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MODEL HOME WORK: 1

School: Department: CSE/ITName of the faculty member: Course No: CSE 303 Course Title: Management Support SystemsClass: Diploma- B.tech Integrated Term: 310111 Section: Batch: 2008Max. Marks:

Date of Allotment: Date of Submission:

PART-A

Ques1) Whenever decision-makers has to make a decision what are the various steps need to be taken by the decision-makers?

Ques2) What criteria should be used as a basis for making decision? Explain the various phases involved?

Ques3) Explain the need of the computerized Decision support?

PART-B

Ques4) You are in a coffee shop across the street from school having lunch. A customer walks up to the counter. You observe the following:

Customer: Hi Jane, I’d like a burger to go. Everything but onions.

Jane (waitress): Anything else?

Customer: Yes, a small order of fries and a root beer.

Jane: That’ll be $ 2.35.

She collects the cash and places the order through an electronic cash register that automatically displays the order on a TV screen in the back room where orders are prepared. When the order is ready, Jane puts it in a bag and hands it to the customer. Explain the pattern of this system in action. Specifically discuss the following:

a) The organization system’s characteristics.b) The type of information system involved.c) The types of computing involved in the organization’s sytem.

d) If you were to improve the performance of this establishment, what would you do? How? Explain.

Ques5) An computer based information system is combination of various technologies to support operations, management, and decision-making. Explain the Statement.

Ques6) Explain the difference between Data, Information and Knowledge.\

http://searchbusinessanalytics.techtarget.com/tutorial/How-decision-support-systems-DSS-can-help-business-decision-making

http://wiki.answers.com/Q/Why_do_you_need_computerized_decision_support_system&alreadyAsked=1&rtitle=Is_Decision_support_needs_to_be_computerized

Data, Information, Knowledge, and Wisdomby Gene Bellinger, Durval Castro, Anthony Mills

There is probably no segment of activity in the world attracting as much attention at present as that of knowledge management. Yet as I entered this arena of activity I quickly found there didn't seem to be a wealth of sources that seemed to make sense in terms of defining what knowledge actually was, and how was it differentiated from data, information, and wisdom. What follows is the current level of understanding I have been able to piece together regarding data, information, knowledge, and wisdom. I figured to understand one of them I had to understand all of them.

According to Russell Ackoff, a systems theorist and professor of organizational change, the content of the human mind can be classified into five categories:

1. Data: symbols

2. Information: data that are processed to be useful; provides answers to "who", "what", "where", and "when" questions

3. Knowledge: application of data and information; answers "how" questions

4. Understanding: appreciation of "why"

5. Wisdom: evaluated understanding.

Ackoff indicates that the first four categories relate to the past; they deal with what has been or what is known. Only the fifth category, wisdom, deals with the future because it incorporates vision and design. With wisdom, people can create the future rather than just grasp the present and past. But achieving wisdom isn't easy; people must move successively through the other categories.

A further elaboration of Ackoff's definitions follows:

Data... data is raw. It simply exists and has no significance beyond its existence (in and of itself). It can exist in any form, usable or not. It does not have meaning of itself. In computer parlance, a spreadsheet generally starts out by holding data.

Information... information is data that has been given meaning by way of relational connection. This "meaning" can be useful, but does not have to be. In computer parlance, a relational database makes information from the data stored within it.

Knowledge... knowledge is the appropriate collection of information, such that it's intent is to be useful. Knowledge is a deterministic process. When someone "memorizes" information (as less-aspiring test-bound students often do), then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, an integration such as would infer further knowledge. For example, elementary school children memorize, or amass knowledge of, the "times table". They can tell you that "2 x 2 = 4" because they have amassed that knowledge (it being included in the times table). But when asked what is "1267 x 300", they can not respond correctly because that entry is not in their times table. To correctly answer such a question requires a true cognitive and analytical ability that is only encompassed in the next level... understanding. In computer parlance, most of the applications we use (modeling, simulation, etc.) exercise some type of stored knowledge.

