About Mike Doherty

Mike Doherty

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!

Is the juice worth the squeeze?

Squeezing-Oranges

A little over 10 years ago I was on a project to help one of Canada’s largest grocery and general merchandise retailers design and implement new planning processes and technology. My role was the co-lead of the Integrated Planning, Forecasting & Replenishment Team and, shockingly, we ended up with a Flowcasting-like design.

The company was engaged in a massive supply chain transformation and the planning component was only one piece of the puzzle. As a result of this, one of the world’s preeminent consulting firms, Accenture, was retained to help oversee and guide the entire program.

One of the partners leading the transformation was a chap named Gary. Gary was a sports lover, a really decent person, great communicator and good listener. He also had a number of “southern sayings” – nuggets of wisdom gleaned from growing up in the southern United States.

One of his saying’s that’s always stuck with me is his question, “is the juice worth the squeeze?”, alluding to the fact that sometimes the result is not worth the effort.

I can remember the exact situation when this comment first surfaced. We were trying to help him understand that even for slow and very slow selling items, creating a long term forecast by item/store was not only worth the squeeze, but also critical. As loyal and devoted Flowcasting disciples know this is needed for planning completeness and to be able to provide a valid simulation of reality and work to a single set of numbers – two fundamental principles of Flowcasting.

The good news was that our colleague did eventually listen to us and understood that the squeeze was not too onerous and today, this client is planning and using Flowcasting – for all items, regardless of sales velocity.

But Gary’s question is an instructive one and one that I’ve been pondering quite a bit recently, particularly with respect to demand planning. Let me explain.

The progress that’s been made by leading technology vendors in forecasting by item/store has been impressive. The leading solutions essentially utilize a unified model/approach (sometimes based on AI/ML, and in other cases not), essentially allowing demand planners to largely take their hands off the wheel in terms of generating a baseline forecast.

The implications of this are significant as it allows the work of demand planning to be more focused and value added – that is, instead of learning and tuning forecasting models, they are working with Merchants and Leaders to develop and implement programs and strategies to drive sales and customer loyalty.

But, I think, perhaps we might be reaching the point where we’re too consumed with trying to squeeze the same orange.

My point is how much better, or more accurate, can you make an item/store forecast when most retailers’ assortments have 60%+ items selling less than 26 units per year, by item/store? It’s a diminishing return for sure.

Delivering exceptional levels of daily in-stock and inventory performance is not solely governed by the forecast. Integrating and seamlessly connecting the supply chain from the item/store forecast to factory is, at this stage, I believe, even more crucial.

Of course, I’m talking about the seamless integration of arrival-based, time-phased, planned shipments from consumption to supply, and updated daily (or even in real time if needed) based on the latest sales and inventory information. This allows all partners in the supply chain to work to a single set of numbers and provides the foundation to make meaningful and impactful improvements in lead times and ordering parameters that impede product flow.

The leading solutions and enabling processes need to produce a decent and reasonable forecast, but that’s not what’s going to make a difference, in my opinion. The big difference, now, will be in planning flexibility and agility – for example, how early and easily supply issues can be surfaced and resolved and/or demand re-mapped to supply.

You and your team can work hard on trying to squeeze an extra 1-3% in terms of forecast accuracy. You could also work to ensure planning flexibility and agility. Or you could work hard on both.

It’s a bit like trying to get great orange juice. To get the best juice, you need to squeeze the right oranges.

Which ones are you squeezing?

Stoplights and Roundabouts

Stoplight-roundabout

As someone who’s been doing project work for a long time, anytime I read something that makes me ponder, I take note.

Consider stop lights and roundabouts.

Stop lights are the dominant way that we use to manage intersections and flows of traffic for two roads that cross.  Have you ever thought about the assumptions behind this approach?

  1. People can’t make decisions on their own approaching an intersection and need to be told what to do
  2. The intersections must be managed with complex rules and technology with cables, lights, switches and a control center
  3. A plan and logic must be determined for every scenario, thus requiring a solution with multi-colored signals, arrows, etc

Now, think about roundabouts.  In a roundabout, cars enter and exit a shared circle that connects travel in all four directions.  The assumptions for this method are significantly different:

  1. People make their own decisions on entry and exit and trust one another to use good judgment
  2. The intersections are managed with simple rules and agreements: give the right of way to cars already in the circle and go with the flow
  3. Lots of scenarios happen, but co-ordination and common sense will be good enough to handle them

How about the performance of each approach?  Ironically, the roundabout outperforms the more complicated and sophisticated system on the three key performance metrics:

  1. They have 75% less collisions and 90% less fatal collisions;
  2. They reduce delays by 89%; and
  3. They are between $5,000 and $10,000 less costly to operate/maintain each year (and, of course, function as normal during power outages)

There’s some pretty profound insights and learning’s from this comparison.  Obviously, if you’re involved in designing and implementing new thinking and technology, keep it as simple as possible and don’t try to automate every decision.

