Smart Software recently announced a Software as a Service (SaaS) option for SmartForecasts—SFCloud™. Premises-based perpetual licenses will continue to be the preferred software implementation method for many organizations, but there are many reasons why demand for cloud-based solutions is taking off. A vintage post by Bill Richardson at ApplicantStack Team Blog summarizes key benefits of the SaaS model.

A readable, well-organized textbook could be invaluable to “help corporate forecasters-in-training understand the basics of time series forecasting,” as Tom Willemain notes in the conclusion to this review, originally published in Foresight: The International Journal of Applied Forecasting. Principally written for an academic audience, the review also serves inexperienced demand planning professionals by pointing them to an in-depth resource.

This neat little book aims to “introduce the reader to quantitative forecasting of time series in a practical, hands-on fashion.” For a certain kind of reader, it will doubtless succeed, and do so in a stylish way.

The author, Dr. Galit Shmueli, is the SRITNE Chaired Professor of Data Analytics and Associate Professor of Statistics and Information Systems at the Indian School of Business, Hyderabad. She has authored or coauthored several other books on applied statistics and business analytics.

The book is meant to be a text for a “mini-semester” course for graduate or upper-level undergraduate students. I think it would be a stretch to believe there is enough technical material here to serve as the basis for a graduate course, but I could see it working well for undergraduates in industrial engineering or management who have had a prior statistics course (and therefore will indeed be able to “recall that a 95% prediction interval for normally distributed errors is…”).

There are end-of-chapter exercises of appropriate size and even setups for three real-world semester projects, so instructors could use the book as envisioned by the author. The book illustrates its points using XLMiner, an Excel add-in, and students can use the free demo version for almost all the exercises. Text datasets are available from the book’s web site, which also provides a free time series analysis “dashboard” application. The author notes that other software can be used in place of XLMiner and mentions Minitab, JMP, and Rob Hyndman’s forecast library in R.

While reading this book, I was delighted by its clarity. Having spent time recently correcting the technical prose of two otherwise good graduate students, I found the writing in this book to be a refreshing contrast, making technical concepts understandable.

Another virtue of this book is its selection of topics. The technical ones are reasonably standard (smoothing methods, regression using polynomial trends, and dummy variables) but also range a bit toward the more exotic (logistic regression, neural nets, a bit of ARIMA). More impressive is the inclusion of what might be called “meta-topics” relevant to forecasting: performance assessment, an overview of alternative technical approaches, and one on the forecasting process, from definition of goals to ways to gear reports differently for managerial and technical audiences. This is the kind of forecasting wisdom we find in Chris Chatfield’s book (2004), though presented rather less tartly and with less mathematical exposition. I typically recommend Chatfield’s introductory book for more technical readers interested in getting into time series; I would recommend Shmueli’s book for a more general audience.

No review is complete without quibbles. Here are a few—too few to undo my very positive view of this impressive little book:

• The text makes a good case for “well formatted and easily readable” charts (p. 179). But I found many of the screen shots to be poorly printed and difficult to see. The book is otherwise so visually pleasing that these defects seem very out of character. It uses luxurious amounts of white space and whimsical marginal art to great effect, producing a very “light” feel that must surely help comprehension.

• The author claims (p. 115) that smoothing methods (e.g., moving averages, exponential smoothing) cannot be fully automated because “the user must specify smoothing constants.” Of course, this is not so, since there are several software packages that do this, and the text later contradicts itself on this point on page 127.

• The otherwise good discussion of autocorrelation misleads when it claims (p. 88) that negative lag-1 autocorrelation means that “high values are immediately followed by low values and vice versa.” Well, usually, but not always.

When I finished reading this book, I realized immediately that there is another target audience outside the classroom. My company often conducts training sessions on the use of our software, and these include some general background on forecasting methods and processes. If we could excise the material on XLMiner, and even if we couldn’t, this text would make a wonderful “leave behind” to help corporate forecasters-in-training understand the basics of time series forecasting. The book is so well written, well organized and well designed that it might even be read. We can certainly use it to help our new programmers understand the applications they are developing. And this book might even serve as guilty reading for a graduate student who wants to really “get” what’s going on in Box, Jenkins and Reinsel (2008).

Thomas Willemain, PhD, co-founded Smart Software and currently serves as Senior Vice President for Research. Dr. Willemain also serves as Professor Emeritus of Industrial and Systems Engineering at Rensselaer Polytechnic Institute and as a member of the research staff at the Center for Computing Sciences, Institute for Defense Analyses.

At year’s end, we are often caught up in thinking and planning for the coming year. Did 2013 turn out the way you expected? Will 2014 be dramatically different? Are there other factors—things we are planning to do; things we think our competitors might do; outside forces like changing taste, demographics or economics—that might change the course of business in the coming year?

Most companies that do a formal forecast start out with a statistical projection of past sales patterns into the future. Your forecast model should detect and characterize any seasonality inherent in your markets and include that in the projection. But that’s just the first step.

