Accounting Information System 200908

TOPICS:

1. Introduction To Accounting Systems

2. Concepts And Fundamental Documentations In System Management

3. Overview Of Business Processes

4. Control And Accounting Information System

5. System Documentation Techniques

6. Revenue Cycle – Sales And Cash Collections

7. Expenditure Cycle – Purchasing And Cash Disbursements

8. Production Cycle

9. The Human Resources Management And Payroll Cycle

10. General Ledger And Reporting System

11. Introduction To E-Business

12. Relational Database

13. Computer-Based Information Systems Control

14. Computer Fraud And Security

Writer's Handbook

1. Stage of writing process
2. Common Types Of Writing Assignments
3. Grammar and Punctuation
4. Improving Your Writing Style
5. Citing References In Your Paper

• Download compiled HTML for the above material in ZIP file from http://www.mediafire.com/file/jzd5nkhztj2/theWritingCenter.zip.

IS Planning And Management

TOPIC 1

• Understanding the role of decision maker
  
• The decision making process
  
• Decision making under uncertainty
  
• Decision making model
  

TOPIC 2

• Decision making under risk
  
• Consequtive decision making in workflow
  

TOPIC 3

• Decision Tree
  

TOPIC 4

• Sensitivity Analysis (What-If Analysis)
  
• Expected Monetary Value (EMV)
  

TOPIC 5

• Linear Programming
• Excel Solver Linear Programming Tutorial

DISCUSSION

• Managing Corporate Information Systems Evolution and Maintenance
  Text: Khaled Khan and Yan Zhang "Managing Corporate Information Systems Evolution and Maintenance",IDEA GROUP PUBLISHING, 2005, P.1
• The EVolution Of Planning For Information Systems
  Text: Gwo-Guang Lee and Wei-LinHsu, "The EVolution Of Planning For Information Systems" In "Planning For Information System",AMIS,2009, P.19
• How Do I Know If The System Is Successful
  Text: "How Do I Know If The System Is Successful" In David James, "Buying Information Systems-Selecting,Implementing and Assessing off-The Shelf System",ASHGATE, P.95 

CRITICAL REVIEW

• Writing A Critical Review (http://www.lc.unsw.edu.au/onlib/critrev.html)
• Criteria For Evaluating Text (http://www.lc.unsw.edu.au/onlib/critrev1.html)
• Sample Extract (http://www.lc.unsw.edu.au/onlib/critrev2.html)
• Download PDF for the above pages (http://www.lc.unsw.edu.au/onlib/pdf/critical_review.pdf)
• What makes a good essay? (Sample essay PDF 271KB)
• What makes a good critical review? (Sample review PDF 275KB)

Linear Programming

Linear Programming (LP) is a mathematical procedure for determining optimal allocation of scarce resources. LP is a procedure that has found practical application in almost all facets of business, from advertising to production planning. Transportation, distribution, and aggregate production planning problems are the most typical objects of LP analysis. In the petroleum industry, for example a data processing manager at a large oil company recently estimated that from 5 to 10 percent of the firm's computer time was devoted to the processing of LP and LP-like models.

 

Linear programming deals with a class of programming problems where both the objective function to be optimized is linear and all relations among the variables corresponding to resources are linear. This problem was first formulated and solved in the late 1940's. Rarely has a new mathematical technique found such a wide range of practical business, commerce, and industrial applications and simultaneously received so thorough a theoretical development, in such a short period of time. Today, this theory is being successfully applied to problems of capital budgeting, design of diets, conservation of resources, games of strategy, economic growth prediction, and transportation systems. In very recent times, linear programming theory has also helped resolve and unify many outstanding applications.

 

It is important for the reader to appreciate, at the outset, that the "programming" in Linear Programming is of a different flavor than the "programming" in Computer Programming. In the former case, it means to plan and organize as in "Get with the program!", it programs you by its solution. While in the latter case, it means to write codes for performing calculations. Training in one kind of programming has very little direct relevance to the other. In fact, the term "linear programming" was coined before the word "programming" became closely associated with computer software. This confusion is sometimes avoided by using the term linear optimization as a synonym for linear programming.

