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A dynamic, digital twin

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Frank Gehry is widely acclaimed as one of the world’s greatest architects. His most famous and celebrated building, the Guggenheim Museum in Bilbao, is the design and subsequent construction that elevated him to superstardom.

The story of how Gehry designs and the technologies he used to develop this, and subsequent masterpieces, is instructive and very relevant for supply chain planning and management.

Gehry usually begins by sketching ideas on paper with scrawls that would mystify most folks. Then he mostly works with models – usually working with wooden building blocks of different sizes that he stacks, and restacks, always looking for something that might be functional and is visually appealing.

Until recently, he’s worked with these types of models his whole life. His studio is filled with them – the culmination of decades of model building. He usually starts at one scale, then tries another and then another to see the project from varying perspectives. He zeroes in on some aspects of the design in his model, zooming in and out until he better understands the design from many different viewpoints and angles. He’s always trying new ideas, reviewing his designs with his team and client, eventually deciding on what works or doesn’t. Eventually he settles on the design and then they get on with it.

After landing the project to design the Guggenheim Bilbao, he and his team spent the better part of two years working through these iterative models, using the decidedly analog world of building blocks and cardboard to visualize the result.

Then, our old friend technology made a house call and changed his design capability forever.

Gehry would be introduced to computer simulation software called CATIA, allowing Gehry to build his designs on a computer. Originally the software was built to help design jets but was modified to allow buildings to be designed – on a computer – in three dimensions. Early in his career his designs were mostly straight lines and box-like shapes, but this technology would allow him to design curves and spirals that would be beautiful and aesthetically pleasing.

CATIA’s capabilities proved incredible. Gehry and his team could alter the design quickly, change curves or shapes, and the system would instantly calculate the implications for the entire design – from structural integrity to electrical/plumbing requirements, to overall cost. They could iterate new ideas and concepts on the computer, simulate the results, then rinse and repeat, and only then, once happy, begin construction.

The Guggenheim building was first fully designed on a computer.

In a moment of foreshadowing, the design and digital design process was labelled a “digital twin”. Once the digital twin was finalized and agreed to, only then did construction begin.

The term “digital twin” has become somewhat fashionable and, to be honest, quite important in supply chain. And what do people mean by the term “digital twin”, when thinking about the supply chain? Here’s one definition…

A digital twin is a digital replica of a physical supply chain. It helps organizations recreate their real supply chain in a virtual world so they can test scenarios, model different nodes, modes, flows, and policies and understand how decisions and disruptions will impact network operations.

For most supply chain folks, the digital twin is relatively static and represents the current state, or outlines a snapshot of the supply chain, as of today – for example, what’s happening in the supply chain, as of right now.

But, like Gehry’s ability to dynamically change design elements and immediately see the impact overall, wouldn’t the best digital twin for supply chains also be dynamic, complete, and forward-looking?

It would.

And isn’t that what Flowcasting is?

It’s a future-dated, up-to-date, complete model of the business. It depicts all current and projected demand, supply, inventory, and financial flows and resource requirements, based on the strategies and tactics that are driving a retailer and their trading partners. If something changes, then the dynamic model re-calculates the projections – so the forward-looking digital twin is always current. Everyone can see the projections in their respective language of the business (e.g., units, cases, dollars, capacities, resources) and work to a single set of numbers.

The architectural “digital twin” was a breakthrough approach for Frank Gehry and architecture in general.

The forward-looking, dynamic “digital twin” – that is, Flowcasting – is a similar breakthrough approach for supply chain planning.

New Model for Retailer-Supplier Collaboration

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Thank you to Supply Chain Management Review for publishing our latest article, “A New Model for Retailer-Supplier Collaboration” in the March/April edition of their always excellent magazine.

Check it out here: https://bt.e-ditionsbyfry.com/publication/?m=24891&i=816750&p=34&ver=html5

The article outlines a new approach to collaborative inventory planning, based on the profound insight of Dr. Joseph Orlicky and the ideas and concepts developed and implemented by our long-time colleague, Andre Martin.

