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Repetito est mater studiorum

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“Repetition is the mother of learning, the father of action, which makes it the architect of accomplishment.” – Zig Ziglar

When I was growing up, I was a very competitive dude, particularly in sports. When it came to sports, for me, winning was everything. Basically, I was an asshole. Many of my friends and foes called me by a different name. What’s the name they used? Oh yeah, a cunt. It’s OK, it was the truth and they often called me that to my face.

Here’s a story, from high school, that helps confirm my then status. 

In the basement of our school, down near the gym, there was a ping pong table. And, during lunch and breaks, students would play. The rule was simple: the winner stays on the table, until someone beats them, and then they take over. So, a very good player could play for quite a while.

One day, me and my pals sauntered down and watched as a girl named Dana beat all comers. Then, at the insistence of my mates, it was my turn to challenge her. She crushed me. My buddies, of course, knowing how competitive I was, absolutely shamed me that day and for many weeks after.

So, what did I do?

I did what any red-blooded, super competitive dude would do. I bought my own racket and a ping pong table.  I was determined to win.

Ping pong tables fold in the middle where the net is so that, when folded, the other side of the table is upright. It allows a single player to hit the ball, over the net, against the other side of the table and it pretty much guarantees that the ball will be returned. So, I set up the folded table in my parents’ basement and, every chance I got, would go down and pound balls against the returning wall.

Over and over. Harder and faster. Learning how to put overspin on both a forehand and backhand. Learning how to smash and return a smash. Repetition after repetition – for hours and days on end.  

I would soon get to challenge Dana again. And, with my buddies watching, I would demolish her and would become not only the ping pong champion of my school, but also the best high school player in the county.

The moral of this true story isn’t to confirm that I was an asshole. The moral of the story is highlighting the importance of repetition.

There’s an old Latin saying, “repetito est mater studiorum” which means repetition is the mother of learning.

When it comes to instilling new ways of working, turns out repetition really is the mother of learning.

Implementing a new planning approach like Flowcasting in retail benefits greatly from repetition. You’re essentially teaching the planners and the wider organization (including suppliers) how to think differently about integrated demand and supply planning, so the more often people are exposed to the idea, the better. 

I recently read a great book about change called The Human Element.  In it they outline one of the most important strategies for instilling change is to “Acclimate the Idea” through repetition and repeated exposure (i.e., give people time to think and internalize the idea/change)

In a recent implementation of Flowcasting, the idea of repetition was leveraged extensively to help people make the change journey, including:

  • An ongoing education program which started with a cascade from the CEO and delivered repeated educational sessions to help people internalize the change in thinking and underlying principles of the new process
  • Process prototypes where the Buying Teams (Merch and Supply Chain) would execute a day in the life scenario, with company-specific data for every major planning scenario – like product life cycle, promotions planning, seasonal planning, etc.
  • A supplier education & training program to teach suppliers and the Buying Teams the new approach to collaboration
  • Training sessions to demonstrate how people would execute the new ways of working
  • Coaching sessions and ongoing coaching with job aides to help people transition from the old to the new

What do all these activities do? 

They constantly repeat and demonstrate to people the underlying change and principles of the new process. As an example, in each of the process prototypes, the Buying Teams could see what was meant by a valid simulation of reality, what the supplier would see in their supplier schedules, why postponing creating a purchase order for promotional volume was better for everyone, plus many other learnings. Repetition, with real scenarios, helped them instill new thinking and helped acclimate the ideas.

Getting good at anything (Flowcasting or ping pong) requires learning.  And learning needs repetition.

After all, she really is the mother of learning.

The Legends

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Honor lies in honest toil. – Grover Cleveland

Moving a retailer from a firefighting mindset to a planning mindset is no small task and requires a lot of emphasis on education, change and butchering sacred cows.

It also invariably requires a technology investment in a new planning system. In theory, the technology piece of this is pretty straightforward, particularly if you choose off-the-shelf planning software that adheres to a few key fundamental principles and meets your core requirements. You bolt it on top of your ERP, master data flows in and you run the batch. Then forecasts, plans and orders flow out. Easy peasy.

