About Mike Doherty

Mike Doherty

Noise is expensive

Noise

Did you know that the iHome alarm clock, common in many hotels, shows a small PM when the time is after 12 noon?  You wonder how many people fail to note the tiny ‘pm’ isn’t showing when they set the alarm, and miss their planned wake up.  Seems a little complicated and unnecessary, wouldn’t you agree?

Did you also know that most microwaves also depict AM or PM? If you need the clock in the microwave to tell you whether it’s morning or night, somethings a tad wrong.

More data/information isn’t always better. In fact, in many cases, it’s a costly distraction or even provides the opportunity to get the important stuff wrong.

Contrary to current thinking, data isn’t free.

Unnecessary data is actually expensive.

If you’re like me, then your life is being subjected to lots of data and noise…unneeded and unwanted information that just confuses and adds complication.

Just think about shopping now for a moment.  In a recent and instructive study sponsored by Oracle (see below), the disconnect between noise and what consumers really want is startling:

  1. 95% of consumers don’t want to talk or engage with a robot
  2. 86% have no desire for other shiny new technologies like AI or virtual reality
  3. 48% of consumers say that these new technologies will have ZERO impact on whether they visit a store and even worse, only 14% said these things might influence them in their purchasing decisions

From the consumers view what this is telling us, and especially supply chain technology firms, we don’t seem to understand what’s noise and what’s actually relevant. I’d argue we’ve got big time noise issues in supply chain planning, especially when it relates to retail.

I’m talking about forecasting consumer sales at a retail store/webstore or point of consumption.  If you understand retail and analyze actual sales you’ll discover something startling:

  1. 50%+ of product/store sales are less than 20 per year, or about 1 every 2-3 weeks.

Many of the leading supply chain planning companies believe that the answer to forecasting and planning at store level is more data and more variables…in many cases, more noise. You’ll hear many of them proclaim that their solution takes hundreds of variables into account, simultaneously processing hundreds of millions of calculations to arrive at a forecast.  A forecast, apparently, that is cloaked in beauty.

As an example, consider the weather.  According to these companies not only can they forecast the weather, they can also determine the impact the weather forecast has on each store/item forecast.

Now, since you live in the real world with me, here’s a question for you:  How often is the weather forecast (from the weather network that employs weather specialists and very sophisticated weather models) right?  Half the time?  Less?  And that’s just trying to predict the next few days, let alone a long term forecast.  Seems like noise, wouldn’t you agree?

Now, don’t get me wrong.  I’m not saying the weather does not impact sales, especially for specific products.  It does.  What I’m saying is that people claiming to predict it with any degree of accuracy are really just adding noise to the forecast.

Weather.  Facebook posts.  Tweets.  The price of tea in China.  All noise, when trying to forecast sales by product at the retail store.

All this “information” needs to be sourced.  Needs to be processed and interpreted somehow.  And it complicates things for people as it’s difficult to understand how all these variables impact the forecast.

Let’s contrast that with a recent retail implementation of Flowcasting.

Our most recent retail implementation of Flowcasting factors none of these variables into the forecast and resulting plans.  No weather forecasts, social media posts, or sentiment data is factored in at all.

None. Zip. Zilch.  Nada.  Heck, it’s so rudimentary that it doesn’t even use any artificial intelligence – I know, you’re aghast, right?

The secret sauce is an intuitive forecasting solution that produces integer forecasts over varying time periods (monthly, quarterly, semi-annually) and consumes these forecasts against actual sales. So, the forecasts and consumption could be considered like a probability.  Think of it like someone managing a retail store. They can say fairly confidently that “I know this product will sell one this month, I just don’t know what day”!

The solution also includes simple replenishment logic to ensure all dependent plans are sensible and ordering for slow selling products is based on your opinion on how probable you think a sale is likely in the short term (i.e., orders are only triggered for a slow selling item if the probability of making a sale is high).

In addition to the simple, intuitive system capabilities above, the process also employs and utilizes a different kind of intelligence – human.  Planners and category managers, since they are speaking the same language – sales – easily come to consensus for situations like promotions and new product introductions.  Once the system is updated then the solution automatically translates and communicates the impact of these events for all partners.

So, what are the results of using such a simple, intuitive process and solution?

The process is delivering world class results in terms of in-stock, inventory performance and costs.  Better results, from what I can tell, than what’s being promoted today by the more sophisticated solutions.  And, importantly, enormously simpler, for obscenely less cost.

