Lecture 3

Question 1

What is forecast?

Answer 1

A calculation or estimate to predict future events. It is based on past and present data.

Question 2

What is the purpose of forecasting?

Answer 2

To explain as much of the systematic variability as possible and quantify the unsystematic variability to eventually support a decision.

Question 3

What is the importance of forecasting?

Answer 3

Forecasts are the basis for nearly all planning decisions in a SC. Poor forecasts cause high inventory costs, underused capacity, and poor customer service.

Question 4

What are the three golden rules of forecasting?

Answer 4

1. The forecast is always wrong
   - errors always occur due to the presence of randomness and uncertainty. Forecasts will never be a solution to our problems, but it is something to help in our decision making. It is more a range not an exact number.
2. The longer the forecast horizon, the harder it is for the forecast to be accurate
   - easier to know what happens tomorrow than in a month, but sometimes we have to make planning decisions on long-term
3. Aggregate forecasts are more accurate
   - because it enables us to get a big picture
   the relative accuracy of the forecast will tend to be higher when we aggregate across products or locations.

Question 5

There are two main distinctions for forecasting methods, could you name them?

Answer 5

Qualitative and quantitative forecasting methods. Which one depends on the type of data we want to analyse and predict.

Question 6

What are the 4 different qualitative forecasting techniques?

Answer 6

1. Panel of experts/executives
2. Delphi method
3. Sales force composite
4. Market research

Question 7

Explain the forecasting with panel of experts/executives.

Answer 7

This technique is based on emotions, intuitions of the company’s executives which have experience. It can involve interviews, discussions and/or surveys.

Question 8

Explain the forecasting with the Delphi method. What is the difference between the Delphi method and the panel of experts/executives?

Answer 8

The Delphi method involves a systematic and interactive discussion between experts on a given problem or situation. The difference from the panel of experts/executives is that with the Delphi method a consensus is required to determine the final results.

interviews separately -> general conclusion -> consensus?

Question 9

Explain the forecasting with the sales force composite.

Answer 9

The sales force composite involves interviews and surveys completed by the sales representative of each particular region or area. So, regional customer needs are forecasted based on experience and intuition of the company’s salesperson.

Question 10

Explain the forecasting with market research

Answer 10

Market research involves qualitative studies on consumer behaviour. Based on interviews, focus groups, and surveys distributed among customers. It aims to understand the changing customers’ needs.

Question 11

What are the time series components?

Answer 11

Level: an “average” around which observations vary

Trend: a predictable increase or decrease in the level over time

Seasonality: a pattern of predictable and recurring shifts in the level

Random noises: unpredictable variations in the data

Question 12

What are the different types of trends?

Answer 12

Upward, downward, up-to-down, down-to-up trend

Question 13

What does a good forecast contain?

Answer 13

It explains level, trend, seasonality, and describes random poses. It should be robust and not sensitive (reacting quickly to random noise).

Question 14

What is systematic and unsystematic variability?

Answer 14

systematic: trend and seasonality -> explain

unsystematic: random noises -> quantify

Question 15

When do you apply the moving average? What is moving average? What is the effect of N?

Answer 15

Time series with no trend nor seasonality, so we focus on forecasting the level. It is the average of the most recent N observations. A larger N is used if you want smoother forecast, and a smaller N is used for a more responsive forecast.

Question 16

When do you apply exponential smoothing? What is exponential smoothing? What is the effect of the weighted average (alpha)?

Answer 16

Time series with no trend nor seasonality, so we focus on forecasting the level. It is the weighted average of the previous forecast and the last demand point. The weighted average can be between 0 and 1. A smaller alpha is used for a more smooth forecast, and a larger alpha for a more reactive forecast (chases demand). It is important to decide which level we have in the data. A changing level (up down, not a lot of noise) should have a high alpha (fast learning) and a stable level (more noise) should have a low alpha (slow learning).

Question 17

When do you apply Holt’s method? What is Holt’s method? How do we get level and trend at timepoints 0?

Answer 17

Time series with trend but no seasonality. Holt’s method predicts both the level and trend, so it has a level component and a trend component each with its own weighted average (alpha or beta). You can get L0 and T0 using linear regression. Follow D(t)=a+bt, then L0 is the intercept and T0 is the slope.

Question 18

When do you apply seasonal decomposition? What is seasonal decomposition? How would the forecast steps be?

Answer 18

Time series with seasonality but no trend. Seasonal decomposition removes the seasonal effect from data before conducting a forecast using the seasonal relatives.

1. compute the season averages
2. compute the overal average of the seasonal averages
3. compute the seasonal relatives (season average/overall average)
4. divide sales data by its seasonal relatives -> deseasonalized data
5. apply forecasting methods like MA or ES on the deseasonalized data
6. incorporate the seasonality again by multiplying with seasonal relatives

Question 19

How do you calculate the forecast error? What different types of forecast error do you have? What can you do with the forecast errors?

Answer 19

You calculate forecast error by subtracting the original demand from the forecast. The other types are absolute forecast error and absolute percentage forecast error. You can sue forecast errors to determine the accuracy and bias of the forecast.

Question 20

What is the difference between the measures of accuracy and the measures of bias?

Answer 20

The measures of accuracy tell us how big the error is and the measures of bias tell us what direction the error is.

Question 21

What do the measures of accuracy tell you? What are the three different measures of accuracy?

Answer 21

Measures of accuracy tell you about the average size of the error.
Mean absolute deviation (MAD)
Mean squared error (MSE)
Mean absolute percentage error (MAPE)

Question 22

What is the difference between the measures of accuracy and the measures of bias?

Answer 22

The measures of accuracy tell us how big the error is and the measures of bias tell us what direction the error is.

Question 23

What do the measures of bias tell you? What are the three different measures of bias?

Answer 23

It tells you if the forecast tends to be constantly too high or consistently too low.
Mean forecast errors (MFE)
Running sum of forecast errors (RSFE)
Tracking signal (TS)

Question 24

What do the measures of accuracy and bias mean?
MAD
MSE
MAPE
MFE
RSFE
TS

Answer 24

• MAD: average of absolute errors over a period
  good if MAD is small
• MSE: average of squared errors over a period
  large MSE means higher presence of large errors
• MAPE: average of absolute percentage forecast error over a period
  forecast accuracy is often defined as 1-MAPE
  it is useful when demand range is wide
• MFE & RSFE
  positive -> too optimistic
  negative -> too pessimistic
• TS
  unbiased if TS is between -0.5 and 0.5