Using Collaboration to Improve
Supply Chain Profitability
6310 N Camden Ave Apt B
Kansas City, MO 64151
University of Missouri, Kansas City
David Hakanson is currently an MBA student
at the University of Missouri, Kansas City.
Kansas City, Missouri Chapter
Using Collaboration to Improve
Supply Chain Profitability
E-business has advanced supply chain management to a new level of efficiency
and responsiveness. Even greater improvements are possible through implementation of
e-business systems with collaborative environments including information sharing,
intelligent agents and demo models. Such improvements can dramatically increase supply
chain member profitability and return on investment.
E-business has revolutionized global competition. For example, the use of e-
business in supply chain management can dramatically reduce lead times; reductions
from 16 weeks to only a few days are not uncommon. Also, stock-outs can be minimized
while companies are able to reduce safety stock inventory. The result is a supply chain
that is both efficient and responsive, one that reduces cost while improving quality and
value for the customer. E-business in simple terms is the ability for separate links of the
supply chain to communicate electronically. This use of the Internet in a business setting
improves value for both consumer and business. Consumers can use e-commerce to
communicate directly with retailers on the Internet. The result is the ability of consumers
to research, shop, and purchase products without leaving their offices or living rooms. E-
business allows information to be shared more effectively and is used by companies to
improve the overall supply chain, thereby lowering costs while increasing both profits
and return on investment (ROI). The supply chain used in many industries includes a
supplier, manufacturer, distributor, retailer and customer, as shown in Figure 1.
Supply Chain Design
E-business is not new but has recently become increasingly more important as
electronically enabled global competition drives companies to cut costs and response
times to become more competitive. In fact, supply chain management software is
expected to be the fastest growing e-business software market in 2004 (1). The growth of
uncoordinated e-business solutions by members of a supply often yield suboptimal,
overall effectiveness of the supply chain. For example, companies within a supply chain
may not use the same e-business software therefore adding confusion and the possibility
of integration problems. Several different factors can affect the effectiveness of the
supply chain. The most important of these factors is e-business communication and
collaboration between members of the supply chain.
A supply chain model based on collaboration can help firms achieve increased
profitability. A few steps should be taken to fully realize the value of a collaborative
supply chain. The most important factor in solving supply chain ineffectiveness is a
strong collaborative environment that includes all supply chain members. A collaborative
environment requires members of the supply chain to communicate, either verbally or
electronically, to determine the needs of each link in the supply chain and how product
communications can be optimized to improve overall effectiveness. Once a collaborative
environment has been achieved, the use of intelligent agents can be used to add efficiency
and responsiveness. This added efficiency is achieved by allowing different links of the
supply chain to communicate electronically using sophisticated programs, which quickly
gather information and perform supply chain performance assessments. Once supply
chains performance is measurable the use of demo models can help members of the
supply chain to further improve efficiency and responsiveness.
By working with other members of the supply chain, a company can optimize the
supply chain and lower costs while becoming more effective. The discussion in this paper
focuses on how e-business can be augmented with the additional collaboration techniques
of information sharing, intelligent agents, and demo models to achieve improved
customer value and global competitiveness.
II. THE RISE OF E-BUSINESS
Customers are demanding more customized rather than homogeneous products.
The primary task for businesses is to effectively supply these customized products in a
timely manner at low cost. The emergence of Information Technology (IT) has replaced
former methods of business communication including: postal mail, phone calls, and
faxes. As IT has emerged, its value has been realized by corporations trying to gain a
competitive advantage in a global marketplace. Companies have found that IT can be
used in their supply chain to produce a fast, responsive, low cost, and flexible solution to
meeting customer demands. It has become essential that companies use IT, primarily e-
business, as an integral part of the supply chain design to remain competitive.
Dell Computer Corporation is one example of a company that has used e-business
to improve customer value and efficiency by providing customized computers quickly at
a low price. Dell’s primary advantage is its supply chain design and management which
works to improve overall efficiency while lowering costs (2). Ordering a computer is as
easy as a click of the mouse with the use of e-commerce on the company’s website. Once
the order has been placed, the customized computer is built from parts at Dell’s
manufacturing facility in Texas and then sent to the consumer within days. This process
allows the consumer to receive the product within a week. The custom supply chain also
works well for Dell who has agreements with manufacturers to have parts delivered in a
Just-In-Time (JIT) fashion resulting in low costs of holding a minimal amount of
inventory. If the consumer orders a monitor with their computer, Dell will have the
monitor manufacturer ship the monitor directly to the consumer thus eliminating
inventory needs by Dell. Dell is one example of how a company uses e-business to
greatly improve efficiency while lowering cost for the consumer.
