The Sun Came Up Today

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

“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

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

“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.

Rising Tides

It is better to aim high and miss than to aim low and hit. – Les Brown

Why is the shelf empty?

I could go on and on about the myriad factors that could be at play, but it really boils down to one of two things:

  1. Failure to anticipate (or quickly react to) demand or;
  2. Not enough available supply, even if demand is properly planned

The first one is pretty obvious. No how matter how much effort you put into forecasting, sometimes shit just happens. The season breaks way earlier than expected. A mundane product that’s been selling steadily for years “goes viral” because of a TikTok trend. Or a customer just comes in and wipes you out unexpectedly for no discernible reason.

The second one is also obvious – at least on the surface. Clogged ports, pandemics and strikes immediately come to mind. But what often doesn’t come to mind is store inventory accuracy. Of all of the millions of item/locations in retail stores with a computerized on hand balance, how many times does the replenishment system think there’s stock available to sell when there is actually none? And how often does this condition go undetected until the store manager starts seeing shelf gaps? With store inventory accuracy hovering in the 50-60% range, I’m going to say “probably a lot”. 

I would argue that it’s probably the single biggest “supply issue” in retail, but the subject is so universally ignored and not measured that there’s no way of actually proving me right or wrong on that. But it sure feels like I may be right.

Regardless, lack of incontrovertible evidence aside, an informal quorum of people seem to agree with me anyhow, even if they may not know it. 

Why do I say that?

Whenever we talk to retailers about fully integrated planning from supplier to shelf and explain the process in detail, someone inevitably connects these dots:

  • Store on hands are a critical input to the planning process – TRUE!
  • Store on hands aren’t particularly accurate – TRUE!
  • Therefore, you can’t properly plan anything until inventory accuracy is under control – hmmm, well…

Here’s the thing about that last point: Inaccurate inventory has been a problem in retail for time immemorial. Computer assisted ordering, stock checking for customers, online order pickup in store – these are all common practices in retail today and have delivered significant benefits in terms of growth, efficiency and customer service. The most critical input for all of these processes is store inventory balances, which everybody knows are not accurate. Yet I haven’t seen any retailers shutting down automated ordering or buy online/pick up in store programs until their store inventory accuracy is up to snuff. 

Could greater benefits be achieved if store inventory records were more accurate? Duh! But that doesn’t change the fact that significant benefits can be (and have been) achieved in spite of inaccurate inventories. Implementing an end-to-end planning approach that relies on store inventory balances is no different in that regard. There is no hard dependency on some arbitrary level of inventory accuracy to start building or improving upon planning capabilities.

Transforming a retailer from a reactionary firefighter to an integrated planning organization takes hard work and discipline and delivers enoromous benefit.

Sustainably improving inventory accuracy in stores also takes hard work and discipline and also delivers enormous benefit.

Either of these initiatives can appear daunting individually, so – except for the most courageous among us – creating a false dependency (i.e. “we can’t plan without accurate inventory”) is a surefire way to ensure that nothing gets done about either of them.

As John F. Kennedy famously popularized: “The rising tide lifts all the boats.”

A retailer with so-so inventory accuracy will be made better with improved planning capabilities. A retailer with poor (or virtually nonexistent) planning capabilities will be made better with improved inventory accuracy.

So work on planning and shelve inventory accuracy for awhile.
Or work on inventory accuracy and shelve planning for awhile. Or be extra brave and work on both at the same time.

But work on something.

Remember Warren Buffet’s addendum: “A rising tide may lift all boats, but only when the tide goes out do you realize who’s been swimming naked.” 

Retail Stock Management: The Cycle of Insanity

The object in life is not to be on the side of the majority, but to escape finding oneself in the ranks of the insane. – Marcus Aurelius Antoninus (121AD – 180AD)

Is this a comedy of errors or a tragedy?


RICK – a retail department manager – sees a confused looking CUSTOMER in front of one of the shelves he manages. He approaches to see if he can help.

Is there something I can help you with?

I need one of these, but I can’t find any.

RICK scans the shelf tag (Item #542317) and the system shows an on hand balance of 24 units.

That’s weird. I better check the back room. Give me a few minutes.

RICK walks back to the stockroom and opens the door.


Upon opening the door, RICK is confronted with a disorganized mess of cartons – some already opened – haphazardly packed onto shelves and pallets of stock on the floor stacked 3 deep against the wall.


I’m never going to find it. I’ll just write it off and the system will order more.

He takes out his trusty handheld device and, with a few keystrokes, wipes out the 24 on hand units in the system. He heads back to the CUSTOMER who is patiently waiting for him in Aisle 6.


