©the mcgraw-hill companies, inc. 2008mcgraw-hill/irwin chapter 10 demand forecasting: building the...

©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin

Chapter 10

Demand Forecasting: Building the Foundation

for Resource Planning

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Learning Objectives

• Describe the benefits of effective resource planning.• Explain how the planning horizon affects planning tasks.• Describe how lead times determine the planning horizon.• Explain how product and service life cycles can aid in the planning process.• Describe the benefits of collaborative planning, forecasting & replenishment• Describe the different types of forecasting methods.• Compute a causal forecast using simple linear regression.• Recognize the components of a time series and appropriate forecasting

techniques for each component.• Compute forecasts using averages, exponential smoothing, seasonal indexes,

and a multiplicative model.• Compute measures of forecast accuracy.• Describe how enterprise resource planning (ERP) systems benefit businesses.

shot

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• Resource Planning - Determining what is needed, and making arrangements to get it, in order to achieve objectives.

• Contingency Plans – Alternative or back-up plans to be used if an unexpected event makes the normal plans infeasible.

Operations Management Framework

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• Increasing Alternatives – Management has more options if it plans ahead.

• Profitability Enhancement– Planning can both reduce costs and increase sales.

The further ahead we plan, however, the less we know about future conditions. There is a tradeoff between increasing alternatives and increasing uncertainty.

Financial Benefits of Effective Planning

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• Planning Horizon– The distance into the future one plans.

Looking into the Future:The Planning Horizon

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• A business may have many different planning horizons depending on the resources in question – Inventory- Usually very short

– Employees - Generally pretty short• Temps, new hires, etc.

– Equipment - A little longer• Purchasing and installation lead times

– Facilities - Longest• Purchase property, build the building

Looking into the Future:The Planning Horizon

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Life Cycle: A pattern of demand growth and decline that occurs from the introduction of a product to its obsolescence.

The five stages of a life cycle:

Introduction

Growth – Demand begins to increase.

Maturation – Demand begins to level off.

Saturation – Demand shifts to the beginning of its decline.

Decline – Final stage as demand disappears.

Exhibit 10.2 Product Life Cycle

Product and Service Life Cycles

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Market leaders sometimes try to create entry barriers by replacing products and maintaining intentionally short life cycles.

Exhibit 10.3 Product Life Cycles Interrupted by New Product Introduction

Product and Service Life Cycles

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Demand Forecasting

• Qualitative Forecasts– Do not use past data. Usually used when such data is not

available (such as planning for a new product).

– Customer surveys, expert opinions, etc.

• Quantitative Forecasts– Divided into causal forecasting and time series forecasting

techniques.

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Collaborative Planning, Forecasting, and Replenishment (CPFR)

• A shared process of creation between two or more parties with diverse skills and knowledge delivering a unified approach that provides the optimal framework for customer satisfaction.

• CPFR requires that data be shared among supply chain partners and that partners collaborate on developing demand forecasts..

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• Causal Techniques– Uses external data to predict future demand

– Looking for the factors that “cause” demand

– Linear regression is often used.

• Time Series Techniques– Use past demand to predict future demand

Demand Forecasting: Quantitative Analysis

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Some external variable is a leading indicator (independent variable) for the demand you want to predict (dependent variable)

• The example (10.1) uses temperature as the independent variable, but you could use others as well.

• e.g., exam schedule, promotions, sporting events, day of the week, etc.

Causal Models

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Demand Forecasting: Simple Linear Regression Example

Predicted High Temp. Beer Sales Predicted High Temp. Beer Sales

62 4,000 63 6,150

85 13,000 88 14,800

80 9,000 90 18,500

58 2,500 92 17,100

68 7,000 86 13,000

72 7,400 89 13,800

82 11,600 94 19,100

86 12,900 91 18,450

93 18,000 87 16,700

91 18,200 82 15,100

79 9,100 71 8,350

84 10,200 77 8,900

85 11,000

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Demand Forecasting: Simple Linear Regression Example

If we believe that fluctuations in demand for beer, Y, are partly due to changes in X, the predicted temperature: Given a particular temperature prediction, what will demand be?

Predicted

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Demand Forecasting:Simple Linear Regression Example

X1, Y1

X2, Y2

Regression analysis provides the formula for the line that best fits through the data points.

Underlying model: Y = a + bx

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Regression Line: Demand = -23,535 + 438.44 (Predicted Temperature)


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Y = -23,535 + 438.44x

For an 80-degree day, the demand forecast would be:

Y = -23,535 + 438.44(80)

= 11,540.2


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• There are four potential components of a time series:– Cycles

• A pattern that repeats over a long period of time (such as 20 years).

• Cycles are less important for demand forecasting, since we rarely have 20 years’ worth of data.

– Trend

– Seasonality

– Randomness

Demand Forecasting:Components of a Time Series

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• Trend – Component of a time series that causes demand to increase or decrease.

Exhibit 10.6 Example of a Time Series with Trend

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• Seasonality – A pattern in a time series that repeats itself at least once a year.

