Repetito est mater studiorum

“Repetition is the mother of learning, the father of action, which makes it the architect of accomplishment.” – Zig Ziglar

When I was growing up, I was a very competitive dude, particularly in sports. When it came to sports, for me, winning was everything. Basically, I was an asshole. Many of my friends and foes called me by a different name. What’s the name they used? Oh yeah, a cunt. It’s OK, it was the truth and they often called me that to my face.

Here’s a story, from high school, that helps confirm my then status. 

In the basement of our school, down near the gym, there was a ping pong table. And, during lunch and breaks, students would play. The rule was simple: the winner stays on the table, until someone beats them, and then they take over. So, a very good player could play for quite a while.

One day, me and my pals sauntered down and watched as a girl named Dana beat all comers. Then, at the insistence of my mates, it was my turn to challenge her. She crushed me. My buddies, of course, knowing how competitive I was, absolutely shamed me that day and for many weeks after.

So, what did I do?

I did what any red-blooded, super competitive dude would do. I bought my own racket and a ping pong table.  I was determined to win.

Ping pong tables fold in the middle where the net is so that, when folded, the other side of the table is upright. It allows a single player to hit the ball, over the net, against the other side of the table and it pretty much guarantees that the ball will be returned. So, I set up the folded table in my parents’ basement and, every chance I got, would go down and pound balls against the returning wall.

Over and over. Harder and faster. Learning how to put overspin on both a forehand and backhand. Learning how to smash and return a smash. Repetition after repetition – for hours and days on end.  

I would soon get to challenge Dana again. And, with my buddies watching, I would demolish her and would become not only the ping pong champion of my school, but also the best high school player in the county.

The moral of this true story isn’t to confirm that I was an asshole. The moral of the story is highlighting the importance of repetition.

There’s an old Latin saying, “repetito est mater studiorum” which means repetition is the mother of learning.

When it comes to instilling new ways of working, turns out repetition really is the mother of learning.

Implementing a new planning approach like Flowcasting in retail benefits greatly from repetition. You’re essentially teaching the planners and the wider organization (including suppliers) how to think differently about integrated demand and supply planning, so the more often people are exposed to the idea, the better. 

I recently read a great book about change called The Human Element.  In it they outline one of the most important strategies for instilling change is to “Acclimate the Idea” through repetition and repeated exposure (i.e., give people time to think and internalize the idea/change)

In a recent implementation of Flowcasting, the idea of repetition was leveraged extensively to help people make the change journey, including:

  • An ongoing education program which started with a cascade from the CEO and delivered repeated educational sessions to help people internalize the change in thinking and underlying principles of the new process
  • Process prototypes where the Buying Teams (Merch and Supply Chain) would execute a day in the life scenario, with company-specific data for every major planning scenario – like product life cycle, promotions planning, seasonal planning, etc.
  • A supplier education & training program to teach suppliers and the Buying Teams the new approach to collaboration
  • Training sessions to demonstrate how people would execute the new ways of working
  • Coaching sessions and ongoing coaching with job aides to help people transition from the old to the new

What do all these activities do? 

They constantly repeat and demonstrate to people the underlying change and principles of the new process. As an example, in each of the process prototypes, the Buying Teams could see what was meant by a valid simulation of reality, what the supplier would see in their supplier schedules, why postponing creating a purchase order for promotional volume was better for everyone, plus many other learnings. Repetition, with real scenarios, helped them instill new thinking and helped acclimate the ideas.

Getting good at anything (Flowcasting or ping pong) requires learning.  And learning needs repetition.

After all, she really is the mother of learning.

A dynamic, digital twin

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.

Subtract

“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.

Soak Time

“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

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

“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.

Just In Time… For What, Exactly?

I have noticed that the people who are late are often so much jollier than the people who have to wait for them. – E.V. Lucas

The time-phased, arrival based planning logic that underpins Flowcasting has frequently been described (sometimes disparagingly) as “pull-based, just in time”. Depending on your definition of “pull-based” and “just in time” (do any two people actually agree on what these terms mean?), there’s more truth to that than fiction.

The “pull-based” part is easy. The retail supply chain hasn’t finished its job until a customer has made a purchase. While it’s possible to encourage a stronger customer pull with promotional offers, pricing and markdowns, you can’t push unwanted stock into a customer’s shopping cart and force them to pay for it. This is true for every saleable item in every retail store.