I couldn’t find any back there, but we should have some more by the end of the week.

Oh, okay.


SALLY – the receiving manager – is looking at her daily reports and sees that there are quite a few trucks coming over the next few days. The mayhem in the stockroom has caused inventory to pile up in her receiving area and she anticipates disaster by week’s end. She leaves a note to the night crew asking them to get as much stock out of the stockroom onto the sales floor as possible.


Lights are blinking in the dark room as the computer assisted ordering system (CAO) is combing through the stock records for RICK’s store. CAO comes across a zero balance for Item #542317.

They’re out of stock! I had better send them another case!

CAO sends a replenishment request to the DC for 24 units of Item #542317, due into the store on their Thursday shipment.


RICK arrives to see that the night crew has been very busy. There are pallets of stock in every aisle waiting to be put on the shelves. He gets to work.


As RICK is working his stock, he comes across a case of 24 units of Item #542317 that was somewhere in the stockroom.

There you are! I’ve been looking for you! Well, not really.

He fills up the shelf with stock and admires his work. He brings back all of the stock he couldn’t fit on the shelf back to the stockroom. It’s still messy in there, but at least there’s some room to move around. He doesn’t update the on hand balance to reverse the writeoff he processed on Monday.


The Thursday shipment has arrived. SALLY has been struggling to keep up all week and the stockroom is really getting jammed again. Before leaving for the day, she approaches PHIL, the night crew manager.

PHIL, I really need you to clear out Receiving tonight! I have more trucks coming tomorrow to get us through the weekend and I don’t have any room to bring them in!

No problem, will do!


RICK arrives and sees that there are lots of pallets of stock in his aisles that need to be put away. It’s the weekend, so he isn’t surprised. He gets to work cutting open cartons and filling shelves.


RICK picks up a case of 24 units of Item #542317 that CAO requested in the wee hours of Tuesday morning. A few units have sold since Tuesday, but the shelf is still mostly full and the new stock won’t fit.

Stupid replenishment system! Either I’m out of stock or I have way too much!

He sets the case back down onto the pallet to take back to the stockroom after he’s done filling the shelves.


RICK starts shoving his excess stock wherever it can fit, including the case of Item #542317. He puts in on a shelf, slides it to the back and then puts a couple boxes of other stock in front of it. It’s been a rough morning so he heads to the break room to get something to eat. The writeoff he processed on Monday is now a distant memory. Even though there are 40+ pieces of Item #542317 in the store, the system thinks there are only 24. Hopefully it will get corrected when they do the physical inventory after Christmas.


RICK sees a confused looking CUSTOMER in front of one of the shelves he manages. He approaches to see if he can help.

Is there something I can help you with?

I need one of these, but I can’t find any.

RICK scans the shelf tag (Item #542317) and the system shows an on hand balance of 24 units.

That’s weird. I better check the back room. Give me a few minutes…


What Demand Planners Really Need

Necessity never made a good bargain. – Benjamin Franklin (1706-1790)

If you ask someone who thinks they know what a retail demand planner needs from a forecasting system, the response will likely be a list of features and gadgets that they believe will make  forecasts “more accurate”. On the surface, this makes some sense – a more accurate forecast has greater planning value than a less accurate one.

Based on this perceived need, the hunt is on to buy a shiny new forecasting system for the demand planners to use. After some evaluations, the list is narrowed down to a couple of front runners. You send them your historical sales data and challenge them to a “bakeoff” – whoever produces the most “accurate” weekly forecast over a few cycles wins (or at least significantly improves their odds of winning).

And what do you learn from this process? You learn how good a bunch of nerds working for the pre-sales team are at fine tuning the inner workings of their system to produce the desired result they’re looking for – a new customer for their solution. How many person hours did they spend trying to win the sale? What exactly did they do to the models? Is any of what they did even remotely close to what a real demand planner can (and should) do on a daily basis to manage a large number of forecasts? You probably won’t learn any of this until the implementation team arrives after you sign on the dotted line.

What you will probably also learn is that each of the front runners produces a more accurate forecast for about 50% of the forecasts – likely with no clear reason as to why one did better than the other for a particular item in a particular location in a particular week. After rolling up all of the results, you find that one software provider’s accuracy is 0.89% higher overall than the other for the sample set used.

That’s when someone creates a fancy spreadsheet to “prove” that this extra 0.89% of “accuracy” actually equates to millions of dollars of additional benefits when you multiply it through all items at all locations and do a 10 year net present value on it. It’s all complete nonsense of course, but because it’s based on a tiny kernel of “truth” from the evaluation, it’s given outsized weight.