We’d also like to thank two forward-looking companies, Princess Auto Ltd and Watson Gloves, for agreeing to be featured in the article, demonstrating that the approach works and benefits consumers and both retailer and supplier.

It turns out that Joe and Andre are right – you should never forecast what you can calculate!

Forecasting Wordplay

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If it is true that words have meanings, why don’t we throw away words and keep just the meanings? – Ludwig Wittgenstein via Anatol Holt

How do you describe your demand forecasts?

In retail, I’ve had numerous conversations that go something like this:

Me: “Do you really think you’re going to sell 10,000 units on that promotion? You’ve never sold higher than 8,000 at that price point.”

Retail Buyer/Demand Planner: “Yeah, it’s a bit optimistic.”

While less common, forecasts can also be made lower than one would expect (or “pessimistic”, if you will), particularly if an incentive structure exists where people are rewarded when actual sales blow the forecast out of the water.

While these “optimistic” or “pessimistic” numbers are entered into the forecasting system and potentially even drive the replenishment and supply planning functions, it would be a misnomer to call them forecasts.

Saying that a forecast is optimistic is an admission that you believe it’s biased to the high side for no good reason. You can call it a goal, a wish or a dream, but it is NOT a forecast.

In a similar way, a “pessimistic forecast” is a hedge, not a forecast.

Simply put, if you don’t truly believe the numbers you’re producing are a reflection of what’s actually going to happen, then you’re not forecasting. A true forecast is the prediction you’d make if you were obligated to bet $500 of your own money on the outcome.

That’s not to say that the most objective forecast produced by the most unbiased demand planner may not be way off. At its essence, demand planning applies assumptions in an attempt to predict human behaviour. That doesn’t always work out.

That’s also not to say that the words “optimistic” and “pessimistic” have no place in forecasting – so long as there are assumptions to back up the judgments.

For example, “I’m optimistic that we’ll beat last year’s sales, because we’ve sharpened our pricing and caused a key competitor to exit the market for this category.”

Or “I’m pessimistic about the sales outlook for our high price point luxury lines because the economy is in the tank and discretionary spending is going to be way down for the next 12 months.”

But what about the potential missed opportunities? What if we produce an unbiased, objective forecast and it ends up oversold to the point that we run out of stock and leave sales on the table?

Isn’t having a forecast that’s “a bit too high” much less risky in terms of being able to satisfy customer demand?

To be clear, a forecast is just one determinant of the level of stock needed to satisfy customer demand. The other is the uncertainty of the demand. If you apply the uncertainty to the supply side of the equation (i.e. safety stock), then that frees you up to forecast with objectivity.

Yes, the uncertainty needs to be quantified, but if you habitually describe your forecasts as being “optimistic” (which is a euphemism for “biased”), then you’re already doing that anyhow:

  • Optimistic Forecast = What we think customers might buy
  • Objective Forecast = What we think customers will buy
  • Safety Stock = Optimistic Forecast – Objective Forecast

Subtract

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“Life can be improved by adding, or by subtracting. The world pushes us to add because that benefits them. But the secret is to focus on subtracting.”

                    - Derek Sivers

People don’t subtract.

Our minds add before even considering taking away.

Don’t believe me?

Leidy Klotz is a Behavioral Science Professor at the University of Virginia and a student of “less”. He conducted a series of experiments that demonstrate people think “more” instead of “less”.

Consider the following diagram and the ask.

Thousands of participants were asked to make the patterns on the left and right side of the dark middle vertical line match each other, with the least number of changes.

There are two best answers. One is to add four shaded blocks on the left and the other is to subtract four shaded blocks on the right.

Only about 15 percent of participants chose to subtract.

Intrigued, Professor Klotz and his research assistants concocted numerous additional experiments to test whether people would add or subtract. They all produced the same result and conclusion – people are addicted to and inclined to add. It wasn’t close.

Big fucking deal, right?

Not so fast. Unfortunately, adding almost always makes things more complicated, polluted, and worse. You’d be better off subtracting.

