1. Enterprise resource planning
and
Related Technologies
Semester 5th
BSc (IT)

2. • ERP is an abbreviation for Enterprise resource
planning and means the techniques and concepts for
the integrated management of business as a whole,
from the viewpoint of the effective use of
management resources, to improve the efficiency of
an enterprise.
• ERP systems serve an important function by
integrating separate business functions-materials
management, product planning, sales, distribution,
finance and accounting and others-into a single
application.

3. • However, ERP systems have three significant limitations:
• 1. Managers cannot generate custom reports or queries
without help from a programmer and this inhibits them from
obtaining information quickly, which is essential for
maintaining a competitive advantage.
• 2. ERP systems provide current status only, such as open
orders. Managers often need to look past the current status
to find trends and patterns that aid better decision-making.
• 3. the data in the ERP application is not integrated with
other enterprise or division systems and does not include
external intelligence.

4. • There are many technologies that help to overcome these
limitations. These technologies, when used in conjunction
with the ERP package, help in overcoming the limitations of
a standalone ERP system and thus, help the employees to
make better decisions. Some of these technologies are:
• Business Process Reengineering (BPR)
• Management Information System (MIS)
• Decision Support Systems ( DSS)
• Executive Information Systems (EIS)
• Data warehousing
• Data Mining
• On-line Analytical Processing (OLAP)
• Supply Chain Management

5. 1. Business Process Reengineering (BPR)
• Business processes are: simply a set of activities
that transform a set of inputs into a set of outputs
(goods or services) for another person or process
using people and tools. We all do them, and at one
time or another play the role of customer or
supplier.

6. • So why business process improvement?
• Improving business processes is paramount for
businesses to stay competitive in today's
marketplace. Over the last 10 to 15 years companies
have been forced to improve their business
processes because we, as customers, are demanding
better and better products and services.
• And if we do not receive what we want from one
supplier, we have many others to choose from
(hence the competitive issue for businesses). Many
companies began business process improvement
with a continuous improvement model. This model
attempts to understand and measure the current
process, and make performance improvements
accordingly.

7. • Definition of BPR.
• Corporate Reengineering
• The most common definition used in the private sector
comes from the book entitled, Reengineering the
Corporation, a Manifesto for Business Revolution, by MIT
professors Michael Hammer and James Champy. Hammer
and Champy defined business process reengineering as:
• The fundamental rethinking and radical redesign of business
processes to bring about dramatic improvements in critical,
contemporary measures of performance, such as cost,
quality, service, and speed. (Reengineering the Corporation,
Hammer and Champy, 1993)

8. • The major emphasis of this approach is the fact that
an organization can realize dramatic improvements
in performance through radical redesign of its
processes. This is in contrast to the notion of
streamlining processes in order to achieve a
measured level of performance.
• Another aspect to the Hammer/Champy definition is
the notion of breakthroughs. This approach to
reengineering assumes the existing process is not
sound and therefore needs to be replaced. A
properly reengineered process will provide quantum
leaps in performance, achieving breakthroughs in
providing value to the customer.

9. • Even though these definitions focus on different
strategies of implementing change, the common
element is that the change occurs across the whole
process.
• THE BUSINESS PROCESS REENGINEERING
(BPR) VISION
• Business Process Reengineering (BPR) is based on a
vision of the future that is increasingly shared by
enterprises around the world. It is evolving into the
sum total of everything we've learned about
management in the industrial age recast into an
information age framework.

10. • The impact of BPR on organizational
performance
• The two cornerstones of any organization are the
people and the processes. If individuals are
motivated and working hard, yet the business
processes are cumbersome and non-essential
activities remain, organizational performance will be
poor. Business Process Reengineering is the key to
transforming how people work. What appear to be
minor changes in processes can have dramatic
effects on cash flow, service delivery and customer
satisfaction. Even the act of documenting business
processes alone will typically improve
organizational efficiency by 10%.

11. 2. Management Information System (MIS)
• Management Information Systems (MIS), are
information systems, typically computer based, that
are used within an organization. WordNet described
an information system as "a system consisting of the
network of all communication channels used within
an organization".

12. • As an area of study it is commonly referred to as
information technology management.
• The study of information systems is usually a
commerce and business administration discipline,
and frequently involves software engineering, but
also distinguishes itself by concentrating on the
integration of computer systems with the aims of the
organization.
• The area of study should not be confused with
Computer Science which is more theoretical and
mathematical in nature or with Computer
Engineering which is more engineering.

