Celebrating an autotelic

A few days after losing the 2010 French Open Final, Novak Djokovic said to his coach, Marián Vajda, that he wanted to quit. He was ranked third in the world, a grand slam winner, and one of the favorites to win Wimbledon.

His coach asked him, “Why do you play?”

Djokovic immediately sensed the problem: He was focusing on rankings, titles, and playing to impress others. As a result, he said, “I was really messed up, mentally.”

As he pondered the question, he realized something. Most of his fondest childhood memories included his “favorite toy” – a small tennis racket and a soft foam ball. He started playing, “because I loved holding that racket.”

“Do you still love it?” his coach asked. Djokovic thought about it, got excited, and said: “I do. I still love holding a racket in my hand. Whether it’s a final on center court or just horsing around, I like playing for the sake of playing.”

His coach then nodded and said, “That’s your inspiration. That’s what you need to tap into. Put aside rankings, titles and other external stuff, and just play, for the love of it.”

Djokovic agreed. And he has never looked back.

The following season, Djokovic enjoyed one of the greatest seasons in sports history. He won 43 straight matches, including his first Wimbledon title. And he finished the year as the No. 1 ranked player in the world.

“I started to play freely,” he said. “I became the kid again, who just loved to play.” There’s a word for doing something for the love of doing it:

Autotelic.

The word stems from the Greek auto (self) and telos (end) – an autotelic is “someone that has a purpose in, and not apart from, itself.”

For an autotelic,” The work is the win,” as Ryan Holiday says. “You need to get to a place where doing the work is the win and everything else is extra.”

Today I’d like to celebrate an inventory planning autotelic – my colleague and collaborator, Darryl Landvater, of the Oliver Wight Americas Group.

Before we became colleagues, Darryl worked with Andre Martin to build and implement the first Distribution Resource Planning (DRP) system at Abbott Labs in Montreal – connecting distribution and manufacturing operations, working to a single set of numbers, and changing how distribution and manufacturing operations were planned forever.

We met and began our collaboration at Canadian Tire, in Toronto, Canada in the mid-1990s. The team I was leading was in the process of re-engineering how product flow planning was done. Darryl (and Andre) helped us convince the Executive team that our design – essentially Flowcasting – would work and also helped us during the initial implementation, especially with respect to education and supplier scheduling.

Shortly after, he and Andre took their idea of an integrated supply chain to see some of the big technology players in the supply chain planning space – with a goal to get them to build a store-level, integrated solution – even offering to help in the process. But every one of them said no. They didn’t believe the market need was there and/or a solution could be built to scale to the retail volumes and specific planning challenges.

Undaunted, and in keeping with an autotelic philosophy, they said “fuck it, we’ll build it ourselves. And they did. Darryl was the chief architect, along the way teaching himself Java and how to code again.

The result was a stunningly simple and elegant solution, including developing leading retail solutions for slow sellers, seasonal planning, promotions, scalability, and true daily net-change planning, among others.

The love of the work inspired him…and still does.

In fact, he’s just finishing re-architecting the solution to a leading-edge, ultra-modern platform to provide clients a robust, flexible, infinitely scalable, and affordable solution.

At any time, someone is always the best in the world.

In tennis, it’s Novak.

For Flowcasting solutions, it’s Darryl.

Perhaps there’s a lesson here.

Maybe, to be the best in the world, you need to be autotelic!

Retail Stock Management: The Cycle of Insanity

The object in life is not to be on the side of the majority, but to escape finding oneself in the ranks of the insane. – Marcus Aurelius Antoninus (121AD – 180AD)

Is this a comedy of errors or a tragedy?

1. INT. RETAIL STORE (AISLE 6) – MONDAY, 10:34AM

RICK – a retail department manager – sees a confused looking CUSTOMER in front of one of the shelves he manages. He approaches to see if he can help.

RICK
Is there something I can help you with?

CUSTOMER
I need one of these, but I can’t find any.

RICK scans the shelf tag (Item #542317) and the system shows an on hand balance of 24 units.

RICK
That’s weird. I better check the back room. Give me a few minutes.

