Demand Forecasting

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Forecasting
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Transcript of Demand Forecasting

Forecasting

Forecasting Predicting the future demand Qualitative forecast methods Subjective Quantitative forecast methods based on mathematical formulas

Forecasting s ImportanceForecasting is important for: Finance uses the long term forecast to evaluate capital investment needs. Human Resources uses forecasts to evaluate personnel needs. IT designs and implements systems that generate forecasts. Marketing develops sales forecasts used for mid-term to long term planning. Operations develops and uses forecasts to make decisions such as: scheduling, inventory management and long term capacity planning.

Forecasts by Time Horizon Short-range forecast Up to 1 year; usually less than 3 months Job scheduling, worker assignments

Medium-range forecast 3 months to 3 years Sales & production planning, budgeting

Long-range forecast 3+ years New product planning, facility location

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

Seasonal pattern an up-and-down repetitive movement in demand occurring periodically (short term: often annually)

Cycle an up-and-down repetitive movement in demand (long term)

Special events promotion, stock outs

Random variations movements in demand that do not follow a pattern

.

Trend Component Persistent, overall upward or downward pattern Linear, exponential Several years durationResponse

Mo., Qtr., Yr.

Seasonal Component Regular pattern of up & down fluctuations Due to weather, habits etc. Occurs within a predefined period: year, month, week, daySummer Response.

Mo., Qtr.

Cyclical Component Repeating up & down movements Due to interactions of factors influencing economy Usually 2-10 years durationCycle Response

Mo., Qtr., Yr.

Forecasting Methods Judgmental (Qualitative) use management judgment, expertise, and opinion to predict future demand

Time series statistical techniques that use historical demand data to predict future demand

Associative models (Regression methods) attempt to develop a mathematical relationship between demand and factors that cause its behavior

Overview of Qualitative Methods Jury of executive opinion Pool opinions of high-level executives, sometimes augment by statistical models

Delphi method Panel of experts, queried iteratively

Sales force composite Estimates from individual salespersons are reviewed for reasonableness, then aggregated

Consumer Market Survey Ask the customer

Jury of Executive Opinion Involves small group of high-level managers Group estimates demand by working together

Combines managerial experience with statistical models Relatively quick Group-think disadvantage

Sales Force Composite Each salesperson projects his or her sales Combined at district & national levels Sales reps know customers needs Sales Tends to be overly optimistic

1995 Corel Corp.

Delphi Method Iterative group process Reduces group-think

Answer

Consumer Market Survey Ask customers about purchasing plans What consumers say, and what they actually do are often How many hours will different you use the Internet Sometimes difficult to answer next week?

1995 Corel Corp.

Qualitative Methods : Advantages & DisadvantagesAdvantages : Take intangible factors into consideration. Useful when there are little data available (new product, new market, new business unit).

Disadvantages : Long consultation process High risk of getting a biased forecast Expensive Usually not precise

Quantitative Methods : Advantages & DisadvantagesAdvantages : Easy to use once the right model has been developed. Data collection is quick and easy since most of the required information is already in the business systems (ex. previous sales) or readily available (ex. consumer price index).

Disadvantages : Do not take new information into consideration : It s like driving a car by looking in the rear-view mirror.

Forecasting ApproachesQualitative Methods Used when situation is vague & little data exist New products New technology Involves intuition, experience

Quantitative Methods Used when situation is stable & historical data exist Existing products Current technology Involves mathematical techniques

Considerations:Planning horizon Availability and value of historical data Needs (precision and reliability) Time and budget constraints

Realities of Forecasting1. Forecasts are seldom perfect: almost always wrong by some amount 2. Aggregated forecasts are more accurate than individual forecasts 3. More accurate for shorter time periods 4. Most forecasting methods assume that there is some underlying stability in the system: watch out for special events!

Quantitative Forecasting Methods(Non-Naive)Quantitative Forecasting Time Series Models Associative Models

Moving Average

Exponential Smoothing

Trend Projection

Multiple Regression

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 naive forecast simple average moving average exponential smoothing linear trend analysis

Moving Averages Naive forecast Demand of the current period is used as next period s forecast

Simple moving average stable demand with no pronounced behavioral patterns

Weighted moving average weights are assigned to most recent data

Naive forecastORDERS PER MONTH 120 90 100 75 110 50 75 130 110 90 -

MONTH Jan Feb Mar Apr May June July Aug Sept Oct Nov

FORECAST 120 90 100 75 110 50 75 130 110 90

Simple Moving Averagen i=1

7

Di

MAn =where

n

n = number of periods in the moving average Di = demand in period i

3-month Simple Moving AverageMONTH Jan Feb Mar Apr May June July Aug Sept Oct Nov ORDERS PER MONTH 120 90 100 75 110 50 75 130 110 90 MOVING AVERAGE 103.3 88.3 95.0 78.3 78.3 85.0 105.0 110.0

3

7 1 Di i=MA3 = 3 90 + 110 + 130 3

=

= 110 orders for Nov

Weighted Moving Average Adjusts moving average method to more closely reflect data fluctuationsWMAn =where

7 Wi Dii=1

Wi = the weight for period i,between 0 and 100 percent

7 Wi = 1.00

Weighted Moving Average ExampleMONTH August September October November Forecast WEIGHT 17% 33% 50% DATA 130 110 903

WMA3 =

71 Wi Di i=

= (0.50)(90) + (0.33)(110) + (0.17)(130) = 103.4 orders

Exponential SmoothingForm of weighted moving averageWeights decline exponentially Most recent data weighted most

Requires smoothing constant (E)Ranges from 0 to 1 Subjectively chosen

Involves little record keeping of past data

Exponential Smoothing

New forecast = last periods forecast + E (last periods actual demand last periods forecast) Ft = Ft 1 + E(At 1 - Ft 1)where Ft = new forecast Ft 1 = previous forecastE =

smoothing (or weighting) constant (0 e E u 1)

Exponential Smoothing ExamplePredicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant E = .20

Exponential Smoothing ExamplePredicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant E = .20 New forecast = 142 + .2(153 142)

Exponential Smoothing ExamplePredicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant E = .20 New forecast = 142 + .2(153 142) = 142 + 2.2 = 144.2 144 cars

Common Measures of Error

Mean Absolute Deviation (MAD) |actual - forecast| MAD = n

Mean Squared Error (MSE)MSE = (forecast errors)2 n