About Jeff Harrop

Jeff Harrop

Your Sales Plan is NOT a Forecast!

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

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

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

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

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

Ergo, Ferrari = Fire Truck.

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

Purpose

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

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

Level of Detail

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

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

Frequency of Update

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

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

“Reconciling” the Plan and the Forecast

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

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

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

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

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

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

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

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

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

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

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

Your Forecast is Wrong (and That’s Okay)

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

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

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

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

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

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

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

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

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

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

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

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

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

Did you catch it?

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

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

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

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

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

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

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

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

Killing Your Sales With Stock

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

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

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

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

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

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

Tell me if this chain of events sounds familiar:

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

A couple weeks later, you run some reports:

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

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

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

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

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

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

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

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

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

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

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

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

Agile or Waterfall?

 

Just because something doesn’t do what you planned it to do doesn’t mean it’s useless. – Thomas A. Edison (1847-1931)

samagile

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…

Agile Methodology vs Waterfall Model: Pros and Cons

…less risky…

comparison of agile vs waterfall | Waterfall project management, Project  management, Agile

…quicker to achieve benefits…

…all with a greater likelihood of success.

2019 UPDATE] Agile Project Success Rates 2X Higher Than Waterfall
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?

Yeah, pretty much.

Sorry.

 

The Potemkin Village

 

The problem with wearing a facade is that sooner or later life shows up with a big pair of scissors. – Craig D. Lounsbrough

Potemkin Village

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.

Yeah, right.

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.”

Keep Calm And Blame It On The Lag

 

A good forecaster is no smarter than everyone else, he merely has his ignorance better organized. – Anonymous

stopwatch

I’ve written on the topic of forecast performance measurement from many different angles, particularly in the context of forecasting sales at the point of consumption in retail.

Over the years, I’ve opined that:

  • Forecast accuracy (in the traditional sense) is a useless measure
  • Reasonableness is more important than accuracy, given that forecasts are, by their nature, forgiving planning elements
  • The outsized importance placed on forecast accuracy in supply chain planning is a myth
  • Accuracy and precision must be considered simultaneously
  • Forecasts should be judged against what is a reasonable expectation for accuracy
  • Forecasting at higher levels of aggregation to achieve higher levels of “accuracy” is a waste of time

After going back and re-reading all of that stuff, they are all really just different angles and approaches for delivering the message “popular methods of comparing forecasts and actuals may not be as useful as you think, especially in a retail context”.

But in all of this time there is one key aspect of forecast measurement that I have not addressed: forecast lags. In other words, which forecast (or forecasts) should you be comparing to the actual?

Assuming, for example, that you have a rolling 52 week forecasting process where forecasts and actuals are in weekly buckets, then for any given week, you would have 52 choices of forecasts to compare to a single actual. So which one(s) do you choose?

Let’s get the easy one out of the way first. Considering that the forecast is being used to drive the supply chain, the conventional wisdom is that the most important lag to capture for measurement  is the order lead time, when a firm commitment to purchase must be made based on the forecast. For example, if the lead time is 4 weeks, you’d capture the forecast for 4 weeks from now and measure its accuracy when the actual is posted 4 weeks later.

Nope. To all of that.

While it’s true that measuring the cumulative forecast error over the lead time can be useful for determining safety stock levels, it’s not very useful for measuring the performance of the forecasting process itself, for a couple of reasons:

  1. It is a flagrant violation of demand planning principle. Nothing on the supply side of the equation (inventory levels, lead times, pack rounding, purchasing constraints, etc.) has anything to do with true demand. Customers want the products they want, where they want them and when they want them at a price they’re willing to pay, period. The amount of time it happens to take to get from the point of origin to a customer accessible location is completely immaterial to the customer.
  2. A demand planner’s job is to manage the entire continuum of forecasts over the forecast horizon. If they know about something that will affect demand at any point (or at all points) over the next 52 weeks, the forecasts should be amended accordingly.

Suppose that you’re a demand planner who manages the following item/location. The black line is 3 years’ worth of demand history and a weekly baseline forecast is calculated for the next 52 weeks.


