Store Inventory Accuracy and Deming’s 14 Points

Our system of make-and-inspect, which if applied to making toast would be expressed: “You burn, I’ll scrape.” – W. Edwards Deming (1900-1993)

What would Dr. Deming make of retail store inventory accuracy?

Impossible to know for a couple of reasons. First, he died nearly thirty years ago. Second, the bulk of his career was devoted to the attainment of total quality management in manufacturing. That said, the spirit of his famous 14 Points for Management first published in his 1982 book Out of the Crisis applies to – well, pretty much every facet of every business and store inventory accuracy is no exception.

Point 1: Create constancy of purpose toward improvement of product and service, with the aim to become competitive and to stay in business, and to provide jobs.

“Defects are not free. Somebody makes them, and gets paid for making them.”


If there’s one thing that the COVID-19 pandemic has taught retailers, it’s that when your system on hand records don’t match the physical stock in the store, it’s a real problem for customer service and productivity. When your sales primarily come from walk-in business, there’s really no reliable way of knowing how many customers walked out unsatisfied or made a substitution as a result of an empty shelf.

When you assign online pickup orders to be picked at a store because the system on hand balance shows stock, the curtain is unceremoniously ripped away. How many orders couldn’t be fulfilled because the available stock in the system couldn’t be found in the store? How much wasted time was spent fruitlessly trying to find the stock to pick the orders? 

Retailers don’t measure their inventory accuracy. They must.

Retailers don’t adequately research the process and transactional errors that cause their inventory to become inaccurate in the first place. They must. 

Before any of that can happen, management needs to actually care about inventory accuracy.

Point 2: Adopt the new philosophy. We are in a new economic age. Western management must awaken to the challenge, must learn their responsibilities, and take on leadership for change.

“To manage one must lead. To lead, one must understand the work that he and his people are responsible for.”

Inaccurate stock records don’t “just happen”. They are the result of one of two things:

  1. People aren’t following the correct processes for managing stock
  2. People are correctly following flawed processes for managing stock

In either case, the responsibility falls on management to correct these issues. This can’t be accomplished without diving deep to understand the processes and behaviours that are causing errors.

Point 3: Cease dependence on inspection to achieve quality. Eliminate the need for inspection on a mass basis by building quality into the product in the first place.

“Inspection does not improve the quality, nor guarantee quality. Inspection is too late. The quality, good or bad, is already in the product.”

This is one that retailers generally don’t understand. At all. Most “inventory accuracy” programs focus on trying to optimize counting frequency. Items with historically poor inventory accuracy are cycle counted and corrected more frequently than items with fewer historical errors, with little investigation as to why those errors are happening in the first place. This approach is really “problem solving theatre” – there are process issues that are causing the errors and constantly repairing the output without addressing the root causes of why the records became inaccurate in the first place will never lead to sustained inventory accuracy.

Point 4: End the practice of awarding business on the basis of price tag. Instead, minimize total cost. Move toward a single supplier for any one item, on a long-term relationship of loyalty and trust.

“The result of long-term relationships is better and better quality, and lower and lower costs.”

Are you buying products from suppliers that make it more difficult to keep stock accurate in stores? Do they wrap several different items in nearly identical packaging to save money at the expense of confusing store staff and customers? Are the barcodes applied with easily removeable (and switchable) stickers? Are the barcodes easy to find and scan at the checkout?

This is often small potatoes when compared to the in store process and behavioural issues, but every little bit helps. Missed sales caused by inaccurate stock affects the supplier too, so to the extent that they can work with retailers to avoid being part of the problem, everyone will benefit.

Point 5: Improve constantly and forever the system of production and service, to improve quality and productivity, and thus constantly decrease costs.

“Putting out fires is not improvement of the process. Neither is discovery and removal of a special cause detected by a point out of control. This only puts the process back to where it should have been in the first place.”

Focusing on inaccurate stock records is trying to manage the output. Inaccurate inventory is caused by processes that result in inaccurate transactions which in turn result in inaccurate on hand balances.

If you research a variance and determine it was because Mary made a mistake at the checkout, what you’ve found is an explanation for that particular error, not a root cause.

Why did Mary make that mistake? Was it a specific one-off event that won’t likely ever be repeated? Has she been properly trained on proper checkout procedures? Are the checkout procedures themselves flawed? Has management instructed Mary to focus on speed over accuracy? Are other cashiers making similar mistakes for the same reasons?

Point 6: Institute training on the job.

“People don’t like to make mistakes.”

