Role of the sales force in forecasting

Mike Gilliland
Mike Gilliland|February 22, 2013

The war of business forecasting ideas is being waged in the trenches of the online discussion groups. Where else can great disagreement be exacerbated (and sometimes even resolved) by often civilized discussion, with participants from across the globe?

One of the popular groups for business forecasting practitioners is Demand Planning, Sales Forecasting, IBP and Supply Chain Optimization on LinkedIn . There has been a long running discussion about whether the sales team should own forecast accuracy, which has evolved into the broader question of the role of the sales force in forecasting.

[Sidenote: I was thrilled to hear from LinkedIn last week that I have one of the top 100% most viewed LinkedIn profiles for 2012! That is, I was thrilled until a colleague pointed out to me the mathematics of being in the top 100%. Then I became despondent.]

The Role of the Sales Force in Forecasting

It is good to be wary of any inputs into the forecasting process, and this naturally includes inputs from sales. Forecasting process participants have personal agendas, perhaps to drive inventory higher or lower, or to drive sales / revenue expectations higher (as motivational targets) or lower (to make them easier to beat). When we ask someone for a forecast, we shouldn't expect an honest answer.

We can't entirely discount the role of the sales force in forecasting, or as part of the S&OP process. They definitely deserve a seat at the table. But I do want to encourage a healthy skepticism about their input. And let’s be efficient about it – only utilizing their input when they are demonstrably providing value by making the forecast more accurate and less biased.

In some situations, it is more likely that input from the sales force will be valuable. For example, if sales for an item are dominated by one or a small number of customers, insight into the intentions of those dominant customers could dramatically impact our forecast. However, if each item is sold to hundreds or thousands of customers, each accounting for a small percentage of total volume, we probably don’t need the sales force to tell us what individual customers are doing because it just doesn’t matter. Errors in individual customer forecasts just cancel each other out with no net improvement in the forecast for that item.

I would not trust sales to “own” the forecast (and then blindly plan operations around their number). But there may be ways to harvest their information and get some value out of it. As in an example provided by Mark Chockalingam, don’t just give the sales force a blank spreadsheet and tell them to fill in their forecasts – this would be a complete waste of time.

Instead, consider the approach suggested by Eric Wilson of Tempur-Pedic, and appeal to the competitive nature of sales people. Provide them with the forecast generated by your forecasting software, and ask for input where they can improve upon the computer’s forecast. This should limit the adjustments they make (to only those forecasts they are confident they can improve upon), and provides you with the data to determine the effectiveness of their adjustments.

 

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Guest Blogger: Len Tashman previews Winter 2013 issue of Foresight

Mike Gilliland
Mike Gilliland|February 8, 2013

Editor Len Tashman's Preview of Foresight

Foresight has always presented its methods-based articles as either tutorials, which introduce and illustrate a methodology in nontechnical language, or as case studies, with a focus on the practical issues and challenges in generating forecasts. We lead off this issue with two practical issues articles. First, Stephan Kolassa and Roland Martin team up again to examine major challenges of Forecasting to Meet Demand. Then Christian Schäfer presents a case study in the use of simulation and scenarios to forecast the market success of new pharmaceutical products. His article shows How to Separate Risk from Uncertainty in Strategic Forecasting.

Foresight’s feature article in the Fall 2012 issue, “Why Should I Trust Your Forecasts?,” examined the main determinants of trust, including “open communication between forecast users and providers.” In this issue, Joe and Simon Sez expands on the theme with our dynamic duo’s ingredients for Fostering Communication that Builds Trust.

Two very special articles appear in our S&OP section. Jane Lee offers a good dose of reality therapy in her discussion of The Role of S&OP in a Sluggish Economy. Jane argues that, rather than maintaining the pretense of “business as usual,” firms should face the necessity of rapid downward adjustments and utilize the S&OP process for gaining buy-in on new forecasts, new plans, and new production and inventory targets.

Jason Boorman then takes us through the key Five Steps to Gaining Necessary and Appropriate Buy-In for a new S&OP process. It’s a formidable task and success, he says, requires the gaining of commitment from top management and the execution of a successful pilot project.

