Sides of a Coin

Most controversies would soon be ended, if those engaged in them would first accurately define their terms, and then adhere to their definitions. – Tryon Edwards

simple: easy to understand, deal with, use, etc.

simplistic: characterized by extreme simplism; oversimplified

complex: composed of many interconnected parts; compound; composite

complicated: difficult to analyze, understand, explain, etc.

Such small distinctions.

Such calamity when those distinctions are not properly acknowledged, particularly when applied to the retail supply chain.

Let’s start here:

  • Problems that are complex can usually broken down into smaller problems, each of which can be simple
  • Problems that are complicated are often intractable – they can’t easily be broken into chunks and require some sort of breakthrough to be solved (think peace in the Middle East)
  • Problems that are simple – well, they’re not really problems at all, are they?

Now here’s where – in my experience at least – retailers start running into trouble.Problem #1: Seeing complexity as complication

If you’ve ever read articles about the challenges of planning the retail supply chain, you’ve probably seen a diagram or two that looks something like this (if not worse):

The implication is clear: The retail supply chain is a vast, sophisticated web of relationships between and among various entities. Not only that, but the problem becomes more mind boggling the closer you get to the customer, as depicted below:

I mean, look at all those arrows!

To be sure, the retail supply chain planning problem is large in terms of the number of items, locations and source/destination relationships. The combinations can get into the tens – if not hundreds – of millions.

But is it truly an enormous and complicated problem or merely an enormous number of simple problems?

In contradiction to the popular proverb, sometimes it’s necessary to focus on the trees, not the forest.

How you consider the problem will in large part determine your level of success at solving it. Complication is often a self inflicted wound (more on that later).

Problem #2: Seeing simplistic as simple

Just as overcomplication leads to trouble, so does oversimplification.

Best summed up by Albert Einstein:

People love this quote because they feel it very neatly sums up Occam’s Razor. Then they further reduce it to a design philosophy like “the simpler, the better”.

But the real power in that quote doesn’t reside in the first part (“Everything should be made as simple as possible.”), rather in the second: But not simpler.

In a retail planning construct, I’ve most often seen this line crossed when the topic is forecasting for slow selling item/stores. Demand forecasting as a discipline originated in manufacturing where volumes are high and SKU counts are low. As a consequence, most mature forecasting methods are designed to predict time series with continuous demand.

When you look at retail consumer demand at item/store level, a significant portion of those demand streams (often more than 70% for hardlines and specialty retailers) sell fewer than one unit per week.

So we have intermittent demand streams and tools that only really work well with continuous demand streams. So what’s the solution?

One of the most common responses to that question is: “Screw it. It’s not even worth forecasting those items. Just set a minimum and maximum stock value for ordering and move on.”

That position is certainly not difficult to understand, but is this really keeping it simple or being overly simplistic?

Here are some follow-up questions:

  • What if a particular item is fast selling at some locations and slow selling at others? Would you forecast it at some locations but not at others? How would a demand planner manage that?
  • If not every item in every location is being forecasted, then how do plan future replenishment volumes? Not just at the stores, but at DCs as well?
  • If not every item is forecasted, then it’s not possible to do rollups for reporting and analysis, so how do you fill in the gaps?

Because a simplistic solution was proposed that only addresses part of a complex problem, we have created a problem that is complicated.

This is akin to looking at a single tree without even recognizing that there are other trees nearby, let alone seeing a forest.

Things are only as complicated as you choose to make them, but they also may not be as simple as they seem.

Trust the process

Nick Saban is a brilliant college football coach and widely heralded as a football genius. At time of writing, his coaching record stands at 261 wins, 65 losses and 1 tie. He’s won 17 Bowl games along with 7 national titles (the most ever) and counting.

If you’re a fan of the Alabama Crimson Tide, in a state where college football is king, Saban is arguably more popular than Jesus. Fanatics think he is Jesus.

When asked about his unrivaled success, Saban offers a counter-intuitive philosophy that’s guided his coaching career, anchored on two fundamental principles:

  1. Don’t focus on the outcome
  2. Trust the process

Incredibly, Saban doesn’t focus on the result – which, for college football – like most sports – is whether you win or lose. Sure, make no mistake about it, Saban wants to win, it’s just he believes that the path to long term success is by trusting the coaching process.

His belief is that by coaching his team, repeatedly and without fail, so that they can execute their designed plays on offense and coaching schemes on defense, he’ll maximize his odds of winning. Losses are important to this process as it provides the team the opportunity to review the film and see where the process failed – either in execution or, sometimes, in coaching and design. Which leads to more coaching, practice and trust in the process.

Trusting the process is a philosophy that has served Saban and the Crimson Tide incredibly well.

We can learn a lot from Nick’s nuggets of wisdom.

Given that we’re often referred to as the Salty Old Sea Dogs of Flowcasting, it’s fair to say that we’ve been around the block a few times and, over time, changed our thinking often. No more than so than in developing, managing and measuring the retail forecasting/demand planning process.

For most retailers, the number of item/store products that sell less than 26 units a year (about 1 every two weeks) can be pretty significant – often comprising 30-50% of a retailer’s assortment. You wouldn’t expect to be as accurate in determining a forecast for these types of products, since there is a fairly large element of probability involved – as an example, based on history, you can feel confident that 1 unit sells every month but you’re not sure when it will be.

While it’s tempting to aggregate the lower level item/store forecasts up to a higher level and assess forecast performance, that’s of little use to anyone – after all, customers buy products in stores, or online, to be acquired or delivered at their preferred location. They don’t buy them at some aggregate level. Not to mention that item/store replenishment plans are driven from these forecasts and the dependent demand is cascaded throughout the supply network.

Like Saban, we work with our clients to help them understand and hopefully instill the idea of assessing the forecasting process by determining something called forecast reasonableness.

So then, what’s a reasonable forecast?

The following diagram outlines, conceptually, what we’re talking about:

The idea is to assess the reasonableness of the forecasts based on a sliding scale determined by selling rate. As an example, you wouldn’t expect to have as accurate a forecast for a product that sold 12 units a year as you would for something that sold 1200 units a year. Of course, you need to determine what’s a reasonable tolerance and sliding scale but from experience that’s not too difficult.

If the item/store forecast is within tolerance then the process/solution is producing a reasonable forecast. The beautiful thing is that the planner spends no time chasing these forecasts since, in all likelihood very little can be done to improve the process/outcome for these.

In practice, forecast reasonableness is an exception condition for the demand planners to action. For the item/store forecasts that are outside of tolerance, the planner can investigate these, see if the same item is outside of tolerance for a number of stores and determine if anything is systemically happening or could be improved in the process to bring them within tolerance.

To determine how well the forecasting process is working is simple. What percentage of the item/store forecasts is within tolerance?

We believe that, in retail, forecast reasonableness should replace traditional measures of forecast accuracy (like MAPE, WMAPE, etc., that were developed for more continuous demand streams in manufacturing and distribution).

Now before you think we’re completely mental, here’s something to ponder on. One of our retail clients, who plan using the Flowcasting process, does not measure baseline forecast accuracy.

Instead, they use a forecast reasonableness exception to evaluate the process, honing in on any forecasts that are not within tolerance to see if there is anything that can explain this and, potentially, how they might “improve the process”.
During the time they’ve not measured forecast accuracy they improved daily in-stock from an average of 91.7% to an average of 97.7%, while also improving inventory performance.

For a customer, in-stock is everything. They couldn’t care less about forecast accuracy.

Maybe you shouldn’t either.

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.