Top Five Tips for New Demand Planners and Forecasters

In Smart Software’s forty-plus years of providing forecasting software, we’ve met many people who find themselves, perhaps surprisingly, becoming demand forecasters. This blog is aimed primarily at those fortunate individuals who are about to start this adventure (though seasoned pros may enjoy the refresher).

Welcome to the field! Good forecasting can make a big difference to your company’s performance, whether you are forecasting to support sales, marketing, production, inventory, or finance.

There is a lot of math and statistics underlying demand forecasting methods, so your assignment suggests that you are not one of those math-phobic people who would rather be poets. Luckily, if you are feeling a bit shaky and not yet healed from your high school geometry class, a lot of the math is built into forecasting software, so your first job is to leave the math for later while you get a view of the big picture. It is indeed a big picture, but let’s isolate few of the ideas that will most help you succeed.


  1. Demand Forecasting is a team sport. Even in a small company, the demand planner is part of a team, with some folks bringing the data, some bringing the tech, and some bringing the business judgment. In a well-run business, your job will never be to simply feed some data into a program and send out a forecast report. Many companies have adopted a process called Sales and Operations Planning (S&OP) in which your forecast will be used to kick off a meeting to make certain judgments (e.g., Should we assume this trend will continue? Will it be worse to under-forecast or over-forecast?) and to blend extra information into the final forecast (e.g., sales force input, business intelligence on competitors’ moves, promotions). The implication for you is that your skills at listening and communicating will be important to your success.


  1. Statistical Forecasting engines need good fuel. Historical data is the fuel used by statistical forecasting programs, so bad or missing or delayed data can degrade your work product. Your job will implicitly include a quality control aspect, and you must keep a keen eye on the data that are supplied to you. Along the way, it is a good idea to make the IT people your friends.


  1. Your name is on your forecasts. Like it or not, if I send forecasts up the chain of command, they get labeled as “Tom’s forecasts.” I must be prepared to own those numbers. To earn my seat at the table, I must be able to explain what data my forecasts were based on, how they were calculated, why I used Method A instead of Method B to do the calculations, and especially how firm or squishy they are. Here honesty is important. No forecast can reasonably be expected to be perfectly accurate, but not all managers can be expected to be perfectly reasonable. If you’re unlucky, your management will think that your reports of forecast uncertainty suggest either ignorance or incompetence. In truth, they indicate professionalism. I have no useful advice about how best to manage such managers, but I can warn you about them. It’s up to you to educate those who use your forecasts. The best managers will appreciate that.


  1. Leave your spreadsheets behind. It’s not uncommon for someone to be promoted to forecaster because they were great with Excel. Unless you are with an unusually small company, the scale of modern corporate forecasting overwhelms what you can handle with spreadsheets. The increasing speed of business compounds the problem: the sleepy tempo of annual and quarterly planning meetings is rapidly giving way to weekly or even daily re-forecasts as conditions change. So, be prepared to lean on a professional vendor of modern, scalable cloud-based demand planning and statistical forecasting software for training and support.


  1. Think visually. It will be very useful, both in deciding how to generate demand forecasts and in presenting them to management, so take advantage of the visualization capabilities built into forecasting software. As I noted above, in today’s high-frequency business world, the data you work with can change rapidly, so what you did last month may not be the right thing to do this month. Literally keep an eye on your data by making simple plots, like “timeplots” that show things like trend or seasonality or (especially) changes in trend or seasonality or anomalies that must be dealt with. Similarly, supplementing tables of forecasts with graphs comparing current forecasts to prior forecasts to actuals can be very helpful in an S&OP process. For example, timeplots showing past values, forecasted values, and “forecast intervals” indicating the objective uncertainty in the forecasts provide a solid basis for your team to fully appreciate the message in your forecasts.


That’s enough for now. As a person who’s taught in universities for half a century, I’m inclined to start into the statistical side of forecasting, but I’ll save that for another time. The five tips above should be helpful to you as you grow into a key part of your corporate planning team. Welcome to the game!




Forecasting and the Rising Tide of Big Data

The Smart Forecaster

 Pursuing best practices in demand planning,

forecasting and inventory optimization

“Trillions of records of millions of people…Finding the useful and right information, understanding its quality and producing reliable analyzed data in a timely and cost-effective manner are all critical issues.”

Smart Software Senior Vice President for Research Tom Willemain recently had the opportunity to talk with Dr. Mohsen Hamoudia, President of the International Institute of Forecasters (IIF), to discuss current issues with, and opportunities for, big data analytics. The IIF informs practitioners on trends and research developments in forecasting via print and online publications and the hosting of professional conferences.

Dr. Hamoudia begins, by way of introduction:

In all industries, data availability is exploding in volume, variety and velocity. Big data analytics is playing an important role in identifying the data that is most important to the business.

Let me take the example of the Information and Communication Technology (ICT) sector. We are seeing literally exponential growth in the amount of data available to telecoms, Over-the-top (OTT) independent content distributors, government, regulators and other organizations.

Around the world, we are witnessing petabytes of data: trillions of records of millions of people—all coming from multiple sources. Among these sources: internet connections, sales, customer contact centers, social media, mobile and land lines data. Finding the useful and right information, understanding its quality and producing reliable analyzed data in a timely and cost-effective manner are all critical issues. ICT companies are increasingly looking to find actionable insights in their data. How they can increase their customer base and loyalty programs? How can they improve the quality of service (QoS) and reduce customer turnover? With the right big data analytics platforms in place, they can be more competitive and efficient, improving operations, customer service and risk management. Forecasting and predicting customer trends and directions are key for telecoms.

