Topic 2 -Forecasting.ppt

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Operations ManagementTopic 2 – ForecastingTopic 2 – Forecasting

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What is Forecasting?What is Forecasting? Process of predicting a Process of predicting a

future eventfuture event

Can be any or Can be any or combination of:combination of: Mathematical modelMathematical model IntuitiveIntuitive

Hmm…. you gonna get an A for this subject

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Short-range forecast Up to 1 year but generally less than 3 months used for planning purchasing, job scheduling,

workforce levels, job assignments, production levels. Medium-range forecast

Generally spans from 3 months to 3 years useful for sales planning, production planning and

budgeting, cash budgeting, and analyzing various operating plans.

Long-range forecast Generally 3 years or more used in planning for new products, capital

expenditures, facility location or expansion, and R&D

Forecasting Time HorizonsForecasting Time Horizons

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Types of ForecastsTypes of Forecasts Economic forecasts

Address business cycle – inflation rate, money supply, housing starts, etc. Technological forecasts

Predict rate of technological progress Impacts development of new products

Demand forecasts Predict sales of existing products and servicesWe can also forecast the economy or the technology. But for OM, demand

forecasting the most relevant.The forecast is the only estimate of demand until actual demand becomes

known.

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Importance of ForecastingImportance of Forecasting

Human Resources – Hiring, training, laying off Human Resources – Hiring, training, laying off workersworkers

Capacity – Capacity shortages can result in Capacity – Capacity shortages can result in undependable delivery, loss of customers, undependable delivery, loss of customers, loss of market shareloss of market share

Supply Chain Management – Good supplier Supply Chain Management – Good supplier relations and price advantagerelations and price advantage

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Seven Steps in ForecastingSeven Steps in Forecasting1. Determine the use of the forecast2. Select the items to be forecasted3. Determine the time horizon of the

forecast4. Select the forecasting model(s)5. Gather the data6. Make the forecast7. Validate and implement results

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The Realities!

Forecasts are seldom perfectForecasts are seldom perfect Most techniques assume an Most techniques assume an

underlying stability in the systemunderlying stability in the system Product family and aggregated Product family and aggregated

forecasts are more accurate than forecasts are more accurate than individual product forecastsindividual product forecasts

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Forecasting ApproachesForecasting Approaches

Qualitative (subjective) Forecast incorporates the decision maker’s intuition, emotion, personal experiences, and value system in reaching a forecast.

Quantitative Forecast use a variety of mathematical models/ techniques that rely on historical data and/or causal variables to forecast demand.

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Used when situation is vague and little Used when situation is vague and little data existdata exist New productsNew products New technologyNew technology e.g., forecasting sales on Internete.g., forecasting sales on Internet

Qualitative MethodsQualitative Methods

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Used when situation is ‘stable’ and Used when situation is ‘stable’ and historical data existhistorical data exist Existing productsExisting products Current technologyCurrent technology e.g., forecasting sales of color televisionse.g., forecasting sales of color televisions

Quantitative MethodsQuantitative Methods

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Qualitative methodsQualitative methods

1. Jury of executive opinion – uses the opinion of a small group of high level managers to form a group estimate of demand.

2. Delphi method – using a group process that allows

experts to make forecasts.3. Sales force composite – based on salesperson’s

estimates of expected sales.

4. Consumer market survey – solicits inputs from customers or potential customers regarding future purchasing plans.

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Quantitative MethodsQuantitative Methods

1. Naive approach2. Moving averages3. Weighted Moving

Averages4. Exponential smoothing5. Trend projection6. Linear regression

Time-Series Time-Series Models Models

Associative Model Associative Model

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uses a series of past data points to make a forecast. uses a series of past data points to make a forecast. It is based on a sequence of evenly spaced (weekly, monthly, quarterly, etc) data points.

Predict on the assumption that the future is a function of the past.

Forecast based only on past values, no other variables important Look what happened over a period of time and use a

series of past data to make a forecast. For example: to predict the sales of lawn mowers, use

the past sales to make the forecasts.

Time Series ModelsTime Series Models

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Associative ModelsAssociative Models

Incorporate variables or factors that might influence the quantity being forecast. For example: an associative model for lawn

mower sales might use factors such as new housing starts, advertising budgets, and competitors prices.

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Components of DemandComponents of DemandDe

man

d fo

r pro

duct

or s

ervi

ce

| | | |1 2 3 4

Year

Average demand over four years

Seasonal peaks

Trend component

Actual demand

Random variation

Figure 4.1Figure 4.1Product demand charted over 4 years with a Growth Trend and Seasonality added:

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Persistent, overall upward or downward pattern

Changes due to population, technology, age, culture, etc.