Understanding... understanding is an interpolative and probabilistic process. It is cognitive and analytical. It is the process by which I can take knowledge and synthesize new knowledge from the previously held knowledge. The difference between understanding and knowledge is the difference between "learning" and "memorizing". People who have understanding can undertake useful actions because they can synthesize new knowledge, or in some cases, at least new information, from what is previously known (and understood). That is, understanding can build upon currently held information, knowledge and understanding itself. In computer parlance, AI systems possess understanding in the sense that they are able to synthesize new knowledge from previously stored information and knowledge.

Wisdom... wisdom is an extrapolative and non-deterministic, non-probabilistic process. It calls upon all the previous levels of consciousness, and specifically upon special types of human programming (moral, ethical codes, etc.). It beckons to give us understanding about which there has previously been no understanding, and in doing so, goes far beyond understanding itself. It is the essence of philosophical probing. Unlike the previous four levels, it asks questions to which there is no (easily-achievable) answer, and in some cases, to which there can be no humanly-known answer period. Wisdom is therefore, the process by which we also discern, or judge, between right and wrong, good and bad. I personally believe that computers do not have, and will never have the ability to posses wisdom. Wisdom is a uniquely human state, or as I see it, wisdom requires one to have a soul, for it resides as much in the heart as in the mind. And a soul is something machines will never possess (or perhaps I should reword that to say, a soul is something that, in general, will never possess a machine).

Personally I contend that the sequence is a bit less involved than described by Ackoff. The following diagram represents the transitions from data, to information, to knowledge, and finally to wisdom, and it is understanding that support the transition from each stage to the next. Understanding is not a separate level of its own.

Data represents a fact or statement of event without relation to other things.

Ex: It is raining.

Information embodies the understanding of a relationship of some sort, possibly cause and effect.

Ex: The temperature dropped 15 degrees and then it started raining.

Knowledge represents a pattern that connects and generally provides a high level of predictability as to what is described or what will happen next.

Ex: If the humidity is very high and the temperature drops substantially the atmospheres is often unlikely to be able to hold the moisture so it rains.

Wisdom embodies more of an understanding of fundamental principles embodied within the knowledge that are essentially the basis for the knowledge being what it is. Wisdom is essentially systemic.

Ex: It rains because it rains. And this encompasses an understanding of all the interactions that happen between raining, evaporation, air currents, temperature gradients, changes, and raining.

Yet, there is still a question regarding when is a pattern knowledge and when is it noise. Consider the following:

Abugt dbesbt regtc uatn s uitrzt. ubtxte pstye ysote anet sser extess

ibxtedstes bet3 ibtes otesb tapbesct ehracts

It is quite likely this sequence represents 100% novelty, which means it's equivalent to noise. There is no foundation for you to connect with the pattern, yet to me the statements are quite meaningful as I understand the translation with reveals they are in fact Newton's 3 laws of motion. Is something knowledge if you can't understand it?

Now consider the following:

I have a box. The box is 3' wide, 3' deep, and 6' high.

The box is very heavy.

The box has a door on the front of it.

When I open the box it has food in it.

It is colder inside the box than it is outside.

You usually find the box in the kitchen.

There is a smaller compartment inside the box with ice in it.

When you open the door the light comes on.

When you move this box you usually find lots of dirt underneath it.

Junk has a real habit of collecting on top of this box.

What is it?

A refrigerator. You knew that, right? At some point in the sequence you connected with the pattern and understood it was a description of a refrigerator. From that point on each statement only added confirmation to your understanding.

If you lived in a society that had never seen a refrigerator you might still be scratching your head as to what the sequence of statements referred to.

Also, realize that I could have provided you with the above statements in any order and still at some point the pattern would have connected. When the pattern connected the sequence of statements represented knowledge to you. To me all the statements convey nothing as they are simply 100% confirmation of what I already knew as I knew what I was describing even before I started.

The Differences Between Data, Information and Knowledge

We frequently hear the words Data, Information and Knowledge used as if they are the same thing.

You hear people talking about the Internet as a “vast network of human knowledge” or that they’ll “e-mail through the data.”

By defining what we mean by data, information and knowledge – and how they interact with one another – it should be much easier.

Has Anyone Seen My CDs?