RICK walks back to the stockroom and opens the door.

STOCKROOM

Upon opening the door, RICK is confronted with a disorganized mess of cartons – some already opened – haphazardly packed onto shelves and pallets of stock on the floor stacked 3 deep against the wall.

RICK

I’m never going to find it. I’ll just write it off and the system will order more.

He takes out his trusty handheld device and, with a few keystrokes, wipes out the 24 on hand units in the system. He heads back to the CUSTOMER who is patiently waiting for him in Aisle 6.

BACK IN AISLE 6

RICK
I couldn’t find any back there, but we should have some more by the end of the week.

CUSTOMER
(disappointed)
Oh, okay.

2. INT. RETAIL STORE (RECEIVING) – MONDAY, 4:55PM

SALLY – the receiving manager – is looking at her daily reports and sees that there are quite a few trucks coming over the next few days. The mayhem in the stockroom has caused inventory to pile up in her receiving area and she anticipates disaster by week’s end. She leaves a note to the night crew asking them to get as much stock out of the stockroom onto the sales floor as possible.

3. INT. HOME OFFICE (SERVER ROOM) – TUESDAY, 2:25AM

Lights are blinking in the dark room as the computer assisted ordering system (CAO) is combing through the stock records for RICK’s store. CAO comes across a zero balance for Item #542317.

CAO
They’re out of stock! I had better send them another case!

CAO sends a replenishment request to the DC for 24 units of Item #542317, due into the store on their Thursday shipment.

4. INT. RETAIL STORE (RICK’S DEPARTMENT) – TUESDAY, 5:55AM

RICK arrives to see that the night crew has been very busy. There are pallets of stock in every aisle waiting to be put on the shelves. He gets to work.

AISLE 6

As RICK is working his stock, he comes across a case of 24 units of Item #542317 that was somewhere in the stockroom.

RICK
There you are! I’ve been looking for you! Well, not really.

He fills up the shelf with stock and admires his work. He brings back all of the stock he couldn’t fit on the shelf back to the stockroom. It’s still messy in there, but at least there’s some room to move around. He doesn’t update the on hand balance to reverse the writeoff he processed on Monday.

5. INT. RETAIL STORE (RECEIVING) – THURSDAY, 8:51PM

The Thursday shipment has arrived. SALLY has been struggling to keep up all week and the stockroom is really getting jammed again. Before leaving for the day, she approaches PHIL, the night crew manager.

SALLY
PHIL, I really need you to clear out Receiving tonight! I have more trucks coming tomorrow to get us through the weekend and I don’t have any room to bring them in!

PHIL
No problem, will do!

6. INT. RETAIL STORE (RICK’S DEPARTMENT) – FRIDAY, 5:53AM

RICK arrives and sees that there are lots of pallets of stock in his aisles that need to be put away. It’s the weekend, so he isn’t surprised. He gets to work cutting open cartons and filling shelves.

AISLE 6

RICK picks up a case of 24 units of Item #542317 that CAO requested in the wee hours of Tuesday morning. A few units have sold since Tuesday, but the shelf is still mostly full and the new stock won’t fit.

RICK
(frustrated)
Stupid replenishment system! Either I’m out of stock or I have way too much!

He sets the case back down onto the pallet to take back to the stockroom after he’s done filling the shelves.

STOCKROOM

RICK starts shoving his excess stock wherever it can fit, including the case of Item #542317. He puts in on a shelf, slides it to the back and then puts a couple boxes of other stock in front of it. It’s been a rough morning so he heads to the break room to get something to eat. The writeoff he processed on Monday is now a distant memory. Even though there are 40+ pieces of Item #542317 in the store, the system thinks there are only 24. Hopefully it will get corrected when they do the physical inventory after Christmas.

7. INT. RETAIL STORE (AISLE 6) – 3 WEEKS LATER

RICK sees a confused looking CUSTOMER in front of one of the shelves he manages. He approaches to see if he can help.

RICK
Is there something I can help you with?

CUSTOMER
I need one of these, but I can’t find any.

