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Artificial Intelligence
It is the study of how to make computers do things which, at the moment, people do better.
In other words, it can be defined as the study of making of computer with the ability to mimic or
duplicate the human brain functions(Model, 2012).
John McCarthy coined the term in 1956, at Massachusetts Institute of Technology, defines it as
"the science and engineering of making intelligent machines(Gaurav, 2013).
Researchers are creating systems which can mimic human thought, understand speech, beat the
best human chess player, and countless other feats never before possible.
History of Artificial Intelligence
Evidence of Artificial Intelligence folklore can be traced back to ancient Egypt, but with the
development of the electronic computer in 1941, the technology finally became available to
create machine intelligence. The term artificial intelligence was first coined in 1956, at the
Dartmouth conference, and since then Artificial Intelligence has expanded because of the
theories and principles developed by its dedicated researchers. Through its short modern history,
advancement in the fields of AI have been slower than first estimated, progress continues to be
made. From its birth 4 decades ago, there have been a variety of AI programs, and they have
impacted other technological advancements.
Thinking machines and artificial beings appear in Greek myths, such as Talos of Crete, the
golden robots of Hephaestus and Pygmalion'sGalatea. Human likenesses believed to have
intelligence were built in every major civilization: animated statues were worshipped in Egypt
and Greeceand humanoid automatons were built by Yan Shi, Hero of Alexandria, Al-Jazariand
Wolfgang von Kempelen. It was also widely believed that artificial beings had been created by
Jābir ibn Hayyān,Judah Loewand Paracelsus.By the 19th and 20th centuries, artificial beings had
become a common feature in fiction, as in Mary Shelley's Frankenstein or KarelČapek'sR.U.R.
(Rossum's Universal Robots).Pamela McCorduck argues that all of these are examples of an
ancient urge, as she describes it, "to forge the gods”. Stories of these creatures and their fates
discuss many of the same hopes, fears and ethical concerns that are presented by artificial
intelligence.

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Mechanical or "formal" reasoning has been developed by philosophers and mathematicians since
antiquity. The study of logic led directly to the invention of the programmable digital electronic
computer, based on the work of mathematicianAlan Turing and others. Turing's theory of
computation suggested that a machine, by shuffling symbols as simple as "0" and "1", could
simulate any conceivable act of mathematical deduction.[23]
This, along with recent discoveries
in neurology, information theory and cybernetics, inspired a small group of researchers to begin
to seriously consider the possibility of building an electronic brain.[24]
The field of AI research was founded at a conference on the campus of Dartmouth College in the
summer of 1956.The attendees, including John McCarthy, Marvin Minsky, Allen Newell and
Herbert Simon, became the leaders of AI research for many decades.They and their students
wrote programs that were, to most people, simply astonishing: computers were solving word
problems in algebra, proving logical theorems and speaking English. By the middle of the 1960s,
research in the U.S. was heavily funded by the Department of Defense and laboratories had been
established around the world.AI's founders were profoundly optimistic about the future of the
new field: Herbert Simon predicted that "machines will be capable, within twenty years, of doing
any work a man can do" and Marvin Minsky agreed, writing that "within a generation ... the
problem of creating 'artificial intelligence' will substantially be solved".
They had failed to recognize the difficulty of some of the problems they faced.In 1974, in
response to the criticism of England's Sir James Lighthill and ongoing pressure from Congress to
fund more productive projects, the U.S. and British governments cut off all undirected,
exploratory research in AI. The next few years, when funding for projects was hard to find,
would later be called an "AI winter"
Fields of Artificial Intelligence:
Games playing: programming computers to play games such as chess and checkers
Expert systems: programming computers to make decisions in real-life situations (for example,
some expert systems help doctors diagnose diseases based on symptoms)
Natural language: programming computers to understand natural human languages

3. 3
Neural networks: Systems that simulate intelligence by attempting to reproduce the types of
physical connections that occur in animal brains
Robotics: programming computers to see and hear and react to other sensory stimuli(L, 1999)
Business Intelligence
“Business intelligence (BI) is a set of methodologies, processes, architectures, and technologies
that transform raw data into meaningful and useful information. It allows business users to make
informed business decisions with real time data that can put a company ahead of its competitors.
Traditionally, core features like reporting and analytics have been the focus of BI technology
choices, but as those features get commoditized, a whole new set of possibilities has emerged.
Forrester's BI research shows that the technology is evolving and that enterprises on the cutting
edge of these new trends can gain competitive advantage in their industries.”
SOURCE http://www.forrester.com/rb/Research/topic_overview_business_intelligence/q/id/39218/t/2
Business Intelligence components
In most cases Business Intelligence involves the use of multidimensional analysis and reporting.
By providing multidimensional analysis and reporting the company often builds a Company Data
Warehouse to assemble the needed data.
However the term Business Intelligence covers at least the following five components that will
be explained in more detail in the following:
1. Multidimensional analysis
2. Reporting
3. Data mining
4, Financial consolidation and budgeting

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1, Multidimensional analysis
This area covers the possibility to slice-and-dice data (factual information) in many dimensions.
For many this is known at pivoting data.
A pivot table is something that you can build and use in most spreadsheets including Excel. In
Excel you can summarize data in a pivot table on many levels on each dimension and you can
have a table of rows and columns of summarized data in both dimensions as well as filtering the
shown data. Dimensions are often a hierarchy with multiple levels (product groups down to
product level eg.) From a more technical perspective most database suppliers and Business
Intelligence suppliers support multidimensional analysis with a special multidimensional
database. A multidimensional database is often also called a CUBE.
2, Reporting
Most companies have a need for different type of reports. In many cases hundreds of different
type of reports and occasionally often more.
Business Intelligence software often has comprehensive reporting tools available that can extract
and present data in many different media types (like over an internal Web page/Intra net, Internet
(to customers), Excel file format, PDF format e.g. In many cases these reporting facilities will be
controlled by parameters that can be chosen real time and present a report that has been run
directly against data (often a Data Warehouse or multidimensional data).
3, Data mining
“Data mining, a branch of computer science, is the process of extracting patterns from large data
sets by combining methods from statistics and artificial intelligence with database management.”
Source:http://en.wikipedia.org/wiki/Data_mining
In many cases Business Intelligence also covers some functionality to perform Data Mining on
the company data. As the definition describes above the purpose is to find (so far unknown)
patterns from large data sets.