The other key insight from this example is actually more profound and speaks to the nature of work, innovation and teams.

I’ve been very fortunate to have led two fairly important projects with respect to retail Flowcasting.  This dichotomy between stoplights and roundabouts highlighted why we were successful and paints a picture for how projects, and indeed work, could be organized better.

About 25 years ago I was the leader of a team at a large, national Canadian retailer whose mandate was to design a better way to plan the flow of inventory from supplier to store.  We would eventually design what we now call Flowcasting and would implement retail DRP and supplier scheduling for the entire company, including all suppliers – a first in complete integration from retailer to supplier.

As luck would have it, our team would eventually report up to a Director, who was, like the team, a bit of a maverick.  Let’s call him Geoff.

What Geoff did that was brilliant – and consistent with the roundabout philosophy – was to give me and the team almost complete decision making authority.  I remember him telling me, “This team knows what they’re doing and the design is solid.  My job is to clear trail for you, shelter you from unnecessary bureaucracy and make sure you can deliver”.

And he did.  The team had virtually the entire say in all decisions that affected the design and implementation.  That’s not to say we didn’t communicate with Senior Management and give updates and ask for opinions – we did, it’s just we felt like we were given ultimate say.  It was exhilarating and, as it turns out, a model for project work.

Fast forward 20 years and I’m a consultant on another Flowcasting project – this time for a mid-sized national hardgoods Canadian retailer.

In another stroke of good fortune, the business sponsor for the team inherently had a similar view about work and how projects delivered.  Let’s name him Ken.

Ken’s operating style also gave the team the latitude to make the key decisions regarding design and implementation – of course he kept abreast of things and contributed his input and advice but ultimately we were in charge.  His role, he said, was to educate and help the Senior people make the journey.

As an example, I remember Ken telling me before our first steering committee meeting, words to the effect…”We’re not going in looking for approval.  We know what we’re doing and why.  These sessions are about educating and informing the group, and every now and then asking for their opinions and advice”.

It was how the entire project operated.

In one example that demonstrated the team’s authority, I remember one of the analyst’s on the team helping Ken change his thinking on our implementation approach.  It was a great example of the team working with psychological safety and proof positive that ideas were more important than hierarchy.

I’ve been doing a lot of reading lately on the future of work and how companies can innovate.  And what I’m seeing is that a model for work (day to day, and also project work) is starting to emerge.

It’s based on the principle of turning people into self-organized, self-managing teams and giving them the space, freedom and authority to work and innovate – treating them like small, micro-enterprises.

Principles I’ve been fortunate enough to have experienced in two of the most successful and rewarding projects I’ve been involved with.

You can manage, innovate and drive change using the operating principles of the stoplight or the roundabout.

Choose wisely.

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.

The beauty of being wrong

Wrong

Let’s be honest, no one likes to be wrong.  From early schooling and continuing through our careers we’ve been ingrained to do our best to be right.  It keeps us out of trouble, builds our self-esteem and helps us progress.

But what if our views on being wrong were, well, wrong?

There is growing research from a number of disciplines that in order to improve, grow, innovate and lead you need to be able to question your own thinking – allowing for different ideas and views to be heard and essentially being humble enough to admit that you might be wrong about what you think you know.

To illustrate this point of view, consider Julia Galef, co-founder of the Center for Applied Rationality, who asks a beautiful, metaphorical question, “Are you a soldier or a scout?

Soldiers defend and protect. Scouts, in contrast, seek and try to understand.  In her view and a number of others, this worldview shapes how you process information, develop ideas and guides your ability to change.

The mindset of a scout is anchored in curiosity. They love to learn, feel intrigued when something new contradicts their previous views and they are also extremely grounded: their self-worth as a person or team mate isn’t tied to how right or wrong they are about a specific topic.

Scouts have what many refer to as “intellectual humility” – a term that has been popularized in the past several years by a number of influential folks including Google’s Lazlo Block and University of Virginia Professor Edward Hess, who even penned a brilliant book entitled, “Humility is the new smart”.  According to Hess, in order to compete you need to assume the role of lifelong humble inquirer.

Intellectual humility is loosely defined as “a state of openness to new ideas, and a willingness to be receptive to new sources of evidence”.  At the heart of intellectual humility are questions.  Scouts ask lots of questions and are comfortable with all sorts of answers.