13. • In business, information systems support business
processes and operations, support decision making,
and support competitive strategies.
• 2. MIS: How does the company "mine" its
relational database systems for information and
trends to be used in the management of the
business?

14. • The major differences between a management
information system and a Data Processing system are:
• 1. The integrated database of the MIS enables greater
flexibility in meeting the information needs of the
management.
• 2. The MIS integrates the information flow between
functional areas (accounting, marketing, manufacturing,
etc.) whereas data processing systems tend to support a
single functional area.
• 3. MIS caters to the information needs of all levels of
management whereas data processing systems focus on
departmental-level support.
• 4. Management’s information needs are supported on a
more timely basis with the MIS (with its on-line query
capability) than with a data processing system.

15. • The main characteristics of the management
information system are:
• 1. The MIS supports the data processing functions
of transaction handling and record keeping.
• 2. MIS uses an integrated database and supports a
variety of functional areas.
• 3. MIS provides operational, tactical and strategic
levels of the organization with timely, but for the
most part structured information (ad-hoc query
facility is not available0.
• 4. MIS is flexible and can be adapted to the
changing needs of the organization.

16. 3. Decision Support Systems ( DSS)
• In the course of their decision activities managers work with
many pieces of knowledge. Some of this knowledge is
descriptive, characterizing the state of past, present, future,
or hypothetical worlds.
• Such knowledge is commonly called information or data.
Other pieces of knowledge are procedural in nature,
specifying how to accomplish various tasks.
• In addition to "know what" (information) and "know how"
(procedures), a manager may work with reasoning
knowledge on the way toward reaching a decision.

17. • This third kind of knowledge indicates that certain
conclusions are valid under particular
circumstances.
• Two other kinds of knowledge are very much
concerned with communication. One is linguistic
knowledge which enables a manager to understand
incoming messages.
• Conversely, a manager works with presentation
knowledge when constructing outgoing messages.

18. • Managers are first and foremost knowledge workers
who are involved in the making of decisions.
• Sometimes, a manager makes decisions
individually. In other cases, decision-making may
be distributed, involving the combined and
coordinated efforts of many knowledge workers.
• Both individual and distributed decision making are
susceptible to support by systems that facilitate,
expand, or enhance a manager's ability to work with
one or more kinds of knowledge. Such knowledge-
based systems are called decision support systems
(DSSs).

19. • Decision support systems; emphasize a knowledge-
management perspective. With the relentless
advances in the technology and economics of
computers, we are rapidly reaching the point where
a manager's success depends on his or her
understanding of DSS possibilities and skill in DSS
application.
• Many DSSs are oriented toward individual decision
support. There is growing interest in DSSs that
directly support distributed decision making at the
group, organization, and inter-organization levels.

20. • Decision support systems also differ with respect to
the kinds of knowledge they help manage.
• The majority of conventional DSSs have been
devised to help manage primarily descriptive and
procedural knowledge. In contrast, there is a class of
artificially intelligent DSSs concerned mainly with
the representation and processing of reasoning
knowledge.

21. • The main characteristics of DSS are:
• 1. A DSS is designed to address semi-structured and
unstructured problems.
• 2. The DSS mainly supports decision-making at the
top management level.
• 3. DSS is interactive, user-friendly can be used by
the decision-maker with little or no assistance from
a computer professional.
• 4. DSS makes general-purpose models, simulation
capabilities and other analytical tools available to
the decision-maker.

22. • A DSS does not replace the MIS; instead a DSS
supplements the MIS. There are distinct differences
between them. MIS emphasizes on planned reports
on a variety of subjects; DSS focuses on decision-
making. MIS is standard, scheduled, structured and
routine; DSS is quite unstructured and is available
on request. MIS is constrained by the organizational
system; DSS is immediate and user-friendly.

23. 4. Executive Information Systems (EIS)
• Definitions for Executive Information Systems
• A computerized system intended to provide current and
appropriate information to support executive decision
making for managers using a networked workstation.
• The emphasis is on graphical displays and an easy to use
interface that present information from the corporate
database.
• They are tools to provide canned reports or briefing books
to top-level executives. They offer strong reporting and
drill-down capabilities. An early term for a sophisticated
data-driven DSS targeted to senior executives.

