1 IES 371 Engineering Management Chapter 13: Forecasting Week 12 August 24, 2005 Objectives ...

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IES 371 Engineering Management

Chapter 13: Forecasting

Week 12August 24, 2005

Objectives Understand and practice on various forecasting method

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Dr. Karndee Prichanont IES371 1/2005

Strategic Role of Forecasting

Focus on supply chain management Short term role of product demand Long term role of new products, processes, and

technologies

Focus on Total Quality Management Satisfy customer demand Uninterrupted product flow with no defective items

Necessary for strategic planning

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Demand Forecast Applications

Time Horizon

Medium Term Long Term Short Term (3 months– (more than

Application (0–3 months) 2 years) 2 years)

Total salesGroups or familiesof products orservicesStaff planningProductionplanningMaster productionschedulingPurchasingDistribution

CausalJudgment

Forecast quantity Individualproducts orservices

Decision area InventorymanagementFinal assemblyschedulingWorkforceschedulingMaster productionscheduling

Forecasting Time seriestechnique Causal

Judgment

Total sales

Facility locationCapacityplanningProcessmanagement

CausalJudgment

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Components for Forecasting Demand

Time frame Short to mid-range Long-rangeDepend on each firm and industry type

Demand behavior Trend Cycle Seasonal

Forecasting method Forecasting process TimeTime

(c) Seasonal pattern(c) Seasonal pattern

Dem

and

TimeTime(c) Seasonal pattern(c) Seasonal pattern

TimeTime(d) Trend with seasonal pattern(d) Trend with seasonal pattern

Dem

and

Dem

and

TimeTime(d) Trend with seasonal pattern(d) Trend with seasonal pattern

Dem

and

Dem

and

Dem

and

TimeTime(b) Cycle(b) Cycle

Dem

and

Dem

and

TimeTime(b) Cycle(b) Cycle

Dem

and

Dem

and

TimeTime(a) Trend(a) Trend

Dem

and

Dem

and

Random Random movementmovement

TimeTime(a) Trend(a) Trend

Dem

and

Dem

and

Random Random movementmovement

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Forecasting Techniques Judgment method

Qualitative method that translates the opinions of managers, expert opinions, customer surveys, and sale-force estimate into quantitative estimates

Causal method

Use of past data on independent variables to derive mathematical relationship Demand = f(relevant factors)

Linear Regression

Time Series analysis

statistical technique using past data for short-term forecasting applications

Naïve forecast Moving average Weighted moving average

Exponential smoothing Adjusted exponential smoothing Linear trend line

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Causal Method – Linear Regression Method

Mathematical relationship between two or more variables

What causes to behave in a certain way?

Linear regression: y = a + bx Correlation

Strength of the relationship between dependent and independent variables

Range of [-1.00, 1.00] 2222

yynxxn

yxxynr

xbya

xnx

yxnxyb

22

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Causal Method – Linear Regression Method

Dep

end

ent

vari

able

Dep

end

ent

vari

able

Independent variableIndependent variableXX

YYEstimate ofEstimate ofY Y from fromregressionregressionequationequation

RegressionRegressionequation:equation:YY = = aa + + bXbX

ActualActualvaluevalueof of YY

Value of Value of X X used usedto estimate to estimate YY

Deviation,Deviation,or erroror error

{

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Ex 1: Causal Method – Linear Regression Method

Carpet City wants to develop a means to forecast its carpet sales. The carpet store manager believes that the store’s sales are directly related to the number of new housing starts in town. The manager has gathered data from county records of monthly house construction permits and from store records on monthly sales. These data are as follows:

MonthMonthly Carpet

Sales (1000s YD)

Montly Construction

Permits1 5 212 10 353 4 104 3 125 8 166 2 97 12 418 11 159 9 18

10 14 26

Required• Develop a linear regression model for this data and forecast sales if 30 construction permits for new home are filed

• Determine the strength of the casual relationship between monthly sales and new home construction using correlation

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Time Series Method- Simple Moving Average

Use several demand values in the recent past Smooth out randomness Suitable for stable demand Number of periods in the moving average smoother

forecast

n

DDDn

nF

nttt

t

...

demands last of Sum

1

1 n = Total number of periods in the averageDt = Demand in period tFt+1 = Forecast for period t +1

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Ex 2: Time Series Method- Simple Moving Average

The manager forecast weekly demand for his specialty pizza so that he can order pizza shells weekly. Forecast the demand for pizza for June 23 to July 14 by using the simple moving average method with n = 3

Recently demand has been as follows:

Week of Pizzas Week of Pizzas

June 2June 9June 16

506552

June 23June 30July 7

565560

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Time Series Method- Weighted Moving Average

More closely reflect data fluctuations Recent data, more weight Weight: trial-and-error experiment

Wi = weight for period i, [0%, 100%] Wi = 1.00

t

iiit DWF

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t

iiit DWF

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Ex 3: With the demand data in Example 2, forecast the demand for pizza by using WMA method with n = 3 and weights of 0.50, 0.30, and 0.20, with 0.50 applying to the most recent demand

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Time Series Method- Exponential Smoothing

Weights the most recent data more strongly React to recent changes in demand Seasonal pattern of demand Widely used in many businesses Requires minimal data

ttt FDF 11

Ft+1 = the forecast for the next periodDt = actual demand in the current periodFt = the previously determined forecast for the current period = smoothing constant (weighted factor)

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Ex 4: Time Series Method- Exponential Smoothing

Use the exponential smoothing method to forecast the number of units for June to January. The initial forecast for May was 105 units; = 0.2

The monthly demand for units manufactured of a company has been as follows:

Month Units Month Units

MayJuneJulyAugust

10080110115

SeptemberOctoberNovemberDecember

105110125120

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Time Series Method- Linear Trend Line

For demand with obvious trend over time A least squares regression line

y = a + bx Dependent variable (y) = Forecasted

demand Independent variable (x) = Time a = intercept at period 0 b = Slope of the line

xbya

xnx

yxnxyb

22

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Choosing a Time-Series MethodForecast Error

Forecast error

Cumulative sum of forecast errors (CFE)

Mean squared error (MSE)

Standard deviation

Mean absolute deviation (MAD)

Mean absolute percent error (MAPE)

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Tracking Signal

Tracking signal = Tracking signal = CFECFE

MADMAD+2.0 +2.0 —

+1.5 +1.5 —

+1.0 +1.0 —

+0.5 +0.5 —

0 0 —

––0.5 0.5 —

––1.0 1.0 —

––1.5 1.5 —| | | | |

00 55 1010 1515 2020 2525 Observation numberObservation number

Tra

ckin

g s

ign

alT

rack

ing

sig

nal

Control limitControl limit

Control limitControl limit

Out of controlOut of control

1 IES 371 Engineering Management Chapter 13: Forecasting Week 12 August 24, 2005 Objectives ...

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