Forecasting skills, including mathematics, statistics and econometrics, form one of the most important “blocks” of required skills in managing Big Data. Some forecasting activities naturally form part of the big data debate.

In retail industries, forecasting addresses daily demand across thousands of products. Financial forecasting, whether considering customer behavior or financial data series, generates massive on-line data sets. As pointed out by Robert Fildes, Distinguished Professor at Lancaster University, as yet the academic forecasting community is not thoroughly engaged—with only a few exceptions. Hal Varian of Google has looked at some of the work that David Hendry and Jennifer Castle, at Oxford University, have undertaken on searching large data sets for data congruent meaningful models. Stock and Watson have also developed their own approaches to large macro data sets. But despite the attempt, at last year’s symposium on forecasting in Seoul, to explore the theme of big data and its forecasting applications, there remain few convincing applications of using on-line data on real forecasting problems.

Q. One hears a great deal about “predictive analytics” these days, yet the phrase rarely is linked with forecasting. Do you agree that forecasting lies at the heart of predictive analytics? Have you an explanation for why the link has been broken? Have you ideas about how to re-inject forecasting into the conversation?

The results of forecasting (the “what”) are perhaps now perceived as less important than the “how”. Consequently, the trust that users give to traditional forecasting has declined. Who indeed is challenging the accuracy or relevance of forecasting by comparing, a posteriori, the reality vs. forecast—making a case for metholodiges’ effectiveness and therefor building credibility?

With the current perception of “predictive analytics”, there is probably more space in the public imagination allocated to the “how” side of things, and therefor a more credible story to tell to partners, investors or customers.

Q. It appears that there is almost no link between traditional forecasting and mobile technology (smart phones, tablet computers). Is this true, or are some companies migrating forecasting to mobile devices? Do you see a path forward in which traditional forecasting algorithms would routinely reside on mobile devices?

First of all, I am really delighted to invite your readers to have a look at our latest issue of Foresight. An excellent paper on the subject, “Forecasting In the Pocket: Mobile Devices Can Improve Collaboration”, explains that “the increasing popularity of PDAs, smartphones, tablet computers and other mobile devices opens up new opportunities for communication and collaboration on business forecasts.” The authors tell us “mobile forecasting (m-forecasting) applications may streamline approaches to collaboration between retailers and suppliers, thus contributing to the provision and exchange of product information, especially since forecasts are strongly tied to local context knowledge.”

For example, on the ICT & OTT side, a large number of predictive projects, such as those of Google+ and Facebook, are happening thanks to the inclusion of the “user location” data in the OTT IT systems. In my opinion, and what I see in some sectors like retail and logistics, is that traditional forecasting and mobile forecasting (m-forecasting) are complementary. This latter could be seen as a bottom-up forecasting approach that will or will not confirm the top-down forecasting results.

Q. Some people argue that big data will facilitate the replacement of forecasting with “sense and react” systems. Practically speaking, how would you explain “sense and react”, and are there application areas where you think it is or is not likely to take hold?

It seems to me that “sense and react” is fully oriented to the short-term perspective. Forecasting extends this by addressing needs for a variable horizon: short-term and medium-term.

As a side effect of ATAWAD (Anytime, Anywhere, Any Device), the decision-making criteria are, more than ever, “short term”. Big data is a “weak signals” sensing system, which enable the near-real-time detection of business opportunities that would go unnoticed with traditional IT systems. There are not really preferred or non-priority applications for this, the question is more on the “when” side.

Big data is relevant when looking below the surface in difficult economic times, but I am less sure whether it is worth the effort in “normal” economic period. To conclude on this point: I will be happy to see an example on how accurate are forecasts which are based on “sense and react” versus forecast based on traditional models.

Q. I’m asking some big questions. To what extent do you see the IIF community shaping these discussions and outcomes? How can readers join in the dialogue?

We are expecting an increasing availability, and increasing usage, of huge amount of data in many industries—such as energy, transportation, health care, finance, telecommunications and tourism.

Many of the IIF’s members are engaged in different aspects of the big data “movement.” The IIF is doing some work in the forecasting activities that naturally form part of the big data debate. More generally, the IIF is actively participating in, and providing a forum for, the discussion of forecasting in the wider world.

The theme of our last International Symposium on Forecasting (ISF) held in Seoul was “Forecasting with Big Data” and a few presentations were related to health care and telecommunications. A relevant workshop has just been run by the European Central Bank (ECB). If these models are capitalized on, they have the potential to impact the economic policy of Europe quite quickly.

Readers can join in the dialogue by contributing papers to the IIF’s publications (The International Journal of Forecasting, Foresight and The Oracle). Foresight, for one, is an invaluable voice in bringing academics and practitioners together in an ongoing discussion.

Readers also can present papers at the annual conference (the aforementioned ISF). They also can suggest and organize specific workshops for specific applications of big data, like the one that was just organized by the ECB in Frankfurt. Another opportunity is to invite IIF’s members to attend any meeting related to forecasting with big data. All these opportunities form good platforms for networking and working together.

Mohsen Hamoudia, PhD, is the President of the International Institute of Forecasters. He also serves as Head of Strategy for Large Projects (Paris) for Orange Business Services (the former France Telecom).

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 Rensselear Polytechnic Institute, and as a member of the research staff at the Center for Computing Sciences, Institute for Defense Analyses.

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