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.

As we approach the midpoint in 2013, there is still a lot of economic uncertainty complicating your supply chain planning processes. Some look at this shaky economy and postpone needed investments that can position their organizations for a strong future.

However, this is not the time to retreat from your supply chain improvement initiatives. Rather, it’s a time to double-down on your efforts to prepare for the inevitable business opportunities that lie ahead.

Economic recovery is a time of sales opportunities. You want to make sure that you’re prepared to take advantage of them. Good demand and inventory planning can help.With the right software and planning processes, you can achieve a sound statistical basis for decision-making going forward while making informed adjustments as circumstances dictate. You can improve your ability to read demand signals, spot trends, model future events, and bring your inventory into balance with demand.

Here are six areas of demand and inventory planning where changes you make now can lead to big payoffs when new opportunities arise:

1. Optimize your inventories

When the customer calls, you want to be able to ship. At the same time, you want to control your costs. The surest way to meet that goal is to find the inventory “sweet spot.” That’s where you have the minimum amount of inventory required to satisfy product demand over a specified lead time and at a desired service level.

The ability to accurately set safety stock and inventory levels can set you apart from the competition, and make a difference in your bottom line. However, getting to that point requires a shift in your planning focus from just forecasting future demand to optimizing stocking levels to fill future orders.

If you’d like to know more about achieving the “sweet spot,” you can find a good article published in APICS Magazine here.

2. Implement intermittent demand forecasting solutions

Companies in the service parts, auto aftermarket, and capital goods industries commonly experience intermittent, “slow moving” demand for a large percentage of their inventory items. Accurately forecasting demand and estimating safety stock levels for these types of items is probably the toughest challenge demand planners face. If you can accurately forecast your intermittently demanded parts and products, and have the correct amount of inventory and safety stock on the shelf, you’ve got most of the competition beat!

The reason for this is that items that have intermittent demand do not have normal demand patterns or distributions, making them difficult to forecast using traditional forecasting methods (see the diagram below).

Bar chart illustrating intermittent demand

So, if you have an accurate means of forecasting intermittent demand and estimating safety stock requirements, you’ll be ahead of your competitors that don’t.

If you’d like to know more about forecasting and planning items with intermittent demand, you can find an informative white paper here.

3. Improve lead times

The economic downturn has forced companies to rethink their sourcing strategies because of uncertain demand back home, long lead times to obtain their goods, rising labor costs abroad, and increasing transportation costs. Shortening replenishment lead times can reduce the time required to get the products you need and helps make your supply chain more efficient. It also makes it easier to react to changes in demand when recovery comes.

4. Prioritize service levels

Prioritizing service levels for your products can help insure that the items important to your sales are given the attention they need. For items that are highly demanded, consider setting service levels higher than for those with less demand. Also try doing a revenue-based ABC analysis of your company’s stock-keeping units (SKUs) and set service levels accordingly in your software planning solution.

For example, you might set the service levels for your “bread and butter” items at 95-99% or higher, while setting service levels much lower (at 70-80% or even less) for other items. In this way, you may find that you need much less stock for some of your SKUs and more stock for others to effectively achieve your overall service level goals.

5. Use more recent demand history in creating your forecasts

Because the economy has been changing so fast, it may be time to shorten the demand history used in generating your forecasts so more emphasis is placed on recent trends and demand patterns—reflecting the “new normal”—rather than those contained in outdated history from 3 or 4 years ago. This, of course, should be done in consultation with your management team and preferably as part of an organized S&OP process that thoroughly evaluates both the risks and benefits of adopting this strategy.

6. Invest in technologies and resources that help you capitalize on opportunities

Investing in the right tools and processes increases your competitive advantage. If you aren’t doing so already, here are some valuable things to consider:

• Start an S&OP process, or fine tune your current process, to include key stakeholders in the supply chain and also ensure that demand forecasting and inventory planning provide key inputs in that planning process.

• If your forecasting software is not good at picking up trends, or cannot handle the portion of your inventory with intermittent demand, find software that’s up to the task.

• Find software that will take your forecast results and generate accurate inventory stocking levels to satisfy demand for your products, components or raw materials over specified lead times and at service levels you desire.

• Look for software solutions that are scalable, yet have a relatively low total cost of ownership, fast payback and high ROI.

• Finally, don’t scrimp on training; get all the training and consulting you need to get the “biggest bang” from your software investments.

Do you have anything to add? What are you doing to prepare for the economic recovery? Please leave a comment.

Charles Smart is the founding President of Smart Software. He currently serves as Vice Chairman, on Smart Software’s Board of Directors, as a company spokesman and in development of strategic business relationships. Prior to founding Smart Software, he was a management consultant at the Stanford Research Institute (SRI International) and Policy Analysis, Inc., and served as a Lieutenant in the U.S. Navy.

Bud Schultz, CPA, Vice President of Finance for NKK Switches, presented his company’s experience with demand planning during a recent webinar. The following is a brief summary of Bud’s key points; view the complete webinar by clicking here.

Q: Tell us about NKK’s business and demand planning challenges.

NKK Switches, based in Scottsdale, Arizona, is a leading manufacturer and supplier of electromechanical switches. The business involves many different switch types—toggles, push-button, rotary, even some programmable switch types. We are known for our high quality, and for our ability to meet an exceptionally broad range of customer requirements on a turnkey (custom configuration) basis. NKK Switches produces customized solutions from component parts sourced exclusively from manufacturing facilities in Japan and China.

There are literally millions of possible switch configurations, and we never know what configured solutions our customers will order. This makes our demand highly intermittent and exceptionally difficult to forecast. In fact, until fairly recently we considered our demand unforecastable. We operated on a build-to-order basis, which meant that customer orders could not be fulfilled until their component parts were produced and then fashioned into finished goods by NKK. This resulted in long lead-times, painful for our customers and a competitive challenge for our sales organization.