Noise is expensive.

The secret for delivering world class performance (supply chain or otherwise) is deceptively simple…

Strip away the noise.

Lucky the car was dirty

Luck

It’s 1971 and Bill Fernandez would do something that would change the course of history. On that fateful day, Bill decided to go for a nice stroll with his good friend, Steve Jobs. As luck would have it, their walk took them pass the house of another of Bill’s pals, Steve Wozniak.

Luckily, Woz’s car was dirty and he was outside, washing it. Bill introduced the two Steve’s and they instantly hit it off. They both shared a passion for technology and practical jokes. Soon after, they started hanging out, collaborating and eventually working together to form Apple. The rest is history.

It’s incredible, in life and business, how powerful and important Luck is.

People who know me well, know that I’m an avid reader and one of the authors that’s influenced my thinking the most is the legendary Tom Peters – you know, of In Search of Excellence fame, among many other brilliant works.

Tom’s also a big believer in Luck. In fact, he believes it’s the most important factor in anyone’s success. I think he’s right. As he correctly points out in his ditty below, you make your own luck and, when you do, you just get luckier and luckier – which is an ongoing philosophy that helps you learn, change, grow and deliver.

So, today, I’m celebrating and counting my lucky stars. I know that luck is THE factor in any success (and failures) that I’ve had. Just consider…

Years ago, I started my career fresh from school at a prestigious consulting firm in downtown Toronto. As luck would have it, one of my Partners, Gus, gave me some brilliant advice. He said to me, “Mike you don’t know shit. The only way to learn is to read. Tons. I’ll make a deal with you. For every business related book you read, the firm will pay for it”. Luckily, I took the advice of Gus and this propelled me into life-long reading and learning.

Roughly 20 years ago, another massive jolt of luck helped me considerably. I was leading a team at a large Canadian retailer who would eventually design what we now call Flowcasting, along with delivering the first full scale implementation of integrated time-phased planning and supplier scheduling in retail.

The original design was enthusiastically supported by our team, but did not have the blessings of Senior Management. In fact, the VP at the time (my boss) indicated that this would not work, we’d better change it, or I’d be fired.

Luckily one of the IT folks, John, then said to me something like “this is just like DRP at store level. You should call Andre Martin and see what he thinks”. To which I replied, “Who’s Andre Martin and what is DRP?”. The next day John brought me copy of Andre’s book, Distribution Resource Planing. I read it (luckily I’m a reader you know) and agreed. I called Andre the next day and eventually he and his colleague, Darryl, helped us convince Senior Management the design was solid – which led to a very successful implementation and helped change the paradigm of retail planning.

As luck would have it, my director on that initial project would later become CEO of Princess Auto Ltd (PAL) – as you know, an early adopter of the Flowcasting process and solution. Given his understanding of the potential of planning and connecting the supply chain from consumption to supply, it was not surprising that we were called to help. Luck had played an important role again.

Luck also played a significant role in the successful implementation of Flowcasting at PAL. The Executive Sponsor, Ken, and the Team Lead, Kim, were people that:

  1. Could simplify things;
  2. See the potential of the organization working in harmony driven by the end consumer; and
  3. Had credibility within the organization to help drive and instill the change.

We were lucky that the three of us had very similar views and philosophy regarding change – focusing on changing the mental model, and less on spewing what I’d call Corporate Mayonnaise.

In addition to being like-minded, the project team at PAL were lucky in that they used a software solution that was designed for the job. The RedPrairie Collaborative Flowcasting solution was designed for purpose – a simple, elegant, low-touch, intuitive system that is easy to use and even easier to implement.

We were very lucky that as an early adopter, we were given the opportunity to use the solution to prove the concept, at scale. As a result, our implementation focused mainly on changing minds and behaviors rather than the typical system and integration issues that plague these implementations when a solution not fit for purpose is deployed.

So, my advice to you is simple. When you get the chance, jot down all the luck you’ve had in your career and life so far. If you’re honest, you’ll realize that luck has played a huge role in your success and who you are today.

And, by all means, you should continue to welcome and encourage more luck into your life.

Thank you and Good Luck!

What do shoppers really want?

Read an awesome report from Bob Phibbs (What do shoppers really want?) about what customers really want in retail. The key finding is that it’s not necessarily about technology, it’s about something more basic – more human. They want a streamlined, smooth shopping experience and they want help from people…someone to answer questions and make them feel important.

