Quantitative Methods for Business Decision Making

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“And welcome to the class by now hopefully take a look at the syllabus that that was supposed to down web ct and you ve seen that our textbook is spreadsheet modeling. Today. What i m going to do is our first lecture. Which is an introduction to quantitative methods.

And what we re going to have posted here. Ever will be powerpoint lectures like this one. Today. We ll also have some webcam lectures.

Where i m gonna be writing on a sheet of paper and drawing graphs and stuff. And the webcam will pick that up and please in it and we re also gonna do quite a bit of excel work with the spreadsheet. So let s start first of all this is a course on making quality decisions. Well.

What is a quality decision is it one where the outcome is always satisfactory or the best outcome. Not really you know life is sort of a crapshoot so sometimes you ll make a decision you ll do all the right things go through all the steps. And it may not turn out to be the best so make a quality decision you re gonna probably have some expert judgment and some quantitative analysis thrown in the question is how much weight should you put on your expert judgement. How much weight should you put on your quantitative.

Analysis. Where w1 and w2 are the weights that sum to one of course. And how do you become an expert. Anyway of course you become an expert with experience perhaps education and of course the quantitative analysis.

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That s what this class is about we re gonna provide you a toolkit that you can use so the question again how much weight should you put on it if you walk into let s say mcdonald s and you re gonna order lunch are you gonna use any quantitative analysis. There i suspect not because no matter what you order you re gonna come out with that same feeling as long. As you stay within your budget. So when should you use quantitative tools.

Well you re probably gonna use these tools. When you have to make important decisions. Well what makes decision is important well if they involve a lot of money or lives. I ve used this stuff in my personal life for trying to decide you know to buy a house or do certain things and so anyway if it involves a lot of money or lives.

It s an important decision you might want to think about using a tool. If your situation is complex you ll more likely use a tool by complex. I mean that there are variables that some are some of them you control some of you don t control. And if there are many variables one rule of thumb says that the human mind can balance five seven plus or minus two items in their mind.

At one time so from five to nine things and balance those in a decision process. But you know many decision situations are going to have hundreds of variables. And there s there s no way that you could balance that many things and figure out a good solution. If the situation is repetitive meaning that you know you re gonna make this decision every week for the next year.

You d be more likely to use a quantitative tool. Because once you ve got that tool built so that it works. It should be pretty easy to revise the tool set you know put in the new input variables. And and let it run and and you should be able to have a pretty good outcome.

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Then of course solution time comes into it if it it s going to take you a couple days to to figure out a tool and you need a decision in five minutes of course then it would be impossible to use a decision tool. So there s a trade off how much time should you spend modeling. The situation versus what s the benefit of using that tool. There are some problems associated with quantitative analysis.

One problem is there are going to be conflicting viewpoints within your organization. We kind of assume in this textbook. That the organization has the same set of goals and values that that s not true you know most of the organizations that people have their hidden agendas and their pet projects. And they want to go their own way for whatever.

Reason. Well there are some tools game theory for examples it deals with conflict. And there are some negotiation tools that are being developed now and group decision making tools that assume conflict but for the most part this semester in this class. We re going to assume that that everybody within.

The organization has the same set of values and conflict. It s not going to be coming in to the to the problems at all second problems textbook model. Fit you know you re probably gonna have some decision situations. Where you might say well that that kind of fits.

What we would use linear programming for or decision analysis. But it doesn t really fit a hundred percent okay so in that case do you have to use your judgment to figure out well do i use one of these textbook tools or do i go out and develop my own tool. A new one that s possible as well. But we re pretty much going to assume that these tools are out there and and your problems are gonna fit.

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Another problem is is gi geo. Which stands for garbage in garbage out. Where does your data come from in the textbook. We re just is kind of assuming that the data is there you know if your garbage coming into the model.

If you do tato coming into the while as garbage. Then the output is gonna be garbage as well so you ve got to be really careful about where you collect your data are you gonna be using some data mining tools are you gonna be using some kind of database internally or perhaps an external database like the se c s edgar or some other database. But in any case you need to be aware where the your data comes from and how accurate. It is the other thing is whether your decisions are strategic or tactical.

Now what do you think we have more quantitative tools. The strategic or the tactical turns out those more tools dealing with tactical then with strategic that might just be the nature of the beast. Because tactical decisions that are easier to deal with another issue is this optimization versus satisficing optimizing. Is finding the best maximizing or minimizing.

Something. Whereas. Satisficing is a term finding. Something that s good enough now to optimise.

We use optimization algorithms and to satisfice to find something good enough we use heuristics heuristics. Our rules of thumb. Now. This is kind of related to herbert simon s bounded rationality in fact herb came up with the term satisficing bounded rationality meaning.

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We all have this box that we look to the world through and we re limited by that box. We ll we ll be discussing some dss. This is of support systems and decision aiding of course. If you re developing a decision support system for a client.

It s probably best that you get their involvement in the process. They ll be more likely to use the tools that they re involved likewise you need to have communication skills to sell the stuff that you develop. We re not gonna be really working on your communication skills in this class maybe mine. But not yours.

But anyway. I do want to emphasize that communication skills are quite important some vocabulary terms stochastic versus deterministic. We re gonna be working with both kinds of models in this class stochastic dealing in the realm with uncertainty. And when we use probabilities deterministic problems with the optimization linear program and dealing with certainty.

There s no probabilities. So the first half the class will be deterministic. The last half is stochastic. We re also going to be discussing electronic commerce and giving with some online tools as well.

So that s it that s the introduction next lecture will be on linear programming. And we ll go through an example problem. ” ..

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