Maria Simos CEO e-forecasting.com

A big treat in attending a conference is not only learning from attendees, but also having the benefit of one-stop shopping when it comes to vendors in the exhibit hall.  These vendors travel across the country, often times lugging huge displays, screens, white papers and swag to meet with current and potential clients and share with them what their software can do to help assist in their planning needs.

What better way to make this trip even more worthwhile than to share some top trends and news from the companies that have made the trek to exhibit this week at the IBF Best Practices Conference?

Top tier sponsors of the show JDA is coming in with major company news.  They are now the largest single company for supply chain planning and optimizations thanks to their recent acquisitions of competing firms Manugistics and i2 as recently as January.  With this synergy, the company now has over 6,000 companies across different industry segments using their software.  Danny Halim, VP of Industry Strategy and Calvin (Cal) Otto, Business Development Manager shared that what makes JDA truly unique is the company’s intimate knowledge  across the entire supply chain. This includes everything from raw materials to the retail space with the consumer experience.  Their company recently announced record first quarter profits, making Q1 the 22nd consecutive profitable quarter for the firm.  A major trend they see is the idea of supply chains competing versus one another rather than individual companies doing so with a convergence of the supply chain.

Smart Software and their Director of Sales Gregory Hartunian shared some impressive news that they have received not their first, but their second National Science Foundation Research Grant (NSF).  Ten years ago they were awarded their first Small Business Innovation Research Grant from NSF to develop a technology called the Smart-Willemain method of forecasting intermittent demand, also known as slow moving demand.  With their second NSF grant, Smart Software will further expand upon the Smart-Willemain method.  With this research completed, they will be the only vendor to offer a ‘next generation’ forecasting solution for slow moving capital goods, like service and spare parts.  Companies use this technology for a variety of applications, Kimberly Clark is using this to track in-house inventory as an example.

Tom Reilly from Autobox shared news of a new joint project with HP which was presented in more detail  Friday.  For this project, they were approached by a Principal Scientist of HP to work and develop a semi-hourly forecast model.  By breaking the day into 48 discrete time periods they are able to better determine precise demand at specific times throughout the day.  This methodology has been used for the last three to four months with application in call centers.  This method also easily translates using a mixed frequency modeling approach for making power estimations for power plants.

Forecast PRO’s Trac has a neat feature which shows how well the model fits with the history.  Bob Leonard gave a brief demonstration showing the archived forecasts over time.  Using this rich forecast archive helps track the accuracy of lead times.  Their software is off-the-shelf and a 5 user system can be implemented for $15-22K.

Boardwalktech Inc will be launching the 3.2 version of their software this June.  The company’s collaborative platform supports concurrent multi-users  down to the cell level using a back end system.  The software is easy to use and can be role based.  The real-time server recognizes who made the last change and makes notations.  Benefits of this system include integration that takes place in weeks not months, it extends the collaboration process, reflects a complete picture of the business and provides greater visibility.

SAS is excited to announce a new forecasting server plug in for SAP.  The plug in, called SAP Advanced Planning and Optimization (APO) links to read and write from live cache.  In other company news, IBF long standing member Mike Gilliland’s intramural basketball team has won the SAS intramural championships the last 2 out of 3 years.  (It’s not always about the forecasts, demand planners also need to have some fun, too.)

John Galt Solutions Inc. has an Atlas Planning Suite which focuses on the consumer-driven supply chain.  The suite allows for use of POS data to help assist in reaching higher levels of forecast accuracy and has over 30 models built in for planning new product launches and promotional events.  Using POS data and forecasting new product supply are also topics that were touched upon during the speed dating session.

Logility has a supply chain management solution called Logility Voyager Solutions which is internet-based.  Given the global nature of their client’s businesses, they have built in multinational support.  The costs and prices are given not only in the currency of the items ‘home market’ but also in local and regional currencies.  With this built into the system, it helps users build rollups to greater levels of detail for their inventory, production and transportation plans worldwide.

RockySoft Corporation has the Inventory Management Suite with Demand Manager and Requirements Planner, aiding clients in reducing inventory.  The suite also includes S&OP and Economic Order Manager (EOM).  With these tools, clients are able to work with the full supply chain to determine forecasts, procurement needs and replenishment quantities. Using this software also allows practitioners to take advantage of price breaks and volume discounts and also use the suite as a support tool to make decisions on a management level for inventory valuation and performance monitoring.   One key feature with the EOM tool is that you can easily compare annual costs of inventory with the annual cost of ordering based on varying volumes.  The suite is easy to use and training on the new system can be done in only four hours.  RockySoft’s applications are comprehensive but not complex.