24. • Executive information systems (EIS) provide a
variety of internal and external information to top
managers in a highly summarized and convenient
form. EIS are becoming an important tool of top-
level control in many organizations.
• They help an executive spot a problem, an
opportunity, or a trend.

25. Executive information systems have these
characteristics:
• 1. EIS provide immediate and easy access to
information reflecting the key success factors the
company and of its units.
• 2. AUser-seductive@ interfaces, presenting
information through color graphics or video, allow
an EIS user to grasp trends at a glance. 3. EIS
provide access to a variety of databases, both
internal and external, through a uniform interface.
• 4. Both current status and projections should be
available from EIS.

26. • 5. An EIS should allow easy tailoring to the
preferences of the particular users or group of users.
• 6. EIS should offer the capability to drill down into
the data.

27. • DSS are primarily used by middle and lower level managers
to project the future, EIS's primarily serve the control needs
of higher level management.
• 1. EISs primarily assist top management in uncovering a
problem or an opportunity. Analysts and middle managers
can subsequently use a DSS to suggest a solution to the
problem.
• 2. At the heart of an EIS lies access to the data. EISs may
work on the data extraction principal, as DSSs do, or they
may be given access to the actual corporate databases or
data warehouses.
• 3. EISs can reside on personal workstations or servers.

28. • Developing EIS
• EIS's should make it easy to track the critical
success factors (CSF) of the enterprise, that is, the
few vital indicators of the firm's performance.
• With the use of this methodology, executives may
define just the few indicators of corporate
performance they need. With the drill-down
capability, they can obtain more detailed data
behind the indicators.

29. • Strategic business objectives methodology of EIS
development takes a company-wide perspective of
the strategic business objectives of the firm where
the critical businesses are identified and prioritized.
• Then the information needed to support these
processes is defined, to be obtained with the EIS
that is being planned. Finally, an EIS is developed
to report on the CSFs. This methodology avoids the
frequent pitfall of aligning an EIS too closely to a
particular sponsor.

30. • An EIS takes the following into consideration:
• 1. The overall vision and mission of the company
and the company goals.]
• 2. Strategic planning and objectives
• 3. Crisis management/Contingency planning
• 4. Strategic control and monitoring of overall
operations

31. 5. Data warehousing
• Introduction
• Increasingly, organizations are analyzing current
and historical data to identify useful
• Patterns and support business strategies. Emphasis
is on complex, interactive, exploratory analysis of
very large datasets created by integrating data from
across all parts of an enterprise; data is fairly static.

32. • Three Complementary Trends:
• Data Warehousing: Consolidate data from many
sources in one large repository:
• * Loading, periodic synchronization of replicas.
• * Semantic integration.
•

34. 6. Data Mining:
• Exploratory search for interesting trends and
anomalies.

35. • 1. Definitions for Data Warehousing
• The ability of a system to store data resulting
from Data Mining to be used in future inquiries of
that database. Data mining is the process of
identifying valid, novel, potentially useful and
ultimately comprehensible information from
databases that is used to make crucial business
decisions.

36. • The primary concept of data warehousing is that the
data stored for business analysis can be accessed
most effectively by separating it from the data in
operational systems. The most important reason for
separating data for business analysis, from the
operational data, has always been the potential
performance degradation on the operational syatem
that can result from the analysis processes.
• High performance and quick response time is almost
universally critical for operational systems.

37. • The main reasons for needing automated computer
systems for intelligent data analysis are:
• 1. Enormous volume of existing and newly
appearing data that require processing.
• 2. The inadequacy of the human brain when
searching for complex multifactorial dependencies
in the data.
• 3. The lack of objectiveness in analyzing the data
• 4. The automated data mining systems is that this
process has much lower cost than hiring an army of
highly trained professionals’ statisticians.

38. • Data mining. Data mining permits our companies to
profile customers, predict sales trends, and enable customer
relationship management (CRM), among other BI
initiatives.
• Mining must therefore be integrated with the warehouse
data structures and supported by warehouse processes to
ensure both effective and efficient use of the technology and
related techniques.
• As shown in the BI architecture, the atomic layer of the
warehouse as well as data marts is excellent data sources for
mining. Those same structures must also be recipients of
mining results to ensure availability to the broadest
audience.

