Production System, 1995). Stalk (1988) describes how
time has become one of the most important sources
of competitive advantage in manufacturing industries. He describes the background for “Japan’s secret
weapon” (Womack et al., 1991) or “lean thinking”
(Womack and Jones, 1996) by illustrating how the
competitive advantage of Japanese manufacturing
industry evolved from low labor costs—through
scale-based strategy, focused factory and flexible
manufacturing—to time-based competitive advantage.
Stalk describes companies as systems and says that
competitive advantage can be achieved by breaking
the “debilitating loop strangling traditional manufacturing planning”. This means that traditional manufacturing requires long lead-times to resolve conflicts
between various jobs or activities that require the
same resources. The long lead-times require sales
forecasts to guide planning. Long lead-times make the
accuracy of sales forecasts decline. Forecasting errors
increase inventories and the need for safety stocks at
all levels. Errors in forecasts mean more unscheduled
jobs in the production line, increasing the lead-times
for the scheduled jobs. The planning loop expands,
drives up costs, increases delays, and creates system
inefficiencies.
Holmström (1994, 1995) has empirically studied
the efficiency potential of speed in operations. His
main results are empirical indications of a strong
positive correlation between speed and efficiency in
manufacturing and that a focus on speed of operations helps expose and remove self-induced sources
of uncertainty. He claims that the main contributor to
uncertainty in slow operations is distorted communication in the activity system. Based on his findings of
J. Heikkilä / Journal of Operations Management 20 (2002) 747–767
a “speed threshold” he suggests that inventory commitment needs to be reduced to a point where demand
distortion is diminished and a synchronization of production with demand is possible in order to improve
performance by speeding up operations.
One of the main system issues in supply chains is
industrial dynamics and management of the bullwhip
(or Forrester or whiplash or whipsaw) effect. This
refers to the phenomenon where orders to the supplier
tend to have larger variance than sales to the buying
organization (i.e. demand distortion), and the distortion propagates upstream in an amplified form (i.e.
variance amplification). This phenomenon is related to
the information flows among the members in the supply chain. Information flows in terms of orders have a
direct impact on the production scheduling, inventory
control and delivery plans of individual members in
the supply chain.
Information-feedback systems owe their behavior
to three characteristics—structure, delays and amplification (Forrester, 1961; Sterman, 1989). The structure
of a system tells how the parts are related to one another. Delays exist in the availability of information,
in making decisions based on the information, and in
taking action on the decisions. Amplification usually
exists throughout systems and it is observed when an
action is more forceful than might seem to be implied
by the information inputs to the system.
Lee et al. (1997b) claim that the bullwhip effect
is an outcome of the strategic interactions among
rational supply chain members who are optimizing.
They suggest the following sources of the bullwhip
effect: demand signal processing, rationing game, order batching and price variations. In the existence of
any of these four sources, bullwhip is caused by rational behavior of the members in the chain. Lack of
inter-company communication combined with large
time lags between receipt and transmittal of information are at the root of the problem (Metters, 1997).
Consequently, solutions to the problem often involve
increasing the abilities of companies to co-ordinate
activity and cut lead-times.
Uncertainty and the nature of the forecasting problem have a considerable impact on the supply chain
structure. According to Fisher (1997), the first step
in devising an effective supply chain is to consider
the nature of the demand for the products. If products
are classified on the basis of their demand patterns,
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they fall into one of two categories: primarily functional or primarily innovative. Each category requires
a distinctly different kind of supply chain. Fisher argues that with their high profit margins and volatile
demand, innovative products require a fundamentally
different supply chain than stable, low-margin functional products. Two distinct types of functions performed by a supply chain should be recognized: a
physical function and a market mediation function. A
supply chain’s physical function is readily apparent
and includes converting raw materials into parts, components, and eventually finished goods, and transporting all of them from one point in the supply chain to
the next. Less visible but equally important is market
mediation (demand knowledge), the purpose of which
is to ensure that the variety of products reaching the
marketplace matches what consumers need.
Most important in the environment for innovative
products is reading market signals correctly and being
able to react quickly during the product’s short life
cycle. The crucial flow of information occurs from
the marketplace to the chain. The critical decisions
about capacity and inventory are not about minimizing costs but where in the chain to position inventory
and available production capacity in order to hedge
against uncertain demand. Suppliers should be chosen
for their speed and flexibility, not for their low cost
(Fisher, 1997).
The first step in designing a responsive supply chain
is to accept that uncertainty is inherent in innovative products. Uncertainty can be avoided by cutting
lead-times and increasing the supply chain’s flexibility so that it can produce to order or at least assemble
the product at a time closer to when demand materializes and can be accurately forecast. The company can
hedge against the remaining uncertainty with buffers
of inventory or excess capacity (Fisher, 1997).
Many recent texts emphasize that the product,
manufacturing process and supply chain structure
need to be considered together to create a capability
for mass customization (Pine et al., 1993; Lampel
and Minzberg, 1996; Feitzinger and Lee, 1997; Fine,
1998; Duray et al., 2000). Different industries require
different approaches for customization. The BTS configuration and delivery belongs to “menu industries”
in the categorization of Lampel and Minzberg (1996).
In a menu industry, buyers have a menu of choices
from which to select features of the final product.
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Customized standardization tends to be the preferred
customization strategy in this type of industry. Transactions between the buyer and the supplier involve
negotiations and reciprocal relationships between
buyers and sellers. Once the configuration has been
decided, the production function assembles prefabricated components into finished products.
2.2. Industrial customer–supplier relationships
Texts on supply chain structure typically suggest
that great benefits could be achieved by co-operation
between the customer and the supplier and giving the
supplier access to the customer’s real demand data.
