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.

Software Selection: Who’s In Charge?

Decide what you want, decide what you’re willing to exchange for it. Establish your priorities and go to work. – H. L. Hunt


You’re living in a cramped 2 bedroom apartment and you decide it’s time to buy a house.

You have a spouse, 3 kids, 2 cars, a dog and a home-based business. On that basis, you determine that you’ll need a 2 storey home with 4 bedrooms, an office, a 2 stall garage and a medium-sized yard.

You give this information to your realtor and she compiles a list of houses for you to look at. Most of them are 2 bedroom bungalows with no garage on postage stamp lots.

In spite of your stated requirements, the realtor has determined that a 2 bedroom house is less costly to heat and cool, the cars can be parked outside and a small yard requires less effort to maintain. Most importantly, there’s an abundance of 2 bedroom houses on the market right now, so she’ll be able to get you into a house more quickly this way.

All of those things may be true, but you’re the one who’s going to be living there for the next several years, not the realtor. Sure, some tradeoffs are possible (even expected) – maybe the two youngest rugrats can bunk together until the oldest goes to college, or perhaps you can build your home office in an unfinished basement after you move in – but in this scenario, it seems as though the realtor is more interested in meeting her requirements.

All too often, a similar story plays out when companies embark on the journey of selecting new software to run their enterprise. We’ve seen it many times firsthand when it comes to supply chain planning software, but it really applies to any area of an organization that uses technology.

The burning platform may come from the business (“Our performance and productivity is suffering because of this clunky old legacy system!”) or from I.T. (“We can’t support this piece of crap any longer!”)

Regardless of the impetus for change, it’s the next decision where things start to go of the rails: “Well, since we need new software, then I.T. should be in charge of selecting it, right?”

Um, wrong.

I.T. certainly has a critical role to play, but if you’re the manager of a business process (and people) that will need to be supported by new technology, you take a back seat in the selection process at your own peril.

There are 3 things to always remember:

1. I.T. can’t read minds

I.T. folks love business requirements. Lots of them. With as much detail as you can muster. It helps them to shortlist candidates and research alternatives. If you don’t invest the time and energy to carefully think about what you want the future state to look like and write down your requirements, then you’re forcing I.T. to guess them. This would be like telling a real estate agent to ‘find me a house’ without providing any other details.

2. What I.T. considers important in a software package is not what the business considers important

Yes, the business has requirements, but so does I.T. A new software package must perform a number of functions in order to enable a new process. But it must also be supported (and supportable), process appropriate volumes and fit in with the established architecture of the systems that won’t be changing.

3. The business people will be using the new software each and every day to do their jobs

This speaks to the relative weighting between business requirements and technical requirements. The cheapest solution with the best processing speed and architecture that’s impossible for a user to understand and doesn’t support the business requirements isn’t much use. In other words, business requirements are more important and consequential to the running of the business than technical requirements – there, I said it.

So, suppose you get a good set of business requirements and a good set of technical requirements. A few software vendors come in to demonstrate functionality with the business folks and talk tech with the I.T. folks. You short list it down to 2 vendors:

  • Vendor A meets 80% of the critical business requirements, but 65% of the technical requirements
  • Vendor B meets 70% of the business requirements, but 75% of the technical requirements

What do you do now? The same thing you would do if the realtor at the beginning shows you a few houses that lacking in some way when compared to your ‘wish list’. You talk it through. You figure out which tradeoffs you’re willing to make. You discuss where workarounds might be used to bridge a functionality gap.

None of that is possible if you don’t give yourself the choice.


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.

Last Mile Delivery: Really Folks?


One way to boost our will power and focus is to manage our distractions instead of letting them manage us. – Daniel Goleman


Okay, first a confession out of the gate. The title, quote and image above might lead you to believe that I’m judging last mile delivery (and the broader omni-channel retailing discussion that goes along with it) as a ‘shiny object’ distraction.

I know that’s not entirely true. But I believe it is at least partially true.

To be sure, retail is changing and it’s changing rapidly. Customers want more choices in terms of how they make purchases and how they get those purchases to their homes – and they aren’t super keen on paying a lot more for these choices.

Retailers who put their heads in the sand and don’t actively address these challenges will (and in some cases already do) find themselves in serious peril.

Where is last mile delivery headed? It’s still evolving – but getting into those details is not the point of this discussion. I’m going to stay in my lane. At the risk of oversimplifying things, a sale is a sale and the supply chain planning challenge is to have the product available where the sale will be fulfilled.

The beef I have is that all of the discussion about last mile delivery seems to be making the blanket assumption that retailers have everything aced right up to the last mile.

As if to prove my point, I received an unsolicited email today (God only knows how many supply chain related online publications have my email address at this point) asking for my participation in a survey with the title: “Can we solve the last mile?” The opening two sentences read as follows:

“The last mile is bearing the brunt of the eCommerce boom. Yet, it represents a great source of angst and expense for retailers and last mile providers alike.”

After that is a ‘sneak preview’ of survey topics that focus solely on last mile problems – the implication (likely unintended) is that the challenges in the last mile are completely independent of all the activities that precede them.