A great example in supply chain is demand planning.

Demand planning, according to many, is becoming the poster child of adding. Let’s factor in more variables to produce an even more beautiful and voluptuous forecast. Are you sure you all these additional variables will improve the demand plan?

I doubt it.

First, many companies are forecasting what should be calculated. It’s been proven that the farther away from end consumption you’re trying to forecast, the more variables you’ll try to add. And the resulting forecast usually gets worse the more you add – since you’re often adding noise.

We have a retail client that is forecasting consumer demand at the item/store level only and calculating all inventory flows from store to supplier – what we call Flowcasting. Their demand planning process only considers two variables to calculate the baseline forecast:
• the sales history in units
• an indication if the sales was influenced by something abnormal (e.g., like promotions, clearance, out of stock, etc.)

All “other” variables that the “experts” say should be included have been subtracted.

Yet their planning process consistently delivers industry leading daily in-stocks and inventory flows to the store shelf.

The idea is simple, profound, and extremely difficult for us all. For process and solution designs, and pretty much everything, you need to remove what’s unnecessary.

You need to subtract.

The Sun Came Up Today

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It pays to be obvious, especially if you have a reputation for subtlety. – Isaac Asimov (1920-1992)

The sun came up today.

I’ve been tracking it daily in a spreadsheet for months. Please reach out if you’re interested in seeing my data. My suspicion is that you won’t.

In (belated) honour of Groundhog Day, the topic du jour is in stock reporting.

You come into the office on Monday morning, log in to your reporting/BI dashboard and display your company’s overall in stock report for the last 21 days, up to and including yesterday. As you look at it, you’re thinking about all of the conversations about in-stock you’ve had over the last 3 weeks and anticipate what today’s conversations will be about:



For the sake of argument, we’ll assume that there’s no major issue with how you calculate the in stock measure. Everyone understands it and there’s broad agreement that it’s a good approximation of the organization’s ability to have stock in the right place at the right time. (This isn’t always the case, but that’s a topic for another day).

It certainly looks like a bit of a roller coaster ride from one day to the next. That’s where applying some principles of statistical process control can help:



By summarizing the results over the last 21 days using basic statistical measures, we can see that the average in stock performance has been 92% and we can expect it to normally fluctuate between 86% (lower control limit) and 97% (upper control limit) on any given day.

In other words, everything that happens between the green dashed lines above is just the normal variation in the process. When you publish an in stock result that’s between 86% and 97% for any given day, it’s like reporting that “the sun came up today”.

Out of the last 21 days, the only one that’s potentially worth talking about is Day 11. Something obviously happened there that took the process out of control. (Even the so-called “downward trend” that you were planning to talk about today is just 3 or 4 recent data points that are within the control limits).

I used the word “potentially” as a qualifier there, because statistical process control was originally developed to help manufacturers isolate the causes of defects, so that they could then apply fixes to the part of the process that’s failing in order to prevent future defects with the same cause. In most cases, the causes (and therefore the fixes) were completely within their control.

Now when you think of “the process” that ultimately results in product being in front of a customer at a retail store, there are a LOT of things that could have gone wrong and many of them are not in the retailer’s control. In the example above, it was a trucker strike that prevented some deliveries from getting to the stores that caused some of them to run out of stock. Everybody probably knew that in stock would suffer as soon as they heard about the strike. But there was really nothing anybody could have done about it and very little that can be done to prevent it from happening again.

So where does that leave us?

Common Cause Variation is not worth discussing, because that’s just indicative of the normal functioning of the process.

Special Cause Variation is often not worth discussing (in this context) unless you have complete control over the sub-process that failed and can implement a process change to fix it.

So what should we be looking at?

In terms of detecting true process problems that need to be discussed and addressed, you want to look at a few observations in a row that are falling outside the established control limits, investigate what changed in the process and decide if you want to do something to correct it or just accept the “new normal”. For example:



But you can also take a broader view and ask questions like:

  • How can we get our average in stock up from 92% to 96%?
  • How can we reduce the variation between the upper and lower limits to give our customers a more consistent experience?