However, as Lee et al. (1997b) state, a different problem is under what conditions the customer would be
willing to co-operate with the supplier, to give access
to real demand data and to co-ordinate its ordering
policies for the benefit of the supplier.
Economists have recognized that ‘resource owners increase productivity through co-operative
specialization’ (Dyer, 1997; further Alchian and
Demsetz, 1972). Indeed, the supply chains are characterized by inter-firm specialization such that individual firms engage in a narrow range of activities
that are embedded in a complex chain of input–output
relations with other firms. Productivity gains in the
supply chains are possible when firms are willing
to make transaction or relation-specific investments
(Williamson, 1985; Perry, 1989). Recent empirical
work confirms that investments in relation-specific
assets are often correlated with better performance
compared to more arms-length relationships (Parkhe,
1993; Dyer, 1996a).
Recent SCM and relationship marketing research
has attempted to increase understanding of the conditions for win–win partnerships, i.e. customer–supplier
relationships in which close long-term co-operation
simultaneously increases the value produced by the
demand chain and decreases the overall cost of the
chain. Several researchers have come to the conclusion
that companies need to divide their customer–supplier
relationships into classes along the continuum from
‘arms-length’ relationships to true partnerships
(Moody, 1993; Vollmann et al., 1995; Lambert et al.,
1996; Cooper et al., 1997; Friis Olsen and Ellram,
1997; Bensaou, 1999). While true strategic partnerships create new value, they are costly to develop,
nurture and maintain. Also, they are risky given the
specialized investments they require (Cooper et al.,
1997; Bensaou, 1999). The number of real partnerships a company can build and maintain is limited.
Therefore, partnership type of relationships cannot be
expected to be built with a large number of customers
or suppliers, and focusing the resources on building
the right relationships requires careful planning and
decision-making.
Commitment refers to the willingness of buyers
and suppliers to exert effort on behalf of the relationship. Commitment to a relationship is most frequently
demonstrated by committing resources to the relationship, which may occur in the form of an organization’s
time, money, facilities, etc. These types of resources
are often referred to as ‘asset-specific’ resources, in
that they are directed specifically towards the other
party (Dyer, 1997). Several other studies have also
found a relationship between resource commitment
and the joint action or continuity between parties
within inter-organizational relationships (Heide and
John, 1990; Yoshino and Rangan, 1995). These results suggest that successful partnerships result when
both buyers and suppliers demonstrate a willingness
to commit a variety of assets to a set of future transactions.
Two aspects of communication behavior that address the extent to which the information exchanged is
effective in a partnership include information sharing,
and the level of information quality and participation
(Monczka et al., 1998). Both of these aspects of information sharing (quantity and quality) are required
to successfully develop supplier partnerships. Information sharing refers to the extent to which critical
and proprietary information is communicated to one’s
supply chain partner (Mohr and Spekman, 1994).
Suppliers and customers can form joint development
teams to improve various aspects in the supply chain
or suppliers can suggest changes that may lead to
quality or cost improvements (Clark, 1989). Information quality includes such aspects as the accuracy,
timeliness, adequacy, and credibility of information exchanged (Huber and Daft, 1987). Information
participation refers to the extent to which partners
engage jointly in planning and goal setting (Mohr
and Spekman, 1994). These information attributes are
closely related and critical in enabling members of a
partnership to co-ordinate their activities. The earlier
J. Heikkilä / Journal of Operations Management 20 (2002) 747–767
mentioned works suggest that successful supplier alliances are associated with high levels of information
sharing and information quality and participation.
Interdependence exists when one actor does not
entirely control all of the conditions necessary for
achievement of an action or a desired outcome. Resource dependence has been explored in empirical
studies, which investigate the relationship between
dependence and control in buyer–supplier relationships (Handfield, 1993). For instance, dealers are
less opportunistic when they depend on a primary
supplier, whereas suppliers with control over dealer’s
decisions exhibit greater opportunism (Provan and
Skinner, 1989). Resource dependence can also influence supplier just-in-time (JIT) delivery performance
(Handfield, 1993). The above literature suggests that
successful partnerships are expected to be characterized by higher levels of interdependence.
Trust encompasses two essential elements (Kumar
et al., 1995): (1) trust in the partner’s reliability, that
is the belief that the partner stands by its word, fulfills
promised role obligations, and is sincere, and (2) trust
in the partner’s benevolence, that is the belief that the
partner is interested in the firm’s welfare and will not
take unexpected actions that will negatively affect the
firm. Trust, therefore, exists when a firm believes its
partner is reliable and benevolent. Conflict is behavior that impedes, blocks, or frustrates another firm’s
goal pursuit (Kumar et al., 1995). Perceived conflict is
the magnitude of present conflict acknowledged and
perceived by the firm.
3. Research method and data collection
Theory building from inductive case research was
chosen as an appropriate research approach for this
study. The objective is increased understanding of the
phenomenon. The research is directed toward development of testable hypotheses that are generalizable
in various application environments. This research
approach is a suitable method to describe and explore new phenomena (Handfield and Melnyk, 1998;
Eisenhardt, 1989) or to build new operations management theories (Meredith, 1998). This type of theory
building relies on direct observations of the objects or
participants in the theory and its development (Glaser
and Strauss, 1967; Yin, 1989). The research approach
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is inductive, and utilizes both qualitative and quantitative data. The case study allows the investigation
to retain the holistic and meaningful characteristic of
complex real life events (Yin, 1989).
Research constructs direct attention to what should
be studied in order to answer the research questions
(Yin, 1989). In this research, there are three main constructs to be operationalized: information and material flows together forming the structure in a demand
chain, the relationship between an industrial customer
and the supplier, and the performance of a demand
chain. Operationalization of these research constructs
is shown in Table 2.
