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.
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.
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.
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.
Inaccurate stock records don’t “just happen”. They are the result of one of two things:
People aren’t following the correct processes for managing stock
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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
Selling pattern (upcoming peaks and troughs)
Planned promotional and event impacts
Planned price changes
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.
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.
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:
Customers can’t buy product that’s out of stock in the store.
Inventory doesn’t sell when it’s sitting in the warehouse.
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:
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.
In August 1949 a group of fifteen smokejumpers – elite wild land firefighters – descended from the Montana sky to contain an aggressive fire near the Missouri River. After hiking for a few minutes the foreman, Wagner Dodge, saw that the fire was raging – flames stretching over 30 feet in the air and blazing forward fast enough to cover two football fields every minute.
The plan was to dig a trench around the fire to contain it and divert it towards an area with little to burn.
Soon it became clear that the fire was out of control and the plan was out the window. The fire was unstoppable so, instead, they’d try to outrun it, to safer ground.
For the next ten minutes, burdened by their heavy gear and tiring legs, the team raced up an incline, reaching an area that was only a few hundred yards from safety. But the fire was unflinching, gaining ground like a wolf chasing down a wounded animal.
Suddenly, Dodge stopped. He threw off his gear and, incredibly, took out some matches, lit them, and tossed them onto the grass. His crew screamed at him but to no avail – when Dodge didn’t listen, they had no choice and turned and ran as fast as they could, leaving their foreman to what they believed to be certain peril.
But Dodge had quickly devised a different survival strategy: an escape fire. By torching an area in front of him, he choked off the fuel for the fire to feed on. Then, he poured water on a rag, put it over his mouth and lay down, face first, on the freshly burnt grass while the fire raged and sped past and over him. In total, he’d spend close to 15 minutes living off the oxygen close the ground he’d just torched.
Sadly, of the rest of his crew that tried to outrun the blaze, only two would survive.
Wagner Dodge was able to survive not because of his physical fitness, but his mental fitness – the ability to rethink and unlearn. The prevailing paradigm was that, at some stage for an out-of-control blaze, your only option is to try to out run it. But Dodge was able to quickly rethink things – believing that, perhaps, by choking off it’s fuel line and providing his own small wasteland area, the fire might avoid him. The ability to rethink had saved his life.
As it turns out, the ability to rethink and unlearn is also crucial for retails survival and revival.
It’s no secret, many retailers are struggling. The same is true of many retail supply chains.
Do you ever really wonder why?
Lots of people blame retail’s generally slow adoption of new technologies and business models as the main factor, but I think it’s a deeper, more fundamental and chronic problem.
Technology is not eating retail. Fossilized thinking is.
What’s fossilized thinking? It’s people – at all levels in an organization – who are unwilling or unable to challenge their long-held beliefs. Not only challenge them, but be able to rethink, unlearn and change them often.
As a case in point, many people who work in the retail supply chain don’t include the consumer as part of the supply chain. Yet, if you think about it, the retail supply chain begins and ends with the consumer. There are even a number of folks who don’t consider the store part of the supply chain. Once the product has shipped from the DC to the store, then, incredibly, job done according to them.
Don’t believe me?
I won’t embarrass them, but just recently I read a “thought leadership” article from one of the world’s pre-eminent consulting firms regarding the top trends in retail supply chain management. At #3, and I kid you not, was the growing view that the store was a key part of the supply chain.
Flowcasting tribe members know better and think differently. The consumer and store have, and always will be, part of the supply chain. That’s why we understand that, in retail, there is no such thing as a push supply chain – since you can’t push the product to the consumer.
In my opinion (and I’m not alone), fossilized thinking, not technology adoption, is the real disruptor in retail.
If you want to improve, innovate or disrupt then you must…
Constantly rethink, unlearn and challenge your own thinking!
Just because something doesn’t do what you planned it to do doesn’t mean it’s useless. – Thomas A. Edison (1847-1931)
So, which is a better approach to project management? Agile or Waterfall?
The answer is simple: Agile is better. It’s newer, it sounds cooler and it has better terminology (scrums and sprints – that’s awesome!).
Still not convinced? Then check out some of these dank memes that pop up when you to a Google search on “agile or waterfall”.