By asking these questions, what you’re looking for are significant changes you can make to the process that will break current the upper control limit and set a new permanent standard for how the process operates day to day:

But be warned: The things you need to do to achieve this are not for the faint of heart. Things like:

  • Completely tearing apart how you plan stock flow from the supplier to the shelf and starting from scratch
  • Switching from cheap overseas suppliers to ones who are closer and more responsive
  • Refitting your distribution network to flow smaller quantities more frequently to the store

They all have costs and ancillary additional benefits to the operation beyond just improving the in stock measure, but this is the scale of change that’s needed to do it without just blowing your inventory holdings out of the water.

Reporting your in stock rates (or any other process output measure for that matter) regularly is a fine thing to do. Just make sure that you’re drawing the right conclusions about what the report is actually telling you.

Soak Time

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“If a plant gets nothing but sunlight, it’s very harmful. It must have darkness too.” – Robert Pirsig

Did you know that if a plant gets nothing but sunlight, it’s extremely harmful and even deadly? It must have darkness as well. In the sun, it converts carbon dioxide to oxygen, but in the dark, it takes some oxygen and converts it back into carbon dioxide.

People, as it turns out, are the same. We need periods of darkness – essentially doing nothing – to develop ideas and/or internalize new ones. We often refer to it as “soak time” – the time where you let your mind just soak stuff in, subconsciously thinking about things. This, inevitably, helps your understanding and allows your creative juices to work.

Turns out many creative folks embrace the concept of soak time.

The film director Quentin Tarantino outlines his creative process and the role of soak time. This is his approach. He basically writes during the daytime. Then after a while, he stops writing. Now comes the key part of his process, he says. “I have a pool, and I keep it heated. And I jump in my pool and just float around in the water…and then, boom, a lot of shit will come to me. Literally, a ton of ideas. Then I get out of the pool and make notes on that. But not do it. That will be tomorrow’s work. Or the day after. Or the day after that.” Another filmmaker, Darren Aronofsky, said, “procrastination is a critical part of the process. Your brain needs a break…so that even when you’re not working, you’re working. Your brain is putting shit together.”

In his excellent book, Deep Work, Cal Newport teaches the benefits of downtime. One study by Dutch psychologist Ap Dijksterhuis showed that when working on a complex problem or decision, you should let your unconscious mind work on it as much as possible. Anders Ericsson wrote a seminal paper, “The Role of Deliberate Practice in the Acquisition of Expert Performance,” which showed that our brains have a limited window for cognitively demanding efforts. “Decades of study from multiple fields within psychology,” Newport writes, “all conclude that regularly resting your brain improves the quality of your work.”

For us, soak time is a core component of our approach to help people change and embrace new technologies and ideas like Flowcasting. It’s why we start the education process very early – so people have time to soak in the new thinking, ponder it and slowly accept it (hopefully).

Designing new processes and work methods also benefits from regular and ongoing doses of soak time. Instead of plowing through a design phase, grinding your way through session after session, a far better approach is to work on some aspect of the design, then let it sit, or go dark for a while. Subconsciously though, that beautiful brain of yours can’t turn off the tap – and will be thinking and pondering even when you’ve gone dark on that topic. The result? Better designs and solutions, guaranteed.

When I consult with retail clients, I’m aghast at how little soak time is built into people’s calendars, especially for folks that work in Home Office or Support Centres. Everybody seems to be busy doing busywork, and there’s barely time to schedule a 30-minute session, let alone have some soak time. The organization would be far better off by scheduling 20-50% of slack time for all these folks – essentially blocking off time for them to daydream, read, walk, or just do nothing at all. Let the mind wander and subconsciously connect things.

In both my Canadian and UK home offices I have a couch. When I need to let things soak, I sometimes go for a walk, or most of the time, I just lay down and do nothing. Just rest and relax and let the mind soak for a while. I do it often, almost every day and for some period.

