Ima supply chain 2 sales forecasting and the myth of exponential smoothing

IMA SUPPLY CHAIN 2:SALES FORECASTING AND

THE MYTH OF EXPONENTIAL SMOOTHING

Tony [email protected]

Mathematics and Managers

It’s easy to sell Mathematical Algorithms to Managers

Take Sales Forecasting

You are looking for a new Forecasting System

You are considering two vendorsSystem A uses a Moving Average to forecastSystem B uses forecasts with Exponential Smoothing

Which would you choose?


• It’s easy to sell Mathematical Algorithms to Managers

Take Sales Forecasting






• Take Sales Forecasting







• You are looking for a new Forecasting System







• You are considering two vendorsSystem A uses a Moving Average to forecastSystem B uses forecasts with Exponential Smoothing






• You are considering two vendors• System A uses a Moving Average to forecastSystem B uses forecasts with Exponential Smoothing






• You are considering two vendors• System A uses a Moving Average to forecast• System B uses forecasts with Exponential Smoothing






• You are considering two vendors• System A uses a Moving Average to forecast• System B uses forecasts with Exponential Smoothing

• Which would you choose?

The Benefits of Exponential Smoothing

Now you may not have heard of Exponential Smoothing – but it sounds impressive especially when its benefits are explained to you

Exponential Smoothing has two primary advantages.

1. It automatically adjusts for errors in forecasting. If the forecast for the current period is lower than the actual demand, then the forecast for next period is automatically adjusted upwards and vice versa. The larger the error the greater the adjustment made. 2. It gives progressively greater weights to more recent demands because these are more relevant in forecasting the future than demands further back in history.

A simple moving average, on the other hand, makes no adjustment for forecast error and gives equal weight to all the periods of demand history included in the average.

Exponential Smoothing is therefore a superior method of forecasting when compared with averaging as it is able to more readily adapt to a changing pattern of demand.


• Now you may not have heard of Exponential Smoothing – but it sounds impressive especially when its benefits are explained to you

Exponential Smoothing has two primary advantages.






• Exponential Smoothing has two primary advantages.




















• A simple moving average, on the other hand, makes no adjustment for forecast

error and gives equal weight to all the periods of demand history included in the average.






• A simple moving average, on the other hand, makes no adjustment for forecast

error and gives equal weight to all the periods of demand history included in the average.

• Exponential Smoothing is therefore a superior method of forecasting when compared with averaging as it is able to more readily adapt to a changing pattern of demand.

A Slight Problem

• There is a slight problem with this argument – viz that ES adapts more quickly to changes in demand than a simple moving average

It doesn’t

Let’s look at an example

A Slight Problem


• It doesn’t

Let’s look at an example

A Slight Problem


• It doesn’t

• Let’s look at an example

A Slight Problem


• It doesn’t

• Let’s look at an example

Sales are increasing

Comparing Forecast Performance

Let’s see how Exponential Smoothing and Averaging compare in forecasting this sales increase.

Each month we generate a forecast based only on past demand.

A commonly used average is a 6 month average Forecast=(Sum of last 6 months sales)/6

Exponential Smoothing (ES) uses a Smoothing Factor. The most commonly used Smoothing Factor is 0.1.

Forecast = 0.9*(Last Forecast) + 0.1*(Latest Demand)


• Let’s see how Exponential Smoothing and Averaging compare in forecasting this sales increase.

Each month we generate a forecast based only on past demand.






• Each month we generate a forecast based only on past demand.







• A commonly used average is a 6 month average • Forecast=(Sum of last 6 months sales)/6






• A commonly used average is a 6 month average • Forecast=(Sum of last 6 months sales)/6

• Exponential Smoothing (ES) uses a Smoothing Factor. The most commonly used Smoothing Factor is 0.1.• Forecast = 0.9*(Last Forecast) + 0.1*(Latest Demand)

Here’s Exponential Smoothing (0.1)

See how it adapts to the increasing demand

Here’s Exponential Smoothing (0.1)

See how it adapts to the increasing demand

And here’s the 6M Average

The Moving Average adapts more quickly than Exponential Smoothing

And here’s the 6M Average

The Moving Average adapts more quickly than Exponential Smoothing

Now Wait a Minute(says the ES proponent)If you change the Smoothing Factor to 0.2 then it will adapt better than a 6 month average.

True. But you could also change the months used in the average. Change them to 3 and they will perform better than ES (0.2) (Take my word for it – but it’s easy to check)

We could go on like this. In fact the most adaptable method is to use a smoothing factor of 1 – ie next month’s forecast = last month’s demand. You can’t beat this for adaptability - but no uses it

Now here’s the key point: The adaptability of the forecast depends less on the method and more on the parameter you use in the method.

