About Mike Doherty

Wrong becomes right

Posted on September 1, 2021 by Mike Doherty

Phil Tetlock spent almost two decades determining people’s ability to forecast specific events – things like elections and the outcomes of potential geopolitical decisions. Unfortunately, the results were not impressive. Most people were about as accurate as a well-fed, dart-throwing chimpanzee.

There were a small number of notable exceptions. A small group of people consistently provided quite accurate forecasts – people he aptly named “super-forecasters”.

In a subsequent forecasting tournament organized by the Intelligence Advanced Research Projects Activity (IARPA) – a focused branch of the United States intelligence community, the super-forecasters trounced teams of professors and “forecasting experts” by wide margins.

What made the super-forecasters so super?

It wasn’t intelligence or that they had more experience than others. In fact, in many cases, they were mostly amateurs yet they outperformed the CIA’s best and brightest (who also had the advantage of years of experience and classified information). Armed with only Google, the super-forecasters beat the CIA, on average, by 30%.

What made them great at being right was they were great at being wrong!

The difference in their ability to forecast was simple, yet crucial. The super-forecasters changed their minds – a lot.

Not a huge, 180-degree shift, but subtle revisions to their predictions as they learned new information. As an example, one of the consistently top super-forecasters would routinely change his mind at least a dozen times on a prediction and, sometimes, as often as forty or fifty times.

Importantly, most viewed a revised forecast based on new information not as changing an initially wrong forecast but rather as updating it. Turns out, updating is the secret to being a great, or super, forecaster.

The concept of updating is important in Flowcasting as well.

As loyal Flowcasters know, an important component of the process is the sharing of what we call a supplier schedule – that is, a projection, by item and delivery location of how many units are needed to ship over a long time horizon (typically 52+ weeks).

If the schedule indicates that, 39 weeks from now, the supplier will need to ship 10440 units of a product to a location, what’s the chance that this projection (i.e., forecast) is perfectly accurate? Pretty low, right? And it doesn’t need to be – it just needs to be reasonable.

A week later, and guess what? The supplier schedule has been re-calculated and updated to indicate that 10400 units are needed to ship in that particular week (perhaps even on a different day). The updated forecast is more right than the previous one. This process of minor revisions continues as the projections are updated until it’s actually time to ship (i.e., when the planned shipment has reached the agreed-upon order release horizon).

In retail, supplier scheduling is the super-forecaster for suppliers – recalibrating and subtly updating the forward looking projections, based on the latest information until…

Wrong becomes right.

Flipping your thinking

Posted on August 26, 2021 by Mike Doherty

From toddler to teenager, most of us had a fairly similar educational experience: The teacher would stand at the front of the room and spew out information that the students were to absorb. Then they would assign homework that would, theoretically at least, test how well you absorbed the content. The homework assignments would then be scored by the teacher and – if you were lucky – you might find some scrawled notes in the margin to give you a clue as to where you may have gone astray.

All this assumes, of course, that you didn’t just copy your homework from a smart (if gullible) friend, thereby completely circumventing the ability of the homework assignment to test knowledge. Not to say that this approach was completely ineffective. Somehow, most of us did learn what we needed to know to become productive members of society. That said, when the best praise you can muster is that it’s “not completely ineffective”, then you are basically admitting that there is ample room for improvement.

Karl Fish is a veteran teacher with 20 years of experience. He teaches high school math in a town just south of Denver, Colorado. Karl thought he could do better for his students than “not completely ineffective”, so he decided to flip the traditional thinking on its head.

Instead of using class-time to “teach” in the traditional sense, Karl tapes his lessons and uploads them to YouTube. Classroom time is used for application and practice. His students are required to watch the lecture whenever and wherever it is most convenient for them. What would be traditionally considered “homework” is actually done during in-class time.

The result of this flip in thinking has been significantly improved understanding of the content. Working through examples and case studies not only improves the students understanding, but also improves their ability to collaborate with fellow students and Karl himself.

Contrast this approach with doing your homework in the evenings (maybe even the wee hours of the morning). If you get stuck, there are few alternatives other than to become increasingly frustrated and demoralized.