RICK scans the shelf tag (Item #542317) and the system shows an on hand balance of 24 units.

RICK
That’s weird. I better check the back room. Give me a few minutes…

FADE TO BLACK

The hardest math of all

It’s September 1981 and I’d walk into and sit down at my first class at the University of Waterloo, in Waterloo, Ontario. It’s algebra, taught by Professor Lee Dickey. After he introduces himself, he walks to the large blackboard and proceeds to write Fermat’s Last Theorem on the board – a world famous mathematical theorem that Fermat had claimed he’d had a brilliantly wonderful proof that he hadn’t written out before he unexpectedly passed away.

It’s a theorem that had eluded the brightest minds in mathematics for 350 years. Professor Dickey told the class that if anyone could prove this, they’d be immortalized in the mathematics community. After class, I considered looking at it, but instead decided to go to the bomb-shelter (the campus pub) with some new friends for a dozen or so pints.

Mathematics is a wonderful discipline, providing foundational constructs and principles for engineering, computing, and architecture, to name only a few. But mathematics is hard. Differential equations, calculus, algebra, probability distributions, number theory, string theory, etc. – the list of branches of mathematics is amazing.

And, of course, mathematics plays a huge role in business, especially in supply chain management.

In business, one math concept stands out as the most influential, but also the hardest to instill and master.

That concept would be “subtraction”.

Whether you’re designing new products or business processes, everyone worships at the altar of simplicity. And, with good reason.

Simplicity sells. It sticks. Simplicity made hits of the Nest thermostat, Fitbit, and TiVo. Simple brought Apple back from the dead. It’s why we have Netflix. The Fisher Space Pen and the Swiss Army Knife are some of our most enduring products. All marvels of simplicity.

Yet while many mechanical marvels of simplicity remain true to their original form, many electronic ones don’t.

Travel back in time to use an early microwave and you’ll likely see a box with three buttons (High, Medium, Low) and a timer. Today, one of LG’s current models boasts 33 buttons. Do you press Auto Defrost or Express Defrost? And what does Less/More do? None of these make your popcorn or pizza cook faster, or taste better. And it’s not easy to use. Why do products almost always become more complex as they evolve?

“Simplicity is about subtraction,” says Mike Monteiro, author of Design Is a Job. “We live in a culture of consumption, where quality is associated with more. Designers and manufacturers tend to believe that to succeed you must provide more”.

Consider Apple. Simplicity saved the company. Starting with the iMac, it rolled out a series of stunningly simple hits: OS X, iTunes, the iPod, iPhone, and iPad. Google was also built on simplicity. Google won dominance with its sparse and very simple search page. Simplicity made Google a verb.

Executives, managers, and project teams alike struggle with keeping things simple. That’s because they can’t subtract. I read a recent Wall Street Journal report that workers now spend over two full days a week either in meetings or on emails. That’s incredibly ineffective and hardly the way to innovate or drive change.

To innovate or change, your calendar needs white space. Lots of it. Ideas rarely happen in meetings. They usually occur when you’re daydreaming, showering, walking, taking a dump, or when you’re bored and not doing anything. So, why don’t we have more white space in our calendars? Easy – we can’t seem to subtract those meetings.

The same chronic issue happens with meeting sizes. Most of the time there’s too many people, who could have been more easily informed or communicated with via email. Again, most people can’t subtract participants from the sessions.

It’s a similar phenomenon in product and process design. Virtually everyone wants to add when you really should be looking to subtract. Is it any wonder that most business processes and their corresponding solutions are too complex?

Simplicity is quite easy to say, but very hard to achieve. It requires paring things away when market forces want you to add. It means removing layers rather than adding them.

It’s achieved by subtraction.

Which is, in fact, the hardest math of all.

What Demand Planners Really Need

Necessity never made a good bargain. – Benjamin Franklin (1706-1790)

If you ask someone who thinks they know what a retail demand planner needs from a forecasting system, the response will likely be a list of features and gadgets that they believe will make  forecasts “more accurate”. On the surface, this makes some sense – a more accurate forecast has greater planning value than a less accurate one.