To make all of this work, you need a dedicated team that includes:

  • A mixture of folks from the business who can drive change – grizzled veterans with a lot of credibility across the functional areas (especially Merchandising, Supply Chain and Store Operations) combined with some whiz kids who may be a bit wet behind the ears, but are eager to learn. Their job will be to design new processes, change existing processes within the business and be the “tip of the spear” for driving the change, both internally and with suppliers. You can optionally augment this team with consultants who specialize in this space and bring experience from prior projects.
  • A technical team who can understand the mission, develop data maps, built and test the integrations, design the batch schedules and course correct when things don’t work out exactly as planned. This team can optionally be supported by the software company and/or system integrators to do some of the heavy lifting on many of those tasks.
  • An implementation team from the software provider who can work with the business folks to train your team on the new system and aid with configuration, data mapping/structure and workflow design.

That sounds like a dream team, doesn’t it? But is it enough?

Not quite.

Remember earlier when I said that the technology piece of the puzzle is “easy peasy”? Well, that’s only a relative description when compared to the change effort. In absolute terms – and from bitter experience – the technology stuff is often NOT “easy peasy”. At all.

This is why you always need one more person to augment your dream team: The Legend.

Every retailer has at least one of them, but usually no more than a handful. It’s the person whose name always comes up when these types of questions are asked:

  • Where the hell are we supposed to find that data and who manages it?
  • I can see the number on the screen, but how the hell was it calculated?
  • Why the hell did we decide to set things up this way?

These people often (but not always) have grey hair, are closer to the bottom of the org structure than the top and generally toil away in anonymous obscurity until a really big problem needs to be solved – then they’re the ones called upon to solve it. Losing one of these people would be more risky and disruptive to the organization than if the CEO was taken away in handcuffs for insider trading.

The Legend could have virtually any job title in any functional area of the organization, but the actual job description can be summed up in one word: Everything.

The Legend is a critical resource for any initiative that requires master data or touches legacy systems in any way. So, basically all of them.

They know where all of the bodies are buried and often need to throw some cold water on project teams who may have the notion that things are “easy peasy”. They don’t do that to block the path or to be a buzzkill. They just don’t want people wasting their time or making unrealistic assumptions that will foul things up. Don’t worry, they’ll eagerly help you to steer clear of the rocks, because they know where all of the rocks are.

But getting their time to help will be difficult, because they are always being pulled in ten different directions, not to mention tasked with keeping the lights on when more mundane day-to-day issues arise.

They are the critical resources that must contribute to any major transformation, but they are also the people that the business can’t afford to lose to a long term project.

In spite of all the power they wield, they are generally not protective of their knowledge or interested in defending turf. If a promotion offer came along, they may seriously consider it, but they probably won’t be actively seeking one out.

They’d be glad to write up detailed documentation and/or transfer some of their expertise. Maybe you can get some time on their calendar to get that going – there’s probably a 1/2 hour block available 4 months from now.

For ethical and technological reasons, cloning is not really an option right now, so how do you get valuable time from people who have none?

  • In the interest of long term success and stability, put some of your initiatives on hold and free up some of their time to cross train some junior folks on the more mundane tasks.
  • Bring in some contractors to backfill some of their “lights on” work and produce documentation while they work on more important matters.
  • Recognize their value and set aside an important role for them when you get to the other side of your change endeavour.

And it never hurts to give them a hug every now and then.

A dynamic, digital twin

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Frank Gehry is widely acclaimed as one of the world’s greatest architects. His most famous and celebrated building, the Guggenheim Museum in Bilbao, is the design and subsequent construction that elevated him to superstardom.

The story of how Gehry designs and the technologies he used to develop this, and subsequent masterpieces, is instructive and very relevant for supply chain planning and management.

Gehry usually begins by sketching ideas on paper with scrawls that would mystify most folks. Then he mostly works with models – usually working with wooden building blocks of different sizes that he stacks, and restacks, always looking for something that might be functional and is visually appealing.

