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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: SAS Institute Inc
- Sprache: Englisch
Go beyond technique to master the difficult judgement calls of forecasting A variety of software can be used effectively to achieve accurate forecasting, but no software can replace the essential human component. You may be new to forecasting, or you may have mastered the statistical theory behind the software's predictions, and even more advanced "power user" techniques for the software itself--but your forecasts will never reach peak accuracy unless you master the complex judgement calls that the software cannot make. Profit From Your Forecasting Software addresses the issues that arise regularly, and shows you how to make the correct decisions to get the most out of your software. Taking a non-mathematical approach to the various forecasting models, the discussion covers common everyday decisions such as model choice, forecast adjustment, product hierarchies, safety stock levels, model fit, testing, and much more. Clear explanations help you better understand seasonal indices, smoothing coefficients, mean absolute percentage error, and r-squared, and an exploration of psychological biases provides insight into the decision to override the software's forecast. With a focus on choice, interpretation, and judgement, this book goes beyond the technical manuals to help you truly grasp the more intangible skills that lead to better accuracy. * Explore the advantages and disadvantages of alternative forecasting methods in different situations * Master the interpretation and evaluation of your software's output * Learn the subconscious biases that could affect your judgement toward intervention * Find expert guidance on testing, planning, and configuration to help you get the most out of your software Relevant to sales forecasters, demand planners, and analysts across industries, Profit From Your Forecasting Software is the much sought-after "missing piece" in forecasting reference.