The authors conclude that “When customers have an emotional connection to the brand, they feel more confident buying and that can’t be created by just putting a robot in your store; you need a branded shopping experience”.

My colleagues and friends from the LEVEL5 Strategy Group not only understand, but they can help. They have the capability to assess and measure, in depth, how customers “feel” emotionally about a retail brand. This always uncovers revelations and insights that were previously missed or unknown. From that, they help craft winning brand strategies to amplify the positives and address the negatives.

Business is not about algorithms. It’s about people. It’s social. And it’s emotional. Winning strategies always start within the hearts of people. Win the heart and the mind will follow.

Unvarnished

It’s an altercation that’s stuck with me for decades.

Roughly twenty years ago I was leading a retail team that would eventually design what we now call Flowcasting. We were an eclectic team, full of passion and dedicated to designing and implementing something new, and much better.

After a particularly explosive team session – that saw tensions and ideas run hot – everyone went back to their workstations to let sleeping dogs lie. One business team member, who’d really gotten into it with one of the IT associates, could not contain his passion. He promptly walked over to the team member’s cubicle and said…

“Oh, one more thing…F**k You!!”

Like most of the team, I was a little startled. I went over and talked to the team member and we had a good chat about how inappropriate his actions were. Luckily the IT team member was one cool dude and he didn’t take offence to it – the event just rolled off his back. To his credit, the next day my team member formally apologized and all was forgiven.

Now, please don’t think I’m condoning this type of action. I’m not. However, as a student of business, change and innovation I’ve been actively learning and trying to understand what really seeds innovation and, in particular, what types of people seem to be able to make change happen.

And, during my research and studies, I keep coming back to this event. It’s evidence of what seems to be a key trait and characteristic of innovative teams and people. They are what many refer to as…

Unvarnished.

If I think back to that team from two decades ago, we were definitely unvarnished. We called a spade a spade. Had little to no respect to the company hierarchy and even less for the status quo. And, as a team, we were brutally honest with each other and everyone on the team felt very comfortable letting me know when I was full of shit – which was, and continues to be, often.

But that team moved, as Steve Jobs would say, mountains – not only designing what would later morph into Flowcasting, but implementing a significant portion of the concept and, as a result, changing the mental model of retail planning.

I had no idea at the time, but being unvarnished was the key trait we had. Franseca Gino has extensively studied what makes great teams and penned a brilliant book about her learnings, entitled “Rebel Talent”.

She dedicates consider time to unvarnishment and quotes extensively from Ed Catmull, famed leader of Pixar Animation Studios who’s worked brilliantly with another member of the unvarnished hall of fame – Steve Jobs.

According to Catmull, “a hallmark of creative cultures is that people feel free to share ideas, opinions and criticisms. When the group draws on the unvarnished perspectives of all its members, the collective knowledge and decision making benefits.”

According to Catmull, and others (including me), “Candor is the key to constructive collaboration”. The KEY to disruptive innovation.

Here’s another example to prove my point. When I was consulting at a national western Canadian retailer, our team was lucky to have an Executive Sponsor who was, as I now understand, unvarnished as well.

As the project unfolded I was amazed how he operated and the way he encouraged and responded to what I’d call dissent. Most leaders of teams absolutely abhor dissent – having been unfortunately schooled over time that company hierarchy was there for a reason and was the tie-breaker on decision making and direction setting.

Our Sponsor openly encouraged people to dissent with him and readily and openly changed his mind whenever required. I vividly remember a very tense and rough session around job design and rollout in which he was at loggerheads with the team, including me. When I think back, it was amazing to see how “safe” team members felt disagreeing with him – and, in this case, very passionately.

As it turned out, over the next few days, we continued the dialogue and he changed his opinion 180 degrees – eventually agreeing with his direct report.

Neuroscience refers to this as being able to work with “psychological safety” – which is a fancier way of saying people are free to be unvarnished. To say what they believe, why and to whom with no consequences whatsoever.
Without question, as I’ve been thinking and studying great teams and innovation I realize just how brilliant this Sponsor was and the environment he helped to foster.

How many Executives, Leaders or teams are really working in an unvarnished environment – with complete psychological safety? I think you’d agree, not many.

If you, your company and your supply chain is going to compete and continually evolve and improve, won’t ongoing innovation need to become a way of life? And that means people need to collaborate better, disrupt faster and feel completely comfortable challenging and destroying the status quo.