39. Data Mining
• Generally, data mining (sometimes called data or
knowledge discovery) is the process of analyzing data from
different perspectives and summarizing it into useful
information - information that can be used to increase
revenue, cuts costs, or both.
• Data mining software is one of a number of analytical tools
for analyzing data. It allows users to analyze data from
many different dimensions or angles, categorize it, and
summarize the relationships identified. Technically, data
mining is the process of finding correlations or patterns
among dozens of fields in large relational databases.

40. Continuous Innovation
• Although data mining is a relatively new term, the
technology is not. Companies have used powerful
computers to sift through volumes of supermarket
scanner data and analyze market research reports for
years. However, continuous innovations in computer
processing power, disk storage, and statistical
software are dramatically increasing the accuracy of
analysis while driving down the cost.

41. What can data mining do?
• Data mining is primarily used today by companies
with a strong consumer focus - retail, financial,
communication, and marketing organizations. It
enables these companies to determine relationships
among "internal" factors such as price, product
positioning, or staff skills, and "external" factors
such as economic indicators, competition, and
customer demographics. And, it enables them to
determine the impact on sales, customer satisfaction,
and corporate profits. Finally, it enables them to
"drill down" into summary information to view
detail transactional data.

42. • With data mining, a retailer could use point-of-sale
records of customer purchases to send targeted
promotions based on an individual's purchase
history. By mining demographic data from comment
or warranty cards, the retailer could develop
products and promotions to appeal to specific
customer segments.
• For example, Blockbuster Entertainment mines its
video rental history database to recommend rentals
to individual customers. American Express can
suggest products to its cardholders based on analysis
of their monthly expenditures.

43. • WalMart is pioneering massive data mining to
transform its supplier relationships. WalMart
captures point-of-sale transactions from over 2,900
stores in 6 countries and continuously transmits this
data to its massive 7.5 terabyte Teradata data
warehouse. WalMart allows more than 3,500
suppliers, to access data on their products and
perform data analyses. These suppliers use this data
to identify customer buying patterns at the store
display level. They use this information to manage
local store inventory and identify new
merchandising opportunities. In 1995, WalMart
computers processed over 1 million complex data
queries.

44. • The National Basketball Association (NBA) is exploring a
data mining application that can be used in conjunction with
image recordings of basketball games. The Advanced Scout
software analyzes the movements of players to help coaches
orchestrate plays and strategies. For example, an analysis of
the play-by-play sheet of the game played between the New
York Knicks and the Cleveland Cavaliers on January 6,
1995 reveals that when Mark Price played the Guard
position, John Williams attempted four jump shots and
made each one! Advanced Scout not only finds this pattern,
but explains that it is interesting because it differs
considerably from the average shooting percentage of
49.30% for the Cavaliers during that game.

45. • By using the NBA universal clock, a coach can
automatically bring up the video clips showing each
of the jump shots attempted by Williams with Price
on the floor, without needing to comb through hours
of video footage. Those clips show a very successful
pick-and-roll play in which Price draws the Knick's
defense and then finds Williams for an open jump
shot.

46. • How does data mining work?
• While large-scale information technology has been
evolving separate transaction and analytical
systems, data mining provides the link between the
two. Data mining software analyzes relationships
and patterns in stored transaction data based on
open-ended user queries. Several types of analytical
software are available: statistical, machine learning,
and neural networks. Generally, any of four types of
relationships are sought:

47. • Classes: Stored data is used to locate data in predetermined
groups. For example, a restaurant chain could mine
customer purchase data to determine when customers visit
and what they typically order. This information could be
used to increase traffic by having daily specials.
• Clusters: Data items are grouped according to logical
relationships or consumer preferences. For example, data
can be mined to identify market segments or consumer
affinities.
• Associations: Data can be mined to identify associations.
The beer-diaper example is an example of associative
mining.
• Sequential patterns: Data is mined to anticipate behavior
patterns and trends. For example, an outdoor equipment
retailer could predict the likelihood of a backpack being
purchased based on a consumer's purchase of sleeping bags
and hiking shoes.

48. • Data mining consists of five major elements:
• Extract, transform, and load transaction data onto
the data warehouse system.
• Store and manage the data in a multidimensional
database system.
• Provide data access to business analysts and
information technology professionals.
• Analyze the data by application software.
• Present the data in a useful format, such as a graph
or table.