 

Any LP problem consists of an objective function and a set of constraints. In most cases, constraints come from the environment in which you work to achieve your objective. When you want to achieve the desirable objective, you will realize that the environment is setting some constraints (i.e., the difficulties, restrictions) in fulfilling your desire or objective. This is why religions such as Buddhism, among others, prescribe living an abstemious life. No desire, no pain. Can you take this advice with respect to your business objective?

 

What is a function: A function is a thing that does something. For example, a coffee grinding machine is a function that transform the coffee beans into powder. The (objective) function maps and translates the input domain (called the feasible region) into output range, with the two end-values called the maximum and the minimum values.

When you formulate a decision-making problem as a linear program, you must check the following conditions:

l        The objective function must be linear. That is, check if all variables have power of 1 and they are added or subtracted (not divided or multiplied)

l        The objective must be either maximization or minimization of a linear function. The objective must represent the goal of the decision-maker

l        The constraints must also be linear. Moreover, the constraint must be of the following forms ( £, ³, or =, that is, the LP-constraints are always closed).

 

For example, the following problem is not an LP: Max X, subject to X < 1. This very simple problem has no solution.

 

As always, one must be careful in categorizing an optimization problem as an LP problem. Here is a question for you. Is the following problem an LP problem?

 

Max X2
subject to:
X1 + X2 £ 0
X1² - 4 £££ 0

Although the second constraint looks "as if" it is a nonlinear constraint, this constraint can equivalently be written as:
X1 ³ -2, and X2 £ 2. 

Therefore, the above problem is indeed an LP problem.

 

For most LP problems one can think of two important classes of objects: The first is limited resources such as land, plant capacity, or sales force size; the second, is activities such as "produce low carbon steel", "produce stainless steel", and "produce high carbon steel". Each activity consumes or possibly contributes additional amounts of the resources. There must be an objective function, i.e. a way to tell bad from good, from an even better decision. The problem is to determine the best combination of activity levels, which do not use more resources than are actually available. Many managers are faced with this task everyday. Fortunately, when a well-formulated model is input, linear programming software helps to determine the best combination.

The Simplex method is a widely used solution algorithm for solving linear programs. An algorithm is a series of steps that will accomplish a certain task.

Expected Monetary Value (EMV)

EMV is a balance of probability and its impact over the range of possible scenarios. If you have to make a decision between two scenarios, which one will provide the greater potential payoff?

Scenario 1

Best case provides a 20% probability of making $180,000	BC = 20%	X $180,000= $36,000
Worst case provides a 15% probability of loosing [-$20,000]	WC = 15%	X(-$20,000) =(-$3,000)
Most likely case provides a 65% probability of making $ 75,000	MLC = 65%	X $75,000 = $48,750

Total Expected Monetary Value 100% $81,750 

Scenario 2

Best case provides a 15% probability of making $200,000	BC=15%	X $200,000 =$30,000
Worst case provides a 25% probability of making $15,000	WC= 25%	X $ 15,000 = $ 3,750
Most likely case provides a 60% probability of making $45,000	MLC=60%	X $45,000 = $27,000

Total Expected Monetary Value 100% $60,750

Which scenario do you choose? Number one, because it has the highest EMV, or $81,750

Sensitivity analysis

Simulation analysis in which key quantitative assumptions and computations (underlying a decision, estimate, or project) are changed systematically to assess their effect on the final outcome. Employed commonly in evaluation of the overall risk or in identification of critical factors, it attempts to predict alternative outcomes of the same course of action. In comparison, contingency analysis uses qualitative assumptions to paint different scenarios. It is also called what-if analysis.

 

What-if analysis basically involves asking questions of the type “What if a value changes?” and is used every day by those involved in data analysis and decision making. “What will our profit be if our sales increase?” or “What will the future value of an investment be if we increase the annual deposit?” are common examples of what-if type questions. The flexible nature of spreadsheets makes them ideal for carrying out what-if analyses, and Excel provides a number of tools to simplify the process of creating dynamic models, such as data tables, the Scenario Manager, Goal Seek, and Solver. 

Decision Tree

An important technique in machine learning, decision trees are used extensively in data mining. They are able to produce human-readable descriptions of trends in the underlying relationships of a dataset and can be used for classfication and prediction tasks. The technique has been used successfully in many different areas, such as medical diagnosis, plant classification, and customer marketing strategies.

