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Demand Planning: Part 2

Collaboration requires shared information

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Objectives Hands on experience using smoothing

procedures Enhanced trend and seasonal

smoothing models Forecasting into the future Parameter & initialization estimation

considerations Error measures

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Moving Average modelFt+1 = Lt = (Dt + Dt-1 + ….+ Dt-n )/ n

Simple Exponential Smoothing

Ft+1 = Lt = α Dt + ( 1- α )Lt-1

Smoothing Models

Dt = sales in tLt = average in tFt = forecast in t

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Forecasting Tools

Spreadsheets Example: Excel

install the Data Analysis Toolpack (Tools/Add-Inns/Analysis Toolpack)

open the file containing the data click on: Tools - Data Analysis (different options are

available) Other Add-ins: e.g., KADD and StatTools

Forecasting application software (2 types) statistical packages forecasting packages specifically designed for

forecasting applications

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Hands on Exercise Hot Pizza exercise, problem 2, page 214

Use moving average (4 period) and simple exponential smoothing (alpha = .2 & .4) models with data, forecast weeks 13 to 16 into the future.

Northwestern Parts, (in class exercise for seasonal and tend enhanced models)

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Components of demand

Trend component: growth or decline over an extended period of time

Cyclical component: wavelike fluctuation around the trend

Seasonal component: pattern of change that repeats itself year after year

Random component: after removal of other components

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Pattern Issues

Which patterns are present in data? Things are not constant over time Need a process to identify change Need a procedure to update quickly

Enhancing Smoothing Procedures

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Grow in SalesTrend Pattern

0

100

200

300

400

500

600

700

0 4 8 12 16 20 24 28 32 36

Quarter

Un

its

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Update Equations with Trend:

Level: Lt = α ( Dt ) + ( 1- α ) ( Lt- 1+Tt-1 )

Trend: Tt = β (Lt - Lt-1 ) + ( 1- β ) Tt-1

Forecast Equation for ‘n’ period in the future:

Ft+n = Lt + n Tt

Expo with Trend - Update Equations and Forecasting Model

Dt = sales in tLt = average in tTt = trend in tFt = forecast in t

Basic Exponential Smoothing: Ft+1 = Lt = α Dt + ( 1- α )Lt-1

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Trend Adjustment

Update smoothed average for recent trend Update Trend Factor

Difference of two period “Average” Weighted combination of

Past trend factor Current Forecast of trend factor

Trend the forecast

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Seasonal Sale PatternSeason Pattern

0

100

200

300

400

500

600

700

0 4 8 12 16 20 24 28 32 36

Quarter

Un

its

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Update Equations:

Level: Lt = α ( Dt / St ) + ( 1- α ) ( Lt- 1)

Season: St+p = γ ( Dt / Lt ) + ( 1- γ ) St

Forecast Equation for ‘n’ period in the future:

Ft+n = (Lt ) St+n

Expo with Season - Update Equations and Forecasting Model

Dt = sales in tLt = average in tSt = season in tFt = forecast in t p = season

Basic Exponential Smoothing: Ft+1 = Lt = α Dt + ( 1- α )Lt-1

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Seasonality Adjustment Deseasonalize recent sales data Calculate smoothed average Update Seasonal Factor

Ratio of Actual to “Average” Weighted combination of

Past deseasonalized seasonal factor Current Forecast of seasonal factor

Seasonalize the forecast

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Trend & Seasonality Common

Coca Cola Quarterly Sales in Millions of Dollars

$1,000

$1,500

$2,000

$2,500

$3,000

$3,500

$4,000

$4,500

$5,000

$5,500

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Update Equations:

Level: Lt = α ( Dt / St ) + ( 1- α ) ( Lt- 1+Tt-1 )

Trend: Tt = β (Lt - Lt-1 ) + ( 1- β ) Tt-1

Season: St+p = γ ( Dt / Lt ) + ( 1- γ ) St

Forecast Equation for ‘n’ period in the future:

Ft+n = (Lt + n Tt ) St+n

Trend & Season (Winter’s) Update Equations and Forecasting Model

Dt = sales in tLt = average in tTt = trend in tSt = season in tFt = forecast in t p = season

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Forecast Error Building a Forecast

Fit to historical data Project future data

Forecast Error How well does model fit historical data? Do we need to tune or refine the model? Can we offer confidence intervals about

our predictions?

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Measuring Forecast Error MAD or MAE

average of the absolute errors Bias (tendency measurement)

Sum of all errors (plus & minus) MAPE (mean absolute percentage

error) Average absolute ratio of error to actual

MSE (mean square error) Square of all errors divided by ‘n’

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Evaluating Forecast Models with Different Measures

Error in period t Mean Absolute Deviation Mean Absolute

Percentage Error Mean Squared Error

nfdMSE

nd

fdMAPE

nfdMAD

fde

tt

n

t

t

ttn

t

tt

n

t

ttt

2

1

1

1

)100(

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Mabert Web Page Prepare: Specialty Packaging Corp. (A),

pp. 216-217. Develop forecasts for each quarter of 2007 for Clear and Black Plastic containers. Seasonal time series. Try using KADD analysis tool vs. provided Excel workbook.

Quiz: There will be a short quiz covering the fundamentals of demand planning and smoothing forecast models. Open book and notes.

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Mabert Web Page

URL address with useful files:

http://kelley.iu.edu/mabert/class-e730.html