Author Archives: Jeff Harrop

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Jeff Harrop

Empty Calories

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There is not any memory with less satisfaction than the memory of some temptation we resisted. – James Branch Cabell (1879-1958)

What are my current stock levels? What’s the status of my inbound orders? How were the weekend sales for my products?

A great deal of effort has been spent over the last 2 decades to provide this information to planners and decision makers in near real time. But how useful is it, really?

We like to call this the “salt, sugar and fat” of supply chain planning. It’s extremely satisfying to get answers to these questions in the moment, but the satiation wears off quickly and you find yourself asking the same questions a few days later.

These types of supply chain visibility metrics are merely a glimpse in the rearview mirror. The myriad activities that give rise to a particular inventory level, a change to an order status or a weekend sales result have already happened and have been happening for days, weeks or even months before the question was even asked.

It’s like sitting at the gate and your airline announces a departure delay. You would rather have that information than not, but if that’s all the information you get, you have no control over the outcome. All you know is that you won’t be getting to your destination on time.

Now, suppose that you’re a savvy traveller. Hours before you even leave for the airport, you check the tail number for the inbound flight. Then you check the origin city of that flight and a massive storm is rolling through right around the time it’s supposed to depart, virtually guaranteeing a significant delay.

What are you going to do? Try to get booked on a different airline whose inbound aircraft is not coming from the city that’s about to get pummelled? Extend your hotel stay for another night because there’s no way you’ll be getting out at a reasonable time? Rent a car and just make it a road trip instead? Or just suck it up and leave on your scheduled flight, even though you know you’re going to be significantly delayed.

Any of those options may be acceptable, depending on your needs and constraints (e.g. cost, how urgently you need to get to your destination, whether or not the distance is reasonably driveable). But you only have one option available if you didn’t see the problem coming and only learned about it when you were sitting at the gate.

The point here is that knowing where things currently sit is certainly useful, but nowhere near as useful as being able to anticipate what things will be like in the future. Constantly checking in on up-to-the-minute information about the very recent past may give you a sense of control, but in reality, you’re just sitting in the back seat bingeing on cheeseburgers and donuts.

In a supply chain context, focusing too much on “real time current” information can lead to false conclusions and bad decisions (or non-decisions).

You look at your current DC and store stock levels and everything looks nice and healthy, so you breathe a sigh of relief and move on to the next item. But a promotion is scheduled in 2 weeks that’s going to virtually wipe you out. And your lead time from the supplier is 4 weeks. This is an example of something that is a big problem, but it doesn’t look like a problem in the current data. The cost is lost sales that could have been avoided.

You move on to another item and you see that 30% of your stores are out of stock. So, you panic. You spend the morning trying to figure out how can this be? What happened? And you have a bunch of higher-ups (who are looking at the same “here and now” data that you are) asking the same questions. Meanwhile, an order was just triggered with the supplier that covers the shortfalls and is due to arrive in a few days. Within a week or so, all of the stores will be back in stock. This is an example of something that looks like a big problem in the current data, but really isn’t much of a problem at all. The cost is stress and lost productivity trying to solve a problem that has already been automatically solved.

Subsisting on a diet consisting mainly of salt, sugar and fat is not good for one’s long term health. So, how do you kick the habit?

Like the savvy air traveller, you need to give yourself a window into the future to know all of your options and make the best decisions in advance.

Properly cooked, an end-to-end planning process that is designed to always maintain a valid simulation of reality is a very tasty and nutritious vegetable.

Are You Sure You Want High In-Stock?

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All exact science is dominated by the idea of approximation. – Bertrand Russell (1872-1970)

Okay, so that title might seem a bit “clickbait-y” and even a little dumb without some context, so bear with me here.

Before we get started, this piece is not about optimizing inventory investment by paring back inventory (and risking out of stocks) on long tail items “for the greater good” or any other such nonsense.

If you’re in the retail business in 2025, then you’re competing with Amazon at least to some degree. Those long tail items are probably as important to your long term success as a business than the so-called “bread and butter” fast sellers.