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In general to use the methods to find some “new information” in regards of already collected
data and with this new information of “patterns” to be able to make “informed business
decisions”
4 Financial consolidations and budgeting
In many cases the Business Intelligence area also covers systems and functionality for Groups to
perform financial group consolidation and budgeting.
This area covers the whole aspect of elimination of intercompany transactions within a group to
present the financial figures as these intercompany transactions had never happened and that the
group is presented as one entity.
In some cases this area within consulting companies also covers some elements of Performance
Management and metrics like Key Performance Indicators
BI converts data into useful information and, through human analysis, into knowledge. Some of
the tasks performed by BI are(S.Negash, 2004):
 Creating forecasts based on historical data, past and current performance, and estimates
of the direction in which the future will go.
 “What if” analysis of the impacts of changes and alternative scenarios.
 Ad hoc access to the data to answer specific, non-routine questions.
 Strategic insight.
Artificial neural networks
One type of network sees the nodes as „artificial neurons‟. These are called artificial neural
networks (ANNs). Anartificialneuronisacomputationalmodelinspiredinthenaturalnens. When the
signals received are strong enough (surpass a certain threshold), the neuron is activated and emits
asignal though the axon. This signal might be sent to another synapse, and might activate other
neurons.
The complexity of real neurons is highly abstracted when modelling artificial neurons
. These basically consist of inputs(likesynapses), which aremultiplied by weights(strengthof
theirelectivesignals),and then computed byamathematical function which determines the

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activation of theneuron. Another function (which maybethe identity)computes the outputof the
artificial neuron (sometimes in dependence of a certainthreshold).ANNs
combineartificialneuronsin order toprocessinformation.
Since the invention of the digital computer, the human being has attempted to create machines
which directly interact with the real world without his intervention.
In this sense the Artificial Intelligence, in general, and particularly the Artificial Neural
Networks (ANN‟s) represents alternative for endowing to the computers one of the
characteristics that makes the difference between humans and other alive beings.
The intelligence an artificial neural network is an abstract simulation of a real nervous system
and its study corresponds to a growing interdisciplinary field which considers the systems as
adaptive distributed and mostly nonlinear, three of the elements found in the real applications
Characteristics of Artificial Neural Networks
Alarge number of very simple processing neuron likeprocessing elements.
Alarge number of weighted connections between the elements.
Distributed representation of knowledge over the connections Knowledge is acquired by network
through a learning process.
Benefits of artificial neural networks
It is evident that the ANN obtains their efficacyfrom:
1. Its structure massively distributed in parallel. The information processing takes place through
the iteration of a great amount of computational neurons, each one of them send exciting or
inhibiting signals to other nodes in the network. Differing from other classic Artificial
Intelligence methods where the information processing can be considered sequential– this is step
by step even when there is not a predetermined order ‐ ,in the
ArtificialNeuralNetworksthisprocessisessentiallyinparallel,whichistheoriginofitsflexibility.
Becausethecalculationsaredividedinmanynodes,ifanyofthemgetsastrayfromthe
Expected behavior itdoesnotaffectthe behavior ofthenetwork.

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2.Itsabilitytolearnandgeneralize.TheANNhasthecapabilitytoacquireknowledgefrom
itssurroundingsbytheadaptationofitsinternalparameters,whichisproducedasaresponsetothepresenc
eofanexternalstimulus.Thenetworklearnsfromtheexampleswhicharepresentedtoit,andgeneralizesk
nowledgefromthem.The generalization can be interpreted
asthepropertyofartificialneuralnetworkstoproduceanadequateresponsetounknownstimuluswhichar
erelatedtotheacquiredknowledge(Gasco).
ThesetwocharacteristicsforinformationprocessingmakeanANNabletogivesolutiontocomplexprobl
emsnormallydifficulttomanagebythetraditionalwaysofapproximation.
Additionally,usingthemgivesthefollowingbenefits
No linearity, the answer from the computational neuron can be linear or not. A neural network
formed by the interconnection ofnon‐ linear neurons, is in itself non ‐linear a trait which is
distributed to the entire network. No linearity is important over all in the cases
Adaptive learning, the ANN is capable of determine the relationship between the different
examples which are presented to it, or to identify the kind to which belong, without requiring a
previous model. Self –
organization,thispropertyallowstheANNtodistributetheknowledgeintheentirenetworkstructure,the
reisnoelementwithspecificstoredinformation.
Faulttolerance,this characteristicisshownintwosenses:Thefirstisrelated tothesamples
Showntothenetwork,inwhichcaseitanswerscorrectlyevenwhentheexamplesexhibit
Variabilityornoise;thesecond,appearswheninanyoftheelementsofthenetworkoccursafailure,which
doesnotimpossibilitiesitsfunctioningduetothewayinwhichitstoresinformation.
SUPPLY CHAIN MANAGEMENT
SCM is management of material and information flow in a supply chain to provide the highest
degree of customer satisfaction at the lowest possible cost. SCM requires commitment of supply
chain partners to work closely to coordinate order generation, order taking and order fulfillment
thus, creating an “extended enterprise” spreading far beyond the producer’s location. Supply
chains encompass the companies and the business activities needed to design, make, deliver

8. 8
and use a product or service. Businesses depend on their supply chains to provide them with
what they need to survive and thrive. Every business fits into one or more supply chains and has
a role pay in each of them.
And also supply chain management is the integration of key business processes from initial raw
material extraction to the final or end customer, including intermediate processing,
transportation and storage activities and final sale to the end customer. Today, the practice of
supply chain management is becoming extremely important to achieve and maintain
competitiveness. Many firms are just now beginning to realize the advantages of supply chain
integration.
Supply chain management is an out-growth and expansion of logistic and purchasing activities
and has grown in popularity and use since the 1980s. Important elements in supply chain
management are in the areas of purchasing, operations and production and distribution. Finally,
as markets, political forces, technology and competition change around the world, the practice
of supply chain management must also change.
The concept of Supply Chain Management is based on two core ideas. The first is that
practically every product that reaches an end user represents the cumulative effort of multiple
organizations. These organizations are referred to collectively as the supply chain.
The second idea is that while supply chains have existed for a long time, most organizations have
only paid attention to what was happening within their “four walls.” Few businesses
understood, much less managed, the entire chain of activities that ultimately delivered products
to the final customer. The result was disjointed and often ineffective supply chains.
Supply chain management, then, is the active management of supply chain activities to maximize
customer value and achieve a sustainable competitive advantage. It represents a conscious effort
by the supply chain firms to develop and run supply chains in the most effective & efficient ways
possible. Supply chain activities cover everything from product development, sourcing,
production, and logistics, as well as the information systems needed to coordinate these
activities.

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The organizations that make up the supply chain are “linked” together through physical flows
and information flows. Physical flows involve the transformation, movement, and storage of
goods and materials. They are the most visible piece of the supply chain. But just as important
are information flows. Information flows allow the various supply chain partners to coordinate
their long-term plans, and to control the day-to-day flow of goods and material up and down the
supply chain.
Next-generation supply chain management software
Overcome challenges such as volatile customer demand and increasingly complex supply
networks – with SAP for Supply Chain Management (SCM). Our software and technology let you
manage your entire network in real time – so you can quickly leverage vital information and
analyses, meet heightened expectations for responsiveness, and facilitate collaboration across
departments and companies.
Importance of Supply Chin management
Many firms, thought, have discovered value, long term benefits from their supply chain
management efforts. Firms with large system inventories, many suppliers, complex product
assemblies, and highly valued customers with large purchasing budgets have the most to gain
from the practice of supply chain management. For these firms, even moderate supply chain
management success can mean lower purchasing and inventory costs, better product quality, and
higher levels of customer service and sales. Purchasing inventory, and transportation cost
saving is quite sizable for firms utilizing supply chain management strategies.
Firms must realize that their management efforts can start small –for instance, with just one key
supplierand build through time to include more supply chain participants- such as other
important suppliers, key customers, and shippers- and, eventually, second-tier suppliers and
customers. So why is this integration activity important? As alluded to earlier, when a firm, its
customers, and its suppliers all know each others’ future plans, the planning process is easier
and moreaccurate(Handfield, 2011)