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Table of Contents
Cover
Title Page
Acknowledgments
Prologue
CHAPTER 1: Profit from Accurate Forecasting
1.1 THE IMPORTANCE OF DEMAND FORECASTING
1.2 WHEN IS A FORECAST NOT A FORECAST?
1.3 WAYS OF PRESENTING FORECASTS
1.4 THE ADVANTAGES OF USING DEDICATED DEMAND FORECASTING SOFTWARE
1.5 GETTING YOUR DATA READY FOR FORECASTING
1.6 TRADING-DAY ADJUSTMENTS
1.7 OVERVIEW OF THE REST OF THE BOOK
1.8 SUMMARY OF KEY TERMS
1.9 REFERENCES
CHAPTER 2: How Your Software Finds Patterns in Past Demand Data
2.1 INTRODUCTION
2.2 KEY FEATURES OF SALES HISTORIES
2.3 AUTOCORRELATION
2.4 INTERMITTENT DEMAND
2.5 OUTLIERS AND SPECIAL EVENTS
2.6 CORRELATION
2.7 MISSING VALUES
2.8 WRAP-UP
2.9 SUMMARY OF KEY TERMS
CHAPTER 3: Understanding Your Software's Bias and Accuracy Measures
3.1 INTRODUCTION
3.2 FITTING AND FORECASTING
3.3 FORECAST ERRORS AND BIAS MEASURES
3.4 DIRECT ACCURACY MEASURES
3.5 PERCENTAGE ACCURACY MEASURES
3.6 RELATIVE ACCURACY MEASURES
3.7 COMPARING THE DIFFERENT ACCURACY MEASURES
3.8 EXCEPTION REPORTING
3.9 FORECAST VALUE-ADDED ANALYSIS (FVA)
3.10 WRAP-UP
3.11 SUMMARY OF KEY TERMS
3.12 REFERENCES
CHAPTER 4: Curve Fitting and Exponential Smoothing
4.1 INTRODUCTION
4.2 CURVE FITTING
4.3 EXPONENTIAL SMOOTHING METHODS
4.4 FORECASTING INTERMITTENT DEMAND
4.5 WRAP-UP
4.6 SUMMARY OF KEY TERMS
CHAPTER 5: Box-Jenkins ARIMA Models
5.1 INTRODUCTION
5.2 STATIONARITY
5.3 MODELS OF STATIONARY TIME SERIES: AUTOREGRESSIVE MODELS
5.4 MODELS OF STATIONARY TIME SERIES: MOVING AVERAGE MODELS
5.5 MODELS OF STATIONARY TIME SERIES: MIXED MODELS
5.6 FITTING A MODEL TO A STATIONARY TIME SERIES
5.7 DIAGNOSTIC CHECKS
5.8 MODELS OF NONSTATIONARY TIME SERIES: DIFFERENCING
5.9 SHOULD YOU INCLUDE A CONSTANT IN YOUR MODEL OF A NONSTATIONARY TIME SERIES?
5.10 WHAT IF A SERIES IS NONSTATIONARY IN THE VARIANCE?
5.11 ARIMA NOTATION
5.12 SEASONAL ARIMA MODELS
5.13 EXAMPLE OF FITTING A SEASONAL ARIMA MODEL
5.14 WRAP-UP
5.15 SUMMARY OF KEY TERMS
CHAPTER 6: Regression Models
6.1 INTRODUCTION
6.2 BIVARIATE REGRESSION
6.3 MULTIPLE REGRESSION
6.4 REGRESSION VERSUS UNIVARIATE METHODS
6.5 DYNAMIC REGRESSION
6.6 WRAP-UP
6.7 SUMMARY OF KEY TERMS
6.8 APPENDIX: ASSUMPTIONS OF REGRESSION ANALYSIS
6.9 REFERENCE
CHAPTER 7: Inventory Control, Aggregation, and Hierarchies
7.1 INTRODUCTION
7.2 IDENTIFYING REORDER LEVELS AND SAFETY STOCKS
7.3 ESTIMATING THE PROBABILITY DISTRIBUTION OF DEMAND
7.4 WHAT IF THE PROBABILITY DISTRIBUTION OF DEMAND IS NOT NORMAL?
7.5 TEMPORAL AGGREGATION
7.6 DEALING WITH PRODUCT HIERARCHIES AND RECONCILING FORECASTS
7.7 WRAP-UP
7.8 SUMMARY OF KEY TERMS
7.9 REFERENCES
CHAPTER 8: Automation and Choice
8.1 INTRODUCTION
8.2 HOW MUCH PAST DATA DO YOU NEED TO APPLY DIFFERENT FORECASTING METHODS?
8.3 ARE MORE COMPLEX FORECASTING METHODS LIKELY TO BE MORE ACCURATE?
8.4 WHEN IT'S BEST TO AUTOMATE FORECASTS
8.5 THE DOWNSIDE OF AUTOMATION
8.6 WRAP-UP
8.7 REFERENCES
CHAPTER 9: Judgmental Interventions: When Are They Appropriate?
9.1 INTRODUCTION
9.2 PSYCHOLOGICAL BIASES THAT MIGHT CATCH YOU OUT
9.3 RESTRICT YOUR INTERVENTIONS
9.4 MAKING EFFECTIVE INTERVENTIONS
9.5 COMBINING JUDGMENT AND STATISTICAL FORECASTS
9.6 WRAP-UP
9.7 REFERENCE
CHAPTER 10: New Product Forecasting
10.1 INTRODUCTION
10.2 DANGERS OF USING UNSTRUCTURED JUDGMENT IN NEW PRODUCT FORECASTING
10.3 FORECASTING BY ANALOGY
10.4 THE BASS DIFFUSION MODEL
10.5 WRAP-UP
10.6 SUMMARY OF KEY TERMS
10.7 REFERENCES
CHAPTER 11: Summary: A Best Practice Blueprint for Using Your Software
11.1 INTRODUCTION
11.2 DESIRABLE CHARACTERISTICS OF FORECASTING SOFTWARE
11.3 A BLUEPRINT FOR BEST PRACTICE
11.4 REFERENCES
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 A Probability Distribution of Demand
Chapter 2
Table 2.1 Intermittent Demand
Chapter 3
Table 3.1 Rolling-origin Forecast Evaluation
Table 3.2 Sales and Forecasts for a Product
Table 3.3 Absolute and Squared Forecast Errors
Table 3.4 Absolute Percentage Errors
Table 3.5 Calculation of the Mean Absolute Scaled Error (MASE)
Table 3.6 Features of Accuracy Measures
Table 3.7 Forecast Value Added (FVA) Analysis
Chapter 4
Table 4.1 Simple Exponential Smoothing
Table 4.2 Available Exponential Smoothing Methods
Table 4.3 Simple Exponential Smoothing and Intermittent Demand
Chapter 5
Table 5.1 Estimation of Best-Fitting Moving Average Model
Table 5.2 Overfitting a Model
Table 5.3 Seasonal Differencing
Table 5.4 Results for the Tentative Seasonal Model
Chapter 6