Based on this perceived need, the hunt is on to buy a shiny new forecasting system for the demand planners to use. After some evaluations, the list is narrowed down to a couple of front runners. You send them your historical sales data and challenge them to a “bakeoff” – whoever produces the most “accurate” weekly forecast over a few cycles wins (or at least significantly improves their odds of winning).

And what do you learn from this process? You learn how good a bunch of nerds working for the pre-sales team are at fine tuning the inner workings of their system to produce the desired result they’re looking for – a new customer for their solution. How many person hours did they spend trying to win the sale? What exactly did they do to the models? Is any of what they did even remotely close to what a real demand planner can (and should) do on a daily basis to manage a large number of forecasts? You probably won’t learn any of this until the implementation team arrives after you sign on the dotted line.

What you will probably also learn is that each of the front runners produces a more accurate forecast for about 50% of the forecasts – likely with no clear reason as to why one did better than the other for a particular item in a particular location in a particular week. After rolling up all of the results, you find that one software provider’s accuracy is 0.89% higher overall than the other for the sample set used.

That’s when someone creates a fancy spreadsheet to “prove” that this extra 0.89% of “accuracy” actually equates to millions of dollars of additional benefits when you multiply it through all items at all locations and do a 10 year net present value on it. It’s all complete nonsense of course, but because it’s based on a tiny kernel of “truth” from the evaluation, it’s given outsized weight.

Fast forward to 3 years later. All of the real business challenges rear their ugly heads during the implementation and are solved with some compromises. Actual demand planners can’t seem to get the same “accuracy” results that were touted in the bakeoff. They don’t really understand all of the inner workings and don’t have the time that the pre-sales team had to fine tune everything in the same way. All of the press releases say that they now use Software X for demand planning, but in reality, most of the real work is being done in Excel spreadsheets, which the demand planners actually know how to use.

Now what if you asked demand planners directly what they actually need from a forecasting system? It’s really only 2 things: Comprehension and control.

Comprehension

When a demand planner is reviewing a system calculated forecast, they want to be able to say one thing: “Given the same inputs as the model, I would have come up with the same forecast on my own.”

That doesn’t mean that they agree with the forecast, it just means that they understand what the model was “thinking” to come up with the result. They don’t need code level understanding of the algorithms in order to do this, just knowledge of how the model interprets data and how it can be influenced.

Before they move a dial or switch to alter the model, they want to be able to reliably predict the outcome of their actions.

Control

So long as the behaviour of the model can be understood, a demand planner will want to work with it to get the output they want, rather than just give up and work against it with manual overrides they calculated in Excel.

Knowing what the model did and why it did it is important, but demand planners also need to know how to affect changes in the model to make it behave differently, but also predictably so that the system will produce forecasts that they agree with and for which they are willing to be held accountable.

Accuracy is a rearview mirror measure. Demand planners need to be able to live in the future, not the past. In order to support them, a forecasting system needs to be both understandable and directly controllable so that they can fully accept accountability for the outcome.

Think Different

An accountant and a former pig farmer would take the elevator and head to the 9th floor of an office building located at 2180 Yonge Street in Toronto, Canada. It’s fall, 1994. They’d make their way to a corner of the floor and be joined by a few other new teammates. A recent economics graduate, an operations research PhD candidate, an engineer from overseas, a former grocery IT analyst, and a person from a large multi-national CPG company with an educational background in history and geography.

There they’d meet their team leader – a dude that had worked in industry and in consulting and was self-described as “a bit of a maverick, who had little regard for hierarchy and was equal part genius, equal part buffoon”.

Their mandate?

Completely change the way product was planned and flowed in a retail supply chain, from factory to store shelf, for this $5billion Canadian retailer with more than 450 stores from coast to coast.

To say this team was unconventional was an understatement. As an example, every team was given the latitude to select their own furniture for meeting rooms and workspaces. After reviewing various options and costs, the team would decide that instead of a costly large meeting table they’d instead buy a ping-pong table and use it for working sessions. It was considerably cheaper, and at lunch and during breaks, they could play ping pong.