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Customer Relationship Management
Customer relationship management (CRM) is a multifaceted process, mediated by a set of
information technologies that focuses on creating two-way exchanges with customers so that
firms have an intimate knowledge of their needs, wants, and buying patterns. In this way, CRM
helps companies understand, as well as anticipate, the needs of current and potential customers.
Functions that support this business purpose include sales, marketing, customer service, training,
professional development, performance management, human resource development, and
compensation. Many CRM initiatives have failed because implementation was limited to
software installation without alignment to a customer-centric strategy.
Customer Relationship Management Software
In CRM (customer relationship management), CRM software is a phrase used to describe a
category of enterprise software that covers a broad set of applications and software to help
businesses manage customer data and customer interaction, access business information,
automate sales, marketing and customer support and also manage employee, vendor and partner
relationships.
CRM Software Installations
Customer relationship management software is offered in a number of installations including on-
premises (where the software resides inside the corporate firewall and is managed by IT
operations), or as web-based ( "cloud" applications) where the software is hosted by a CRM
provider and accessed by the client business online via the provider's secure services.
Characteristics of CRM
Well-designed CRM includes the following characteristics:
 Relationship management is a customer-oriented feature with service response based on
customer input, one-to-one solutions to customers‟ requirements, direct online
communications with customer and customer service centers that help customers solve
their questions

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 Salesforce automation. This function can implement sales promotion analysis, automate
tracking of a client‟s account history for repeated sales or future sales, and also
сoordinate sales, marketing, call centers, and retail outlets in order to realize the
salesforce automation.
 Use of technology. This feature is about following the technology trend and skills of
value delivering using technology to make “up-to-the-second” customer data available. It
applies data warehouse technology in order to aggregate transaction information, to
merge the information with CRM solutions, and to provide KPI (key performance
indicators).
 Opportunity management. This feature helps the company to manage unpredictable
growth and demand and implement a good forecasting model to integrate sales history
with sales projections.
Implementing CRM to the company
There are numerous steps companies should follow while implementing CRM system.
The project manager is responsible for the success of this process. Some conditions need
to be checked by the company before they starting implementation directly:
1. Make a strategic decision concerning the CRM desired goal: to improve or to change the
business processes of the organization.
2. Choose an appropriate project manager: usually it is the IT-department that is
responsible for CRM system implementation. However, it is reasonable to hire the
manager with a Customer Service/Sales and Marketing business focus as there are a
number of decisions that are related to the business processes rather than to the
hardware, software or network
3. Executive sponsorship: provide the top management support and systematic introduction
to the project manager
4. Project team commitment and training: make sure team members have enough time and
authority to complete project tasks and are committed to its success

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CRM in customer contact centers
CRM systems are Customer Relationship Management platforms. Their goal is to track, record,
store in databases, and then data mine the information in a way that increases customer relations
(predominantly increased ARPU, and decreased churn). The CRM codifies the interactions
between you and your customers, so you can maximize sales and profits using analytics, KPIs, to
give the users as much information on where to focus your marketing, customer service to
maximize revenue, and decrease idle and unproductive contact with your customers.
The contact channels‟ (now aiming to be Omni-Channel from Multi-Channel) uses such
operational methods as contact centers.
The CRM software is installed in the contact centers, and helps direct customers to the right
agent or self-empowered knowledge. CRM software can also be used to identify and reward
loyal customers over a period of time (Free, 2013).
E-COMMERCE
E-commerce is short for electronic commerce. It is similar to traditional commerce system which
involves the activities of selling and buying, but it perform these operations using any electronic
medium like, TV, fax, radio or internet. Today internet has captured all the e-trade demand with
its comparatively greater features, so here we will consider only internet as an e-commerce
source.
E-commerce is the use of Internet and the web to transact business but when we focus on
digitally enabled commercial transactions between and among organizations and individuals
involving information systems under the control of the firm it takes the form of e-business.
Nowadays, 'e' is gaining momentum and most of the things if not everything is getting digitally
enabled. Thus, it becomes very important to clearly draw the line between different types of
commerce or business integrated with the 'e' factor.

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There are mainly five types of e-commerce models:
1. Business to Consumer (B2C) –
As the name suggests, it is the model involving businesses and consumers.
This is the most common e-commerce segment.
In this model, online businesses sell to individual consumers.
When B2C started, it had a small share in the market but after 1995 its growth was exponential.
The basic concept behind this type is that the online retailers and marketers can sell their
products to the online consumer by using crystal clear data which is made available via various
online marketing tools.
The B2C model sells goods or services to the consumer, generally using online catalog and
shopping cart transaction systems.
E.g. an online pharmacy giving free medical consultation and selling medicines to patients is
following B2C model.
Amazon is an example of one of the first and still one of the most successful B2C e-commerce
companies.
Services such as subscriptions to information sites or online data backup are also examples of
B2C e-commerce.
2. Business to Business (B2B) –
It is the largest form of e-commerce involving business of trillions of dollars.
In this form, the buyers and sellers are both business entities and do not involve an individual
consumer.
It is like the manufacturer supplying goods to the retailer or wholesaler.
The volume of B2B transactions is much higher than the volume of B2C transactions.

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Business-to-business (B2B) describes commerce transactions between businesses, such as
between a manufacturer and a wholesaler, or between a wholesaler and a retailer. Contrasting
terms are business-to-consumer (B2C) and business-to-government (B2G).
The primary reason for this is that in a typical supply chain there will be many B2B transactions
involving subcomponent or raw materials, and only one B2C transaction, specifically sale of the
finished product to the end customer.
For example, an automobile manufacturer makes several B2B transactions such as buying tires,
glass for windscreens, and rubber hoses for its vehicles. The final transaction, a finished vehicle
sold to the consumer, is a single (B2C) transaction.
Cisco is an example of one of the first B2B catalogs online. Other examples of B2B e-commerce
are intranet services and Web meetings.
The term "business-to-business" was originally coined to describe the electronic communications
between businesses or enterprises in order to distinguish it from the communications between
businesses and consumers (B2C). It eventually came to be used in marketing as well, initially
describing only industrial or capital goods marketing. Today it is widely used to describe all
products and services used by enterprises.
Many professional institutions and the trade publications focus much more on B2C than B2B,
although most sales and marketing personnel are in the B2B sector.
3. Consumer to Consumer (C2C) –
It facilitates the online transaction of goods or services between two people.
Where consumers can post classified ads or offers to sell their property to other consumers. This
is the fastest growing segment of e-commerce thanks to the advent of social networking.
The sites are only intermediaries, just there to match consumers. They do not have to check
quality of the products being offered.
Though there is no visible intermediary involved but the parties cannot carry out the transactions
without the platform which is provided by the online market maker such as eBay.