With so many software solutions out there, it’s hard to imagine room for yet another –but here’s one that really is different. Jeff Greer makes a motivating case for adding Geographic Information Systems software to the supply-chain toolbox in GIS: The Missing Tool for Supply-Chain Design.

The United States has just emerged from a presidential election in which the populace was besieged – nay, bludgeoned – with predictions of the outcome from polls, betting markets, expert opinions, regression models, aggregators, and blogs such as Nate Silver’s FiveThirtyEight for the New York Times. Many polls were wrong beyond statistical expectation (e.g. Gallup), but one clear winner was the aggregator approach, which is based on the averaging of different methods. Andreas Graefe and his team at Pollyvote.com had it consistently right throughout the campaign; how this was accomplished is the subject of Combined Forecasts of the 2012 Election: ThePollyVote.

And speaking of Nate Silver – who outperformed Polly and almost every other predictor in the final two weeks before the elections and has achieved superstar status for his campaign-outcome acumen – he tells all in his new book, The Signal and the Noise: Why So Many Predictions Fail -- But Some Don't. In David Orrell’s review of the book in this issue, he shows the breadth of Silver’s forecasting approach reaching well beyond politics to economics, athletics, and climate.

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How to make weather forecasting look good

Mike Gilliland
Mike Gilliland|February 6, 2013

Compare it to predicting the economy.

So concludes an ABC News Australia story by finance reporter Sue Lannin, entitled "Economic forecasts no better than a random walk." The story covers a recent apology by the International Monetary Fund over its estimates for troubled European nations, and an admission by the Reserve Bank of Australia that its economic forecasts were wide of the mark.

An internal study by the RBA found that 70% of its inflation forecasts were close, but its economic growth forecasts were worse, and its unemployment forecasts were no better than a random walk. [Recall the random walk (or "no change" forecasting model) uses the last observed value as the forecast for future values.]

In other words, a bunch of high-priced economists generated forecasts upon which government policies were made, when they could have just ignored (or fired) the economists and made the policies based on the most recent data.

Anyone who has worked in (or paid any attention to) business forecasting will not be surprised by these confessions. Naive forecasts like the random walk or seasonal random walk can be surprisingly difficult to beat. And simple models, like single exponential smoothing, can be even more difficult to beat.

While we assume that our fancy models and elaborate forecasting processes are making dramatic improvements in the forecast, these improvements can be surprisingly small. And frequently, due to use of inappropriate models or methods, and to "political" pressures on forecasting process participants, our costly and time consuming efforts just make the forecast worse.

The conclusion? Everybody needs to do just what these RBA analysts did, and conduct forecast value added analysis. Compare the effectiveness of your forecasting efforts to a placebo -- the random walk forecast. If you aren't doing any better than that, you have some apologizing to do.

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Business forecasting effectiveness at the APICS Triangle Chapter (February 12)

Mike Gilliland
Mike Gilliland|February 5, 2013

If you happen to be in Raleigh, NC next Tuesday evening, please come out for the APICS Triangle Chapter professional development meeting, 6:00-8:00 pm. While I can't make any promises about the caliber of the evening's speaker (me),  you are assured a good meal and good conversation with representatives from many triangle area industries.

I last spoke at this chapter in 2006, on the topic of Lean Forecasting. This time we'll look at the reasons why forecasts are so wrong, and some of the common worst practices. We'll then return to the lean forecasting approach with a dive into Forecast Value Added analysis.

Note: You must register by noon on Thursday, February 7. See you there.

KMWorld Article on Managing the Business Forecasting Process

For additional preview of the APICS dinner topic, see this recent KMWorld article, "Forecast Value Added: The Key to Managing the Business Forecasting Process."

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What good is being a "great place to work"?

Mike Gilliland
Mike Gilliland|January 16, 2013

Even the SAS sheep enjoy a great place to work

Popularized rankings of "best places to work" (such as in 2012, SAS ranked #1 in the world in Great Places to Work®'s list of Multinational Workplaces) tend to focus on why it is so great to be an employee.