The retail industry in general is notorious for high turnover in front line staff – you know, the people who actually transact stock movements within the store. As a result, it can be tempting to skimp on training new people for fear that your investment won’t be returned. When new people have questions, they need to go to a manager for instruction on what to do. More often than not, busy managers will provide shortcut solutions that are designed to get the problem off their plates as quickly as possible.

Is saving money on training actually saving money?

Point 7: Institute leadership. The aim of supervision should be to help people and machines and gadgets to do a better job. Supervision of management is in need of overhaul, as well as supervision of production workers.

“It is not enough to do your best; you must know what to do, and then do your best.”

Training is a good start, but it’s not enough to sustain inventory accuracy. Do people understand why inventory accuracy is important to customers and fellow team members and how their role can impact it?

Point 8: Drive out fear, so that everyone may work effectively for the company.

“Where there is fear, you do not get honest figures.”

Poor inventory accuracy should not be seen as a reflection of people’s performance, rather the performance of the process. If inventory discrepancies discovered in cycle counts result in witch hunts that are used to find culprits and lay blame, people will quickly learn “the right amount” of error they can report to avoid suspicion on the low end and recriminations on the high end. The true problems will remain buried under rosy reports that everyone can reference to argue that a problem doesn’t exist.

Point 9: Break down barriers between departments. People in research, design, sales, and production must work as a team, to foresee problems of production and in use that may be encountered with the product or service.

“Quality is everyone’s responsibility.”

Contributors to inaccurate inventory records can be found anywhere and processes (both internal and external to the store) can be the cause. Is an inventory accuracy lens being used when designing new processes and procedures in Loss Prevention, Merchandising, Sourcing, DC Picking, Store Receiving and Stock Management?

Point 10: Eliminate slogans, exhortations, and targets for the work force asking for zero defects and new levels of productivity. Such exhortations only create adversarial relationships, as the bulk of the causes of low quality and low productivity belong to the system and thus lie beyond the power of the work force.

“Hopes without a method to achieve them will remain mere hopes.”

There may well be some store employees who are deliberately trying to sabotage the business, but they are in a very small minority. Telling people “We need you to keep your stock more accurate” without first investing in education, training and the proper tools is like giving them a mule and telling them to go out and win the Kentucky Derby. 

Point 11: 11a. Eliminate work standards (quotas) on the factory floor. Substitute leadership. 11b. Eliminate management by objective. Eliminate management by numbers, numerical goals. Substitute leadership.

“Every system is perfectly designed to get the results it gets.”

Don’t let this one fool you. Deming was all about data collection and measurement of results. It’s what you do with the data that counts. Stock variance reports can alert management to where the problems may lie, but the only path to a solution is to dig in and understand the process in detail. Once a process change is made that you feel should have solved the problem, future rounds of data collection will tell you whether or not you were successful. If you weren’t successful, you need to dig in again, because there is something you missed.

Point 12: 12a. Remove barriers that rob the hourly worker of his right to pride of workmanship. The responsibility of supervisors must be changed from sheer numbers to quality. 12b. Remove barriers that rob people in management and in engineering of their right to pride of workmanship. This means, inter alia, abolishment of the annual or merit rating and of management by objective.

“When one understands who depends on me, then I may take joy in my work.”

See Points 6 and 7 above. The impact to customers and fellow team members of doing things that cause stock to become inaccurate is easy to explain. People want to do a good job, but they need to be given the right education, training and tools.

13. Institute a vigorous program of education and self-improvement.

“Learning is not complusary. Neither is survival.”

When it comes to store inventory accuracy, there’s never a point at which you are finished. There will always be new causes of errors and processes that need improving.

14. Put everybody in the company to work to accomplish the transformation. The transformation is everybody’s job.

“Long-term commitment to new learning and new philosophy is required of any management that seeks transformation. The timid and the fainthearted, and people that expect quick results, are doomed to disappointment.”

Store perpetual inventory has been around for decades. So have the process gaps, bad habits and lack of care that makes inventory records inaccurate. There are a lot of people involved and a lot of moving parts that will make it difficult to attain and sustain high levels of inventory accuracy at stores. It will take effort. It will cost some money. It won’t be easy.

But living with the impacts of poor on hand accuracy is no walk in the park either. It’s taking MORE effort, costing MORE money and making things MORE difficult on a daily basis. 

Wrong becomes right

Phil Tetlock spent almost two decades determining people’s ability to forecast specific events – things like elections and the outcomes of potential geopolitical decisions. Unfortunately, the results were not impressive. Most people were about as accurate as a well-fed, dart-throwing chimpanzee.