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4, Consumer-to-business (C2B)
C2B is an electronic commerce business model in which consumers (individuals) offer products
and services to companies and the companies pay them. This business model is a complete
reversal of traditional business model where companies offer goods and services to consumers.
This kind of economic relationship is qualified as an inverted business type. The advent of the
C2B scheme is due to major changes:
Connecting a large group of people to a bidirectional network has made this sort of commercial
relationship possible. The large traditional media outlets are one direction relationship whereas
the internet is bidirectional one.
Decreased cost of technology : Individuals now have access to technologies that were once only
available to large companies ( digital printing and acquisition technology, high performance
computer, powerful software)
Elance is an example of C2B where the consumer posts a project and businesses answer with bid
proposals. Another example of C2B is online loan brokers.
5, Peer to Peer (P2P) –
Though it is an e-commerce model but it is more than that. It is a technology in itself which helps
people to directly share computer files and computer resources without having to go through a
central web server. To use this, both sides need to install the required software so that they can
communicate on the common platform. This type of e-commerce has quite low revenue
generation as from the beginning it has been inclined to the free usage due to which it sometimes
got entangled in cyber laws.
5. m-Commerce - It refers to the use of mobile devices for conducting the transactions. The
mobile device holders can contact each other and can conduct the business. Even the web design
and development companies optimize the websites to be viewed correctly on mobile devices.
There are other types of e-commerce business models too like

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Business to Employee (B2E), electronic commerce uses an intra-business network which allows
companies to provide products and/or services to their employees. Typically, companies use B2E
networks to automate employee-related corporate processes.
Examples of B2E applications include:
• Online insurance policy management
• Corporate announcement dissemination
• Online supply requests
• Special employee offers
• Employee benefits reporting
• 401(k) Management
Government-to-Business (abbreviated G2B) is the online non-commercial interaction between
local and central government and the commercial business sector, rather than private individuals
(G2C). For example http://www.dti.gov.uk is a government web site where businesses can get
information and advice on e-business 'best practice'. http://g2b.perm.ru is another example.
Government-to-Citizen (abbreviated G2C) is the communication link between a government and
private individuals or residents. Such G2C communication most often refers to that which takes
place through Information Communication Technologies (or ICTs), but can also include direct
mail and media campaigns. G2C can take place at the federal, state, and local levels. G2C stands
in contrast to G2B, or Government-to-Business networks.
One such Federal G2C network is USA.gov: the United States' official web portal, though there
are many other examples from governments around the world.[1]
Business-to-government (B2G) is a derivative of B2B marketing and often referred to as a
market definition of "public sector marketing" which encompasses marketing products and
services to various government levels - including federal, state and local - through integrated
marketing communications techniques such as strategic public relations, branding, marcom,
advertising, and web-based communications.

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B2G networks provide a platform for businesses to bid on government opportunities which are
presented as solicitations in the form of RFPs in a reverse auction fashion. Public sector
organizations (PSO's) post tenders in the form of RFP's, RFI's, RFQ's, Sources Sought, etc. and
suppliers respond to them but in essence they are similar to the above mentioned types.
Moreover, it is not necessary that these models are dedicatedly followed in all the online
business types. It may be the case that a business is using all the models or only one of them or
some of them as per its needs.
E-commerce benefits
Benefits to Organization
 Expends the marketplace to national and international markets.
 Decrease the cost of creating, processing, distributing, storing and retrieving paper-based
information.
 Allows reduced inventories and overhead by facilitating "pull" type supply chain
management.
 The pull type processing allows for customization of products and services which
provides competitive advantages to its implementers.
 Reduce the time between the outlay of capital and the receipt of products and services.
 Support Business Processes Reengineering (BPR) efforts.
 Lowers telecommunication cost – the internet is much cheaper than Value Added
Networks (VANs).
Benefits to Society
 Enables more individual to work at home, and to do less traveling for shopping, resulting
in less traffic on the roads, and lower air pollution.
 Allows some merchandise to be sold at lower prices benefiting the poor ones.
 Enables people in Third World countries and rural areas to enjoy products and services
which otherwise are not available to them.
 Facilities delivery of public services at reduced cost, increases effectiveness, and/or
improves quality.

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Benefits to Consumer
 Enables customers to shop or do other transactions 24 hours a day, all year round from
almost any location.
 Provides customers with more choices.
 Provides customers with less expensive products and services by allowing them to shop
in many places and conduct quick comparisons.
 Allows quick delivery of products and services in some cases, especially with digitized
products.
 Customers can receive relevant and detailed information in seconds; rather than in days
or weeks.
 Makes it possible to participate in virtual auctions.
 Allows customers to interact with other customers in electronic communities and
exchange ideas as well as compare experiences.
 Electronic commerce facilitates competition, which results in substantial discounts.
Limitations of E-commerce
Technical Limitations
 Lack of sufficient system's security, reliability, standards, and communication protocols.
 Insufficient telecommunication bandwidth.
 The software development tools are still evolving and changing rapidly.
 Difficulties in integrating the Internet and electronic commerce software with some
existing applications and databases.
 The need for special Web servers and other infrastructures, in addition to the network
servers (additional cost).
 Possible problems of interoperability, meaning that some E-commerce software does not
fit with some hardware, or is incompatible with some operating systems or other
components.

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Non-Technical Limitations
The cost of developing an EC in house can be very high, and mistakes due to lack of experience,
may result in delays. There are many opportunities for outsourcing, but where and how to do it is
not a simple issue. Furthermore, to justify the system one needs to deal with some intangible
benefits which are difficult to quantify.
These issues are especially important in the B2C area, and security concerns are not truly so
serious from a technical standpoint. Privacy measures are constantly improving too. Yet, the
customers perceive these issues as very important and therefore the E-commerce industry has a
very long and difficult task of convincing customers that online transactions and privacy are, in
fact, fairly secure.
Customers do not trust an unknown faceless seller, paperless transactions, and electronic money.
So switching from a physical to a virtual store may be difficult (Reisinger).
Other limiting factors
 Lack of touch and feel online.
 Many unresolved legal issues.
 Rapidly evolving and changing E-commerce.
 Lack of support services.
 Insufficiently large enough number of sellers and buyers.
 Breakdown of human relationships.
 Expensive and/or inconvenient accessibility to the Internet.
M- COMMERCE
Advances in e-commerce have resulted in progress towards strategies, requirements and
development of e-commerce application. Nearly all the e-commerce applications envisioned so
far assume fixed or stationary users with wired infrastructure, such as a browser on PC connected
to the internet using phone lines on LAN.

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Many people do not use a PC outside the office, but keep the mobile phone at their side all the
times. Mobile commerce is perfect for this group.
M-commerce allows one to reach the consumer directly, not his fax machine, his desk, his
secretary or his mailbox, but ones consumer directly, regardless of where he is.
M-commerce is "the delivery of electronic commerce capabilities directly into the hands,
anywhere, via wireless technology" and "putting a retail outlet in the customer's hands
anywhere." This can be done with just a mobile phone, a PDA connected to a mobile phone or
even a portable PC connected to a mobile phone. M-commerce is also termed as wireless e-
commerce.
Electronic commerce has attracted significant attention in the last few years. Advances in e-
commerce have resulted in significant progress towards strategies, requirements and
development of e-commerce applications. Nearly all the applications envisioned and developed
so far assume fixed or stationary users with wired infrastructure, such as browser on a PC
connected to the Internet using phone lines or a Local Area Network. A new e-commerce
application such as "Wireless e-commerce" or "Mobile e-commerce" will benefit one to reach
the consumer directly, regardless of where he is.
The emergence of M-commerce, a synonym for wireless e-commerce allows one to do the same
function that can be done over the internet. This can be done by connecting a PDA to a mobile
phone, or even a portable PC connected to a mobile phone. Mobile Commerce is perfect for the
group who always keep a mobile phone by side all the times. A study from the wireless data and
computing service, a division of strategy analytics, reports that the mobile commerce market
may rise to $200 billion by 2004. The report predicts that transactions via wireless devices will
generate about $14 billion a year.
We are aware that consensus within business and industry of future applications is still in its
infancy. However, we are interested in examining those future applications and technologies that
will form the next frontier of electronic commerce. To help future applications and to allow
designers, developers and researchers to strategize and create mobile commerce applications, a
four level integrated framework is proposed.