Q: What did you expect to get from improved product demand forecasting?

When we began to investigate the value of demand forecasting software (SmartForecasts from Smart Software), we tried to view the decision from a Return on Investment (ROI) point of view. We did some capital budgeting, making assumptions about potential reductions in inventory levels, reduced inventory carrying costs and other potential savings. Although the capital budgets returned positive returns on investment, we nevertheless were unable to move forward based on that information. We lacked confidence in our assumptions, and we were worried that we wouldn’t be able to justify the safety stock and inventory levels that the software would suggest.

What we didn’t expect was a challenge from our parent company. In light of the capabilities of a newly implemented ERP system, they would consider a new approach. If we could produce demonstrably reliable demand forecasts, they would consider procuring raw materials and producing switch components on a build-to-forecast rather than build-to-order basis. This opened the door to a much more profound impact. We tracked actuals against forecasts over a twelve-month period and found that our forecasts, particularly in aggregate, were exceptionally accurate: actual demand was within 3% of forecast. Once we were able to prove the validity of our forecasts, we were able to move forward with the parent company’s plan to manufacture product based on those forecasts.

Q: How did accurate forecasts of product lines with intermittent demand data transform NKK’s operations?

From the many different combinations we manufacture to order, individual switch parts can show very intermittent demand (long periods with zero orders and then seemingly random spikes), but we can identify more consistent patterns across switch series. All of the part numbers in a given series have common components and raw materials, such as plastic housing, brackets and other hardware, gold, silver and LEDs.

Providing our manufacturing facilities with reliable forecasts ended up allowing us to make dramatic changes. Our manufacturing plants could start procuring raw materials that in the aggregate would eventually be used in production of different part numbers within that series, even if the specific part numbers to be produced were unknown at the time the forecasts were made. And in many instances, despite the irregular demand history data, it was even possible for the suppliers to manufacture specific part numbers based on the forecast.

Once the program is fully implemented, we anticipate our leads times will be reduced to half the time or even less. Shorter lead times will result in lower reorder points, resulting in higher service levels while reducing our inventory requirements.

Bud Schultz leads all finance and accounting functions at NKK. His background as a Certified Public Accountant, attorney, engineer and pilot for the US Air Force provide unique perspective on finances for engineering and manufacturing operations.

A go-to source for info on cutting-edge forecasting research, the International Institute of Forecasters (IIF) publishes journals and hosts conferences that we have relied on for decades. In this introduction, IIF Business Manager Pam Stroud gives an overview of the organization’s offerings. (Smart Software Senior Vice President for Research Tom Willemain serves on the Editorial Board of the IIF’s practitioner-oriented publication, Foresight.)

When founded in 1981, the IIF set as its goal: “Bridge the gap between theory and practice, with practice helping to set the research agenda and research providing useful results”. The IIF keeps its members abreast of the latest trends and research in forecasting through its publications, events and website. Its members are drawn from corporations and institutes of higher learning in more than one hundred countries, and form a vibrant community for networking and professional development.

The IIF’s practitioner journal, Foresight, is dedicated to improving the practice of business forecasting, enhancing the professional development of business forecasters, and bringing forecasting know-how to those entering the profession. Because forecasting knowledge and wisdom are not concentrated in one segment of forecasters, we publish valuable ideas from across the discipline—from forecasting teachers and scholars, forecasting analysts and managers, and forecasting consultants and vendors. And we strive to ensure that these ideas are presented clearly, are supported by evidence and are free of bias.

In addition to our publications, the IIF sponsors an annual conference, the International Symposium on Forecasting (ISF). The ISF is an opportunity for researchers and practitioners to come together to share experiences and cutting edge research, and to network among their peers. The Foresight Practitioner conference extends this opportunity to practitioners, delivering practical professional development for business forecasters.

Another example of ‘bridging the gap,’ between research and practice, is the annual research grant, offered in partnership with SAS, which supports research on how to improve forecasting methods and business forecasting practice.

IIF Membership benefits include:

The International Journal of Forecasting – The IJF is the leading scholarly journal in the field of forecasting. With an outstanding editorial board of 44 internationally known forecasting experts, it is a highly readable, widely used and often-cited research journal.

Foresight: The International Journal of Applied Forecasting – Foresight publishes concise, readable and timely articles on forecasting processes, methods and solutions. It is the essential read for business forecasters and an invaluable aid for forecasting educators and students.

The International Symposium on Forecasting – Members receive discounted registration to the premier international forecasting conference. This annual IIF event attracts the world’s leading researchers, practitioners, and students. Each symposium offers more than 250 research presentations in a setting which emphasizes social interaction, and networking opportunities.

The Foresight Practitioner Conference – Members receive discounted admission to this professional development event for business forecasters, where they learn from practitioners who have earned their expertise in the field at top companies, and from forecasting researchers sharing the business implications of their work.

Recent topics from The International Journal of Forecasting:
• Economic Time Series: Modeling and Seasonality
• On the use of cross-sectional measures of forecast uncertainty
• Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts
Recent topics from Foresight: The International Journal of Applied Forecasting
• How Good Is a “Good” Forecast?: Forecast Errors and Their Avoidability
• Forecast Methods Tutorial: ARIMA: The Models of Box and Jenkins
• Improve Forecasting of Consumer Purchases Using Google Trends

“Since 1981, the IIF has been central to my career. Why? Because it’s a diverse and clever group of people focused on pragmatic, evidence-based research. The annual symposium is a great place to exchange ideas about forecasting.” – J. Scott Armstrong, Professor of Marketing, The Wharton School