Table 6.1 Typical Computer Output for Bivariate Regression
Table 6.2 Monthly Sales of a Product and Three Potential Predictor Variables
Table 6.3 Computer Output for Multiple Regression
Table 6.4 Output for Refitted Model
Table 6.5 Sales Data with Dummy Variables Added
Table 6.6 Computer Output for Model with Dummy Variables
Chapter 7
Table 7.1
Z
values for Different Customer Service Levels
Table 7.2 Conversion Factors for Standard Prediction Intervals
Table 7.3 Intermittent Demand with a Poisson Distribution
Table 7.4 Poisson Probabilities for Lead Time Demand
Table 7.5 Intermittent Demand with a Negative Binomial Distribution
Table 7.6 Negative Binomial Distribution Probabilities for Lead Time Demand
Table 7.7 Nonoverlapping and Overlapping Temporal Aggregation
Table 7.8 Average of Historical Proportions
Table 7.9 Sales of Individual Products and Their Proportions of Total Sales
Chapter 8
Table 8.1 Absolute Minimum Number of Observations to Apply Forecasting Methods
Chapter 10
Table 10.1 First 10 Months' Sales of a New Product
Chapter 11
Table 11.1 Desirable Features of Forecasting Software
List of Illustrations
Chapter 1
Figure 1.1 A graphical display of the probability distribution
Figure 1.2 A normal distribution of demand
Chapter 2
Figure 2.1 Demand history with trend and seasonal pattern
Figure 2.2 Examples of trend types: (a) Damped; (b) Changing linear; (c) Product life cycle; (d) Exponential
Figure 2.3 A seasonal-cycle graph
Figure 2.4 Additive seasonal patterns
Figure 2.5 Multiplicative seasonal patterns
Figure 2.6 Plot of sales history and associated autocorrelation function
Figure 2.7 A scattergraph
Figure 2.8 Examples of scattergraphs and correlations
Chapter 3
Figure 3.1 Fitting (in-sample) and hold-out periods
Chapter 4
Figure 4.1 Curve fitting
Figure 4.2 Examples of curves
Figure 4.3 Sales and simple exponential smoothing forecasts
Figure 4.4 Forecasts produced by Holt's method and the damped Holt's method
Chapter 5
Figure 5.1 Stationary and nonstationary series
Figure 5.2 A series that has a nonstationary variance
Figure 5.3 An ACF for a series that has a nonstationary mean
Figure 5.4 An ACF for a series that has a stationary mean
Figure 5.5 Theoretical ACF and PACF for a first-order autoregressive model
Figure 5.6 Theoretical ACF and PACF for a first-order moving average model
Figure 5.7 Sales of a product over 50 weeks
Figure 5.8 ACF and PACF for example product
Figure 5.9 Autocorrelations of residuals
Figure 5.10 Assessing whether residuals are normally distributed
Figure 5.11 ACF and PACF for seasonal autoregressive model of order 1 and period 12
Figure 5.12 Freight carried by railroad company per quarter
Figure 5.13 ACF for railroad data
Figure 5.14 ACF and PACF after first differencing
Chapter 6
Figure 6.1 Soup sales and temperature forecast
Figure 6.2 The effect of an outlier on the line of best fit
Figure 6.3 The effect of an influential observation on the line of best fit
Figure 6.4 Using residual plots to detect violations of assumptions
Chapter 7
Figure 7.1 Normal probability distribution of demand
Figure 7.2 Normal distributions with different standard deviations
Figure 7.3 A log-normal distribution of demand
Figure 7.4 Poisson distribution with a mean of 3
Figure 7.5 Negative binomial distribution with a mean of 3 and standard deviation of 3.5
Chapter 8
Figure 8.1a Fitting a line to three observations with little randomness
Figure 8.1b Fitting a line to three observations with much randomness
Figure 8.2a The computer's original forecast
Figure 8.2b Increasing the responsiveness of the forecasts
Figure 8.2c Changing the forecasting method
Figure 8.2d Changing the amount of past data used
Chapter 10
Figure 10.1 Monthly sales of analogous products as percentages of their total sales over 48 months
Figure 10.2 Plot of cumulative sales of candidate analogies
Figure 10.3 Average cumulative percentage sales of four analogous products and fitted curve
Figure 10.4 A typical Bass model of numbers of adopters over time
Figure 10.5 Bass model fitted to data
Guide
Cover
Table of Contents
Begin Reading
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Profit from Your Forecasting Software
A Best Practice Guide for Sales Forecasters
Paul Goodwin
Copyright © 2018 by John Wiley & Sons, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750–8400, fax (978) 646–8600, or on the Web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748–6011, fax (201) 748–6008, or online at www.wiley.com/go/permissions.
Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762–2974, outside the United States at (317) 572–3993, or fax (317) 572–4002.
Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com.
Library of Congress Cataloging-in-Publication Data is Available:
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ISBN 978-1-119-41600-5 (ePub)
Cover Design: Wiley
Cover Image: © kraphix/iStockphoto
To my parents, Sidney and Norma Goodwin
Acknowledgments
A number of people have assisted me during the preparation of this book. The teams at SAS and Wiley have been helpful in speedily answering my queries and encouraging me to progress the book. In particular, I would like to thank Sheck Cho, Mike Gilliland, Lauree Shepard, Emily Paul, Banurekha Venkatesan, and Siân Roberts. Thanks are also due to Eric Stellwagen of Business Forecast Systems, Inc.
Much of the book reflects what I have learned from my fellow researchers and those with whom I have conducted consultancy work. These are too numerous to mention in their entirety, but they include Robert Fildes, George Wright, Richard Lawton, Dilek Önkal, Kostas Nikolopoulos, John Boylan, Aris Syntetos, Michael Lawrence, and Fotios Petropoulos. Nevertheless, any views expressed in the book are my own and any errors are my responsibility.
I must also thank Len Tashman who has allowed me, for the last 12 years, to write a regular Hot New Research column for Foresight: The International Journal of Applied Forecasting, which he edits. This has motivated me to keep abreast of the very latest research into applied forecasting, much of which is covered in the book.
Finally, I thank my wife, Chris, for her patience and encouragement during the many hours I have spent in my study absorbed in the fascinating and challenging topic of demand forecasting.
Prologue
I once heard of a woman who was working late on a Friday afternoon in her office when her boss appeared.
“We've just lost our sales forecaster,” he said. “He's transferring to Customer Relations so we need someone to do his job from Monday. I'd like you to take on that role.”
When the woman protested that she had no relevant experience in forecasting or any knowledge of statistics, the boss was reassuring.
“You'll soon pick it up. I think it's mostly done by the computer, anyway.”
And with that he was gone.
If you find yourself in a similar position, then this is the book for you. Research indicates that many forecasters in companies, who may be experts in their products and markets, have little or no formal knowledge of forecasting methods. They are also not mathematicians, so explanations of these methods that befuddle them with reams of formulae and complex notation are of little help. This leads to a temptation to sidestep the methods. Allow the computer to produce its mysterious forecasts, but then replace them with finger-in-the-air judgments; or even avoid the computer altogether and fit a ruler roughly across a hand-drawn sales graph.
Even if you are willing to work with the computer, the technical terms it displays may seem forbidding. MSEs, MAPEs, AICs, exponential smoothing, R-squared, ARIMA models, and autocorrelations can sound as meaningful as the language of quantum physics is to the layperson. And yet, unlike quantum physics, all of these terms can be made understandable to a nonspecialist manager, at least at the intuitive level. In fact, many of them represent very simple concepts that are much easier to comprehend than a typical tax return.
It's a pity if forecasters aren't harnessing the full power of methods that are embodied in modern forecasting software because they don't understand the methods or their output. This book aims to remedy this situation by providing accessible explanations and guidance on when to use different methods and measures. It addresses key practical questions such as:
When, if ever, should management judgment be used to adjust or override a computer's forecast?
Should I choose my own forecasting method or let the expert system in the software choose it?