Dell Supply Chain
Dell’s use of e-business in their supply chain is not an isolated example. E-
business will continue to grow as global competition drives companies to become lean by
lowering costs. E-business has several advantages to companies which use it effectively.
First, when humans are involved mistakes can be easily made. An e-business supply
chain is an efficient way for communication between links of the supply chain that does
not need the intervention of a human. A second advantage is speed. An e-business
transaction can take place in seconds rather than hours or days when done by phone, fax,
or mail. Wal-Mart uses a custom e-business system that collects the UPC code of every
item sold and places an order to the manufacturer to refill the item when stock gets low.
This almost completely eliminates the need for store clerks to check inventory and allows
employees to focus more on the customer rather than the product. The only time a
manager may need to get involved in the e-business transaction is during a cyclical or
weather-related event that will produce untypical demand.
III. INFORMATION SHARING
In any supply chain, supply is stimulated by demand and the goal is to meet that
demand in a low cost manner without stock outs. The success of a firm depends on a
quick response time in meeting customer demand. In the past, firms have used safety
stock in the event that actual demand was higher than forecasted demand, but now faster
order replenishment times are possible with e-business. The ability to hold less safety
stock reduces cost for the firm while achieving a quick response time. Electronic
information sharing (called e-knowledge) between links in the supply chain makes this
form of e-business possible. A supply chain optimized by e-business can be more
effective when incorporating collaborative solutions between members in the supply
chain; thereby improving overall supply chain profitability while reducing costs. Table 1
shows how the customer can be positively affected by supply chain e-knowledge, which
decreases the demand uncertainty and stock outs. Average forecast errors, stock-out rates,
and markdowns are greatly reduced with the availability of low demand uncertainty.
Effects of Low Demand Uncertainty
Attribute Uncertainty Uncertainty
Profit Margin Low High
Average Forecast Error 10% 40%-100%
Average Stock-out Rate 1%-2% 10%-40%
Average Forced end-season 0% 10%-25%
The Bull-Whip Effect
Before the emergence of e-business, a serious problem found in most supply
chains was the “bull-whip effect.” The “bull-whip effect” occurs due to the order time lag
between supply chain members (3). The “bull-whip effect” is amplified as it goes away
from the consumer and up the supply chain. E-business greatly reduces the “bull-whip
effect” by allowing supply chain members access to demand information quickly. When
the information distortion along the supply chain is reduced, this reduces the possibility
of the “bull-whip effect”.
Consumption Consumer Retailer Distributor Manufacturer Supplier
E-business supply chain systems using e-knowledge can improve the way supply
chain members work together. A collaborative environment can be created to share
demand forecasts, cyclical factors, and business ideas to achieve synergy along the
supply chain. The goal of using such e-knowledge is the “exchange of strategic
knowledge in order to achieve mutually beneficial objectives” (4). The use of e-
knowledge became possible only a few years ago as a result of evolving collaborative
networks producing, currently accepted platforms for the continuous exchange of
information concerning markets, customers, demand, and inventories. Now, when e-
knowledge is used in a supply chain, the automated exchange of information is
instantaneous to all supply chain members. Toyota has used e-knowledge to increase its
suppliers’ involvement by requiring a collaborative environment in its supply chain (5).
Researchers found that in the Toyota system the suppliers were developing a “dynamic
learning capability” that improves their competitive capabilities. The use of this
collaborative environment to streamline information sharing saves the cost of information
search, evaluation, transactions, and administration. The low costs of Japanese
automobile companies result from economies of scale, repeated transactions with a small
group of suppliers, a wide range of information sharing to reduce information asymmetry,
long-term performance orientation, and investments in shared (co-specialized) assets (4).