Another vendor is working to optimize the time it takes to make demand forecasts.  OM Partners USA has  Abhi Patel at the show sharing information on their supply chain planning software.  Their core strength comes with the ability to integrate the forecast with S&OP planning and scheduling.  The company has a variety of suites that peel time down from a 4-week to possibly one or two week cycle.

A lot was learned by walking around and visiting with the vendors during the Best Practices Conference.  At times, and I know this because I have exhibited at a fair number of shows myself, attendees are not necessarily jumping at the chance to come talk to vendors.  Being on the other-other side of things this time around working as an ambassador and live-tweeting and blogging about the event though, I found that the folks exhibiting at the show were just truly excited about the new things their companies are doing.  So many new applications are being developed in this space and it is a real energizing time in the field.  So next time you are at a show, take some time to hear what’s new in the industry.  Visit with the vendors and simply ask, ‘what’s new?’  It just may be the best way to see what’s next.

Maria E. Simos is CEO of e-forecasting.com, an economic research and consulting company based in Durham, NH with clients ranging from media, academics, federal banks, major manufacturers to other consulting firms.  In her role, Ms. Simos works to further develop the reach of e-forecasting’s economic data and reporting capabilities. She also works closely with clients to ensure that they are receiving the important forecasts, economic data and support needed to be successful. She promotes the work of e-forecasting.com and provides economic analysis through her twitter account (@mesimos) and via other social media outlets.  Ms. Simos holds a Master’s Degree in Management from Carnegie Mellon University where she focused her research on management and network analysis. Her research explored social and business networks and their tie in to culture in organizations.  Her undergraduate study was completed at the Tepper School of Business at Carnegie Mellon.

Mike Gilliland

Below details Questions & Answers from IBF’s Webinar “What Management Must Know About Forecasting.”  If you missed it, no worries.  You can view it complimentary by clicking HERE.

1. If a product is not forecastable, what’s the most appropriate step to move the product to become forecastable?

Answer: The most effective way to improve forecast accuracy is to “make the demand forecastable” and a great way to do that is to lower the volatility of demand.  Most of what we do with our organizational policies and practices is to add volatility.  We encourage our customers to buy in spikes, and we encourage our sales people to sell that way.  This is completely contrary to quality management practices, which are all about removing volatility and making everything more stable and predictable.

Review sales and financial practices that are encouraging volatility, and either re-engineer or eliminate them and replace with practices that encourage everything to operate more smoothly.  (Examples of practices that encourage volatility are pricing and promotional activities, and the quarter end “hockey stick” to meet short term revenue goals.)  You should question whether these sorts of practice make sense by contributing to the long term profitability of your business.  If not, pursue ways to reduce volatility and encourage smooth, stable growth.  This will allow you to forecast more accurately and will reduce overall costs, which you can then pass along to your customers.

2. All of this is relative to the base line forecast, correct? What if your items are heavily promotional driven?

Answer: The accuracy of a naïve forecasting model serves as the baseline against which the performance of alternative forecasting methods should be compared.  Thus if the naïve model (say, a moving average) achieves MAPE of 40%, then I want to know how well my statistical model is forecasting, and how well my overall process is forecasting, and compare them to the baseline of 40% MAPE that the naïve model delivered.  This is what I’m talking about as a “baseline.”

This should not be confused with what is commonly called the “baseline” forecast when you try to distinguish baseline demand from promoted demand.  How do you know what demand was baseline and what was due to the promotion?  How do you distinguish the two?  I don’t believe that you can distinguish the baseline demand from promoted demand in a clean or easy or certain manner, so I would suggest not bothering trying to do so.  What matters is “how much total demand is there going to be.”  It isn’t necessary for me to care how much of it is “baseline” and how much of it is due to “promotion” – and I can never know for sure anyway?  Don’t assume you are making your forecasts  more accurate by trying to distinguish the two – you may just be making things more complex.