3.1. Case selection and data collection
Six supply chains of Nokia Networks were studied
with a different customer in each case in a different
European country. These six cases were selected using
the following criteria:
• The first two cases (customers Alpha and Beta,
the names used here to identify the firms are
pseudonyms) represented two extremes in supply
chain performance in Nokia. The higher performing supply chain (Alpha) represented an industrial customer–supplier relationship in which the
two organizations had co-operated for a relatively
long time, the two organizations had jointly carried out a Handshake supply chain improvement
project successfully, and the supply chain performance was considered good by Nokia’s managers.
The lower-performing case (Beta) represented a
relationship in which the two organizations had
co-operated for a few years and implementing SCM
improvement was perceived as difficult.
• Another high performing (Delta) and another low
performing (Theta) case were selected for study after the first two cases were analyzed. The objective
was to either reinforce or reject patterns emerging
from the first two cases related to success or failure
in supply chain relationships.
• The two other cases (Gamma and Epsilon) that were
selected after the analysis of the first two cases represented relationships that were just recently established and the customers’ cellular network was not
yet fully opened for traffic. Right from the beginning
of the relationship, Nokia was trying to implement
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Table 2
Operationalization of the research constructs: data collection protocol for each of the issues
Issues in the research constructs
Data collection protocol
Demand chain structure
Members of the demand chain
Information and material flows (delays)
between the members
Process description from the interviews
The database of order-to-delivery cycles
Sources of delays and distortions in the information flows
Demand signaling
Forecasting practice from the interviews
Rationing game
Forecasting accuracy from the forecasting data and interviews
Order batching
Frequency of ordering from the interviews
Customer–supplier relationship
Commitment to future interaction
Duration of the relationship from the interviews
Communication
Amount of information sharing, information
quality
Participation
Communication patterns from the interviews
Trust
Reliability, benevolence and perceived conflict from the survey
Demand chain performance
Customer satisfaction (effectiveness)
Efficiency
Information sharing (both quantity and quality of information) from the survey
Customer respondents’ perception of the support received from the supplier to
achieve customer’s demand chain management objectives—from the survey
Delivery lead-times from the database of order-to-delivery cycles
Inventory commitment in terms of days-of-supply from the database of
order-to-delivery cycles and from the interviews. Share of order changes from the
database of order-to-delivery cycles and from the interviews
the same lean SCM practices (such as funnel forecasting, low inventories and assemble-to-order deliveries of BTSs) as in the Handshake improvement
projects with the customers in the other cases. In
one (Epsilon) of these two new customer relationships, Nokia delivered a full turnkey cellular network to the customer.
Theory-building research typically combines multiple data collection methods. This triangulation
provides stronger substantiation of constructs and
hypotheses (Jick, 1979). Combination of data types
should be highly synergistic. Quantitative evidence
can indicate relationships, which may not be salient
from pure qualitative data. Qualitative data is useful
for understanding the rationale of the underlying relationships. In our research, data collection consisted
of the following three parts in each of the six cases:
• Quantitative data was collected of the information
and material flows and supply chain performance.
(forecasting and delivery data of the BTS volumes, a
database of 605 order-to-delivery cycles, inventory
commitment in all the supply chains, data of order
changes in all the six cases).
• Interviewing the supplier and customer representatives provided qualitative data of the
customer–supplier relationship (35 informants,
out of which 27 were representatives of Nokia
country organizations and eight represented the
customers).
• Based on the results of the qualitative interviews,
a survey questionnaire was developed and sent to
respondents in the customer and supplier organizations in order to collect quantified perceptions of the
relationship and co-operation in the network building process (46 responses received for 63 questionnaires mailed, 73% response rate).
3.2. Data analysis
Data collection, data analysis and theory building
were closely linked in this research, and together
formed an iterative process. The research process
started by definition of the research questions and
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selection of initial research constructs based on the
preliminary practical and theoretical understanding of
the phenomenon studied.
There were three levels of analysis in each of the
data analysis steps of the research process:
• within-case analysis;
• cross-case analysis;
• expert analysis: presentation and discussion of the
results with the research project steering group consisting of industry experts and research advisors.
Within-case analysis of each case involved detailed
case study write-ups for each case. These write-ups
were descriptions structured according to the constructs used in the data collection. They were central
in the generation of the insight into each case, because
they helped to cope with the analysis process of the
large volume of data (Eisenhardt, 1989). The written
case descriptions were essential for the reliability of
the research. They also enabled the informants to
review the case analysis of each case, thereby improving the construct validity. This process allowed
the unique patterns of each case to emerge before
generalized patterns across the cases were created.
The three types of data collected—quantitative data,
interview responses and survey results—were combined in the case study write-ups. All three types of
data were used as important sources of evidence when
developing an understanding of demand chain management in each case; no single type of data was allowed to dominate. Descriptive statistical measures
(arithmetic mean, S.D., and skew) were calculated for
interpretation of survey results and quantitative data.
Because of the research approach used and the relatively small sample sizes this was considered as an
appropriate way to analyze the quantitative data.
The second level in the case analysis was the
search for cross-case patterns. In the analysis of the
first two cases this meant looking at the potential
reasons for differences in the supply chain performance. This helped to sharpen the research constructs
and to focus the data collection in the further steps
of the research. Cross-case analysis of all the six
cases started by analyzing the three types of relationships that were originally used in selecting the
cases (successful, non-successful and new demand
chains). Within-group similarities were sought first
coupled with inter-group differences. The second
method used was to compare the cases across the
initial groups. Overall, the idea behind the cross-case
searching method was to force the investigation to go
beyond initial impressions (Eisenhardt, 1989). Also,
cross-case searching tactics enhance the probability to
capture the novel findings that may exist in the data.
4. How to combine efficiency and customer
satisfaction?