The “just in time” part is what can sometimes make people (particularly buyers) a little queasy. The term evokes images of stock running almost to zero just before the perfectly executing supply chain delivers more stock. There seems to be a pervading fear that such approaches will cut inventory to the bone in a blind bid to increase stock turns at a all costs.

While it’s certainly possible to run your supply chain (including the stores) super lean, it’s definitely not necessary – nor recommended. A store with just enough stock to cover anticipated demand and variability for every item will look like it’s perpetually going out of business.

“Just in time” doesn’t mean “just enough to support sales”. It means just in time to prevent the stock level from dipping below a minimum floor that you decide

Do you want to maximize turns with minimal safety stock? No problem!

Do you want to have a nice, full looking display with at least 5 facings, 3 deep on the shelf at all times? Go for it!

(Same item, same store, same sales forecast).

Do you want to augment the normal shelf stock with secondary promotional displays for a few weeks? Nobody’s stopping you!

Would you rather have a minimum of 4 weeks of supply in the store at all times? Sure! Why not?

Just in time isn’t about stock levels, it’s about stock flow. So long as you can articulate what minimum stock holding you require for each item/location and when (and can justify it to Finance), a proper just in time planning approach does what it’s told and flows in stock to ensure you never fall below that level.

Merchants and space planners rejoice and be glad! You’re not slaves to just in time planning. Just in time planning is a slave to your merchandising needs.

Changing the game

In 1972, for my 10th birthday, my Mom would buy me a wooden chess set and a chess book to teach me the basics of the game.  Shortly after, I’d become hooked and the timing was perfect as it coincided with Bobby Fischer’s ascendency in September 1972 to chess immortality – becoming the 11th World Champion.

As a chess aficionado, I was recently intrigued by a new and different chess book, Game Changer, by International Grandmaster Matthew Sadler and International Master Natasha Regan.

The book chronicles the evolution and rise of computer chess super-grandmaster AlphaZero – a completely new chess algorithm developed by British artificial intelligence (AI) company DeepMind.

Until the emergence of AlphaZero, the king of chess algorithms was Stockfish.  Stockfish was architected by providing the engine the entire library of recorded grandmaster games, along with the entire library of chess openings, middle game tactics and endgames.  It would rely on this incredible database of chess knowledge and it’s monstrous computational abilities.

And, the approach worked.  Stockfish was the king of chess machines and its official chess rating of around 3200 is higher than any human in history.  In short, a match between current World Champion Magnus Carlsen and Stockfish would see the machine win every time.

Enter AlphaZero.  What’s intriguing and instructive about AlphaZero is that the developers took a completely different approach to enabling its chess knowledge.  The approach would use machine learning.

Rather than try to provide the sum total of chess knowledge to the engine, all that was provided were the rules of the game.

AlphaZero would be architected by learning from examples, rather than drawing on pre-specified human expert knowledge.  The basic approach is that the machine learning algorithm analyzes a position and determines move probabilities for each possible move to assess the strongest move.

And where did it get examples from which to learn?  By playing itself, repeatedly. Over the course of 9 hours, AlphaZero played 44 million games against itself – during which it continuously learned and adjusted the parameters of its machine learning neural network.

In 2017 AlphaZero would play a 100 game match against Stockfish and the match would result in a comprehensive victory for AlphaZero.  Imagine, a chess algorithm, architected based on a probabilistic machine learning approach would teach itself how to play and then smash the then algorithmic world champion!

What was even more impressive to the plethora of interested grandmasters was the manner in which AlphaZero played.  It played like a human, like the great attacking players of all time – a more precise version of Tal, Kasparov, and Spassky, complete with pawn and piece sacrifices to gain the initiative.

The AlphaZero story is very instructive for us supply chain planners and retail Flowcasters in particular.

As loyal disciples know, retail Flowcasting requires the calculation of millions of item/store forecasts – a staggering number.  Not surprisingly, people cannot manage that number of forecasts and even attempting to manage by exception is proving to have its limits.

What’s emerging, and is consistent with the AlphaZero story and learning, is that algorithms (either machine learning or a unified model approach) can shoulder the burden of grinding through and developing item/store specific baseline forecasts of sales, with little to no human touch required.

