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's Galatea. Human likenesses believed to have

intelligence were built in every major civilization: animated statues were worshipped in Egypt

and Greece and humanoid automatons were built by Yan Shi, Hero of Alexandria, Al-Jazari and

Wolfgang von Kempelen. It was also widely believed that artificial beings had been created by

Jābir ibn Hayyān, Judah Loew and Paracelsus. By the 19th and 20th centuries, artificial beings

had become a common feature in fiction, as in Mary Shelley's Frankenstein or Karel Čapek's

R.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 mathematician Alan 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

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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). An artificial neuron is a computational model inspired in the naturalnens.

When the signals received are strong enough (surpass a certain threshold), the neuron is activated

and emits a signal 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 (like synapses), which are multiplied by weights(strength of

their elective signals),and then computed by a mathematical function which determines the

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activation of the neuron. Another function (which may be the identity) computes the output of

the artificial neuron (sometimes in dependence of a certain threshold).ANNs combine artificial

neurons in order to process information.

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 a live 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

A large number of very simple processing neuron like processing elements.

A large 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 efficacy from:

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 Artificial Neural Networks this

process is essentially in parallel, which is the origin of its flexibility.

Because the calculations are divided in many nodes, if any of them gets astray from the

Expected behavior it does not affect the behavior of the network.

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2. Its ability to learn and generalize. The ANN has the capability to acquire knowledge from its

surroundings by the adaptation of its internal parameters, which is produced as a response to the

presence of an external stimulus. The network learns from the examples which are presented to

it, and generalizes knowledge from them. The generalization can be interpreted as the property of

artificial neural networks to produce an adequate response to unknown stimulus which are

related to the acquired knowledge (Gasco).

These two characteristics for information processing make an ANN able to give solution to

complex problems normally difficult to manage by the traditional ways of approximation.

Additionally, using them gives the following benefits

No linearity, the answer from the computational neuron can be linear or not. A neural network

formed by the interconnection of non ‐ 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, this property allows the ANN to distribute the knowledge in

the entire network structure, there is no element with specific stored information.

Fault tolerance, this characteristic is shown in two senses: The first is related to the samples

Shown to the network, in which case it answers correctly even when the examples exhibit

Variability or noise; the second, appears when in any of the elements of the network occurs a

failure, which does not impossibilities its functioning due to the way in which it stores

information.

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

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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 more accurate (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.

20

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.

21

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

22

applications. This applies also to mobile devices that exhibit some major drawbacks comp a red

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

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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

24

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

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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

26

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.

27

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

28

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.

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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 Lotfi Zadeh 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

30

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 Free scale 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).

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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.

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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.

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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

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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

36

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.

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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 a competitive

disadvantage, or meet other strategic enterprise objectives.

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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 the many 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

.

39

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