Author Archives: Jeff Harrop

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Jeff Harrop

What Demand Planners Really Need

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

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

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

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

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

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

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

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

Comprehension

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

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

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

Control

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Bread and Butter

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

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

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

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

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

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

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

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

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

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

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

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

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

There’s your real bread and butter.

Just In Time… For What, Exactly?

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

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

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

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

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

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

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

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

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

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

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

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

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

Customer Satisfaction Theatre

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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:

  1. 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.
  2. 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.

All That Glitters is Not Gold

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Man is a credulous animal and must believe something; in the absence of good grounds for belief, he will be satisfied with bad ones. – Bertrand Russell (1872-1970)

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”:

  1. I determine that I have a need for Product A.
  2. I know that Retailer X sells Product A and that Retailer X has a location (Store 1) near me.
  3. I check Retailer X’s website and it shows that they have 6 on hand at Store 1.
  4. I drive to Store 1 to pick up one unit of Product A.
  5. 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.
  6. 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.
  7. 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.
  8. The pickup email doesn’t arrive that day, so I go to bed.
  9. 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.
  10. 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.

Hunting Unicorns

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Perfection is achieved not when there is nothing more to add, but when there is nothing left to take away.

Antoine de Saint-Exupery (1900-1944)

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.

I’m skeptical, but I’m eager to be won over.

You know the types of questions I’ll be asking.

The Veil Has Been Lifted

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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.

According to McKinsey, e-commerce experienced 10 years’ worth of growth in 90 days at the onset of the pandemic.



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

Makes perfect sense, but it only works if the stock records are reasonably accurate and the store knows where the stock is.

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?

Orders, Allocations and Cursive Writing

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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.

My response?

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.