Until recently, he’s worked with these types of models his whole life. His studio is filled with them – the culmination of decades of model building. He usually starts at one scale, then tries another and then another to see the project from varying perspectives. He zeroes in on some aspects of the design in his model, zooming in and out until he better understands the design from many different viewpoints and angles. He’s always trying new ideas, reviewing his designs with his team and client, eventually deciding on what works or doesn’t. Eventually he settles on the design and then they get on with it.

After landing the project to design the Guggenheim Bilbao, he and his team spent the better part of two years working through these iterative models, using the decidedly analog world of building blocks and cardboard to visualize the result.

Then, our old friend technology made a house call and changed his design capability forever.

Gehry would be introduced to computer simulation software called CATIA, allowing Gehry to build his designs on a computer. Originally the software was built to help design jets but was modified to allow buildings to be designed – on a computer – in three dimensions. Early in his career his designs were mostly straight lines and box-like shapes, but this technology would allow him to design curves and spirals that would be beautiful and aesthetically pleasing.

CATIA’s capabilities proved incredible. Gehry and his team could alter the design quickly, change curves or shapes, and the system would instantly calculate the implications for the entire design – from structural integrity to electrical/plumbing requirements, to overall cost. They could iterate new ideas and concepts on the computer, simulate the results, then rinse and repeat, and only then, once happy, begin construction.

The Guggenheim building was first fully designed on a computer.

In a moment of foreshadowing, the design and digital design process was labelled a “digital twin”. Once the digital twin was finalized and agreed to, only then did construction begin.

The term “digital twin” has become somewhat fashionable and, to be honest, quite important in supply chain. And what do people mean by the term “digital twin”, when thinking about the supply chain? Here’s one definition…

A digital twin is a digital replica of a physical supply chain. It helps organizations recreate their real supply chain in a virtual world so they can test scenarios, model different nodes, modes, flows, and policies and understand how decisions and disruptions will impact network operations.

For most supply chain folks, the digital twin is relatively static and represents the current state, or outlines a snapshot of the supply chain, as of today – for example, what’s happening in the supply chain, as of right now.

But, like Gehry’s ability to dynamically change design elements and immediately see the impact overall, wouldn’t the best digital twin for supply chains also be dynamic, complete, and forward-looking?

It would.

And isn’t that what Flowcasting is?

It’s a future-dated, up-to-date, complete model of the business. It depicts all current and projected demand, supply, inventory, and financial flows and resource requirements, based on the strategies and tactics that are driving a retailer and their trading partners. If something changes, then the dynamic model re-calculates the projections – so the forward-looking digital twin is always current. Everyone can see the projections in their respective language of the business (e.g., units, cases, dollars, capacities, resources) and work to a single set of numbers.

The architectural “digital twin” was a breakthrough approach for Frank Gehry and architecture in general.

The forward-looking, dynamic “digital twin” – that is, Flowcasting – is a similar breakthrough approach for supply chain planning.

New Model for Retailer-Supplier Collaboration

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Thank you to Supply Chain Management Review for publishing our latest article, “A New Model for Retailer-Supplier Collaboration” in the March/April edition of their always excellent magazine.

Check it out here: https://bt.e-ditionsbyfry.com/publication/?m=24891&i=816750&p=34&ver=html5

The article outlines a new approach to collaborative inventory planning, based on the profound insight of Dr. Joseph Orlicky and the ideas and concepts developed and implemented by our long-time colleague, Andre Martin.

We’d also like to thank two forward-looking companies, Princess Auto Ltd and Watson Gloves, for agreeing to be featured in the article, demonstrating that the approach works and benefits consumers and both retailer and supplier.

It turns out that Joe and Andre are right – you should never forecast what you can calculate!

Forecasting Wordplay

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If it is true that words have meanings, why don’t we throw away words and keep just the meanings? – Ludwig Wittgenstein via Anatol Holt

How do you describe your demand forecasts?