Now, I’m not saying that when you don’t agree with someone to tell them to go F-themselves.

What I am saying – and other folks who are a lot smarter than me – is that hiring, promoting, encouraging and fostering people and a working environment that is unvarnished will be a crucial!

So here’s to being unvarnished. To being and working in safety. To real collaboration and candor.

And to looking your status quo in the eye and saying…”F**k you!”

Questions and Answers

Questions

Did you know that most, if not all, organizations and innovations started with a question, or series of questions?

Reed Hastings concocted Netflix by asking a simple question to himself…”what if DVD’s could be rented through a subscription-type service, so no one ever had to pay late fees?” (Rumor was that this was just after he’d been hit with a $40 late fee).

Apple Computer was forged by Woz and Jobs asking, “Why aren’t computers small enough for people to have them in their homes and offices?”

In the 1940’s, the Polaroid instant camera was conceived based on the question of a three year old. Edwin H. Land’s daughter grew impatient after her father had taken a photo and asked, “Why do we have to wait for the picture, Daddy?”

Harvard child psychologist Dr. Paul Harris estimated that between the ages of two to five, a child asks about 40,000 questions. Yup, forty thousand!

Questions are pretty important. They lead to thinking, reflection, discovery and sometimes breakthrough ideas and businesses.

The problem is that we’re not five years old anymore and, as a result, we just don’t seem to ask enough questions – especially the “why” and “what if” kinds of questions. We should.

Turns out our quest for answers and solutions would be much better served by questions. To demonstrate the power of questions, let’s consider the evolution of solutions to develop a forward looking, time-phased forecast of consumer demand by item/store.

Early solutions realized that at item/store level a significant number of products sold at a very slow rate. Using just that items sales history, at that store, made it difficult to determine a selling pattern – how the forecasted demand would happen over the calendar year.

To solve this dilemma, many of the leading solutions used the concept of the “law of large numbers” – whereby they could aggregate a number of similar products into a grouping of those products to determine a sales pattern.

I won’t bore you with the details, but that is essentially the essence behind the thinking that, for the retail/store, the forecast pattern would need to be derived from a higher level forecast and then each individual store forecast would be that stores contribution to the forecast, spread across time using the higher level forecast’s selling pattern.

It’s the standard approach used by many solutions, one who’s even labelled it as multi-level forecasting. Most retail clients who are developing a time-phased forecast at item/store are using this approach.

Although the approach does produce a time-phased item/store forecast, it has glaring and significant problems – most notably in terms of complexity, manageability and reasonableness of using the same selling pattern for a product across a number of stores.

To help you understand, consider a can of pork and beans at a grocery retailer. At what level of aggregation would you pick so that the aggregate selling pattern could be used in every store for that product? If you think about it for a while, you’ll understand that two stores even with a few miles of each other could easily have very different selling patterns. Using the same pattern to spread each stores forecast would yield erroneous and poor results. And, in practice, they do.
Not only that, but you need to manage many different levels of a system calculated forecast and ensuring that these multi-level forecasts are synchronized amongst each level – which requires more system processing. Trying to determine the appropriate levels to forecast in order to account for the myriad of retail planning challenges has also been a big problem – which has tended to make the resulting implementations more complex.

As an example, for most of these implementations, it’s not uncommon to have 3 or more forecasting levels to “help” determine a selling pattern for the item/store. Adding to the issue is that as the multi-level implementation becomes more complex, it’s harder for planners to understand and manage.

Suffice it to say, this approach has not worked well. It’s taken a questioner, at heart, to figure out a better, simpler and more effective way.

Instead of the conventional wisdom, much like our 3 year old above, he asked some simple questions…

“What if I calculated a rolling, annual forecast first?” “Couldn’t I then spread that forecast into the weekly/daily selling pattern?”

As it turns out, he was right.

Then, another question…

“Why do I have to create a higher level forecast to determine a pattern?” Couldn’t I just aggregate sales history for like items, in the same store, to determine the selling pattern?”

Turns out, he could.

Finally, a last question…

“Couldn’t I then multiple the annual forecast by the selling pattern to get my time-phased, item/store forecast?”

Yes, indeed he could.

Now, the solution he developed also included some very simple and special thinking around slow selling items and using a varying time period to forecast them – fast sellers in weekly periods, slower sellers monthly and even slower sellers in quarterly or semi-annual periods.

The questions he asked himself were around the ideas of “Why does every item, at the retail store, need to be forecast in weekly time periods?”