Because you’re a very good demand planner who keeps tabs on the drivers of demand for this product, you know that:

  • The warm weather that drives the demand pattern for this item/location has arrived early and it looks like it’s going to stay that way between now and when the season was originally expected to start.
  • There are 2 one week price promotions coming up that have just been signed off and all of the pertinent details (particularly timing and discount) are known.
  • For the last 3 years, there have been 3 similar products to this one being offered at this location. A decision has just been made to broaden the assortment with 2 additional similar products half way through the selling season.

On that basis, I have 2 questions:

  1. How does the baseline forecast need to change in order to incorporate this new information?
  2. How would your answer to question 1 change if you also knew that the order-to-delivery lead time for this item/location was 1 week? 2 weeks? 12 weeks?

Hint: Because it was established at the outset that “you’re a very good demand planner who keeps tabs on the drivers of demand for this product”, the answer to question 2 is: “Not at all.”

So if measuring forecast error at the lead time isn’t the right way to go, then what lag(s) should be captured for measurement?

As with all things forecasting related, there is no definitive answer to this question. But as a matter of principle, the lags chosen to measure the performance of a demand planning process should based on when facts become “knowable” that could affect future demand and would prompt a demand planner to “grab the stick” and override a baseline forecast modeled based on historical patterns.

In some cases, upstream processes that create or shape demand can provide very specific input to the forecasting process.

For example, it’s common for retailers to have promotional planning processes with specific milestones, for example:

  • Product selection and price discounts are decided 12 weeks out
  • Final design of media to support the ad is decided 8 weeks out
  • Last minute adds, deletes and switches are finalized 3 weeks out

At each of those milestones, decisions can be made that might impact a demand planner’s expectation of demand for the promotion, so in this case, it would be valuable to store forecasts at lags 3, 8 and 12. Similar milestone schedules generally exist for assortment decisions as well.

In other cases, what’s “knowable” to the demand planner can be subject to judgment. For example, if actuals come in higher than forecast for 3 weeks in a row, is that a trend change or a blip? How about 4 weeks in a row?

Lags that are closer in time (say 0 through 4) are often useful in this regard, as they can show error trends forming while they are still fresh.

Unless tied to a demand shaping process with specific milestones as described above, long term lags are virtually useless. Reviewing actuals posted over the weekend and comparing it to a forecast for that week that was created 6 months ago might be an interesting academic exercise, but it’s a complete waste of time otherwise.

The key of measuring is to inform so as to improve the process over the long term.

With the right tools and mindset, today’s “I wish I knew that ahead of time” turns into tomorrow’s knowable information.

The Great Lever of Power

I shan’t be pulling the levers there, but I shall be a very good back-seat driver. – Margaret Thatcher

lever

A number of years ago, I saw a television interview with President Ronald Reagan after he left office. In that interview, he reminisced on his political career, including when he first stepped into the Oval Office in 1981.

I can’t find any transcripts or direct quotes from that interview, but I do distinctly remember him saying something to the effect of: “Before I assumed the presidency, I imagined a great lever of power on the Resolute Desk. When I took office, I learned that the lever actually existed – but it wasn’t connected to anything.” (If anyone out there has the exact quote, please share!)

I think of that whenever I hear senior leaders in retail say things like “our inventory is too high – we need to get it under control”.

What often follows this declaration is a draconian set of directives to “bring the inventory down”:

  • “Look at all of our outstanding purchase orders and cancel anything that’s not needed”
  • “We can’t sell excess stock out of the DCs, so return as much as possible and push the rest out to the stores where it can sell”

[One quarter later…]:

  • “Oh shit, our in-stock has nosedived and we’re losing sales! Buy! Buy! Buy!”

Rinse and repeat.

It has been described to me as a “swinging pendulum” in terms that would lead one to believe that these inventory imbalances are cyclical in nature, like the rate of inflation in the economy. When it gets too high, the central bank steps in with an interest rate hike to steer it to an acceptable range.