In retail, I’ve had numerous conversations that go something like this:

Me: “Do you really think you’re going to sell 10,000 units on that promotion? You’ve never sold higher than 8,000 at that price point.”

Retail Buyer/Demand Planner: “Yeah, it’s a bit optimistic.”

While less common, forecasts can also be made lower than one would expect (or “pessimistic”, if you will), particularly if an incentive structure exists where people are rewarded when actual sales blow the forecast out of the water.

While these “optimistic” or “pessimistic” numbers are entered into the forecasting system and potentially even drive the replenishment and supply planning functions, it would be a misnomer to call them forecasts.

Saying that a forecast is optimistic is an admission that you believe it’s biased to the high side for no good reason. You can call it a goal, a wish or a dream, but it is NOT a forecast.

In a similar way, a “pessimistic forecast” is a hedge, not a forecast.

Simply put, if you don’t truly believe the numbers you’re producing are a reflection of what’s actually going to happen, then you’re not forecasting. A true forecast is the prediction you’d make if you were obligated to bet $500 of your own money on the outcome.

That’s not to say that the most objective forecast produced by the most unbiased demand planner may not be way off. At its essence, demand planning applies assumptions in an attempt to predict human behaviour. That doesn’t always work out.

That’s also not to say that the words “optimistic” and “pessimistic” have no place in forecasting – so long as there are assumptions to back up the judgments.

For example, “I’m optimistic that we’ll beat last year’s sales, because we’ve sharpened our pricing and caused a key competitor to exit the market for this category.”

Or “I’m pessimistic about the sales outlook for our high price point luxury lines because the economy is in the tank and discretionary spending is going to be way down for the next 12 months.”

But what about the potential missed opportunities? What if we produce an unbiased, objective forecast and it ends up oversold to the point that we run out of stock and leave sales on the table?

Isn’t having a forecast that’s “a bit too high” much less risky in terms of being able to satisfy customer demand?

To be clear, a forecast is just one determinant of the level of stock needed to satisfy customer demand. The other is the uncertainty of the demand. If you apply the uncertainty to the supply side of the equation (i.e. safety stock), then that frees you up to forecast with objectivity.

Yes, the uncertainty needs to be quantified, but if you habitually describe your forecasts as being “optimistic” (which is a euphemism for “biased”), then you’re already doing that anyhow:

  • Optimistic Forecast = What we think customers might buy
  • Objective Forecast = What we think customers will buy
  • Safety Stock = Optimistic Forecast – Objective Forecast

Subtract

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“Life can be improved by adding, or by subtracting. The world pushes us to add because that benefits them. But the secret is to focus on subtracting.”

                    - Derek Sivers

People don’t subtract.

Our minds add before even considering taking away.

Don’t believe me?

Leidy Klotz is a Behavioral Science Professor at the University of Virginia and a student of “less”. He conducted a series of experiments that demonstrate people think “more” instead of “less”.

Consider the following diagram and the ask.

Thousands of participants were asked to make the patterns on the left and right side of the dark middle vertical line match each other, with the least number of changes.

There are two best answers. One is to add four shaded blocks on the left and the other is to subtract four shaded blocks on the right.

Only about 15 percent of participants chose to subtract.

Intrigued, Professor Klotz and his research assistants concocted numerous additional experiments to test whether people would add or subtract. They all produced the same result and conclusion – people are addicted to and inclined to add. It wasn’t close.

Big fucking deal, right?

Not so fast. Unfortunately, adding almost always makes things more complicated, polluted, and worse. You’d be better off subtracting.

A great example in supply chain is demand planning.

Demand planning, according to many, is becoming the poster child of adding. Let’s factor in more variables to produce an even more beautiful and voluptuous forecast. Are you sure you all these additional variables will improve the demand plan?

I doubt it.

First, many companies are forecasting what should be calculated. It’s been proven that the farther away from end consumption you’re trying to forecast, the more variables you’ll try to add. And the resulting forecast usually gets worse the more you add – since you’re often adding noise.