The cross-case analysis results are organized according to the background of the customer–supplier
relationship in the cases studied (Table 3), and according to the three main research constructs used to
guide the research: demand chain structure (Table 4),
customer–supplier relationship (Table 5), and demand
chain performance (Table 6).
Refer to Table 3 for the comparison of the cases in
terms of the background of the relationships. For further illustration, Nokia’s Project Implementation Manager for Epsilon and Country Logistics Manager for
Gamma described the planning challenge in a new
demand chain relationship as follows:
Logistics is a big mess in these projects. The
approach is too theoretical, not practical enough.
Logistics is causing our problems. Putting a complete site package together is in principle a good
idea, as suggested in the Handshake model. However, it does not work. In the planning there is
70–80% reliability for the following week, but there
is no understanding of the needs for 3 weeks out.
Use of site packages is possible in an established
project in which the required competencies, systems and planning processes are in place. In a new
project, it is better to start with a big warehouse
(that can deliver materials fast).
The following conclusions are made to explain how
the background of the customer–supplier relationships
might be related to the demand chain efficiency in the
cellular network building:
Competence of the customer, duration of the relationship between the customer and the supplier
and experience of the employees working on the
customer–supplier relationship increase demand
chain efficiency (this proposition is based on the
finding nos. 1–3, see Table 3).
J. Heikkilä / Journal of Operations Management 20 (2002) 747–767
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J. Heikkilä / Journal of Operations Management 20 (2002) 747–767
Table 5
Relationship characteristics
Communication and trust
Alpha
Delta
Epsilon
Gamma
Beta
Communication patterns
Frequent communication forums established on all levels and in all
functions between the two organizations
Theta
Finding
number
13
Information sharing
Nokia respondents
Customer respondents
4.97
5.01
4.43
5.01
3.76
4.75
5.09
4.89
4.78
4.66
4.63
14
Reliability
Nokia respondents
Customer respondents
4.91
5.68
5.16
4.88
3.96
2.60
3.90
3.27
4.38
3.85
4.10
Benevolence
Nokia respondents
Customer respondents
4.20
5.60
4.12
5.00
3.32
3.40
3.80
3.93
3.93
4.70
4.20
Perceived conflict
Nokia respondents
Customer respondents
1.75
1.90
2.30
2.90
5.10
3.50
2.63
4.00
3.25
3.13
4.33
15
16
17
The numbers given are arithmetic means of responses from the indicated respondent group. The scale was a 7-point Likert scale with 7
representing high and 1 representing low information sharing, reliability, benevolence, and conflict in the relationship with the other party.
The importance of efficiency as a demand chain
management objective increases when the network
building stage matures and the growth of the network stabilizes. Demand chain efficiency grows because of the increased attention to it (finding nos. 4
and 5).
The research data on the demand chain structure in
the six cases are given in Table 4. The following conclusions are made to explain how the demand chain
structure might be related to the demand chain efficiency in the cellular network building:
Lower demand chain efficiency is related to
multi-step forecasting process and consistent
over-forecasting from the customer to the supplier’s
country organization and from the supplier’s country organization to the factory (finding no. 6).
Lower demand chain efficiency is related to changing orders, order batching or delay in the ordering
information (finding no. 7).
The waiting time of a delivery in the target country (customer-specific inventory) is a major part of
the total inventory commitment in the chain and
is related to the overall efficiency of the demand
chains. Removal of a customer-specific inventory
is a “speed threshold” that allows radical improvement of supply chain performance (finding no. 10).
The research data on the characteristics of the
customer–supplier relationships in the six cases is
shown in Table 5. In addition to the survey results
in the table, the following quotes from the interview
informants in the high-performance Alpha case and
the lower-performing Gamma case indicate considerable difference in how trust was perceived in the
customer–supplier relationships:
There is a spirit of co-operation between Alpha and
Nokia. Both organizations are willing to do extra
work for the partner in order to help them forward.
The contract between the two organizations defines
prices, but otherwise it is not followed in every detail. An example is failure reports. Alpha wants to
have monthly Nokia’s failure reports for the network. This is more often than the contract defines,
but we are not going to charge any extra for this
service. (Nokia Product Manager for Alpha).
Current relationship with Nokia country organization is improving. Our biggest issue is that we do
not feel they are open and honest with us. They
J. Heikkilä / Journal of Operations Management 20 (2002) 747–767
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J. Heikkilä / Journal of Operations Management 20 (2002) 747–767
continue to tell what we want to hear and deliver
the bad news at the 23rd hour. This impacts our
planning. . . . We feel they need to improve the
management of their subcontractors and the flow of
materials. . . . Their different organizational units
have conflicting goals, for example site implementation versus logistics. (Gamma Network Planning
and Implementation Manager).
The following conclusions are made to explain how
the customer–supplier relationship might influence the
demand chain efficiency in the cellular network building:
Good demand chain efficiency is related to good
trust between the customer and the supplier, i.e. high
reliability and benevolence and low conflict in the
customer–supplier relationship (this proposition is
based on finding nos. 15–17 in Table 5).
High quantity of information sharing is a necessary
but not always sufficient condition for good quality of information sharing and high demand chain
efficiency (finding nos. 13 and 14).
Low perceived reliability of the supplier is interpreted as low quality of information sharing. Lower
quality of information sharing in the relationship is
related to lower demand chain efficiency (finding
no. 15).
The research data on the demand chain performance
is given in Table 6. The following conclusions are
suggested to explain how the various demand chain
performance issues are related in the cellular network
building:
High demand chain efficiency is not sufficient to
explain the overall customer satisfaction in the cellular network building. For example, technology
and its perceived quality might contribute more to
the overall customer satisfaction than demand chain
efficiency (finding nos. 21–23).