A couple of problems with that:

  1. The behaviour of consumers drives the inflation rate and this behaviour can’t be directly controlled. In contrast, the processes that drive inventory flow are internal to the retailer and, as such, are directly controllable.
  2. The pendulum swings themselves are caused by management’s efforts to control the pendulum swings – that popping sound you heard was my head exploding

I should note that I rarely hear “We need to review our inventory management policies and processes to determine what’s causing our inventory levels to be higher than expected, so that we can improve the process to ensure that we can flow stock better in the future without sacrificing in stock.”

Inventory is not an “input variable” that can be directly manipulated by management and brought to “the right level” in the aggregate. It is an output of policies and processes being executed day in, day out for every item at every location over a period of time. Believing that inventory levels can be directly controlled with blunt instruments is like believing that you can directly impact your gross margin without changing the price or the cost (or both).

It may sound trite, but if management doesn’t like the output of the process, then they must necessarily be taking issue with the process inputs or the process itself (both of which, by the way, are owned by management).

On the input side:

  • Are your stocking policies excessive compared to variability in demand?
  • Are you purchasing in higher quantities or with higher lead times than you used to (e.g. container loads from overseas versus pallets from a domestic source)?
  • Are you buffering poor inbound performance from suppliers with more safety stock?

On the process side:

  • Are demand planners striving to predict what will happen in an unbiased way or are they encouraged to be optimistic?
  • Are people buying first and figuring out how to sell it later?
  • Is your inventory higher because your sales have been increasing?

Management does not “own results”.

Management owns the processes that give rise to the results. If you make the determination that “inventory is too high” and you don’t know why, then you’re not doing your job.

Or to put it another way:

The aim of leadership should be to improve the performance of man and machine, to improve quality, to increase output, and simultaneously to bring pride of workmanship to people. Put in a negative way, the aim of leadership is not merely to find and record failures of men, but to remove the causes of failure: to help people to do a better job with less effort. – W. Edwards Deming

On Shelf Symbiosis (Robots Optional)

 

The cows shorten the grass, and the chickens eat the fly larvae and sanitize the pastures. This is a symbiotic relation. – Joel Salatin

interlinked

Daily In Stock.

It’s the gold standard measure of customer service in retail. The inventory level for each item at each selling location is evaluated independently on a daily basis to determine whether or not you are “in stock” for that item at that store.

The criteria to determine whether or not you are “in stock” can vary (e.g. at least one unit on hand, enough to cover forecasted sales until the next shipment arrives, X% of minimum display stock covered, etc.), but the intent is the same. To develop a single, quantifiable metric that represents how well customers are being served (at least with regard to inventory availability).

One strength of this measure is that – unless you get crazy with conditions and filters – it’s relatively easy to calculate with available information. A simple version is as follows:

  • Collect nightly on hands for all item/locations where there is a customer expectation that the store should have stock at all times (e.g. currently active planogrammed items)
  • If there’s at least 1 unit of stock recorded, that item/location is “in stock” for that day. If not, that item/location is “out of stock” for that day.
  • Divide the number of “in stock” records by the number of item/locations in the population and that’s your quick and easy in stock percentage.

By calculating this measure daily, it becomes less necessary to worry about selling rates in the determination. If an item/location is in stock with 2 units today, but the selling rate is 5 units per day, it stands to reason that the same item/location will be out of stock tomorrow. What’s important is not so much the pure efficacy of the measure, rather that it’s evaluated daily and moving in the right direction.

Using this measure, people can picture the physical world the customer is seeing. If your in-stock is 94% at a particular store on a particular day, then that means that 6% of the shelf positions in the stores were empty, representing potential lost sales.

Here’s the problem, though: Customers don’t care about the percentage of the time that your digital stock records are >0 (or some other formula) – they want physical products on the shelf to buy.

That’s the major weakness of the in stock measure – in order to interpret it as a true customer service measure, the following (somewhat dubious) assumptions must be made:

  1. The number of units of an item that the system says is in the store is actually physically in the store. You can deduct 5 points from your in stock just by making this assumption alone.
  2. Even if assumption #1 is true, you then need to assume that the inventory within the 4 walls of the store is in a customer accessible location where they would expect to find it.

That’s where shelf scanning robots come in – quiet, unassuming sentinels traversing the aisles to find those empty shelves and alert staff to take action.

