I had the pleasure of speaking with supply chain VP Noha Tohamy of Gartner/AMR Research recently. We were discussing exotic new taxonomies of demand forecasting and demand planning — demand sensing in particular, and where it fits within our understanding of the S&OP process. We also talked about the value of a holistic approach to demand planning — something we discussed earlier on this blog.

Noha agreed with the Foresight premise that a complete, holistic approach to demand planning and forecasting was best: the ability to look at short, medium and long time frames at multiple levels of detail (store/customer to entire networks and everything in between) all at the same time. However, she highlighted a couple of critical caveats. One was the assumption that a company has a platform to enable such a comprehensive approach. Enough said there.

The other caveat is data. In some instances, forecasting and analytical computing ability exceeds the timeliness and accuracy of the data available; in others, there is more data available than some technologies, processes and personnel are capable of handling. Often both these scenarios can be found at the same company. Which problem do you tackle first? Or can you take them both on at once?

Putting POS data in play: a field example

Let’s get a bit more detailed. In terms of importance for improving supply chain efficiency and building partnerships with your customers, POS (point of sale) data ranks high. But it also serves as a good example of the conundrum expressed above.

More advanced platforms and better collaboration have given supply chain technology vendors the ability to grab POS data that is made available either directly by the retail stores or through aggregators such as VIP or IRI. This data is often made available on a weekly basis and aggregated at a chain/region level rather than by store. Historically, the reasons for this have been data size, network connections etc., but these reasons are becoming less relevant as technology improves.

For POS data, even the weekly schedule is frequent enough to allow demand planning applications to grab it and leverage it for modeling, inventory management, trend reporting, and more. In fact, these same applications would allow for the same manipulation and value to be derived from more timely POS — say, on a daily basis and at a store level — which would allow suppliers to more actively respond to trends such as overselling in response to a promotion. Demand Foresight brings its clients this capability, as do others such as Terra Technologies.

Wal-Mart and Home Depot: stunning implications

However, now groups like Wal-Mart and Home Depot are advancing an even more immediate data model. They are actively prototyping a business model wherein they do not take ownership of the product at their stores until the transaction at the cash register — basically a back-to-back where the retailer collects cash from the customer, only having “owned” the product for a fraction of a second, then paying the supplier of the product 30, 45 or 60 days later.

There are several ramifications for this model, but let’s focus on the potentially stunning impact in data alone. How many of you (and your companies that supply retailers, or supply companies that supply retailers) have platformed themselves to take in huge amounts of POS data once a day? Four times a day? Hourly? Fractions of an hour? Given the model described above and the facts that:

  • You have responsibility for the product until it is actually sold
    • You are still operating under service level agreements that demand coverage levels for each square foot of shelf space

Are you prepared? Most are not and this is an example of data overtaking technical capabilities.

Broadening the data question

And remember, this is just one type of data. What about data associated with capacity and the ability to promise orders? If a customer calls and requests 10,000 pieces of product X, can customer service respond immediately with all the applicable availability, time and requirement answers? What if your products have a short shelf life? These are all critical to your forecast.

What about external data such as weather? Planalytics — a great company run by some outstanding ex-Air Force badasses — provides highly accurate and detailed weather info. Some are using the data to help with seasonality and impact on big sales days such as 4th of July. Now companies can profitably tackle formerly esoteric questions such as “How much more beer will we sell if the temp is 100 instead of 80?”

But even all this only scratches the surface of the minute and critical indicators available within Planalytics’ functionality. There’s still demographics, raw material pricing, market capacity…the list is endless, but also critical to answer as these indicators provide huge value for forecast accuracy in the short, medium and long term. Few applications are set up to fully take advantage of everything offered. This is the essence of the data conundrum.

So which side of the problem do you attack first to impact revenue in a positive way? Ideally, both. It will ever be our position that a holistic approach that marries detail with high-level strategy and the very short term with the long term is always going to provide the most value to an enterprise and its bottom line performance.

However, I think professionals are best served by being honest with what they really have to work with and then building from there — so if POS data is available on a weekly basis and it is not being used, then focus on taking advantage of the POS; learn the possibilities and build from there (invest in the correct applications). If however, you and POS are old friends, then tackle the other side, start to invest for the future by following tracks laid by companies like Best Buy and Sony, who are executing complete and real-time data sharing.

If you platform yourself correctly, not only can you get ahead of the data curve, but you’ll be armed with a unique competitive weapon: the ability to approach your retail customers and say, “I know how you can optimize the dollars generated from each square foot of your stores and distribution centers.”

Hmmm – helping each partner of your value chain maximize their efficiency and profitability…Now there is something that can positively impact your EBITDA both for the long term and in a way that allows for competitive differentiation- and all of it tied to a focus on improving forecasting and demand planning.