Collaborative Network within Supply Chain
A collaborative environment can also be used in research and development
(R&D) departments to improve the overall supply chain network. Development of
modular and customized product development systems can be used to improve individual
performance while working with other supply chain members to provide products which
will better suite customer needs. More specifically, the co-engineering infrastructures
built in a collaborative environment enable the firms in the supply chain to provide
customized products, without significant costs and sacrifice of efficiency. Figure 5 shows
how collaboration between supply chain member R&D departments is possible. By using
this model supply chain members work together as a team to complete a common goal
instead of viewing the task as a competition between links. By putting the goal of the
overall supply chain first, the R&D departments focus on a common goal and not the
company’s own interests.
Collaboration for Research and Development
Collaborative Planning Forecasting and Replenishment
A major use of information sharing is the demand forecast. With an accurate
demand forecast, a supplier can produce and transport products to reduce overall costs.
Collaborative sharing of customer’s forecasts to suppliers, a concept called Collaborative
Planning Forecasting and Replenishment (CPFR) provides opportunities for cost savings.
The phrase “partnering” is overused in the retail business but not when it involves CPFR
(6). Since CPFR does not necessarily involve the exchange of money it can be
overlooked. The benefits of CPFR are great and should be viewed as realized revenue.
The goal of CPFR is to ensure that supply walks hand-in-hand with demand. The
inability for a supplier to meet demand of a product will mean a loss of possible revenue.
This loss of revenue does not just impact the retailer but also impacts the whole supply
chain since each firm’s profitability depends on the profitability and revenue of the whole
Information sharing does not just involve the behind-the-scenes members of the
supply chain. Customers also have the advantage of using online ordering and tracking to
add value to their purchase(s). In addition to the cost savings earned by improved supply
chain efficiency, the customer also has the ability to track their package and view order
status from their computer. Information given on a retailer’s website may also save the
customer time when making a purchase. Online customer service and technical support
allow a reduction in customer service costs thus increasing efficiency. While the
increased efficiency may come with an intangible cost of generic answers and slow
response time, the balance of available customer service and online support can provide
both value for the customer and cost savings for the retailer.
Incorporating information sharing, whether within the supply chain or from a
customer standpoint, is only the beginning of what need to be done. Methods, such as
intelligent agents and the use of demo models, exist to build upon the existing
collaborative. Without collaboration in a supply chain, the efficiency of an e-business
supply chain can not be maximized to produce the highest gain for all parties.
IV. INTELLIGENT AGENTS
An intelligent agent (IA) is software that automates information and decision
making based on the available data. In the e-business era speed is focused on as a
competitive advantage. A firm can greatly increase value to the customer if it can share
forecasting information and make decisions quickly and effectively. An information
sharing environment may consist of meetings, telephone calls, emails, and/or faxes. An
intelligent agent can take parameters given by management and make decisions based on
those parameters. Very important decisions can be flagged and analyzed by senior
Example of intelligent agent software 
Intelligent Agent Software
Figure 6 gives an example of an intelligent agent solution, TradeMatrix Platform,
by the i2 company, which augments information sharing by automating the process. The
TradeMatrix Platform by i2 manages transactions, messaging, and presentation of
information to customers, and integrates with back-end Enterprise Resource Planning
(ERP) systems (7). i2 Technologies states that it “provides a wide variety of
collaborative e-services for both early-stage and next-generation e-business adoption,
with each offering supported by decision optimization, transaction management, and
content management solutions.” An intelligent agent solution like that offered by i2
integrates into an existing supply chain and does the work of optimizing the supply chain.
A disadvantage of using a specific intelligent agent software solution is that each supply
chain link is required to have software that communicates efficiently with the intelligent
agent software. This may require members of the supply chain to upgrade and/or install
new and costly software. The installation of software is only the beginning of the
integration since management and forecasters will require training to work with the
system to provide constraints and configure the software to make decisions based on the
The uses of intelligent agents can already be seen in a customer setting .
Websites such as Google (www.google.com), mySimon (www.mysimon.com), and Ask
Jeeves (www.askjeeves.com) work with intelligent agents to search the Internet and
provide valid information based on the search criteria given by the user. When going to
mySimon a consumer can type in the name of a product and the software will search to
find the lowest prices of that product. A search engine such as Google will search its
databases for pages containing the information requested by the user and Ask Jeeves will
provide information based on a question that is typed in by the user. All this information
gathering and reporting is automated and transparent to the user. The intelligent agent
software will do all the work of searching through the information. The artificial
intelligence (AI) module will then determine whether the information found has any
value to the consumer. If so the customer will see a link to the information and will never
see completely invalid information.