As cool and futuristic as that notion is, it must be noted that this is still a reactive approach, no matter how quickly the holes can be spotted.

The real question is: Why did the shelf become empty in the first place?

Let’s consider that in the context of our 2 assumptions:

  1. It could very well be that a shortage of stock is the result of shitty planning. But for the sake of argument, let’s say that you have the most sophisticated and responsive planning process and system in the world. If there is no physical stock anywhere in the store, but the planning system is being told that the store is holding 12 units, what exactly would you expect it to do? Likewise, if there is “extra” physical stock in the store that’s not accounted for in the on hand balance, the replenishment system will be sending more before it’s actually needed, which results in a different set of problems – more on that later.
  2. To the extent that physical stock exists in the 4 walls of the store (whether the system inventory is accurate or not) and it is not in a selling location, the general consensus is that this is a stock management issue within the store (hence the development of robots to more quickly and accurately find the holes).

While the use of a daily recalculating planning process is the best way to achieve high levels of in stock, more needs to be done to ensure that the in stock measure more closely resembles on shelf availability, which is what the customer actually sees.

Instituting a store inventory accuracy program to find and permanently fix the process failures that cause mismatches between the stock records and the physical goods to occur in the first place will make the in stock measure more reliable from a “what’s in the 4 walls” perspective.

Flowing product directly from the back door to the shelf location as a standard operating procedure gives confidence that any stock that is within the store is likely on the shelf (and, ideally, only on the shelf). This goes beyond just speeding up receiving and putaway (although that could be a part of it). It’s as much about lining up the space planning, replenishment planning and physical flow of goods such that product arrives at the store in quantities that can fit on the shelf upon arrival. This really isn’t super sophisticated stuff:

  1. From the space plan, how much capacity (in units) is allocated to the item at the store? How much of that capacity is “reserved” by the minimum display quantity?
  2. Is the number of units in a typical shipment less than the remaining shelf space after the minimum display quantity is subtracted from the shelf capacity?

If the answer to question 2 is “no”, then you’re basically guaranteeing that at least some of the inbound stock is going to go onto an overhead or stay in the back room. The shelf might be filled up shortly after the shipment arrives, but you can’t count on the replenishment system to send more when the shelf is low a few weeks later, because the backroom or overhead stock is still in the store, leading to potential holes.

Solving this problem requires thinking about the structural policies that allocate space and flow product into the store:

  • Is enough shelf space allocated to this item based on the demand rate?
  • Are shipping multiples/delivery frequency suitable to the demand rate and shelf allocation?

Finding this balance on as many items as possible serves to ensure – structurally – that any product in the store exists briefly on the receiving dock, then only resides in the selling location after that (similar to a DC flowthrough operation with no “putaway” into storage racking).

Like literally everything in retail, the number 100% doesn’t exist – it’s highly unlikely that you’ll be able to achieve this balance for all items in all locations at all times. But the more this becomes standard criteria for allocating space and setting replenishment policies, the more you narrow the gap between “in stock” and “on the shelf”.

So if the three ingredients to on shelf availability are 1) continuous daily replanning, 2) maintaining accurate inventory records and 3) organizing the supply chain and space plans to flow product directly to the shelf while avoiding overstock, then any work done in any of these areas in isolation will definitely help.

Taken together, however, they work symbiotically to provide exponential value in terms of customer service:

  • More accurate inventory balances means that the right product is flowing into the back of the store when it’s needed to fulfill demand, decreasing the potential for holes on the shelf due to stockout.
  • Stocking product only on the shelf without any overhead/backroom stock keeps it all in one place so that it doesn’t end up misplaced or miscounted, increasing inventory accuracy.
  • Improved inventory accuracy increases the likelihood that when a shipment arrives, the free shelf space that’s expected to be there is actually there when the physical stock arrives.

The (stated) intent of utilizing shelf scanning robots is to help humans more effectively keep the shelves stocked, not to make them obsolete.

I think it a nobler goal to design from end-to-end for the express purpose of maximizing on shelf availability as part of day in, day out execution.

