“We think in generalities, but we live in detail.” - Alfred North Whitehead (1861-1947)

It’s official.

The most overused phrase in the retail industry for 2008 is (drum roll please)... “customer centric”.

Like many of you, I also subscribe to and collect many industry journals, white papers and research reports. In the last 2-3 months alone, here are some of the titles that have made it into my email inbox:

The Customer-Centric Store
Customer Centric Retailing
Deeper Customer Insight
Customer-Centric Merchandising
The New Customer-Centric Supply Chain

The tone and focus of each is slightly different, but the underlying message is the same: High level segmentation and demographic analysis is not good enough anymore. Retailers must strive to be flexible enough to understand and respond to the needs of individual customers.

More and more, store layouts and assortments – even those of mass merchandisers - need to be at least partially customized to meet the unique demands of the communities they serve.

Unfortunately, these discussions do little to address the kind of supply chain needed to support such a strategy. Most large retail organizations continue to use a “top down” process for planning their supply chains – an approach that’s inherently flawed when it comes to trying to support a customer centric model.

In the survey titled The New Customer-Centric Supply Chain from Retail Systems Research, the responses tell us that retailers intuitively understand the “bits and pieces” required to have a customer centric supply chain:

When asked about some of the challenges they face in adapting their supply chains to be customer centric, the most popular responses are:

“The forecast never matches demand - leaving us to scramble in execution”

“Customer service requirements demand ore precision from the supply chain for delivery quantities and time”

“Optimizing inventory across channels”

Even more promising are the responses when asked about the top opportunities for the supply chain to contribute to the company’s performance:

“Increase supply chain visibility”

“The ability to re-optimize plans in real time based on what’s happening in execution”

“Increase our ability to recognize and respond to supply chain exceptions”

And specific to the question of what capabilities are needed to provide the necessary visibility to be customer centric, it’s becoming clear that retailers are really starting to “get it”. Here were some of the responses at or near the top of that list:

“A rolling forecast of demand”

“Real-time inventory visibility in store”

“Promotion planning tied to supply forecast”

The real question now is: How will retailers tie all of this together? How quickly will they realize that they must abandon “top down” strategies in favour of a “bottom up” approach – starting with the act of a customer making a purchase at a store?

For me, the need for retailers to be more customer centric became clear many years ago – long before it became the “flavour of the month”.

In a simulation of the Flowcasting process, consumer demand for several different beer brands was modeled for a small convenience store chain with 10 stores in the greater Montreal area. When analyzing the history, we noticed that 9 of the 10 stores had a classic seasonal pattern for beer in Canada – higher sales in the summer months, lower sales in the fall and winter with a spike during the holidays.

The tenth store, however, had an inverted seasonal profile (higher sales during the fall and winter with lower sales in the summer). What could explain such a difference in the seasonal profile, given that this store was less than 30 miles from any other store in the study?

It turns out that this tenth store was located adjacent to a university campus. Far fewer students in the neighbourhood during the summer months means lower demand for beer in that store.

The same type of phenomenon happens regularly in the grocery business. Stores that are geographically very close can have vastly different demand patterns on certain items due to the ethnic makeup of the neighbourhoods they serve.

Looking at aggregate numbers would never allow us to capture these types of important demand influencers – they only apply at store level.

“Consumer segments” don’t buy “product categories” from “store clusters”... customers buy products from stores. To be effective at customer centric retailing, a top-down approach simply won’t do - we need to forecast and plan where it is most useful and required, not where it’s most accurate or convenient.

Bottoms up!

Posted by Jeff Harrop at 01:00 AM | Permalink | Comments (0) | TrackBacks (0)

Last week we gave a presentation about Flowcasting at the Massachusetts Institute of Technology summer professional development program.

To download a copy of the talk, along with the notes, click the link below:

Download file

The highlight of the talk was when Andre described how a large US retailer and one of their suppliers was using Flowcasting to manage their business. While we can’t name names, this involves 100 or so items supplied to about 600 stores. Using the Flowcasting process the results to date have been brilliant:

• Shelf availability of 99.9%
• Store inventory turns of 38+
• Planner productivity increase of 30%

Thanks to Professor Jeremy Shapiro and MIT for giving us the opportunity to talk about our passion.