Typically several years duration

Trend ComponentTrend Component

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Regular pattern of up and down fluctuations

Due to weather, customs, etc. Occurs within a single year

Seasonal ComponentSeasonal Component

Number ofPeriod Length Seasons

Week Day 7Month Week 4-4.5Month Day 28-31Year Quarter 4Year Month 12Year Week 52

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Repeating up and down movements Affected by business cycle, political, and

economic factors Multiple years duration Often causal or

associative relationships

Cyclical ComponentCyclical Component

00 55 1010 1515 2020

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Erratic, unsystematic, ‘residual’ fluctuations

Due to random variation or unforeseen events

Short duration and nonrepeating

Random ComponentRandom Component

MM TT WW TT FF

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Naive ApproachNaive Approach

Assumes demand in next Assumes demand in next period is the same as period is the same as demand in most recent perioddemand in most recent period e.g., If e.g., If JanJanuary sales were uary sales were 6868, , thenthen

FebFebruary sales will be ruary sales will be 6868

Sometimes cost effective and efficientSometimes cost effective and efficient Can be good starting pointCan be good starting point

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Moving averageWeighted moving averageExponential smoothing

Techniques for AveragingTechniques for Averaging

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Moving Average MethodMoving Average Method

Moving average =Moving average =∑ ∑ demand in previous n periodsdemand in previous n periods

nn

A forecasting method that uses an average of the ‘n’ most recent periods of data to forecast the next period. Useful if we can assume that market demands will stay fairly steady over time.

e.g. a 4-month moving average is found by summing the demand during the past 4 months and dividing by 4. This practice tends to smooth out short term irregularities in the data series.

Where n is the number of periods in the moving average.

The above is used as an estimate of the next period’s demand

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Moving Average Example

Storage shed sales at a Garden Supply shop are as shown in the following Table.

Example 1:

Calculate the 3-month moving average forecast.

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JanuaryJanuary 1010FebruaryFebruary 1212MarchMarch 1313AprilApril 1616MayMay 1919JuneJune 2323JulyJuly 2626

ActualActual 3-Month3-MonthMonthMonth Shed SalesShed Sales Moving AverageMoving Average

(12 + 13 + 16)/3 = 13 (12 + 13 + 16)/3 = 13 22//33

(13 + 16 + 19)/3 = 16(13 + 16 + 19)/3 = 16(16 + 19 + 23)/3 = 19 (16 + 19 + 23)/3 = 19 11//33


101012121313

((1010 + + 1212 + + 1313)/3 = 11 )/3 = 11 22//33

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e.g. the forecast for December is 20.7The forecast for coming January is (18+16+14)/3=16.0

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Graph of Moving Average

| | | | | | | | | | | |JJ FF MM AA MM JJ JJ AA SS OO NN DD

Shed

Sal

esSh

ed S

ales

30 30 –28 28 –26 26 –24 24 –22 22 –20 20 –18 18 –16 16 –14 14 –12 12 –10 10 –

Actual Actual SalesSales

Moving Moving Average Average ForecastForecast

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Weighted Moving Average

WeightedWeightedmoving averagemoving average ==

∑∑ ((weight for period nweight for period n)) x x ((demand in period ndemand in period n))

∑∑ weightsweights

When a detectable trend or pattern is present, weights can be used to place more emphasis on recent values. This makes forecasting techniques more responsive to changes because more recent periods may be more heavily weighted.

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Weighted Moving Average Ex:Example 2The shop in Example 1 decides to forecast storage shed sales by weighting the past 3 months as

follows:Period Weight appliedLast month 32 months ago 23 months ago 1_____________________________Solution:∑ (weights) = 6

Based on the weightings above, the forecast for any month [(3 x Sales last month) + (2 x Sales 2 months ago) + (1 x Sales 3 months ago)]

= ------------------------------------------------------------------------------------------------- ∑ (weights)

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JanuaryJanuary 1010FebruaryFebruary 1212MarchMarch 1313AprilApril 1616MayMay 1919JuneJune 2323JulyJuly 2626

ActualActual 3-Month Weighted3-Month WeightedMonthMonth Shed SalesShed Sales Moving AverageMoving Average

[(3 x 16) + (2 x 13) + (12)]/6 = 14[(3 x 16) + (2 x 13) + (12)]/6 = 1411//33

[(3 x 19) + (2 x 16) + (13)]/6 = 17[(3 x 19) + (2 x 16) + (13)]/6 = 17[(3 x 23) + (2 x 19) + (16)]/6 = 20[(3 x 23) + (2 x 19) + (16)]/6 = 2011//22

Weighted Moving Average

101012121313

[(3 x [(3 x 1313) + (2 x ) + (2 x 1212) + () + (1010)]/6 = 12)]/6 = 1211//66

Weights Applied Period3 Last month2 Two months ago1 Three months ago6 Sum of weights

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Weighted Moving Average Ex:

Note that in this situation more heavily weighting the latest month provides a much more accurate projection.Note also that moving averages are effective in smoothing out sudden fluctuations in the demand pattern to provide stable estimates.