3. What is FVA?  A tool?  Expert judgment?  Or what?

Answer: Forecast Value Added is a metric, defined as the change in a forecasting performance metric (such as MAPE, forecast accuracy, or bias), that can be attributed to a particular step or participant in the forecasting process.  When a process step or participant makes the forecast more accurate or less biased, they are “adding value.”  FVA is negative when the step or participant is just making the forecast worse.  FVA analysis is the method of reviewing the performance of your process and identifying those non-value adding (or negative-value adding) activities that should be eliminated.  For more information on FVA analysis, see the webinar “Forecast Value Added Analysis: Step-by-Step” or the accompanying white paper.  You are also encouraged to attend the IBF Supply Chain Forecasting conference in Phoenix, February 22-23, 2010, to learn how to do FVA and hear case studies about several organizations (such as Intel) that are using this method.

4. What are the methods used commonly to measure Forecast Accuracy?  (Is MAPE the most common?) And what is a good process to determine forecast accuracy?

Answer: Mean Absolute Percent Error (MAPE) or its variations like Weighted MAPE or Symmetric MAPE seem to be the most popular metrics of forecasting performance.  MAPE has many well known limitations (such as being undefined when the denominator (the Actual demand) is zero), and is not suitable for use with data with a lot of zeroes (intermittent demand).  Also note that with MAPE you can have absolute errors greater than 100%, so you cannot simply define forecast accuracy as 100% – MAPE.

For management reporting I use a “Forecast Accuracy” (FA) metric, defined as:

1 – { Σ | Forecast – Actual |  /  Σ Maximum (Forecast, Actual) }

Note: FA is defined as 100% when both Forecast and Actual are zero.

By using Maximum of Forecast or Actual in the denominator, FA is always scaled between 0 and 100%, so it is very easy for management to understand.  That is why I favor it, even though some professional forecasters are very critical of this metric.

5. What are your perspectives on how do you differentiate volatile demand from uncertain demand?  In my opnion, uncertainty is related to an event and volatility is related to demand fluctuations. Is that right?

Answer: Volatility is expressed by the Coefficient of Variation (CV), which is the standard deviation divided by the mean.  For example, look at the last 52 weeks of sales, and compute the CV of that pattern.  In general, the more volatile (i.e. erratic and variable) the demand, the more difficult it is to forecast accurately.  Recall the Accuracy vs. Volatility scatterplot in the webinar.

Sometimes we can forecast volatile demand quite accurately, where there is structure to the volatile pattern.  You might see this for highly seasonal items, where you can always count on a big spike in demand at a certain time.  (E.g. bunny costumes and egg painting kits before Easter.)  Note: I’m not claiming we can forecast bunny costume or egg painting kits accurately, just using them as an illustration of volatility due to seasonality.

Volatility is measured looking back at what really happened.  If we expect high volatility to continue, we would probably have less confidence or certainty in our future forecasts.  If volatility is very low, we can probably feel more secure (and certain) of our forecasts.

6. Is there any ratio to determine the horizon for the forecast to be measured?  Any industry correlation to lead times?

Answer: Forecasting performance should be reported relative to the supply lead times.  Thus, if it takes 3 months to make changes in your supply, you should measure the accuracy of your forecasts made 3 months in advance. Once inside this lead time, it is ok to continue to make adjustments to the forecast, and many companies even report their forecast accuracy based on a forecast immediately prior to the period being forecast.  (Some companies even allow adjustments to the forecast within the time period (e.g. week or month) being forecast – and then report that as their forecast accuracy.)  However, it is the forecast made at the lead time that really tells you how well (or how poorly) you understand your business.  Don’t congratulate yourself on good forecasts made within the month being forecast!
Regarding forecasting horizon – how far into the future you should forecast – this will vary based on your business needs.  A power company forecasts years (even decades) ahead to know if it will need to make capital investments in new power plants.  For most companies, forecasting 12-18 months ahead is sufficient.  And the forecasting process should always be “rolling,” so that you always maintain that horizon of forecasts ahead of you.

Routinely doing 5-year ahead forecasts if you don’t really need them seems like a silly exercise.  If management insists on forecasting farther ahead than you really need, don’t waste much time doing it.  It is very unlikely you can forecast very accurately that far ahead.  It is much better to keep your organization nimble and able to adapt to however your market changes over time, rather than fool yourself into thinking you can accurately predict that far into the future.

7. How can you do calculate “appropriateness for forecasting” when your time series is too short for out-of-sample testing?

Answer: When there is enough data, out-of-sample testing is a great way to help evaluate and select forecasting models.  Good software, such as SAS Forecast Server, allows you to define and utilize a holdout sample in your model selection process.  Poorly designed software will select a model based solely on “best fit” to recent history, and as was illustrated in the webinar, the best fitting model may be a very poor choice for generating forecasts.

