DEMAND FORECASTING Chapter 5 Prepared by Mark A. Jacobs, PhD ©2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or.

DEMAND DEMAND FORECASTINGFORECASTING

Chapter 5Chapter 5

Prepared by Mark A. Jacobs, PhD

©2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

©2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. 2

LEARNING OBJECTIVESLEARNING OBJECTIVES

You should be able to:• Explain the role of demand forecasting in a supply

chain• Identify the components of a forecast• Compare & contrast qualitative & quantitative

forecasting techniques• Assess the accuracy of forecasts• Explain collaborative planning, forecasting, &

replenishment

©2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. 3

CHAPTER OUTLINECHAPTER OUTLINE

• Introduction • Demand Forecasting• Forecasting Techniques

• Qualitative Methods• Quantitative Methods

• Components of Time Series Data• Time Series Forecasting Methods• Forecast Accuracy• Useful Forecasting Websites• Collaborative Planning, Forecasting, & Replenishment

(CPFR)• Software Solutions

Introduction

• Supply chain members find it important to manage demand, especially in pull manufacturing environments.

• Suppliers must find ways to better match supply & demand to achieve optimal levels of cost, quality, & customer service to enable them to compete with other supply chains.

• Improved forecasts benefit all trading partners in the supply chain & mitigates supply-demand mismatch problems.

Demand Forecasting

A forecast is an estimate of future demand & provides the basis for planning decisions

The goal is to minimize forecast error The factors that influence demand must be

considered when forecasting. Managing demand requires timely & accurate

forecasts Good forecasting provides reduced inventories,

costs, & stockouts, & improved production plans & customer service

Forecasting Techniques

Qualitative forecasting is based on opinion & intuition.

Quantitative forecasting uses mathematical models & historical data to make forecasts.

Time series models are the most frequently used among all the forecasting models.

Forecasting Techniques (Continued)

Qualitative Forecasting MethodsGenerally used when data are limited, unavailable, or not currently relevant. Forecast depends on skill & experience of forecaster(s) & available information

Four qualitative models used are –

1. Jury of executive opinion

2. Delphi method

3. Sales force composite

4. Consumer survey

Forecasting Techniques (Continued)

Quantitative Methods Time series forecasting – based on the assumption

that the future is an extension of the past. Historical data is used to predict future demand

Cause & Effect forecasting – assumes that one or more factors (independent variables) predict future demand

It is generally recommended to use a combination of quantitative & qualitative techniques

Forecasting Techniques (Continued)

Components of Time SeriesData should be plotted to detect for the following components –

Trend variations: increasing or decreasing Cyclical variations: wavelike movements that are

longer than a year (e.g., business cycle) Seasonal variations: show peaks & valleys that

repeat over a consistent interval such as hours, days, weeks, months, seasons, or years

Random variations: due to unexpected or unpredictable events

Forecasting Techniques (Continued)

Time Series Forecasting Models

Naïve Forecast – the estimate of the next period is equal to the demand in the past period.

Ft+1 = At

Where Ft+1 = forecast for period t+1

At = actual demand for period t

Forecasting Techniques (Continued)

Time Series Forecasting Models

Simple Moving Average Forecast – uses historical data to generate a forecast. Works well when demand is stable over time.

Where Ft+1 = forecast for period t+1

At = actual demand for period t

n = number of periods to calculate moving average

Forecasting Techniques (Continued)

Simple Moving Average

(Fig. 5.1)

Forecasting Techniques (Continued)

Time Series Forecasting Models

Weighted Moving Average Forecast – is based on an n-period weighted moving average

Where Ft+1 = forecast for period t+1

Ai = actual demand for period i

n = number of periods to calculate moving average

wi = weight assigned to period i (Σwi = 1)

Forecasting Techniques (Continued)

Weighted Moving Average

(Fig. 5.2)

Forecasting Techniques (Continued)

