Maria Simos CEO

When you come to a conference, all you want is to talk to as many people as you can so you can learn what everyone else is doing and learn from them.  Speaking with one attendee, they shared how in their group, there is only four demand planners, spread across the globe.  The benefit of attending events like the IBF Best Practices Conference is that you are now in a room with hundreds in the same position.  But still, how to talk to as many as possible?  How about some speed dating!  So that’s what about 100 or so attendees chose to do with the late afternoon session at the conference.  We were broken up into several different groups each with a different topic to discuss. We worked our way around the room a few times so that we would have a chance to discuss the topics we were most interested in. Each topic was led by table monitors.  With so many great topics to choose from, it was a hard decision, but  I chose to head  over to the JDA led table first and listened in on the discussion about “Improving forecasting and planning with consumption (POS) and syndicated data” led by Danny Halim.

The group quickly began sharing how they are all ‘trying’ to use consumption data in their demand forecast.  Digging deeper into the reason for the repeated use of the word “trying”, several issues came up regarding the reliability of  POS data.  Several fellow date-ees shared their systems for cleaning the data, or merging several different sources to make it more comprehensive.   Some companies manually merge them together, while others have built complex systems to forecast inventory levels based on POS data provided by major retailers such as Walmart. It is essential to find a system that works to clean the bad data in order to make it usable. The phrase ‘garbage in, garbage out’ was used frequently although I would say these daters were real pros at sharing and I found myself  not  wanting to get up and move on to another table as the discussion was really exciting.  I hope to see some break-out sessions on this topic at future IBF events.

I headed over to the next table where the topic was “what forecasting system works best for you”?  All daters were sharing at first was whether  their organization goes top bottom or bottom up.  Not to be left out, there were also a few working from the middle out.  The demand forecasting world does not discriminate and accepts all creeds! Two daters shared that their group does both (bottom up and top bottom) and then reconcile the forecasts.  Another shared how they begin with a price line item forecast then dollarize it by working with the marketing team and use this to drive the financial forecast.  To do this, they create assumptions upfront on the industry, start with a baseline and use this target for demand planning functions.  Not everyone  shared happy stories of their forecasting process.  One person explained that their sales department does not participate in providing input into the forecast, event though they are the closest to the demand and the customers..  The ideas presented in the Keynote Presentation by Gerry Fay of Avnet EM Velocity came up as ways to combat some of the issues the table faced. Some of these were demand sensing and responding, and command and control.  A few other different approaches presented were forecasting at the SKU level then weighting forecasts by different group functions at certain levels depending on the timing, conflicts with upper management when they do not see what they want in the forecast, forecast ownership and having the sales team provide forecast as a change in trend, rather than level.  Again, when table switching was called for, it was hard to leave but by this point everyone was so warmed up to sharing I looked forward to seeing how the last table would go.

Our last group date was led by Mike Gilliand of SAS and we talked about “new product forecasting”.  Around the table the range of new products spanned  from 5-40%.  Forecasting by analogy is the method most commonly used for new product forecasting.  The focus on the higher levels of uncertainty and risk were brought up, and the strong need to make sure management realizes this as new products roll out.  Also, the importance of tracking past new product forecast reports was part of the discussion as well.  Is your sales team consistently over-shooting? Keep this in mind.  One major takeaway was to make sure and track what assumptions were used when you are making the forecast.  If you carefully track these, it will assist in making the forecast better and help the team in the long run.  The general consensus of the table was that it takes roughly six months, for the most part, to know if a new product is going to succeed before entering it into the standard S&OP process for the organization.

And with that, speed dating was done and all minds were racing.  The level of sharing within the group continued to grow and we all moseyed over to the cocktail reception where the speed dating conversations continued and mini-crab cakes, succulent ripe California strawberries and JDA signature martinis were our award for being such great daters and sharers of demand planning lessons.

Maria E. Simos is CEO of, 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 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.