 

A simple decision tree

 

An example dataset : Imagine we have the following data about a fictitious marketing strategy. Say some company sent out some promotion to various houses and recorded a few facts about each house and also whether the people responded or not:

 

District	House Type	Income	Previous  Customer	Outcome
Suburban	Detached	High	No	Nothing
Suburban	Detached	High	Yes	Nothing
Rural	Detached	High	No	Responded
Urban	Semi-detached	High	No	Responded
Urban	Semi-detached	Low	No	Responded
Urban	Semi-detached	Low	Yes	Nothing
Rural	Semi-detached	Low	Yes	Responded
Suburban	Terrace	High	No	Nothing
Suburban	Semi-detached	Low	No	Responded
Urban	Terrace	Low	No	Responded
Suburban	Terrace	Low	Yes	Responded
Rural	Terrace	High	Yes	Responded
Rural	Detached	Low	No	Responded
Urban	Terrace	High	Yes	Nothing

 

Imagine that we had thousands and thousands of instances (records) of this stuff. Here we have only 14, but if we had a lot, then it would be reasonable to assume that there would be some patterns in it. What sort of patterns? What could we find out? Well, we might discover some underlying relationships between some of the attributes, in particular it would be good to know which factors influence whether someone responds or not. That is, which factors most strongly affect a household's response to the promotion. In the data above for example, we can see that all rural households responded. This would be useful to know, as next time we might only have so many promotional brochures and so we would like to be selective as to where we send them in order to get the most responses. The example above is pretty trivial and we could probable analyse it manually just by looking, but the general idea if we had more instances would be to build some sort of classifier which could be used to examine the underlying relationships and make future predictions about the target concept (in this case the outcome of a mailed promotion). This is where automated building of decision trees comes in - a technique that can be used to generate some rules about data and then perform generalisation and prediction tasks.

 

We'll stick with the example data above in this tutorial and use it to show how we could build a decision tree to analyse it. Of course we're not going to 'build' any trees ourselves-we'd get some software to do it, or some seeds and a pot- but it's important to examine the techniques involved.

 

The Decision Tree

How can a tree help here? Well, in order to generate a set of rules we can construct a decision tree. This is done top-down from a root node and involves partitioning the data into subsets that contain instances that have similar values. Doing this for the dataset above can result in such a tree:

Explanation

Ok, so the nodes in brown in the tree correspond to attributes. At each node the dataset is split into subsets based on the value of the attribute at the node. For instance, at the root node, we split the entire dataset into three subsets. One that contains only instances (rows, tuples, whatever) that have the value 'Suburban' for the 'District' attribute, one that that contains only instances where the District attribute is 'Urban', and one where the all the instances are 'Rural' for that attribute. The numbers on the branches are important here: They correspond to the number of instances in each subset that have one and only one value for the target attribute ('Outcome'). This basically says how well the given value of the attribute we split on relates to the target attribute. What? Look at the tree - the middle branch of the first node. '4/4' below 'Rural' indicates that all four of the instances with District=Rural have the same value for 'Outcome' (in this case 'Responded'). This is good, because we have split the data using this attribute=value pairing to perfectly classify all instances that have this pairing. In other cases the value of the District attribute does not lead to a perfect, or pure subset. These ideas are related to entropy which we shall examine later. Anyway, continuing with the above tree - look at the first branch of the first node. This tells us that when District=Suburban, only 3 of 5 instances have the same value of the target attribute. In this case, it is necessary to continue splitting up this subset using other attribute tests until we have only pure subsets. The 5 instances which have District=Suburban on the left-most branch are then tested with 'House-Type' and are split into further subsets. The tree construction continues until purity, or until all the subsets are pure (with respect to the target attribute). When this occurs the branch terminates in a green leaf node that specifies what value the target attribute takes for all instances that have been filtered down this branch.

 

Rules From The Tree

Ok, so we can represent the data with a tree? So what? Well we can extract rules from the tree quite easily. Just read off the paths of all the leaf nodes. This gives us (from left to right in the tree):

 

#

 

# => (Outcome = Nothing)

 

# (District=Suburban) AND (House Type=Terrace) AND (Income=Low) => (Outcome = Responded)

 

(District=Suburban) AND (House Type=Detached) => (Outcome = Nothing)

(Outcome = Responded)

(District=Suburban) AND (House Type=Terrace) AND (Income=High) => (Outcome = Nothing)

(District=Suburban) AND (House Type=Terrace) AND (Income=Low) => (Outcome = Responded)

(District=Urban) AND (Previous Customer=No) => (Outcome = Responded)

(District=Urban) AND (Previous Customer=Yes) => (Outcome = Nothing)

(District=Rural) => (Outcome = Responded)

A disjunction of conjunctions. This is useful for summarising the data and extracting the underlying relationships.