Sites such as mySimon use AI to find pricing information
Intelligent Agents in the Supply Chain
While the use of AI and intelligent agents may abound in the consumer market, it
is not necessarily the case in the business world. Decisions may place large amounts of
money on the line and if an automated system makes a mistake it will be felt heavily by
all members of the supply chain. Businesses are cautious to use this improvement to
information sharing because the decision is not made by a person but by a machine.
Currently intelligent agents “help to facilitate dynamic trade environments; that is, they
help companies develop custom responses to current demands” (8). In a supply chain
environment that task consists of supporting communication among factories,
warehouses, distribution centers, and retailers, in addition to other members such as
suppliers, customers, and partners. An example of this is an electronics manufacturer who
dispatches intelligent agents to monitor the progress of contract. The electronics
manufacturer can use automation to check the progress of work and set triggers to contact
a senior manager if the work being done by the outsourced company is falling behind
The example of the electronics manufacturer is found in an e-marketplace. In an
e-marketplace, intelligent agents query factory management systems to determine which
manufacturers have the most inventory of a needed item and how fast and costly that
shipment will be. A retailer, for example, may need a fast shipment of 100 computers. By
using intelligent agents the retailer can find the lowest cost supplier with fastest response
time. This reduces the cost to the retailer and provides value to the consumer by better
guaranteeing that the item will be in stock. Before the introduction of intelligent agents,
management would have to analyze inventories and cost of each supplier before making a
decision. With the automation of the process, management is now able to focus on
customer service and other avenues to add value to the consumer.
Intelligent agents can be used in all sections of the business, and not just supplier
intelligence. Factory production, schedules, maintenance and product specifications can
all be influenced by the use of intelligent agent software. Given a set of constraints, the
software can make decisions based on demand, pricing, and availability information
collected from multiple sources. Management overrides can influence decisions but for
most day-to-day operations the intelligent agents can do the work to optimally buy and
purchase goods to meet demand and reduce costs. Using this type of information sharing
tool allows fast and effective decisions to be made without the need of information
inputted manually by management or the workforce. Figure 8 gives a figure of
information flow using intelligent agents in a retail environment.
Use of Intelligent Agents in a Retail Environment
(Chooses best Supplier)
Supplier 1 Supplier 4
Supplier 2 Supplier 3
Intelligent Agent Concerns
So why doesn’t every business use intelligent agents? There are several factors
that affect the success of this software. Intelligent agents, like all software, should be easy
to use and have an easy to use interface. As intelligent agents have developed for supply
chains the interface has not been the primary focus. Since technological functionality has
been achieved, software developers must focus on the interface and documentation to
provide an easy to install, configure, and maintain system that any manager can use.
Another factor is the collection of possible sensitive information. When
purchasing a product online a customer will not want the whole world to view their credit
card number. This is the same problem that haunts intelligent agents. Since the software
collects information using the Internet other parties seeking this information can read it.
Several solutions exist to this problem but are not trivial. The first and easiest solution
would be to use a secure network for data collection. If the intelligent agent can gather
information over an encrypted channel, the data will not be available to hackers and other
parties. A possible problem to this solution is that if the data is unencrypted in any area of
the communication, an external source might be able to view all the information. The
second option, while harder to implement, can provide a better solution. Using exclusive
networks can eliminate the possibility of other external entities receiving the information.
An exclusive network contains only the members of the supply chain so that all
information is only passed in that network and not through the Internet.
Another problem found using intelligent agents is the variety of information
standards that exist (9). A software system may store all its demand forecast data in
Microsoft Excel while another software system may provide information in a comma
separated text file. Some software is available to act as a middleman and translate
different formats, but the best solution is a common information storage standard which
is uniformly used. One such format is XML which can be shared by both Windows and
UNIX platforms. Information stored in XML format can easily be parsed by software
systems and is platform independent. The use of this possible standard can make
intelligent agent software implementations easy for most businesses.
Intelligent agents build upon the foundation set using information sharing in the
supply chain. The use of intelligent agents can optimize forecasting but a business must
use trial and error to determine whether the supply chain is optimal or not. The next
section discusses demo models, which can be used in a supply chain environment to
benchmark a supply chain by comparing the results of using different intelligent agents
and collaborative environments.