A few years ago, the UK Government Tax office lost some CDs containing 25 million people’s records, when they were posted unsecurely. The fear was that there was enough information contained on them to allow criminals to set up bank accounts, get loans, and do their Christmas shopping… all under someone else’s name.

In the fallout, the main argument in the press was about security, and inevitably there were many that were using it to attack Government ministers. Anyone who’s ever worked in a bureaucracy will know that this kind of thing goes on more often that we would like to think, as people cut corners. No procedure or official process is water-tight. It’s just this time, they didn’t get away with it.

The media used the terms “data” and “information” interchangeably.

For example, one of the frequent mistakes was that they lost “data.” However, you can’t physically lose data. You can’t physically pick up data, move it about, etc.

Confused?

Let me explain, but – before we go any further - I should point out that I’m using the Infogineering defintions of the three words (data, information, knowledge) here. They’ve been so muddled up over the past few years that the various definitions don’t match up. So, let me explain how Infogineering views them all.

Knowledge

Firstly, let’s look at Knowledge. Knowledge is what we know. Think of this as the map of the World we build inside our brains. Like a physical map, it helps us know where things are – but it contains more than that. It also contains our beliefs and expectations. “If I do this, I will probably get that.” Crucially, the brain links all these things together into a giant network of ideas, memories, predictions, beliefs, etc.

It is from this “map” that we base our decisions, not the real world itself. Our brains constantly update this map from the signals coming through our eyes, ears, nose, mouth and skin.

You can’t currently store knowledge in anything other than a brain, because a brain connects it all together. Everything is inter-connected in the brain. Computers are not artificial brains. They don’t understand what they are processing, and can’t make independent decisions based upon what you tell them.

There are two sources that the brain uses to build this knowledge - information and data.

Data

Data is/are the facts of the World. For example, take yourself. You may be 5ft tall, have brown hair and blue eyes. All of this is “data”. You have brown hair whether this is written down somewhere or not.

In many ways, data can be thought of as a description of the World. We can perceive this data with our senses, and then the brain can process this.

Human beings have used data as long as we’ve existed to form knowledge of the world.

Until we started using information, all we could use was data directly. If you wanted to know how tall I was, you would have to come and look at me. Our knowledge was limited by our direct experiences.

Information

Information allows us to expand our knowledge beyond the range of our senses. We can capture data in information, then move it about so that other people can access it at different times.

Here is a simple analogy for you.

If I take a picture of you, the photograph is information. But what you look like is data.

I can move the photo of you around, send it to other people via e-mail etc. However, I’m not actually moving you around – or what you look like. I’m simply allowing other people who can’t directly see you from where they are to know what you look like. If I lose or destroy the photo, this doesn’t change how you look.

So, in the case of the lost tax records, the CDs were information. The information was lost, but the data wasn’t. Mrs Jones still lives at 14 Whitewater road, and she was still born on 15th August 1971.

The Infogineering Model (below) explains how these interact…

Why does it matter that people mix them up?

When people confuse data with information, they can make critical mistakes. Data is always correct (I can’t be 29 years old and 62 years old at the same time) but information can be wrong (there could be two files on me, one saying I was born in 1981, and one saying I was born in 1948).

Information captures data at a single point. The data changes over time. The mistake people make is thinking that the information they are looking at is always an accurate reflection of the data.

By understanding the differences between these, you can better understand how to make better decisions based on the accurate facts.

In Brief

Data: Facts, a description of the WorldInformation: Captured Data and KnowledgeKnowledge: Our personal map/model of the World

Decision support systemFrom Wikipedia, the free encyclopedia

Example of a Decision Support System for John Day Reservoir.

A decision support system (DSS) is a computer-based information system that supports business or organizational decision-making activities. DSSs serve the management, operations, and planning levels of an organization and help to make decisions, which may be rapidly changing and not easily specified in advance.

DSSs include knowledge-based systems. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from a combination of raw data, documents, personal knowledge, or business models to identify and solve problems and make decisions.

Typical information that a decision support application might gather and present are:

inventories of information assets (including legacy and relational data sources, cubes, data warehouses, and data marts),

comparative sales figures between one period and the next,

projected revenue figures based on product sales assumptions.