 

How can this be used for classification and prediction?

 

Well, say for example that we wanted to predict the outcome of mailing to a certain house. We could just of course do a look-up on our dataset to see if the characteristics of this new house matched any we had mailed to before with the assumption that the new house will respond in the same way. This won't always be possible though, as our dataset here doesn't represent all the possible combinations. Instead we use the decision tree to generalise.

 

E.g. If we know the District we're going to mail to is Urban and the person was a previous customer, then the tree predicts that the person will not respond (Follow the attributes and values down the tree).

 

Practically?

Ok, so this illustrates the basic idea how we can use Decision Tree Learning. You might be thinking that this is a very small, contrived example and that really it's all fairly random what happened. Well, yes, yes and yes, but the same basic idea is used in practical situations. Imagine if we had thousands of records of data for a concept like the one above and maybe lots more attributes, perhaps some of them with numeric attributes. We wouldn't be able to analyse such data by just looking at it and so constructing a decision tree would help. Furthermore, the more data we have, then the more chance that we can get a real insight into the any underlying function or relationship between the attributes. This is because the tree generalises when used for predictions and we would be more confident about it's accuracy if it had been constructed from many examples instead of just a few. There are many other complications and details to worry about, but all that can be looked at later. Right now, we are going to look at the tree building process a bit more.

 

One Dataset: Many Trees

For any given dataset there are a lot of possible trees that we could construct. Instead of having the root node as 'District', it could have been 'Income' for example. Likewise, the second child node could have been 'House-Type' instead of 'Previous-Customer'. Have a go building a tree from this dataset on the next page. You will see that there are many possible trees that can be built to model this data. They are all perfectly legitimate decision trees. Try to find the shortest (least number of nodes).

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Reference:

http://www.springerlink.com/content/r22154877045541t/

http://www.eogogics.com/talkgogics/tutorials/decision-tree/

Consequtive decision making in workflow

The workflow represents the transferring of data, documents and tasks during the work. Often it is not easy to trace the path of a concrete task or a document. To make easier the study and analysis or working processes and for presenting them in a simple visual manner workflow diagrams are used. With the help of such diagram it is possible to see the path of the task in a workflow, the person who is responsible for its execution on each stage, which documents the task is accompanied by and which resources it needs. Knowing this it is possible to optimize the workflow and to discover weak points in it. Workflow diagram is a sort of flowcharts. Such diagrams consist of three types of blocks: Start/End Block, Process Block, Decision Making Block.

 

During the study and analysis the accuracy of decisions made earlier and the availability of all necessary information and resources for work continuation in a workflow are checked. Study and analysis are need for start of any working operation. For example before sending the ready production, the fulfillment of the product repayment terms, product intactness and completeness are controlled.

When the stage of study and analysis is complete the decision making is required. If the stage of study and analysis is complete successfully then the decision about the process start is made. If the above mentioned stage failed, than the blocking of a decision making indicates the point of the workflow where it is necessary to return and to repeat actions for reaching the result which allows to start the process. The making decision block has two outputs- YES and NO. If the task can�t be solved unambiguously, several conditions are studied and analyzed and there are several decision variants, than several consecutive decision making blocks are applied. Answers to all questions of each block lead to necessary actions.

 

After making a decision, the process representing the totality of events starts. If the negative decision is made, the workflow may be stopped or even the return in a workflow may happen.

For building a workflow diagram it is necessary to create a list of all processes constituting the working process so that connecting them between each other with blocks of study and analysis and block of decisions making to obtain the complete picture of the workflow.

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Reference:

 http://www.conceptdraw.com/en/products/cd5/ap_workflow.php

Decision making under risk

Because making decisions involves a degree of risk, it would be helpful to examine risk and risk analysis in this chapter in order to gain an understanding of what is involved. Risk and uncertainty create anxiety, yet they are necessary components of an active life.

 

General Comments on Risk Taking

 

1. Only the risk takers are truly free. All decisions of consequence involve risk. Without taking risks, you cannot grow or improve or even live.

