Noise is expensive

Posted on May 1, 2019 by Mike Doherty

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:

95% of consumers don’t want to talk or engage with a robot
86% have no desire for other shiny new technologies like AI or virtual reality
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:

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

Posted on March 6, 2019 by Mike Doherty

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:

Could simplify things;
See the potential of the organization working in harmony driven by the end consumer; and
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

Posted on September 5, 2018 by Mike Doherty

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

Posted on July 4, 2018 by Mike Doherty

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

Posted on May 2, 2018 by Mike Doherty

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

Posted on March 7, 2018 by Mike Doherty

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

Posted on January 3, 2018 by Mike Doherty

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:

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.

Princess Auto’s Flowcasting journey featured in Canadian Retailer magazine

Posted on November 7, 2017 by Jeff Harrop

Our client Princess Auto Ltd. is the subject of a feature article in the inaugural Supply Chain issue of Canadian Retailer magazine (published by the Retail Council of Canada). Click here to learn about how they are using the Flowcasting planning process to significantly improve in-stocks and profits while unleashing a new omnichannel fulfilment model. You can also download a PDF copy here.

Virtual Reality for the Retail Supply Chain

Posted on October 4, 2017 by Jeff Harrop

Whenever we discuss Flowcasting, we always describe it as ‘a valid simulation of reality inside a system’. This term originated with our longtime colleague Darryl Landvater and it is the most concise and accurate way to describe what Flowcasting really is that we’ve heard.

In fact, we use that term so much that I think we sometimes assume its meaning is self evident.

It’s not.

Over the last 11 years since Flowcasting the Retail Supply Chain was first published, I’ve noticed that, more and more, the terms ‘Flowcasting’ and ‘valid simulation of reality’ have been treated somewhat like ink blots, with some folks (intentionally or otherwise) using them to mean whatever they want them to mean.

To set the record straight, a true ‘valid simulation of reality’ for the retail supply chain has some very specific characteristics, all of which must be present. To the extent that they are not, the value of the plan suffers – as do the results.

Before diving into the nitty gritty details, consider this: Virtually all retailers have a data warehouse that captures daily sales summaries for every product in every location, all upstream product movements, every on hand balance and certain attributes of every product and every location. This data is usually archived over several years and the elemental level of information is kept intact so that rollups, reports and analysis of that data can be trusted and flexibly done.

Think of a valid simulation of reality for the retail supply chain as a data warehouse – with a complete set of the exact same data elements at the same granular level of detail – except that all of the dates are in the future instead of the past.

However, it must also be said that ‘valid’ does not mean ‘perfect’. Unlike the historical data warehouse that remains fixed after each day goes into the books, the future simulation can and will change over time based on what happened yesterday and new assumptions about the future. Updating the simulation daily at all levels is the key to ensuring it remains valid.

Now let’s get into some of the specifics. A valid simulation of reality has 4 dimensions:

Information about the physical world as of this moment
Forecasts of expected demand over the next 52 weeks for each individual product at each individual point of consumption (could be a retail store or a virtual store)
A simulation of future product movements driven by the forecasts and future planned changes to the physical world
Rollups of the elemental data to support aggregate planning in the future

While it may seem that meeting all of these requirements is onerous, it is actually quite simple compared to trying to do things several different ways to account for variations in how products sell or are replenished.

Current information about the physical world includes things like:

Master information about products (e.g. cube, weight, pricing, case packs, introduction and discontinuation dates) and locations (stores, DCs, supplier ship points)
Relatively accurate on hand balances
Planogram details such as store assortment, facings and depth
Source to destination relationships with lead-times that are representative of physical activity and travel times

This information is ‘table stakes’ for getting to a valid simulation of the future and is readily available for most retailers. High levels of accuracy for these items (even store on hands) is achievable – so long as the processes that create this information have some discipline – because they are directly observable in the here and now.