If holding stock on those long tail items gives you heartburn, then you’re better off increasing the selling price to offset the carrying cost than trimming your inventory. Customers will pay a premium if they know you’re the only game in town (or at least the most reliable game in town) to get those hard-to-find items.

Now, back to the topic at hand.

If you read the title of this piece and thought to yourself “What a dumb question!”, I’d wager that you were probably conflating the terms “in-stock” and “on shelf availability”.

What’s the difference? Customers don’t care that you have available stock somewhere within the 4 walls of the store. They want it on the shelf.

While it’s true that stock can’t be on the shelf if it’s not in the store in the first place, there are times when putting additional inventory in the store to boost your in-stock metric can actually harm on shelf availability – and sales.

Consider a simple example for a particular item where the shelf capacity for the item is 10 units. The shelf is completely full and the store is currently holding an additional 20 units in inventory in the back room or some other overflow location. 

A couple of weeks go by. The 10 units of shelf stock has sold and the selling location is now empty. For a few days, either nobody notices the hole, the stock has been misplaced or is in a difficult to access overflow location within the store.

What will the replenishment system do? Probably nothing, because there is still plenty of stock in the store to sell. How does the in-stock report look for this item? Fantastic! But you’re losing sales.

In other words, boosting inventory levels in the store will definitely improve your in-stock metric, but if it’s done to excess, inventory can actually harm sales. 

One problem we have is that in-stock is relatively easy to reliably measure, while on shelf availability is not. So we are forced to use in-stock as our proxy measure for customer service, when that’s not always the case.

So getting back to the original question: Are you really sure you want high in-stock?

The answer is yes – so long as you’re actively doing everything you can to effectively make in-stock a reliable proxy for on shelf availability:

  • Keeping your store level inventory accurate
  • Developing and maintaining accurate planograms (and compliance to those planograms in store)
  • Triangulating your planograms with stocking policies and pack sizes to ensure that inbound stock can flow directly from the receiving bay to the shelf

We Don’t Need a Ferrari

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

When a retailer seriously embarks on an effort to completely reshape how they plan the flow of goods from supplier to shelf, the discussion inevitably turns to what software they will need to do the job. (And ideally, this isn’t Step 1 of the process).

As the time approaches to evaluate software vendors, someone in company leadership is bound to utter the phrase “We don’t need a Ferrari”. After that, everyone in the room will nod their heads sagely in agreement. You can almost set your watch by it.

The message they’re trying to send is “We don’t need unnecessary sophistication and we don’t want to spend a ridiculous amount of money. We just need to get the basics right.”

I believe the intention is correct. You don’t want the design and implementation team to go off on a wild search for the most sophisticated system they can find – whether or not it’s proven or even necessary. But the advice may not be as useful as you think.

People who are tasked with transforming supply chain planning generally don’t need to be constrained or reined in. They need to be led. By the time you get to this point, you should already have assembled a team with strong convictions and a bias toward pragmatism – they won’t run around chasing shiny objects. Sometimes they will need leadership to be led by them.

Using well-meaning platitudes like “we don’t need a Ferrari” doesn’t really clarify anything and could potentially lead them down the road of picking a simplistic system over a simple one.

The team needs to understand time-tested and proven planning principles, what the true requirements of the organization are (including taking into account future strategic direction) and what results they are expected to deliver.

A system with a simple data structure, easy navigation and limited options that doesn’t adhere to solid planning principles and doesn’t meet the requirements will not deliver results.

Just because some functionality may be more complex or sophisticated than what you have today, that doesn’t make it “too fancy”, unless the mandate is to implement a new system and process that does the same thing you’ve always done (with the same results). Not all sophistication is unnecessary.

Just because people will need to acquire more skills – some of which may be difficult to learn – doesn’t mean that the system or process is “too complicated”.

No, you do not need a Ferrari – because nobody “needs” a Ferrari. 