Does it help? Yes, I think it does. Sure, over time, most of my ideas have been shit, but the odd decent one slips through. I’m convinced it’s because of nurturing it with soak time.

If you’re delivering projects or have a job that requires you to develop new ideas and initiatives, then my advice is simple: plan and schedule lots of soak time.

Maybe also get a couch so when you need to, you can just lie down and relax, breath slowly and let your mind wander and connect stuff, all while converting oxygen to carbon dioxide.

Sorta like a plant does.

A Forecast By Any Other Name

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What’s in a name? That which we call a rose
By any other name would smell as sweet. – William Shakespeare (1564-1616), Romeo and Juliet, Act 2 Scene 2

Scenario 1: A store associate walks down the aisles. She sees 6 units of an item on the shelf and determines that more is needed on the next shipment, so she orders another case pack of 12 units.

Scenario 2: In the overnight batch run, a centralized store min/max system averages the last 6 weeks of sales for every item at every store. This average selling rate is used to set a replenishment policy – a replenishment request is triggered when the stock level reaches 2 weeks’ worth of on hand (based on the 6 week average) and the amount ordered is enough to get up to 4 weeks’ worth of on hand, rounded to the nearest pack size.

Scenario 3: In the overnight batch run, a centralized store reorder point system calculates a total sales forecast over the next 2 shipping cycles. It uses 2 years’ worth of sales history so that it can capture a trend and weekly selling pattern for each item/store being replenished and calculate a proper safety stock based on demand variability. On designated ordering days, the replenishment system evaluates the current stock position against the total of expected sales plus safety stock over the next two ordering cycles and triggers replenishment requests as necessary to ensure that safety stock will not be breached between successive replenishment days.

Scenario 4: In the overnight batch run, a centralized supply chain planning system calculates a sales forecast (with expected trend and weekly selling pattern) for the next 52 weeks. Using this forecast, merchandising minimums, store receiving calendars and the current stock position, it calculates when future arrivals of stock are needed at the store to ensure that the merchandising minimums won’t be breached over the next 52 weeks. Using the transit lead time, it determines when each of those planned arrivals will need to be shipped from the supplying distribution centre over the next 52 weeks. The rolled up store shipment projections become the outbound plans for each item/DC, which then performs the same logic to calculate when future inbound arrivals are needed and their corresponding ship dates. Finally the projected inbound shipments to the DC are communicated to suppliers so that they can properly plan their finished goods inventory, production and raw material procurement. For both stores and DCs, the plans are turned into firm replenishment requests at the ordering lead time.

With that out of the way, let’s do some audience participation. I have a question for you: Which of the above replenishment methods are forecast based? (You can pause here to scroll up to read each scenario again before deciding, or you can just look down to the very next line for the answer).

The answer is… they are ALL forecast based.

Don’t believe me?

In Scenario 1, how did the store associate know that a visual stock position of 6 units meant they were “getting low”? And why did she order a single case of 12 in response? Why didn’t she wait until there were 3 units? Or 1 unit? And why did she order 12? Why not 120?

For Scenario 2, you’re probably saying to yourself: “Averaging the past 6 weeks’ worth of sales is looking backward – that’s NOT forecasting!” Au contraire. By deciding to base your FUTURE replenishment on the basis of the last 6 weeks’ worth of sales, an assumption is being made that upcoming sales will be similar to past sales. That assumption IS the forecast. I’m not saying it’s a good assumption or that it will be a good forecast. I’m just saying that the method is forecast based.

Using the terms “trend” and “selling pattern” in Scenarios 3 and 4 probably spoiled the surprise for those ones.

So why did I go through such pains to make this point?

Quite simply, to counter the (foolish and naive) narrative that “forecasts are always wrong, so you shouldn’t bother forecasting at all”. 

The simple fact is that unless you are in a position where you don’t need to replenish stock until AFTER your customer has already committed to buying it, any stock replenishment method you use must by definition be forecast based. I have yet to run across a retailer in the last 28+ years that has that luxury.