 

2. There is really no such thing as permanent security in anything on earth. Not taking risks is really not more secure than taking them, for your present state can always be changed without action on your part. If you don't take the risk of dying by driving to the store, your house could collapse on you and kill you anyway.

 

3. You are supposed to be afraid when you risk. Admit your fears--of loss, of rejection, of failure.

 

4. Risking normally involves a degree of separation anxiety--the anxiety you feel whenever you are removed from something that makes you feel secure. Many children feel this when they first leave their parents for school. Some college students feel this when they go off to college. Travelers sometimes feel it when they get homesick. The way to overcome separation anxiety is to build a bridge between the familiar and secure and the new. Find out what the new place--school or country--is like and how its elements compare to familiar and secure things at home. Take familiar things with you--books, teddy bear, popcorn popper, whatever.

Decision making model

The many decision making models that exist nowadays means that you even have to make a decision as to which one to use! There are rational models, intuitive models, rational-iterative models as well as 5, 6, 7 and even 9 step decision models.

Most, however, move through each of the basic stages in decision making.

Some of these decision making models presuppose that decision making is the same as problem solving. Frequently, the first step in the decision making process is to identify the problem. I don't believe that every decision is solving a problem. For example, deciding whether you want dark chocolate or milk chocolate is not, in and of itself, a problem frame.


Rational decision making models

This is the most popular type of model and is based around a cognitive judgement of the pros and cons of various options. It is organised around selecting the most logical and sensible alternative that will have the desired effect. Detailed analysis of alternatives and a comparative assessment of the advantages of each is the order of the day.

Rational decision making models can be quite time consuming and often require a lot of preparation in terms of information gathering. The six step decision making process is a classic example in this category.

The Vroom-Jago decision model helps leaders decide how much involvement their teams and subordinates should have in the decision making process.


Seven step decision making model

The seven step model was designed for choosing careers and may be classed as a rational decision making model. The seven steps are designed to firstly identify the frame of the decision. Based on the information available, alternatives are generated. Further information is then gathered about these alternatives in order to choose the best one.

But what happens when there's too much information? How do you separate the useful from the worthless? And then, of course, the world is changing so rapidly that the information is also changing rapidly. But waiting for things to stabilize may cause a delay in decision making which may, in turn, lead to missed opportunities.

Many think the way forward involves reharnessing the power of our intuition.

Intuitive decision making models

Some people consider these decisions to be unlikely coincidences, lucky guesses, or some kind of new-age hocus-pocus. Many universities are still only teaching rational decision making models and suggest that if these are not used, failure results. Some researchers are even studying the logic behind the intuitive decision making models!

The groups who study intuitive decision making models are realising that it's not simply the opposite of rational decision making. Carl Jung pointed out that it is outside the realm of reason.

In military schools the rational, analytical models have historically been utilised. It is also long been recognised, however, that once the enemy is engaged the analytical model may do more harm than good. History is full of examples where battles have more often been lost by a leader?s failure to make a decision than by his making a poor one.


"A good plan, executed now, is better than a perfect plan next week."
- General George S. Patton, Jr.


The military are educating the soldiers of every rank in how to make intuitive decisions. Information overload, lack of time and chaotic conditions are poor conditions for rational models. Instead of improving their rational decision making, the army has turned to intuitive decision models. Why? Because they work!


Recognition primed decision making model

Psychologist Dr Gary Klein has been studying decision making for many years and he suggests that people actually use an intuitive approach 90% of the time. His recognition primed decision making model describes that in any situation there are cues or hints that allow people to recognise patterns. Obviously the more experience somebody has, the more patterns they will be able to recognise. Based on the pattern, the person chooses a particular course of action. They mentally rehearse it and if they think it will work, they do it.
If they don't think it will work, they choose another, and mentally rehearse that. As soon as they find one that they think will work, they do it. Again past experience and learning plays a big part here. There is no actual comparison of choices, but rather a cycling through choices until an appropriate one is found.

Obviously people become better with this over time as they have more experiences and learn more patterns. But can this be taught?


The ultimate decision making model

The ultimate model will allow you to rapidly assimilate the available information in a situation, bring all the relevant learning and past experiences to bear and allow you to quickly and easily decide what to do, while knowing for certain that you're making the right decision.