Forecasts of demand over the next 52 weeks must:

Include every item at every selling point
Model demand realistically for slow selling items (i.e. integer values as opposed to small decimals that will model an inventory ‘sawtooth’ that won’t actually happen)
Include all known positive and negative future influences for each individual item at each individual selling location (e.g. promotions, assortment changes, trends)
Allow for ‘uncertainty on the high side’ for promotions to be modeled independently from the true sales expectation so as not to bias the forecast, especially for promotions
Account for periods where inventory is planned to be unavailable at store level (e.g. sales forecast for a discontinued item should continue while the item is in stock and drop to zero when it’s projected to run out in the future)

The rule here is simple: if the item at a location is selling (no matter how slowly or for how long) it must have a sales forecast and that sales forecast must be an unbiased and reasonable representation of what future sales will look like.

A simulation of future product movements driven by the forecasts and future planned changes to the physical world means that:

Replenishment and ordering constraints are respected in the plan (e.g. rounding up to case packs if that’s how product ships, rounding at lane level if truckload minimums across all products on a lane are required)
Activity calendars are respected (e.g. an arrival of stock is not scheduled when a location is not open for receiving, a shipment is not scheduled during known future shutdown)
Carryover targets are respected for seasonal items (e.g. before the season even begins, the planning logic suppresses shipments at the end of the season so as to intentionally run out of stock at the stores and DCs)
Future changes to stocking requirements (e.g. changing the number of facings for an item or adding/subtracting it from the store assortment) are known in advance and the effect is visible in the plan on the future day when it will take effect
Future changes to network and sourcing relationships (e.g. changing a group of stores to be served by a different DC starting 2 months from now) are known in advance and the effect is visible in the plan on the future day when it will take effect
Future price changes (whether temporary or permanent) are known in advance and the effect is visible in the plan on the future day when it will take effect
Pre-distributions of promotional stock are scheduled in advance to allow display setup time ahead of the sale
Except in rare cases, the creation and release of orders or stock transfers at any location is a fully automated, administrative ‘non event’ that requires no human intervention

Here it can be tempting to take shortcuts that seem ‘easier’:

‘We don’t bother forecasting or planning slow moving items at store level. We just wait for a reorder point to trigger.’
‘For items we only buy once from a vendor, we just manually buy it into the DC and push it all out to the stores.’
‘We know our inventory isn’t very accurate for some items at store level, so we just get the store to order those items manually based on a visual review of available stock.’

In order to have a valid simulation of reality that supports higher levels of planning beyond immediate replenishment (see next section below), you need a system and process that can model these things in a way that is representative of what is actually going to happen.

If an item/location is selling/sellable, then it must have a forecast for those sales.

There is actually no such thing as ‘push’ in retail (unless you are able to ‘push’ product into a customer’s cart against their will and get them to pay for it).

Rollups of the elemental data to support aggregate planning in the future means:

You can do proper capacity planning with a complete view of the future because a common process is being used at the elemental level, cube/weight data is accurate and so-called shortcuts are not being taken at the elemental level
S&OP is possible because the elemental plans are complete, have future pricing changes applied – instead of looking in the mirror with ‘budget vs actual’, senior leaders and decision makers can look through the windshield and compare ‘budget vs operational plan‘

While all of these elements that define ‘valid simulation of reality’ may seem intuitive and reasonable, it doesn’t stop some people from saying things like:

‘A lot of items, especially slow movers, can’t be forecasted, so the whole idea kinda falls apart right there.’
‘That’s a great theory, but it’s actually not possible to use a pull-based system for every item.’
‘Just because of sheer volume, it’s impossible to manage every product at every location in this way.’

Again, as mentioned previously, a single process framework that can be used for all possible scenarios is actually much simpler to implement and maintain over the long run.

Plus, well… it’s already been done, which kinda deflates the whole ‘it’s impossible’ argument.

The EACH Supply Chain

Posted on September 6, 2017 by Mike Doherty

It’s no secret that retail is undergoing some pretty big changes and will undoubtedly see even more significant shifts in the coming years. While most people are extolling the impact of digitization on retail, there is, in my opinion, a very fundamental and profound shift underway.

The shift is to the EACH supply chain.

Supply chain efficiency is about density. Filling up trucks, planes and trains. Delivering bigger orders. Driving the unit handling cost down. Makes sense and for any retailer, having a reasonable and competitive cost structure is important.