But there is a wide range of options between a Ferrari and a tricycle. Your requirements need to dictate whether you need a Corolla, a minivan or a pickup truck.

Don’t choose a tricycle just because it’s the farthest option away from a Ferrari. A simplistic system that doesn’t meet requirements makes the implementation just as complicated as an over-engineered system that you don’t need.

A Rather Unassuming Approach

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When we make assumptions, we contribute to the complexity rather than the simplicity of a problem, making it more difficult to solve. – Julie A., M.A. Ross and Judy Corcoran

Planning the retail supply chain not only requires, but is entirely predicated upon making assumptions about the future.

Why?

Because when a customer walks into a store, they already expect the products they want to be on the shelf in sufficient quantity to satisfy their demand – and they give no advance notice of their planned visit. So depending on the cumulative lead times from the ultimate source of supply to the store shelves, the decisions you make regarding product movements today must be made based on what you anticipate (i.e. assume) customers will want – how much, where and when – days, weeks or even months into the future.

So for retailers of any size, that could add up to millions of assumptions that need to be made each day just for the expected consumer demand element alone. And each assumption you make is a risk – if an assumption doesn’t hold, then the decisions you made based on that assumption will cost you in some way.

If the supply chain is disconnected, there are more assumptions to be made. So there are greater risks and higher costs in the form of customer service failures and/or inefficient use of labour and capital.

As an example, it’s not uncommon for a retailer to have different planning and replenishment systems for stores and distribution centres. And those systems usually have a “what do I need to request today?” focus – think min/max or reorder point.

The store replenishment problem is relatively straightforward:

  1. What is the current on hand in the store minus the display minimum (or “cycle stock” for want of a better term)?
  2. What do you anticipate (assume) you will sell between now and the next scheduled delivery day?
  3. If what you expect to sell exceeds the cycle stock, request the difference, rounded to the nearest ship pack

In this case, the only assumption you’re making is the expected sales, with a few “sub-assumptions” with regard to trend, seasonality, promotional activity, etc. going into that.

(NOTE: You are also assuming that your store on hand balance is accurate, which is a whole other lengthy discussion in and of itself.)

Okay, so far so good. Now we need to make sure that there will be sufficient stock in the distribution centre to satisfy the store requests. Because the supply chain is disconnected, the requests that the stores drop onto the DC today need to be picked within a day or two, so that means that the DC must anticipate (assume) what the stores will request in advance.

A common way to do this is to use historical store requests to forecast future DC withdrawals. In this case, you are making a number of additional assumptions:

  • That store inventories are largely balanced across all stores served by the DC and have been so historically
  • That any growth/decline in consumer sales will be accurately reflected in the DC withdrawals with a consistent lag
  • That there are no expected changes in store merchandising requirements that will increase or decrease their need for stock irrespective of sales

Going further back to the supplier, they have their own internal planning processes whereby they are trying to guess what each of their retailer customers are going to want from them in order to plan their inventories of finished goods. They are now several steps removed from the ultimate consumer of their products and have to apply their own additional set of assumptions.

It’s like a game of telephone where each successive person in the queue passes what they think they heard on to the next.

And if something doesn’t go according to plan, a whole bunch of people need to revisit their assumptions to figure out where the breakdown happened. At least they should, but that rarely happens. Everyone is too busy dealing with the fallout in “crisis mode” to actually figure out what went wrong.

The result?

  • In stock rates to the consumer in the 92-93% range
  • Excessive amounts of “buffer stock” to try to cover for all of the self-inflicted uncertainty (assumptions) in the process
  • Margin loss from taking markdowns on excess stock that’s in the wrong place at the wrong time

So how does an approach like Flowcasting – a fully integrated end-to-end planning process – sustain in-stocks in the high 90s while simultaneously (and significantly) reducing stock levels throughout the supply chain?