There were a small number of notable exceptions. A small group of people consistently provided quite accurate forecasts – people he aptly named “super-forecasters”.

In a subsequent forecasting tournament organized by the Intelligence Advanced Research Projects Activity (IARPA) – a focused branch of the United States intelligence community, the super-forecasters trounced teams of professors and “forecasting experts” by wide margins.

What made the super-forecasters so super?

It wasn’t intelligence or that they had more experience than others. In fact, in many cases, they were mostly amateurs yet they outperformed the CIA’s best and brightest (who also had the advantage of years of experience and classified information). Armed with only Google, the super-forecasters beat the CIA, on average, by 30%.

What made them great at being right was they were great at being wrong!

The difference in their ability to forecast was simple, yet crucial. The super-forecasters changed their minds – a lot.

Not a huge, 180-degree shift, but subtle revisions to their predictions as they learned new information. As an example, one of the consistently top super-forecasters would routinely change his mind at least a dozen times on a prediction and, sometimes, as often as forty or fifty times.

Importantly, most viewed a revised forecast based on new information not as changing an initially wrong forecast but rather as updating it. Turns out, updating is the secret to being a great, or super, forecaster.

The concept of updating is important in Flowcasting as well.

As loyal Flowcasters know, an important component of the process is the sharing of what we call a supplier schedule – that is, a projection, by item and delivery location of how many units are needed to ship over a long time horizon (typically 52+ weeks).

If the schedule indicates that, 39 weeks from now, the supplier will need to ship 10440 units of a product to a location, what’s the chance that this projection (i.e., forecast) is perfectly accurate? Pretty low, right? And it doesn’t need to be – it just needs to be reasonable.

A week later, and guess what? The supplier schedule has been re-calculated and updated to indicate that 10400 units are needed to ship in that particular week (perhaps even on a different day). The updated forecast is more right than the previous one. This process of minor revisions continues as the projections are updated until it’s actually time to ship (i.e., when the planned shipment has reached the agreed-upon order release horizon).

In retail, supplier scheduling is the super-forecaster for suppliers – recalibrating and subtly updating the forward looking projections, based on the latest information until…

Wrong becomes right.

Flipping your thinking

From toddler to teenager, most of us had a fairly similar educational experience: The teacher would stand at the front of the room and spew out information that the students were to absorb. Then they would assign homework that would, theoretically at least, test how well you absorbed the content. The homework assignments would then be scored by the teacher and – if you were lucky – you might find some scrawled notes in the margin to give you a clue as to where you may have gone astray.

All this assumes, of course, that you didn’t just copy your homework from a smart (if gullible) friend, thereby completely circumventing the ability of the homework assignment to test knowledge. Not to say that this approach was completely ineffective. Somehow, most of us did learn what we needed to know to become productive members of society. That said, when the best praise you can muster is that it’s “not completely ineffective”, then you are basically admitting that there is ample room for improvement. 

Karl Fish is a veteran teacher with 20 years of experience. He teaches high school math in a town just south of Denver, Colorado.  Karl thought he could do better for his students than “not completely ineffective”, so he decided to flip the traditional thinking on its head.

Instead of using class-time to “teach” in the traditional sense, Karl tapes his lessons and uploads them to YouTube. Classroom time is used for application and practice.  His students are required to watch the lecture whenever and wherever it is most convenient for them.  What would be traditionally considered “homework” is actually done during in-class time.

The result of this flip in thinking has been significantly improved understanding of the content. Working through examples and case studies not only improves the students understanding, but also improves their ability to collaborate with fellow students and Karl himself.

Contrast this approach with doing your homework in the evenings (maybe even the wee hours of the morning). If you get stuck, there are few alternatives other than to become increasingly frustrated and demoralized.

In Karl’s class, you can pose your question to a group (many of whom are likely struggling with the same question) and work together to solve the problem. What better skill and habits are there for someone to learn in high school?

Retail supply chain planning is also in need of a flip in thinking.

Traditional thinking in retail has ingrained into people’s heads that ordering is the key decision a supply chain planner needs to make. Day in and day out the retail supply chain planner only has 2 questions:

1) Should I order today?

2) How much should I order?

All anyone can talk about nowadays is “demand driven supply chains” that are super-responsive to consumer demand – yet the entire planning approach is geared toward figuring out when to place an order upstream with, at best, an indirect link to the actual customer demand.

Flowcasting flips that thinking completely. In a nutshell, the decision to place an order is a mere after-effect of the planning process (not the one and only decision) and, generally, it should be performed by a computer, not a human being.