When there is not enough history to use a holdout sample, the appropriateness of a model is based on the judgment, experience, and domain expertise of the forecaster.  In the webinar example, Model 4 fit the history perfectly, but the forecast exploded to huge values which probably weren’t realistic (unless you had domain knowledge that demand would be significantly increasing, you were rolling out to new regions, etc.).  Without any other information, using the mean (Model 1) or a simple trendline (Model 2) seemed to be “most appropriate.”

8. Statistical modeling can be difficult in planning service parts demand. Can you give further input for planning service demand volatility.

Answer: Demand for service parts if often intermittent, with lots of periods of zero demand.  Intermittent demand is difficult to forecast accurately.  Although there are various methods to help you forecast and manage inventory in these situations (see Croston’s method and its variations), you should not have high expectations for accuracy.  It may be easier (and just about as effective) to simply forecast the mean demand each period.

Sometimes there is sufficiently high demand for the service parts that you could use standard time series methods to forecast.  It may be helpful to incorporate known sales of the items requiring the parts, so you can base your forecasts on failure rates.  Thus, if you know 100,000 units of a product were sold, and that 10% require servicing every year, this could help you decide that about 10,000 of the service parts will be needed each year.

One other approach, more applicable to high value machinery (e.g. jet engines, ships, factory production lines), is knowledge of the routine maintenance schedule.  If you sell 1000 jet engines and the maintenance schedule says a part is replaced every 6 months, then you can use this to forecast demand for that part.

9. Do you have examples available of cost of inaccuracy metrics?

Answer: I do not have access to the Cost of Inaccuracy metric used at Yokohama Tire Canada by Jonathon Karelse.  However, Jonathan will be speaking at the IBF’s Demand Planning & Forecasting: Best Practices Conference in San Francisco (April 28-30), so you could follow up with him there.

IBF members have access to a cost of inaccuracy spreadsheet available on their website.  Also, analyst firm AMR has published research (which you could access if you are an AMR subscriber) on the costs of forecast inaccuracy.
Any such cost calculators are based on a number of assumptions which you provide, so be cautious in your use of them and in your interpretation of the results.  Personally, I’m very skeptical of claims such as “Reducing forecast error 1% will reduce your inventory costs x%.” If nobody in your organization trusts your forecasts now, reducing the error by 1% is not going to make anybody more trusting of the forecast, and they won’t change any behavior, so you won’t reduce inventory.  It may take more substantial improvement to reap the cost benefits.

10. Does anyone work in the call or contact center environment for an inbound customer service center?

Answer: These principles can be applied to forecasting demand for services, such as in forecasting needs for call center staffing.  The major difference is the time bucket that is of interest.  Call centers often forecast in 15 or 30 minutes increments (rather than in weeks or months for a manufacturer), to make sure they are sufficiently staffed during peak call periods, and not overstaffed during the low call times.

Michael Gilliland
Product Marketing Manager, SAS
IBF Board of Advisor

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February 22-23, 2010
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Michael Gilliland

Emily Rodriguez

Are there some things you wish your organization’s management knew about forecasting?  Those of us who have served time in the forecasting profession know that “serving time” is an apt description of the job.  Being a business forecaster is sort of like being in county lock-up – without the benefit of free meals, charming bunkmates, and periodic delousing.  Forecasting is difficult – we never seem to forecast accurately enough to please management.  And forecasting is thankless – even when we come up with good models that forecast reasonably well, someone above us is likely to change the numbers to whatever they darn well please.  Those forecasters that aren’t already on mood altering substances probably should be.

What the forecaster really needs, are the tools to educate management, and forecast as accurately and efficiently as can reasonably be expected given the nature of your demand.

There are four main reasons why forecasts are wrong:

• Unsound or misused software
• Unskilled or inexperienced forecasters
• Politicized forecasting process
• Unforecastable demand

The best accuracy you can achieve is limited by the forecastability of your demand patterns.  So accuracy expectations have to take that into consideration.  The naïve forecasting model is the proper baseline for accuracy objectives, and industry benchmarks should never be used to set accuracy targets.