And obsolete those robots.

Store Inventory Accuracy: Getting It Right

 

A man who has committed a mistake and doesn’t correct it, is committing another mistake. – Confucius (551BC – 479BC)

correct and incorrect

 

A couple months ago, I wrote a piece entitled What Everybody Gets Wrong About Store Inventory Accuracy. Here it is in a nutshell:

  • Retailers are pretty terrible at keeping their store inventory accurate
  • It’s costing them a lot in terms of sales, customer service and yes, shrink
  • The problem is pervasive and has not been properly addressed due to some combination of willful blindness, misunderstanding and fear

I think what mostly gives rise to the inaction is the assumption that the only way to keep inventory accurate is to expend vast amounts of time and energy on counting.

Teaching people how to bandage cuts, use eyewash stations or mend broken bones is not a workplace health and safety program. Yes, those things would certainly be part of the program, but the focus should be far more heavily weighted to prevention, not in dealing with the aftermath of mishaps that have already occurred.

In a similar vein, a store cycle counting program is NOT an inventory accuracy program!

A recent trend I’ve noticed among retailers is to mine vast quantities of sales and stock movement data to predict which items in which stores are most likely to have inventory record discrepancies at any given time. Those items and stores are targeted for more frequent counting so as to minimize the duration of the mismatch. Such programs are often described as being “proactive”, but how can that be so if the purpose of the program is still to correct errors in the stock ledger after they have already happened?

Going back to the workplace safety analogy, this is like “proactively” locating an eyewash station near the key cutting kiosk. That way, the key cutter can immediately wash his/her eyes after getting metal shavings in them. Perhaps safety glasses or a protective screen might be a better idea.

Again, what’s needed is prevention – intervening in the processes that cause the inaccurate records in the first place.

Think of the operational processes in a store that adjust the electronic stock ledger on a daily basis:

  • Receiving
  • POS Scanning
  • Returns
  • Adjustments for damage, waste, store use, etc.

Two or more of those processes touch every single item in every single store on a fairly frequent basis. To the extent that flaws exist in those processes that result in the wrong items and quantities being recorded in the stock ledger (or even the right items and quantities at the wrong time), then any given item in any given store at any given time can have an inaccurate inventory balance without anyone knowing about it or why until it is discovered long after the fact.

By the same token, fixing defects in a relatively small number of processes can significantly (and permanently) improve inventory accuracy across a wide swath of items.

So how do you find these process defects?

At the outset, it may not be as difficult as you think. In my experience, a 2 hour meeting with anyone who works in Loss Prevention will give you plenty of things to get started on. Whether it’s an onerous and manual receiving process that is prone to error, poor shelf management or lackadaisical behaviour at the checkout, identifying the problems is usually not the hard part – it’s actually making the changes necessary to begin to address them (which could involve system changes, retraining, measurement and monitoring or all of the above).

If your organization actually cares about keeping inventory records accurate (versus fixing them long after they have been allowed to degrade), then there’s nothing stopping you from working on those things immediately, before a single item is ever counted (see the Confucius quote at the top). If not, then I hate to say it but you’re doomed to having inaccurate inventory in perpetuity (or at least until someone at or near the top does start caring).

Tackling some low hanging fruit is one thing, but to attain and sustain high levels of accuracy – day in and day out – over the long term, rooting out and correcting process defects needs to become part of the organization’s cultural DNA. The end goal is one that can never be reached – better every day.

This entails moving to a three pronged approach for managing stock:

  • Counting with purpose and following up (Control Group Cycle Counting)
  • Keeping the car between the lines on the road (Inspection Counting)
  • Keeping track of progress (Measurement Counting)

Control Group Cycle Counting

The purpose of this counting approach is not to correct inventory balances that have become inaccurate. Rather, it’s to detect the process failures that cause discrepancies in the first place.