It’s not magic. By connecting the supply chain with long term supply projections and keeping those projections up to date, the number of assumptions you need to make are drastically reduced:

  • You already know the inventory for every item at every location, so you can model the long term need for each individually and roll them up rather than assuming averages.
  • The planning approach automatically models the impact of any changes in consumer demand by netting against available stock in store and applying the necessary constraints and rounding rules using simple calculations – there’s no need to guess how a changing demand picture will affect upstream supply.
  • Inventory level decisions (e.g. changes to display quantities and off locations) can be discretely modeled separately from demand and incorporated directly into the store projections in a time-phased manner. You don’t need to make a “same as last year” assumption if you already know that won’t be the case.

However, it’s not perfect and things can still go wrong. You still need to have long term forecasts about consumer demand, which means assumptions still need to be made. But when bad things happen, the information travels quickly and transparently up and down the supply chain assumption-free after that. Everyone knows exactly how they are affected by any botched assumptions about consumer demand in near real time and can start course correcting much sooner. “Bad news early is better than bad news late.”

It’s like playing telephone, except that the first player doesn’t whisper to the next – he uses a megaphone to ensure that everyone hears the same phrase at the same time.

Customer Disservice

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The best offense is a good defense, but a bad defense is offensive. – Gene Wolfe

“We want our people helping customers, not doing back office tasks.”

This seems to be the prevailing wisdom in retail these days, particularly since Amazon became a significant threat to brick and mortar retailers.

This view is backed up by many articles that have made their way to my inbox and LinkedIn feed recently – retailers need to be doting on their customers, going above-and-beyond, providing an experience, etc., etc…

As I read these pieces and hear the arguments, the words all make sense to me, but they tend to make me feel a little out of touch. As a customer, I generally know what I want. If I need product knowledge or advice to choose between options, I turn to Google, not retail sales associates.

In other words, when I shop in person, I just want to be left alone. My idea of a spectacular shopping experience is walking into a store, finding everything on my list and leaving in record time, ideally without any human interaction aside from some pleasantries with the cashier.

After doing a bit of research on this, I began to feel a bit less isolated. Here’s an excerpt from Stop Trying to Delight Your Customers, published in Harvard Business Review in 2010:

“According to conventional wisdom, customers are more loyal to firms that go above and beyond. But our research shows that exceeding their expectations during service interactions (for example, by offering a refund, a free product, or a free service such as expedited shipping) makes customers only marginally more loyal than simply meeting their needs.”

And another from Your In-Store Customers Want More Privacy, also from HBR in 2016:

“Shoppers want a certain level of privacy in a store — and they want to have control over that privacy. In other words, people generally prefer being left alone, but also want to be able to get help if and when they need it.”

These references are a bit old, so I conducted my own (informal and definitely not scientific) survey in the Consumer Goods and Retail Professionals LinkedIn group. Here are the results:

So, the respondents who wanted to be left alone outnumbered those who wanted interaction by a factor of 6. And while LinkedIn’s rudimentary polling feature doesn’t accommodate deep dives on the responses, I would hazard to guess that a majority of the “It depends” respondents probably only want to talk to someone if they can’t find what they’re looking for – that is, the experience has already turned negative and the customer is hoping they can find someone to – hopefully – make it slightly less negative.

Even if I’m wrong about that, answering “It depends” doesn’t indicate that those folks are exactly yearning for staff interaction as an integral part of their shopping experience.

To be sure, there are certainly cases where interaction with knowledgeable staff is a necessary part of the customer experience – e.g. if you’re looking for a luxury watch or need help designing a custom home theatre for your basement – but what percentage of your total retail transactions does that represent?

It’s time for brick and mortar retailers to drop their somewhat defeatist attitude – “We can’t compete with online sellers on convenience, so we’ll compete on service.”

Firstly, for most customers, convenience IS the service they’re looking for.

Secondly, yes you can compete on convenience – by getting better at those mundane “back office tasks” (like stock accuracy and speed to shelf) that puts stock at customers’ fingertips.