How should I use the software to handle product hierarchies or to plan safety stock levels?
How much past data should I use to fit and test my forecasting model?
The book is not tied to any specific forecasting software product. Nor is it intended to duplicate a user's manual, so it won't tell you which button to press or describe particular menu structures. Instead, it has an important complementary role. It draws on the very latest forecasting research to enable you to interact with your software with confidence and insight so you can aim for maximum forecast accuracy, while also making the best use of your time. Depending on what you need to know, you can either read the book in its entirety or use it as a reference guide when you need an explanation, or an evaluation, of a particular method or metric. The focus is on commercial demand forecasting software products, so the book does not consider facilities that may be available in free software, such as R, though much of the content will still be relevant to R users. Also, there is no coverage of specialist software that uses neural networks or conjoint analysis.
Sales forecasts can rarely be perfectly accurate – if they are, the forecaster was either very lucky or was told the exact quantity of orders that were in the pipeline. The true challenge of forecasting is to avoid unnecessary inaccuracy caused by systematic bias, inefficient use of available information, or the wrong choice of method or its application. This book should help you to meet that challenge by employing best practices, so you can ultimately profit from your forecasting software.
CHAPTER 1Profit from Accurate Forecasting
1.1 THE IMPORTANCE OF DEMAND FORECASTING
Forecasts of demand for products and services can be crucial to the operations of most companies. Inventory planning, logistics planning, production scheduling, cash flow planning, decisions on staffing levels, and purchasing decisions can all depend on forecasts. Making these forecasts perform as well as possible will lead to improved customer service levels and so foster customer goodwill and retention. It will also lower costs. There will be less need for expensive emergency production runs, and there should be a reduction in the waste associated with excessive stock levels and unsold products.
Figures for the cost reductions or increased profits that companies achieve through improved forecasting can be hard to come by – most organizations don't publish them. However, one forecasting software company (www.catchbull.com) estimates that avoidable forecast errors can add between 2% and 4% to costs of production. They quote the case of one $15 billion firm where executives estimated that “we can drive up to $200 m of avoidable costs out of the business.” A survey carried out by a Triple Point Technology in 2013 indicated that reductions in inventory levels resulting from improved forecast accuracy meant that a company with a $1 billion turnover could expect savings of between $5 million and $10 million. However, the same survey found that 40% of respondents admitted that they were “not currently leveraging the advantages of statistical modeling in their demand planning operations.” Of course, it's in the interests of software companies to advertise these huge benefits, but common sense suggests that better-performing forecasts will significantly benefit a company's bottom line.
1.2 WHEN IS A FORECAST NOT A FORECAST?
A forecast is an honest statement of what we expect to happen at a future data, based on the information available at the time when we make the forecast. It is not necessarily what we hope will happen, so it is not the same as a target. In fact, in some circumstances we may be doubtful that a target will be achieved – we simply created it to motivate people to try to get as close to it as possible. Nor is it the same as a plan. A plan is what we intend to happen, assuming the future is under our control. As we shall see, ideally a forecast will acknowledge that we are uncertain about the future and provide a measure of that uncertainty.
It is also important to distinguish a forecast from a decision. A decision is what we choose to do in the light of a forecast. We may have a demand forecast for next week of 2,000 units, so we decide to hold 2,200 units in stock at the start of the week in case demand exceeds the forecast. The 2,200 units is not a forecast – it's a decision.
Sometimes people are tempted to look at the possible demand levels that may occur at a future period and decide which one will best suit their interests. For example, I might forecast a demand for next month of 3,500 units, knowing that it will please senior managers and gain me some kudos, even though I don't truly expect this demand level to be achieved. Although I might call this a forecast, in reality I'm making a decision.
1.3 WAYS OF PRESENTING FORECASTS
1.3.1 Forecasts as Probability Distributions
We can present forecasts in several different ways. A probability distribution indicates the possible levels of demand and their associated probabilities. Table 1.1 is an example. Figure 1.1 displays the distribution. It shows that relatively low levels of demand are more probable than very high demands, so the distribution is skewed. Forecasts in this form are useful because they show the risks of particular decisions we may make. For example, if we decide to hold 69 units of stock at the start of the month, there will be a 5% + 1% = 6% probability that we will be unable to meet demand and will disappoint customers.