Instead of asking “When and how much should I order”, maybe the first question should be “When does more stock need to arrive?” – Isn’t that what’s really important to your ability to stay in stock and serve a customer?

By focusing on this question first, new thinking emerges. Once you understand when product needs to arrive, then you can calculate when you need product to ship (based on where it’s coming from) and when you need to order.

Of course, to answer this question you’ll need a system to project inventory and product arrival dates well into the future.  And to calculate the corresponding ship dates and order dates. The basic change in philosophy is simple – to really know when product needs to be ordered, you must first know when it needs to arrive at the destination.

While this may sound quite simple and logical, in practice it requires a great deal of education and understanding to make such a flip – particularly if people have been “ordering” for a long time.

In our experience, most retailers continue to subscribe to the “order first, ask questions later” philosophy and flipping to an arrival based planning approach like Flowcasting will not be a slam dunk, regardless of how logical it sounds.

If you’re struggling with the concept, we’d be happy to schedule some homework time with you and your team.

Principles

Folks that know us well and have worked with us, know we’re what you might call principles freaks.  A decent chunk of time and effort we spend helping retailers implement Flowcasting is oriented to instilling a set of principles, which guide our thinking and, hopefully over time, our clients.

Julia Galef, in her brilliant book, The Scout Mindset, beautifully outlines the paradox of principles in a chapter aptly named, “How To Be Wrong”…

Many principles sound obvious and that you know them already.  But “knowing” a principle, in the sense that you read it and say, “Yes, I know that,” is different from having internalized it in a way that actually changes how you think.

Your Sales Plan is NOT a Forecast!

Man is the only animal that laughs and weeps, for he is the only animal that is struck with the difference between what things are and what they ought to be. – William Hazlitt (1778-1830)

A Ferrari has a steering wheel. A fire truck also has a steering wheel.

A Ferrari has a clutch, brake and accelerator. A fire truck also has a clutch, brake and accelerator.

Most Ferraris are red. Most fire trucks are also red.

A new Ferrari costs several hundred thousand dollars. A new fire truck also costs several hundred thousand dollars.

Ergo, Ferrari = Fire Truck.

That was an absurd leap to make, I know, but no more absurd than using the terms “sales plan” and “sales forecast” interchangeably in a retail setting. Yes, they are each intended to represent a consensus view of future sales, but that’s pretty much where the similarity ends. They differ significantly with regard to purpose, level of detail and frequency of update.

Purpose

The purpose of the sales plan is to set future goals for the business that are grounded in strategy and (hopefully) realism. Its job is to quantify and articulate the “Why” and with a bit of a light touch on the “What” and the “How”. It’s about predicting what we’re trying to make happen.

The purpose of the operational sales forecast is to subjectively predict future customer behaviour based on observed customer demand to date, augmented with information about known upcoming occurrences – such as near term weather events, planned promotions and assortment changes – that may make customers behave differently. It’s all about the “What” and the “How” and its purpose is to foresee what we think is going to happen based on all available information at any one time.

Level of Detail

The sales plan is an aggregate weekly or monthly view of expected sales for a category of goods in dollars. Factored into the plan are category strategies and assumptions (“we’ll promote this category very heavily in the back half” or “we will expand the assortment by 20% to become more dominant”), but usually lacking in the specific details which will be worked out as the year unfolds.

The operational sales forecast is a detailed projection by item/location/week in units, which is how customers actually demand product. It incorporates all of the specific details that flow out of the sales plan whenever they become available.

Frequency of Update

The sales plan is generally drafted once toward the end of a fiscal year so as to get approval for the strategies that will be employed to drive toward the plan for the upcoming year.

The operational sales forecast is updated and rolled forward at least weekly so as to drive the supply chain to respond to what’s expected to happen based on everything that has happened to date up to and including yesterday.

“Reconciling” the Plan and the Forecast

Being more elemental, the operational forecast can be easily converted to dollars and rolled up to the same level at which the sales plan was drafted for easy comparison.

Whenever this is done, it’s not uncommon to see that the rolled up operational forecast does not match the sales plan for any future time period. Nor should it. And based on the differences between them discussed above, how could it?

This should not be panic inducing, rather a call to action:

“According to the sales plan that was drafted months ago, Category X should be booking $10 million in sales over the next 13 weeks.”

“According to the sales forecast that was most recently updated yesterday to include all of the details that are driving customer behaviour for the items in Category X, that ain’t gonna happen.”

Valuable information to have, is it not? Especially since the next 13 weeks are still out there in a future that has yet to transpire.

Clearly assumptions were made when the sales plan was drafted that are not coming to pass. Which assumptions were they and what can we do about them?