If you think about it, it’s not as far-fetched as you might think.  It will facilitate a game changing paradigm shift in demand planning.

First, it will relieve the burden of demand planners from learning and understanding different algorithms and approaches for developing a reasonable baseline forecast. Keep in mind that I said a reasonable forecast.  When we work with retailers helping them design and implement Flowcasting most folks are shocked that we don’t worship at the feet of forecast accuracy – at least not in the traditional sense.

In retail, with so many slow selling items, chasing traditional forecast accuracy is a bit of a fool’s game.  What’s more important is to ensure the forecast is sensible and assess it on some sort of a sliding scale.  To wit, if you usually sell between 20-24 units a year for an item at a store with a store-specific selling pattern, then a reasonable forecast and selling pattern would be in that range.

Slow selling items (indeed, perhaps all items) should be forecasted almost like a probability…for example, you’re fairly confident that 2 units will sell this month, you’re just not sure when.  That’s why, counter-intuitively, daily re-planning is more important than forecast accuracy to sustain exceptionally high levels of in-stock…whew, there, I said it!

What an approach like this means is that planners will no longer be dilly-dallying around tuning models and learning intricacies of various forecasting approaches.  Let the machine do it and review/work with the output.

Of course, sometimes, demand planners will need to add judgment to the forecast in certain situations – where the future will be different and this information and resulting impacts would be unknowable to the algorithm.  Situations where planners have unique market insights – be it national or local.

Second, and more importantly, it will allow demand planners to shift their role/work from analytic to strategic – spending considerably more time on working to pick the “winners” and developing strategies and tactics to drive sales, customer loyalty and engagement.

In reality, spending more time shaping the demand, rather than forecasting it.

And that, in my opinion, will be a game changing shift in thinking, working and performance.

Fossilized thinking

Fossilized

In August 1949 a group of fifteen smokejumpers – elite wild land firefighters – descended from the Montana sky to contain an aggressive fire near the Missouri River.  After hiking for a few minutes the foreman, Wagner Dodge, saw that the fire was raging – flames stretching over 30 feet in the air and blazing forward fast enough to cover two football fields every minute.

The plan was to dig a trench around the fire to contain it and divert it towards an area with little to burn.

Soon it became clear that the fire was out of control and the plan was out the window.  The fire was unstoppable so, instead, they’d try to outrun it, to safer ground.

For the next ten minutes, burdened by their heavy gear and tiring legs, the team raced up an incline, reaching an area that was only a few hundred yards from safety.  But the fire was unflinching, gaining ground like a wolf chasing down a wounded animal.

Suddenly, Dodge stopped.  He threw off his gear and, incredibly, took out some matches, lit them, and tossed them onto the grass.  His crew screamed at him but to no avail – when Dodge didn’t listen, they had no choice and turned and ran as fast as they could, leaving their foreman to what they believed to be certain peril.

But Dodge had quickly devised a different survival strategy: an escape fire.  By torching an area in front of him, he choked off the fuel for the fire to feed on.  Then, he poured water on a rag, put it over his mouth and lay down, face first, on the freshly burnt grass while the fire raged and sped past and over him.  In total, he’d spend close to 15 minutes living off the oxygen close the ground he’d just torched.

Sadly, of the rest of his crew that tried to outrun the blaze, only two would survive.

Wagner Dodge was able to survive not because of his physical fitness, but his mental fitness – the ability to rethink and unlearn.  The prevailing paradigm was that, at some stage for an out-of-control blaze, your only option is to try to out run it.  But Dodge was able to quickly rethink things – believing that, perhaps, by choking off it’s fuel line and providing his own small wasteland area, the fire might avoid him.  The ability to rethink had saved his life.

As it turns out, the ability to rethink and unlearn is also crucial for retails survival and revival.

It’s no secret, many retailers are struggling.  The same is true of many retail supply chains.

Do you ever really wonder why?

Lots of people blame retail’s generally slow adoption of new technologies and business models as the main factor, but I think it’s a deeper, more fundamental and chronic problem.

Technology is not eating retail.  Fossilized thinking is.

What’s fossilized thinking?  It’s people – at all levels in an organization – who are unwilling or unable to challenge their long-held beliefs.  Not only challenge them, but be able to rethink, unlearn and change them often.