Given the very slow rate of sales for most item/stores the answer is they don’t and shouldn’t.

The solution described above was arrived at by asking questions. It works beautifully and if you’re interested in learning more and perhaps asking a few questions of your own, you know how to find me.

So, if you’re a retailer and are using the complicated, hard-to-manage, multi-level forecasting approach outlined above, perhaps you should ask a question or two as well…

1. “Why are we doing it like this?”
2. “Who is using the new approach and how’s it working?”

They’re great questions and, as you now know, questions will lead you to the answers!

The Autonomous Self-learning Supply Chain

I have to admit, its hard work trying to keep up with the latest lingo and thinking when it comes to supply chain planning. Suffice it to say, the concept of digitizing the supply chain is not only cool, but offers tremendous value to those companies that achieve it…and it will, over time, become the norm, in my humble opinion.

A number of companies and supply chain technologists are pursuing a vision they describe as the Autonomous Supply Chain – a supply chain that is largely self-learning, adapting and holistically focused on continuously meeting the needs of consumers and customers.

A lot of folks, when they hear this, shutter at the thought, or dismiss it out of hand…poppycock they say, this will never happen and is a futurist’s wet dream.

I beg to differ and not only essentially agree with the vision, but can offer initial proof that the concept not only has merit, but also tremendous potential.

At one of our most recent retail clients, they use the Flowcasting process to plan and manage the flow of inventory from supplier to consumer. What’s brilliant and consistent with the idea of the autonomous and self-learning supply chain is that they have, within their Flowcasting solution, a digital twin of their entire, extended supply chain.

What’s a digital twin?

A digital twin is a complete model of the business, whereby all physical product flows, both current and planned, are digitally represented within the solution – a complete, up-to-date, real time view of their business; containing all projected flows from supplier to consumer for an extended planning horizon of 52 or more weeks.

The Flowcasting solution and digital model of the business enables what we often refer to as continuous planning.

The process and solution re-plans and re-calibrates the entire value chain, digitally, based on what happens physically. Changes in sales, inventories, or shipments will result in re-forecasting and re-planning product flows – to stay in stock, flow inventory, and respond to real exceptions or unplanned events. The process, solution and supply chain is self-learning.

The result is that the Flowcasting process/solution can manage the flow of information and trigger the movement of goods, digitally, on auto-pilot, a vast majority of the time—requiring planner input only when judgment and experience are needed.

When I think about how our client is using the Flowcasting process/solution to plan, I would estimate 95% of the product flows are initiated automatically (e.g., digitally) based on the solution interpreting what yesterday’s sales and inventory movements mean, and then re-adjusting, self-correcting, and altering current and planned product flows.

Furthermore, as part of the implementation, we worked with the planners and semi-automated how they would handle certain exceptions, based on learning from initial planners responding to these exceptions. It’s certainly not a stretch to think that, at some point, a machine/algorithm could learn too and respond to these types of anomalies in order to enable the smooth and continuous flow of product.

And what are the results of using a self-learning, self-correcting and fairly autonomous planning process (i.e., Flowcasting)?

Highest in-stocks in company history, increased sales, improved inventory turns, reduced costs and, most importantly, happier customers.

Please understand I’m not talking about a Skynet scenario here. I firmly believe that supply chain planning solutions can largely become autonomous and self-learning, but will always require some human input for situations where intuition and judgement are required. But, I’d argue this will be the exception and is also a form of a self-learning supply chain (e.g., people learn from experience).

The autonomous, self-learning supply chain is quite a vision. And, like all visions, it needs initial pilots and examples to move the ball forward, provide initial learnings and help people understand what is and might be possible. Our recent retail implementation of Flowcasting, we believe, helps the cause and should provide food for thought for any retailer.

So to the folks and companies pursuing this vision (most notably JDA Software), I can only offer best wishes and the advice from Calvin Coolidge…

“Press on. Nothing in this world can take the place of persistence”.

Fundamentals

Imagine you’re a hotshot US high school basketball player during the late 1960’s. You’re all American and can pretty much choose any collegiate program you want, with an almost for certain scholarship attached and a better than decent chance that someday you’ll play professionally in the NBA.

You finally decide to join the famed UCLA program, under the tutelage of the acclaimed John Wooden, aptly named The Wizard of Westwood – an eventual winner of 10 NCAA titles in 12 seasons, including a still record 7 in a row.