As a potential customer of one of these best places to work, why should I care? In fact, if a company is such a great place to work, doesn't that mean they pay fair wages and provide good benefits -- things I as a customer would ultimately pay for in the cost of their products and services? Wouldn't it be in my interest to do business with a company that shortchanges its employees, spending the least possible on human resources, and thereby (at least theoretically) passing that savings on to me?

That's one way of thinking about it. Or there is another way:

  • Employees at great places to work recognize they have a good quality of work and life, and are motivated to continue delivering value to the company to keep their jobs. (Personally, I would feel really bad to be fired from my job at SAS. Other jobs I’ve had, not so much.)
  • Low employee turnover (3.3% at SAS versus the industry norm of 22%) means much less money spent by HR to recruit, relocate, and train new employees – and more money to invest in R&D and services that directly benefit the customer.
  • Low turnover also means customers can build long term relationships, working with the same people year after year, who understand the their business problems and can partner to solve them.
  • Specifically, customers dealing with SAS (e.g. calling for support) are dealing with competent & experienced personnel, not a bunch fresh-faced newbies reading from a script.

In short, SAS being good to us (its employees), can not only reduce HR costs (compared to a high-employee-turnover organization). It also helps SAS be a much more effective partner in delivering success to our customers.

By the way, it was just announced that SAS is #2 on the 2013 Fortune 100 Best Companies to Work For list in the US. This is after #1 rankings in 2010 and 2011, and #3 ranking last year. Ho hum...

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Larry Lapide receives Lifetime Achievement award from IBF

Mike Gilliland
Mike Gilliland|January 9, 2013

Larry receiving award from IBF Managing Director Anish Jain

The Institute of Business Forecasting has named Larry Lapide, Research Affiliate at MIT, as recipient of its "Lifetime Achievement in Business Forecasting & Planning" award -- a much deserved honor! Larry has written a quarterly column for Journal of Business Forecasting for 15 years, and I've been a longtime follower.

In contributing over 60 columns to JBF, Larry has covered a lot of territory. Yet, no matter what the topic, I've always found some useful takeaway from his writings. Three favorites that come to mind are:

  • Forecasting is About Understanding Variations (Winter 1998) -- advocates a simple method called Percent of Variation Explained (PVE) for evaluating forecasting performance.
  • Forecasting Heroes Catch Turning Points (Fall 2001) -- suggests ways to detect turning points in your company's business.
  • Where Should the Forecasting Function Reside? (Winter 2002) -- surveys the pro and con arguments for locating forecasting in various departments (standalone, marketing, sales, production/operations/logistics, finance, strategic planning).

Note that IBF members receive free online access to all 30+ years of their journal, including all of Larry's columns.

Thank you Larry for all your valuable contributions, and congratulations on this honor!

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Research Fellowship at Lancaster Centre for Forecasting

Mike Gilliland
Mike Gilliland|December 20, 2012

Robert Fildes

The Lancaster Centre for Forecasting is led by two of my favorites in the forecasting world, Robert Fildes and Sven Crone.  The Centre is home to cutting edge research and consulting, covering the range of forecasting models and methods, as well as real-world forecasting process.

Sven Crone

The Centre has announced a Management & Business Development Fellowship, allowing the recipient to develop a research area in Forecasting or Marketing Analytics, as well as gain a PhD.

This is an amazing career opportunity for an academically inclined business forecasting practitioner -- a chance to study with the best and contribute to the field -- and being funded to do so!

From the announcement:

Management & Business Development Fellowship

Salary:   Grade 8, £37,012 to £46,486
Ref:        A587

If you’ve been working in Forecasting, Demand Planning, Time Series Analysis/Prediction, or in the area of Data Mining, Business or Marketing Analytics for 5 - 15 years and would like to move into academia, the Lancaster University Department of Management Science is recruiting to a Management and Business Development Fellowship partly funded by ESRC.

We’ll work with you to identify and develop a research area in Forecasting or Marketing Analytics and if you don’t already have a Management Science related PhD, you’ll be expected to gain one whilst on the Fellowship. You’ll need to have at least an excellent first degree and will need to demonstrate that you wish to move into a research active career as a Forecasting/ Analytical Marketing academic in the wider field of Operational Research / Management Science (OR/MS).