Fast forward to 3 years later. All of the real business challenges rear their ugly heads during the implementation and are solved with some compromises. Actual demand planners can’t seem to get the same “accuracy” results that were touted in the bakeoff. They don’t really understand all of the inner workings and don’t have the time that the pre-sales team had to fine tune everything in the same way. All of the press releases say that they now use Software X for demand planning, but in reality, most of the real work is being done in Excel spreadsheets, which the demand planners actually know how to use.

Now what if you asked demand planners directly what they actually need from a forecasting system? It’s really only 2 things: Comprehension and control.


When a demand planner is reviewing a system calculated forecast, they want to be able to say one thing: “Given the same inputs as the model, I would have come up with the same forecast on my own.”

That doesn’t mean that they agree with the forecast, it just means that they understand what the model was “thinking” to come up with the result. They don’t need code level understanding of the algorithms in order to do this, just knowledge of how the model interprets data and how it can be influenced.

Before they move a dial or switch to alter the model, they want to be able to reliably predict the outcome of their actions.


So long as the behaviour of the model can be understood, a demand planner will want to work with it to get the output they want, rather than just give up and work against it with manual overrides they calculated in Excel.

Knowing what the model did and why it did it is important, but demand planners also need to know how to affect changes in the model to make it behave differently, but also predictably so that the system will produce forecasts that they agree with and for which they are willing to be held accountable.

Accuracy is a rearview mirror measure. Demand planners need to be able to live in the future, not the past. In order to support them, a forecasting system needs to be both understandable and directly controllable so that they can fully accept accountability for the outcome.

Probabilistic Forecasting – One Man’s (Somewhat Informed) Opinion

A reasonable probability is the only certainty. – E. W. Howe

My, how forecasting methods for supply chain planning have evolved over time:

  • Naive, flat line forecasts (e.g. moving averages) were once used to estimate demand for triggering orders.
  • Time series decomposition type mathematical models added more intelligence around detecting trends and seasonality to enable better long term forecasting.
  • Causal forecasting models allowed different time series to influence each other (e.g. the effect of future planned price changes on forecasted volumes)

All of these methods are deterministic, meaning that their output is a single value representing the “most likely outcome” for each future time period. Ironically, the “most likely outcome” almost never actually materializes.

This brings us to probabilistic forecasting. In addition to calculating a mean (or median) value for each future time period (can be interpreted as the most likely outcome), probabilistic methods also calculate a distinct confidence interval for each individual future forecast period. In essence, instead of having an individual point for each time period into the future, you instead have a cloud of “good forecasts” for various types of scenario modeling and decision making.

But how do you apply this in supply chain management where all of the physical activities driven by the forecast are discrete and deterministic? You can’t submit a purchase order line to a supplier that reads “there’s a 95% chance we’ll need 1 case, a 66% chance we’ll need 2 cases and a 33% chance we’ll need 3 cases”. They need to know exactly how many cases they need to pick, full stop.

The probabilistic forecasting approach can address many “self evident truths” about forecasting that have plagued supply chain planners for decades by better informing the discrete decisions in the supply chain:

  • That not only is demand variable, but variability in demand is also variable over time. Think about a product that is seasonal or highly promotional in nature. The amount of safety stock you need to cover demand variability for a garden hose is far greater in the summer than it is in the winter. By knowing how not just demand but demand variability changes over time, you can properly set discrete safety stock levels at different times of the season. 
  • That uncertainty is inherent in every prediction. Measuring forecasts using the standard “every forecast is wrong, but by how much” method provides little useful information and causes us to chase ghosts. By incorporating a calculated expectation of uncertainty into forecast measurements, we can instead make meaningful determinations about whether or not a “miss” calculated by traditional means was within an expected range and not really a miss at all. The definition of accuracy changes from an arbitrary percentage to a clear judgment call, forecast by forecast, because the inherent and unavoidable uncertainty is treated as part of the signal (which it actually is), allowing us to focus on the true noise.
  • That rollups of granular unit forecasts by item/location to higher levels for capacity and financial planning can be misleading and costly. The ability to also roll up the specific uncertainty by item/location/day allows management to make much more informed decisions about risk before committing resources and capital.

Now here’s the “somewhat informed” part. In order to gain widespread adoption, proponents of probabilistic methods really do need to help us old dogs learn their new tricks. It’s my experience that demand planners can be highly effective without knowing every single rule and formula driving their forecast outputs. If they use off the shelf software packages, the algorithms are proprietary and they aren’t able to get that far down into the details anyhow.

What’s important is that – when looking at all of the information available to the model – a demand planner can look at the output and understand what it was “thinking”, even if they may disagree with it. All models make the general assumption that patterns of the past will continue into the future. Knowing that, a demand planner can quickly address cases where that assumption won’t hold true (i.e. they know something about why the future will be different from the past that the model does not) and take action.