49. 7. OLAP
• OLAP is an acronym for On Line Analytical
Processing. It is an approach to quickly provide the
answer to analytical queries that are dimensional in
nature.
• It is part of the broader category business
intelligence, which also includes Extract transform
load (ETL), relational reporting and data mining.

50. • The typical applications of OLAP are in business
reporting for sales, marketing, management
reporting, business performance management
(BPM), budgeting and forecasting, financial
reporting and similar areas.
• The term OLAP was created as a slight modification
of the traditional database term OLTP (On Line
Transaction Processing).

51. • Databases configured for OLAP employ a
multidimensional data model, allowing for complex
analytical and ad-hoc queries with a rapid execution
time.
• Nigel Pendse has suggested that an alternative and
perhaps more descriptive term to describe the
concept of OLAP is Fast Analysis of Shared
Multidimensional Information (FASMI). They
borrow aspects of navigational databases and
hierarchical databases that are speedier than their
relational

52. Functionality
• OLAP takes a snapshot of a set of source data and
restructures it into an OLAP cube. The queries can then be
run against this. It has been claimed that for complex
queries OLAP can produce an answer in around 0.1% of the
time for the same query on OLTP relational data.
• The cube is created from a star schema or snowflake schema
of tables. At the centre is the fact table which lists the core
facts which make up the query. Numerous dimension tables
are linked to the fact tables. These tables indicate how the
aggregations of relational data can be analyzed. The number
of possible aggregations is determined by every possible
manner in which the original data can be hierarchically
linked.

53. • For example a set of customers can be grouped by
city, by district or by country; so with 50 cities, 8
districts and two countries there are three
hierarchical levels with 60 members.
• These customers can be considered in relation to
products; if there are 250 products with 20
categories, three families and three departments then
there are 276 product members.
• With just these two dimensions there are 16,560
(276 * 60) possible aggregations. As the data
considered increases the number of aggregations can
quickly total tens of millions or more.

54. • The calculation of the aggregations AND the base
data combined make up an OLAP cube, which can
potentially contain all the answers to every query
which can be answered from the data (as in Gray,
Bosworth, Layman, and Pirahesh, 1997). Due to the
potentially large number of aggregations to be
calculated, often only a predetermined number are
fully calculated while the remainder are solved on
demand.

55. Types of OLAP
• There are three types of OLAP.
• Multidimensional or MOLAP
• MOLAP is the 'classic' form of OLAP and is
sometimes referred to as just OLAP. MOLAP uses
database structures that are generally optimal for
attributes such as time period, location, product or
account code. The way that each dimension will be
aggregated is defined in advance by one or more
hierarchies.

56. • Relational or ROLAP
• ROLAP works directly with relational databases.
The base data and the dimension tables are stored as
relational tables and new tables are created to hold
the aggregated information. Depends on a
specialized schema design.

57. • Hybrid Or HOLAP
• There is no clear agreement across the industry as to
what constitutes "Hybrid OLAP", except that a
database will divide data between relational and
specialized storage.
• For example, for some vendors, a HOLAP database
will use relational tables to hold the larger quantities
of detailed data, and use specialized storage for at
least some aspects of the smaller quantities of more-
aggregate or less-detailed data.

58. Comparison
• Each type has certain benefits, although there is
disagreement about the specifics of the benefits between
providers.
• Some MOLAP implementations are prone to database
explosion. Database explosion is a phenomenon causing
vast amounts of storage space to be used by MOLAP
databases when certain common conditions are met: high
number of dimensions, pre-calculated results and sparse
multidimensional data. The typical mitigation technique for
database explosion is not to materialize all the possible
aggregation, but only the optimal subset of aggregations
based on the desired performance vs. storage trade off.

59. • MOLAP generally delivers better performance due
to specialized indexing and storage optimizations.
MOLAP also needs less storage space compared to
ROLAP because the specialized storage typically
includes compression techniques.
• ROLAP is generally more scalable. However, large
volume pre-processing is difficult to implement
efficiently so it is frequently skipped. ROLAP query
performance can therefore suffer.

60. • Since ROLAP relies more on the database to
perform calculations, it has more limitations in the
specialized functions it can use.
• HOLAP encompases a range of solutions that
attempt to mix the best of ROLAP and MOLAP. It
can generally pre-process quickly, scale well, and
offer good function support.