New product forecasting is an area of particular angst.  Managers realize that these forecasts are usually way off, yet they forge ahead with supply and revenue plans in full confidence.  We suggest that assessing uncertainty and risk is more useful than forecasting alone.  When management has a good understanding of the likely range of new product demand outcomes, the organization can better align resources to all the possibilities.

We also support Forecast Value Added (FVA) analysis – a method now used by many major organizations to identify forecasting process waste and to achieve better forecasts.  FVA evaluates every step and participant in the forecasting process, identifying those that are not adding value by making the forecast better.  Many process activities are found to be making the forecast worse – and these activities need to be fixed or eliminated.

Intel extensively uses FVA analysis.  Over the last three years, Intel has taken the basic idea of FVA and applied it to a broader range of forecasting and supply chain process issues.  Intel has gone through paradigm shifts in thinking, and how to address the change management issues.

On Monday February 22, at the IBF’s Supply Chain Forecasting Conference in Phoenix, perhaps we (Emily Rodriguez, Program Manager at Intel) and Michael Gilliland at SAS) can help improve your forecasting performance. We are delivering a morning workshop entitled “A Primer for Management: Fundamentals of Business Forecasting and Conducting Forecast Value Added (FVA) Analysis.”  The theme for our presentation is “what management must know about forecasting.”  We will be providing step-by-step instructions for gathering, analyzing, and reporting the data needed for a thorough FVA analysis, along with several brief case studies at organizations, where it has been applied.  Furthermore, Emily will provide an in-depth case study of the use of FVA analysis at Intel.

Whether FVA is a new concept to you, or you are an experienced practitioner of the approach, we look forward to having you join us in Phoenix.  Meantime, stay abreast of the latest innovations and defamations in forecasting, in The Business Forecasting Deal.  See you in February at the IBF Supply Chain Forecasting Conference.

Emily Rodriguez, Program Manager
Intel Corporation

Michael Gilliland, Product Marketing Manager
SAS Institute

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February 22-23, 2010
Phoenix, Arizona USA

Ritu Jain

DOW up, DOW down; oil prices spike, oil prices spiral down; jobless claims up, jobless claims steady!! The news is full of contradictions. Is it the start of recovery after what was termed the greatest economic crisis since the Great Depression or are we still in the midst of a perfect storm?  We think that debate is better suited to qualified economists than to a couple of practitioners like us.

What we know for sure is that this is a different world – past trends are no longer indicative of future.  Still, it is surprising to see how many companies continue to adhere to demand planning practices of past.  In a recent survey on current state of demand forecasting practices, conducted by SAS and Purdue University, it was found that Excel Spreadsheets are still the most frequently used demand management tool with about 85% of respondents reporting use of the same for demand forecasting and planning. Interestingly, in the same survey, about 54% of respondents reported using jury of executive opinion as a frequently or occasionally used technique in demand forecasting process.

Tom Vogel

No wonder in past two years, we saw a large number of companies, across multiple disciplines, write off huge inventories, run continuous promotions at rock-bottom prices, and in general do anything to survive.  But that is not the complete picture! We also saw a handful of companies that stood firm in face of market volatility and even emerged stronger.  What is it that made these companies deal with market pressures better?  How were their demand forecasting and planning practices different from others to make them more resilient?

The SAS-Purdue survey included  more than 180 forecasting managers, planners and supply chain executives from 170+ unique companies. The findings revealed interesting differences between demand forecasting leaders and laggards– differences in terms of key performance metrics such as fill rates, customer satisfaction levels and forecast accuracy, as well as in terms of organizational and process level maturity.

Best-in-class organizations consistently shared many characteristics, including:

• Higher degree of organizational alignment between various functional groups.
• Strong focus on measuring and managing to relevant performance metrics
• Reliance on advanced forecasting technology to support sophisticated functional capabilities including causal techniques, attribute based modeling, and simulation.

If you are one of those leading organizations, we would like to hear your experiences. How important do you think is a pro-active, process view of demand forecasting for an organization to become best-in-class?  If you are somewhere along the curve – working your way up to the demand planning maturity ladder – don’t despair, you are in good company.

At the upcoming IBF Best Practices conference, we will share the specific characteristics of best-in-class companies as well as real life case studies on how you can transform your demand management processes.

Send us your comments on anything specific you would like to hear about from us. Remember, learning from each others’ experiences can only help accelerate our way to maturity!

Tom Vogel, Director of Supply Chain Integration
Dreyer’s Grand Ice Cream

Ritu Jain, Industry Marketing Manager
SAS

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