Covered in Warts

It’s the early 1990’s and Joanne is down on her luck. A recently divorced, single mother who’s jobless, she decides to move back from England to Scotland to at least be closer to her sister and family.

During her working days in Manchester she had started scribbling some ideas and notes about a nonsensical book idea and, by the time she’d moved home, had three chapters written of a book. Once back near Edinburgh, she continued to write and improve her manuscript until she had a first draft completed in 1995 – fully five years from her first penned thoughts.

During the next two years she pitched the very rough manuscript to a dozen major publishers. They all rejected it and believed the story would not resonate with people and, as a result, sales would be dismal.

Undismayed she eventually convinced Bloomsbury to take a very small chance on the book – advancing her a paltry $1500 pounds and agreeing to print 1,000 copies, 500 of which would be sent to various libraries.

In 1997 and 1998 the book, Harry Potter by J. K. Rowling, would win both the Nestle Book award and the British Book Awards Children’s book of the year. That book would launch Rowling’s worldwide success and, to date, her books have sold over 400 million copies.

The eventual success of the Harry Potter series of books is very instructive for breakthroughs and innovation.

The most important breakthroughs—the ones that change the course of science, business, or history — are fragile. They rarely arrive dazzling everyone with their brilliance.

Instead, they often arrive covered in warts — the failures and seemingly obvious reasons they could never work that make them easy to dismiss. They travel through tunnels of skepticism and uncertainty, their champions often dismissed as crazy.

Luckily most of the champions of breakthrough items are what many would describe as loons – people that refuse to give up on their ideas and will work, over time, to smooth and eliminate the warts.

When it comes to supply chain planning innovation, you’d have to put Andre Martin into the loon category as well.

In the mid 1970’s Andre invented Distribution Resource Planning (DRP) and, along with his colleague Darryl Landvater, designed and implemented the first DRP system in 1978 – connecting distribution to manufacturing and changing planning paradigms forever.

Most folks don’t know but around that time Andre saw that the thinking of DRP could be extended to the retail supply chain – connecting the store to the factory using the principles of DRP and time-phased planning.

The idea, which has since morphed and labelled as Flowcasting, was covered in warts. During the course of the last 40 years Andre and Darryl have refined the thinking, smoothed the warts, eliminated dissention, educated an industry and, unbelievably, built a solution that enables Flowcasting.

I’ve been a convert and a colleague in the wart-reduction efforts over the last 25 years – experiencing first-hand the some irrational responses and views from, first, a large Canadian retailer, and more recently the market in general.

But, like JK, the warts are largely being exposed as pimples and people and retailers are seeing the light – the retail supply chain can only deliver if it’s connected from consumer to supplier – driven only by a forecast of consumer demand. Planned and managed using the principles of Flowcasting.

The lesson here is to realize that if you think you’ve got a breakthrough idea, there’s a good chance it’ll be covered in warts and will need time, effort, patience and determination to smooth and eliminate them.

It can, however, be done.

And you can do it.

Godspeed.

Managing the Long Tail

If you don’t mind haunting the margins, I think there is more freedom there. – Colin Firth

long-tail

 

A couple of months ago, I wrote a piece called Employing the Law of Large Numbers in Bottom Up Forecasting. The morals of that story were fourfold:

  1. That when sales at item/store level are intermittent (fewer than 52 units per year), a proper sales pattern at that level can’t be properly determined from the demand data at that level.
  2. That any retailer has a sufficient percentage of slow selling item/store combinations that the problem simply can’t be ignored in the planning process.
  3. That using a multi level, top-down approach to developing properly shaped forecasts in a retail context is fundamentally flawed.
  4. That the Law of Large Numbers can be used in a store centric fashion by aggregating sales across similar items at a store only for the purpose of determining the shape of the curve, thereby eliminating the need to create any forecasts above item/store level.

A high level explanation of the Profile Based Forecasting approach developed by Darryl Landvater (but not dissimilar to what many retailers were doing for years with systems like INFOREM and various home grown solutions) was presented as the antidote to this problem. Oh and by the way, it works fabulously well, even with such a low level of “sophistication” (i.e. unnecessary complexity).

But being able to shape a forecast for intermittent demands without using top-down forecasting is only one aspect of the slow seller problem. The objective of this piece is to look more closely at the implications of intermittent demands on replenishment.

The Bunching Problem

Regardless of how you provide a shape to an item/store forecast for a slow selling item (using either Profile Based Forecasting or the far more cumbersome and deeply flawed top-down method), you are still left with a forecasted stream of small decimal numbers.