We have a retail client that is forecasting consumer demand at the item/store level only and calculating all inventory flows from store to supplier – what we call Flowcasting. Their demand planning process only considers two variables to calculate the baseline forecast:
• the sales history in units
• an indication if the sales was influenced by something abnormal (e.g., like promotions, clearance, out of stock, etc.)

All “other” variables that the “experts” say should be included have been subtracted.

Yet their planning process consistently delivers industry leading daily in-stocks and inventory flows to the store shelf.

The idea is simple, profound, and extremely difficult for us all. For process and solution designs, and pretty much everything, you need to remove what’s unnecessary.

You need to subtract.

The Sun Came Up Today

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It pays to be obvious, especially if you have a reputation for subtlety. – Isaac Asimov (1920-1992)

The sun came up today.

I’ve been tracking it daily in a spreadsheet for months. Please reach out if you’re interested in seeing my data. My suspicion is that you won’t.

In (belated) honour of Groundhog Day, the topic du jour is in stock reporting.

You come into the office on Monday morning, log in to your reporting/BI dashboard and display your company’s overall in stock report for the last 21 days, up to and including yesterday. As you look at it, you’re thinking about all of the conversations about in-stock you’ve had over the last 3 weeks and anticipate what today’s conversations will be about:



For the sake of argument, we’ll assume that there’s no major issue with how you calculate the in stock measure. Everyone understands it and there’s broad agreement that it’s a good approximation of the organization’s ability to have stock in the right place at the right time. (This isn’t always the case, but that’s a topic for another day).

It certainly looks like a bit of a roller coaster ride from one day to the next. That’s where applying some principles of statistical process control can help:



By summarizing the results over the last 21 days using basic statistical measures, we can see that the average in stock performance has been 92% and we can expect it to normally fluctuate between 86% (lower control limit) and 97% (upper control limit) on any given day.

In other words, everything that happens between the green dashed lines above is just the normal variation in the process. When you publish an in stock result that’s between 86% and 97% for any given day, it’s like reporting that “the sun came up today”.

Out of the last 21 days, the only one that’s potentially worth talking about is Day 11. Something obviously happened there that took the process out of control. (Even the so-called “downward trend” that you were planning to talk about today is just 3 or 4 recent data points that are within the control limits).

I used the word “potentially” as a qualifier there, because statistical process control was originally developed to help manufacturers isolate the causes of defects, so that they could then apply fixes to the part of the process that’s failing in order to prevent future defects with the same cause. In most cases, the causes (and therefore the fixes) were completely within their control.

Now when you think of “the process” that ultimately results in product being in front of a customer at a retail store, there are a LOT of things that could have gone wrong and many of them are not in the retailer’s control. In the example above, it was a trucker strike that prevented some deliveries from getting to the stores that caused some of them to run out of stock. Everybody probably knew that in stock would suffer as soon as they heard about the strike. But there was really nothing anybody could have done about it and very little that can be done to prevent it from happening again.

So where does that leave us?

Common Cause Variation is not worth discussing, because that’s just indicative of the normal functioning of the process.

Special Cause Variation is often not worth discussing (in this context) unless you have complete control over the sub-process that failed and can implement a process change to fix it.

So what should we be looking at?

In terms of detecting true process problems that need to be discussed and addressed, you want to look at a few observations in a row that are falling outside the established control limits, investigate what changed in the process and decide if you want to do something to correct it or just accept the “new normal”. For example:



But you can also take a broader view and ask questions like:

  • How can we get our average in stock up from 92% to 96%?
  • How can we reduce the variation between the upper and lower limits to give our customers a more consistent experience?

By asking these questions, what you’re looking for are significant changes you can make to the process that will break current the upper control limit and set a new permanent standard for how the process operates day to day:

But be warned: The things you need to do to achieve this are not for the faint of heart. Things like:

  • Completely tearing apart how you plan stock flow from the supplier to the shelf and starting from scratch
  • Switching from cheap overseas suppliers to ones who are closer and more responsive
  • Refitting your distribution network to flow smaller quantities more frequently to the store

They all have costs and ancillary additional benefits to the operation beyond just improving the in stock measure, but this is the scale of change that’s needed to do it without just blowing your inventory holdings out of the water.

