Basic principles and demand forecasting

Logistics Tools for Management 1. DuPont chart 2. ABC-analysis 3. Relative Contribution 4. Forecasting 5. Qualitative forecasting 6. Quantitative Methods Conclusions

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hesselvisser@chello.nlLogistics: Principles and Practice

Basic principles and demand forecasting

February, 8th 2009 Hessel Visser

Lecture 3

Chapter 5

Rough Version

Program for Today

Logistics Tools for Management

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1 DuPont Chart

Definition

• DuPont Chart calculates the key

components of any business for easy

evaluation of performance.

www.businessplans.org/DuPontChart.html

Page 167 WML4 174 / WML5 132 Page 170 WML4 177 / WML5 135 Page 171 WML4 178 / WML5 136

2 ABC-analysis

Definition

• Analysis of a range of items, from

inventory levels to customers and sales

territories, into three groups: A = very

important; B = important; C = marginal

significance The goal is to categorize

items which would be prioritized,

managed, or controlled in different ways

ABC analysis is also called 'usage-value

analysis'.

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3 Relative Contribution

Definition

• Average contribution margin that is

weighted to reflect the relative contribution

of each operating department of a

multi-department firm to its ability to pay fixed

costs and to generate income.

Page 175 WML4 182 / WML5 141 Page 176 WML4 183 / WML5 142

4 Forecasting

Definition

• Forecasting is the process of estimation

in unknown situations Prediction is a

similar, but more general term, and usually

refers to estimation of time series,

cross-sectional or longitudinal data

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Types of Forecasts

• Economic forecasts

– Address business cycle, e.g., inflation rate,

money supply etc.

• Technological forecasts

– Predict rate of technological progress

• Demand forecasts

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Demand Patterns

Dependent versus independent

– Only independent demand needs to be

What Should Be Forecasted?

Business plan Market direction 2 to 10 years

Sales and operations

planning Product lines and families 1 to 3 years

Master production

schedule End items and options

6 to 18 Months

Forecast Time Frame Level

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Seven Steps in Forecasting

• Determine the use of the forecast

• Select the items to be forecasted

• Determine the time horizon of the

forecast

• Select the forecasting model(s)

• Gather the data

• Make the forecast

• Validate and implement results

Product Demand Charted over 4

Years with Trend and Seasonality

Year

1 Year 2 Year 3 Year 4

Seasonal peaks Trend component

Actual demand line

Average demand over four years

Demand for product or service Random variation

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Actual Demand, Moving Average,

Weighted Moving Average

• Forecasts are seldom perfect

• Most forecasting methods assume that

there is some underlying stability in the

system

• Both product family and aggregated

product forecasts are more accurate

than individual product forecasts

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Forecasting Approaches

• Used when situation

is ‘stable’ & historical data exist

– Existing products – Current technology

• Involves mathematical techniques

– e.g., forecasting sales

of color televisions

Quantitative Methods

• Used when situation

is vague & little data

• Qualitative forecasting methods are

based on educated opinions of appropriate

persons

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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

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• Involves small group of high-level

Jury of Executive Opinion

Sales Force Composite

• Each salesperson projects his

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survey)

(Sales will be 45, 50, 55)

(Sales will be 50!)

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Consumer Market Survey

• Ask customers about

purchasing plans

• What consumers say,

and what they

actually do are often

• Time series forecasting methods are

based on analysis of historical data (time

series: a set of observations measured at

successive times or over successive

periods) They make the assumption that

past patterns in data can be used to

forecast future data points

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Quantitative Forecasting Methods

(Non-Naive)

Quantitative Forecasting

Linear Regression

Associative Models

Exponential Smoothing

Moving

Average

Time Series Models

Trend Projection

– Obtained by observing response variable at regular

time periods

– Assumes that factors influencing past and present

will continue influence in future

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Trend

Seasonal

Cyclical

Random

Time Series Components

• Persistent, overall upward or downward

pattern

• Due to population, technology etc.

• Several years duration

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• Regular pattern of up & down

fluctuations

• Due to weather, customs etc.

• Occurs within 1 year

• Repeating up & down movements

• Due to interactions of factors influencing

economy

• Usually 2-10 years duration

Cyclical Component

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• Erratic, unsystematic, ‘residual’

• Assumes demand in next period is the

same as demand in most recent

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Page 180 WML4 187 / WML5 95

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Forecast errors

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Tracking the Forecast

Forecasts are rarely 100% correct over time.

Why track the forecast?

– To plan around the error in the future

– To measure actual demand versus forecasts

– To improve our forecasting methods

Conclusions about Logistic

Tools for management

• Start with Simple Tools

• Collect Data in an Early Stage

• Integrate Tools as much as possible

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