Exhibit 10.7 Time Series with Seasonality


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• Random Fluctuation – Unpredictable variation in demand that is not due to trend, seasonality, or cycle.

Exhibit 10.8 Time Series with Random Fluctuation


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Time Series Techniques: Averages

• Averaging is used to remove random fluctuations in historical data.

• Various kinds of averages can be used– Differences between them are exploited to create varying

degrees of responsiveness.

Responsiveness: The degree to which the forecast responds to the most immediate change in demand.

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• Averages constructed from bigger data sets (i.e., more history) are less responsive to sudden changes.– An average that uses the eight most recent data points is less responsive than

one that uses the past three:Exhibit 10.11 Three-Period and Eight-Period Moving Average Forecasts

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• Moving averages can be weighted to change responsiveness• Weights must sum up to 1.0• For more responsiveness, assign heavier weights to more recent data

points

Period 1 2 3 4

Demand 133 130 134 146

Example 10 .2Use weighted averages and the past four weeks’ demand to predict the next week’s demand. Demands for the past four weeks are:

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• A variant of moving average (weighted average):– Premise: More recent observations are better indicators of future demand

than past observations.

– Reduces the need to hold lots of data.

• Uses a smoothing constant, ‘alpha’ () to weight the previous demand and establish the responsiveness of the forecast.

Ft+1 = At + (1- )Ft

Where:Ft+1 = The forecast for the next time period

= A smoothing constant, between 0 and 1 At = The actual demand for the most recent periodFt = The forecast for the most recent period

Time Series Techniques:Exponential Smoothing

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• A higher alpha makes the forecast more responsive to changes:

Exhibit 10 .13 Comparison of .1 and .4 Alpha Values for Exponential Smoothing

Time Series Techniques:Exponential Smoothing

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• Example 10 .3: To forecast February’s demand using exponential smoothing with an alpha of .3. (assume an initial January forecast of 90)

Ft+1 = At + (1- )Ft

= .3(100) + (1-.3)90

= 30 + 63

= 93

Time Series Techniques:Exponential Smoothing Example

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Continue the process until the forecast for July is determined:

Time Series Techniques:Exponential Smoothing Example

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• Trend-adjusted Exponential Smoothing adds a smoothing constant to account for trend– Also called “forecast including trend” (FIT)

• FIT t+1 = Ft + Tt

• Where Ft is the smoothed forecast, Tt is the trend estimate and

– Ft = FIT t + (At – FITt)

– Tt = Tt-1 + (FITt - FITt-1 - Tt-1)

Time Series Techniques:Trend-Adjusted Exponential Smoothing

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• Example 10 .4:

• Using the following data = 0.2 = 0.9

– Initial trend (F1) = 3

– Initial forecast (T1) = 25

• Calculate the demand– For period two

– For period three

– For subsequent periods

48464241383934302925Demand (A)

10987654321Week (t)


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• For period 2

FITt+1 = Ft + Tt

Initial forecast (F1) = 25Initial Trend (T1) = 3

FIT2 = F1 + T1 = 25 + 3 = 28

48464241383934302925Demand (A)

10987654321Week (t)

FIT t+1 = Ft + Tt

Ft = FIT t + (At – FITt)

Tt = Tt-1 + (FITt - FITt-1 - Tt-1)


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• For period 3FITt+1 = Ft + Tt

FIT3 = F2 + T2

F2 = FIT2 + (A2 – FIT2)F2 = 28 + .2(29-28)F2 = 28 + .2 = 28.2

T2 = T1 + (FIT2 - FIT1 - T1)T2 = 3 + .9(28 - 25 - 3)T2 = 3 + .9(0) = 3

FIT3 = F2 + T2

FIT3 = 28.2 + 3FIT3 = 31.20

48464241383934302925Demand (A)

10987654321Week (t)

FIT t+1 = Ft + Tt




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• For period 4FITt+1 = Ft + Tt

FIT4 = F3 + T3

F3 = FIT3 + (A3 – FIT3)F3 = 31.2 + .2(30-31.2)F3 = 31.2 + .2(-1.2) = 30.96

T3 = T2 + (FIT3 - FIT2 - T2)T3 = 3 + .9(31.2 - 28 - 3)T3 = 3 + .9(.2) = 3.18

FIT4 = F3 + T3

FIT4 = 30.96 + 3.18FIT4 = 34.14

48464241383934302925Demand (A)

10987654321Week (t)

FIT t+1 = Ft + Tt




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Time Series Techniques:Using the Linear Trend Equation

• Identical to using linear regression as a causal technique– Time period is the independent variable

– Demand is the dependent variable

• Example 10.5:– Consider the following 10-month time series with an apparent trend:

Month: 1 2 3 4 5 6 7 8 9 10

Demand: 308 315 360 391 412 423 445 456 471 482

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Time Series Techniques:Using the Linear Trend Equation

Exhibit 10.16 Partial Excel Regression Analysis Output for Backpack Sales for Example 10.5

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• Seasonality: a common component in time series– Sell more ski equipment in Fall and Winter

• Seasonality is described by using a ratio of the average demand for a period to the average demand across all periods– If July has a seasonal index of 1.8, it means that average July demand is 1.8 times greater than overall average monthly demand

Time Series Techniques:Including Seasonality

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Day Overall SeasonalDay 1 2 3 4 Average Average Index

Monday 5 7 3 4 4.75 13.83 0.3434Tuesday 6 9 6 7 7.00 13.83 0.5060Wednesday 12 10 9 13 11.00 13.83 0.7952Thursday 15 17 13 19 16.00 13.83 1.1566Friday 27 24 30 28 27.25 13.83 1.9699Saturday 15 20 16 17 17.00 13.83 1.2289

Week

• Calculate average demand for each “season” (period average)– e.g. all Mondays, all January, etc.

