Nothing is less sincere than our mode of asking and giving advice. – Francois de la Rochefoucauld (1613 – 1680)
Actually, that quote above the title block is only partial. Here’s the entire quote:
Nothing is less sincere than our mode of asking and giving advice. He who asks seems to have a deference for the opinion of his friend, while he only aims to get approval of his own and make his friend responsible for his action. And he who gives advice repays the confidence supposed to be placed in him by a seemingly disinterested zeal, while he seldom means anything by his advice but his own interest or reputation. – Francois de la Rochefoucauld (1613 – 1680)
It’s with that context in mind that I’d like to discuss so-called “customer satisfaction” surveys.
If you use Microsoft Teams, you’ve certainly seen this pop up after ending a call:
If you give them 5 stars, you see this:
Aw, that’s nice. However, if you give them 4 stars (or anything below 5 stars), you get this:
If you click on one of the “Audio”, “Video” or “Presenting” links (I selected “Video”), you get this:
And after checking the box that best describes your problem, you get this:
TRANSLATION: “Thanks for the feedback! It’s been saved somewhere for someone to look at someday – maybe.”
The most cynical (or paranoid) interpretation of this is that they are trying to train their customers to either give them the highest possible rating or skip giving feedback altogether. “If you give us 5 stars, you can move on with your day. Anything less than 5 stars, and we’re giving you work to do.”
A kinder interpretation is that they didn’t really think through their data collection method, as it’s clearly flawed and unlikely to give them anything useful.
It should be noted that I’m not picking on Microsoft here (and if I were, they would hardly care). Their feedback collection has actually improved recently by at least trying to make it easier to get something useful from their users.
More often than not, the only option after giving fewer than 5 stars is something like: “Oh, we’re sorry to hear that. Please type a short essay into the box below explaining your problem and NOBODY will get back to you.”
But it’s not just online. At my favourite grocery store (which I won’t name), every cashier has started asking me “Did you find everything you were looking for today?”
If I reply “Yes”, the cashier will respond with something like “That’s good to hear!”
If I reply “No, I needed black beans for a recipe, but you’re all out”, the response is something like “Oh, I’m sorry to hear that.”
That’s it. End of conversation.
If I were more of a jerk, I would ask them “Aren’t you going to write that down? Don’t you want to know the brand and size I was looking for? Aren’t you going to call a supervisor to talk to me about it?”
Of course, I’m not going to do that – the cashiers are just doing what they’ve been asked to do. I imagine this extra little task at the end of each transaction opens them up to abuse from people who ARE jerks and don’t understand that the cashier has zero control over stock availability in the store.
Now when I get asked this question, I just say “Yes”, regardless of whether it’s true or not.
Okay, so now that I’ve done my complaining, I’ll propose a couple of nominal solutions:
If you don’t care about my feedback, don’t ask. I’m actually being sincere here. I find not being asked preferable to feigning interest in my experience for the sake of having an interaction, while making it quite obvious at the end of the interaction that you really don’t give a shit.
If you actually do value the feedback, then do SOMETHING to show it other than saying “Thanks for that, now leave me alone.”
Just spitballing here, but in the MS Teams example, what if they linked you to a simple support page that describes the most common causes of the problem you indicated with some quick fixes to try?
Or at a minimum, they could tell you exactly what happens to your feedback after you hit Submit and send a follow-up message whenever they’ve actually done something on their end to address the problem you raised.
As for the retail store example, the most obvious sincere remedies for a customer expressing dissatisfaction at the checkout (offering to switch to a higher priced brand, providing a discount on the order or a gift card for a future trip, etc.) are all admittedly very costly and/or rife with the potential for abuse. But could you at least have a tally sheet next to the cash register where a cashier can record which department is logging the most customer complaints to see if there’s an operational issue?
Or better yet, provide me with an app on my phone that allows me to scan and report the empty shelf for the item I wanted to purchase. Then you could follow up with me electronically later to let me know when it’s available, maybe send me a discount coupon for it, etc.
Look, every customer feedback mechanism has its flaws, but if you’re just fishing for compliments (or punishing customers for lodging complaints), then you’re not really collecting any useful information anyhow, no matter how “cheap and easy” it was to do.
And when a customer gives you negative feedback without any follow-up, then that’s just one additional thing you’ve done to annoy them today.