As a case in point, many people who work in the retail supply chain don’t include the consumer as part of the supply chain.  Yet, if you think about it, the retail supply chain begins and ends with the consumer.  There are even a number of folks who don’t consider the store part of the supply chain.  Once the product has shipped from the DC to the store, then, incredibly, job done according to them.

Don’t believe me?

I won’t embarrass them, but just recently I read a “thought leadership” article from one of the world’s pre-eminent consulting firms regarding the top trends in retail supply chain management.  At #3, and I kid you not, was the growing view that the store was a key part of the supply chain.

Flowcasting tribe members know better and think differently.  The consumer and store have, and always will be, part of the supply chain.  That’s why we understand that, in retail, there is no such thing as a push supply chain – since you can’t push the product to the consumer.

In my opinion (and I’m not alone), fossilized thinking, not technology adoption, is the real disruptor in retail.

If you want to improve, innovate or disrupt then you must…

Constantly rethink, unlearn and challenge your own thinking!

Limit your inputs

Meditations

Marcus Aurelius is widely considered to be one of the wisest people of all time. His classic and colossal writings, ‘Meditations’, is a bible for crystal clear thinking, famously outlining the principles of stoicism.

In Meditations, he asks a profound question that all designers and implementers should ponder often…

”Is this necessary?”

Knowing what not to think about. What to ignore and not do. It’s an important question, especially when it comes to designing and implementing new ways of working.

As an example, consider the process of developing a forward looking forecast of consumer demand, by item, by store. Of course, loyal readers and disciples know that this is the forecast that provides a key input to allow a retail supply chain to be planned using Flowcasting.

You might get your knickers in a knot to learn that in one of our most recent and successful implementations of Flowcasting, the store level forecasting process uses only two key inputs:

  1. The actual sales history by item/store in units
  2. An indication if that sales history was during an abnormal period (e.g., a promotion, an unplanned event, a stock-out period, a different selling price, etc).

Now, I know what you’re thinking. What about all those ‘other’ things that influence consumer demand that many people espouse? You know, things like the weather, competitor activities and any other causal variables?

Counter-intuitively, all these additional ‘factors’ are not really required at the retail store level and for very good reasons.

First, did you know that for most retailers 50-60% of the item/store combinations sell 24 or less units per year. That’s less than one unit every two weeks. Furthermore, about 70-80% of the item/store combinations sell less than 52 units per year, or about 1 unit per week.

Consider the very slow sellers – selling 24 or less units a year. If the last 52 weeks sales was 24 units and so was the previous year’s, it would stand to reason that a reasonable forecast for the upcoming 52 weeks would be around 24 units.

Keep in mind, that as actual sales happen the forecasting process would always be re-forecasting, looking ahead and estimating the upcoming 52 weeks consumer demand based on most recent history.

Now, consider a 52 week forecast of 24 units. That breaks down to a weekly forecast of 0.46.

Factoring in additional variables is not likely to make actual sales of 30 units happen (assuming stock outs were not excessive) – which would be the equivalent of increasing previous year’s sales by 25%. A higher forecast does not mean sales will actually happen.

Even a 10% increase in the forecast would only increase the annual forecast by about 2 units, or about .04 units per week.

Is there really a difference between an average weekly forecast of 0.46 and 0.50 units? Isn’t that essentially the same number? In terms of a forecast, they are both reasonable (in reality, the forecast would also have a pattern to the expected sales and would be expressed as integers, but you get the point).

One of the keys of the retail Flowcasting process is using only a limited number of inputs to build the item/store forecast, while allowing people to easily understand and thus manage it – by a very limited number of exceptions that could not be automatically resolved using system rules.

Add some basic supply information (like inventory balances and ordering rules) to the forecast and voila – the entire supply chain can be calculated/planned from store to supplier – every day, for every planned inventory flow and projection for a rolling 52 weeks into the future.

What’s elegant and inherently beautiful about Flowcasting is that daily re-planning the entire supply chain provides the agility to adjust current and planned inventory flows and ensures everyone is working to a single set of numbers – based on the drumbeat of the consumer. It negates the need to find the ‘perfect forecast’ and, as such, allows us to limit our inputs to the bare essentials.

Marcus Aurelius was right.

Limit your inputs and always ask, “Is this necessary?”

It’s good advice in business, in life and especially store level forecasting.