I think it’s safe to say you’d be both nervous and excited.

First practice day arrives and you’re intrigued to see what you’ll learn from the Wizard. What techniques does he employ? What’s his secret sauce? How has he been so successful?

Wooden arrives, promptly introduces himself and his staff and new recruits begin to practice a drill that Wooden starts every season with…

learning to put on your socks and tie your laces.

Astonished, your first practice with the top program in the country would include practicing, over and over, how to put on your socks and tie your shoe laces.

John Wooden understood how important fundamentals are. If the players got blisters from their socks or improperly laced shoes, they wouldn’t be able to move as well. If they couldn’t move as well, they couldn’t rebound or shoot as well and they’d miss shots. And if they missed rebounds and shots, they’d lose.

Master the fundamentals, Wooden realized, and the team would excel.

It’s impressive thinking and very insightful for all us working in supply chain planning.

Flowcasting is based on fundamentals. I remember talking recently to an Executive from a technology-focused and hyped firm who proclaimed to me, “Flowcasting is not new or innovative”. After all he proclaimed, “the concept has been around for more than 15 years and the book is 12 years old”.

Ok, so what’s that got to do with anything? Does something have to be new, exciting and, to date, unproven to add value? I think not and I know you do too.

Flowcasting is based on sound fundamentals that will stand the test of time. A forecast of consumer demand, at customer touchpoint only, is used to calculate and translate that only unknown into all resource requirements in the extended retail supply chain (or any industrial supply chain).

Joe Orlicky, one of the Godfathers of time-phased planning, MRP and DRP, said it best, almost 50 years ago when he proclaimed ”Never forecast what you can calculate”.

I’ve never met Dr. Orlicky or Dr. Wooden but I believe that they are kindred spirits. They understood the power and importance of fundamentals.

Now you do too.

A Symphony of Placid Beauty

Game6-FinalPosition

July 23, 1972. Reykjavík, Iceland. An event would occur that day that would rock the chess world and would continue to be talked about, even to this day. It was game 6 of the acclaimed world chess championship match between the World Champion, Boris Spassky of Russia, and the challenger, Bobby Fischer of the United States.

Fischer opened 1. C4, the English Opening – the first time in his entire career that he’d deviated from his beloved 1. E4 opening move. Spassky quickly transformed the opening into the Queen’s Gambit Declined, for which he was one of the world’s foremost experts in this line of defense.

What followed was a masterpiece. Fischer’s moves were new, exciting and novel on a variation that had been played by chess grandmasters for centuries. The moves were pure, clean and deceptively simple, yet powerful and profound.

When Spassky resigned after Fischer’s 41st move, not only did the crowd stand and applaud, so too did Spassky. They all knew that they’d witnessed a masterpiece. A game of such beauty and purity – still talked about to this day, as chess perfection.

Dr Anthony Saidy, a Fischer confidant and assistant described it magically when he proclaimed, “it was like a symphony of placid beauty”.

Fischer’s play in game 6 captures his signature style: crystalline – transparent but ingenious and incredibly profound and powerful. Nigel Short, the highest ranked British Grandmaster of all time sums up Fischer’s play nicely, “The thing that strikes me about Fischer’s chess,” he says, “is that it’s very clear. There are no mysterious rook moves or obscure manoeuvrings. There’s a great deal of logic to the chess. When you look at it you can understand it – afterwards. He just makes chess look very easy.”

There are a lot of parallels to Fischer’s chess, particularly game 6 from 1972, and Flowcasting.

Flowcasting, as you know, seamlessly connects the supply chain from the consumer to the factory in a natural and logical way. That’s easy to understand and most people seem to get that.

However, like analyzing Fischer’s moves, the nuances of the process are deceptively powerful and profound. I’ll outline a couple of important ones, though there are others and they follow the Fischer-like mantra – deceptively easy to understand, simple, yet profound.

The forecasting approach used by the leading Flowcasting solutions does not use any sophisticated algorithms. Instead it uses and builds on profile based forecasting techniques that have been around for decades – the subtle improvement (like some of Fischer’s novelties in game 6) is the use of differing forecast time periods by SKU, converting them to integer forecasts for slow selling items and then consuming these forecasts as actual sales happen.

Why is that placid-like beauty? Because if you study retail, and real sales history, you’ll uncover that the vast majority of products sell less than 26 units per year per store for virtually any retailer. Trying to find a fancy algorithm that can predict when these sales will occur is a fool’s game.