Contents[hide]

1 History 2 Taxonomies

3 Components

o 3.1 Development Frameworks

4 Classification

5 Applications

6 Benefits

7 See also

8 References

9 Further reading

[edit] History

According to Keen (1978),[1] the concept of decision support has evolved from two main areas of research: The theoretical studies of organizational decision making done at the Carnegie Institute of Technology during the late 1950s and early 1960s, and the technical work on interactive computer systems, mainly carried out at the Massachusetts Institute of Technology in the 1960s. It is considered that the concept of DSS became an area of research of its own in the middle of the 1970s, before gaining in intensity during the 1980s. In the middle and late 1980s, executive information systems (EIS), group decision support systems (GDSS), and organizational decision support systems (ODSS) evolved from the single user and model-oriented DSS.

According to Sol (1987)[2] the definition and scope of DSS has been migrating over the years. In the 1970s DSS was described as "a computer based system to aid decision making". Late 1970s the DSS movement started focusing on "interactive computer-based systems which help decision-makers utilize data bases and models to solve ill-structured problems". In the 1980s DSS should provide systems "using suitable and available technology to improve effectiveness of managerial and professional activities", and end 1980s DSS faced a new challenge towards the design of intelligent workstations.[2]

In 1987 Texas Instruments completed development of the Gate Assignment Display System (GADS) for United Airlines. This decision support system is credited with significantly reducing travel delays by aiding the management of ground operations at various airports, beginning with O'Hare International Airport in Chicago and Stapleton Airport in Denver Colorado.[3][4]

Beginning in about 1990, data warehousing and on-line analytical processing (OLAP) began broadening the realm of DSS. As the turn of the millennium approached, new Web-based analytical applications were introduced.

The advent of better and better reporting technologies has seen DSS start to emerge as a critical component of management design. Examples of this can be seen in the intense amount of discussion of DSS in the education environment.

DSS also have a weak connection to the user interface paradigm of hypertext. Both the University of Vermont PROMIS system (for medical decision making) and the Carnegie Mellon ZOG/KMS system (for military and business decision making) were decision support systems which also were major breakthroughs in user interface research. Furthermore, although hypertext researchers have generally been concerned with information overload, certain researchers, notably Douglas Engelbart, have been focused on decision makers in particular.

[edit] Taxonomies

As with the definition, there is no universally-accepted taxonomy of DSS either. Different authors propose different classifications. Using the relationship with the user as the criterion, Haettenschwiler[5] differentiates passive, active, and cooperative DSS. A passive DSS is a system that aids the process of decision making, but that cannot bring out explicit decision suggestions or solutions. An active DSS can bring out such decision suggestions or solutions. A cooperative DSS allows the decision maker (or its advisor) to modify, complete, or refine the decision suggestions provided by the system, before sending them back to the system for validation. The system again improves, completes, and refines the suggestions of the decision maker and sends them back to him for validation. The whole process then starts again, until a consolidated solution is generated.

Another taxonomy for DSS has been created by Daniel Power. Using the mode of assistance as the criterion, Power differentiates communication-driven DSS, data-driven DSS, document-driven DSS, knowledge-driven DSS, and model-driven DSS.[6]

A communication-driven DSS supports more than one person working on a shared task; examples include integrated tools like Microsoft's NetMeeting or Groove [7]

A data-driven DSS or data-oriented DSS emphasizes access to and manipulation of a time series of internal company data and, sometimes, external data.

A document-driven DSS manages, retrieves, and manipulates unstructured information in a variety of electronic formats.

A knowledge-driven DSS provides specialized problem-solving expertise stored as facts, rules, procedures, or in similar structures.[6]

A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model. Model-driven DSS use data and parameters provided by users to assist decision makers in analyzing a situation; they are not necessarily data-intensive. Dicodess is an example of an open source model-driven DSS generator.[8]

Using scope as the criterion, Power[9] differentiates enterprise-wide DSS and desktop DSS. An enterprise-wide DSS is linked to large data warehouses and serves many managers in the company. A desktop, single-user DSS is a small system that runs on an individual manager's PC.