In just a few simple pictures, you can plainly see that the Agile approach is easier…
…quicker to achieve benefits…
…all with a greater likelihood of success.
So if it’s been settled for the ages, then why am I writing this piece? And why are you detecting a very slight hint of sarcasm in my tone thus far?
The Agile framework has its roots in software development and in that context, I have no doubt about its superiority. That’s because new software is (generally speaking) easy to modularize, easy to test and easy to change when you find errors, vulnerabilities or awkwardness for the user all with very little risk or significant capital outlay.
I’m certainly no expert, but intuitively, Agile project management seems to be exactly the right tool for that kind of job.
Where I take issue is when I hear it being bandied about as an outright replacement for all previous project management methods because it’s trendy, regardless of whether or not it’s the right fit.
Some Dude: “We’re embarking on an Agile transformation!”
Me: “That sounds great! What do you mean exactly?”
Same Dude: “It means we’ll be more agile!”
Me: “Uh, okay.”
Building a new skyscraper is a project that consumes time, materials and resources and is expected to achieve a benefit upon completion.
You can’t approach such a project with the mindset that “we’ll buy some land, start building upwards and make adjustments as we go”. When you get up to the 20th storey, it’s not so easy to make a decision at that point to go up another 20 storeys (did you put in a foundation at step 1 to support that?). Everything needs to be thoroughly thought through and planned in a significant amount of detail before you even purchase the land, otherwise the project will fail. Yes, there are opportunities to make small changes along the way as needs arise, but you really do need to be following a “grand plan” and you can’t start renting it out until it’s done.
Similarly, if you had a medical condition requiring several complex surgeries to be performed over several months, would you opt instead for a surgeon who says “I’ll just cut you open, start messing around in there and quickly adapt as I go – we can probably shave 20% off the total time”?
This brings me to supply chain planning in retail. On the face of it, the goal is to get people out of spreadsheets and into a functional planning system that can streamline work and improve results. It would seem that an Agile approach might fit the bill.
But building out a new planning capability for a large organization is much more like building a skyscraper than coding a killer app. Yes, the numbers calculated in a planning system are just data that can be easily changed, but that data directly drives the deployment of millions of dollars in physical assets and resources. It requires:
Tons of education and training to a large pool of people, grounded in principles that they will initially find unfamiliar. The unlearning is much harder than the learning.
A thorough understanding of what data is needed, where it resides and what needs to be done to improve quality and fill gaps.
A thorough understanding of how the new planning process and system will fit in with existing processes and systems in the organization that won’t be changing.
All of this needs to happen before you “flip the switch” to start moving goods AND the business has to keep running at the same time. A former colleague described projects of this nature as “performing open heart surgery while the patient is running a marathon”.
After this foundation is in place and stabilized, there are opportunities aplenty to apply Agile techniques for continuous improvement, analysis and a whole host of other things. But there needs to be a firm base on which to build. Even constructing a killer app with the Agile approach still requires a programming language to exist first.
So what am I saying here? That large scale organizational change programs are complex, risky, take a lot of time and require significant upfront investment before benefits can be realized?
A little over 10 years ago I was on a project to help one of Canada’s largest grocery and general merchandise retailers design and implement new planning processes and technology. My role was the co-lead of the Integrated Planning, Forecasting & Replenishment Team and, shockingly, we ended up with a Flowcasting-like design.
The company was engaged in a massive supply chain transformation and the planning component was only one piece of the puzzle. As a result of this, one of the world’s preeminent consulting firms, Accenture, was retained to help oversee and guide the entire program.
One of the partners leading the transformation was a chap named Gary. Gary was a sports lover, a really decent person, great communicator and good listener. He also had a number of “southern sayings” – nuggets of wisdom gleaned from growing up in the southern United States.
One of his saying’s that’s always stuck with me is his question, “is the juice worth the squeeze?”, alluding to the fact that sometimes the result is not worth the effort.
I can remember the exact situation when this comment first surfaced. We were trying to help him understand that even for slow and very slow selling items, creating a long term forecast by item/store was not only worth the squeeze, but also critical. As loyal and devoted Flowcasting disciples know this is needed for planning completeness and to be able to provide a valid simulation of reality and work to a single set of numbers – two fundamental principles of Flowcasting.