A forecast that happens in someone’s head, isn’t recorded anywhere and only manifests itself physically as a replenishment request is still a forecast.

An assumption that next week will be like the average of the last few weeks is still a forecast.

As the march continues and retailers gradually transition from Scenarios 1, 2 or 3 to Scenario 4, the forecasting process will become more formalized and measurable. And it can be a lot of work to maintain them (along with the replenishment plans that are driven by them). 

But the overall effort pays off handsomely. Retailers in Scenarios 1, 2 and 3 have experienced in stock rates of 92-93% with wild swings in inventory levels and chronic stock imbalances. This has been documented time and again for 30 years.

Only by formalizing your forecasting your forecasting process, using those forecasts to drive long term plans and sharing those plans up and down the supply chain can you achieve 97-98% in stock while simultaneously reducing inventory investment, with reduced overall effort.

Collaborate, then calculate

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“Never forecast what you can calculate.” – Dr. Joseph Orlicky

Collaboration promises much to the retail supply chain, and rightly so. Retailers and their trading partners are beginning to understand they are not alone. The retail supply chain does not act as a series of islands – each independent entity working for its own purpose. Rather, smart companies understand that they are really working as part of one, completely integrated network that is designed (or should be) to deliver products to their end customers.

Almost 50 years ago, at the 1975 APICS conference in San Diego, Dr. Joseph Orlicky (the pioneer of MRP – Material Requirements Planning) made a profound statement regarding supply chain planning. Having just learned of Andre Martin’s idea to calculate factory demand from the distribution centre requirements, he told Andre that his idea was good since you should “never forecast what you can calculate”.

Leveraging this profound truth is the key to improved collaboration, addressing the shortcomings of CPFR and, importantly, making a significant dent in stock outs, overstocks, and the bullwhip effect.

The retail/CPG supply chain should be driven only by a forecast of consumer demand – time-phased by item/selling-location (e.g., store, webstore, etc.). The consumer demand forecast should then be used to calculate a series of integrated, time-phased product flow plans and planned shipments (for a 52+ week planning horizon) from the store to the supplier factory – what we call Flowcasting.

Sharing planned shipments allows the retailer to inform the supplier about future product flow requirements, by item and shipping location, with all known variables factored in – what we often refer to as the supplier schedule. This allows the supplier to eliminate all efforts previously expended to attempt to forecast that retailer orders. The planned shipments replace all this effort – improving the supplier order plan and allowing the collaborative process to work using the profound power of silence.

In the new collaborative model, since the planned shipments provide a long-term view of future required inventory flows, the expectation is that the retailer and supplier work to the principle of “silence is approval”. What that means is that the retailer expects the supplier to be prepared to deliver to the up-to-date, forward-looking schedule and only when they cannot supply to the schedule and/or they don’t understand the projected schedule, is collaboration required.

Collaboration based on a shared view of planned shipments (i.e., the supplier schedule) allows for the collaborative model to become more strategic and value added. In this new approach retailers and suppliers will collaborate on strategies to drive sales and potentially inventory plans – in essence, the inputs to drive joint business plans.

That’s a complete reversal of the traditional CPFR model where each company developed their own independent order forecasts and then spent considerable time and effort to reconcile these forecasts. In the new approach, the collaboration mostly focuses on a common language: sales to the end consumer. And, again, largely by exception. There is no need to collaborate on the plethora of retail forecasts and planned shipments since these have been automatically translated into the requirements, product flows and various languages of the business (e.g., dollars, cube, capacity, resource needs) for all trading partners.

The following diagram depicts the paradigm shift in collaborative planning between retailers and their merchandise suppliers – collaborating primarily on the inputs to the joint business plans, and only by exception if any issues or opportunities arise based on the resultant operational product flow plans:

Leading retailers and their suppliers will collaborate where they believe it is worthy of each partner’s time and largely on strategies (i.e., inputs to the joint plans) that drive growth and/or improved performance. That could be on promotional forecasts, new items, and ideas and concepts about product flows – to name a few. Both partners understand that the planned shipments resulting from these strategies are calculated – so collaboration on these shared projections is only needed if supply is at risk.