V. DEMO MODELS
A demo model is a software program that models the effect of using a new supply
chain configuration. Past demand information coupled with cost variables are used to
determine the return on investment of alternative supply chain configurations. A demo
model allows an organization to simulate the effects of different supply chain
configurations without reconfiguring the actual supply chain. The value of a demo model
is that efficiency and responsiveness can be cost-effectively improved by identifying the
supply chain that best fits the organization. Since supply chain re-configuration costs can
seriously affect the profitability of the supply chain, demo models should be used to find
the configuration that increases profitability and return on investment.
Demo Model Simulation
When simulating a demo model, several variables need to be set. The holding cost
will affect the amount of safety stock held and the response time of the suppliers (10). A
high holding cost will cause a company to ask suppliers to be very responsive whereas a
low holding cost may allow a company to hold more inventories. The order cost will
affect the order quantities and the number of orders placed to suppliers. A higher order
cost may require the company to hold more inventories to reduce the number of orders. A
low order cost will allow the company to place orders whenever new inventories are
needed without the high costs of order placement. The planning horizon and demand
refresh/update period are related to forecasting information. The planning horizon sets
how far in the future to forecast the demand of a product. A shorter planning horizon will
result in a better forecast. The demand refresh/update period reanalyzes the forecast to
determine whether the forecasting system is accurate. If accurate, the forecast will not
change. However, if the forecast is inaccurate, a new forecasting model may need to be
developed particularly if the forecast starts producing values outside the bounds set by
management. Depending on the company, a sample of past forecasts and demand
information may be used to plug into the demo models. Figure 9 provides a graph of how
demo models can be used to provide management with the optimal supply chain
configuration. With the use of intelligent agents and the variables discussed earlier the
costs of each possibly supply chain configuration can be found and compared to other
demo models. The results can be presented to management in a form that supports more
Use of Demo Models to Find Optimal Model
Demo Model 1 Demo Model 4
Demo Model 2 Demo Model 3
The use of these demo models, or simulations, allows management to virtually
use a supply chain with past demand and forecasting data to make decisions concerning
supply chain configuration. Without this type of simulation an organization needs to use
trial and error to find supply chain configurations that yield low costs. The trial and error
method can be very costly to companies. For example, an airplane manufacturer begins
using a supply chain that involves shipping parts at low costs from Asia. On paper the
supply chain may look very profitable but initial analysis may not take currency changes,
trade restrictions, or increased response time costs into account when determining overall
profitability. By using demo models, the company can quickly simulate multiple models
using a wide variety of currency changes to view their effect on the supply chain
profitability. So instead of investing millions into an inefficient supply chain the effects
can be bounded by the cost of the simulations. Since a supply chain is the core to a
company’s product delivery, it is very important that alternative supply chain
configurations be tested to find the one that produces the lowest cost, greatest value for
the customer, and highest return on investment.
The role of the demo models is crucial to assuring management that supply-chain-
wide development and optimization is possible (11). Psychological barriers may exist
between members of the supply chain concerning collaboration and decision making.
Demo models allow the simulation of common scenarios so that each member of the
supply chain can view the overall profitability effect, in additional to the cost savings
realized by each member. The use of demo models requires that a collaborative
information-sharing environment exist between members of the supply chain. Once the
foundation is in place, intelligent agents and demo models can be used to find the best
model for all members of the supply chain.
VI. PROPOSED IMPROVED MODEL
Figure 10 gives a proposed update to the supply chain outlined in Figure 1. This
update includes the augmented information flow between supply chain members to
incorporate a collaborative environment. Thus, as shown in Figure 10, the building of
optimal supply chain models involves collaboration between all members of the supply
chain. Without collaboration, members may work to improve their link of the chain but
will not receive the highest benefits until a system-wide optimal model is identified.
Optimal models can be found by combining intelligent agent software and demo models;
this combination can be used to construct a supply chain effective for all supply chain
Proposed Supply Chain Design
Once an optimal model is found using demo modeling and intelligent agents, the
supply chain configuration is shared with all involved supply chain members in order to
reach a consensus to whether to implement the model or not. Therefore demo models
allow each member to be involved in the supply chain design and formal acceptance; thus
guaranteeing mutual benefits from implementing the new model. Once a new model has
been approved, careful consideration must be made to determine how to best implement
the new model. Collaboration must continue between members to collectively plan an
upgrade or modification of current supply chain models. This process may not be trivial
and may take anywhere from a few months to years. The value realized by the supply
chain model upgrade is worth the implementation costs as new models will yield
significant supply chain wide benefits from initial supplier to final consumer.