Radical demand chain improvement requires good
co-operation from the customer. The customer’s
clear perceived gain from co-operation is related
to good success in demand chain improvement
(removing customer’s central warehouse is a sufficient incentive for the customer to co-operate in
radical improvement; incremental reduction of the
supplier’s demand chain cost is not) (finding nos.
24 and 25).
4.1. Cross-case analysis results according
to effectiveness and efficiency
The cross-case analysis results are summarized in
Fig. 1. Each of the six cases is positioned in a matrix according to the demand chain effectiveness (i.e.
the match between the customer need and the demand
chain structure) and the demand chain efficiency (i.e.
the total inventory commitment from BTS assembly
start until integration to the telecommunications network).
The direct BTS delivery Handshake model implemented in the Alpha and Delta cases matched well
with the customers’ situation and need, making the
demand chain structure effective. The following reasons contributed to the successful implementation of
the direct delivery Handshake model:
• There was an established relationship between the
customers and Nokia. The customers had good
competence of working in their industry and market.
Organizations and communication mechanisms
were well established.
• The network building was in an advanced stage,
with stabilized growth, focusing on optimizing the
network and building new end-customer features in
the network.
• High information sharing and trust made it possible to work together to improve the demand chain
performance. High gain perceived by the customer
of the demand chain improvement project increased
the motivation of the customers to co-operate.
• The customers had good planning capabilities and
they took responsibility for the planning information provided by them.
• Because of the good co-operation it was possible
to remove the customer-specific inventory, resulting
in major demand chain efficiency improvement and
good customer satisfaction.
Comparison of cases in the same effectiveness
group results in explanations for efficiency differences
between cases. Delta has longer total lead-time, higher
inventory commitment and more order changes than
Alpha, even if they operate with the same demand
J. Heikkilä / Journal of Operations Management 20 (2002) 747–767
761
Fig. 1. Summary of the cross-case analysis.
chain structure in a comparable customer situation
and relationship. Similar findings are made when
comparing the Epsilon, Gamma and Beta cases. Epsilon demand chain has higher efficiency than the
other two demand chains because there is no gaming
in the forecasts, there are no delays and no batching
in the ordering process. Beta has lowest efficiency of
these three cases. It also has the highest consistent
bias in forecasting and longest delays in the ordering
process. For this type of customer, a highly reactive
chain is the only possibility, as also demanded by
Beta’s Contract and Negotiations Manager:
Theta forms a special case among the cases studied. In principle, the country warehouse model with
BTS modules in the warehouse would be an effective
model to support fast network building with high flexibility. However, having all elements of low efficiency
in place ruined the effectiveness of the model. There
was gaming in forecasts, long delays in the ordering
process and monthly batches in ordering, resulting in
very high total inventory commitment, total lead-time
and share of order changes.
Flexibility of deliveries is the most important factor influencing the performance of our chain, followed by on-time delivery and quality. Flexibility
means having the possibility to make changes in
the content of the delivery if needed during the
agreed lead-time. Sometimes a shorter than agreed
lead-time is needed. . . . Lead-time could be reduced
if BTS configuration could be made in our country. International transportation takes too long time.
Site acquisition and preparation make the site process uncertain.
5. Toward a model of demand chain management
This case research of the six customer relationships in cellular network building indicates that
there are a variety of customer relationships that the
supplier needs to adapt to. Therefore, the crucial
question for a supplier is how to design the demand
chain architecture according to the needs and characteristics of distinct customer needs and situations.
Demand chain architecture means understanding
the nature of demand and developing a modular
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J. Heikkilä / Journal of Operations Management 20 (2002) 747–767
demand chain structure—including decisions of the
order-penetration point, inventory buffer locations and
sizes, and assembly capacity. The above described
research results are next positioned to the existing
theoretical literature of demand chain management.
Stalk (1988) suggests demand chain efficiency can
be improved by reducing the time delays in the flow
of information and materials throughout the chain.
Holmström (1995) suggests that inventory commitment needs to be reduced to a point where demand
distortion is diminished and synchronization of production with demand is possible in order to improve
performance by speeding up operations. There is no
reason to disagree with these overall objectives to improve demand chain efficiency. But the research findings indicate that reduction of inventory commitment
might be challenging with a large number of customers
even if it brings radical improvements with some. Inventory reduction and an effort to speed-up operations
can fail with non-co-operative customers, and result
in lowered trust in the relationship, and further in distorted demand information and lowered efficiency.
The research findings comply with those of
Forrester (1958, 1961) and Sterman (1989) showing
that the basic structure of a demand chain and delays and distortions in the information flows cause
inefficiencies in the chain. Also, the findings support
the results of Lee et al. (1997b) that demand signaling, rationing game and order batching are sources
of distorted information flows and result in chain
inefficiencies.
The research findings on the customer–supplier relationship comply with the results of Heide and John
(1990) and Mohr and Spekman (1994) that the historical length of the relationship increases the continuity expectations, which in turn increase the level of
co-operation (joint action); and that co-operation in
terms of co-ordination, participation, and joint problem solving (also Monczka et al., 1998) are good
predictors for the success of a partnership. Dyer
(1997) also sees continuity as an important factor for a
successful partnership, through repeated transactions
with a small set of suppliers.
The research results are also consistent with the
earlier research in finding high trust between the partners being related to good demand chain efficiency.
Mohr and Spekman (1994), Dyer (1997), and
Monczka et al. (1998) all found trust an important
factor contributing to partnership success. There is a
difference between the results of some of the earlier
research and our findings in the information sharing
between customer and supplier in industrial relationships. Dyer (1997) found that extensive inter-firm
information sharing reduces asymmetric information
and results in lower transaction costs. Monczka et al.
(1998) found that bilateral communication behavior
played a significant role in determining partnership
success.