In Karl’s class, you can pose your question to a group (many of whom are likely struggling with the same question) and work together to solve the problem. What better skill and habits are there for someone to learn in high school?

Retail supply chain planning is also in need of a flip in thinking.

Traditional thinking in retail has ingrained into people’s heads that ordering is the key decision a supply chain planner needs to make. Day in and day out the retail supply chain planner only has 2 questions:

1) Should I order today?

2) How much should I order?

All anyone can talk about nowadays is “demand driven supply chains” that are super-responsive to consumer demand – yet the entire planning approach is geared toward figuring out when to place an order upstream with, at best, an indirect link to the actual customer demand.

Flowcasting flips that thinking completely. In a nutshell, the decision to place an order is a mere after-effect of the planning process (not the one and only decision) and, generally, it should be performed by a computer, not a human being.

Instead of asking “When and how much should I order”, maybe the first question should be “When does more stock need to arrive?” – Isn’t that what’s really important to your ability to stay in stock and serve a customer?

By focusing on this question first, new thinking emerges. Once you understand when product needs to arrive, then you can calculate when you need product to ship (based on where it’s coming from) and when you need to order.

Of course, to answer this question you’ll need a system to project inventory and product arrival dates well into the future. And to calculate the corresponding ship dates and order dates. The basic change in philosophy is simple – to really know when product needs to be ordered, you must first know when it needs to arrive at the destination.

While this may sound quite simple and logical, in practice it requires a great deal of education and understanding to make such a flip – particularly if people have been “ordering” for a long time.

In our experience, most retailers continue to subscribe to the “order first, ask questions later” philosophy and flipping to an arrival based planning approach like Flowcasting will not be a slam dunk, regardless of how logical it sounds.

If you’re struggling with the concept, we’d be happy to schedule some homework time with you and your team.

Principles

Posted on August 10, 2021 by Mike Doherty

Folks that know us well and have worked with us, know we’re what you might call principles freaks. A decent chunk of time and effort we spend helping retailers implement Flowcasting is oriented to instilling a set of principles, which guide our thinking and, hopefully over time, our clients.

Julia Galef, in her brilliant book, The Scout Mindset, beautifully outlines the paradox of principles in a chapter aptly named, “How To Be Wrong”…

Many principles sound obvious and that you know them already. But “knowing” a principle, in the sense that you read it and say, “Yes, I know that,” is different from having internalized it in a way that actually changes how you think.

Times Ten

Posted on July 7, 2021 by Mike Doherty

It’s hard to believe and counter-intuitive but it’s often easier to make something 10 times better than it is to make it 10 percent better.

Yes, really.

That’s because when you’re working to make things a bit better – like 10 percent better – you always start with the existing tools, assumptions and paradigms, and focus on tweaking an existing approach/solution that’s become the accepted norm.

This kind of progress is driven by extra effort, extra money, and extra resources – attempting to squeeze just a wee bit more from the current system. While making a minor improvement is generally a good thing, often we find ourselves stuck in the same old paradigms.

But when you aim for a 10 times improvement, you need to lean in and focus on being brave and creative – the kind of thinking that, literally and metaphorically, can put a man on Mars.

Retail Flowcasting solutions have been emerging for a while with some technology companies claiming that Flowcasting is in the DNA of their solution. More often than not that’s debatable but what’s not is that a retail Flowcasting solution needs to be at least 10 times more scalable and simpler than similar solutions that have been used in distribution and manufacturing – because in retail we’re dealing with 10’s of millions of item/location combinations.

We’ve been lucky enough to implement a purpose-built, times-ten retail Flowcasting solution. In our experience, it’s 10 times faster, 10 times simpler, and costs about 10 times less to implement compared to others who claim to have Flowcasting native solutions.

Over the course of the implementation, the chief architect shared with us how he was able to build something that’s a times-ten solution.

His secret:

Start from scratch
Solve the hard problems first

Most existing solutions are too heavy and burdensome – the result of solution providers not knowing how to say “no” to a specific ask – usually from a paying client that doesn’t know better, but also sometimes from competitive peer pressure. Checklist Charlie said our solution needs 11 different ways to handle safety stock, so we’ll make it so. The result is a system that cannot be the foundational starting point to build a times-ten solution.