The introduction of a ping pong table as a work area became the stuff of legends. Lunch times were dominated with ferocious matches and the Senior Vice President of Supply Chain (the ultimate boss of this team) would famously have his ass handed to him one day during a match with one of the team members. No one worried about taking it easy on him. Both his forehand and backhand were weak and, as a result, he got what he deserved – crushed!

Work wise, the team would stutter and stumble. They’d take too long and get bogged down numerous times. They’d spend considerable time in stores, listening to store owners lament about poor product flows and shit service levels. Several ideas were documented, debated, and eventually scrapped.

One idea, though, would survive and the team continued to refine and improve it. Eventually, the idea of what we now call Flowcasting would be documented, and the team was certain the idea was simple, intuitive, and potentially game changing.

Unfortunately, the Senior Executive team didn’t concur. Several executives considered the idea a pipe dream and told the team, “Change the design since this will never work”.

Luckily the team leader was also a bit of a c*nt and was not too keen at being “told” to change the design, especially by speculators – no one knows if something will work before you do it, so how can anyone say, with certainty, that “this will never work”. Never is long time.

As it turned out, one of the technology team members would connect the business folks with Andre Martin and Darryl Landvater and they would reassure the team with the fact that they had already conceived and ran some pilots of this design idea a few years before. A turning point for the design and the Flowcasting concept in general.

They would help convince the Senior Executive team that arrival-based, integrated, time-phased planning (the foundation that Flowcasting is built on) would be a critical capability retailers would need to enable their supply chains to flow product and deliver. The design direction would be approved, and subsequently implemented from DC to supplier and the rest is history.

What was the secret sauce of that pioneering team from Canadian Tire?

Diversity.

There’s lots of talk today, and rightly so, about diversity. I think, however, many companies and especially teams are missing the real key to diversity – that is, cognitive diversity.

Are you looking for ways to inject fresh perspectives and innovative thinking into work?

Step forward, cognitive diversity.

Numerous studies have shown that people like working with others who think like them and have similar values.

The problem, however, is that it leads to groupthink, stifles creativity, and can limit the solutions that are proposed. Instead, you should embrace cognitive diversity, which means forming teams of people who are quite likely to disagree and bring widely varying perspectives and experiences to the table.

Look for people with different beliefs and/or personalities. Seek out colleagues with different educational backgrounds, widely different work experiences, from different parts of the world, or with different levels of risk acceptance.

The best teams, who deliver real and meaningful change, are ones with diverse perspectives and skills that complement each other. The best teams are cognitively diverse.

Think about many supply chain transformation initiatives. More often, the teams are composed of people with a similar background – type A personalities with a technical, mathematical, or engineering background. And what does that almost for certain guarantee? Groupthink and that the designs will be factual and logical.

Everyone who’s implemented something knows that logic plays a small role. Implementations are about people and people have feelings, emotions, wants and needs. They can, at times, seem quite illogical. A cognitively diverse team will have better understanding, wider perspectives, better questioning & listening skills, and more empathy – all ingredients needed for successful change.

So, the next time you need to recruit a new team member and you have a candidate that’s got pig farming (or other seemingly oddball experiences that don’t fit the standard mold) in their experience, my advice is to hire them.

You’ll be glad you did.

HBR article included as chapter in upcoming book

We’re happy to announce that our recent Harvard Business Review article (A Better Way to Match Demand and Supply in the Retail Supply Chain) about Flowcasting was selected to be included as a chapter in an upcoming Harvard Insights Series book entitled, “Supply Chain: The Insights You Need from Harvard Business Review”. This book will outline the latest thinking, insights, case studies and leading practices in supply chain, from several authors.

Thank you, HBR, for including our piece in this upcoming book (due out in October in paperback and as an eBook) and for recognizing the strategic significance of Flowcasting and a consumer-driven, integrated inventory flow planning process.