No major differences could be observed in this
research in the quantity of information sharing between high and low efficiency relationships. It seems
that information sharing is perceived as open in all
customer–supplier relationships in cellular network
building, as far as it can be concluded by studying six
relationships of one supplier in the industry. However,
there were differences in how the supplier’s reliability
was perceived. Reliability was perceived as higher in
the high efficiency relationships than in the low efficiency relationships. A conclusion is drawn here that
even if the quantity of information sharing might be
a necessary precondition for well-performing supply
chain relationships, it is not always sufficient. Information sharing quality comes into focus, particularly
in an industry with a large number of new companies,
new markets, new employees, and new relationships.
Cellular network systems clearly fall in the category of “innovative products” in the typology of Fisher
(1997). The highly uncertain receptiveness of the market increases the risk of shortages or excess supplies.
The cost of shortages is that the customer loses sales
in an emerging (sometimes exponentially growing)
market. The high number of final product configurations increases the risk of obsolescence and the cost
of excess supplies.
According to Fisher (1997), the most important
factor when designing global delivery chains is to understand the behavior of demand in a particular industry and organize the chain to serve it accordingly. In
such an environment, the crucial flow of information
occurs from the marketplace to the chain. It is important to cut lead-times to produce the product close to
the time when demand materializes. The critical decisions about inventory and capacity are where in the
chain to position inventory and available production
and assembly capacity in order to have maximum
flexibility to deal with the highly fluctuating demand.
J. Heikkilä / Journal of Operations Management 20 (2002) 747–767
Fisher suggests that selection of demand chain structure primarily depends on the industry characteristics.
We feel that this is too simple an approach. Based on
the findings of this study, it is suggested that several
different chains are needed within a single industry
to meet various customer needs and situations.
This oversimplification by Fisher makes us suggest
that strategic SCM needs to be well integrated with the
market segmentation ideas in marketing. In operations
management, Hill (1994) has earlier talked about this
integration in the context of manufacturing strategy.
Companies require a strategy not based solely on marketing, manufacturing, logistics, or any other function,
but one that embraces the interface between markets
and functions. The link between functional strategies
comes from the markets the business serves.
SCM must choose its processes and design its infrastructure in ways that help a company’s bundles
of goods and services to win orders, and that choice
and design must be adaptable to changing business
needs. Thus, the company as a whole needs to agree
on the markets and segments within these markets in
which it decides to compete. In no way can these critical decisions be the responsibility of a single function. As a function, marketing will have an important
and essential (but not the only) view. An essential perspective of a firm’s markets has to come from operations. This perspective is established by determining
the order-qualifiers and order-winners that operations
needs to provide (compare to Hill, 1994).
Order-qualifiers are the criteria that a firm must
meet for a customer to even consider it as a possible
supplier. Order-winners are those criteria that win the
orders. In the cellular network building—as in most
other project businesses as well—order-qualifiers
are related to the customer’s perception of the
supplier’s capability to fulfill the contract requirements. Order-winners for new relationships in this
type of high-technology industry are probably often related to technological matters. But essential
order-winners for continuous business are developed
during the co-operation between the customer and the
supplier when building the network.
For a new operator striving for launching their services to market, and thereafter aggressively competing
for the market share with the established telecommunications operators, speed and support from an experienced technology vendor are above all other criteria.
763
Efficiency that is perceived to slow down the network
building and expansion is not acceptable. Established
operators in an advanced stage of network optimization are more willing to appreciate the cost advantage of a lean supply chain. The technology vendor
needs to understand the differing customer needs and
situations, implement best demand chain structure in
co-operation with the customer, and through improved
customer satisfaction contribute to better relationship
and co-operation.
The research findings are summarized in the demand chain management model presented in Fig. 2.
The model consists of the following five propositions,
emerging from the research of the six cases in the mobile telecommunication industry:
Proposition 1. Good relationship characteristics
contribute to reliable information flows.
Proposition 2. Reliable information flows contribute
to high efficiency.
Proposition 3. Understanding the customer situation and need and good relationship characteristics
contribute to co-operation between the customer and
supplier.
Proposition 4. Good co-operation in implementing
demand chain improvement contributes to high efficiency and high customer satisfaction.
Proposition 5. High customer satisfaction contributes
to good relationship characteristics.
The first two propositions state the already wellknown relationships in industrial supply chains that
good customer–supplier relationships contribute to
reliable information flows, that in turn result in high
supply chain efficiency. However, this well-known
equation needs stratification for fast-growing system
businesses, where the high growth results in a large
number of different types of customers with different
situations and needs. A technology vendor that wants
to achieve overall good customer satisfaction, needs to
understand the individual customers’ needs and objectives and to be able to support the customer in meeting
their objectives. Good understanding of the customer
needs builds a good basis for fruitful co-operation
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J. Heikkilä / Journal of Operations Management 20 (2002) 747–767
Fig. 2. The model of demand chain management.
between the customer and the supplier, increasing the
demand chain efficiency and customer satisfaction.
following priority order of decision-making criteria
was proposed to design alternative modular demand
chain processes:
6. Managerial implications
1. Supporting the customer’s network building process by sufficiently fast deliveries.
2. Building a product structure to enable decisions
on the order-penetration point for a base station
according to the customer need.
The main argument resulting from this research is
that several demand chain structures are necessary to
adapt to varying customer needs and situations. The
Fig. 3. The three alternative demand chain structures suggested to serve different customers in the mobile network business.
J. Heikkilä / Journal of Operations Management 20 (2002) 747–767
3. Flexibility in the assembly capacity to meet the
market uncertainty.
4. Inventory optimization within the constraints
resulting from the above criteria.
Three demand chain processes as variations of
generic demand chain architecture were proposed
to serve the different customer needs of Nokia Networks in the cellular networks industry (Fig. 3). An
important aspect was to see the alternative processes
as modular, supporting a consistent move of particular customer–supplier relationships from one demand
chain process to a more advanced one when the
relationship characteristics allow.
