Forecasting.ppt

3-1 Forecasting

William J. Stevenson

Operations Management

8th edition

3-2 Forecasting

FORECAST: A statement about the future value of a variable of

interest such as demand. Forecasts affect decisions and activities throughout

an organization It involve taking historical data and projecting them

into the future with some sorts of mathematical model. It may be subjective or institutive prediction

It may involve combination of mathematical model adjusted by a manager’s good judgment.

There is seldom single superior method

3-3 Forecasting

Accounting Cost/profit estimates

Finance Cash flow and funding

Human Resources Hiring/recruiting/training

Marketing Pricing, promotion, strategy

MIS IT/IS systems, services

Operations Schedules, MRP, workloads

Product/service design New products and services

Uses of ForecastsUses of Forecasts

3-4 Forecasting

Assumes causal systempast ==> future

Forecasts rarely perfect because of randomness

Forecasts more accurate forgroups vs. individuals

Forecast accuracy decreases as time horizon increases I see that you will

get an A this semester.

Features common to all Features common to all forecasts forecasts

3-5 Forecasting

It can be classified by the future time horizon that it covers. Time horizons fall into three categories:-

Short range forecast: 3 months to 1year

Planning purchasing, job scheduling, workforce levels, job assignments, and production levels

Medium Range forecast: 3 months to 3years

For sales planning, production planning, cash budgeting and analyzing various operating plans

Large Range forecast: 3year or more

Planning for new products, capitial expenditures, facility location, or expansion, and R&D

FORECASTING TIME FORECASTING TIME HORIZONS HORIZONS

3-6 Forecasting

Elements of a Good ForecastElements of a Good Forecast

Timely

AccurateReliable

Mea

ningfu

l

Written

Easy

to u

se

3-7 Forecasting

Economics Forecast addresses the business cycle by predicting inflation rates, money supplies housing states and other planning indicators.

Technological Forecast are concerned with rates of technological program which can result in the birth of new exciting products, requiring new plants and equipments.

Demand Forecasts are projections of demand for a company’s products or services. These forecasts are also called “sales forecasts” drives a company production capacity and scheduling system.

TYPES OF FORECASTSTYPES OF FORECASTS

3-8 Forecasting

Lets look at the impact of product forecast on three activities

Human Resources: Hiring firing training of workers all depend on anticipated demand.

Capacity: if you capacity without proper demand and forecast would result in higher cost of production

Supply Chain Management: Good supplier relations and the ensuring price advantages for materials and parts depend on accurate forecasts.

The strategic importance of The strategic importance of forecastsforecasts

3-9 Forecasting

Steps in the Forecasting ProcessSteps in the Forecasting Process

Step 1 Determine purpose of forecast

Step 2 Establish a time horizon

Step 3 Select a forecasting technique

Step 4 Gather and analyze data

Step 5 Prepare the forecast

Step 6 Monitor the forecast

“The forecast”

3-10 Forecasting

Types of ForecastsTypes of Forecasts

Judgmental - uses subjective inputs

Time series - uses historical data assuming the future will be like the past

Associative models - uses explanatory variables to predict the future

3-11 Forecasting

Judgmental ForecastsJudgmental Forecasts

Executive opinions Sales force opinions Consumer surveys Outside opinion Delphi method

Opinions of managers and staff Achieves a consensus forecast

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Time Series ForecastsTime Series Forecasts

Trend - long-term movement in data Seasonality - short-term regular variations in

data Cycle – wavelike variations of more than one

year’s duration Irregular variations - caused by unusual

circumstances Random variations - caused by chance

3-13 Forecasting

Forecast VariationsForecast Variations

Trend

Irregularvariation

Seasonal variations

908988

Figure 3.1

Cycles

3-14 Forecasting

Naive ForecastsNaive Forecasts

Uh, give me a minute.... We sold 250 wheels lastweek.... Now, next week we should sell....

The forecast for any period equals the previous period’s actual value.

3-15 Forecasting

Simple to use Virtually no cost Quick and easy to prepare Data analysis is nonexistent Easily understandable Cannot provide high accuracy Can be a standard for accuracy

Naïve ForecastsNaïve Forecasts

3-16 Forecasting

Stable time series data F(t) = A(t-1)

Seasonal variations F(t) = A(t-n)

Data with trends F(t) = A(t-1) + (A(t-1) – A(t-2))

Uses for Naïve ForecastsUses for Naïve Forecasts

3-17 Forecasting

Techniques for AveragingTechniques for Averaging

Moving average

Weighted moving average

Exponential smoothing

3-18 Forecasting

Moving AveragesMoving Averages

Moving average – A technique that averages a number of recent actual values, updated as new values become available.

Weighted moving average – More recent values in a series are given more weight in computing the forecast.

MAn = n

Aii = 1n

3-19 Forecasting

Simple Moving AverageSimple Moving Average

MAn = n

Aii = 1n

35

37

39

41

43

45

47

1 2 3 4 5 6 7 8 9 10 11 12

Actual

MA3

MA5

3-20 Forecasting

Exponential SmoothingExponential Smoothing

• Premise--The most recent observations might have the highest predictive value.

Therefore, we should give more weight to the more recent time periods when forecasting.