Now Wait a Minute(says the ES proponent)• If you change the Smoothing Factor to 0.2 then it will adapt better

than a 6 month average.

True. But you could also change the months used in the average. Change them to 3 and they will perform better than ES (0.2) (Take my word for it – but it’s easy to check)





• True. But you could also change the months used in the average. Change them to 3 and they will perform better than ES (0.2) (Take my word for it – but it’s easy to check)






• We could go on like this. In fact the most adaptable method is to use a smoothing factor of 1

ie next month’s forecast = last month’s demand. You can’t beat this for adaptability - but no uses it





• We could go on like this. • In fact the most adaptable method is to use a smoothing factor of 1



Now Wait a Minute(says the ES proponent)• If you change the Smoothing Factor to 0.2 then it will adapt better than

a 6 month average.


• We could go on like this. • In fact the most adaptable method is to use a smoothing factor of 1


• Now here’s the key point: The adaptability of the forecast depends less on the method and more on the parameter you use in the method.

ES – History and Why It SurvivesHistory: Exponential Smoothing was introduced not because it was a superior method of forecasting but rather because it required less data to be held on computer. To calculate a six months average we need to hold at least six buckets of data. The calculation for ES requires only two buckets – last month’s demand and a forecast bucket. ES was introduced primarily because it required less disk space in the 1950s when disk space was at a premium.

Survival: A primary reason for it being still so widely used in an era of vast gigabyte data retention availability is that managers think it seems ‘scientific’ when compared with a simple average. If our forecasting is lousy then we are likely to be less criticised if we say we use exponential smoothing rather than a moving average. Or if we sell systems it sounds quite impressive when we tell you how we use Exponential Smoothing to forecast. (This is why ES is found in so many software packages.)

ES – History and Why It Survives• History: Exponential Smoothing was introduced not because it was a superior method of

forecasting but rather because it required less data to be held on computer. To calculate a six months average we need to hold at least six buckets of data. The calculation for ES requires only two buckets – last month’s demand and a forecast bucket. ES was introduced primarily because it required less disk space in the 1950s when disk space was at a premium.

Survival: A primary reason for it being still so widely used in an era of vast gigabyte data retention availability is that managers think it seems ‘scientific’ when compared with a simple average. If our forecasting is lousy then we are likely to be less criticised if we say we use exponential smoothing rather than a moving average. Or if we sell systems it sounds quite impressive when we tell you how we use Exponential Smoothing to forecast. (This is why ES is found in so many software packages.)

ES – History and Why It Survives• History: Exponential Smoothing was introduced not because it was a superior method of

forecasting but rather because it required less data to be held on computer. To calculate a six months average we need to hold at least six buckets of data. The calculation for ES requires only two buckets – last month’s demand and a forecast bucket. ES was introduced primarily because it required less disk space in the 1950s when disk space was at a premium.

• Survival: A primary reason for it being still so widely used in an era of vast gigabyte data retention availability is that managers think it seems ‘scientific’ when compared with a simple average. If our forecasting is lousy then we are likely to be less criticised if we say we use exponential smoothing rather than a moving average. Or if we sell systems it sounds quite impressive when we tell you how we use Exponential Smoothing to forecast. (This is why ES is found in so many software packages.)

In ConclusionThere is a moral to this story.

This issue goes well beyond Exponential Smoothing.

Many – certainly not all – people in business are impressed by mathematics which they do not understand, especially when the claimed advantages of the algorithm can be presented in an appealing manner by a competent proponent of the approach.

It is often assumed that more complex algorithms work better than simple algorithms, which is not necessarily the case. This is rather like assuming that a medicine that tastes horrible must be better than one that is easy to swallow.

Finally we constantly see many examples of business operations misusing forecasting and planning algorithms they do not understand to achieve dismal performance – often coupled with a lot of wasted time.

Commonsense is more important than algorithms.

In Conclusion• There is a moral to this story.

This issue goes well beyond Exponential Smoothing.






• This issue goes well beyond Exponential Smoothing.







• Many – certainly not all – people in business are impressed by mathematics which they do not understand, especially when the claimed advantages of the algorithm can be presented in an appealing manner by a competent proponent of the approach.







• It is often assumed that more complex algorithms work better than simple algorithms, which is not necessarily the case. This is rather like assuming that a medicine that tastes horrible must be better than one that is easy to swallow.







• Finally we constantly see many examples of business operations misusing forecasting and planning algorithms they do not understand to achieve dismal performance – often coupled with a lot of wasted time.






• Finally we constantly see many examples of business operations misusing forecasting and planning algorithms they do not understand to achieve dismal performance – often coupled with a lot of wasted time.

• Commonsense is more important than algorithms.

Business

Ima supply chain 2 sales forecasting and the myth of exponential smoothing