While a retailer can’t directly control customer behaviour (wouldn’t that be grand?), they have many weapons in their arsenal to influence it significantly: advertising, pricing, promotions, assortment, cross-selling – the list goes on.

The predicted gap between the plan and the forecast drives tactical action to close the gap:

Maybe it turns out that the tactics you employ will not close the gap completely. Maybe you’re okay with it because the category is expected to track ahead later in the year. Maybe another category will pick up the slack, making the overall plan whole. Or maybe you still don’t like what you’re seeing and need to sharpen your pencil again on your assumptions and tactics.

Good thing your sales plan is separate and distinct from your sales forecast so that you can know about those gaps in advance and actually do something about them.

Times Ten

It’s hard to believe and counter-intuitive but it’s often easier to make something 10 times better than it is to make it 10 percent better.

Yes, really.

That’s because when you’re working to make things a bit better – like 10 percent better – you always start with the existing tools, assumptions and paradigms, and focus on tweaking an existing approach/solution that’s become the accepted norm.

This kind of progress is driven by extra effort, extra money, and extra resources – attempting to squeeze just a wee bit more from the current system. While making a minor improvement is generally a good thing, often we find ourselves stuck in the same old paradigms.

But when you aim for a 10 times improvement, you need to lean in and focus on being brave and creative – the kind of thinking that, literally and metaphorically, can put a man on Mars.

Retail Flowcasting solutions have been emerging for a while with some technology companies claiming that Flowcasting is in the DNA of their solution. More often than not that’s debatable but what’s not is that a retail Flowcasting solution needs to be at least 10 times more scalable and simpler than similar solutions that have been used in distribution and manufacturing – because in retail we’re dealing with 10’s of millions of item/location combinations.

We’ve been lucky enough to implement a purpose-built, times-ten retail Flowcasting solution. In our experience, it’s 10 times faster, 10 times simpler, and costs about 10 times less to implement compared to others who claim to have Flowcasting native solutions.

Over the course of the implementation, the chief architect shared with us how he was able to build something that’s a times-ten solution.

His secret:

  1. Start from scratch
  2. Solve the hard problems first

Most existing solutions are too heavy and burdensome – the result of solution providers not knowing how to say “no” to a specific ask – usually from a paying client that doesn’t know better, but also sometimes from competitive peer pressure. Checklist Charlie said our solution needs 11 different ways to handle safety stock, so we’ll make it so. The result is a system that cannot be the foundational starting point to build a times-ten solution.

Our architectural hero said the reason he started from scratch is that only what was necessary would go into the solution and not a drop more. A bit like Einstein once said, “everything should be made as simple as possible, but no simpler”.

In tandem with making things as simple as possible, our colleague believes that another reason he was successful is that he also solved the hard problems first.

To wit, he worked, tested and developed stunningly intuitive and simple ways to handle a variety of problems that have plagued existing providers from morphing their current solutions to a retail-focused Flowcasting solution, notably:

  1. Extreme scalability
  2. Handling slow selling items properly (both the forecast and the replenishment plans)
  3. Planning for product phase out (to provide a valid simulation of reality for any item at any location)
  4. Managing seasonal items (to maximize sales and minimize inventory carryover)

I won’t bore you with the specifics on how our architectural and design colleague solved each of these chronic retail planning challenges. He did say that he’d spoken to a number of grassroots folks in retail about each of these challenges and then – from a blank sheet of paper and some fundamental principles – developed, tested, refined and eventually delivered a times-ten solution.

Once the hard problems were solved, he believed that the rest of the planning challenges would be chicken-shit to deal with. Turns out, he was right.

The lesson here is simple, yet profound. If you’re looking for a quantum leap improvement – a times-ten solution if you will – then you need to start from scratch and solve the hard stuff first.

Subtraction

“What gets left out is as important as what gets put in.”

 Steve Jobs philosophy

Subtraction

I have a good friend named April, who has the coolest son, Lincoln.  Lincoln is in grade one and looking forward to grade two.  When I asked him his favorite subject, he declared “math”.  So, probing further, I asked him what he liked about math…

“Subtraction”.

“I like taking things away and seeing what’s left”.

I was startled. I’d never heard anyone claim their love of subtraction.  Or taking things away from something and seeing what’s left.  Of course, it got me thinking.

Flowcasting, as a concept and business process, is gaining lots of momentum.  People are really beginning to understand that, when it comes to supply chain planning, most companies have been planning looking in the rear view mirror.  Most are forecasting what should be calculated.