Informal enquiries; Prof Robert Fildes (+44 (0)1524 593870, r.fildes@lancaster.ac.uk) or Dr. Sven F. Crone (+44 (0)1524 592991, s.crone@lancaster.ac.uk)

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Simple methods and ensemble forecasting of elections

Mike Gilliland
Mike Gilliland|November 8, 2012

Two enduring principles of forecasting are that simple methods can work as well as fancy methods, and that combining (averaging)  forecasts, also known as "ensemble forecasting," will usually result in more accurate predictions than the individual methods being averaged. We saw a good demonstration of these principles in Tuesday's election forecasts by Nate Silver on his FiveThirtyEight blog, and PollyVote.com. But let me digress...

Six Methods of Election Forecasting

 There are at least six kinds of methods used in election forecasting:

  • Nonsense: Basing the forecast on an observed historical correlation between the election outcome and a causally irrelevant variable. For example, the "Redskins rule," which asserted that when the Washington Redskins football team wins their last home game prior to the election, the party that holds the White House wins the election. When this rule failed for the first time in 2004, it was amended to assert that when the Redskins win, the party that won the popular vote in the previous election wins the election. (Recall Bush v. Gore in 2000.) Result: On November 4 the Redskins lost their home game, thus foretelling a Romney win.

 

  • Punditry: One step beyond nonsense (but just a baby step), are the forecasts of the once employed politicians (Newt Gingrich I, Newt Gingrich II), once relevant consultants (Dick Morris), once funny comedians (Jim Cramer), and current intellectual leaders (Rush Limbaugh). Such forecasts are based on the nebulous concepts of "experience" and "gut feel."  If you have a lot of money, there is no shortage of Washington, DC operatives willing to sell you their opinions, and part you from your political contributions. A laudable attribute of the pundits is that they don't let data and scientific evidence get in the way of their viewpoints. (See Karl Rove vs. the quants at the Fox News Decision Desk.)

 

  • Econometric Models: The University of Colorado model stresses state-level economic data, including unemployment and changes in per capita income. This approach didn't do so well. It forecast a Romney win with 330 electoral votes, and correctly called just 3 of 13 battleground states (with Florida still to be determined). Yale economist Ray Fair's model is interesting in that it is claimed to have correctly predicted 21 of 24 presidential elections from 1916 through 2008. What should give one pause, however, is that Ray Fair wasn't born until 1942, so how did his model "predict" those elections that occurred before the model existed? Even if he were a child forecasting prodigy and perfected his model at age 2 in time for the 1944 elections, that would be 7 fewer election predictions to brag about. In fact, the model was first used only in 1980, and miscalled 1992, 2000, and now 2012 (Obama 49%), making it correct in just 6 of 9 elections, or not much better than tossing a fair coin. (Note: To be fair, Fair has stated that the 2012 prediction is within the margin of error, so too close to call. But this renders the model both uninteresting and irrelevant.)

 

  • Prediction Markets: Relying on the "wisdom of crowds," the Iowa Electronic Market and Intrade are the two best known examples. On Monday Intrade priced Obama's chances at 72.4%, and IEM at about 75.7% (average price for the day in the winner-take-all market). Of course, just like a meteorologist predicting rain, if you don't forecast something as either 0% or 100%, one instance is not going to prove you wrong. IEM's vote share market averaged 50.9% for Obama on Monday, so that was pretty close.

 

  • Combination Models: PollyVote.com is an unweighted average of forecasts from five sources: polls, the IEM vote share prediction market, econometric models, expert surveys, and indexes based on voter perception and candidate biographies. In just its 3rd presidential election, PollyVote has always come within 0.5% of the two-party vote percentages, and was about 0.2% off this year (giving Obama 51.0% of the two-party vote).

 

  • Polling: The definitive source for election news is, of course, The Colbert Report, where New York Times blogger Nate Silver explained his prediction methodology: “Go and look at the polls and take an average and add up the states and see who has 270 electoral votes. It’s not really that complicated, but people treat it like it’s Galileo or something.” Just as I predicted on Tuesday, someone would pretty much nail the results and become famous, and Nate Silver is the one. 