Time Series Forecasting Models

Exponential Smoothing Forecast – a type of weighted moving average where only two data points are needed

Ft+1 = Ft+(At - Ft) or Ft+1 = At + (1 – ) Ft

Where Ft+1 = forecast for Period t + 1

Ft = forecast for Period t

At = actual demand for Period t

= smoothing constant (0 ≤ ≤1)

Forecasting Techniques (Continued)

Exponential Smoothing

(Fig. 5.3)

Forecast Accuracy

The formula for forecast error, defined as the difference between actual quantity & the forecast –

Forecast error, et = At - Ft

Where et = forecast error for Period t

At = actual demand for Period t

Ft = forecast for Period t

Forecast Accuracy (Continued)

Several measures of forecasting accuracy follow –

Mean absolute deviation (MAD)- a MAD of 0 indicates the forecast exactly predicted demand

Mean absolute percentage error (MAPE)- provides a perspective of the true magnitude of the forecast error

Mean squared error (MSE)- analogous to variance, large forecast errors are heavily penalized

Forecast Accuracy (Continued)

Mean absolute deviation (MAD)- MAD of 0 indicates the forecast exactly predicted demand.

Where et = forecast error for period tAt = actual demand for period tn = number of periods of evaluation

Forecast Accuracy (Continued)

Mean absolute percentage error (MAPE) –

provides a perspective of the true magnitude of the forecast error.

Where et = forecast error for period tAt = actual demand for period tn = number of periods of evaluation

Forecast Accuracy (Continued)

Mean squared error (MSE) –

analogous to variance, large forecast errors are heavily penalized

Where et = forecast error for period tn = number of periods of evaluation

Forecast Accuracy (Continued)

Running Sum of Forecast Errors (RSFE) – indicates bias in the forecasts or the tendency of a forecast to be consistently higher or lower than actual demand.

Running Sum of Forecast Errors, RSFE =

n

tte

1

Where et = forecast error for period t

Forecast Accuracy (Continued)

Tracking signal – determines if forecast is within acceptable control limits. If the tracking signal falls outside the pre-set control limits, there is a bias problem with the forecasting method and an evaluation of the way forecasts are generated is warranted.

Tracking Signal =

MAD

RSFE

MAD

RSFE

Useful Forecasting Websites

Institute for Forecasting Education

www.forecastingeducation.com International Institute of Forecasters

www.forecasters.org Forecasting Principles

www.forecastingprinciples.com Stata (Data analysis & statistical software)

www.stata.com/links/stat_software.html

Collaborative Planning, Forecasting, & Replenishment (CPFR)

A business practice that combines the intelligence of multiple trading partners in the planning & fulfillment of customer demands.

Links sales & marketing best practices, such as category management, to supply chain planning processes to increase availability while reducing inventory, transportation & logistics costs.

Real value of CPFR comes from sharing of forecasts among firms rather than sophisticated algorithms from only one firm.

Does away with the shifting of inventories among trading partners that suboptimizes the supply chain.

CPFR provides the supply chain with a plethora of benefits but requires a fundamental change in the way that buyers & sellers work together.

Collaborative Planning, Forecasting, & Replenishment (Continued)

VICS’s CPFR Model

Collaborative Planning, Forecasting, & Replenishment (Continued)

(Fig. 5.5)

CPFR ModelStep 1: Collaboration Arrangement

Step 2: Joint Business Plan

Step 3: Sales Forecasting

Step 4: Order Planning/Forecasting

Step 5: Order Generation

Step 6: Order Fulfillment

Step 7: Exception Management

Step 8: Performance Assessment

Collaborative Planning, Forecasting, & Replenishment (Continued)

Software Solutions

Forecasting Software Business Forecast Systems www.forecastpro.com John Galt www.johngalt.com Just Enough www.justenough.com SAS www.sas.com JDA Software Group www.jda.com i2 Technologies www.i2.com Oracle www.oracle.com