With Flowcasting, you only need to forecast at the item/store level and calculate everything else – all demand, supply, inventory, capacity, financial projections can be determined using this forecast.

The legendary Steve Jobs is a disciple of “subtraction thinking” – his colleagues claim that Steve was more interested in what gets left out versus what gets put in when designing a new product.

Flowcasting systems are systems cut from the same cloth. 

Think about today’s planning systems and all their features.  All kinds of bells and whistles – you know the drill, a different algorithm for each type of demand stream…special systems to help you decide on the algorithm…multiple forecasts and automatically picking the best one…a different planning system for slow selling items…a special system for allocation……etc… On and on it goes.

For retailers, these systems were built for a world that will someday no longer exist.  A world where we were forecasting at the wrong level – DC’s, plants, etc. – anywhere other than the point of final consumption.

Technology companies are also beginning to understand and embrace the principles of Flowcasting.

Hopefully they’ll embrace the concept of “subtraction”.  Instead of adding to their systems, they should be subtracting. 

For the retail supply chain, forecasting is at the store level and only there.  Simple approaches are all that’s required to forecast at store level and further remind yourself that all other demands can and should be calculated.

Are there systems available that support the Flowcasting process?  Absolutely.  They are elegant, simple and were built with “subtraction thinking” in mind.

It’s amazing what you can learn talking with a first-grader.

I plan on talking to Lincoln again soon. 

Your Forecast is Wrong (and That’s Okay)

Just because you made a good plan, doesn’t mean that’s what’s gonna happen. – Taylor Swift

I was 25 years old the first time I met with a financial advisor. I was unmarried, living in a small midtown Toronto apartment and working in my first full time job out of university. 

I can’t say I remember all of the details, but we did go through all of the standard questions:

  • Will I be getting married? Having kids? How many kids?
  • How do I see my career progressing?
  • When might I want to retire?
  • What kind of a lifestyle do I want to have in retirement?

On the basis of that interview, we developed a savings plan and I started executing on it.

The following is an abridged list of events that have happened since that initial plan was created a quarter century ago, only a couple of which were accounted for (vaguely) in my original plan:

  • I left my stable job to pursue a not-so-stable career in consulting
  • I moved from my first apartment to a slightly larger apartment
  • I got married
  • We moved into an even bigger apartment
  • We had a kid
  • We moved into a house
  • We had two more kids
  • I co-authored a book
  • My wife went back to school for her Masters
  • The 2008 financial crisis happened
  • The Canadian government made numerous substantial changes to personal and corporate tax rules and registered savings programs
  • We sold our house and built a new house
  • Numerous cars were bought, many of which died unexpectedly
  • COVID-19 happened

You get the idea. Many of these events (and numerous others not listed) required a re-evaluation of our goals, a change in the plan to achieve those goals or both.

The key takeaway from all of this is obvious: That because the original plan bears no resemblance to what it is today, planning for an unknown and unknowable future is a complete waste of time. 

At this point, you may be feeling a bit bewildered and thinking that this conclusion is – to put it kindly – somewhat misinformed. 

I want you to recall that feeling of bewilderment whenever you hear or read people saying things (in a supply chain context) like “You shouldn’t be forecasting because forecasts are always wrong” or “Forecasting is a waste of time because you can’t predict the future anyhow”.

This viewpoint seems to hinge on the notion that a forecast is not needed if your minimum stock levels are properly calculated. To replenish a location, you just need to wait until the actual stock level is about to breach the minimum stock level and automatically trigger an order. No forecasting required!

Putting aside the fact that properly constructed and maintained forecasts drive far more than just stock replenishment to a location, a bit of trickery was employed to make the argument.

Did you catch it?

It’s the “minimum stock levels are properly calculated” part.

In order for the minimum stock level for an item at a location at any point in time to be “properly calculated”, it would by necessity need to account for (at a minimum):

  • The expected selling rate
  • Expected trends
  • Selling pattern (upcoming peaks and troughs)
  • Planned promotional and event impacts
  • Planned price changes
  • Etc.

Do those elements look at all familiar to you? A forecast by any other name is still a forecast.

The simple fact is that customers don’t like to wait. They’re expecting product to be available to purchase at the moment they make the purchase decision. Unless someone has figured out how to circumvent the laws of time and space, the only way to achieve that is to anticipate customer demand before it happens.

It’s true that any given prediction will be “wrong” to one degree or another as the passage of time unfolds and the correctness of your assumptions about the future are revealed. That’s not just a characteristic of a business forecasting process – it’s a characteristic of life in general. Casting aspersions on forecasting because of that fact is tantamount to casting aspersions upon God Himself.