 A Win for the Quants

While the PollyVote and Nate Silver results were certainly a win for the principles of forecasting, remember one more important principle:

Don't jump to too many conclusions based on just one data point!

Sometimes good (or bad) results are just due to chance.

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The predictive power of nonsense

Mike Gilliland
Mike Gilliland|November 6, 2012

The 2012 US Presidential race comes to a close today (thankfully), and there is no shortage of wacky indicators predicting the winner:

In primitive times a diviner could foretell the future by poisoning a chicken -- whether it lived or died provided the answer. Today we have Halloween masks, coffee cups, cookie sales, and winners of sporting events all being credited with forecasting ability. But if you search hard enough (and it won't even be that hard), you can always find perfect associations in history that have no predictive power.

For example, in time series forecasting, we can always find a model that fits the history perfectly (with a polynomial of sufficiently high order). But will this model be able to forecast the future perfectly? Or will it even be an appropriate model, generating forecasts that are at least in the ballpark and not too awful?

Here is my prediction: Out of all the organizations and people making predictions, somebody will get it just right, and be lauded as a genius for the next four years.

This evening, as we sit on our sofas eating popcorn and watching the election returns, please let us reflect on the classic bad forecasting practices that have been on display. And let us be thankful we didn't have to kill any chickens in the process.

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Guest Blogger: Len Tashman previews Fall 2012 issue of Foresight

Mike Gilliland
Mike Gilliland|November 5, 2012

Editor Len Tashman's Preview of Foresight

The importance of trust in the dissemination of forecasts through an organization cannot be overstated. Lack of trust, due in no small measure to the biases arising from “silo mentalities” and misplaced incentives, can and too often does undermine the forecasting process. Foresight has devoted many articles to organizational biases and game playing, with warnings against tolerating such behaviors and recommendations for how to avoid and negate them. Now Sinan Gönül, Dilek Önkal, and Paul Goodwin examine the latest research into the factors that instill trust in the work of the forecast providers. Their article, "Why Should I Trust Your Forecasts?," is followed by commentaries from forecast leaders at IBM (John Parks), Boise Paper (John Unger), and the Lego Group (Lauge Valentin) as well as an extension to supply chain partners by supply-chain specialist Ram Ganeshan.

This issue begins a new series of Forecasting Methods Tutorials with Eric Stellwagen's introduction to “Exponential Smoothing: The Workhorse of Business Forecasting.” Providing nontechnical overviews of important methodologies and illustrating method strengths and limitations, these tutorials should enable forecasters to make more informed use of the software that supports the forecasting function.

The Sales and Operations Planning (S&OP) process is the infrastructure through which most companies develop, disseminate, and reconcile forecasts to guide decision making across their functional areas. S&OP gurus Bob Stahl and Tom Wallace team up once again to offer an examination of “S&OP Principles: The Foundation for Success.”  Their discussion addresses the key elements of proper S&OP implementation as well as the tactics for overcoming organizational pushback.

Principles provide a guidepost—but the devil is always in the details. Amy Mansfield, Production Planning Manager at V&M Star, gives us a step-by-step set of instructions for proper implementation of an S&OP process. Her article is aptly entitled “Executive S&OP Implementation – Do It Right.”

We conclude the fall 2012 issue with Associate Editor Stephan Kolassa’s review of two new introductory forecasting textbooks. Principles of Business Forecasting is a collaborative effort of two renowned forecasting scholars, Keith Ord of Georgetown University and Robert Fildes of Lancaster University. This volume should be a valuable addition to the forecaster’s reference shelf. The second, Forecasting: Principles and Practice, is a digital text which is available free online. The authors are Rob Hyndman and George Athanasopoulos of Monash University in Melbourne, Australia. Rob is Editor in Chief of Foresight's sister publication, the International Journal of Forecasting. 

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  • About this blog

    Michael Gilliland is a longtime business forecasting practitioner and currently Product Marketing Manager for SAS Forecasting. He initiated The Business Forecasting Deal to help expose the seamy underbelly of the forecasting practice, and to provide practical solutions to its most vexing problems.

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