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Reference:

 http://www.decision-making-confidence.com/decision-making-models.html

Decision making under uncertainty

Every decision is made within a decision environment, which is defined as the collection of information, alternatives, values, and preferences available at the time of the decision. An ideal decision environment would include all possible information, all of it accurate, and every possible alternative. However, both information and alternatives are constrained because the time and effort to gain information or identify alternatives are limited. The time constraint simply means that a decision must be made by a certain time. The effort constraint reflects the limits of manpower, money, and priorities. (You wouldn't want to spend three hours and half a tank of gas trying to find the very best parking place at the mall.) Since decisions must be made within this constrained environment, we can say that the major challenge of decision making is uncertainty, and a major goal of decision analysis is to reduce uncertainty. We can almost never have all information needed to make a decision with certainty, so most decisions involve an undeniable amount of risk.

The fact that decisions must be made within a limiting decision environment suggests two things. First, it explains why hindsight is so much more accurate and better at making decisions that foresight. As time passes, the decision environment continues to grow and expand. New information and new alternatives appear--even after the decision must be made. Armed with new information after the fact, the hindsighters can many times look back and make a much better decision than the original maker, because the decision environment has continued to expand.

The second thing suggested by the decision-within-an-environment idea follows from the above point. Since the decision environment continues to expand as time passes, it is often advisable to put off making a decision until close to the deadline. Information and alternatives continue to grow as time passes, so to have access to the most information and to the best alternatives, do not make the decision too soon. Now, since we are dealing with real life, it is obvious that some alternatives might no longer be available if too much time passes; that is a tension we have to work with, a tension that helps to shape the cutoff date for the decision.

Delaying a decision as long as reasonably possible, then, provides three benefits:

1. The decision environment will be larger, providing more information. There is also time for more thoughtful and extended analysis.
2. New alternatives might be recognized or created. Version 2.0 might be released.
3. The decision maker's preferences might change. With further thought, wisdom, and maturity, you may decide not to buy car X and instead to buy car Y.

The decision making process

1. Identify the decision to be made together with the goals it should achieve. Determine the scope and limitations of the decision. Is the new job to be permanent or temporary or is that not yet known (thus requiring another decision later)? Is the new package for the product to be put into all markets or just into a test market? How might the scope of the decision be changed--that is, what are its possible parameters?

When thinking about the decision, be sure to include a clarification of goals: We must decide whom to hire for our new secretary, one who will be able to create an efficient and organized office. Or, We must decide where to go on vacation, where we can relax and get some rest from the fast pace of society.

2. Get the facts. But remember that you cannot get all the facts. Get as many facts as possible about a decision within the limits of time imposed on you and your ability to process them, but remember that virtually every decision must be made in partial ignorance. Lack of complete information must not be allowed to paralyze your decision. A decision based on partial knowledge is usually better than not making the decision when a decision is really needed. The proverb that "any decision is better than no decision," while perhaps extreme, shows the importance of choosing. When you are racing toward a bridge support, you must decide to turn away to the right or to the left. Which way you turn is less important than the fact that you do indeed turn.

As part of your collection of facts, list your feelings, hunches, and intuitive urges. Many decisions must ultimately rely on or be influenced by intuition because of the remaining degree of uncertainty involved in the situation.

Also as part of your collection of facts, consult those who will be affected by and who will have to implement your decision. Input from these people not only helps supply you with information and help in making the decision but it begins to produce the acceptance necessary in the implementers because they feel that they are part of the decision making process. As Russell Ackoff noted in The Art of Problem Solving, not consulting people involved in a decision is often perceived as an act of aggression.

3. Develop alternatives. Make a list of all the possible choices you have, including the choice of doing nothing. Not choosing one of the candidates or one of the building sites is in itself a decision. Often a non decision is harmful as we mentioned above--not choosing to turn either right or left is to choose to drive into the bridge. But sometimes the decision to do nothing is useful or at least better than the alternatives, so it should always be consciously included in the decision making process.

Also be sure to think about not just identifying available alternatives but creating alternatives that don't yet exist. For example, if you want to choose which major to pursue in college, think not only of the available ones in the catalog, but of designing your own course of study.