In short, scouts have a completely different view about being wrong.  They’re actually cool with it.  They embrace it.  And understand that being wrong is as important as being right – since they understand that being wrong helps you learn, change, iterate and, ultimately, make breakthroughs.

While you might feel a tad uncomfortable with this assertion, I would contend that virtually every major innovation, change or scientific breakthrough started out, at some point, being “wrong”.

While I’m not sure I’d consider myself a scout (though I do like the term and metaphor), I can surely confess I’ve been wrong a lot.  Maybe even more often than I’ve been right.

As just one example, a few years ago I was on a team working to achieve inventory accuracy in stores.  We’d followed a sensible approach that we’ve outlined in previous newsletters – frequently counting a control group of items to uncover and correct the root causes of the errors.

The team had surfaced and resolved some important discipline and housekeeping errors and consistently had the control group of products between 92-94% accurate.  Unfortunately, I helped to convince the team that we needed to get to virtually 100% accurate before we could roll it out to all stores.

Unfortunately, I was wrong.

A couple of years ago I was talking to a colleague who has more experience and, importantly, a different view.  He asked me why I thought that we needed to be 100% accurate and, during the discussion, politely reminded me that “perfection is the enemy of great”.  According to him, what we’d done could have been rolled out to all stores, instead of only rolling out minor procedural changes.

Here’s a great example of why I think being wrong is actually pretty instructive.  This learning helped me change my perspective on change and altered my thinking and approach.  Now “perfection” will never be the goal and good enough truly is – since good enough gets done (implemented) and done can be built on and improved.

Now, I’m not saying you should try to be wrong.  It’s just that being wrong has gotten a bad rap. Innovation and change require that you get good and comfortable with the notion of being wrong.  Wrong leads to right.

Of course, that’s my view and, well, I could be wrong.

Experts on the future

In 1971, Judah Folkman, a doctor working in Boston, developed a new approach to treat cancer – essentially by stopping the blood vessels supplying the tumors. Blocking the flow, he concluded, would halt the growth of the tumors.

At the time, the only accepted and endorsed approach to treating cancer was chemotherapy. Dr Folkman’s idea was scorned and ridiculed by the medical establishment consisting of a group of PhD insiders – mostly from the field of biology.

According to Dr Folkman, when he attempted to share his idea and thinking to the scientific community, the entire room would get up and leave – as if, collectively, they all had to take a piss at the exact same time. Over time, the criticism got so bad that special committees were developed to review his ideas and not only judged his idea to be of little value, they also threatened to revoke his medical license if he did not cease – in one letter, writing to him to reject his ideas and calling him a ‘clown’.

Folkman, however, was undaunted and pressed on. Painstakingly, his ideas were slowly starting to be accepted by more “open” thinkers and eventually morphed into drugs available for cancer trials. To his credit, in the summer of 2003, at a major medical conference, the results from a large trial for patients with advanced colon cancer validated Dr. Folkman’s thinking.

The way to treat cancer had been transformed. At the event, the crowd rose in a standing ovation. The presenter, at the time, said, to the effect, “it’s a shame that Dr. Folkman couldn’t be here to experience this” – little did he know that, sitting in a back row, Dr. Folkman had just smiled.

Eventually he couldn’t hide his fame and was asked about his achievement – which had taken the better part of 32 years. Most folks wanted to know how he felt and why he continued on his journey in light of all the criticism and personal attacks.

His answers were and still are very insightful.

First, in terms of the ridicule he proclaimed, “You can always tell the leader of new thinking from all the arrows in their ass”.

And even more profound about why he never gave up: “There are no experts of the future.”

Presently we’re living through an unprecedented time and there are a lot of questions about the future – how will the world look after the virus is subdued and what will the new normal look like? Some of these questions are focused on retail and supply chain management.

How will consumers change their behaviors? How much sales will be transacted online? Will home delivery become even more significant? Will supply chain networks become more diverse and less susceptible to a single country’s supply disruption? What other customer delivery methods will emerge?

All good questions for which my answer is the same as yours, “I really don’t know”. In my opinion, a lot will change, I’m just not sure where, when, how much and how fast.

Remember, “There are no experts of the future”.

What I do know is that human behavior will not change. We are social animals and like and need to acquire stuff. We just might shift, perhaps dramatically over time, how we go about this.

For us supply chain planners – especially retailers – that means having the supply chain driven by and connected to consumer demand will be crucial. As consumer demand shifts and evolves having a complete model of the business and providing longer term visibility to all stakeholders will be a core capability – both in the short to medium term but also longer term to proactively plan for and respond to the next disruption.