The next thing to consider is product lifecycle. Nearly all products go through a predictable cycle of introduction, acceptance and growth, maturity (demand levels off) and finally decline to obsolescence. These cycles can be as short as weeks or as long as decades. Clothing fashions and consumer electronics would be on the shorter end of the scale, while products like plumbing fixtures and construction equipment would experience longer cycles. In specialized situations like bus fleet management, entire fleets may be replaced over defined transition periods. In any case, the demand forecast should be adjusted to reflect increasing or decreasing demand according to the product’s position in its lifecycle.

Now comes the hardest part—predicting the unpredictable. In general, the future is likely to look a lot like the recent past in a similarity to Newton’s first law of motion: a body in motion tends to stay in motion unless acted upon by an external force. But it’s those external forces that can send your carefully calculated forecast right into the gutter. A competitor might slash their prices to take away some of your market share. New technologies might obsolete your product before the end of its expected life span. Changing tastes or new regulations might stop sales in their tracks.

But good things might happen as well. You might be the one to slash prices or improve your product and take away a competitor’s business. Your product may catch the fancy of the market and sales will skyrocket. A competitor may abandon the business or go bankrupt, leaving you with more opportunity.

Should you plan for these kinds of things? Certainly, to the extent that you can. You may know when you’ll run promotions or phase in the next product line. But the future, by nature, is uncertain. History and your business knowledge of the past lay the foundation for your view of the future. Statistically-based tools can help you create a risk-adjusted forecast, with safety stock recommendations that correspond with the level of risk you are willing to take. Beyond this, your key to success is agility—the ability to adapt to changing conditions. Prepare the best forecast you can, build your plans around that forecast—then monitor sales and market conditions closely and continuously. Look for early warning that things may be going in a direction other than you predicted.

You must be willing to recognize and adapt to changing conditions—in other words, don’t fall in love with your forecast and ignore evidence that it may be wrong. Pride of authorship in this case can be deadly to the business.

It is also important to have contingency plans in place so you will be prepared to make the necessary changes to procurement, production and inventory to respond to the new estimate of demand. The best tools for this are the shortest possible lead times (both production and supplier), good supplier relationships and a clear view of the world and your markets.

Forecasting is difficult mainly because people know it is likely to be wrong and nobody likes to be publicly and visibly wrong. Nevertheless, a good forecast is necessary to position the resources necessary to satisfy customer demands. Just be open to the first signs of change and be prepared to react quickly and decisively.

Dave Turbide, CFPIM, CIRM, CSCP, CMfgE is a New Hampshire-based independent consultant and freelance writer. He can be reached via e-mail at dave at daveturbide dot com.

In my previous post in this series on essential concepts, “What is ‘A Good Forecast’”, I discussed the basic effort to discover the most likely future in a demand planning scenario. I defined a good forecast as one that is unbiased and as accurate as possible. But I also cautioned that, depending on the stability or volatility of the data we have to work with, there may still be some inaccuracy in even a good forecast. The key is to have an understanding of how much.

This topic, managing uncertainty, is the subject of post by my colleague Tom Willemain, “The Average is not the Answer”. His post lays out the theory for responsibly confronting the limits of our predictive ability. It’s important to understand how this actually works.

As I briefly touched on at the end of my previous post, our approach begins with something called a “sliding simulation”. We estimate how accurately we are predicting the future by using our forecasting techniques on an older portion of history, excluding the most recent data. We can then compare what we would have predicted for the recent past with our actual real world information about what happened. This is a reliable method to estimate how closely we are predicting future demand.

Safety stock, a carefully measured buffer in inventory level we stock above our prediction of most likely demand, is derived from the estimate of forecast error coming out of the “sliding simulation”. This approach to dealing with the accuracy of our forecasts efficiently balances between ignoring the threat of the unpredictable and costly overcompensation.

In more technical detail: the forecasts errors that are estimated by this sliding simulation process indicate the level of uncertainty. We use these errors to estimate the standard deviation of the forecasts. Now, with regular demand, we can assume the forecasts (which are estimates of future behavior) are best represented by a bell-shaped probability distribution—what statisticians call the “normal distribution”. The center of that distribution is our point forecast. The width of that distribution is the standard deviation of the “sliding simulation” forecast from the known actual values—we obtain this directly from our forecast error estimates.

Once we know the specific bell shaped curve associated with the forecast, we can easily estimate the safety stock buffer that is needed. The only input from us is the “service level” that is desired, and the safety stock at that service level can be ascertained. (The service level is essentially a measure of how confident we need to be in our inventory stocking levels, with increasing confidence requiring corresponding expenditures on extra inventory.) Notice, we are assuming that the correct distribution to use is the normal distribution. This is correct for most demand series where you have regular demand per period. It fails when demand is sporadic or intermittent.

In the next piece in this series, I’ll discuss how Smart Forecasts deals with estimating safety stock in those cases of intermittent demand, when the assumption of normality is incorrect.

Nelson Hartunian, PhD, co-founded Smart Software, formerly served as President, and currently oversees it as Chairman of the Board. He has, at various times, headed software development, sales and customer service.

In a recent post at SupplyChainBrain, Robert Bowman takes a look at excellence in demand planning. Focusing on admirable qualities and techniques, it should be an interesting read for any demand planner seeking to improve his or her craft.

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