As the pool of early adopters of probabilistic methods grows, I’m looking forward to seeing heaps of case studies and real world examples covering a wide range of business scenarios from the perspective of a retail demand planner – without having to go back to school for 6 more years to earn a PhD in statistics. Some of us are just too old for that shit.

I see great promise, but for the time being, I remain only somewhat informed.

Bread and Butter

Man shall not live by bread alone. – Matthew 4:4

“Make sure you focus on the bread and butter items!”

Anybody who’s worked for a retailer – particularly in supply chain – has either heard or said these words at least a dozen times. And everybody knows what those “bread and butter” items are: The fast sellers. The products that customers take out of the stores by the cartload. If you were ever stocked out on one of those items, the damage to your brand would be catastrophic.

Hence the perceived need to make sure your people in charge of replenishment are watching those items like a hawk.

Here’s the thing though: Fast selling items with continuous demand in every store are precisely the ones that require virtually no effort whatsoever. They turn so quickly and the volumes are so well established that they basically manage themselves on autopilot. In most cases, these are the items that your competitors also sell (and potentially consider “bread and butter” items themselves).

The reality for most brick and mortar retailers is that they are in one of the following two categories:

  1. You’re competing with Amazon, or;
  2. You will soon be competing with Amazon

Unless you’re Walmart or Costco, you really do need to be a category killer to overcome the perceived advantages while exploiting the weaknesses that “endless aisle” retailers like Amazon provide to customers. Yes, you need to have an online presence and offer as many channels to the customer as possible, but that won’t be enough.

You can drive to Walmart right now and get a pack of wood screws, but are you sure will they have the size you need?

You can order the exact wood screws you need from Amazon, but will they be easy to find and can you get them right now if you need them?

If you’re like me, you don’t even ask those questions. The moment you identify a need for a particular size and type of screw, you jump in your car and go straight to Home Depot or Lowes and march straight to the aisle that has every type of screw and fastener you can imagine, confident that you’ll find what you need.

Sure, there’s a lot of slow selling dog crap in there when you look at the assortment SKU by SKU, but if you only pay attention to the fast selling items, then you’re competing head to head with Walmart and Costco – probably not a winning strategy.

It’s a broad assortment of those long tail items that really make you stick out in the customer’s mind. They’re the key differentiators that can automatically and subconsciously disqualify your competitors when people are in the market for what you’re selling.

There’s your real bread and butter.

Just In Time… For What, Exactly?

I have noticed that the people who are late are often so much jollier than the people who have to wait for them. – E.V. Lucas

The time-phased, arrival based planning logic that underpins Flowcasting has frequently been described (sometimes disparagingly) as “pull-based, just in time”. Depending on your definition of “pull-based” and “just in time” (do any two people actually agree on what these terms mean?), there’s more truth to that than fiction.

The “pull-based” part is easy. The retail supply chain hasn’t finished its job until a customer has made a purchase. While it’s possible to encourage a stronger customer pull with promotional offers, pricing and markdowns, you can’t push unwanted stock into a customer’s shopping cart and force them to pay for it. This is true for every saleable item in every retail store.

The “just in time” part is what can sometimes make people (particularly buyers) a little queasy. The term evokes images of stock running almost to zero just before the perfectly executing supply chain delivers more stock. There seems to be a pervading fear that such approaches will cut inventory to the bone in a blind bid to increase stock turns at a all costs.

While it’s certainly possible to run your supply chain (including the stores) super lean, it’s definitely not necessary – nor recommended. A store with just enough stock to cover anticipated demand and variability for every item will look like it’s perpetually going out of business.

“Just in time” doesn’t mean “just enough to support sales”. It means just in time to prevent the stock level from dipping below a minimum floor that you decide

Do you want to maximize turns with minimal safety stock? No problem!

Do you want to have a nice, full looking display with at least 5 facings, 3 deep on the shelf at all times? Go for it!

(Same item, same store, same sales forecast).

Do you want to augment the normal shelf stock with secondary promotional displays for a few weeks? Nobody’s stopping you!

Would you rather have a minimum of 4 weeks of supply in the store at all times? Sure! Why not?

Just in time isn’t about stock levels, it’s about stock flow. So long as you can articulate what minimum stock holding you require for each item/location and when (and can justify it to Finance), a proper just in time planning approach does what it’s told and flows in stock to ensure you never fall below that level.

Merchants and space planners rejoice and be glad! You’re not slaves to just in time planning. Just in time planning is a slave to your merchandising needs.