Ft = Ft-1 + (At-1 - Ft-1)

3-21 Forecasting


Weighted averaging method based on previous forecast plus a percentage of the forecast error

A-F is the error term, is the % feedback

Ft = Ft-1 + (At-1 - Ft-1)

3-22 Forecasting

Period Actual Alpha = 0.1 Error Alpha = 0.4 Error1 422 40 42 -2.00 42 -23 43 41.8 1.20 41.2 1.84 40 41.92 -1.92 41.92 -1.925 41 41.73 -0.73 41.15 -0.156 39 41.66 -2.66 41.09 -2.097 46 41.39 4.61 40.25 5.758 44 41.85 2.15 42.55 1.459 45 42.07 2.93 43.13 1.87

10 38 42.36 -4.36 43.88 -5.8811 40 41.92 -1.92 41.53 -1.5312 41.73 40.92

Example 3 - Exponential SmoothingExample 3 - Exponential Smoothing

3-23 Forecasting

Picking a Smoothing ConstantPicking a Smoothing Constant

35

40

45

50

1 2 3 4 5 6 7 8 9 10 11 12

Period

Dem

and .1

.4

Actual

3-24 Forecasting

Trend adjusting Exponential SmoothingTrend adjusting Exponential Smoothing

3-25 Forecasting

3-26 Forecasting

Common Nonlinear TrendsCommon Nonlinear Trends

Parabolic

Exponential

Growth

Figure 3.5

3-27 Forecasting

Linear Trend EquationLinear Trend Equation

Ft = Forecast for period t t = Specified number of time periods a = Value of Ft at t = 0 b = Slope of the line

Ft = a + bt

0 1 2 3 4 5 t

Ft

3-28 Forecasting

Calculating a and bCalculating a and b

b = n (ty) - t y

n t2 - ( t)2

a = y - b t

n

3-29 Forecasting

Linear Trend Equation ExampleLinear Trend Equation Example

t yW e e k t 2 S a l e s t y

1 1 1 5 0 1 5 02 4 1 5 7 3 1 43 9 1 6 2 4 8 64 1 6 1 6 6 6 6 45 2 5 1 7 7 8 8 5

t = 1 5 t 2 = 5 5 y = 8 1 2 t y = 2 4 9 9( t ) 2 = 2 2 5

3-30 Forecasting

Linear Trend CalculationLinear Trend Calculation

y = 143.5 + 6.3t

a = 812 - 6.3(15)

5 =

b = 5 (2499) - 15(812)

5(55) - 225 =

12495-12180

275 -225 = 6.3

143.5

3-31 Forecasting

SEASONALITY EFFECT SEASONALITY EFFECT

3-32 Forecasting

3-33 Forecasting

3-34 Forecasting

3-35 Forecasting

Summarizing Forecast AccuracySummarizing Forecast Accuracy

3-36 Forecasting

3-37 Forecasting

3-38 Forecasting

3-39 Forecasting

Linear Model Seems ReasonableLinear Model Seems Reasonable

A straight line is fitted to a set of sample points.

0

10

20

30

40

50

0 5 10 15 20 25

X Y7 152 106 134 15

14 2515 2716 2412 2014 2720 4415 347 17

Computedrelationship

3-40 Forecasting

Controlling the ForecastControlling the Forecast

Control chart A visual tool for monitoring forecast errors Used to detect non-randomness in errors

Forecasting errors are in control if All errors are within the control limits No patterns, such as trends or cycles, are

present

3-41 Forecasting

Sources of Forecast errorsSources of Forecast errors

Model may be inadequate Irregular variations Incorrect use of forecasting technique

3-42 Forecasting

Choosing a Forecasting TechniqueChoosing a Forecasting Technique

Many different forecasting techniques are available, and no single technique works best in every situation.

When selecting a technique for a given situation, the manager or analyst must take a number of factors into consideration.

The two most important factors are: Cost and Accuracy

How much money is budgeted for generating the forecast? What are the possible costs of error? What are the benefits that might accrue from an accurate

forecast?

3-43 Forecasting


Generally speaking, the higher the accuracy, the higher the cost, so it is important to weigh cost-accuracy trade-offs carefully.

The time needed to gatherer and analyze data, and prepare the forecast.

Time horizon are also important:- MA & Exp smoothing are short range techniques since they produce forecast for the next period.

Trend equations can be used to project over much long range forecast.

Delphi method & executive opinion methods are often used for long range planning.

3-44 Forecasting


New products and services lack historical data, so forecast, for them must be based on subjective estimates.

In some instances, a manager might use more than one forecasting technique to obtain independent forecasts.

If different techniques produced approximately the same predictions, that would give increased confidence in the results; disagreement among the forecast would indicate that additional analysis may be needed.

3-45 Forecasting

Tracking SignalTracking Signal

Tracking signal = (Actual-forecast)

MAD

•Tracking signal

–Ratio of cumulative error to MAD

Bias – Persistent tendency for forecasts to beGreater or less than actual values.

3-46 Forecasting


No single technique works in every situation Two most important factors

Cost Accuracy

Other factors include the availability of: Historical data Computers Time needed to gather and analyze the data Forecast horizon

3-47 Forecasting


3-48 Forecasting

Linear Trend EquationLinear Trend Equation

3-49 Forecasting

Simple Linear RegressionSimple Linear Regression

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