Dependent versus independent Only independent demand needs to be

forecast Dependent demand should never be forecast

What Should Be Forecast?

Business plan Market direction 2 to 10 years

Sales and operationsplanning

Product lines andfamilies

1 to 3 years

Master productionschedule

End item andoption

Months

Forecast Time FrameLevel

2-8

Forecasts Are rarely 100% accurate over time Should include an estimate of error Are more accurate for product lines and

families Are more accurate for nearer periods of time

2-9

Record data in terms needed for the forecast Record circumstances relating to the data Record demand separately for different

customer groups

2-10

Are based on intuition and informed opinion Tend to be subjective Are used for business planning and

forecasting for new products Are used for medium-term to long-term

forecasting

2-11

Based on historical data usually available in the company

Assume future will repeat past

2-12

Based on external indicators Useful in forecasting total company demand

or demand for families of products

2-13

Month SalesJanuary 92February 83March 66April 74May 75June 84July 84August 81September 75October 63November 91December 84January ?

2-14

3

90) + 84 + (91

3

84) + 91 + (63 = forecastJanuary

Forecast sales as an average of past months

An average of the past 3 months:

If January sales are 90, forecast for February

2-15

Can be used to filter out random variation. Longer periods smooth out random variation. If a trend exists, it is hard to detect. Manual calculations can be cumbersome when

dealing with more periods.

2-16

Problem 2.1

2-17

Problem 2.1

Month DemandThree-Month

TotalNext MonthForecast

1 1022 913 954 1055 946 101

2-18

2-18a

Provides a routine method of updating item forecasts

Works well for stable items Is satisfactory for short-range forecasts Detects trends, but lags them

2-19

periods all for sales Average

sales average Period =index Seasonal

Measures the amount of seasonal variation of demand for a product

Relates the average demand in a particular period to the average demand for all periods

2-20

Quarter Average Quarterly Sales/100 Seasonal Index

1 128/100 = 1.28

2 102/100 = 1.02

3 75/100 = 0.75

4 95/100 = 0.95

Total = 4.00

Sales History

Year Quarter Total

1 2 3 41 122 108 81 90 4012 130 100 73 96 3993 132 98 71 99 400

Average 128 102 75 95 400

units 1004

400quarters all for sales Average ==

2-21

Average Salesfor All Periods

2-22

Forecasts are rarely 100% correct over time.Why monitor the forecast? To plan around the error in the future To measure actual demand versus forecasts To improve our forecasting methods

2-23

Bias: Cumulative sales may not be the same as forecast.

Bias exists since the cumulative variation is not zero.

Month Forecast Actual Variation1 100 90 –102 100 125 +253 100 120 +204 100 125 +255 100 120 +206 100 110 +10

Total 600 690 +90

2-24

Month Forecast Actual Variation

1 1,000 1,050 +50

2 1,000 940 –60

3 1,000 980 –20

4 1,000 1,040 +40

5 1,000 1,030 +30

6 1,000 960 –40

Total 6,000 6,000 0

Random variation: Sales will vary plus and minus about the average.

There is no bias, but there is random variation each month.

2-25

Understand the factors influencing demand Recognize basic demand patterns Describe the basic principles of forecasting Understand the principles of data collection Compare basic forecasting techniques Understand the concept of seasonality Understand the sources and types of forecast

error

2-26

2-27

Month Demand Next Month Forecast

1 102

2 91

3 95 96

4 105 97

5 94 98

6 101 100

7 108 101

8 91 100

9 101 100

10 99 97