Probabilistic Forecasting – One Man’s (Somewhat Informed) Opinion

A reasonable probability is the only certainty. – E. W. Howe

My, how forecasting methods for supply chain planning have evolved over time:

  • Naive, flat line forecasts (e.g. moving averages) were once used to estimate demand for triggering orders.
  • Time series decomposition type mathematical models added more intelligence around detecting trends and seasonality to enable better long term forecasting.
  • Causal forecasting models allowed different time series to influence each other (e.g. the effect of future planned price changes on forecasted volumes)

All of these methods are deterministic, meaning that their output is a single value representing the “most likely outcome” for each future time period. Ironically, the “most likely outcome” almost never actually materializes.

This brings us to probabilistic forecasting. In addition to calculating a mean (or median) value for each future time period (can be interpreted as the most likely outcome), probabilistic methods also calculate a distinct confidence interval for each individual future forecast period. In essence, instead of having an individual point for each time period into the future, you instead have a cloud of “good forecasts” for various types of scenario modeling and decision making.

But how do you apply this in supply chain management where all of the physical activities driven by the forecast are discrete and deterministic? You can’t submit a purchase order line to a supplier that reads “there’s a 95% chance we’ll need 1 case, a 66% chance we’ll need 2 cases and a 33% chance we’ll need 3 cases”. They need to know exactly how many cases they need to pick, full stop.

The probabilistic forecasting approach can address many “self evident truths” about forecasting that have plagued supply chain planners for decades by better informing the discrete decisions in the supply chain:

  • That not only is demand variable, but variability in demand is also variable over time. Think about a product that is seasonal or highly promotional in nature. The amount of safety stock you need to cover demand variability for a garden hose is far greater in the summer than it is in the winter. By knowing how not just demand but demand variability changes over time, you can properly set discrete safety stock levels at different times of the season. 
  • That uncertainty is inherent in every prediction. Measuring forecasts using the standard “every forecast is wrong, but by how much” method provides little useful information and causes us to chase ghosts. By incorporating a calculated expectation of uncertainty into forecast measurements, we can instead make meaningful determinations about whether or not a “miss” calculated by traditional means was within an expected range and not really a miss at all. The definition of accuracy changes from an arbitrary percentage to a clear judgment call, forecast by forecast, because the inherent and unavoidable uncertainty is treated as part of the signal (which it actually is), allowing us to focus on the true noise.
  • That rollups of granular unit forecasts by item/location to higher levels for capacity and financial planning can be misleading and costly. The ability to also roll up the specific uncertainty by item/location/day allows management to make much more informed decisions about risk before committing resources and capital.

Now here’s the “somewhat informed” part. In order to gain widespread adoption, proponents of probabilistic methods really do need to help us old dogs learn their new tricks. It’s my experience that demand planners can be highly effective without knowing every single rule and formula driving their forecast outputs. If they use off the shelf software packages, the algorithms are proprietary and they aren’t able to get that far down into the details anyhow.

What’s important is that – when looking at all of the information available to the model – a demand planner can look at the output and understand what it was “thinking”, even if they may disagree with it. All models make the general assumption that patterns of the past will continue into the future. Knowing that, a demand planner can quickly address cases where that assumption won’t hold true (i.e. they know something about why the future will be different from the past that the model does not) and take action.

As the pool of early adopters of probabilistic methods grows, I’m looking forward to seeing heaps of case studies and real world examples covering a wide range of business scenarios from the perspective of a retail demand planner – without having to go back to school for 6 more years to earn a PhD in statistics. Some of us are just too old for that shit.

I see great promise, but for the time being, I remain only somewhat informed.

Timing (is everything)

There’s an old saying that “timing is everything”. And while it may not always be true, more often than not, it is. Especially when it comes to implementing new planning approaches, like Flowcasting.

Even though implementing Flowcasting usually means implementing new technology, it’s got very little to do with software. It’s about changing the mental model and how organizations work, plan, and collaborate. As a result, these implementations are about change – helping people unlearn old ways, learn, and ingrain new ones.

And that requires time for change.

