Cumulative Mean Screencast

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來自 Rutgers the State University of New Jersey 的課程

Supply Chain Planning

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Rutgers the State University of New Jersey

Supply Chain Planning

551 個評分

課程 3（共 5 門，Specialization Supply Chain Management）

Have you ever wondered how companies know how much to produce in advance so that they do not make too much or too little? Matching supply and demand requires planning. This course introduces you to the exciting area of supply chain planning. Part of a broader specialization on Supply Chain Management, you will master different forecasting techniques, essential for building a Sales and Operations Plan. At the completion of this course you will have the tools and techniques to analyze demand data, construct different forecasting techniques, and choose the most suitable one for projecting future demand.

從本節課中

Simple Forecasting Methods, Naive Forecast and Cumulative Mean

Welcome to the exciting world of planning! This module introduces you to the professor who is teaching the courses in the Supply Chain Management Specialization. You will also construct forecasting models that enable you to predict future demand. By the end of this short module, you'll know what to expect in the course and hopefully be as excited to learn about Supply Chain Planning as I will be to teach you.In this module learners will review two simple forecasting methods, the naive method and the cumulative mean. Finally, you will create forecasts of your own based on a data set and provide the correct answers in the quiz.

The Cumulative Mean2:33

Cumulative Mean Screencast4:38

與講師見面

Rudolf Leuschner, Ph.D.
Assistant Professor
Department of Supply Chain Management

Cumulative Mean Screencast.

The cumulative mean is a forecast method

that is taking an average of all prior demand.

So as we discussed in the lecture, as we go through time,

and there are more and more periods available,

our average rows over more and more data points.

In order to implement it, it's actually quite simple.

There's only a few little things that we need to be careful about.

So the first period in which we can implement accumulative mean is really

period three, because before then,

we don't really have anything to average together.

And in that period, we have two terms that we average together.

As we go through the time, we will have more and more periods to average together.

So, In order to get an average,

we type in the word average, open the parenthesis, and

then select the two cells that we want to average together.

Now, if I were to just hit enter at this point,

what will happen later as I try to copy that formula across the multiple rows

is that those two prior periods of demand are all that I'm going to average.

But that's not what I want.

I want it to always start at this cell, B2, and

then go up until the last period of demand that we have just observed.

So in order to achieve that,

I have to put in a dollar sign in between the B and the 2.

You can also put 1 in front of the B for good measure if you wanted to, but

that's optional, it works either way.

So once I hit enter, we have our average.

You'll see the dollar signs in here, and now I can copy this formula down and

copy it all the way here, and here is our cumulative mean.

So now, one thing jumps out at you right away, I hope.

We see a lot of decimals because the average is not always going to be

an integer, so that is something that we need to take care of.

And one recommendation I have is whenever you have a forecast,

try to forecast in the same way that demand comes in.

So in this case, it's integer values.

There's no halves, or quarters, or any decimals.

So we want to make sure your forecast comes out exactly the same way.

And there's this round function,

which basically converts our result into a rounded

output where you don't want any decimal points.

So there's that, and if I copy this formula down,

here is our result, and there you go.

Now, some of you at this point may wonder,

why don't you just remove the decimals with this part here?

Well, you could do that.

And here we see there are no decimals.

So I could remove them that way.

However, in my calculations later on, they will still be there.

And as we calculate accuracy, for example, it's still going to impact it.

So it is better to have a clean forecast just the way you would use it rather

than have all those decimal points hidden in there.

So here we have a chart of our demand versus our accumulative mean forecast.

And what see here is that over time, our forecast becomes basically

like a flat line because we have more and more periods that we can average together.

And that each new period provides very little weight on the overall average.

So that's basically one of the things that you get with accumulative mean.

You get almost a straight line, and

it takes a lot to really move your forecast off of that straight line.

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