Table 1.1 A Probability Distribution of Demand
Next Month's Demand (Units)
Probability (%)
20 to 29
5
30 to 39
30
40 to 49
41
50 to 59
10
60 to 69
8
70 to 79
5
80 to 89
1
Figure 1.1 A graphical display of the probability distribution
Accurately estimating probability distributions can be difficult, particularly if we have limited past data. Usually, it is assumed that a particular distribution applies and, most commonly, this is the bell-shaped normal or Gaussian distribution. Figure 1.2 shows an example. Notice that the distribution is symmetrical about its highest point. While there are theoretical reasons why a normal distribution will apply, in many circumstances it can at best only offer a rough approximation to the probabilities of future demand. When the “true” distribution is highly skewed, the approximation will be very poor.
Figure 1.2 A normal distribution of demand
1.3.2 Point Forecasts
Most software products don't currently display full probability distributions (sometimes these are called density forecasts). Instead, they produce point forecasts and prediction intervals. A point forecast is a forecast expressed as a single number. It usually represents the mean (or average) of the probability distribution. Imagine if the month referred to in Figure 1.2 was repeated many times. On some occasions, we see demand greater than 300 units. On very rare occasions, it would exceed 350 units. In other months, demand might be well below 200 units. More often than not, it would be between 200 and 280. If we averaged all of the demands, we observed we would find that the mean demand was 240 units. This would be our point forecast. If we did the same for the month referred to in Figure 1.1, we would find that the mean demand was 45 units – slightly to the left of the range of possible demands because of the skewness in the distribution. Therefore, a point forecast produced by software is simply an average of all the possible levels of demand – taking into account their probabilities. It is not a statement that we think that that specific level of demand will occur – we know that the actual demand is likely to stray from its value as shown by the probability distributions. You might tell me the mean height of American males aged over 21, but I don't expect every American male I meet in this age group to be that height. In fact, meeting someone who conforms exactly to the mean would be rare.
This point is worth emphasising. I have heard of cases of senior managers who expect point forecasts always to be “100% accurate” and criticize forecasters who are not achieving this. Their attitude shows a fundamental misunderstanding of what a point forecast is. In most forecasting situations, there are bound to be random or unpredictable factors that cause the actual demand to stray from the average represented by the point forecast. In particularly unpredictable situations, such as when we forecast a long way ahead, we should not be surprised if the demand strays a significant distance from the point forecasts. However, as we will see, if we try to anticipate these random factors, we will be wasting our time and probably damaging the forecasts to boot.
1.3.3 Prediction Intervals
Point forecasts don't tell us anything about the level of uncertainty associated with a forecast. We can't tell how far actual demand might stray from the forecast, and you generally need this information to plan inventory levels. However, some idea of the level of uncertainty can be obtained from a prediction interval. A prediction interval is a range that has a stated probability of capturing the actual demand. For example, if we have the distribution shown in Figure 1.2, our software would produce a 95% prediction interval for next month's demand of 177 to 303 units. This means that there's a 95% chance that the actual demand will be captured within this range, and therefore, a 5% chance that the demand will be outside it.
The 95% is sometimes known as the coverage probability. Higher coverage probabilities and more uncertainty both lead to wider prediction intervals. Because of the greater uncertainty, prediction intervals therefore tend to be wider the further ahead you are forecasting. In Chapter 7, we will see how prediction intervals can be used to determine safety stocks and reorder levels. Note that sometimes prediction intervals are referred to as confidence intervals, though many statisticians prefer not to use that term in this context.
1.4 THE ADVANTAGES OF USING DEDICATED DEMAND FORECASTING SOFTWARE
Research into how companies make their sales forecasts indicates that spreadsheets are the most common of type of software employed. In one survey of US corporations, 48% of respondents used spreadsheets, while only 11% used specialized forecasting software. While spreadsheets may be accessible and allow plenty of flexibility, good-quality dedicated demand forecasting software offers many advantages.