Increased rate of returns in implementing demand
chain management was experienced at Nokia when the
positive feedback of good customer satisfaction feeding into the relationship characteristics started taking
effect (Proposition 5 in the model, see also Sterman,
2000; Senge and Sterman, 1994; Senge, 1990 for modeling organizational learning). The original Handshake
model of Nokia Networks was developed into a new
program called breakthrough inventory rotation days
(BIRD). The BIRD program aimed at improving customer satisfaction and implementing efficient demand
chains for a large number of Nokia Networks’ customers. By the end of the year 2000, during 1.5 years
of the BIRD program implementation, about 40% average reduction in inventory levels was reached despite of substantial growth in sales. BIRD focused on
Nokia Networks’ European customers. The new processes were implemented for customer projects in 17
different European countries by the end of year 2000
(Tissari and Heikkilä, 2001).
7. Conclusions
Companies in the fast-growing industries need to
be constantly developing their supply chain efficiency.
At the same time, they are all the time facing a variety of new customers, with new situations and needs.
Our study of six customer cases of Nokia Networks
explored how to combine high supply chain efficiency
with good customer satisfaction. We propose that supply chain improvement should start from the customer
end, and the concept of SCM should be changed into
demand chain management.
765
Demand chain management understands the need
for good customer–supplier relationships and reliable
information flows as contributors to high efficiency.
But in a fast-growing systems business such as mobile telecommunications industry, the supplier also
needs to be able to adapt its offering to a wide variety of customers. Understanding the customer’s need
together with the right demand chain structure results
in good co-operation in improving the joint demand
chain, which further leads to superior demand chain
efficiency and customer satisfaction.
The current article addresses the process of
demand chain improvement in the fast-growing,
high-technology environment of the mobile telecommunications industry. This industry is admittedly a
special case, an environment placing an extraordinary
emphasis on continuous and rapid changes in the
mode of operations to remain ahead of the competition. However, the ideas here are offered for further
empirical testing. If they survive these tests, they
provide learning for all organizations wishing to stay
competitive in this ever-changing world.
References
Alchian, A.A., Demsetz, H., 1972. Production, information costs,
and economic organization. American Economic Association
62 (5), 777–795.
Bensaou, M., 1999. Portfolios of buyer–supplier relationships.
Sloan Management Review 40 (3), 35–44.
Burbidge, J.L., 1961. The new approach to production. The
Production Engineer 40 (12), 769–784.
Clark, K., 1989. Project scope and project performance: the
effect of parts strategy and supplier involvement on product
development. Management Science 35 (10), 1247–1263.
Cooper, M.C., Ellram, L.M., Gardner, J.T., Hanks, A.M., 1997.
Meshing multiple alliances. Journal of Business Logistics
18 (1), 67–89.
Duray, R., Ward, P.T., Milligan, G.W., Berry, W.L., 2000.
Approaches to mass customization: configurations and empirical
validation. Journal of Operations Management 18 (6), 605–625.
Dyer, J.H., 1996a. Specialized supplier networks as a source
of competitive advantage: evidence from the auto industry.
Strategic Management Journal 17 (4), 271–291.
Dyer, J.H., 1996b. Does governance matter? Keiretsu alliances and
asset specificity as sources of Japanese competitive advantage.
Organization Science 7 (6), 649–666.
Dyer, J.H., 1996c. How Chrysler created an American Keiretsu.
Harvard Business Review 74 (4), 42–56.
Dyer, J.H., 1997. Effective inter-firm collaboration: how firms
minimize transaction costs and maximize transaction value.
Strategic Management Journal 18 (7), 535–556.
766
J. Heikkilä / Journal of Operations Management 20 (2002) 747–767
Eisenhardt, K.M., 1989. Building theories from case study
research. Academy of Management Review 14 (4), 532–550.
Feitzinger, E., Lee, H.L., 1997. Mass customization at
Hewlett-Packard: the power of postponement. Harvard Business
Review 75 (1), 116–121.
Fine, C.H., 1998. Clockspeed: Winning Industry Control in the
Age of Temporary Advantage. Perseus Books, Reading.
Fisher, M.L., 1997. What is the right supply chain for your product?
Harvard Business Review 75 (2), 105–116.
Forrester, J.W., 1958. Industrial dynamics: a major breakthrough
for decision makers. Harvard Business Review 36 (4), 37–66.
Forrester, J.W., 1961. Industrial Dynamics. MIT Press, NY.
Friis Olsen, R., Ellram, L.M., 1997. A portfolio approach to
supplier relationships. International Marketing Management
26 (2), 101–113.
Glaser, B., Strauss, A., 1967. The Discovery of Grounded Theory:
Strategies for Qualitative Research. Aldine, Chicago.
Hakansson, H., Snehota, I., 1995. Developing Relationships in
Business Networks. Routledge, London.
Handfield, R.B., 1993. A resource dependence perspective of
just-in-time purchasing. Journal of Operations Management
11 (3), 289–311.
Handfield, R.B., Melnyk, S.A., 1998. The scientific theory-building
process: a primer using the case of TQM. Journal of Operations
Management 16 (4), 321–339.
Heide, J.B., John, G., 1990. Alliances in industrial purchasing:
the determinants of joint action in buyer–supplier relationships.
Journal of Marketing Research 27 (1), 24–36.
Hill, T., 1994. Manufacturing Strategy: Text and Cases. Richard
D. Irwin, Boston.
Holmström, J., 1994. The relationship between speed and
productivity in industry networks: a study of industrial statistics.
International Journal of Production Economics 34 (1), 91–98.
