Lecture Operations and supply chain management: The Core (3/e) – Chapter 3: Forecasting

The main goals of this chapter are to: Understand the role of forecasting as a basis for supply chain planning; identify the basic components of demand: average, trend, seasonal, and random variation; show how to make a time series forecast using moving averages, exponential smoothing, and regression;...

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Forecasting

Chapter 03

Learning Objectives

1 Understand the role of forecasting as a basis for

supply chain planning

2 Identify the basic components of demand:

average, trend, seasonal, and random variation

3 Show how to make a time series forecast using

moving averages, exponential smoothing, and regression

4 Use decomposition to forecast when trend and

seasonality is present

5 Show how to measure forecast error

6 Describe the common qualitative forecasting

techniques, such as the Delphi method and

The Role of Forecasting

 Forecasting is a vital function and impacts every significant management decision

 Finance and accounting use forecasts as the basis for budgeting and cost control

 Marketing relies on forecasts to make key decisions such as new product planning and personnel

compensation

 Production uses forecasts to select suppliers,

determine capacity requirements, and to drive

decisions about purchasing, staffing, and inventory

 Different roles require different forecasting

approaches

 Decisions about overall directions require strategic

forecasts

 Tactical forecasts are used to guide day-to-day

decisions

Components of Demand

Excel: Components of Demand

Time Series Analysis

 Using the past to predict the future

Forecasting Method Selection

Guide

Forecas ting Method Amount of His torical 

Data Data Pattern Forecas t Horizon

Simple moving

average 6 to 12 months; weekly data are often used Stationary (i.e no trend or

seasonality)

Short

Weighted moving

average and simple

exponential smoothing

5 to 10 observations needed to start Stationary Short

Exponential smoothing

with trend 5 to 10 observations needed to start Stationary and trend Short

Linear regression 10 to 20 observations Stationary, trend,

and seasonality Short to Medium

Forecast Error Measurements

 Ideally, MAD will be zero

(no forecasting error)

 Larger values of MAD

indicate a less accurate

model

 MAPE scales the forecast error

to the magnitude of demand

 Tracking signal indicates whether forecast errors are accumulating over time (either positive or negative errors)

Computing Forecast Error

Causal Relationship

Forecasting

 Causal relationship forecasting uses

independent variables other than time to

predict future demand

 This independent variable must be a leading

indicator

 Many apparently causal relationships are

actually just correlated events – care must be taken when selecting causal variables

Multiple Regression Techniques

 Often, more than one independent variable

may be a valid predictor of future demand

 In this case, the forecast analyst may utilize

multiple regression

 Analogous to linear regression analysis, but with multiple independent variables

 Multiple regression is supported by statistical

software packages

Qualitative Forecasting

Techniques

 Generally used to take advantage of expert

knowledge

 Useful when judgment is required, when

products are new, or if the firm has little

experience in a new market

 Market research

 Panel consensus

 Historical analogy

 Delphi method

Collaborative Planning,

Forecasting, and Replenishment

(CPFR)

 A web-based process used to coordinate the efforts of a supply chain

 Demand forecasting

 Production and purchasing

 Inventory replenishment

 Integrates all members of a supply chain –

manufacturers, distributors, and retailers

 Depends upon the exchange of internal

information to provide a more reliable view of

CPFR Steps

 Forecasting is a fundamental step in any

planning process

 Forecast effort should be proportional to the

magnitude of decisions being made

 Web-based systems (CPFR) are growing in

importance and effectiveness

 All forecasts have errors – understanding and minimizing this error is the key to effective

forecasting processes