The world, however, is changing. Two major shifts are driving us towards the EACH supply chain – customer expectations and product proliferation/marketing.

Most retail supply chain professionals gained their supply chain knowledge in the world of logistics. It was about moving boxes and cases between facilities. Many, even to this day, don’t really consider the store part of the supply chain. They’ve been conditioned to be content once the product leaves a facility, destined to a store. And measures, to date, reflected that. Fill rates were measured and reported on based on if orders left a facility on time and in full.

It’s only relatively recently that retail supply chain executives began to measure store in-stock and, sometimes, on-shelf availability. Try to find a retailer that measures and reports on store inventory accuracy? It’s a tough search. Yet, having an accurate on-hand balance at store level is becoming more and more a necessity – not only to deliver an excellent customer experience but, importantly, to enable the supply chain to perform, particularly in an omni-channel world.

Retail supply chain thinking, however, needs to extend beyond the store. It needs to, and does, include the customer. The customer is now empowered – armed with finger-tip product and delivery knowledge and their expectations are on the rise.

Customers buy in EACHes, not cases – whether in store, or online and, as we all know, online volume is increasing and that trend will likely continue. Meaning, of course, that the retail supply chain needs to be re-thought and re-architected to deliver in EACH.

When omni-channel was in its infancy, many retailers set up a dedicated distribution centre (DC) to process and fulfill online orders. DC’s, however, traditionally don’t deal with EACHes very well. Combine that with the cost of home delivery and omni-channel fulfillment has been, to date, a losing proposition for most retailers.

Retailers have traditionally set their shelf configurations to hold more than a case quantity and have set their replenishment strategy such that once the on hand balance reaches a minimum level, then an additional case can be ordered to, hopefully, flow directly to and fit on the shelf.

The problem is that all these cases take up too much space and inventory and, often, the arrival of a case of a specific product results in enough inventory to last for weeks and sometimes months. What retailers are beginning to realize is that if they can flow product closer to the EACH rate in which they are demanded, more products can fit on the shelf and be available in the store for presentation to customers.

It’s hard enough for retailers to encourage and entice customers to shop in store and one way is to increase the breadth of assortment – along, of course, with making the shopping experience unique, entertaining and fun.

Marketing and advertising is also evolving to an EACH philosophy and that’s also pressuring the retail supply chain to be EACH-capable. The proliferation of mobile device ads and offers are targeted specifically to people based on data and learnings from individual consumers shopping and search habits. The result? Further demands in EACHes.

Product proliferation is also necessitating the EACH supply chain. The “endless aisle”, as it’s sometimes called, is upon us, and think about the number of possible products available for purchase from Amazon, or on the web in general – it’s staggering and, again, will only increase.

Amazon, of course, is the poster-child of the EACH supply chain. Think about what they’ve been up to over their 20 years of existence. Making online shopping as easy and seamless as possible, while slowly and steadily moving fulfillment closer and closer to the consumer – reducing times and costs in the process. Inherently they understood and have shaped the thinking towards the EACH supply chain and continue to work to reduce costs and, more importantly, cycle times and customer friction.

As customers shift more of their consumption to online this necessitates thinking and designing the flow of inventory from supply to consumer. What’s emerging as a viable model is for retailers to leverage the store to deliver to the customer – either by encouraging/rewarding for picking up in store, or by delivering from store to home.

On the planning front, Flowcasting should be considered a foundational process to facilitate and enable the EACH supply chain. Given many retailers will evolve to delivering the EACH demand from store level, then it’s logical and sensible to plan the total consumer demand from store level. Some of the sales in the store will be through the cash register, some by customer pick-up and still others by shipping from store to home.

Regardless, it’s still a sale and this sales history would be used, in aggregate, at store level to forecast future consumer demand for that store/supply point. Since Flowcasting re-forecasts and re-plans the entire supply chain daily, shifts in demand are quickly assessed, and translated into meaningful plans for all partners in the retail supply chain.

Retail and retail supply chains are undergoing massive changes. I believe that, at the heart of this transformation, is the shift to the EACH supply chain.

Of course, you may not agree and that’s cool.

After all, as the old saying goes – to EACH their own.