Our architectural hero said the reason he started from scratch is that only what was necessary would go into the solution and not a drop more. A bit like Einstein once said, “everything should be made as simple as possible, but no simpler”.

In tandem with making things as simple as possible, our colleague believes that another reason he was successful is that he also solved the hard problems first.

To wit, he worked, tested and developed stunningly intuitive and simple ways to handle a variety of problems that have plagued existing providers from morphing their current solutions to a retail-focused Flowcasting solution, notably:

Extreme scalability
Handling slow selling items properly (both the forecast and the replenishment plans)
Planning for product phase out (to provide a valid simulation of reality for any item at any location)
Managing seasonal items (to maximize sales and minimize inventory carryover)

I won’t bore you with the specifics on how our architectural and design colleague solved each of these chronic retail planning challenges. He did say that he’d spoken to a number of grassroots folks in retail about each of these challenges and then – from a blank sheet of paper and some fundamental principles – developed, tested, refined and eventually delivered a times-ten solution.

Once the hard problems were solved, he believed that the rest of the planning challenges would be chicken-shit to deal with. Turns out, he was right.

The lesson here is simple, yet profound. If you’re looking for a quantum leap improvement – a times-ten solution if you will – then you need to start from scratch and solve the hard stuff first.

Subtraction

Posted on June 29, 2021 by Mike Doherty

“What gets left out is as important as what gets put in.”

Steve Jobs philosophy

Subtraction

I have a good friend named April, who has the coolest son, Lincoln. Lincoln is in grade one and looking forward to grade two. When I asked him his favorite subject, he declared “math”. So, probing further, I asked him what he liked about math…

“Subtraction”.

“I like taking things away and seeing what’s left”.

I was startled. I’d never heard anyone claim their love of subtraction. Or taking things away from something and seeing what’s left. Of course, it got me thinking.

Flowcasting, as a concept and business process, is gaining lots of momentum. People are really beginning to understand that, when it comes to supply chain planning, most companies have been planning looking in the rear view mirror. Most are forecasting what should be calculated.

With Flowcasting, you only need to forecast at the item/store level and calculate everything else – all demand, supply, inventory, capacity, financial projections can be determined using this forecast.

The legendary Steve Jobs is a disciple of “subtraction thinking” – his colleagues claim that Steve was more interested in what gets left out versus what gets put in when designing a new product.

Flowcasting systems are systems cut from the same cloth.

Think about today’s planning systems and all their features. All kinds of bells and whistles – you know the drill, a different algorithm for each type of demand stream…special systems to help you decide on the algorithm…multiple forecasts and automatically picking the best one…a different planning system for slow selling items…a special system for allocation……etc… On and on it goes.

For retailers, these systems were built for a world that will someday no longer exist. A world where we were forecasting at the wrong level – DC’s, plants, etc. – anywhere other than the point of final consumption.

Technology companies are also beginning to understand and embrace the principles of Flowcasting.

Hopefully they’ll embrace the concept of “subtraction”. Instead of adding to their systems, they should be subtracting.

For the retail supply chain, forecasting is at the store level and only there. Simple approaches are all that’s required to forecast at store level and further remind yourself that all other demands can and should be calculated.

Are there systems available that support the Flowcasting process? Absolutely. They are elegant, simple and were built with “subtraction thinking” in mind.

It’s amazing what you can learn talking with a first-grader.

I plan on talking to Lincoln again soon.

Changing the game

Posted on May 5, 2021 by Mike Doherty

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

Posted on March 3, 2021 by Mike Doherty

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?

Posted on January 6, 2021 by Mike Doherty

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

Posted on November 4, 2020 by Mike Doherty

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?

People can’t make decisions on their own approaching an intersection and need to be told what to do
The intersections must be managed with complex rules and technology with cables, lights, switches and a control center
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:

People make their own decisions on entry and exit and trust one another to use good judgment
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
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:

They have 75% less collisions and 90% less fatal collisions;
They reduce delays by 89%; and
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

Posted on September 2, 2020 by Mike Doherty

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:

The actual sales history by item/store in units
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.