21. 21
Mobile commerce is a broad term that can be applied to a variety of different transactions. For
example, mobile commerce may describe a consumer using their mobile device to send an SMS
to subscribe to a ring tone service; a consumer using a mobile device to access the internet to
purchase clothing; or a consumer swiping a mobile device over a scanner at a café to pay for a
coffee. The cost of the transaction may be deducted from pre- paid funds stored on a phone,
charged to a phone bill, or charged to a credit card for example.
As Kisieloweska-Lipman (2009) notes, the most popular products and services in the mobile
commerce market include ringtones, screensavers and games, broadly referred to as mobile
premium services, as well as goods such as e-tickets, digital music, e-books and physical goods.
Transactions of this type are often low in value and relatively simple to conduct.
Increasingly, m-commerce technology is developing to allow for more varied transactions. In
some countries, for example, consumers can pay for train tickets or buy a coffee via their mobile
phone. With developments in near field communication (NFC) technology, the types of goods
and services available to consumers in this market are further likely to diversify. NFC
technology uses radio communications to send information between two devices to process a
transaction; for example, a phone with a chip stored on its SIM card, and a „reader‟ on the gates
at a train station.
NFC technology is already in use in countries like Japan. In Australia, systems such as Myki and
Paypass give an indication of where this technology is likely to head. The Paypass system
involves tapping (as opposed to swiping) a credit card against a reader to make a payment. For
transactions under AU$100 no signature or PIN is required. Paypass has also been designed to
operate via mobile phones; using NFC technology a customer can tap a mobile phone against a
reader to make a payment. MasterCard launched a trial of this technology in Australia in 2007; it
is already available in Taiwan, Korea and the US.

22. 22
Who is using mobile commerce?
According to a recent review, several factors affect m-commerce uptake including a person‟s
nationality and age, with young consumers and people from the Asia-Pacific region the strongest
adopters of m-commerce services (E-Marketer, 2010).
In Australia, a study by Vittles, Rintoul, Power and Keevy (2008) found that:
•45 per cent of young people (12-17 years) had shopped via their mobile phone or internet
•18 per cent reported that they entered competitions run by TV stations using SMS or by calling
in
•13 per cent voted using SMS (for example, for Big Brother or Australian Idol)
•9 per cent bought services using SMS for their mobile phone (for example, ringtones).
According to this same report, young Australians have the second highest mobile phone
ownership rates in the world, second to Hong Kong.
Typical examples of m-commerce are:
 Purchasing airline tickets
 Purchasing movie tickets
 Restaurant booking and reservation
 Hotel booking Hotel booking and reservation.
CONCLUSIONS
M-Commerce is an evolving area of e-Commerce, where users can interact with the service
providers through a mobile and wireless network, using mobile devices for information retrieval
and transaction processing.
M-Commerce services and applications can be adopted through different wireless and mobile
networks, with the aid of several mobile devices. Although there are many systems supporting
mobility and many solutions for wireless access, there are issues influencing the performance of
the various mobile systems that need to be considered in the design of m-Commerce services and

23. 23
applications. This applies also to mobile devices that exhibit some major drawbacks comp ared
to desktop systems. An important factor in designing m-Commerce services and applications is
the need for proper identification of mobile users‟ requirements, as well as mobile devices and
technologies constraints. Services and applications are designed and developed according to
these requirements and constraints.
M-Commerce services and applications can be classified based on the functionality they provide
to the mobile users for allowing easier identification of constraints posed on the design and
development process. This kind of classification results in two major classes: the directory and
the transaction-oriented services and applications, with their unique properties.
This paper suggested a new approach for designing and developing m-Commerce services and
applications. The proposed approach relies on mobile user needs and requirements, then
classification of m-Commerce services and applications, as well as the current technologies for
mobile and wireless computing and their constraints. Future work will include the verification of
the methodology described through the actual development of m-Commerce services and
applications for each of the two classes reported (A.S.Andreous, 2011).
Data warehouse
A fundamental concept of a data warehouse is the distinction between data and information. Data
is composed of observable and recordable facts that are often found in operational or
transactional systems. At Rutgers, these systems include the registrar‟s data on students (widely
known as the SRDB), human resource and payroll databases, course scheduling data, and data on
financial aid. In a data warehouse environment, data only comes to have value to end-users when
it is organized and presented as information. Information is an integrated collection of facts and
is used as the basis for decision- making. For example, an academic unit needs to have
diachronic information about its extent of output of its different faculty members to gauge if it is
becoming more or less reliant on part-time faculty.
A data warehouse is the concept of data extracted from operational systems and made available
as historical snapshots for ad-hoc queries and scheduled reporting. Characteristics that
distinguish data in the data warehouse from data found in the operational environment are that it
is: organized in such as way that relevant data is clustered together for easy access, several

24. 24
copies of the data from various points in time are kept together, and once the data is placed into
the data warehouse it is not updated. Rather, the historical snapshots stored in the Data
Warehouse are periodically refreshed with data from the operational databases.
The main problem addressed by a data warehouse is that end-users have a difficult time
producing ad-hoc or other specialized queries and reports. This is due to several factors;
 Most of the data is stored in ADABAS, which is difficult for end-users to access. The
data stores were designed for transaction processing not ad-hoc reporting.
 Obtaining the data or a report usually requires waiting for a programmer to either develop
the report or provide a customized download program.
 All of the data may not be consistent as of the same point in time.
 There may not be enough copies of the data kept for historical reporting in the
operational systems. End-users do not have the knowledge of what is kept in the existing
data stores.
Data Warehousing is an evolutionary/iterative process that follows a spiral pattern
 The warehouse architecture is initially developed at the start.
 The first increment is developed based on the architecture.
 Building the first increment causes architectural changes.
 Operation of the warehouse brings architectural changes.
 Each additional increment extends the warehouse.
 Each new increment may cause architectural adjustments.
 Continued operation may cause architectural adjustments.
Advantages
The data warehouse addresses these factors and provides many advantages to the end-users of
the University including (Uiversity, 2013)
 Improved end-user access to a wide variety of University data
 Increased data consistency
 Additional documentation of the data
 Potentially lower computing costs and increased productivity