There are a lot of low maintenance customers like me out there who just want to interact with your cash register. We are better served by finding the products we want in your aisles, more so than your staff (no matter how friendly or helpful they may be).

Just make it easier for me to do that and I won’t be a bother, I promise.

The Legends

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Honor lies in honest toil. – Grover Cleveland

Moving a retailer from a firefighting mindset to a planning mindset is no small task and requires a lot of emphasis on education, change and butchering sacred cows.

It also invariably requires a technology investment in a new planning system. In theory, the technology piece of this is pretty straightforward, particularly if you choose off-the-shelf planning software that adheres to a few key fundamental principles and meets your core requirements. You bolt it on top of your ERP, master data flows in and you run the batch. Then forecasts, plans and orders flow out. Easy peasy.

To make all of this work, you need a dedicated team that includes:

  • A mixture of folks from the business who can drive change – grizzled veterans with a lot of credibility across the functional areas (especially Merchandising, Supply Chain and Store Operations) combined with some whiz kids who may be a bit wet behind the ears, but are eager to learn. Their job will be to design new processes, change existing processes within the business and be the “tip of the spear” for driving the change, both internally and with suppliers. You can optionally augment this team with consultants who specialize in this space and bring experience from prior projects.
  • A technical team who can understand the mission, develop data maps, built and test the integrations, design the batch schedules and course correct when things don’t work out exactly as planned. This team can optionally be supported by the software company and/or system integrators to do some of the heavy lifting on many of those tasks.
  • An implementation team from the software provider who can work with the business folks to train your team on the new system and aid with configuration, data mapping/structure and workflow design.

That sounds like a dream team, doesn’t it? But is it enough?

Not quite.

Remember earlier when I said that the technology piece of the puzzle is “easy peasy”? Well, that’s only a relative description when compared to the change effort. In absolute terms – and from bitter experience – the technology stuff is often NOT “easy peasy”. At all.

This is why you always need one more person to augment your dream team: The Legend.

Every retailer has at least one of them, but usually no more than a handful. It’s the person whose name always comes up when these types of questions are asked:

  • Where the hell are we supposed to find that data and who manages it?
  • I can see the number on the screen, but how the hell was it calculated?
  • Why the hell did we decide to set things up this way?

These people often (but not always) have grey hair, are closer to the bottom of the org structure than the top and generally toil away in anonymous obscurity until a really big problem needs to be solved – then they’re the ones called upon to solve it. Losing one of these people would be more risky and disruptive to the organization than if the CEO was taken away in handcuffs for insider trading.

The Legend could have virtually any job title in any functional area of the organization, but the actual job description can be summed up in one word: Everything.

The Legend is a critical resource for any initiative that requires master data or touches legacy systems in any way. So, basically all of them.

They know where all of the bodies are buried and often need to throw some cold water on project teams who may have the notion that things are “easy peasy”. They don’t do that to block the path or to be a buzzkill. They just don’t want people wasting their time or making unrealistic assumptions that will foul things up. Don’t worry, they’ll eagerly help you to steer clear of the rocks, because they know where all of the rocks are.

But getting their time to help will be difficult, because they are always being pulled in ten different directions, not to mention tasked with keeping the lights on when more mundane day-to-day issues arise.

They are the critical resources that must contribute to any major transformation, but they are also the people that the business can’t afford to lose to a long term project.

In spite of all the power they wield, they are generally not protective of their knowledge or interested in defending turf. If a promotion offer came along, they may seriously consider it, but they probably won’t be actively seeking one out.

They’d be glad to write up detailed documentation and/or transfer some of their expertise. Maybe you can get some time on their calendar to get that going – there’s probably a 1/2 hour block available 4 months from now.

For ethical and technological reasons, cloning is not really an option right now, so how do you get valuable time from people who have none?

  • In the interest of long term success and stability, put some of your initiatives on hold and free up some of their time to cross train some junior folks on the more mundane tasks.
  • Bring in some contractors to backfill some of their “lights on” work and produce documentation while they work on more important matters.
  • Recognize their value and set aside an important role for them when you get to the other side of your change endeavour.