Here’s a beautiful view of change management, highlighting the critical importance of timing:

About 25 years ago we were blessed with a dose of shit luck. We were working as employees at one of Canada’s most iconic and successful retailers, designing and implementing what we now call Flowcasting. As luck would have it, two former Oliver Wight planning pioneers would somehow emerge from the wilderness and join the party. They would ingrain the project team with an implementation approach, called the Proven Path – aptly named given thousands of successful implementations of integrated planning over several decades.

Over the years we’ve retail-ized the approach but the basic principles and fundamentals have endured.

At the heart of the approach is early and repetitive education for executives, management, planners, and suppliers. The diagram above nicely outlines the importance of engaging people early – teaching them how the new process will work, what’s different and why it will be better.

We’re often asked why start educating and engaging people so early. It’s simple, yet instructive. People really don’t like to be surprised and they need time to think. The term we use is “soak time”. The sooner people begin to understand the change, the longer time they can think about it, question it, challenge it, even improve on it. Of course, education is not a one-time event and constant and refresher education happens throughout the change.

It’s why we typically follow up educational sessions with process prototypes – where people, who are now more knowledgeable, can “test” the new approach in a guided, lab-like environment.

And what does that do? It gives people time to experience the new process and more time to “soak” in the new approach and thinking. Helping the change effort considerably.

The reason the diagram above is so instructive is that it reflects the difference between super successful implementations and successful ones. Many teams view these implementations from a technology lens. You’re installing new software to improve things. With a view like that, typically teams scrunch the change effort much closer to when the software go-live will be. And, almost always, it’s too much, too fast for people and the implementations suffer.

In contrast, if you understand that the implementation is about changing people’s behaviors and corresponding mental models (throughout the extended organization, including suppliers) then the importance of starting early should be apparent.

To drive the point home, years ago the Oliver Wight team surveyed over 1000 companies regarding how successful their implementations of integrated planning were. The results were enlightening. The companies that started the change program early, with early and ongoing education, realized an average Return on Investment (ROI) of 200%, compared with 30% for the companies who thought they were installing software.

I suppose if you’re looking for a super successful implementation and a big, fat, juicy ROI then timing is, indeed, everything.

Bread and Butter

Man shall not live by bread alone. – Matthew 4:4

“Make sure you focus on the bread and butter items!”

Anybody who’s worked for a retailer – particularly in supply chain – has either heard or said these words at least a dozen times. And everybody knows what those “bread and butter” items are: The fast sellers. The products that customers take out of the stores by the cartload. If you were ever stocked out on one of those items, the damage to your brand would be catastrophic.

Hence the perceived need to make sure your people in charge of replenishment are watching those items like a hawk.

Here’s the thing though: Fast selling items with continuous demand in every store are precisely the ones that require virtually no effort whatsoever. They turn so quickly and the volumes are so well established that they basically manage themselves on autopilot. In most cases, these are the items that your competitors also sell (and potentially consider “bread and butter” items themselves).

The reality for most brick and mortar retailers is that they are in one of the following two categories:

  1. You’re competing with Amazon, or;
  2. You will soon be competing with Amazon

Unless you’re Walmart or Costco, you really do need to be a category killer to overcome the perceived advantages while exploiting the weaknesses that “endless aisle” retailers like Amazon provide to customers. Yes, you need to have an online presence and offer as many channels to the customer as possible, but that won’t be enough.

You can drive to Walmart right now and get a pack of wood screws, but are you sure will they have the size you need?

You can order the exact wood screws you need from Amazon, but will they be easy to find and can you get them right now if you need them?

If you’re like me, you don’t even ask those questions. The moment you identify a need for a particular size and type of screw, you jump in your car and go straight to Home Depot or Lowes and march straight to the aisle that has every type of screw and fastener you can imagine, confident that you’ll find what you need.

Sure, there’s a lot of slow selling dog crap in there when you look at the assortment SKU by SKU, but if you only pay attention to the fast selling items, then you’re competing head to head with Walmart and Costco – probably not a winning strategy.

It’s a broad assortment of those long tail items that really make you stick out in the customer’s mind. They’re the key differentiators that can automatically and subconsciously disqualify your competitors when people are in the market for what you’re selling.

There’s your real bread and butter.