25. 25
 Providing a place to combine related data from separate sources
 Creation of a computing infrastructure that can support changes in computer systems and
business structures
 Empowering end-users to perform any level of ad-hoc queries or reports without
impacting the performance of the operational systems.
Each page listed above represents a typical data warehouse design phase, and has several
sections:
Task Description: This section describes what typically needs to be accomplished during this
particular data warehouse design phase.
Time Requirement: A rough estimate of the amount of time this particular data warehouse task
takes.
Deliverables: Typically at the end of each data warehouse task, one or more documents are
produced that fully describe the steps and results of that particular task. This is especially
important for consultants to communicate their results to the clients.
Possible Pitfalls: Things to watch out for. Some of them not so obvious. All of them are real
(data.com, 2014)
Business use of a data warehouse
No discussion of the data warehousing systems is complete without review of the type of activity
supported by a data warehouse. Some of the activity against today‟s data warehouses is
predefined and not much different from traditional analysis activity. Other processes such as
multi-dimensional analysis and information visualization were not available with traditional
analysis tools and methods.
There is a very interesting phenomenon that is observed with many data warehousing projects.
The users of a new data warehouse only wish to get the information that they were able to get
using the old tools and methods. They wish to replicate their queries and reports with the data
warehouse and make sure that all the numbers match. Often there is as much apprehension of
the new tools and the data warehouse as there is excitement. It is only after using the new data

26. 26
warehouse for a period of time that they start to explore and discover the new capabilities that
are available to them. Soon after, they start to have significant input into the data warehouse
enhancement process and they happily become the mentors for the new users.
Summary
It is important to note that data warehousing is a science that continues to evolve. Many of the
design and development concepts introduced here greatly influence the quality of the analysis
that is possible with data in the data warehouse. If invalid or corrupt data is allowed to get into
the data warehouse, the analysis done with this data is likely to be invalid.
After the rapid acceptance of data warehousing systems during past three years, there will
continue to be many more enhancements and adjustments to the data warehousing system model.
Further evolution of the hardware and software technology will also continue to greatly influence
the capabilities that are built into data warehouses.
Data warehousing systems have become a key component of information technology
architecture. A flexible enterprise data warehouse strategy can yield significant benefits for a
long period (R.Gupta, 1997).
Data Mining
Data Mining is an analytic process designed to explore data (usually large amounts of data -
typically business or market related) in search of consistent patterns and/or systematic
relationships between variables, and then to validate the findings by applying the detected
patterns to new subsets of data. The ultimate goal of data mining is prediction - and predictive
data mining is the most common type of data mining and one that has the most direct business
applications. The process of data mining consists of three stages: (1) the initial exploration, (2)
model building or pattern identification with validation/verification, and (3) deployment (i.e., the
application of the model to new data in order to generate predictions).
Stage 1: Exploration. This stage usually starts with data preparation which may involve cleaning
data, data transformations, selecting subsets of records and - in case of data sets with large
numbers of variables ("fields") - performing some preliminary feature selection operations to
bring the number of variables to a manageable range (depending on the statistical methods which

27. 27
are being considered). Then, depending on the nature of the analytic problem, this first stage of
the process of data mining may involve anywhere between a simple choice of straightforward
predictors for a regression model, to elaborate exploratory analyses using a wide variety of
graphical and statistical methods (see Exploratory Data Analysis (EDA)) in order to identify the
most relevant variables and determine the complexity and/or the general nature of models that
can be taken into account in the next stage.
Stage 2: Model building and validation. This stage involves considering various models and
choosing the best one based on their predictive performance (i.e., explaining the variability in
question and producing stable results across samples). This may sound like a simple operation,
but in fact, it sometimes involves a very elaborate process. There are a variety of techniques
developed to achieve that goal - many of which are based on so-called "competitive evaluation of
models," that is, applying different models to the same data set and then comparing their
performance to choose the best. These techniques - which are often considered the core of
predictive data mining - include: Bagging (Voting, Averaging), Boosting, Stacking (Stacked
Generalizations), and Meta-Learning.
Stage 3: Deployment. That final stage involves using the model selected as best in the previous
stage and applying it to new data in order to generate predictions or estimates of the expected
outcome.
The concept of Data Mining is becoming increasingly popular as a business information
management tool where it is expected to reveal knowledge structures that can guide decisions in
conditions of limited certainty. Recently, there has been increased interest in developing new
analytic techniques specifically designed to address the issues relevant to business Data Mining
(e.g., Classification Trees), but Data Mining is still based on the conceptual principles of
statistics including the traditional Exploratory Data Analysis (EDA) and modeling and it shares
with them both some components of its general approaches and specific techniques.
However, an important general difference in the focus and purpose between Data Mining and the
traditional Exploratory Data Analysis (EDA) is that Data Mining is more oriented towards
applications than the basic nature of the underlying phenomena. In other words, Data Mining is
relatively less concerned with identifying the specific relations between the involved variables.

28. 28
For example, uncovering the nature of the underlying functions or the specific types of
interactive, multivariate dependencies between variables are not the main goal of Data Mining.
Instead, the focus is on producing a solution that can generate useful predictions. Therefore, Data
Mining accepts among others a "black box" approach to data exploration or knowledge discovery
and uses not only the traditional Exploratory Data Analysis (EDA) techniques, but also such
techniques as Neural Networks which can generate valid predictions but are not capable of
identifying the specific nature of the interrelations between the variables on which the
predictions are based.
Data Mining is often considered to be "a blend of statistics, AI [artificial intelligence], and data
base research" (Pregibon, 1997, p. 8), which until very recently was not commonly recognized as
a field of interest for statisticians, and was even considered by some "a dirty word in Statistics"
(Pregibon, 1997, p. 8). Due to its applied importance, however, the field emerges as a rapidly
growing and major area (also in statistics) where important theoretical advances are being made
(see, for example, the recent annual International Conferences on Knowledge Discovery and
Data Mining, co-hosted by the American Statistical Association).
For information on Data Mining techniques, please review the summary topics included below in
this chapter of the Electronic Statistics Textbook. There are numerous books that review the
theory and practice of data mining; the following books offer a representative sample of recent
general books on data mining, representing a variety of approaches and perspectives:
2. Crucial Concepts in Data Mining
The concept of bagging (voting for classification, averaging for regression-type problems with
continuous dependent variables of interest) applies to the area of predictive data mining, to
combine the predicted classifications (prediction) from multiple models, or from the same type
of model for different learning data. It is also used to address the inherent instability of results
when applying complex models to relatively small data sets. Suppose your data mining task is to
build a model for predictive classification, and the dataset from which to train the model
(learning data set, which contains observed classifications) is relatively small. You could
repeatedly sub-sample (with replacement) from the dataset, and apply, for example, a tree
classifier (e.g., C&RT and CHAID) to the successive samples. In practice, very different trees

29. 29
will often be grown for the different samples, illustrating the instability of models often evident
with small datasets. One method of deriving a single prediction (for new observations) is to use
all trees found in the different samples, and to apply some simple voting: The final classification
is the one most often predicted by the different trees. Note that some weighted combination of
predictions (weighted vote, weighted average) is also possible, and commonly used. A
sophisticated (machine learning) algorithm for generating weights for weighted prediction or
voting is the Boosting procedure.
Boosting
The concept of boosting applies to the area of predictive data mining, to generate multiple
models or classifiers (for prediction or classification), and to derive weights to combine the
predictions from those models into a single prediction or predicted classification (see also
Bagging).
A simple algorithm for boosting works like this: Start by applying some method (e.g., a tree
classifier such as C&RT or CHAID) to the learning data, where each observation is assigned an
equal weight. Compute the predicted classifications, and apply weights to the observations in the
learning sample that are inversely proportional to the accuracy of the classification. In other
words, assign greater weight to those observations that were difficult to classify (where the
misclassification rate was high), and lower weights to those that were easy to classify (where the
misclassification rate was low). In the context of C&RT for example, different misclassification
costs (for the different classes) can be applied, inversely proportional to the accuracy of
prediction in each class. Then apply the classifier again to the weighted data (or with different
misclassification costs), and continue with the next iteration (application of the analysis method
for classification to the re-weighted data).
Boosting will generate a sequence of classifiers, where each consecutive classifier in the
sequence is an "expert" in classifying observations that were not well classified by those
preceding it. During deployment (for prediction or classification of new cases), the predictions
from the different classifiers can then be combined (e.g., via voting, or some weighted voting
procedure) to derive a single best prediction or classification.