And it never hurts to give them a hug every now and then.

Forecasting Wordplay

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If it is true that words have meanings, why don’t we throw away words and keep just the meanings? – Ludwig Wittgenstein via Anatol Holt

How do you describe your demand forecasts?

In retail, I’ve had numerous conversations that go something like this:

Me: “Do you really think you’re going to sell 10,000 units on that promotion? You’ve never sold higher than 8,000 at that price point.”

Retail Buyer/Demand Planner: “Yeah, it’s a bit optimistic.”

While less common, forecasts can also be made lower than one would expect (or “pessimistic”, if you will), particularly if an incentive structure exists where people are rewarded when actual sales blow the forecast out of the water.

While these “optimistic” or “pessimistic” numbers are entered into the forecasting system and potentially even drive the replenishment and supply planning functions, it would be a misnomer to call them forecasts.

Saying that a forecast is optimistic is an admission that you believe it’s biased to the high side for no good reason. You can call it a goal, a wish or a dream, but it is NOT a forecast.

In a similar way, a “pessimistic forecast” is a hedge, not a forecast.

Simply put, if you don’t truly believe the numbers you’re producing are a reflection of what’s actually going to happen, then you’re not forecasting. A true forecast is the prediction you’d make if you were obligated to bet $500 of your own money on the outcome.

That’s not to say that the most objective forecast produced by the most unbiased demand planner may not be way off. At its essence, demand planning applies assumptions in an attempt to predict human behaviour. That doesn’t always work out.

That’s also not to say that the words “optimistic” and “pessimistic” have no place in forecasting – so long as there are assumptions to back up the judgments.

For example, “I’m optimistic that we’ll beat last year’s sales, because we’ve sharpened our pricing and caused a key competitor to exit the market for this category.”

Or “I’m pessimistic about the sales outlook for our high price point luxury lines because the economy is in the tank and discretionary spending is going to be way down for the next 12 months.”

But what about the potential missed opportunities? What if we produce an unbiased, objective forecast and it ends up oversold to the point that we run out of stock and leave sales on the table?

Isn’t having a forecast that’s “a bit too high” much less risky in terms of being able to satisfy customer demand?

To be clear, a forecast is just one determinant of the level of stock needed to satisfy customer demand. The other is the uncertainty of the demand. If you apply the uncertainty to the supply side of the equation (i.e. safety stock), then that frees you up to forecast with objectivity.

Yes, the uncertainty needs to be quantified, but if you habitually describe your forecasts as being “optimistic” (which is a euphemism for “biased”), then you’re already doing that anyhow:

  • Optimistic Forecast = What we think customers might buy
  • Objective Forecast = What we think customers will buy
  • Safety Stock = Optimistic Forecast – Objective Forecast

The Sun Came Up Today

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It pays to be obvious, especially if you have a reputation for subtlety. – Isaac Asimov (1920-1992)

The sun came up today.

I’ve been tracking it daily in a spreadsheet for months. Please reach out if you’re interested in seeing my data. My suspicion is that you won’t.

In (belated) honour of Groundhog Day, the topic du jour is in stock reporting.

You come into the office on Monday morning, log in to your reporting/BI dashboard and display your company’s overall in stock report for the last 21 days, up to and including yesterday. As you look at it, you’re thinking about all of the conversations about in-stock you’ve had over the last 3 weeks and anticipate what today’s conversations will be about:



For the sake of argument, we’ll assume that there’s no major issue with how you calculate the in stock measure. Everyone understands it and there’s broad agreement that it’s a good approximation of the organization’s ability to have stock in the right place at the right time. (This isn’t always the case, but that’s a topic for another day).

It certainly looks like a bit of a roller coaster ride from one day to the next. That’s where applying some principles of statistical process control can help:



By summarizing the results over the last 21 days using basic statistical measures, we can see that the average in stock performance has been 92% and we can expect it to normally fluctuate between 86% (lower control limit) and 97% (upper control limit) on any given day.