The good news was that our colleague did eventually listen to us and understood that the squeeze was not too onerous and today, this client is planning and using Flowcasting – for all items, regardless of sales velocity.
But Gary’s question is an instructive one and one that I’ve been pondering quite a bit recently, particularly with respect to demand planning. Let me explain.
The progress that’s been made by leading technology vendors in forecasting by item/store has been impressive. The leading solutions essentially utilize a unified model/approach (sometimes based on AI/ML, and in other cases not), essentially allowing demand planners to largely take their hands off the wheel in terms of generating a baseline forecast.
The implications of this are significant as it allows the work of demand planning to be more focused and value added – that is, instead of learning and tuning forecasting models, they are working with Merchants and Leaders to develop and implement programs and strategies to drive sales and customer loyalty.
But, I think, perhaps we might be reaching the point where we’re too consumed with trying to squeeze the same orange.
My point is how much better, or more accurate, can you make an item/store forecast when most retailers’ assortments have 60%+ items selling less than 26 units per year, by item/store? It’s a diminishing return for sure.
Delivering exceptional levels of daily in-stock and inventory performance is not solely governed by the forecast. Integrating and seamlessly connecting the supply chain from the item/store forecast to factory is, at this stage, I believe, even more crucial.
Of course, I’m talking about the seamless integration of arrival-based, time-phased, planned shipments from consumption to supply, and updated daily (or even in real time if needed) based on the latest sales and inventory information. This allows all partners in the supply chain to work to a single set of numbers and provides the foundation to make meaningful and impactful improvements in lead times and ordering parameters that impede product flow.
The leading solutions and enabling processes need to produce a decent and reasonable forecast, but that’s not what’s going to make a difference, in my opinion. The big difference, now, will be in planning flexibility and agility – for example, how early and easily supply issues can be surfaced and resolved and/or demand re-mapped to supply.
You and your team can work hard on trying to squeeze an extra 1-3% in terms of forecast accuracy. You could also work to ensure planning flexibility and agility. Or you could work hard on both.
It’s a bit like trying to get great orange juice. To get the best juice, you need to squeeze the right oranges.
The problem with wearing a facade is that sooner or later life shows up with a big pair of scissors. – Craig D. Lounsbrough
Russia had recently annexed Crimea from the Ottoman Empire and after a long war, the region of New Russia now found itself under the rule of Empress Catherine II (a.k.a. Catherine the Great).
In 1787, Catherine embarked on a 6 month journey down the Dnieper River to New Russia to survey her new territory. Accompanying her on this journey was her boyfriend, Grigory Potemkin.
Unbeknownst to Catherine, the region had been devastated by the war. According to folklore, Potemkin – in an effort to placate Catherine – sent ahead an “advance team” to erect a fake village bustling with people before Catherine’s flotilla sailed by. After she had passed, the village would be taken down, rushed further downstream and reassembled to give Catherine the false impression that New Russia was a vibrant and welcome addition to her empire and that all of the treasure and bloodshed to obtain it was not in vain.
It’s been over 230 years, but the tradition of the Potemkin Village is alive and well today.
Don’t believe me?
Try visiting a retail store on a day when the store manager (Grigory) has just been informed that the bigwigs from home office (Catherine) will be stopping by for a visit. In all likelihood, an advance communication went to the store telling them that they don’t need to do anything to prepare in advance and they should just carry on as usual – the bigwigs don’t want to get in the way.
A flurry of activity soon ensues. The receiving area and back room are cleaned up and all stock is run out to the floor. Shelves and pegs are filled up, faced up and looking neat. Any aisle clutter is either put away or hidden. This is the kind of stuff that should be happening daily if people had the time – and yet, oddly, the time can be found to do two weeks’ worth of work in 3 days ahead of a VIP visit.
Sidebar: I once worked at a retailer (who shall remain nameless) with hundreds of stores each stocking thousands of products. But there was one store in particular that had its own unique set of stocking policies and ordering rules. This same store was always the top priority location when stock was low in the DC and needed to be rationed. What made this store so special? It happened to be located near the CEO’s home and he was known to shop there frequently. Not making that up.
