Coefficient of Variation and Demand

Planning in a Mixed ETO/MTS

Environment

Presented by:

Yur P. Ness

Demand Planning and Variation

Sometimes you work at places with a mix of

engineer to order and make to stock products.

If you are not careful, you will end up some fucked

up forecasting.

To avoid this, you to clearly segment the product

lines or channels driving the variation.

One method is to see if a product line has a high

or low coefficient of variation.

Coefficient of Variation

What is a coefficient of variation? It is a measure of dispersion around a mean average.

It helps you understand the relative importance of the standard deviation of a population better.

It wont get you laid, but it may help you keep your job, which might help you get laid. At least it should not hurt your chances.

How do you calculate it? Take you Standard deviation and divide it into you mean average

If you Std Dev is 2, and your mean average is 10, your coefficient of average is .2

If you Std Dev is 2, and your mean average is 4, your coefficient of average is .5

The bigger the number, the more variation you have around the mean (the bell curve will be flatter)

The smaller the number, the more peaked or boner-esque the curve

Attention Ladies: Bigger is not always better!

What we know about Cakes Standard cakes have a low coefficient of variation and are typically US customers.

International cakes tend to be the focus of the pre-demand review and typically are associated with festivals.

Festival cakes have high demand variability

4

High COV is tough

to forecast

statistically

Fruities Creamie Naughties

Fruities

Creamie

Naughties

Generating a flat MRP demand plan

For Naughtie Brownies units, the proposal is to take the statistically generated demand plan

On a 240 day year, this would be approximately 300 units a day

For Fruitie Brownies, the proposal is to load a flat forecast of 30 units a day (7,000 a year)

This will give us some pipeline materials

It may lead to heavier inventories at times

It does not guarantee we can make any given rate at any given time

It will have to be managed up or down based on the data from the demand review

Major expected changes would be added incrementally to the base forecast

For Creamies, the proposal is to load a flat forecast of 30 units a day (7,000 a year)

The same logic applies as above for Fruities.

Getting MRP Less Wrong On flat demand we should get the components in

time to make the parts in time and to get to MRPs goal of zero inventory with perfect service

Zero inventory!

No shortages!

Level loads!

HAPPY BABY!

Lumpy Demand With Lumpy demand, we need to bump forecast on Fruities and extra

Creamies to avoid the following conditions Excess Inventory

No enough inventory (airfreight)

Production stops

Sad Babies

Demand but no parts to produce

Excess Inventory

Key Points of this Presentation

Forecasts are always wrong.

The key is to understand the assumptions and scenarios underlying them to make them less wrong in the future.

The Coefficient of variation helps you know when to take a statistical forecast model.

Cute baby pictures can distract you from the fact that the MRP slides have nothing to do with demand planning,

and are just filler.

Penis jokes are funny!