In other words, everything that happens between the green dashed lines above is just the normal variation in the process. When you publish an in stock result that’s between 86% and 97% for any given day, it’s like reporting that “the sun came up today”.

Out of the last 21 days, the only one that’s potentially worth talking about is Day 11. Something obviously happened there that took the process out of control. (Even the so-called “downward trend” that you were planning to talk about today is just 3 or 4 recent data points that are within the control limits).

I used the word “potentially” as a qualifier there, because statistical process control was originally developed to help manufacturers isolate the causes of defects, so that they could then apply fixes to the part of the process that’s failing in order to prevent future defects with the same cause. In most cases, the causes (and therefore the fixes) were completely within their control.

Now when you think of “the process” that ultimately results in product being in front of a customer at a retail store, there are a LOT of things that could have gone wrong and many of them are not in the retailer’s control. In the example above, it was a trucker strike that prevented some deliveries from getting to the stores that caused some of them to run out of stock. Everybody probably knew that in stock would suffer as soon as they heard about the strike. But there was really nothing anybody could have done about it and very little that can be done to prevent it from happening again.

So where does that leave us?

Common Cause Variation is not worth discussing, because that’s just indicative of the normal functioning of the process.

Special Cause Variation is often not worth discussing (in this context) unless you have complete control over the sub-process that failed and can implement a process change to fix it.

So what should we be looking at?

In terms of detecting true process problems that need to be discussed and addressed, you want to look at a few observations in a row that are falling outside the established control limits, investigate what changed in the process and decide if you want to do something to correct it or just accept the “new normal”. For example:



But you can also take a broader view and ask questions like:

  • How can we get our average in stock up from 92% to 96%?
  • How can we reduce the variation between the upper and lower limits to give our customers a more consistent experience?

By asking these questions, what you’re looking for are significant changes you can make to the process that will break current the upper control limit and set a new permanent standard for how the process operates day to day:

But be warned: The things you need to do to achieve this are not for the faint of heart. Things like:

  • Completely tearing apart how you plan stock flow from the supplier to the shelf and starting from scratch
  • Switching from cheap overseas suppliers to ones who are closer and more responsive
  • Refitting your distribution network to flow smaller quantities more frequently to the store

They all have costs and ancillary additional benefits to the operation beyond just improving the in stock measure, but this is the scale of change that’s needed to do it without just blowing your inventory holdings out of the water.

Reporting your in stock rates (or any other process output measure for that matter) regularly is a fine thing to do. Just make sure that you’re drawing the right conclusions about what the report is actually telling you.

A Forecast By Any Other Name

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What’s in a name? That which we call a rose
By any other name would smell as sweet. – William Shakespeare (1564-1616), Romeo and Juliet, Act 2 Scene 2

Scenario 1: A store associate walks down the aisles. She sees 6 units of an item on the shelf and determines that more is needed on the next shipment, so she orders another case pack of 12 units.

Scenario 2: In the overnight batch run, a centralized store min/max system averages the last 6 weeks of sales for every item at every store. This average selling rate is used to set a replenishment policy – a replenishment request is triggered when the stock level reaches 2 weeks’ worth of on hand (based on the 6 week average) and the amount ordered is enough to get up to 4 weeks’ worth of on hand, rounded to the nearest pack size.

Scenario 3: In the overnight batch run, a centralized store reorder point system calculates a total sales forecast over the next 2 shipping cycles. It uses 2 years’ worth of sales history so that it can capture a trend and weekly selling pattern for each item/store being replenished and calculate a proper safety stock based on demand variability. On designated ordering days, the replenishment system evaluates the current stock position against the total of expected sales plus safety stock over the next two ordering cycles and triggers replenishment requests as necessary to ensure that safety stock will not be breached between successive replenishment days.