It works like this:

  1. Select a sample of items that is representative of the entire store, yet small enough to detail count in a reasonable amount of time (for the sake of argument, let’s say that’s 50 items in a store). This sample is the control group.
  2. Perform a highly detailed count of the control group items, making sure that every unit of stock has been located. Adjust the inventory balances to set the baseline for the first “perfect” count.
  3. One week later, count the exact same items in detail all over again. Over such a short duration, the expectation is that the stock ledger should exactly match the number of units counted. If there are any discrepancies, whatever caused the discrepancy must have occurred in the last 7 days.
  4. Research the transactions that have happened in the last week to find the source of the error. If the discrepancy was 12 units and a goods receipt for a case of 12 was recorded 3 days ago, did something happen in receiving? If the system record shows 6 units but there are 9 on the shelf, was the item scanned once with a quantity override, even though 4 different items may have actually been sold? The point is that you’re asking people about potential errors that have recently happened and will have a better chance of successfully isolating the source of the problem while it’s in everyone’s mind. Not every discrepancy will have an identifiable cause and not every discrepancy with an identifiable cause will have an easy remedy, but one must try.
  5. Determine the conditions that caused the problem to occur. Chances are, those same conditions could be causing problems on many other items outside the control group.
  6. Think about how the process could have been done differently so as to have avoided the problem to begin with and trial new procedure(s) for efficiency and effectiveness.
  7. Roll out new procedures chainwide.
  8. Repeat steps 3 to 7 forever (changing the control group every so often to make sure you continue to catch new process defects).

Eight simple steps – what could be easier, right?

Yes, this process is somewhat labour intensive.
Yes, this requires some intestinal fortitude.
Yes, this is not easy.

But…

How much time does your sales staff spend running around on scavenger hunts looking for product that “the system says is here”?

How much money and time do you waste on emergency orders and store-to-store transfers because you can’t pick an online order?

How long do you think your customers will be loyal if a competitor consistently has the product they want on the shelf or can ship it to their door in 24 hours?

Inspection Counting

In previous pieces written on this topic, I’ve referred to this as “Process Control Counting” – so coined by Roger Brooks and Larry Wilson in their book Inventory Record Accuracy – which they describe as being “controversial in theory, but effective in practice”.

We’ve found that moniker to be not very descriptive and can be confusing to people who are not well versed in inventory accuracy concepts (i.e. every retailer we’ve encountered in the last 25 years).

The Inspection Counting approach is designed to quickly identify items with obvious large discrepancies and correct them on the spot.

Here’s how it works:

  1. Start at the beginning of an aisle and inquiry the first item using a handheld scanner that can instantly display the inventory balance.
  2. Quickly scan the shelf and determine whether or not it appears the system balance is correct.
  3. If it appears to be correct, move on to the next item. If there appears to be a large discrepancy, do some simple investigation to see if it can be located – if not, then perform a count, adjust the balance and move on.

It may seem like this approach is not very scientific and subject to interpretation and judgment on the part of the person doing the inspection counting. That’s because it is. (That’s the “controversial” part).

But there are clear advantages:

  • It is fast – Every item in the store can be inspection counted every few weeks.
  • It is efficient – The items that are selected to be counted are items that are obviously way off (which are the ones that are most important to correct).
  • It is more proactive – “Hole scans” performed today are quite often major inventory errors that occurred days or weeks ago and were only discovered when the shelf was empty – bad news early is better than bad news late.

No matter how many process defects are found and properly addressed through Control Group Counting, there will always be theft and honest mistakes. Inspection Counting ensures that there is a stopgap to ensure that no inventory record goes unchecked for a long period of time, even when there are thousands of items to cycle through.

As part of an overall program underpinned by Control Group Counting and process defect elimination, the number of counts triggered by an inspection (and the associated time and effort) should decrease over time as fewer defects cause the discrepancies in the first place.

Measurement Counting

The purpose of this counting approach is to use sampling to estimate the accuracy of the population based on the accuracy of a representative group.

It works like this:

  1. Once a month, select a fresh sample of items that is representative of the entire store, yet small enough to detail count in a reasonable amount of time, similar to how a control group is selected. This sample is the measurement group.
  2. Perform a highly detailed count of the measurement group items, making sure that every unit of stock has been located.
  3. Post the results in the store and discuss it in executive meetings every month. Is accuracy trending upward or downward? Do certain stores need some additional temporary support? Have new root causes been identified that need to be addressed?