Once companies begin to realize the value of e-business-enabled supply chain
integration, they often discover entirely new ways of pursuing business objectives,
developing strategies, and business models that were neither apparent nor possible prior
to the Internet (12). E-business allows orders to be received and processed by suppliers
within minutes and allows faster methods of communication. The value of e-business can
be realized by both the consumer and supplier with lower prices and more available
E-business itself cannot fix all problems associated with supply chain models.
Since the profitability of a firm will depend on the overall profitability of the supply
chain an overall integrated solution needs to be created to add value to all supply chain
members. To effectively make this optimization possible, information sharing and
collaboration need to be the foundation of work between members. Without
collaboration, the efforts of each supply chain member cannot be integrated in pursuit of
common purpose. Once a collaborative environment has been created, intelligent agents
can help improve supply chain performance by automating the information gathering and
decision making tasks. Intelligent agent software uses artificial intelligence (AI) to
search, collect, and analyze demand, inventory, and pricing information. Demo models
can be used to simulate and test possible supply chain models to view performance given
a set of constraints; this allows supply chain performance to be measured before investing
in the new model. Demo models also help bring supply chain members together by
working to run demo tests that will simulate individual supply chain performance. Figure
11 shows the hierarchy of building an optimal supply chain model based on information
Building optimal model based on information sharing
Optimal Model ++++
The advantages of working in a collaborative environment do not only include
lower costs and higher profitability. Collaborative environments foster communications
between supply chain members that may not happen in normal settings. Relationships can
be formed between members that can result in long term contracts between retailers and
suppliers. The improvements of supply chain relationships yield synergies that enable
every member to work more effectively as a group than individually. Once new models
have been explored, firms can be on the leading edge of their industries by providing a
more efficient and responsive supply chain with optimal value for all.
(1) “SCM sales set for fastest growth in software market”, Supply Management, Sep 18,
2003, pg. 13.
(2) Fine, Charles (1998), “Clockspeed-Winning Industry Control In the Age of
Temporary Advantage”, Perseus Books, 1998.
(3) Kim, Ki-Chan, and Il Im, (2002), “The Effects of Electronic Supply Chain Design (e-
SCD) on Coordination and Knowledge Sharing: An Empirical Investigation”, Resource
Paper, Annual Hawaii International Conference on System Sciences.
(4) Warkentin, Merrill, Ravi Bapna, and Vijayan Sugumaran, (2001), “E-knowledge
networks for inter-organizational collaborative e-business”, Logistics Information
Management, Vol. 14, pg. 149, 2001.
(5) Dyer, J. H and K. Nobeoka (2000), “Creating and Managing a High-performance
Knowledge Sharing Network: The Toyota Case”, Strategic Management Journal, Vol. 21,
2000, pp. 345-367.
(6) Clark, Ken, (2002), “Collaborators and proud of it”, Chain Store Age, Vol. 78, pg. 76,
(7) i2 Technologies, Strengthening the Supply Chain, 2001, Dell
(8) Zaniker, Jeff and Jay Holata, (2001) “Intelligent Agents in the B2B Value Chain”,
Resource Paper, Supply Chain e-business, April 2001.
(9) Goodwin, Richard, Pinar Keskinocak, Sesh Murthy, Frederick Wu, and Akkiraju
Rama, (1999) “Intelligent Decision Support for the e-Supply Chain”, Resource Paper,
American Association for Artificial Intelligence.
(10) Wadhwa, S, John Jose, and A. Gandhi (2002) “Managing Innovation in e-Business
Based Supply Chain Structures: Role of Demo Models”, Studies in Informatics and
Control, Vol. 11, No. 3, September 2002.
(11) Wadhwa, S., (2001) “Judicious use of IT in Manufacturing SMEs: Case
Experiences”, Resource Paper, E-Commerce: Opportunities and challenges for SMEs,
(12) Lee, H and S. Whang, (2001) “E-Business and Supply Chain Integration”, Resource
Paper, Stanford Global Supply Chain Management Forum, Stanford University.