Okay, back to the VIP visit. The big day arrives and the store is looking fantastic. The VIP entourage arrives and the store manager is waiting at the entrance to give the grand tour. Pleasantries are exchanged. How have sales been? Lots of customers in today! Any issues we need to know about?
Then comes the much anticipated Walking of the Aisles. The VIPs are escorted throughout the store, commenting on the attractiveness of the displays, asking questions and making suggestions….
Then someone in the entourage sees a shelf tag with no stock above it. “Why don’t you have stock? That’s sales we could be losing!”
The sheepish store manager replies: “I dunno. The ordering is centralized at headquarters. We just run product to the shelf when it arrives. We actually haven’t had that item in weeks and I can’t get a straight answer as to why not.”
“We need to support the stores better than this!”, exclaims one of the VIPs. “I’ll get this straightened out!”. Out comes the cell phone to snap a picture of the shelf tag below the void where stock should be. And for good measure, a few more pics of other holes in the same aisle.
A couple of taps and the pics are on their way to whichever VP is in charge of store replenishment with the subject line: “please look into this” (no time for proper capitalization or punctuation).
Ten minutes later, a replenishment analyst receives an email from her manager with the subject line: “FW: FW: FW: FW: please look into this”.
Another sidebar: I happened to be shadowing a replenishment analyst for another retailer for the purpose of learning her current state processes when one of those emails with pictures came in. There were 6 or 7 pictures of empty shelf positions and she researched each one. For all but one of the items, the system showed that there was stock in the store even though there was apparently none on the shelf. The last one was indeed stocked out, but a delivery was due into the store on that very same day. Was this a good use of her time?
Look, I know the tone of this piece is probably a bit more snarky than it needs to be. And although this whole scenario is clearly absurd when laid out this way, I’m not projecting malice of intent on anyone involved:
The VIP spotted a potential customer service failure and wanted to use her power to get it rectified. It never occurred to her that the culprit might be within the 4 walls of the store because: a) the store looked so nice and organized when she arrived; and b) the organization doesn’t measure store inventory accuracy as a KPI. If shrink is fairly low, it’s just assumed that stock management is under control.
In all likelihood, the store manager truly has no idea how replenishment decisions are made for his store. And while there’s a 4 inch thick binder in the back office with stock management procedures and scanning codes of conduct, nobody has actually properly connected the dots between those procedures and stock record accuracy more generally.
The replenishment analyst wants to help by getting answers, but she can’t control the fact that the wrong question is being asked.
The problem here is that there are numerous potential points of failure in the retail supply chain, any of which would result in an empty shelf position for a particular item in a particular store on a particular day. Nothing a senior manager does for 20-30 minutes on the sales floor of a store will do anything to properly identify – let alone resolve – which process failures are contributing to those empty shelves.
Jumping to the conclusion that someone on the store replenishment team must have dropped the ball is not only demoralizing to the team, it’s also a flat-out wrong assumption a majority of the time.
If you happen to be (or are aspiring to be) one of those VIPs and you truly want the straight goods on what’s happening in the stores, you need to change up your game:
Every so often, visit a store unannounced – completely unannounced and spend some time in the aisles by yourself and soaking in the true customer experience for awhile before speaking to store management.
When it’s time to get a feel for what can be done to keep the shelves full, put down the phone and pick up a handheld reader. Just because the stock isn’t on the shelf right now, that doesn’t mean that it isn’t elsewhere in the store or on its way already.
Spend the time you would normally spend on pleasantries and somewhat meaningless measures on a deep dive into some of those shelf holes with the store manager in tow:
Shelf is empty, but the system says there’s 6 in the store? Let’s go find it!
Truly out of stock with 0 reported on hand and none to be found? Let’s look at what sales have been like since the last delivery.
Can’t figure out why the replenishment system doesn’t seem to be providing what’s needed? Work through the calculations and see if there’s something wrong with the inputs (especially the on hand balance).
Will looking past the facade of the Potemkin Village solve the problems that it’s been hiding? Probably not. But you need to start somewhere.
In the words of George Washington Carver: “There is no shortcut to achievement. Life requires thorough preparation – veneer isn’t worth anything.”