Wait a minute…that sounds like Flowcasting, doesn’t it?

The key to being in-stock

Key

Abraham Lincoln is widely considered the greatest President in history. He preserved the Union, abolished slavery and helped to strengthen and modernize government and the economy. He also led a fragile America through one of her darkest and most crucial periods – the American Civil War.

In the early days of the war, there were lots of competing ideas about how to secure victory and who should attempt it. Most of the generals at that time had concluded that the war could only be won through long, savage and bloody battles in the nation’s biggest cities – like Richmond, New Orleans and even Washington.

Lincoln – who taught himself strategy by reading obsessively – had a different plan. He laid out a large map and pointed to Vicksburg, Mississippi, a small city deep in the South. Not only did it control important navigation waterways, but it was also a junction of other rivers, as well as the rail lines that supplied Confederate armies and plantations across the South.

“Vicksburg is the key”, he proclaimed. “We can never win the war until that key is ours”.

As it turns out, Lincoln was right.

It would take years, blood, sweat and ferocious commitment to the cause, but his strategy he’d laid out was what won the war and ended slavery in America forever. Every other victory in the Civil War was possible because Lincoln had correctly understood the key to victory – taking the city that would split the South in half and gaining control of critical shipping lanes.

Lincoln understood the key. Understanding the key is paramount in life and in business.

It’s no secret that many retailers are struggling – especially in terms of the customer journey – most notably when it comes to retail out of stocks. Retail out of stocks have remained, on average, sadly, at 8% for decades.

So what’s the key to finally ending out-of-stocks?

The key is speed and completeness of planning.

First, we all know that the retail supply chain can and should only be driven by a forward looking forecast of consumer demand – how much you think you’ll sell, by product and consumption location.

Second, everyone also agrees (though few understand the key to solving this thorn in our ass) that store/location on-hands need to be accurate.

But the real key is that, once these are in place, the planning process must be at least done daily and must be complete – from consumption to supplier.

Daily re-forecasting and re-planning is necessary to re-orient and re-synch the entire supply chain based on what did or didn’t sell yesterday. Forecasts will always be wrong and speedy re-planning is the key to mitigating forecast error.

However, that is not enough to sustain exceptionally high levels of daily in-stock. In addition, the planning process must be complete – providing the latest projections from consumption to supply, giving all trading partners their respective projections in the language in which they operate (e.g., units, volume, cube, weight, dollars). The reason is simple – all partners need to see, as soon as possible, the result of the most up to date plans. All plans are re-calibrated to help you stay in stock. And the process repeats, day in, day out.

We have retail clients that are achieving, long term, daily in-stocks of 98%+, regardless of the item, time of year or planning scenario.

They understand the key to making it happen.

Now you do too.

Grandmaster Collaboration

Garry Kasparov is one of the world’s greatest ever chess grandmasters – reigning as World Champion for 15 years from 1985-2000, the longest such reign in chess history. Kasparov was a brilliant tactician, able to out-calculate his opponents and “see” many moves into the future.

In addition to his chess prowess, Kasparov is famous for the 1997 chess showdown, aptly billed as the final battle for supremacy between human and artificial intelligence. The IBM supercomputer, Deep Blue, defeated Kasparov in a 6 game match – the first time that a machine beat a reigning World Champion.

Of course chess is a natural game for the computational power of AI – Deep Blue reportedly being able to calculate over 200 million moves per second. Today, virtually all top chess programs that you and I can purchase are stronger than any human on earth.

The loss to Deep Blue intrigued Kasparov and made him think. He recalled Moravec’s paradox: machines and humans frequently have opposite strengths and weaknesses. There’s a saying that chess is “99 percent tactics” – that is, the short combinations of moves players use to get an advantage in position. Computers are tactically flawless compared to humans.

On the flip side, humans, especially chess Grandmasters were brilliant at recognizing strategic themes of positions and deeply grasping chess strategy.

What if, Kasparov wondered, if the computational tactical prowess were combined with the human big-picture, strategic thinking that top Grandmasters had honed after years of play and positional study?

In 1998 he helped organize the first “advanced chess” tournament in which each human player had a machine partner to help during each game. The results were incredible and the combination of human/machine teams regularly beat the strongest chess computers (all of which were stronger than Kasparov). According to Kasparov, “human creativity was more important under these conditions”.

By 2014, and to this day, there continue to be what is described as “freestyle” chess tournaments in which teams made up of humans and any combination of computers compete against each other, along with the strongest stand-alone machines. The human-machine combination wins most of the time.

In freestyle chess the “team” is led by human executives, who have a team of mega-grandmaster tactical advisers helping decide whose advice to probe in depth and ultimately the strategic direction to take the game in.