The simple combination of integer forecasting, consumption and daily re-planning simplifies the solution to deliver results. And, once explained to planners, makes sense to them and is easy to understand and manage. Much like Fischer’s moves, the ideas and concepts are deceptively simple and profound.

Consider also the simple and profound concept within Flowcasting of daily, net change re-planning. At our most recent implementation the solution works like this: for any product that had a sale or change that occurred yesterday, then the entire supply chain is re-forecast and re-planned from consumer to factory for that item.

In retail, on a daily basis, only about 5-15% of the products experience a change daily. Only these are re-planned daily, adjusting the future flows of inventory to ensure you remain in-stock and your inventory is productive. All projections are easily and simply converted into the language of the business so that the entire organization is working to a single set of numbers. One other benefit of net change, daily re-planning is that it also dramatically reduces system processing requirements.

Flowcasting is an easy concept and solution to understand and most people wholeheartedly agree with the premise. The trick to making it successful is to understand the nuances, embrace its simplicity and instill, over time, that kind of thinking in your planning organization.

Once you do, then, from experience, your supply chain and indeed your company will work like a Symphony of Placid Beauty!

 

Telephone Poles

telephone poles

It’s no secret that the Navy Seals are one of the most elite teams on the planet. Highly skilled, trained and motivated, they operate with exceptional levels of commitment and teamwork, performing missions around the world that demand excellence and pinpoint precision – like the missions to kill Bin Laden, or rescue Captain Phillips.

If you visit their training facilities in either Coronado or Virginia Beach you’re likely to notice one of their secrets to consistently churning out elite teams.

You’ll notice a stack of telephone poles.

They look like remains from a construction project or a stockpile for a utility, but for Seal Commanders they are sacred. They form the basis of a training routine called Log PT – an approach that instills teamwork, discipline, vulnerability and commitment.

Log PT is not complicated. Essentially six trainees perform a collection of maneuvers that look more like a barn raising. They lift them. Roll them. Carry them and move them from shoulder to shoulder. Do sit-ups while cradling them. Stand for long periods holding them above their heads.

There is no defined strategy for a team of trainees to follow. They must learn to work together, to build commitment and teamwork.

When done poorly, the poles buck and roll, and the team fights with each other, boiling emotions. However, when done well, it looks smooth, quiet and efficient. It has nothing to do with strength – rather it’s performed well when teamwork and harmony emerge. When a team member falters, almost invisibly another team member adjusts their efforts to keep the poles level and steady.

Log PT is the brainchild of Draper Kauffman, a WWII Veteran who got the idea for Log PT (and others that help form the core of Seal training) from being stationed with and serving with the Corps Franc, on the front lines in Germany.

Log PT was designed and first implemented in the late 1940s. And still, to this day, is used to train and prepare elite teams.

Think about that for a moment. With all the new and exciting technologies available today, a simple program based on teams working together and in harmony moving telephone poles around is the core technology used to produce elite teams and performance.

Let that sink in and the lesson on offer.

Everyday, if you’re like me, you’re being bombarded with claims of incredible breakthroughs of potential future performance with new and brilliant technologies – like AI, Big Data, Augmented Reality, Virtual Reality, Internet of Things, just to name a few. And to be fair, I believe the potential is and will be enormous.

The lesson here is that the most elite producing teams on the planet has yet to see the need or benefit of changing their approach – an approach that literally hasn’t changed since the 1940s.

Here’s an example from one of our clients that is consistent with the lesson.

When we demonstrate the Flowcasting planning process for one of our retail clients, many people are shocked to understand how the promotional sales forecast is derived.

It’s basically built from a demand planner looking at POS sales history for that item from past promotions and then, if needed, collaborating with the Category Leader – for situations where there is limited or no history and/or the promotional offer is significantly different than past offers.

They agree on what they think they will sell for the event and the system spreads that forecast down to the participating stores based on simple rules about that items contribution to sales, store by store.

That’s it. Pure simplicity.

Yet, like Log PT, it is delivering awesome results – better than any approaches used before. Helping to deliver industry leading in-stock for promotional events – a thorn for most retailers.

Planners and Category Leaders understand they need to work together, and they do, building commitment and accountability for the promotional sales forecast.

Please don’t think that I’m shitting on new technologies like AI, IoT and any others. I’m not. I believe that there is and will be enormous potential for these technologies and that they will also largely deliver on these promises.