In the example below, the shape of the sales curve cannot be determined using only sales history from two years ago (blue line) and the most recent year (orange line), so the pattern for the forecast (green dashed line) was derived from an aggregation of sales of similar items at the same store and multiplied through the selling rate of the item/store itself (in this case 13.5 units per year):

You can see that the forecast indeed has a defined shape – it’s not merely a flat line that would be calculated from intermittent demand data with most forecasting approaches. However, when you multiply the shape by a low rate of sale, you don’t actually have a realistic demand forecast. In reality, what you have is a forecast of the probability that a sale will occur.

Having values to the right of the decimal in a forecast is not a problem in and of itself. But when the value to the left of the decimal is a zero, it can create a huge problem in replenishment.

Why?

Because replenishment calculations always operate in discrete units and don’t know the difference between a forecast of true demand and a forecast of a probability of a sale.

Using the first 8 weeks of the forecast calculated above, you can see how time-phased replenishment logic will behave:

The store sells 13 to 14 units per year, has a safety stock of 2 units and 2 units in stock (a little less than 2 months of supply). By all accounts, this store is in good shape and doesn’t need any more inventory right now.

However, the replenishment calculation is being told that 0.185 units will be deducted from inventory in the first week, which will drive the on hand below the safety stock. An immediate requirement of 1 unit is triggered to ensure that doesn’t happen.

Think of what that means. Suppose you have 100 stores in which the item is slow selling and the on hand level is currently sitting at the safety stock (not an uncommon scenario in retail). Because of small decimal forecasts triggering immediate requirements at all of those stores, the DC needs to ship out 100 pieces to support sales of fewer than 20 pieces at store level – demand has been distorted 500%.

Now, further suppose that this isn’t a break-pack item and the ship multiple to the store is an inner pack of 4 pieces – instead of 100 pieces, the immediate requirement would be 400 pieces and demand would be distorted by 2,000%!

The Antidote to Bunching – Integer Forecasts

What’s needed to prevent bunching from occurring is to convert the forecast of small decimals (the probability of a sale occurring) into a realistic forecast of demand, while still retaining the proper shape of the curve.

This problem has been solved (likewise by Darryl Landvater) using simple accumulator logic with a random seed to convert a forecast of small decimals into a forecast of integers.

It works like this:

  • Start with a random number between 0 and 1
  • Add this random number to the decimal forecast of the first period
  • Continue to add forecasts for subsequent periods to the accumulation until the value to the right of the decimal in the accumulation “tips over” to the next integer – place a forecast of 1 unit at each of these “tip-over” points

Here’s our small decimal forecast converted to integers in this fashion:

Because a random seed is being used for each item/store, the timing of the first integer forecast will vary by each item/store.

And because the accumulator uses the shaped decimal forecast, the shape of the curve is preserved. In faster selling periods, the accumulator will tip over more frequently and the integer forecasts will likewise be more frequent. In slower periods, the opposite is true.

Below is our original forecast after it has been converted from decimals to integers using this logic:

And when the requirements across multiple stores are placed back on the DC, they are not “bunched” and a more realistic shipment schedule results:

Stabilizing the Plans – Variable Consumption Periods

Just to stay grounded in reality, none of what has been described above (or, for that matter, in the previous piece Employing the Law of Large Numbers in Bottom Up Forecasting) improves forecast accuracy in the traditional sense. This is because, quite frankly, it’s not possible to predict with a high degree of accuracy the exact quantity and timing of 13 units of sales over a 52 week forecast horizon.

The goal here is not pinpoint accuracy (the logic does start with a random number after all), but reasonableness, consistency and ease of use. It allows for long tail items to have the same multi-echelon planning approach as fast selling items without having separate processes “on the side” to deal with them.

For fast selling items with continuous demand, it is common to forecast in weekly buckets, spread the weekly forecast into days for replenishment using a traffic profile for that location and consume the forecast against actuals to date for the current week:

In the example above, the total forecast for Week 1 is 100 units. By end of day Wednesday, the posted actuals to date totalled 29 units, but the original forecast for those 3 days was 24 units. The difference of -5 units is spread proportionally to the remainder of the week such as to keep the total forecast for the week at 100 units. The assumption being used is that you have higher confidence in the weekly total of 100 units than you have in the exact daily timing as to when those 100 units will actually sell.

For slow moving items, we would not even have confidence in the weekly forecasts, so consuming forecast against actual for a week makes no sense. However, there would still be a need to keep the forecast stable in the very likely event that the timing and magnitude of the actuals don’t match the original forecast. In this case, we would consume forecast against actuals on a less frequent basis:

The logic is the same, but the consumption period is longer to reflect the appropriate level of confidence in the forecast timing.