It’s one thing to recognize that forecasts have error, it’s quite another to argue that because forecasts have error, the forecasting process itself has no value.

Forecasting is not about trying to make every forecast exactly match every actual. Rather it’s a voyage of discovery about your assumptions and continuously changing course as you learn.

Changing the game

In 1972, for my 10th birthday, my Mom would buy me a wooden chess set and a chess book to teach me the basics of the game.  Shortly after, I’d become hooked and the timing was perfect as it coincided with Bobby Fischer’s ascendency in September 1972 to chess immortality – becoming the 11th World Champion.

As a chess aficionado, I was recently intrigued by a new and different chess book, Game Changer, by International Grandmaster Matthew Sadler and International Master Natasha Regan.

The book chronicles the evolution and rise of computer chess super-grandmaster AlphaZero – a completely new chess algorithm developed by British artificial intelligence (AI) company DeepMind.

Until the emergence of AlphaZero, the king of chess algorithms was Stockfish.  Stockfish was architected by providing the engine the entire library of recorded grandmaster games, along with the entire library of chess openings, middle game tactics and endgames.  It would rely on this incredible database of chess knowledge and it’s monstrous computational abilities.

And, the approach worked.  Stockfish was the king of chess machines and its official chess rating of around 3200 is higher than any human in history.  In short, a match between current World Champion Magnus Carlsen and Stockfish would see the machine win every time.

Enter AlphaZero.  What’s intriguing and instructive about AlphaZero is that the developers took a completely different approach to enabling its chess knowledge.  The approach would use machine learning.

Rather than try to provide the sum total of chess knowledge to the engine, all that was provided were the rules of the game.

AlphaZero would be architected by learning from examples, rather than drawing on pre-specified human expert knowledge.  The basic approach is that the machine learning algorithm analyzes a position and determines move probabilities for each possible move to assess the strongest move.

And where did it get examples from which to learn?  By playing itself, repeatedly. Over the course of 9 hours, AlphaZero played 44 million games against itself – during which it continuously learned and adjusted the parameters of its machine learning neural network.

In 2017 AlphaZero would play a 100 game match against Stockfish and the match would result in a comprehensive victory for AlphaZero.  Imagine, a chess algorithm, architected based on a probabilistic machine learning approach would teach itself how to play and then smash the then algorithmic world champion!

What was even more impressive to the plethora of interested grandmasters was the manner in which AlphaZero played.  It played like a human, like the great attacking players of all time – a more precise version of Tal, Kasparov, and Spassky, complete with pawn and piece sacrifices to gain the initiative.

The AlphaZero story is very instructive for us supply chain planners and retail Flowcasters in particular.

As loyal disciples know, retail Flowcasting requires the calculation of millions of item/store forecasts – a staggering number.  Not surprisingly, people cannot manage that number of forecasts and even attempting to manage by exception is proving to have its limits.

What’s emerging, and is consistent with the AlphaZero story and learning, is that algorithms (either machine learning or a unified model approach) can shoulder the burden of grinding through and developing item/store specific baseline forecasts of sales, with little to no human touch required.

If you think about it, it’s not as far-fetched as you might think.  It will facilitate a game changing paradigm shift in demand planning.

First, it will relieve the burden of demand planners from learning and understanding different algorithms and approaches for developing a reasonable baseline forecast. Keep in mind that I said a reasonable forecast.  When we work with retailers helping them design and implement Flowcasting most folks are shocked that we don’t worship at the feet of forecast accuracy – at least not in the traditional sense.

In retail, with so many slow selling items, chasing traditional forecast accuracy is a bit of a fool’s game.  What’s more important is to ensure the forecast is sensible and assess it on some sort of a sliding scale.  To wit, if you usually sell between 20-24 units a year for an item at a store with a store-specific selling pattern, then a reasonable forecast and selling pattern would be in that range.

Slow selling items (indeed, perhaps all items) should be forecasted almost like a probability…for example, you’re fairly confident that 2 units will sell this month, you’re just not sure when.  That’s why, counter-intuitively, daily re-planning is more important than forecast accuracy to sustain exceptionally high levels of in-stock…whew, there, I said it!

What an approach like this means is that planners will no longer be dilly-dallying around tuning models and learning intricacies of various forecasting approaches.  Let the machine do it and review/work with the output.

Of course, sometimes, demand planners will need to add judgment to the forecast in certain situations – where the future will be different and this information and resulting impacts would be unknowable to the algorithm.  Situations where planners have unique market insights – be it national or local.