First, they usually offer a wider range of forecasting methods than nondedicated software, and they automatically provide metrics allowing you to compare the accuracy of different methods (see Chapter 3). This increases the chances that you will find the most accurate forecasting method for a particular product. In addition, they are designed to support processes that are specific to demand forecasting such as bottom-up or top-down forecasting when you have a product hierarchy (see Chapter 7). Some software packages will also directly link your demand forecasts to inventory control, advising you on what your reorder level should be and how much safety stock you need to carry. Good-quality forecasting packages also have tried and tested algorithms to implement the different methods. It is known that statistical algorithms in some spreadsheet products contain errors that can result in serious inaccuracy in forecasting. Then there's the danger that you will introduce errors if you are setting up forecasting formulae in a spreadsheet yourself. Mistakes in cutting and pasting and references to the wrong cell ranges can compound the problem. One study found that companies who employed forecasting packages achieved average forecast errors that were almost 7% lower than those of spreadsheet users.
My experience when visiting some companies suggests that if people use a spreadsheet, there is also a danger they will set up an idiosyncratic forecasting system that no one else understands. If they leave the company, it's impossible for their successor to take over the system. Moreover, unlike methods available in good forecasting packages, these self-designed methods usually lack a theoretical underpinning, and they haven't been tested on lots of data sets or compared with other methods.
The second major reason for choosing dedicated software is that many aspects of the forecasting process can be automated and hence performed speedily. This can be particularly important in saving effort if you have to make a large number of forecasts on a frequent basis, and where timely forecasts are crucial to an organization's effectiveness. Even where fewer forecasts are needed, automation can take the form of automatic selection of a forecasting method, ideally with an associated explanation of why the method has been selected. Though automation may have some downsides (see Chapter 8), you will usually have the option of overruling the software's choice in cases where this seems necessary.
Finally, effective demand forecasting is often a team effort, involving forecasters, accounts managers, sales, and operations staff. Dedicated software is likely to be better at supporting collaboration than individuals' spreadsheets. In too many companies, there are islands of analysis – people producing their forecasts separately on noninterfacing, and sometimes homemade, systems. Often these systems have no direct connection with those used by production planners or other departments. Forecasters in a major retail company I visited had to copy data from one system using pen and paper, before manually reentering it into another system.
Some companies don't use computers at all to make their forecasts – they rely solely on the gut-feel of managers. As we'll see in Chapter 9, managers' judgments can bring benefits to forecasts if they are used where they are most appropriate. But we'll also see that, more often than not, judgmental forecasts suffer from both political and psychological biases that are inimical to accuracy. Replacing these with the forecasts from a dedicated forecast package is not only likely to reduce costs but also will allow managers to make more effective use of their time.
1.5 GETTING YOUR DATA READY FOR FORECASTING
Garbage in, garbage out has been a well-known phrase in computing for years and, of course, it applies to demand forecasting using computers as well. If you are supplying the software with erroneous data, then it will produce erroneous forecasts. Data cleansing is the process of removing errors and anomalies from the available data. It can be a time consuming and tedious task, but as we'll see, these days – with the availability of modern software – it should not be carried out too far. Moreover, if you use appropriate sources of data, it may be largely unnecessary.
First, we should note that our objective is to forecast future demand for a product or service, but historical data usually relates to sales. The two are not necessarily the same. Last month there may have been a demand for 2,500 units of your product, but because you only had 2,000 units available, you had sales of 2,000. In many situations, it's difficult to know how much demand was unfulfilled. Customers who saw an empty shelf where your product is usually displayed are unlikely to let you know that they were disappointed. However, some types of historical data are likely to be closer to demand than others.
Data on the quantities of a product shipped on particular dates may not reflect demand at that time because they might simply be delayed deliveries. Analysts agree that either point-of-sale (POS) or syndicated scanner data generally provide a better guide to demand. Syndicated scanner data can be obtained from specialist providers who use individually scanned purchases from large numbers of locations to build and then manipulate a database. This allows sales of products to be viewed at different levels of granularity – nationally, regionally, or at individual retail locations, or annually, quarterly, monthly weekly, or even daily. POS or syndicated scanner data is less likely to contain errors than data on past shipments that might have involved manual data entry, obviating the need to spend many joyless hours attempting to correct mistakes and chase down missing figures.