Controlling Store Inventory – Selective Order Release

Let’s assume for a moment a 1 week lead time from DC to store. In the example below, a shipment is planned in Week 2, which means that in order to get this shipment in Week 2, the store needs to trigger a firm replenishment right now:

Using standard replenishment rules that you would use for fast moving items, this planned shipment would automatically trigger as a store transfer in Week 1 to be delivered in Week 2. But this replenishment requirement is being calculated based on a forecast in Week 2 and as previously mentioned, we do not have confidence that this specific quantity will be sold in this specific week at this specific store.

When that shipment of 1 unit arrives at the store (bringing the on hand up to 3 units), it’s quite possible that you won’t actually sell it for several more weeks. And the overstock situation would be further exacerbated if the order multiple is greater than 1 unit.

This is where having the original decimal forecast is useful. Remember that, as a practical matter, the small decimals represent the probability of a sale in a particular week. This allows us to calculate a tradeoff between firming this shipment now or waiting for the sale to materialize first.

Let’s assume that choosing to forgo the shipment in Week 2 today means that the next opportunity for a shipment is in Week 3. In the example below, we can see that there is a 67.8% chance (0.185 + 0.185 + 0.308) that we will sell 1 unit and drop the on hand below safety stock between now and the next available ship date:

Based on this probability, would you release the shipment or not? The threshold for this decision could be determined based on any number of factors such as product size, cost, etc. For example, if an item is small and cheap, you might use a low probability threshold to trigger a shipment. If another slow selling item is very large and expensive, you might set the threshold very high to ensure that this product is only replenished after a sale drives the on hand below the safety stock.

Remember, the probabilities themselves follow the sales curve, so an order has a higher probability of triggering in a higher selling period than in a lower selling period, which would be the desired behaviour.

The point of all of this is that the same principles of Flowcasting (forecast only at the point of consumption, every item has a 52 week forecast and plan, only order at the lead time, etc.) can still apply to items on the long tail, so long as the planning logic you use incorporates these elements.

Ordinary Observation

OrdinaryObservation

It’s September 28, 1928 in a West London lab. A young physician, Alex, was doing some basic research that had been assigned to him regarding antibacterial agents. He’d been doing the same thing for a number of days when one day he noticed something odd.

What caught his eye and attention that fateful day was that mold had actually killed some bacteria in one of his plates. Usually samples like this are discarded, but instead Alex kept this sample and began to wonder. If this mold could kill this type of bacteria, could it be used to kill destructive bacteria in the human body?

Alexander Fleming would spend the next 14 years working out the kinks and details before “penicillin” was officially used to treat infections. It was an invention that would revolutionize medicine by discovering the world’s first antibiotic.

Dr. Fleming was able to develop this innovation through the simple power of ordinary observation. Sherlock Holmes famously said once to Watson: “You see, but you do not observe. The distinction is clear.” According to psychologist and writer Maria Konnikov…“To observe, you must learn to separate situation from interpretation, yourself from what you are seeing.

Here’s another example of the power of observation. Fast forward to 1955, a relatively unknown and small furniture store in Almhult, Sweden. One day, the founder and owner noticed something odd. An employee had purchased a table to take home to the family. Rather than struggling to try to cram the assembled table into his car, this employee took the legs off and carefully placed them in a box, which, in turn, would fit nicely in his car for delivery home.

As it turned out, the owner of the store, Ingvard Kamprad, would observe this unpacking phenomena regularly. Carefully he observed what his employees were doing and why it was so effective. And, if this concept was better for his employees, it would stand to reason that it would also be better for his customers – and the bottom line.

Soon after, Kamprad would work tirelessly to perfect the idea of selling dis-assembled furniture – changing the customer journey for furniture acquisition forever, and making IKEA synonymous with this brand promise and a worldwide household name. All because of the power of ordinary observation.

A final story about observation and its impact on supply chain planning.

Ken Moser is one of Canada’s top retailers – leading and managing one of Canadian Tire’s best stores in northern Ontario. About 15 years ago, he was visited by a chap who would eventually build the world’s first and, to date, best Flowcasting solution.

This person followed Ken around the store, asking questions and observing how the store operated and how Ken thought – particularly about how to manage the inventory of tens of thousands of items. Rumour has it that when Ken got to a section of the store, he proclaimed something like…”these items are like a set-it-and-forget-it. I have no idea when they’ll sell, and neither do you. All I know is that, like clockwork, they’ll only sell one a month. For others, it’s like one every quarter.”

Our Flowcasting architect was fascinated with this observation and spent time watching/observing customers perusing this section of the store. And like the two examples above, deep observation and reflection would eventually morph into an approach to forecasting and planning slow selling items that is, to date, the only proven solution in retail. All from the awesome power of ordinary observation.

Yogi Berra, the great Yankee catcher and sometimes philosopher, hit the nail on the proverbial head regarding the importance of ordinary observation when he proclaimed…

You can observe a lot, just by watching.

Turns out, you can.

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!

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.