Posted by Mike Doherty at 12:55 PM | Permalink | Comments (0) | TrackBacks (0)

Everybody knows that it’s impossible to forecast with 100% accuracy. This simple truth inevitably leads to a lot of discussion about what is the “right” level of accuracy.

If it’s a “bread and butter” item with a high rate of sale, is 80% accuracy good enough? What level of accuracy should I expect on a seasonal product? Or a slow mover? Or an item that’s promoted frequently?

Look, we’re not in elementary school anymore. There is no grading system for forecasts. Sometimes 80% accuracy is a poor result and under a different set of circumstances 30% accuracy may be good enough.

Your achievable level of accuracy depends on two things: 1) the level of random variation in the demand pattern you're trying to predict (and which is largely out of your control); and 2) the business process you follow to produce forecasts. If you are consistently following a good process, then your accuracy will be as good as it possibly can be.

So how do you know if you're following a good business process?

The Ten Commandments

The Ten Commandments are recognized by Judaism, Christianity and Islam as an important set of rules for guiding human behaviour. No matter what your religion, if you followed the commandments, particularly the ones pertaining to how we interact with each other (Thou Shalt Not Kill, Thou Shalt Not Steal, Thou Shalt Not Bear False Witness, etc.), then you could safely say that you’re not an evil person.

Over the years came many more laws based on the Commandments. It didn't take long for this to deteriorate into a complex web of rules and regulations, many of which contradicted each other. It became more and more difficult to know how to be good.

Then, about 1,000 years after the original Commandments were given to Moses, a young carpenter from Galilee boiled them down nicely into a single Golden Rule:

“Do unto others as you would have them do unto you” (or, alternatively, “Treat others as you yourself would like to be treated”).

If you were to follow this one rule, you would have at least six of the Ten Commandments covered.

The Rules of Good Forecasting

It’s fairly well documented that good supply chain forecasting processes have the following characteristics (or, if you will, commandments):

1) Thou Shalt Be Free of Procedural Bias – This means that there aren’t steps designed into the process itself that cause habitual overforecasting or underforecasting.

2) Thou Shalt Be Free of Organizational Bias – In other words, performance measures are aligned with the goal of your operational forecasting process, so that people aren’t given incentive to intentionally “sandbag” the numbers one way or the other.

3) Thou Shalt Account For Time, Place and Magnitude – You can’t schedule your supply chain around an operational forecast that says you’re “going to sell 10,000 units across all locations over the next 8 weeks”. It needs to be very specific with respect to timing, location and quantity to be of any operational use.

4) Thou Shalt Not Buffer – This is often the toughest to detect, because it’s caused by the best of intentions. In a nutshell, this is the practice of forecasting “a little bit extra, just in case” to guard against service failures.

Like the Ten Commandments, volumes of material has been written in support of these rules. But if you adopt one simple Golden Rule in your forecasting process, you won’t have to worry about being one of the contributors to forecast error.

The $500 Rule

The $500 Rule of Forecast Management States:

“The operational forecast that runs your supply chain is the one on which you would bet $500 of your own money.”

Think about it. Suppose you were obligated to enter a forecasting competition and pay a $500 entry fee. You can use any resources and information at your disposal. But for a particular item in a particular week, whoever comes closest - in absolute terms - to the actual number wins the pot.

If these were the rules of the contest, would you be inclined to take steps that inflate the number beyond what is realistic? Would you simply average a big number across a number of weeks? Would you forecast what the facts are telling you “plus a little bit extra”?

While there’s no antidote for random error and completely unforeseeable events, you can still choose to spend your time and energy (i.e. money) on coming up with forecasts that are as honest as possible (i.e. you’d plunk down 500 bucks of your own money on it).

The great part is that, to your organization, the benefits of unbiased forecasting will make $500 look like chump change.

Posted by Jeff Harrop at 08:00 PM | Permalink | Comments (0) | TrackBacks (0)