Second, and more importantly, it will allow demand planners to shift their role/work from analytic to strategic – spending considerably more time on working to pick the “winners” and developing strategies and tactics to drive sales, customer loyalty and engagement.

In reality, spending more time shaping the demand, rather than forecasting it.

And that, in my opinion, will be a game changing shift in thinking, working and performance.

Killing Your Sales With Stock

Can one desire too much of a good thing? – William Shakespeare (1564-1616)

Here is one of the most widely accepted logical propositions in retail:

  1. Customers can’t buy product that’s out of stock in the store.
  2. Inventory doesn’t sell when it’s sitting in the warehouse.
  3. Ergo, the more stock you have in your stores, the better it is for sales

It makes some sense, so long as you don’t think about it too hard.

While this thought process can manifest in good ways – reorganizing the supply chain to flow product quickly through a stockless DC based on what’s needed at the store, for example – it can (and often does) result in behaviour that can actually harm sales and productivity.

The old “You can’t sell it out of the warehouse!” chestnut is most often trotted out when the warehouse is packed and they need to make room.

Tell me if this chain of events sounds familiar:

  • The warehouse is running out of space
  • The decision is made to clear out some stock
  • Products are identified that are the biggest contributors to the capacity issue (i.e. they’re taking up a lot of space and not being drawn out as quickly as everyone would like)
  • Push it out to the stores!

A couple weeks later, you run some reports:

  • Warehouse picking efficiency has skyrocketed as a result of shipping out oodles of pallets out to the stores – SUCCESS!
  • Warehouse is unclogged and has sufficient space to maneuver for the next few weeks – SUCCESS!
  • Stores now have all kinds of stock to support sales – SUCCESS!

If we just stop there, we’re feeling pretty good about ourselves. Unfortunately, there’s usually a bit more to the story:

  • The store receives way more stock that can fit on the shelf, so they need to put it somewhere – stores don’t have the luxury of being able to push product out the door to unwilling recipients.
  • Where the stock ultimately ends up is scattered throughout the store – on promotional end caps, in the back room, on overhead storage racks, shoved into a corner in receiving, sometimes even in offsite storage – solving a capacity issue in one location has just created capacity issues in dozens of other locations.

In the best case scenario after this has happened, stores are extremely disciplined and organized in their stock management and can always replenish the shelf from their overstock once it starts to get empty. But protecting sales comes at a significant cost. After the initial receipt of the overstock goods, the product will need to be moved around many times again before it leaves the store:

  • Shelf gets empty, go to the back room and bring out some more, fill the retail displays, bring what didn’t fit back to the back room again, repeat.
  • The overstock product is finally cleared out of the back room, but now you need to start taking down secondary displays as they deplete to replenish the home and fill them up with something more deserving that should have been there in the first place.

In the second best case scenario, the stock is within the 4 walls of the store – somewhere. When the shelf is empty, the vast majority of your customers will seek out a staff member to find the product and wait patiently while said staff member recruits other staff members to go on a costly scavenger hunt that hopefully… eventually… turns up the stock that the customer is waiting for. Crisis averted! Sale retained! But again, at a steep cost.

In the worst case (and most common) scenario, the customer sees an empty shelf and just leaves the store without alerting anyone to his/her dissatisfaction. A couple days later, a staff member walks by, sees the empty shelf and thinks “I’m sure the replenishment system will take care of that.” But it won’t. According to the stock ledger, the store has tons of stock to sell. After a couple more weeks of lost sales, someone realizes that they need to try to find the stock somewhere within the store. After an hour of searching, they give up and just write the stock off in the hopes that more will be sent to fill the hole in the shelf, further exacerbating the overstock problem until it turns up months later during the physical count.

And in all of the above scenarios, the management of overstock is consuming finite store resources that could negatively impact sales for all products in the store, not just the problem children.

So there you have it – rather than an enabler, inventory can be an impediment to sales. Even though inventory is in the store, it might as well be on Mars if it’s not accessible to the customer.

In an ideal world, you would set up your processes, systems and constraints in such a way that product can flow into the back door of the store in such a way that what’s coming in can largely flow directly to the shelf with minimal overstock. it’s not super easy to accomplish this, but it’s not advanced calculus either.

But in the event that you do end up with overstock in your supply chain, the best place to have it is upstream where the product is not yet fully costed, better processes and tools exist to manage it and you still have options to dispose of it or clear it out as cost effectively as possible – you know, postponement and all that.

Arbitrarily pushing stock out to the stores in the hopes that they’ll figure out what to do with it is about the worst thing you can do.