Transcript Enterprise resource planning - Best Educational websites

Enterprise resource
planning
and
Related Technologies
th
Semester 5
BSc (IT)
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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.
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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.
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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
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Business Process Reengineering (BPR)
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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.
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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 6
accordingly.
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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)
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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.
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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.
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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%.
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Management Information System (MIS)
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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".
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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.
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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?
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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.
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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.
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Decision Support Systems ( DSS)
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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.
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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.
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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 knowledgebased systems are called decision support systems
(DSSs).
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Decision support systems; emphasize a knowledgemanagement 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.
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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.
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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.
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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 decisionmaking. 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.
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Executive Information Systems (EIS)
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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.
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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 toplevel control in many organizations.
They help an executive spot a problem, an
opportunity, or a trend.
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Executive information systems have
these characteristics:
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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.
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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 Adrill down@
into the data.
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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.
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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.
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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.
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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
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Data warehousing
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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.
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Three Complementary Trends:
Data Warehousing: Consolidate data from many
sources in one large repository:
* Loading, periodic synchronization of replicas.
* Semantic integration.
ON-LINE Analytical Processing (OLAP):
* Complex SQL queries and views.
* Queries based on spreadsheet-style operations
and “multidimensional” view of data.
* Interactive and “online” queries.
3. Data Mining:
Exploratory search for interesting trends and
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anomalies.
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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.
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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.
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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.
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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.
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Data Mining
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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.
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Continuous Innovation
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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.
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What can data mining do?
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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.
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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.
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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.
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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.
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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.
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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.
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How does data mining work?
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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:
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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.
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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.
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