30. 30
Note that boosting can also be applied to learning methods that do not explicitly support weights
or misclassification costs. In that case, random sub-sampling can be applied to the learning data
in the successive steps of the iterative boosting procedure, where the probability for selection of
an observation into the subsample is inversely proportional to the accuracy of the prediction for
that observation in the previous iteration (in the sequence of iterations of the boosting
procedure).
Fuzzy Logic
Fuzzy logic is derived from fuzzy set theory dealing with reasoning that is approximate rather
than precisely deduced from classical predicate logic. It can be thought as the application side of
fuzzy set theory dealing with well thought out real world expert values for a complex problem.
Degrees of truth are often confused with probabilities. However, they are conceptually distinct;
fuzzy truth represents membership in vaguely defined sets, not likelihood of some event or
condition. To illustrate the difference, consider this scenario: Bob is in a house with two adjacent
rooms: the kitchen and the dining room. In many cases, Bob's status within the set of things "in
the kitchen" is completely plain: he's either "in the kitchen" or "not in the kitchen". What about
when Bob stands in the doorway? He may be considered "partially in the kitchen". Quantifying
this partial state yields a fuzzy set membership. With only his little toe in the dining room, we
might say Bob is 99% "in the kitchen" and 1% "in the dining room", for instance. No event (like
a coin toss) will resolve Bob to being completely "in the kitchen" or "not in the kitchen", as long
as he's standing in that doorway. Fuzzy sets are based on vague definitions of sets, not
randomness.
Fuzzy logic allows for set membership values between and including 0 and 1, shades of gray as
well as black and white, and in its linguistic form, imprecise concepts like "slightly", "quite" and
"very". Specifically, it allows partial membership in a set. It is related to fuzzy sets and
possibility theory. It was introduced in 1065 by Prof LotfiZadeh at the University of California,
Berkley.
Fuzzy logic is controversial in some circles, despite wide acceptance and a broad track record of
successful applications. It is rejected by some control engineers for validation and other reasons,
and by some statisticians who hold that probability is the only rigorous mathematical description

31. 31
of uncertainty. Critics also argue that it cannot be a superset of ordinary set theory since
membership functions are defined in terms of conventional.
Applications
Fuzzy logic can be used to control household appliances such as washing machines (which sense
load size and detergent concentration and adjust their wash cycles accordingly) and refrigerators.
A basic application might characterize sub ranges of a continuous variable. For instance, a
temperature measurement for anti-lock brakes might have several separate membership functions
defining particular temperature ranges needed to control the brakes properly. Each function maps
the same temperature value to a truth value in the 0 to 1 range. These truth values can then be
used to determine how the brakes should be controlled.
Examples where fuzzy logic is used
 Automobile and other vehicle subsystems, such as ABS and cruise control (e.g. Tokyo
monorail)
 Air conditioners
 The Massive engine used in the Lord of the Rings films, which helped show huge scale
armies create random, yet orderly movements
 Cameras
 Digital image processing, such as edge detection
 Rice cookers
 Dishwashers
 Elevartors
 Washing machines and other home appliances
 Video game artificial intelligence
 Massage boards and chat rooms
 Fuzzy logic has also been incorporated into some microcontrollers and microprocessors,
for instance Freescale 68HC12.

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How fuzzy logic is applied
Fuzzy logic usually uses IF/THEN rules, or constructs that are equivalent, such as fuzzy
associative matrices.
Rules are usually expressed in the form:
IF variable IS set THEN action
For example, an extremely simple temperature regulator that uses a fan might look like this:
IF temperature IS very cold THEN stop fan
IF temperature IS cold THEN turn down fan
IF temperature IS normal THEN maintain level
IF temperature IS hot THEN speed up fan
Notice there is no "ELSE". All of the rules are evaluated, because the temperature might be
"cold" and "normal" at the same time to differing degrees.
The AND, OR, and NOT operators of boolean logic exist in fuzzy logic, usually defined as the
minimum, maximum, and complement; when they are defined this way, they are called the
Zadeh operators, because they were first defined as such in Zadeh's original papers. So for the
fuzzy variables x and y:
NOT x = (1 - truth(x))
x AND y = minimum(truth(x), truth(y))
x OR y = maximum(truth(x), truth(y))
There are also other operators, more linguistic in nature, called hedges that can be applied. These
are generally adverbs such as "very", or "somewhat", which modify the meaning of a set using a
mathematical formula (Earl, 1994).

33. 33
SUMMARY
FL was conceived as a better method for sorting and handling data but has proven to be a
excellent choice for many control system applications since it mimics human control logic. It can
be built into anything from small, hand-held products to large computerized process control
systems. It uses an imprecise but very descriptive language to deal with input data more like a
human operator. It is very robust and forgiving of operator and data input and often works when
first implemented with little or no tuning.
In application, the programming language Prolog is well geared to implementing fuzzy logic
with its facilities to setup a database of "rules" which are queried to deduct logic. This sort of
programming is known as logic programming.
Enterprise Resource Planning
The term Enterprise Resource Planning (ERP) is most commonly referenced in the context of
commercially available software systems. ERP systems provide an integrated suite of
information technology applications that support the operations of an enterprise and are not, as
the acronym ERP implies, limited to planning functions. The activities supported by ERP
systems include all core functions of an enterprise, including financial management, human
resources management, and operations. Increasingly, ERP vendors are offering “bolt-on”
products that provide specialized functionality to augment the core, such as Advanced Planning
and Scheduling (APS), and Customer Relationship Management (CRM).
Although the term ERP generally refers to a software system, it also encompasses the business
processes that drive system requirements and capabilities. ERP systems support and enable the
transformation of enterprises through the deployment of best practices and integrated business
processes. Transformation of business processes can be achieved using a Continuous Business
Process Improvement (CBPI) approach. CBPI refers to both incremental and larger, more radical
process changes. Transformation through the use of CBPI frequently leverages the
implementation of an ERP solution as a key enabler. In CBPI, business processes, work flows,
information, organizational design and position descriptions are changed.

