Ennis Siemsen - Demand Forecasting for Managers

Demand Forecasting for Managers

Demand Forecasting for Managers

Stephan Kolassa
Enno Siemsen

Demand Forecasting for Managers

Copyright Business Expert Press, LLC, 2016.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any meanselectronic, mechanical, photocopy, recording, or any other except for brief quotations, not to exceed 250 words, without the prior permission of the publisher.

First published in 2016 by

Business Expert Press, LLC

222 East 46th Street, New York, NY 10017

www.businessexpertpress.com

ISBN-13: 978-1-60649-502-5 (paperback)

ISBN-13: 978-1-60649-503-2 (e-book)

Business Expert Press Supply and Operations Management Collection

Collection ISSN: 2156-8189 (print)

Collection ISSN: 2156-8200 (electronic)

Cover and interior design by S4Carlisle Publishing Services Private Ltd., Chennai, India

First edition: 2016

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Abstract

Most decisions and plans in a firm require a forecast. Not matching supply with demand can make or break any business, and that is why forecasting is so invaluable. Forecasting can appear as a frightening topic with many arcane equations to master. We therefore start out from the very basics and provide a nontechnical overview of common forecasting techniques as well as organizational aspects of creating a robust forecasting process. We also discuss how to measure forecast accuracy to hold people accountable and guide continuous improvement. This book does not require prior knowledge of higher mathematics, statistics, or operations research. It is designed to serve as a first introduction to the nonexpert, such as a manager overseeing a forecasting group, or an MBA student who needs to be familiar with the broad outlines of forecasting without specializing in it.

Keywords

forecasting; sales and operations planning; decision making; service levels; statistics thinking; choice under uncertainty; forecast accuracy; intermittent demand; forecasting competition; judgmental forecasting

Contents

We would like to thank Aris Syntetos, Len Tashman, Doug Thomas, Paul Goodwin, and Jordan Tong for their valuable feedback on earlier drafts of this manuscript.

Stephan Kolassa dedicates this book to I., S., & P. Enno Siemsen dedicates this book to O., T., & M.

1.1. The Value of a Good Forecasting Process

It is common to become frustrated about forecasting. The necessary data is often dispersed throughout the organization. The algorithms used to analyze this data are often opaque. Those within the organization trained to understand the algorithms often do not understand the business, and those who breathe the business do not understand the algorithms. The actual forecast is then discussed in long and unproductive consensus meetings between diverse stakeholders with often conflicting incentives; in between, the forecast is often confused with goals, targets, and plans. The resulting consensus can be a political compromise that is far removed from any optimal use of information. These forecasts are in turn often ignored by decision makers, who instead come up with their own guess since they do not trust the forecast and the process that created it. Even if the forecasting process appears to work well, the actual, inherent demand uncertainty often creates numbers that are far away from the forecast. It is hard to maintain clarity in such a setting and not become frustrated by how hard it is to rely on forecasts.

Yet, what alternative do we have to preparing a forecast? The absence of a good forecasting process within an organization will only create worse parallel shadow processes. Every plan, after all, needs a forecast, whether that forecast is an actual number based on facts or just the gut feeling of a planner. Some companies can change their business model to a make-to-order system, eliminating the need to forecast demand and manufacture their products to stock, but this alternative model still requires ordering components and raw materials based on a forecast, as well as planning capacity and training the workforce according to an estimate of future demand. A central metric for every supply chain is how long it would take for all partners in the supply chain to move one unitfrom the beginning to the endinto the market. This metric shows the total lead time in the supply chain. As long as customers are not willing to wait that long for a product, a supply chain cannot change to a complete make-to-order system. Someone in the supply chain will need to forecast and hold inventory. If that forecasting system does not work well, the resulting costs and disruptions will be felt throughout the supply chain.

One central tenet every manager involved in forecasting needs to accept is that there are no good or bad forecasts. There are only good or bad ways of creating or using forecasts. Forecasts should contain all the relevant information that is available to the organization and its supply chain about the market. Information is everything that reduces uncertainty. If a forecast is far away from the actual demand, but the process that generated the forecast made effective use of all available information, the organization simply had bad luck. Conversely, if a forecast is spot on, but the process that created it neglected important information, the organization was lucky but should consider improving their forecasting process. Bad forecasts in this sense can only be the result of bad forecasting processes. As with decision making under uncertainty in general, one should not question the quality of the decision or forecast itself given the actual outcome; one should only question the process that led to this decision or forecast. Betting money in roulette on the number 20 does not become a bad choice just because a different number is rolledand neither does it become a better choice if the ball happens to actually land on the 20!

Different time series are more or less predictable, and if a series has a lot of unexplainable variation, there is a limit to how well it can be forecast. .

Easy- and hard-toforecast time series

From this perspective, one may be surprised to see how many organizations still exclusively rely on the use of point forecasts. A point forecast is a single numberan estimate of what an unknown quantity will most likely be. It is, however, highly unlikely that the actual number will be exactly equal to the point forecast. Thus, one always needs to think about and deal with the remaining uncertainty in the forecast. Ideally, one should conceptualize a forecast as a probability distribution. That distribution can have a center, which is usually equivalent to the point forecast. Yet that distribution also has a spread, representing the remaining uncertainty around the point forecast. Good forecasting processes will communicate this spread effectively; bad forecasting processes will remain silent on this issue, projecting unrealistic confidence in a single number. Further, not making explicit the inherent forecast uncertainty can lead to decision makers using both highly uncertain and highly certain forecasts in a similar way. It is not uncommon, for example, for firms to require equivalent safety stocks across different products, even though the uncertainty inherent in these products may vary vastly. The root cause of this problem often lies in insufficient reporting of uncertainty. We will further explore the idea of probabilistic forecasting in .