Reporting your in stock rates (or any other process output measure for that matter) regularly is a fine thing to do. Just make sure that you’re drawing the right conclusions about what the report is actually telling you.

Soak Time

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“If a plant gets nothing but sunlight, it’s very harmful. It must have darkness too.” – Robert Pirsig

Did you know that if a plant gets nothing but sunlight, it’s extremely harmful and even deadly? It must have darkness as well. In the sun, it converts carbon dioxide to oxygen, but in the dark, it takes some oxygen and converts it back into carbon dioxide.

People, as it turns out, are the same. We need periods of darkness – essentially doing nothing – to develop ideas and/or internalize new ones. We often refer to it as “soak time” – the time where you let your mind just soak stuff in, subconsciously thinking about things. This, inevitably, helps your understanding and allows your creative juices to work.

Turns out many creative folks embrace the concept of soak time.

The film director Quentin Tarantino outlines his creative process and the role of soak time. This is his approach. He basically writes during the daytime. Then after a while, he stops writing. Now comes the key part of his process, he says. “I have a pool, and I keep it heated. And I jump in my pool and just float around in the water…and then, boom, a lot of shit will come to me. Literally, a ton of ideas. Then I get out of the pool and make notes on that. But not do it. That will be tomorrow’s work. Or the day after. Or the day after that.” Another filmmaker, Darren Aronofsky, said, “procrastination is a critical part of the process. Your brain needs a break…so that even when you’re not working, you’re working. Your brain is putting shit together.”

In his excellent book, Deep Work, Cal Newport teaches the benefits of downtime. One study by Dutch psychologist Ap Dijksterhuis showed that when working on a complex problem or decision, you should let your unconscious mind work on it as much as possible. Anders Ericsson wrote a seminal paper, “The Role of Deliberate Practice in the Acquisition of Expert Performance,” which showed that our brains have a limited window for cognitively demanding efforts. “Decades of study from multiple fields within psychology,” Newport writes, “all conclude that regularly resting your brain improves the quality of your work.”

For us, soak time is a core component of our approach to help people change and embrace new technologies and ideas like Flowcasting. It’s why we start the education process very early – so people have time to soak in the new thinking, ponder it and slowly accept it (hopefully).

Designing new processes and work methods also benefits from regular and ongoing doses of soak time. Instead of plowing through a design phase, grinding your way through session after session, a far better approach is to work on some aspect of the design, then let it sit, or go dark for a while. Subconsciously though, that beautiful brain of yours can’t turn off the tap – and will be thinking and pondering even when you’ve gone dark on that topic. The result? Better designs and solutions, guaranteed.

When I consult with retail clients, I’m aghast at how little soak time is built into people’s calendars, especially for folks that work in Home Office or Support Centres. Everybody seems to be busy doing busywork, and there’s barely time to schedule a 30-minute session, let alone have some soak time. The organization would be far better off by scheduling 20-50% of slack time for all these folks – essentially blocking off time for them to daydream, read, walk, or just do nothing at all. Let the mind wander and subconsciously connect things.

In both my Canadian and UK home offices I have a couch. When I need to let things soak, I sometimes go for a walk, or most of the time, I just lay down and do nothing. Just rest and relax and let the mind soak for a while. I do it often, almost every day and for some period.

Does it help? Yes, I think it does. Sure, over time, most of my ideas have been shit, but the odd decent one slips through. I’m convinced it’s because of nurturing it with soak time.

If you’re delivering projects or have a job that requires you to develop new ideas and initiatives, then my advice is simple: plan and schedule lots of soak time.

Maybe also get a couch so when you need to, you can just lie down and relax, breath slowly and let your mind wander and connect stuff, all while converting oxygen to carbon dioxide.