But, I also believe in what is simple and works.

So do my client’s customers.

Ungrain

In 1983 Benjamin Libet, researcher in the Department of Physiology at the University of California, performed one of the most famous and controversial experiments in the history of neuroscience.

In simple terms, Libet’s experiment measured and timed the response of the neural circuitry of the brain, based on some very basic commands – like moving your left wrist, followed by your right wrist.  What he discovered is that there is a time lapse between the decisions our neural circuitry makes for us and our awareness of the situation.

What that means, in a nutshell, is that for basic operations and requests the brain has already been hardwired, or ingrained, into a conditioned response, basically without thought.  The brain has seen this movie (or ask) so many times that the response is automatic.

For us folks who are working on changing people’s behaviors and habits, we can relate.  People become ingrained in current practices, processes and ways of thinking and it usually takes considerable time and effort to change – the thinking and the response.

Libet, however, didn’t stop there.  Further work, research and experiments concluded that there were generally only two ways to change the history of the brain as it relates to a specific ask or task.  They are asking WHY and making a JOKE of the situation.

Let’s look at an important supply chain planning example and focus on the WHY.

To date, most retail planners, consultants and solution providers have firmly cemented and ingrained the thinking that to systemically create a forward looking time-phased forecast by item/store (or webstore) requires that you forecast at multiple levels and then spread the higher level forecasts down to the lower (store) level.

Initially the thinking was that the aggregate level forecast would be more accurate, and that is usually the case.  But some people realized that the higher level forecast was of no value – it’s the lowest level of forecast that drives the integrated supply chain.

Asking and wondering WHY enough times eventually surfaced that the higher level forecast was really only helpful in determining a selling pattern, especially for slower selling products where a pattern was difficult to detect.

Our colleague, Darryl, not only understood but asked WHY it was necessary to forecast at a higher level.  Couldn’t the pattern be determined, at the selling location, without the need and complexity of forecasting at a higher level.

Eventually, he arrived at a simple, sensible solution.  In a previous newsletter, I outlined the key elements of the approach but the key elements of the approach are:

  • An annual forecast is calculated, along with a decimal forecast (by day and week) for the 52 weeks that comprise the annual forecast
  • A category or department level selling pattern is calculated at the store location (or other’s if needed)
  • Simple user forecast thresholds are applied against the annual forecast to determine the forecast time period and how to determine the selling pattern – including using the category/department level pattern from above for slow sellers (to get the sales pattern)
  • The same thresholds determine whether to convert the decimal forecast to integers
  • For the forecasts that will be converted to integers, a random number between 0 and 1 is calculated, then the small decimal forecasts are added from there and once the cumulative forecast hits 1, then an integer forecast is 1 is used in that period, and the counter and randomizer starts again…this logic is applied to the 52 week forward looking forecast

Now, while the above is tougher to write to help understand, our experience in outlining this to people is that they not only understand, but it makes intuitive sense to them.

This solution was originally key functionality of the RedPrairie Collaborative Flowcasting solution and is now available within the JDA solution set, aptly named JDA Slow Mover Forecasting and Replenishment.

Yes, but does it work?

The graph below outlines the sales forecasts of our recent implementation of Flowcasting at a Canadian hardgoods retailer, using this exact approach:

Slow-sellers2

As you can see, a significant number of products are slow or very slow sellers (54% sell less than one unit per month at a store).  However, using this approach the company was able to improve in-stock by 6%, while also reducing and improving inventory performance.

Having an integer-like forecast for all these item/store combinations is important since it allows them to calculate time-phased DC and vendor replenishment plans, along with complete capacity and financial projections – allowing them to work to a single set of numbers.

In addition, the solution is so much simpler in terms of understanding, flexibility and processing requirements.

Given the above, people should embrace this solution full tilt.  This should be a no-brainer, right?

Nope, wrong.

Our old villain, ingrained, has helped cement the view of higher level forecasting in retail.

It’s ironic, and a little sad, that a number of people and companies who advise and help companies change and learn new and presumably better ways have not embraced this approach, and instead are still pushing old, tired and ineffective solutions.

They need to ungrain their thinking (ungrain is the opposite of ingrain and yes, I made this word up!).

My advice is simple: if you’re a retailer who is forecasting at a higher level, or you’re someone who’s pushing this approach, please stop.

Learn. Understand. See it yourself.  Ask WHY.  And, importantly…

Ungrain the old and begin to ingrain the new.