Lecture 10 OM

LECTURE 10

Forecasting

Forecasting

• Predicting the future• Qualitative forecast methods

• subjective

• Quantitative forecast methods• based on mathematical formulas

© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e 12-2

The Effect of Inaccurate Forecasting


Forecasting

• Quality Management• Accurately forecasting customer demand is a key to

providing good quality service

• Strategic Planning• Successful strategic planning requires accurate

forecasts of future products and markets


Components of Forecasting Demand

• Time frame• Demand behavior• Causes of behavior


Time Frame

• Indicates how far into the future is forecast• Short- to mid-range forecast

• typically encompasses the immediate future• daily up to two years

• Long-range forecast• usually encompasses a period of time longer than

two years


Demand Behavior

• Trend• a gradual, long-term up or down movement of demand

• Random variations• movements in demand that do not follow a pattern

• Cycle• an up-and-down repetitive movement in demand

• Seasonal pattern• an up-and-down repetitive movement in demand

occurring periodically


Forms of Forecast Movement


Forecasting Methods

• Time series• statistical techniques that use historical demand data

to predict future demand

• Regression methods• attempt to develop a mathematical relationship

between demand and factors that cause its behavior

• Qualitative• use management judgment, expertise, and opinion to

predict future demand


Qualitative Methods

• Management, marketing, purchasing, and engineering are sources for internal qualitative forecasts

• Delphi method• involves soliciting forecasts about technological

advances from experts


Forecasting Process

6. Check forecast accuracy with one or more measures

4. Select a forecast model that seems appropriate for data

5. Develop/compute forecast for period of historical data

8a. Forecast over planning horizon

9. Adjust forecast based on additional qualitative information and insight

10. Monitor results and measure forecast accuracy

8b. Select new forecast model or adjust parameters of existing model

7.Is accuracy of

forecast acceptable?

1. Identify the purpose of forecast

3. Plot data and identify patterns

2. Collect historical data

No

Yes


Time Series

• Assume that what has occurred in the past will continue to occur in the future

• Relate the forecast to only one factor - time• Include

• naïve forecast• moving average• exponential smoothing• linear trend line


Moving Average

• Naive forecast• demand in current period is used as next period’s

forecast• Simple moving average

• uses average demand for a fixed sequence of periods• good for stable demand with no pronounced

behavioral patterns• Weighted moving average

• weights are assigned to most recent data



Moving Average: Naïve Approach

Jan 120

Feb 90

Mar 100

Apr 75

May 110

June 50

July 75

Aug 130

Sept 110

Oct 90

ORDERSMONTH PER MONTH

Nov -

FORECAST


Moving Average: Naïve Approach

Jan 120

Feb 90

Mar 100

Apr 75

May 110

June 50

July 75

Aug 130

Sept 110

Oct 90

ORDERSMONTH PER MONTH

-120

90100

751105075

13011090Nov -

FORECAST


Simple Moving Average

MAn =

n

i = 1 Di

nwhere

n = number of periods in the moving

averageDi = demand in

period i


3-month Simple Moving Average

Jan 120

Feb 90

Mar 100

Apr 75

May 110

June 50

July 75

Aug 130

Sept 110

Oct 90Nov -

ORDERS

MONTH PER MONTH

MA3 =

3

i = 1 Di

3

MOVING AVERAGE



Jan 120

Feb 90

Mar 100

Apr 75

May 110

June 50

July 75

Aug 130

Sept 110

Oct 90Nov -

ORDERS

MONTH PER MONTH

MA3 =

3

i = 1 Di

3

=90 + 110 + 130

3

= 110 orders for Nov

–––

103.388.395.078.378.385.0

105.0110.0

MOVING AVERAGE



MA5 =

5

i = 1 Di

5

Jan 120

Feb 90

Mar 100

Apr 75

May 110

June 50

July 75

Aug 130

Sept 110

Oct 90Nov -

ORDERS

MONTH PER MONTH

MOVING AVERAGE



MA5 =

5

i = 1 Di

5

=90 + 110 + 130+75+50

5

= 91 orders for Nov

Jan 120

Feb 90

Mar 100

Apr 75

May 110

June 50

July 75

Aug 130

Sept 110

Oct 90Nov -

ORDERS

MONTH PER MONTH –

–– –

– 99.085.082.088.095.091.0

MOVING AVERAGE


Smoothing Effects


Weighted Moving Average

• Adjusts moving average method to more closely reflect data fluctuations

WMAn = i = 1 Wi Di

where

Wi = the weight for period i,

between 0 and 100 percent

Wi = 1.00

n



Weighted Moving Average Example

MONTH WEIGHT DATA

August 17% 130September 33% 110October 50% 90

WMA3 = 3

i = 1 Wi DiNovember Forecast


Weighted Moving Average Example

MONTH WEIGHT DATA

August 17% 130September 33% 110October 50% 90

WMA3 = 3

i = 1 Wi Di

= (0.50)(90) + (0.33)(110) + (0.17)(130)