While the pandemic has recently pushed the trend into overdrive, click & collect has been steadily growing for years. And by all accounts, it will continue to grow in the years to come.
The two main reasons most often cited for why click & collect is so popular with consumers (versus home delivery) are:
Avoidance of delivery charges
Faster fulfillment (i.e. they can most often get the items they’re looking for at a nearby location on the same day, rather than waiting for it to ship from a remote fulfillment centre)
What seems to have escaped notice is that there’s another fulfillment method that delivers both of those benefits to customers: Driving to the store, getting the product themselves and bringing it home.
In fact, with regard to the second benefit (faster fulfillment), the “go get it yourself” method is superior. Depending on how far away the store is, a customer can have an item in his/her possession within minutes of deciding they want it, without having to wait for a pickup email.
This begs the question (that nobody seems to be asking, at least as far as I can tell): For customers who are looking to avoid delivery charges and fulfillment delays, why would they choose click & collect versus just picking it up themselves, given that both methods require a trip to the store anyhow?
In the absence of surveys or studies on this topic, I’ll postulate an explanation based on my personal experience. I do frequently use click & collect, but not because I find it convenient. I use it as a tool to avoid inconvenience.
Here is an early version of my personal “click & collect customer journey”:
I determine that I have a need for Product A.
I know that Retailer X sells Product A and that Retailer X has a location (Store 1) near me.
I check Retailer X’s website and it shows that they have 6 on hand at Store 1.
I drive to Store 1 to pick up one unit of Product A.
When I get to Store 1, the shelf is empty. I ask a team member to help me, but after 10 minutes of searching, they can’t find it either.
I angrily drive home and look up Product A at Store 2. It’s not as close, but still within a reasonable driving distance. The website shows that Store 2 has 4 units on hand.
Before driving to Store 2, I place a click & collect order for Product A and wait for the pickup email. Even though I have time to go get it now, I’m not in a particularly trusting mood – I’m not willing to spend more time and gas driving there only to find that Store 2 is out of stock too.
The pickup email doesn’t arrive that day, so I go to bed.
The next afternoon, I receive a “your order has been cancelled” email from Store 2. I check the on hand balance on the website and it now shows that Store 2 is out of stock on Product A. Clearly they went to pick it, couldn’t find any and zeroed out their on hand balance.
I give up and order Product A from Amazon and just wait for it to be delivered to my home (so much for the click & collect benefits).
On the basis of that experience, I’ve streamlined the process to jump straight from step 1 to step 7 – let the retailer spend their time and energy trying to find it before I waste any of mine.
To be sure, there are some customers out there who do find click & collect “convenient” in its own right – being able to (hopefully) get what they want on the same day without having to push a cart through the aisles, even though they still need to make a trip to the store.
But in many cases, click & collect may not be the “win-win” that everyone is claiming it to be. Customers aren’t necessarily rewarding retailers for providing added convenience – they may be punishing them after being burned for poor in stock performance now that click & collect has given them the opportunity to do so. And retailers now need to pay staff to perform tasks that customers used to do for free, in addition to losing out on impulse purchases and cross-selling opportunities in the store.
Perhaps retailers should be working harder on the basics (keeping stock accurate, in stock and on the shelf) to make it truly convenient for customers to get what they want where they want it and when they want it.
Here’s my high level analysis of the technology landscape for retail planning systems:
I’ve seen systems that intersect with any two of those circles, but I’ve never seen one in the “sweet spot”:
Built for retail
Holistic supply chain planning capabilities
Commercially available with a solid track record
Built For Retail and Commercially Available, Limited Supply Chain Planning Capabilities
Systems falling into this category generally have the “look and feel” that retailers are looking for and speak the language that most retailers find natural: orders. Suggesting orders. Optimizing orders. Managing the release of orders.
Over time, these systems have evolved to include long term demand forecasting, time-phasing out their ordering logic into the future and even connecting time-phased store order plans to distribution centres in an attempt to encroach into the “Supply Chain Planning Capabilities” circle. But the logical DNA of these systems is to work out the administrative part of the supply chain (the orders) first and understand the shipments and arrivals after the fact.
