I saw this linked in comment in a discussion group:

“I hate to get technical”. That expression always bothered me, but I am wondering who IS getting technical….who is using Orders to help predict POS? Who is using a “day of the week” dummy variable?

It is my belief that the current simplistic solutions aren’t able to deal with issues like this. Let me explain why….because they can’t solve the technical modeling issues to solve the problem correctly. I defy someone to tell me TWO solutions that can bring in orders to help forecast POS demand and identify and adjust the model based on the lead/lag relationship between orders and POS demand. The same holds true for the “day of the week” dummy variable added in automatically when you have daily data. Also “week of the year”….” day of the month(when appropriate)”….fridays before a WEEKEND holiday…do you see my point?….. I could go on all day here…..How about Interventions?….identifying and adjusting the model for a “level shift”….” local time trend”….the challenge has made…..one more thought….the conclusion from the M3 competition that simpler was better did have a lot to do with the “KISS methodology” conclusion which didn’t necessarily help besides the technical difficulty.

“”I hate to get technical”. That expression always bothered me, but I am wondering who IS getting technical….who is using Orders to help predict POS? Who is using a “day of the week” dummy variable?

It is my belief that the current simplistic solutions aren’t able to deal with issues like this. Let me explain why….because they can’t solve the technical modeling issues to solve the problem correctly. I defy someone to tell me TWO solutions that can bring in orders to help forecast POS demand and identify and adjust the model based on the lead/lag relationship between orders and POS demand. The same holds true for the “day of the week” dummy variable added in automatically when you have daily data. Also “week of the year”….” day of the month(when appropriate)”….fridays before a WEEKEND holiday…do you see my point?….. I could go on all day here…..How about Interventions?….identifying and adjusting the model for a “level shift”….” local time trend”….the challenge has made…..one more thought….the conclusion from the M3 competition that simpler was better did have a lot to do with the “KISS methodology” conclusion which didn’t necessarily help besides the technical difficulty.”

I was initially puzzled by the order of the POS versus orders – what is forecasting what?  or even Why?  Is it useful to predict POS using the orders?  Even if there is a significance, would that be a spurious variable just proxying inefficiently for the inventory levels and perhaps promotional activity?

We have always tried to solve client problems that needed the POS data to try to predict the orders.  Now you mention using Orders to predict POS, which seems to be a novel idea.

Orders can predict POS?  If so, how?  If I am the retailer and I can place orders, how will I use my historical order pattern to predict what my consumer is going to buy?

Perhaps just use the old adage “stack ’em high and see ’em fly”?!

Just order from the manufacturer and build your shelves and stores with the inventory, so the mere visibility of this inventory creates POS demand?!  If so, this will make the push concept very usable.  Seems very consistent with the supply-side argument of the economists on the monetarist side.

It is good to see a refreshing view amidst all the supply chain forecasters subscribing to the pull-based forecasting methodology.  And I am one of them too……. I believe that using the POS can better predict Orders, the opposite of what you are proposing.

And this is what we preach and educate our constituents. How to better use your POS demand and inventory patterns to better create an order forecast.