Sorta like a plant does.

A Forecast By Any Other Name

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What’s in a name? That which we call a rose
By any other name would smell as sweet. – William Shakespeare (1564-1616), Romeo and Juliet, Act 2 Scene 2

Scenario 1: A store associate walks down the aisles. She sees 6 units of an item on the shelf and determines that more is needed on the next shipment, so she orders another case pack of 12 units.

Scenario 2: In the overnight batch run, a centralized store min/max system averages the last 6 weeks of sales for every item at every store. This average selling rate is used to set a replenishment policy – a replenishment request is triggered when the stock level reaches 2 weeks’ worth of on hand (based on the 6 week average) and the amount ordered is enough to get up to 4 weeks’ worth of on hand, rounded to the nearest pack size.

Scenario 3: In the overnight batch run, a centralized store reorder point system calculates a total sales forecast over the next 2 shipping cycles. It uses 2 years’ worth of sales history so that it can capture a trend and weekly selling pattern for each item/store being replenished and calculate a proper safety stock based on demand variability. On designated ordering days, the replenishment system evaluates the current stock position against the total of expected sales plus safety stock over the next two ordering cycles and triggers replenishment requests as necessary to ensure that safety stock will not be breached between successive replenishment days.

Scenario 4: In the overnight batch run, a centralized supply chain planning system calculates a sales forecast (with expected trend and weekly selling pattern) for the next 52 weeks. Using this forecast, merchandising minimums, store receiving calendars and the current stock position, it calculates when future arrivals of stock are needed at the store to ensure that the merchandising minimums won’t be breached over the next 52 weeks. Using the transit lead time, it determines when each of those planned arrivals will need to be shipped from the supplying distribution centre over the next 52 weeks. The rolled up store shipment projections become the outbound plans for each item/DC, which then performs the same logic to calculate when future inbound arrivals are needed and their corresponding ship dates. Finally the projected inbound shipments to the DC are communicated to suppliers so that they can properly plan their finished goods inventory, production and raw material procurement. For both stores and DCs, the plans are turned into firm replenishment requests at the ordering lead time.

With that out of the way, let’s do some audience participation. I have a question for you: Which of the above replenishment methods are forecast based? (You can pause here to scroll up to read each scenario again before deciding, or you can just look down to the very next line for the answer).

The answer is… they are ALL forecast based.

Don’t believe me?

In Scenario 1, how did the store associate know that a visual stock position of 6 units meant they were “getting low”? And why did she order a single case of 12 in response? Why didn’t she wait until there were 3 units? Or 1 unit? And why did she order 12? Why not 120?

For Scenario 2, you’re probably saying to yourself: “Averaging the past 6 weeks’ worth of sales is looking backward – that’s NOT forecasting!” Au contraire. By deciding to base your FUTURE replenishment on the basis of the last 6 weeks’ worth of sales, an assumption is being made that upcoming sales will be similar to past sales. That assumption IS the forecast. I’m not saying it’s a good assumption or that it will be a good forecast. I’m just saying that the method is forecast based.

Using the terms “trend” and “selling pattern” in Scenarios 3 and 4 probably spoiled the surprise for those ones.

So why did I go through such pains to make this point?

Quite simply, to counter the (foolish and naive) narrative that “forecasts are always wrong, so you shouldn’t bother forecasting at all”. 

The simple fact is that unless you are in a position where you don’t need to replenish stock until AFTER your customer has already committed to buying it, any stock replenishment method you use must by definition be forecast based. I have yet to run across a retailer in the last 28+ years that has that luxury.

A forecast that happens in someone’s head, isn’t recorded anywhere and only manifests itself physically as a replenishment request is still a forecast.

An assumption that next week will be like the average of the last few weeks is still a forecast.

As the march continues and retailers gradually transition from Scenarios 1, 2 or 3 to Scenario 4, the forecasting process will become more formalized and measurable. And it can be a lot of work to maintain them (along with the replenishment plans that are driven by them). 