For us folks in supply chain, and especially in supply chain planning, there’s a lot to be learned from the surprisingly beneficial collaboration of chess grandmaster and supercomputer.

Humans excel at certain things. So do computers.

Combine them, effectively, like Kasparov inspired and you’ll undoubtedly get…

Grandmaster Collaboration.

Pissed Off People

Jim is basically your average bloke. One Saturday afternoon, about 25 years ago, he’s doing something a lot of average blokes do; cleaning his home – a small farmhouse in the west of England.

After some dusting, it’s time to vacuum. Like everyone at the time, he’s shocked how quickly his top-of-the-line Hoover cleaner loses its suction power.

Jim is pissed. Royally pissed off. Madder than a wet hen.

So mad, in fact, that he took the cleaner out to his shed, took it apart and examined why it would lose suction power so quickly. After a few experiments he correctly deduced that the issue was that fine dust blocked the filter almost immediately and that’s why performance in conventional cleaners dips so fast.

Jim continued to be pissed until one day he visited a timber mill, looking for some wood. In those days, timber mills planed the logs on the spot for you. Jim watched as he saw his wood travel along until it reached a cyclone specifically designed to change the dynamics of airflow, separating the dust from the air via centrifugal force.

BOOM! James Dyson, still pissed at how shit traditional vacuum cleaners were, got the core idea of the Dyson cyclone cleaner. An idea that he would use to eventually deposit over £3 billion into his back pocket.

Unbelievably it took Dyson three years and 5,127 small, incremental prototypes to finally “perfect” his design and revolutionize cleaning forever. Can you imagine how pissed you’d need to be to work, diligently, over that many iterations to finally see your idea through?

Dyson’s story is incredible and enlightening – offering us a couple of key insights into the innovative process.

First, most folks think that innovation happens as a result of ideas just popping into people’s heads. That’s missing the key piece of the puzzle: the problem! Without a problem, a flaw, a frustration, innovation cannot happen. As Dyson himself states, “creativity should be thought of as a dialogue. You have to have a problem before you can have game-changing innovation”.

Second, for innovative solutions to emerge you need pissed off people. People like Dyson who are mad, frustrated and generally peeved with current solutions and approaches for the problem at hand. So they are always thinking, connecting and, at times, creating a breakthrough solution – sometimes years after initially surfacing the problem. So, while it’s easy to say that the “idea” just happened, more often than not you’ve been mulling it over, subconsciously, because you’re pissed about something.

Here’s a true story about Flowcasting and how it eventually saw the light of day as a result of some pissed off people.

About 25 years ago, I was the leader of a team whose mandate was to improve supply chain planning for a large, very successful Canadian retailer. I won’t bore you with the details but eventually we designed, on paper, what we now call Flowcasting.

Problem was that it was very poorly received by the company’s Senior Leadership team, especially the Supply Chain executives. On numerous occasions I was informed that this idea would never work and that we needed to change the design. I was also threatened to be fired more than once if we didn’t change.

The problem was, our team loved the design and could see it potentially working. As I was getting more pressure and “never” from the leadership team, I was getting more and more pissed. Royally pissed off as a matter of fact.

As luck would have it, as a pissed off person, I didn’t back down (there’s a lesson here too – “never” is not a valid reason why something might not work, regardless who says it). One person on the team suggested I contact Andre Martin and he and his colleague, Darryl Landvater, helped us convince the non-believers that it would be the future and that we should pilot a portion of the design. The rest is, of course, history.

The Flowcasting saga didn’t stop there. As we were embarking on our early pilot of the DC-supplier integration, Andre and Darryl tried, unsuccessfully, to convince a few major technology planning vendors that an integrated solution, from store/consumption to supply was needed and that they needed to build it, from scratch.

All the major technology players turned them down, citing lots of “nevers” themselves as to why this solution was either not needed, or would not scale and/or work.

To be honest, it pissed them off, as they’ve admitted to me many times over the years.

So much so, that, despite all the warnings from the experts they “put their money where their mouth is” and built a Flowcasting solution that connects the store to supplier in an elegant, intuitive and seamless fashion – properly planning for crucial retail planning scenarios like slow sellers, promotions, and seasonal items just to name a few.

In 2015, using the concept of Flowcasting and the technology that they developed, a retailer seamlessly connected their supply chain from consumption to supply – improving in-stocks, sales and profits and instilling a process that facilitates any-channel planning however they wish to do it.

Sure, having a reasonably well thought out design was important. As was having a solution suited for the job.

But what really enabled the breakthrough were some pissed-off people!