4. Rate each alternative. This is the evaluation of the value of each alternative. Consider the negative of each alternative (cost, consequences, problems created, time needed, etc.) and the positive of each (money saved, time saved, added creativity or happiness to company or employees, etc.). Remember here that the alternative that you might like best or that would in the best of all possible worlds be an obvious choice will, however, not be functional in the real world because of too much cost, time, or lack of acceptance by others.

Also don't forget to include indirect factors in the rating. If you are deciding between machines X, Y, and Z and you already have an employee who knows how to operate machine Z, that fact should be considered. If you are choosing an investigative team to send to Japan to look at plant sites and you have very qualified candidates A, B, and C, the fact that B is a very fast typist, a superior photographer or has some other side benefit in addition to being a qualified team member, should be considered. In fact, what you put on your hobbies and interests line on your resume can be quite important when you apply for a job just because employers are interested in getting people with a good collection of additional abilities.

5. Rate the risk of each alternative. In problem solving, you hunt around for a solution that best solves a particular problem, and by such a hunt you are pretty sure that the solution will work. In decision making, however, there is always some degree of uncertainty in any choice. Will Bill really work out as the new supervisor? If we decide to expand into Canada, will our sales and profits really increase? If we let Jane date Fred at age fifteen, will the experience be good? If you decide to marry person X or buy car Y or go to school Z, will that be the best or at least a successful choice?

Risks can be rated as percentages, ratios, rankings, grades or in any other form that allows them to be compared. See the section on risk evaluation for more details on risking.

6. Make the decision. If you are making an individual decision, apply your preferences (which may take into account the preferences of others). Choose the path to follow, whether it includes one of the alternatives, more than one of them (a multiple decision) or the decision to choose none.

And of course, don't forget to implement the decision and then evaluate the implementation, just as you would in a problem solving experience.

One important item often overlooked in implementation is that when explaining the decision to those involved in carrying it out or those who will be affected by it, don't just list the projected benefits: frankly explain the risks and the drawbacks involved and tell why you believe the proposed benefits outweigh the negatives. Implementers are much more willing to support decisions when they (1) understand the risks and (2) believe that they are being treated with honesty and like adults.

Remember also that very few decisions are irrevocable. Don't cancel a decision prematurely because many new plans require time to work--it may take years for your new branch office in Paris to get profitable--but don't hesitate to change directions if a particular decision clearly is not working out or is being somehow harmful. You can always make another decision to do something else.

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Reference:

http://www.virtualsalt.com/crebook5.htm

Understanding The Role Of Decision Maker

According to the dictionary, the verb ‘decide’ means ‘to determine, to end, to resolve, to settle and to make up one’s mind’, while the noun ‘decision’ is ‘the act of settling, making up one’s mind’, etc. Someone in a position of power is said to be a ‘decision-maker’ and we refer to those who do make up their mind as ‘resolute’ or ‘decisive’.

The Latin root of the word means to ‘cut away’. This points to what a decision really is: to cut away the surrounding clutter, to enable one to see a path to an objective and, by taking a decision (or a series of decisions), to follow that path with all of its implications.

A decision is not allowing events to take their course willy-nilly. If you did, an outcome would still occur - but one not influenced or decided upon with due regard to the surrounding circumstances. Such an outcome represents an inability or lack of desire to analyse and reach a conclusion; control has been surrendered. This might not matter - for example, when merely choosing what perfume to wear - but can be of major consequence where commercial or other vital decisions are required.

Todays managers depend on information systems for decision making. The managers have handful of data around them but manually they cannot process the data accurately and with in the short period of time available to them due to heavy competition in modern world. Therefore mangers depend on information systems.

Management has been defined in a variety of ways, but for our purposes it comprises the process or activities what managers do in the operation of their organization: Plan, Organize, Initiate and Control operations.

Data are facts and figures that are not currently being used in a decision processes and usually take the form of historical records that are recorded and filed without immediate intent to retrieve for decision making.

Information consists of data that have been retrieved, processed or otherwise used for information or inference purposes, argument, or as a basis for forecasting or decision making.

System can be described simply as a set of elements joined together for a common objective. A subsystem is is part of a larger system with which we are concerned. All systems are part of larger systems.

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References:

Information System for Modern Management – ROBERT G. MURDICK, JOEL E. ROSS, JAMES R. CLAGGETT.

Management Information Systems: The Management View – ROBERT SCHILTHESIS and MARY SUMNER.

The Decision-making Pocketbook By Neil Russell-Jones,

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