But the overall effort pays off handsomely. Retailers in Scenarios 1, 2 and 3 have experienced in stock rates of 92-93% with wild swings in inventory levels and chronic stock imbalances. This has been documented time and again for 30 years.

Only by formalizing your forecasting your forecasting process, using those forecasts to drive long term plans and sharing those plans up and down the supply chain can you achieve 97-98% in stock while simultaneously reducing inventory investment, with reduced overall effort.

Collaborate, then calculate

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“Never forecast what you can calculate.” – Dr. Joseph Orlicky

Collaboration promises much to the retail supply chain, and rightly so. Retailers and their trading partners are beginning to understand they are not alone. The retail supply chain does not act as a series of islands – each independent entity working for its own purpose. Rather, smart companies understand that they are really working as part of one, completely integrated network that is designed (or should be) to deliver products to their end customers.

Almost 50 years ago, at the 1975 APICS conference in San Diego, Dr. Joseph Orlicky (the pioneer of MRP – Material Requirements Planning) made a profound statement regarding supply chain planning. Having just learned of Andre Martin’s idea to calculate factory demand from the distribution centre requirements, he told Andre that his idea was good since you should “never forecast what you can calculate”.

Leveraging this profound truth is the key to improved collaboration, addressing the shortcomings of CPFR and, importantly, making a significant dent in stock outs, overstocks, and the bullwhip effect.

The retail/CPG supply chain should be driven only by a forecast of consumer demand – time-phased by item/selling-location (e.g., store, webstore, etc.). The consumer demand forecast should then be used to calculate a series of integrated, time-phased product flow plans and planned shipments (for a 52+ week planning horizon) from the store to the supplier factory – what we call Flowcasting.

Sharing planned shipments allows the retailer to inform the supplier about future product flow requirements, by item and shipping location, with all known variables factored in – what we often refer to as the supplier schedule. This allows the supplier to eliminate all efforts previously expended to attempt to forecast that retailer orders. The planned shipments replace all this effort – improving the supplier order plan and allowing the collaborative process to work using the profound power of silence.

In the new collaborative model, since the planned shipments provide a long-term view of future required inventory flows, the expectation is that the retailer and supplier work to the principle of “silence is approval”. What that means is that the retailer expects the supplier to be prepared to deliver to the up-to-date, forward-looking schedule and only when they cannot supply to the schedule and/or they don’t understand the projected schedule, is collaboration required.

Collaboration based on a shared view of planned shipments (i.e., the supplier schedule) allows for the collaborative model to become more strategic and value added. In this new approach retailers and suppliers will collaborate on strategies to drive sales and potentially inventory plans – in essence, the inputs to drive joint business plans.

That’s a complete reversal of the traditional CPFR model where each company developed their own independent order forecasts and then spent considerable time and effort to reconcile these forecasts. In the new approach, the collaboration mostly focuses on a common language: sales to the end consumer. And, again, largely by exception. There is no need to collaborate on the plethora of retail forecasts and planned shipments since these have been automatically translated into the requirements, product flows and various languages of the business (e.g., dollars, cube, capacity, resource needs) for all trading partners.

The following diagram depicts the paradigm shift in collaborative planning between retailers and their merchandise suppliers – collaborating primarily on the inputs to the joint business plans, and only by exception if any issues or opportunities arise based on the resultant operational product flow plans:

Leading retailers and their suppliers will collaborate where they believe it is worthy of each partner’s time and largely on strategies (i.e., inputs to the joint plans) that drive growth and/or improved performance. That could be on promotional forecasts, new items, and ideas and concepts about product flows – to name a few. Both partners understand that the planned shipments resulting from these strategies are calculated – so collaboration on these shared projections is only needed if supply is at risk.

Dr. Orlicky’s famous and profound quote, “never forecast what you can calculate” is embedded in my mind and cemented in all the retail clients I’ve worked with. We can, and should, build on this profound truth and work to ingrain this thinking and practice between retailers and their trading partners…

Collaborate, then calculate.