Retail out-of-stocks have been a major problem since they started measuring it (8% on average and double that during promotions). The most prevalent cause cited by all of the major studies is inventory management and replenishment practices at store level. Not surprisingly, the lack of attention on solving for these causes means that they haven’t yet magically vanished. Perhaps someday, if we keep wishing really hard…

It’s pretty clear that ‘non Amazon retailers’ will need to make use of their bricks and mortar store network to enable whatever last mile delivery options they intend to pursue. How will they be successful in that regard with such abysmal out-of-stock performance and no idea what the accuracy of their electronic on hand records are (if they even have them at all)?

The day is coming when customers will expect to see store on hand balances on your web page before they submit a ‘click and collect’ order – what happens when the website says you have 3 in stock, but there isn’t any to be found when the customer goes in to collect?

Finally, we can’t lose sight of the fact that the ‘omni’ in ‘omnichannel’ is a latin prefix meaning ‘all’ or ‘every’. One of those ‘every’ channels is customers walking into a store, getting a cart, selecting products and paying for them at the checkout – kickin’ it old school to the tune of 91.5% of total retail sales.

Yes, e-commerce is growing like crazy, but it’s going to be awhile yet before online selling is truly dominant in retail as a whole.

And if (when) that day comes?

Again, I’m not suggesting that working out the last mile won’t be critically important. I’m just saying that retailers still have some work to do in getting basics right (like being in stock and knowing how much is on hand) in order to make it all work.

Princess Auto’s Flowcasting journey featured in Canadian Retailer magazine


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.


A beautiful mind

Do you remember the movie “A Beautiful Mind”?

The film is based on mathematician Dr. John Nash’s life, and, during one part, attempts to explain how Nash got the idea for his equilibrium theory as a part of game theory. In the scene Dr. Nash is at a bar with three pals, and they are all enraptured by a beautiful blond woman who walks in with her friends.

While his friends banter about which of them would successfully woo the woman, Dr. Nash concludes they should do the opposite – Ignore her. “If we all go for her,” he says, “we block each other and not a single one of us is going to get her. That’s the only way we win.”  That’s the moment when he formulated his idea.

The idea that pops into Dr. Nash’s head at that moment is very instructive in the innovation process.  Often, real innovation happens because you are in a situation and you’re paying attention, or listening, and you just connect the dots.  It’s the subconscious mind at work, finally coming to grips with something you’ve likely been pondering for a while.

It’s a great film and a beautiful story.

Here’s another beautiful story of essentially the same approach that was used to create the breakthrough thinking and solution in demand planning at store level – which, as we know, drives the entire Flowcasting process.

In retail, forecasting at store level, systemically, has been a major challenge for a long time.  Not only do most retailers have millions of store/item combinations, they also need to deal with virtually every imaginable sales pattern.  But, by far, the largest challenge, is the large number of slow selling items – accounting for 50%+ of virtually any retailers assortment.

The main issues with slow selling items is twofold: finding a selling pattern amongst sparse data, and ensuring that the forecast reflected the somewhat random nature of the actual sales.

The hero in our true story is named Darryl.  Darryl is the architect of the RedPrairie Flowcasting solution (now part of JDA) and, specifically, the profile-based, randomized integer forecasting approach that has simplified retail store level forecasting to a beautiful, elegant, intuitive process that does something incredible – it works and is very low touch.

The baseline forecasting process works like this:


In Darryl’s approach, unlike that of other attempts, he first calculates an annual forecast by item/store.  Then simple user defined sales thresholds automatically doing the following:

  1. Determine what time period to use to forecast in (weeks, months, quarters, semi-annual)
  2. Determine which level of already pre-aggregated history to use to spread the annual forecast in the time period
  3. Determine whether to convert the forecast into integers – which he randomizes by store/item, ensuring that the same item across many stores will not have an integer forecast in the same week

How did he think of this?  Well, similar to Dr. Nash, he found himself in a situation where someone said something very interesting and it sparked his thinking and helped him connect the dots.

Rumour has it that Darryl was walking around a Canadian Tire store years ago and was talking to the owner of the store.  They approached a section of the store and the owner grabbed a particular product and said something like, “I don’t know when we’ll sell these, all I know is that we’ll sell two every quarter”!

BOOM!  The idea for a different time period for forecasting by item/store popped into Darryl’s head and this event triggered the thinking and eventual development of the baseline forecasting process.

This is a significant development – so much so that it has been patented and is now available with the JDA product solution set.  What it has done is obsolete the need for multi-level forecasting approaches that, to date, have been the norm in attempting to create store/item level forecasts.

This approach is simple, intuitive, elegant and is computationally blazingly fast – another key requirement in retail store level forecasting.

Oh, and it also works.  We implemented this exact approach during our very successful implementation of Flowcasting at Princess Auto.  The solution is forecasting items in all varying time periods and is creating store/item forecasts for products that sell from 1 unit a year at store level, to over 25,000 units a year.

Even more important is that the people that would become demand planners (with no prior knowledge or experience in demand planning) would understand and become proficient using this approach.  Just another benefit of simplicity.

John Nash looks like any other bloke.  But, without a doubt, he’s got a beautiful mind.

The hero in our story, Darryl is just like John.  If you met him, you’d immediately think he’s another Vermont farmer who’s good with hydraulics.  But behind those coveralls and hay-stained hands is…

A beautiful mind.

Virtual Reality for the Retail Supply Chain


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

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.