Holmström, J., 1995. Speed and efficiency—a statistical enquiry
of manufacturing industries. International Journal of Production
Economics 39 (3), 185–191.
Huber, T., Daft, R., 1987. The information environment of
organizations. In: Jablin, F., et al. (Eds.), Handbook of
Organizational Communication. Sage Publications, Newbury
Park.
Jick, T.D., 1979. Mixing qualitative and quantitative methods:
triangulation in action. Administrative Science Quarterly 24 (4),
602–611.
Kumar, N., Scheer, L.K., Steenkamp, J.-B.E.M., 1995. The effects
of perceived interdependence on dealer attitudes. Journal of
Marketing Research 32 (3), 348–356.
Lambert, D.M., Emmelhainz, M.A., Gardner, J.T., 1996. So you
think you want a partner? Marketing Management 5 (2), 24–33.
Lampel, D., Minzberg, H., 1996. Customizing customization. Sloan
Management Review 38 (1), 21–30.
Lee, H.L., Billington, C., 1992. Managing supply chain inventory:
pitfalls and opportunities. Sloan Management Review 33 (3),
65–73.
Lee, H.L., Padmanabhan, V., Whang, S., 1997a. The bullwhip
effect in supply chains. Sloan Management Review 38 (3),
93–102.
Lee, H.L., Padmanabhan, V., Whang, S., 1997b. Information
distortion in a supply chain: the bullwhip effect. Management
Science 43 (4), 546–558.
Meredith, J., 1998. Building operation management theory through
case and field research. Journal of Operations Management
16 (4), 441–454.
Metters, R., 1997. Quantifying the bullwhip effect in supply chains.
Journal of Operations Management 15 (2), 89–100.
Mohr, J., Spekman, R., 1994. Characteristics of partnership
success: partnership attributes, communication behavior, and
conflict resolution techniques. Strategic Management Journal
15 (2), 135–152.
Monczka, R.M., Petersen, K.J., Handfield, R.B., Ragatz, G.L.,
1998. Success factors in strategic supplier alliances: the buying
company perspective. Decision Sciences 29 (3), 553–573.
Moody, P.E., 1993. Breakthrough Partnering: Creating a Collective
Enterprise Advantage. Omneo, Essex Junction, UK.
Parkhe, A., 1993. Strategic alliance structuring: a game theoretic
and transaction cost examination of inter-firm co-operation.
Academy of Management Journal 36 (4), 794–829.
Perry, M.K., 1989. Vertical integration. In: Schmalensee, R., Willig,
R. (Eds.), Handbook of Industrial Organization. North-Holland,
Amsterdam, pp. 185–255.
Pine III, B.J., Victor, B., Boynton, A.C., 1993. Making mass
customization work. Harvard Business Review 71 (4), 108–119.
Provan, K., Skinner, S., 1989. Inter-organizational dependence
and control as predictors of opportunism in dealer–supplier
relations. Academy of Management Journal 32 (1), 202–212.
Senge P.M., 1990. The Fifth Discipline: The Art and Practice of
the Learning Organization. Currency Doubleday, NY.
Senge, P.M., Sterman, J.D., 1994. Systems thinking and
organizational learning: acting locally and thinking globally in
the organization of the future. In: Morecroft, J.D.W., Sterman,
J.D. (Eds.), Modeling for Learning Organizations. Productivity
Press, Portland, pp. 195–216.
Sharman, G., 1984. The rediscovery of logistics. Harvard Business
Review 62 (5), 65–77.
Stalk, G., 1988. Time: the next strategic advantage. Harvard
Business Review 66 (4), 41–51.
Stalk, G., Holt, T.M., 1990. Competing against time, Free Press,
NY.
Sterman, J.D., 1989. Modeling managerial behavior: misperceptions of feedback in a dynamic decision-making experiment.
Management Science 35 (3), 321–339.
Sterman, J.D., 2000. Business Dynamics: Systems Thinking and
Modeling for a Complex World. McGraw-Hill, Boston.
Tissari, T., Heikkilä, J., 2001. Successful re-engineering: learning
by doing. International Journal of Logistics: Research and
Applications 4 (3), 229–344.
Towill, D.R., Naim, M.M., Wikner, J., 1992. Industrial dynamics
simulation models in the design of supply chains. International
Journal of Physical Distribution and Logistics Management
22 (5), 3–13.
Toyota Motor Co., Int. Public Affairs Division. Operations
Management Consulting Division, 1995. The Toyota Production
System, Toyota City.
J. Heikkilä / Journal of Operations Management 20 (2002) 747–767
Vollmann, T.E., Cordon, C., Raabe, H., 1995. From supply chain
management to demand chain management. IMD Perspectives
for Managers. November.
Vollmann, T.E., Berry, W.L., Whybark, D.C., 1997. Manufacturing
Planning and Control Systems, McGraw-Hill, NY.
Vollmann, T.E., Cordon, C., 1998. Building successful customer–
supplier alliances. Long Range Planning 31 (5), 684–694.
Vollmann, T.E., Cordon, C., Heikkilä, J., 2000. Teaching supply
chain management to business executives. Production and
Operations Management Journal 9 (1), 81–90.
Williamson, O.E., 1985. The Economic Institutions of Capitalism.
Free Press, NY.
767
Womack, J.P., Jones, D.T., 1996. Lean Thinking: Banish Waste
and Create Wealth in Your Corporation. Simon & Schuster,
NY.
Womack, J.P., Jones, D.T., Roos, D., 1991. The Machine
that Changed the World: The Story of Lean Production.
HarperPerennial, NY.
Yin, R.K., 1989. Case Study Research: Design and Methods. Sage
Publications, Newbury Park.
Yoshino, M., Rangan, S., 1995. Strategic Alliances: An
Entrepreneurial Approach to Globalization. Harvard Business
School Press, Boston.