Whether retailers like it or not, inventory accuracy is a KPI that customers are measuring anecdotally and it’s colouring their viewpoint on their shopping experience. Probably a good idea to actually measure and report on it properly, right?

If you’re doing a good job detecting and eliminating process defects that cause inaccurate inventory and continuously making corrections to erroneous records, then this should be reflected in your measurement counts over time. Who knows? If you can demonstrate a high level of accuracy on a continuously changing representative sample, maybe you can convince the Finance and Loss Prevention folks to do away with annual physical counts altogether.

What Everybody Gets Wrong About Store Inventory Accuracy

 

Don’t build roadblocks out of assumptions. – Lorii Myers

red herring

Retailers are not properly managing the most important asset on their balance sheets – and it’s killing customer service.

I analyzed sample data from 3 retailers who do annual “wall to wall” physical counts. There were 898,526 count records in the sample across 92 stores. For each count record (active items only on the day of the count), the system on hand balance before the count was captured along with the physical quantity counted. The products in the sample include hardware, dry grocery, household consumables, sporting goods, basic apparel and all manner of specialty hardlines items. Each of the retailers report annual shrink percentages that are in line with industry averages.

A system inventory record is considered to be “accurate” if the system quantity is adjusted by less than +/- 5% after the physical count is taken. Here are the results:

So 54% of inventory records were accurate within a 5% tolerance on the day of the count. Not good, right?

It gets worse.

For 19% of the total records counted (that’s nearly 1 in every 5 item/locations), the adjustment changed the system quantity by 50% or more!

Wait, there’s more!

In addition, I calculated simple in-stock measures before and after the count as follows:

Reported In Stock: Percentage of records where the system on hand was >0 just before the count

Actual In Stock: Percentage of records where the counted quantity was >0 just after the count

Here are the results of that:

Let’s consider what that means for a moment. If you ran an in-stock report based on the system on hand just before those records were counted, you would think that you’re at 94%. Not world class, but certainly not bad. However, once the lie is exposed on that very same day, you realize that the true in-stock (the one your customer sees) is 5% lower than what you’ve been telling yourself.

Sure, this is a specific point in time and we don’t know how long it took the inventory accuracy to degrade up for each item/location, but how can you ever look at an in-stock report the same way again?

Further, when you look at it store by store, it’s clear that stores with higher levels of inventory accuracy experience a lesser drop in in-stock after the records are counted. Each of the blue dots on the scatterplot below represent one of the 92 stores in the sample:


A couple of outliers notwithstanding, it’s clear that the higher on hand accuracy is, the more truthful the in-stock measure is and vice-versa.

Now let’s do some simple math. A number of studies have consistently shown that an out-of-stock results in a lost sale for the retailer about 1/3 of the time. Assuming the 5% differential between reported and actual in-stock is structural, this means that having inaccurate inventory records could be costing retailers 1.67% of their topline sales. This is in addition to the cost of shrink.

So, a billion dollar retailer could be losing almost $17 million per year in sales just because of inaccurate on hands and nothing else.

Let’s be clear, this isn’t like forecast accuracy where you are trying to predict an unknown future. And it’s not like the myriad potential flow problems that can arise and prevent product from getting to the stores to meet customer demands. It is an erosion in sales caused by the inability to properly keep records of assets that are currently observable in the physical world.

So why hasn’t this problem been tackled?

Red Herring #1: Our Shrink Numbers Are Good

Whenever we perform this type of analysis for a retailer, it’s not uncommon for people to express incredulity that their store inventory balances are so inaccurate.

“That can’t possibly be. Our shrink numbers are below industry average.”

To that, I ask two related questions:

  1. Who gives a shit about industry averages?
  2. What about your customers?

In addition to the potential sales loss, inaccurate on hands can piss customers off in many other ways. For example, if it hasn’t happened already, it won’t be long until you’re forced by competition to publish your store on hand balances on your website. What if a customer makes a trip to the store or schedules a pickup order based on this information?