34. 34
ERP (enterprise resource planning) is an industry term for the broad set of activities supported by
multi-module application software that help a manufacturer or other business manage the
important parts of its business, including product planning, parts purchasing, maintaining
inventories, interacting with suppliers, providing customer service, and tracking orders. ERP can
also include application modules for the finance and human resources aspects of a business.
Typically, an ERP system uses or is integrated with a relational database system. The
deployment of an ERP system can involve considerable business process analysis, employee
retraining, and new work procedures.
Evaluation Criteria
Some important points to be kept in mind while evaluating ERP software include:
1) Functional fit with the Company's business processes
2) Degree of integration between the various components of the ERP system
3) Flexibility and scalability
4) Complexity; user friendliness
5) Quick implementation; shortened ROI period
6) Ability to support multi-site planning and control
7) Technology; client/server capabilities, database independence, security
8) Availability of regular upgrades
9) Amount of customization required
10) Local support infrastructure
11) Availability of reference sites
12) Total costs, including cost of license, training, implementation, maintenance,
customization and hardware requirements.

35. 35
ERP Purpose
The main value ERP systems provide is the opportunity to integrate an entire organization. End
to end business processes that were traditionally disjointed, now share information through a
common database. The information flow is much more efficient in that there are clear lines of
business processes across the enterprise. For example, in a horizontally integrated ERP system, a
purchasing department would process a purchase order in a central database with a common
General Ledger (GL). Both Accounts Payable and Receiving have access to the same GL so the
data would be immediately available to them. There is no time lag, re-entry of information, or
dependency on paper documents. By having a single point of entry the risk of inaccuracy in the
end-to-end transaction is reduced, resulting in fewer reconciliation points. Additionally, the ERP
systems of today provide utilities for vertical integration with suppliers and distributors. When
properly implemented as part of a comprehensive transformation effort, ERP solutions can yield
the following results:
•Integrated processes and information systems
•Consolidation and/or elimination of current systems
•Reduced complexity of application and technology portfolios
•Reduced reliance on programmers to make software changes
•Authoritative data source
•Reduced data redundancy and duplicative data entry
•More effective and efficient business processes
Advantages & Disadvantages of ERP
Advantages of ERP Systems
 Provides integration of the supply-chain, production, and administration
 Creates commonality of databases
 Can incorporate improved best processes

36. 36
 Increases communication and collaboration between business units and sitesHas an off-
the-shelf software database
 May provide a strategic advantage
Disadvantages of ERP Systems
 Is very expensive to purchase and even more so to customize
 Implementation may require major changes in the company and its processes
 Is so complex that many companies cannot adjust to it
 Involves an ongoing, possibly never ending, process for implementation
 Expertise is limited with ongoing staffing problems
Conclusion
ERP is recommended in an organization not only because the advantages outnumber the
disadvantages but also by keeping in mind the ways to overcome the disadvantages. An
organization has to correctly weigh the advantages and disadvantages of ERP before going for
them.
Expert system
An expert system is a computer program that simulates the judgement and behavior of a human
or an organization that has expert knowledge and experience in a particular field. Typically, such
a system contains a knowledge base containing accumulated experience and a set of rules for
applying the knowledge base to each particular situation that is described to the program.
Sophisticated expert systems can be enhanced with additions to the knowledge base or to the set
of rules.
Artificial intelligence based system that converts the knowledge of an expert in a specific subject
into a software code. This code can be merged with other such codes (based on the knowledge of
other experts) and used for answering questions (queries) submitted through a computer. Expert
systems typically consist of three parts: (1) a knowledge base which contains the information
acquired by interviewing experts, and logic rules that govern how that information is applied; (2)
an Inference engine that interprets the submitted problem against the rules and logic of
information stored in the knowledge base; and an (3) Interface that allows the user to express the

37. 37
problem in a human language such as English. Despite its earlier high hopes, expert systems
technology has found application only in areas where information can be reduced to a set of
computational rules, such as insurance underwriting or some aspects of securities trading. Also
called rule based system.
Source:http://www.businessdictionary.com/definition/expert-system.html#ixzz2y3ZJ5CDP
Executive information system
An executive information system (EIS) is a type of management information system that
facilitates and supports senior executive information and decision-making needs. It provides easy
access to internal and external information relevant to organizational goals. It is commonly
considered a specialized form of decision support system (DSS).[1]
EIS emphasizes graphical displays and easy-to-use user interfaces. They offer strong reporting
and drill-down capabilities. In general, EIS are enterprise-wide DSS that help top-level
executives analyze, compare, and highlight trends in important variables so that they can monitor
performance and identify opportunities and problems. EIS and data warehousing technologies
are converging in the marketplace.
Executive information systems (EIS) are management information systems tailored to the
strategic information needs of top management. Top executives get the information they need
from many sources, including letters, memos, periodicals, and reports produced manually as well
as by computer systems.
Other sources of executive information are meetings, telephone calls, and social activities. Thus,
much of a top executive‟s information comes from non-computer services. Computer generated
information as not played a primary role in meeting many top executives‟ information needs.

38. 38
OTHER CLASSIFICATIONS OF INFORMATION SYSTEMS
(i) EXPERT SYSTEMS
An expert system is a knowledge-based information system; that is, it uses its knowledge about a
specific area to act as an expert consultant to users.The components of an expert system are a
knowledge base and software modules that perform inferences on the knowledge and offer
answers to a user‟s questions.
Expert systems are being used in many different fields, including medicine, engineering, the
physical sciences, and business. For example, expert systems now help diagnose illnesses, search
for minerals, analyze compounds, recommend repairs, and do financial planning. Expert systems
can support either operations or management activities.
KNOWLEDGE MANAGEMENT SYSTEMS
Knowledge Management systems (KMS), Workers create, organize, and share important
business knowledge wherever and whenever it is needed. For example, many knowledge
management systems rely on Internet and intranet Web sites, knowledge bases, and discussion
forums as key technologies for gathering, storing, and disseminating business knowledge. In this
way, knowledge management systems facilitate organization learning and knowledge creation
and dissemination within the business enterprise.
(iii) STRATEGIC INFORMATION SYSTEM
The strategic role of information systems involves using information technology to develop
products, services, and capabilities that give a company strategic advantages over the
competitive forces it faces in the global marketplace. This creates strategic information system,
information systems that support or shape the competitive position and strategies of an
enterprise.So a strategic information system can be any kind of information systems (TPS, MIS,
DSS, etc.) that helps an organization gain a competitive advantage, reduce acompetitive
disadvantage, or meet other strategic enterprise objectives.

39. 39
IV BUSINESS INFORMATION SYSTEMS
As a future managerial end user, it is important for you to realize that information systems
directly support both operations and management activities in the business functions of
accounting, finance, human resource management, marketing, and operations management. Such
business information systems are needed by all business functions.
For example, marketing managers need information about sales performance and trends
provided by marketing information systems. Financial managers need information concerning
financing costs and investment returns provided by financial information systems.
(v) INTEGRATED INFORMATION SYSTEM
It is also important to realize that information systems in the real world are typically integrated
combinations of several types of information systems we have just mentioned.That’s because
conceptual classification of information systems are designed to emphasize themany different
roles of information systems. In practice, these roles are integrated into composite or cross-
functional information systems that provide a variety of functions. Thus, most information
systems are designed to produce information and support decision making for various levels of
management and business functions, as well as do record keeping and transaction processing
systems
.

40. 40
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