While these systems are demonstrably and significantly superior to traditional reorder point and min/max approaches when it comes to replenishment, they struggle to provide a valid simulation of reality that can be rolled up to support flow planning, capacity planning and business/financial planning, particularly in scenarios where the “steady state” is being disrupted:
Changes to network flowpaths, such as realigning DC outbound schedules or changing the inbound source of supply
Properly constraining ship dates for things like Chinese New Year and scheduled supplier shutdowns
Properly constraining arrival dates to account for receiving schedules at stores or DCs
These systems are generally streamlined and slick, but will struggle when the following question is posed:
How would you configure the system to accurately plan for a scenario where we are currently sourcing a bunch of items domestically, but will start sourcing those same items from overseas in 5 months?
Supply Chain Planning Capabilities and Commercially Available, Not Built for Retail
Systems falling into this category trace their lineage back to manufacturing and distribution where the discipline of supply chain planning began. Planning stock movement in a backward stepwise fashion from demand to supply (i.e. demand triggers arrivals which trigger shipments which trigger orders for every item at every location) is built right into their DNA.
Over time, these systems have evolved to be able to process the gargantuan data volumes common in retail, but only through brute force and by the grace of Moore’s Law. And bolt-ons have been developed to plan for things like retail promotions and intermittent demand streams in an attempt to encroach on the “Built for Retail” circle.
While these systems excel at being able to holistically plan stock movement from source of supply to source of consumption, it only comes with unnecessary complexity. It’s not easy to genetically modify a system that was built for manufacturing and distribution into a retail solution. These systems are designed to follow the core principles of planning, but will struggle when posed with the following question:
How would a planner update their forecasts and safety stocks for 20 items across 500 locations, roll up the results and then make a few tweaks – all before 10am (on the same day)?
Built for Retail with Supply Chain Planning Capabilities, Not Commercially Available
Systems falling into this category have successfully translated the time-tested planning capabilities originated in manufacturing and distribution to specifically tackle the retail planning problem in a way that’s simple, intuitive and fast.
The biggest problem these systems face is the huge barrier to entry into the market. In spite of their shortcomings, the types of systems discussed previously have developed a track record for delivering significant benefits to their retail customer base – suboptimal planning is better than no planning at all.
These systems have everything retailers need (from a stock flow planning standpoint) and nothing they don’t. But in my experience, retailers aren’t generally known for their willingness to gamble on something new and unproven at scale. They will struggle when posed with the following question:
Tell me about your last 5 full scale implementations at a retailer our size with similar planning challenges?
If you’re a software provider (or a user of said provider) who thinks you’ve hit the trifecta, then I guess I’m implying that you don’t exist. Even though I have never heard of you, I would be thrilled to get to know you.
You never know who’s swimming naked until the tide goes out. – Warren Buffett
Up until a couple of years ago, growth in online sales has been relatively slow and steady overall, with click & collect being the fastest growing channel. This put brick & mortar retailers somewhat back in the driver’s seat versus the pure online players like Amazon.
While brick & mortar retailers have struggled with execution in their online businesses, it represented a relatively small fraction of their sales. Most of their revenue came from foot traffic in their stores and retailers made steady progress investing in and nurturing their online businesses, with plans to grow those channels gradually over many years.
The COVID-19 pandemic changed all of that. Different retailers were affected in different ways depending on what they sell and where they do business, but many retailers needed to shift to nearly 100% online fulfillment for an extended period of time virtually overnight.
Responding to such a massive, unforeseen event in such a short period of time caused unavoidable stress in terms of store operations, staffing and variability in demand and supply, but make no mistake – a great deal of the pain was self inflicted.
You see, for years (decades really), customers have been subsidizing retailers for their poor stock management. When a customer in the aisle finds a gap where the product they wanted should be, about one third of the time the retailer loses the sale. But two thirds of the time, a customer will either switch to a similar product that is in stock or come back and buy it later, preserving the sale for the retailer.
This behaviour has been well documented in numerous studies on retail out-of-stocks, but it was all too easy for retailers to tell themselves “Yes, well maybe those retailers who participated in the studies angered their customers and lost sales, but not us. We’re special.”
Without the ability to definitively capture the absence of a sale that would have otherwise occurred in transaction history, many retailers could console themselves in the belief that the findings of those studies were academic and theoretical – the problem was surely not that bad.
Then the pandemic hit and many retailers were forced to conduct virtually all of their business online. And they got caught with their pants fully down.