The point here is that shrink is a financial measure, on hand accuracy is a customer service measure. Don’t assume that “we have low shrink” means the same thing as “our inventory management practices are under control”.

Red Herring #2: It Must Have Been Theft

It’s true that shoplifting and employee theft is a problem that is unlikely to be completely solved. Maybe one day item level RFID tagging will become ubiquitous and make it difficult for product to leave the store without being detected. In the meantime, there’s a limit to what can be done to prevent theft without either severely inconveniencing customers or going bankrupt.

But are we absolutely sure that the majority of inventory shrinkage is caused by theft? Using the count records mentioned earlier, here is another slice showing how the adjustments were made:

From the second column of this table, you can see that for 29% of all the count transactions, the system inventory balances were decreased by at least 1 unit after the count.

Think about that next time you’re walking the aisles in a store. If you assume that theft is the primary cause for negative adjustments, then by extension you must also believe that one out of every 3 unique items you see on the shelves will be stolen by someone at least once in the course of a year – and it could be higher than that if an “accurate” record on the day of the count was negatively adjusted at other times throughout the year. I mean, maybe… seems a bit much, though, don’t you think?

Now let’s look at the first column (count adjustments that increase the inventory balance). If you assume that all of the inventory decreases were theft, then – using the same logic – you must also believe that for one out of every 5 unique items, someone is sneaking product into the store and leaving it on the shelves. I mean, come on.

Perhaps there’s more than theft going on here.

Red Herring #3: The Problem Is Just Too Big

Yes, it goes without saying that when you multiply out the number of products and locations in retail, you get a large number of individual inventory balances – it can easily get into the millions for a medium to large sized retailer. “There’s no way that we can keep that many inventory pools accurate on a daily basis” the argument goes.

But the flaw in this thinking stems from the (unfortunately quite popular) notion that the only way to keep inventory records accurate is through counting and correcting. The problem with this approach (besides being highly labour intensive, inefficient and prone to error) is that it corrects errors that have already happened and does not address whatever process deficiencies caused the error in the first place.

This is akin to a car manufacturer noticing that every vehicle rolling off the assembly line has a scratch on the left front fender. Instead of tracing back through the line to see where the scratch is occurring, they instead just add another station at the end with a full time employee whose job it is to buff the scratch out of each and every car.

The problem is not about the large number of inventory pools, it’s about the small number of processes that change the inventory balances. To the extent that inventory movements in the physical world are not being matched with proper system transactions, a small number of process defects have the potential to impact all inventory records.

When your store inventory records don’t match the physical stock on hand, it must necessarily be a result of one of the following processes:

  • Receiving: Is every carton being scanned into the store’s inventory? Do you “blind receive” shipments from DCs or suppliers that have not demonstrated high levels of picking accuracy for the sake of speed?
  • POS Scanning and Saleable Returns: Do cashiers scan each and every individual item off the belt or do they sometimes use the mult key for efficiency? If an item is missing a bar code and must be keyed under a dummy product number, is there a process to record those circumstances to correct the inventory later?
  • Damage and Waste: Whenever a product is found damaged or expired, is it scanned out of the on hand on a nightly basis?
  • Store Use, Transformations, Transfers: If a product taken from the shelf to use within the store (e.g. paper towels to clean up a mess) or used as a raw material for another product (e.g. flour taken from the pantry aisle to use in the bakery) are they stock adjusted out? Are store-to-store transfers or DC returns scanned out of the store’s inventory correctly before they leave?
  • Counting: Before a stock record is changed because of a count, are people making sure that they’ve located and counted all units of that product within the store or do they just “pencil whip” based on what they see in front of them and move on?
  • Theft: Are there more things that can be done within the store to minimize theft? Do you actively “transact” some of your theft when you find empty packaging in the aisle?

So how can retailers finally make a permanent improvement to the accuracy of their store on hands?

  • They need to actually care about it (losing 1-2% of top line sales should be a strong motivator)
  • They need to measure store on hand accuracy as a KPI
  • They need an approach whereby process failures that cause on hand errors can be detected and addressed
  • They need an efficient approach for finding and correcting discrepancies as the process issues are being fixed

Stay tuned for more on that.