[edit] Components

Design of a Drought Mitigation Decision Support System.

Three fundamental components of a DSS architecture are:[5][6][10][11][12]

1. the database (or knowledge base),2. the model (i.e., the decision context and user criteria), and

3. the user interface.

The users themselves are also important components of the architecture.[5][12]

[edit] Development Frameworks

DSS systems are not entirely different from other systems and require a structured approach. Such a framework includes people, technology, and the development approach.[10]

DSS technology levels (of hardware and software) may include:

1. The actual application that will be used by the user. This is the part of the application that allows the decision maker to make decisions in a particular problem area. The user can act upon that particular problem.

2. Generator contains Hardware/software environment that allows people to easily develop specific DSS applications. This level makes use of case tools or systems such as Crystal, AIMMS, and iThink.

3. Tools include lower level hardware/software. DSS generators including special languages, function libraries and linking modules

An iterative developmental approach allows for the DSS to be changed and redesigned at various intervals. Once the system is designed, it will need to be tested and revised for the desired outcome.

[edit] Classification

There are several ways to classify DSS applications. Not every DSS fits neatly into one category, but may be a mix of two or more architectures.

Holsapple and Whinston[13] classify DSS into the following six frameworks: Text-oriented DSS, Database-oriented DSS, Spreadsheet-oriented DSS, Solver-oriented DSS, Rule-oriented DSS, and Compound DSS.

A compound DSS is the most popular classification for a DSS. It is a hybrid system that includes two or more of the five basic structures described by Holsapple and Whinston.[13]

The support given by DSS can be separated into three distinct, interrelated categories[14]: Personal Support, Group Support, and Organizational Support.

DSS components may be classified as:

1. Inputs: Factors, numbers, and characteristics to analyze2. User Knowledge and Expertise: Inputs requiring manual analysis by the user

3. Outputs: Transformed data from which DSS "decisions" are generated

4. Decisions: Results generated by the DSS based on user criteria

DSSs which perform selected cognitive decision-making functions and are based on artificial intelligence or intelligent agents technologies are called Intelligent Decision Support Systems (IDSS).[citation needed]

The nascent field of Decision engineering treats the decision itself as an engineered object, and applies engineering principles such as Design and Quality assurance to an explicit representation of the elements that make up a decision.

[edit] Applications

As mentioned above, there are theoretical possibilities of building such systems in any knowledge domain.

One example is the clinical decision support system for medical diagnosis. Other examples include a bank loan officer verifying the credit of a loan applicant or an engineering firm that has bids on several projects and wants to know if they can be competitive with their costs.

DSS is extensively used in business and management. Executive dashboard and other business performance software allow faster decision making, identification of negative trends, and better allocation of business resources.

A growing area of DSS application, concepts, principles, and techniques is in agricultural production, marketing for sustainable development. For example, the DSSAT4 package,[15][16] developed through financial support of USAID during the 80's and 90's, has allowed rapid assessment of several agricultural production systems around the world to facilitate decision-making at the farm and policy levels. There are, however, many constraints to the successful adoption on DSS in agriculture.[17]

DSS are also prevalent in forest management where the long planning time frame demands specific requirements. All aspects of Forest management, from log transportation, harvest scheduling to sustainability and ecosystem protection have been addressed by modern DSSs. A comprehensive list and discussion of all available systems in forest management is being compiled under the COST action Forsys

A specific example concerns the Canadian National Railway system, which tests its equipment on a regular basis using a decision support system. A problem faced by any railroad is worn-out or defective rails, which can result in hundreds of derailments per year. Under a DSS, CN managed to decrease the incidence of derailments at the same time other companies were experiencing an increase.

[edit] Benefits1. Improves personal efficiency2. Speed up the process of decision making

3. Increases organizational control

4. Encourages exploration and discovery on the part of the decision maker

5. Speeds up problem solving in an organization

6. Facilitates interpersonal communication

7. Promotes learning or training

8. Generates new evidence in support of a decision

9. Creates a competitive advantage over competition

10. Reveals new approaches to thinking about the problem space

11. Helps automate managerial processes