The standard approach for fulfilling a click & collect order goes something like this:
A customer submits an online order for pickup at a store of his/her choosing
A check is performed against the store’s inventory balance to make sure that there is sufficient stock at the selected store to fill it
If sufficient stock exists, the order is assigned to the store for picking
The store picks the order and the customer is notified when they can pick it up
Based on discussions with our clients who routinely measured their online order fill rate (with reason codes for failures) during the pandemic, an employee in the store who is given a pick list (that has already been checked against the store stock balance before being issued) runs into an empty shelf up to 20% of the time when they attempt to pick the order.
(Sidebar: There REALLY needs to be a formal study on this)
To be clear, this was happening before the pandemic hit, but when online sales only represent 5-10% of your overall business, it’s easier to just sweep it under the rug and wait for it to become more pressing before doing anything about it. It becomes significantly more problematic when your stores are dealing with nearly 100% online sales volume for weeks or months at a time.
So, given that; a) an online customer isn’t in the store to make an “in the moment” decision to bail you out and; b) it’s not possible to undo years of neglect with regard to store stock management in a few days, what choices are left?
Actually, there are several. From a cost and customer service standpoint, none of them are good:
Take a margin hit by automatically substituting a more expensive version of what the customer ordered (if it’s in stock) in the hopes that the customer will appreciate it (which they may not)
Waste more of your time (and your customer’s) contacting them to find out if they really really wanted the item or if they would be willing to take a substitute.
Delay the order and/or incur significant additional cost having the out-of-stock item(s) rush delivered from the DC or another store who does have the out-of-stock item on hand.
Cancel the customer’s order altogether after exhaustively searching for the item(s) and coming up empty.
Hell, maybe the pandemic (or something like it) won’t repeat itself anytime soon and we can all go back to business as usual and deal with store stock management “at some later date”.
But what would be the downside of tackling it now?
When a subject becomes totally obsolete, we make it a required course. – Peter Drucker (1909-2005)
From third grade through to about the sixth, all of my classrooms had a banner posted above the front chalkboards (for those of you who don’t know what chalkboards are, you can Google it), showing the formation of all the letters of the alphabet – both upper and lower case – in cursive form.
We all used special notebooks with 3 horizontal lines per row to guide you in making your cursive letters with the correct height and shape.
For 40 minutes or so every day, we’d practice. First an entire line of As (both upper and lower). Then Bs, then Cs and so on. After a couple weeks, we’d move on to writing whole words and sentences by joining different letters together in just the right way.
Being a lefty, I finished every class with the heel of my left hand completely coated in dark grey pencil lead. It was all worth it though, because it was a necessary skill to learn. Once mastered, cursive was a far faster and more efficient way of writing than using individually printed letters.
My mom was recently shocked and disappointed to learn that none of my 3 kids (now 22, 19 and 16) could write cursive.
Who cares! None of them can shoe a horse or start a fire with sticks either, but I think they’ll be just fine. Hell, for them, email is considered obsolete technology.
Truth be told – in spite of all the instruction and practice – I’m not sure that I could write five cursive sentences if you put a gun to my head. I type 60 words a minute on a keyboard, though.
Why do people cling so nostalgically to demonstrably inferior methods that they just happen to find more familiar?
That’s the thought that crosses my mind whenever I talk to retailers on the topic of allocating and ordering stock. Some think it’s the bee’s knees, the cat’s pyjamas and the elephant’s adenoids rolled into one.
Over time, the methods for determining which store gets which percentage of available stock become more sophisticated. Historical sales, current inventory levels, safety stocks, on order quantities and the price of Bitcoin are all factored in to make sure that stock is pushed out of the DC and each store gets the perfect allocation for each SKU.
But it’s still nothing more than a blunt instrument. Like sticking with cursive writing, but using a calligraphy pen instead of a number 2 pencil.
The most important factor in determining how much of any given item each store needs at any given time is the anticipated demand from customers for that item at that store. If you focused your energy on that one problem (forecasting customer demand), then simple netting logic can figure out the rest, including what needs to be in the DCs to support the demand in the first place. Ordering stock becomes a menial administrative activity unworthy of a human being’s time or attention.
Forecasts are by no means perfect, but the need to have stock positioned in anticipation of your customers’ arrival still exists and is the primary value a brick-and-mortar retailer gives to the world.
If you build a process around the ultimate goal of constantly learning what makes your customers tick, you will only get better at it.
And leave that number 2 pencil behind once and for all.
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.