61. 8. Supply chain management (SCM)
• Supply chain management (SCM) is the process of
planning, implementing, and controlling the
operations of the supply chain with the purpose to
satisfy customer requirements as efficiently as
possible. Supply chain management spans all
movement and storage of raw materials, work-in-
process inventory, and finished goods from point-of-
origin to point-of-consumption.

62. • According to the Council of Supply Chain Management
Professionals (CSCMP), a professional association that
developed a definition in 2004, Supply Chain Management
"encompasses the planning and management of all activities
involved in sourcing and procurement, conversion, and all
logistics management activities.
• Importantly, it also includes coordination and collaboration
with channel partners, which can be suppliers,
intermediaries, third-party service providers, and customers.
In essence, Supply Chain Management integrates supply
and demand management within and across companies.

63. • Supply chain event management (abbreviated as
SCEM) is a consideration of all possible occurring
events and factors that can cause a disruption in a
supply chain. With SCEM possible scenarios can be
created and solutions can be planned.

64. • Some experts distinguish supply chain management
and logistics management, while others consider the
terms to be interchangeable.
• From the point of view of an enterprise, the scope of
supply chain management is usually bounded on the
supply side by your supplier's suppliers and on the
customer side by your customer's customers. Supply
chain management is also a category of software
products.

65. • Opportunities enabled by Supply Chain Management
• The following strategic and competitive areas can be used to
their full advantage if a supply chain management system is
properly implemented.
• Fulfillment. “Ensuring the right quantity of parts for
production or products for sale arrive at the right
time.”(Haag, Cummings, McCubbrey, et al., 2006, p. 46).
This is enabled through efficient communication, ensuring
that orders are placed with the appropriate amount of time
available to be filled. The supply chain management system
also allows a company to constantly see what is on stock
and making sure that the right quantities are ordered to
replace stock.

66. • Logistics. “Keeping the cost of transporting materials as
low as possible consistent with safe and reliable delivery.”
(Haag, Cummings, McCubbrey, et al., 2006, p. 46).
• Here the supply chain management system enables a
company to have constant contact with its distribution team,
which could consist of trucks, trains, or any other mode of
transportation.
• The system can allow the company to track where the
required materials are at all times. As well, it may be cost
effective to share transportation costs with a partner
company if shipments are not large enough to fill a whole
truck and this again, allows the company to make this
decision.

67. • Production: “Ensuring production lines function
smoothly because high-quality parts are available
when needed.” (Haag, Cummings, McCubbrey, et
al., 2006, p. 46).
• Production can run smoothly as a result of
fulfillment and logistics being implemented
correctly. If the correct quantity is not ordered and
delivered at the requested time, production will be
halted, but having an effective supply chain
management system in place will ensure that
production can always run smoothly without delays
due to ordering and transportation.

68. • Revenue & profit. “Ensuring no sales are lost
because shelves are empty.”(Haag, Cummings,
McCubbrey, et al., 2006, p. 46).
• Managing the supply chain improves a company’s
flexibility to respond to unforeseen changes in
demand and supply.
• Because of this, a company has the ability to
produce goods at lower prices and distribute them to
consumers quicker than companies without supply
chain management thus increasing the overall profit.

69. • Costs. “Keeping the cost of purchased parts and
products at acceptable levels.” (Haag, Cummings,
McCubbrey, et al., 2006, p. 46). Supply chain
management reduces costs by “… increasing
inventory turnover on the shop floor and in the
warehouse” (&ldquo Supply chain management,”
2006) controlling the quality of goods thus reducing
internal and external failure costs and working with
suppliers to produce the most cost efficient means of
manufacturing a product.

70. • Cooperation. “Among supply chain partners
ensures 'mutual success.'” (Haag, Cummings,
McCubbrey, et al., 2006, p. 46). Collaborative
planning, forecasting and replenishment (CPFR) is a
“longer-term commitment, joint work on quality,
and support by the buyer of the supplier’s
managerial, technological, and capacity
development.” (Klassen, Krajewski, Ritzman, 2004,
p.293) higher quality goods provided at a lower
cost.

71. • This relationship allows a company to have access
to current, reliable information, obtain lower
inventory levels, cut lead times, enhance product
quality, improve forecasting accuracy and ultimately
improve customer service and overall profits.
• The suppliers also benefit from the cooperative
relationship through increased buyer input from
suggestions on improving the quality and costs and
though shared savings. Consumers can benefit as
well through