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Moving Average And Weighted Moving Average

Note from the graph that both moving averages lag the actual demand. The weighted moving average, however reacts more quickly to changes in demand.

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Increasing n smooths the forecast but makes it less sensitive to real changes in the data.

Cannot pick up trends very well. Because they are averages, they will always stay within past levels and will not predict changes to either higher or lower levels.

Require extensive historical of past data.

Potential Problems With Moving Average

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Exponential Smoothing

Is a weighted moving average forecasting technique in which data points are weighted by an exponential function.

This technique involves little record keeping of past data.

Easy to use.

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Exponential Smoothing

New forecast =New forecast = Last period’s forecastLast period’s forecast+ + ((Last period’s actual demand Last period’s actual demand

– – Last period’s forecastLast period’s forecast))

FFtt = F = Ft t – 1– 1 + + ((AAt t – 1– 1 - - F Ft t – 1– 1))

wherewhere FFtt == new forecastnew forecastFFt t – 1– 1 == previous forecastprevious forecast

== smoothing (or weighting) smoothing (or weighting) constant constant (0 ≤ (0 ≤ ≤ 1) ≤ 1)

Remember This!!!!!!!!Basic exponential smoothing formula:

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Exponential Smoothing Example

Example 3

In January, a car dealer predicted February demand for 142 Ford Mustangs. Actual February demand was 153. Using a smoothing constant chosen by management of α = 0.20, forecast the March demand using the exponential smoothing model.

Solution:Substituting into the formula above,New forecast (for March demand), FMac = FFeb + α (AFeb – FFeb)

= 142 + 0.20 (153 – 142)= 144.2

Therefore the March demand forecast for Ford Mustang is 144.

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Measuring Forecast ErrorForecast error (or Deviation) = Actual demand – Forecast demand = At - Ft.Several measures in use:•Mean absolute deviation (MAD)•Mean squared error (MSE)•Mean absolute percent error (MAPE)

∑ | Actual - Forecast |MAD = ------------------------------

n ∑ (Forecast error)2

MSE = ------------------------ n n

100 ∑ | Actual i - Forecast i | / Actual i

MAPE = -------i=1--------------------------------------------n

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Trend Projection

•A time-series forecasting method that fits a trend line to a series of historical data points and then projects the line into the future for forecasts.

•It is usually for medium-to-long range forecasts.

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Forecast Error Example:

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Exponential Smoothing Example 2

Demand for the last four months was:Demand for the last four months was:

Predict demand for July using each of these methods:(A)1) A 3-period moving average 2) exponential smoothing with alpha equal to .20 (use naïve to

begin).(B)3) If the naive approach had been used to predict demand for April

through June, what would MAD have been for those months?

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Exponential Smoothing Example 2

Month Demand Forecast

March 6 -

April 8 6

May 10 6 + 0.2(8 – 6) = 6.4

June 8 6.4 + 0.2(10 – 6.4) = 7.12

7.12 + 0.2(8 – 7.12) = 7.296

A) 1. (8+10+8)/3 = 8.33 (July Forecast)2. Use naïve to begin

B)Month March April May June

Demand 6 8 10 8

Naïve - 6 8 10

Error - +2 +2 -2

MAD 6/3 = 2.0

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Moving Average

Weekly sales of ten-grain bread at the local organic food market are in the table below. Based on this data, forecast week 9 using a five-week moving average.

Other Examples

Week 1 2 3 4 5 6 7 8

Sales 415 389 420 382 410 432 405 421

(382+410+432+405+421)= 410.0

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Exponential Smoothing & MADJim's department at a local department store has tracked the sales of a product over the last ten weeks. Forecast demand using exponential smoothing with an alpha of 0.4, and an initial forecast of 28.0. Calculate MAD.

Other Examples

Period Demand1 242 233 264 365 266 307 328 269 25

10 28

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Period Demand Forecast Error Absolute1 24 28.002 23 26.40 -3.40 3.403 26 25.04 0.96 0.964 36 25.42 10.58 10.585 26 29.65 -3.65 3.656 30 28.19 1.81 1.817 32 28.92 3.08 3.088 26 30.15 -4.15 4.159 25 28.49 -3.49 3.49

10 28 27.09 0.91 0.91Total 2.64 32.03

Average 0.29 3.56Bias MAD

Other Examples –Exponential Smoothing

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Other ExamplesProblems: Forecasting

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QUIZ 1The docking rate of ships at the Northport varies monthly and the operations manager is attempting to test the use of exponential smoothing to determine the effectiveness of the technique in forecasting. He begins the analysis in the month of January and continues for an additional 5 months. The initial forecast for January is 320. Actual data for the past 6 month are as follows:

The operation manager has decided on 2 values for a i.e. = 0.1 and a = 0.4. Which of these alpha values will be more accurate? Explain why?

Topic 2 -Forecasting.ppt

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