Dr. Orlicky’s famous and profound quote, “never forecast what you can calculate” is embedded in my mind and cemented in all the retail clients I’ve worked with. We can, and should, build on this profound truth and work to ingrain this thinking and practice between retailers and their trading partners…

Collaborate, then calculate.

Rising Tides

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It is better to aim high and miss than to aim low and hit. – Les Brown

Why is the shelf empty?

I could go on and on about the myriad factors that could be at play, but it really boils down to one of two things:

  1. Failure to anticipate (or quickly react to) demand or;
  2. Not enough available supply, even if demand is properly planned

The first one is pretty obvious. No how matter how much effort you put into forecasting, sometimes shit just happens. The season breaks way earlier than expected. A mundane product that’s been selling steadily for years “goes viral” because of a TikTok trend. Or a customer just comes in and wipes you out unexpectedly for no discernible reason.

The second one is also obvious – at least on the surface. Clogged ports, pandemics and strikes immediately come to mind. But what often doesn’t come to mind is store inventory accuracy. Of all of the millions of item/locations in retail stores with a computerized on hand balance, how many times does the replenishment system think there’s stock available to sell when there is actually none? And how often does this condition go undetected until the store manager starts seeing shelf gaps? With store inventory accuracy hovering in the 50-60% range, I’m going to say “probably a lot”. 

I would argue that it’s probably the single biggest “supply issue” in retail, but the subject is so universally ignored and not measured that there’s no way of actually proving me right or wrong on that. But it sure feels like I may be right.

Regardless, lack of incontrovertible evidence aside, an informal quorum of people seem to agree with me anyhow, even if they may not know it. 

Why do I say that?

Whenever we talk to retailers about fully integrated planning from supplier to shelf and explain the process in detail, someone inevitably connects these dots:

  • Store on hands are a critical input to the planning process – TRUE!
  • Store on hands aren’t particularly accurate – TRUE!
  • Therefore, you can’t properly plan anything until inventory accuracy is under control – hmmm, well…

Here’s the thing about that last point: Inaccurate inventory has been a problem in retail for time immemorial. Computer assisted ordering, stock checking for customers, online order pickup in store – these are all common practices in retail today and have delivered significant benefits in terms of growth, efficiency and customer service. The most critical input for all of these processes is store inventory balances, which everybody knows are not accurate. Yet I haven’t seen any retailers shutting down automated ordering or buy online/pick up in store programs until their store inventory accuracy is up to snuff. 

Could greater benefits be achieved if store inventory records were more accurate? Duh! But that doesn’t change the fact that significant benefits can be (and have been) achieved in spite of inaccurate inventories. Implementing an end-to-end planning approach that relies on store inventory balances is no different in that regard. There is no hard dependency on some arbitrary level of inventory accuracy to start building or improving upon planning capabilities.

Transforming a retailer from a reactionary firefighter to an integrated planning organization takes hard work and discipline and delivers enoromous benefit.

Sustainably improving inventory accuracy in stores also takes hard work and discipline and also delivers enormous benefit.

Either of these initiatives can appear daunting individually, so – except for the most courageous among us – creating a false dependency (i.e. “we can’t plan without accurate inventory”) is a surefire way to ensure that nothing gets done about either of them.

As John F. Kennedy famously popularized: “The rising tide lifts all the boats.”

A retailer with so-so inventory accuracy will be made better with improved planning capabilities. A retailer with poor (or virtually nonexistent) planning capabilities will be made better with improved inventory accuracy.

So work on planning and shelve inventory accuracy for awhile.
Or work on inventory accuracy and shelve planning for awhile. Or be extra brave and work on both at the same time.

But work on something.

Remember Warren Buffet’s addendum: “A rising tide may lift all boats, but only when the tide goes out do you realize who’s been swimming naked.” 

Celebrating an autotelic

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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!