= 103.4 orders

November Forecast


Exponential Smoothing

• Averaging method • Weights most recent data more strongly• Reacts more to recent changes• Widely used, accurate method• Smoothing constant, α

• applied to most recent data



Ft +1 = Dt + (1 - )Ft

where:

Ft +1 = forecast for next period

Dt = actual demand for present period

Ft = previously determined forecast for present period

= weighting factor, smoothing constant


0.0 1.0

If = 0.20, then Ft +1 = 0.20Dt + 0.80 Ft

If = 0, then Ft +1 = 0Dt + 1 Ft = Ft

Forecast does not reflect recent data

If = 1, then Ft +1 = 1Dt + 0 Ft =Dt Forecast based only on most recent data

Effect of Smoothing Constant



Exponential Smoothing (α=0.30)

F2 = D1 + (1 - )F1

F3 = D2 + (1 - )F2

F13 = D12 + (1 - )F12

PERIOD MONTHDEMAND

1 Jan 37

2 Feb 40

3 Mar 41

4 Apr 37

5 May 45

6 Jun 50

7 Jul 43

8 Aug 47

9 Sep 56

10 Oct 52

11 Nov 55

12 Dec 54


Exponential Smoothing (α=0.30)

F2 = D1 + (1 - )F1

= (0.30)(37) + (0.70)(37)

= 37

F3 = D2 + (1 - )F2

= (0.30)(40) + (0.70)(37)

= 37.9

F13 = D12 + (1 - )F12

= (0.30)(54) + (0.70)(50.84)

= 51.79

PERIOD MONTHDEMAND

1 Jan 37

2 Feb 40

3 Mar 41

4 Apr 37

5 May 45

6 Jun 50

7 Jul 43

8 Aug 47

9 Sep 56

10 Oct 52

11 Nov 55

12 Dec 54


Exponential SmoothingFORECAST, Ft + 1

PERIOD MONTH DEMAND ( = 0.3) ( = 0.5)

1 Jan 37 – –2 Feb 403 Mar 414 Apr 375 May 456 Jun 507 Jul 438 Aug 479 Sep 56

10 Oct 5211 Nov 5512 Dec 5413 Jan –


Exponential SmoothingFORECAST, Ft + 1

PERIOD MONTH DEMAND ( = 0.3) ( = 0.5)

1 Jan 37 – –2 Feb 40 37.00 37.003 Mar 41 37.90 38.504 Apr 37 38.83 39.755 May 45 38.28 38.376 Jun 50 40.29 41.687 Jul 43 43.20 45.848 Aug 47 43.14 44.429 Sep 56 44.30 45.71

10 Oct 52 47.81 50.8511 Nov 55 49.06 51.4212 Dec 54 50.84 53.2113 Jan – 51.79 53.61




Forecast Accuracy

• Forecast error• difference between forecast and actual demand

• MAD• mean absolute deviation

• MAPD• mean absolute percent deviation

• Cumulative error• Average error or bias


Mean Absolute Deviation (MAD)

where t = period number

Dt = demand in period t

Ft = forecast for period t

n = total number of periods = absolute value

S Dt - Ft nMAD =


MAD Example

1 37 37.00 – –2 40 37.003 41 37.904 37 38.835 45 38.286 50 40.297 43 43.208 47 43.149 56 44.30

10 52 47.8111 55 49.0612 54 50.84

PERIOD DEMAND, Dt Ft ( =0.3) (Dt - Ft) |Dt - Ft|


MAD Example

1 37 37.00 – –2 40 37.00 3.00 3.003 41 37.90 3.10 3.104 37 38.83 -1.83 1.835 45 38.28 6.72 6.726 50 40.29 9.69 9.697 43 43.20 -0.20 0.208 47 43.14 3.86 3.869 56 44.30 11.70 11.70

10 52 47.81 4.19 4.1911 55 49.06 5.94 5.9412 54 50.84 3.15 3.15

557 49.31 53.39

PERIOD DEMAND, Dt Ft ( =0.3) (Dt - Ft) |Dt - Ft|


MAD Calculation

S Dt - Ft nMAD =

=

= 4.85

53.3911


Other Accuracy Measures

Mean absolute percent deviation (MAPD)

MAPD =|Dt - Ft|

Dt

Cumulative error

E = et

Average error

E =et

n


Comparison of Forecasts

FORECAST MAD MAPD E (E)

Exponential smoothing (= 0.30) 4.85 9.6% 49.31 4.48

Exponential smoothing (= 0.50) 4.04 8.5% 33.21 3.02


Based on the table, the company should choose 5-month forecast because the smaller MAD indicates a more accurate forecast

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Lecture 10 OM

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