• Compute average of all observations (global average)• Divide period averages by global average


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• So if in general I forecast 20 visitors per day, I adjust by the seasonal index to estimate what I expect on a particular day


Day Overall Seasonal Expected DailyAverage Average Index Daily Demand Forecast

4.75 13.83 0.3434 20 6.877.00 13.83 0.5060 20 10.1211.00 13.83 0.7952 20 15.9016.00 13.83 1.1566 20 23.1327.25 13.83 1.9699 20 39.4017.00 13.83 1.2289 20 24.58

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• A regression-based approach (Multiplicative model)– Compute seasonal indexes for each period– Remove seasonal component from the time series

• “Deseasonalize” the data

– Model the trend using linear regression on the

deseasonalized data– Determine the forecast by using the trend equation and seasonal indexes

Time Series Techniques:Dealing with Seasonality and Trend

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• Calculate seasonal indexes to deseasonalize data


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The regression analysis determines the best-fitting line through the deseasonalized demand. The general equation for that line is:

Y = a + bt

Where: Y = a point on the trend linea = Y interceptb = slope

t = time period

Dealing with Seasonality and TrendUsing Regression

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• Regression analysis result:Y = 280.48 + 2.30 (period)

• Forecast (deseasonalized):Y = 280.48 + 2.30 (17)

= 280.48 + 39.10 = 319.58• Forecast (seasonal):

– Multiply back by the appropriate seasonal indexY = 319.58(Q1 index)

= 319.58(1.67) = 533.70

Example: Given the trend in demand over the past 4 years and the effects of seasonality, what do we expect demand to be in period 17?


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Regression StatisticsMultiple R 0.90R Square 0.80Adjusted R Square0.79Standard Error 5.63Observations 16.00

ANOVAdf SS MS F Significance F

Regression 1.00 1793.50 1793.50 56.64 0.00Residual 14.00 443.30 31.66Total 15.00 2236.81

Coefficients Standard Error t Stat P -value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 280.48 2.95 95.05 0.00 274.15 286.81 274.15 286.81X Variable 1 2.30 0.31 7.53 0.00 1.64 2.95 1.64 2.95

Percentage (79%) of the variation in demand for tax services is explained by the time period

Rate of demand growth per period

Base-level service demand (period 0)


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Forecast Accuracy

• Forecast error is the actual demand minus the forecast demand.

• Absolute Error: how far “off” are we, in absolute terms?– Measured by mean absolute deviation (MAD) or mean squared error

(MSE)

• Forecast Bias: Are we consistently high or low?– A forecast should be unbiased (low forecasts are as frequent as high

forecasts)

– Bias is measured by mean forecast error (MFE) or running sum of forecast error (RSFE)

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• The ideal value for both is zero, which would mean there is no forecasting error• The larger the MAD or MSE, the less the accurate the model

n

F-A=MAD

n

1t

tt

n

F-A=MAD

n

1t

tt

1-n

FA=MSE

n

1t

2tt

1-n

FA=MSE

n

1t

2tt

Forecast Accuracy

Two similar approaches are used to measure absolute forecast error

MAD is the mean of the absolute values of the forecast errors

MSE is the mean of the squared values of the forecast errors

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Example 10.8: Calculating MAD

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Example 10.9: Calculating MSE

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Forecast Bias

• Forecast Bias: Tendency of a forecast to be too high or too low.

• Mean forecast error (MFE)– The mean of the forecast errors

• Running sum of forecast errors (RSFE)– The sum of forecast error, updated as each new error is calculated.

• Ideal measure is zero which indicates no bias.– Positive means forecast tends to low

– Negative means forecast tends to high

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Forecast Bias: Calculating MFE and RSFE

Mean Forecast Error = 1.00RSFE (period 8) = 8

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Tracking Signals

• The size of the cumulative forecast error expressed as MADs

• TS = RSFE/MAD

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Integrated Resource Planning Systems

• Enterprise Resource Planning (ERP) Systems– Planning for resources done from common database

– Allows decisions to be made from enterprise perspective

– Everyone uses the same numbers.

– ERP solutions are typically “off the shelf” – not customized to business

• Major providers: SAP, BAAN, PeopleSoft, Oracle

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Integrated Resource Planning Systems

Exhibit 10.28 Conceptual View of a Generic ERP System

©the mcgraw-hill companies, inc. 2008mcgraw-hill/irwin chapter 10 demand forecasting: building the...

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