Scenario 4: In the overnight batch run, a centralized supply chain planning system calculates a sales forecast (with expected trend and weekly selling pattern) for the next 52 weeks. Using this forecast, merchandising minimums, store receiving calendars and the current stock position, it calculates when future arrivals of stock are needed at the store to ensure that the merchandising minimums won’t be breached over the next 52 weeks. Using the transit lead time, it determines when each of those planned arrivals will need to be shipped from the supplying distribution centre over the next 52 weeks. The rolled up store shipment projections become the outbound plans for each item/DC, which then performs the same logic to calculate when future inbound arrivals are needed and their corresponding ship dates. Finally the projected inbound shipments to the DC are communicated to suppliers so that they can properly plan their finished goods inventory, production and raw material procurement. For both stores and DCs, the plans are turned into firm replenishment requests at the ordering lead time.

With that out of the way, let’s do some audience participation. I have a question for you: Which of the above replenishment methods are forecast based? (You can pause here to scroll up to read each scenario again before deciding, or you can just look down to the very next line for the answer).

The answer is… they are ALL forecast based.

Don’t believe me?

In Scenario 1, how did the store associate know that a visual stock position of 6 units meant they were “getting low”? And why did she order a single case of 12 in response? Why didn’t she wait until there were 3 units? Or 1 unit? And why did she order 12? Why not 120?

For Scenario 2, you’re probably saying to yourself: “Averaging the past 6 weeks’ worth of sales is looking backward – that’s NOT forecasting!” Au contraire. By deciding to base your FUTURE replenishment on the basis of the last 6 weeks’ worth of sales, an assumption is being made that upcoming sales will be similar to past sales. That assumption IS the forecast. I’m not saying it’s a good assumption or that it will be a good forecast. I’m just saying that the method is forecast based.

Using the terms “trend” and “selling pattern” in Scenarios 3 and 4 probably spoiled the surprise for those ones.

So why did I go through such pains to make this point?

Quite simply, to counter the (foolish and naive) narrative that “forecasts are always wrong, so you shouldn’t bother forecasting at all”. 

The simple fact is that unless you are in a position where you don’t need to replenish stock until AFTER your customer has already committed to buying it, any stock replenishment method you use must by definition be forecast based. I have yet to run across a retailer in the last 28+ years that has that luxury.

A forecast that happens in someone’s head, isn’t recorded anywhere and only manifests itself physically as a replenishment request is still a forecast.

An assumption that next week will be like the average of the last few weeks is still a forecast.

As the march continues and retailers gradually transition from Scenarios 1, 2 or 3 to Scenario 4, the forecasting process will become more formalized and measurable. And it can be a lot of work to maintain them (along with the replenishment plans that are driven by them). 

But the overall effort pays off handsomely. Retailers in Scenarios 1, 2 and 3 have experienced in stock rates of 92-93% with wild swings in inventory levels and chronic stock imbalances. This has been documented time and again for 30 years.

Only by formalizing your forecasting your forecasting process, using those forecasts to drive long term plans and sharing those plans up and down the supply chain can you achieve 97-98% in stock while simultaneously reducing inventory investment, with reduced overall effort.

Rising Tides

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It is better to aim high and miss than to aim low and hit. – Les Brown

Why is the shelf empty?

I could go on and on about the myriad factors that could be at play, but it really boils down to one of two things:

  1. Failure to anticipate (or quickly react to) demand or;
  2. Not enough available supply, even if demand is properly planned

The first one is pretty obvious. No how matter how much effort you put into forecasting, sometimes shit just happens. The season breaks way earlier than expected. A mundane product that’s been selling steadily for years “goes viral” because of a TikTok trend. Or a customer just comes in and wipes you out unexpectedly for no discernible reason.

The second one is also obvious – at least on the surface. Clogged ports, pandemics and strikes immediately come to mind. But what often doesn’t come to mind is store inventory accuracy. Of all of the millions of item/locations in retail stores with a computerized on hand balance, how many times does the replenishment system think there’s stock available to sell when there is actually none? And how often does this condition go undetected until the store manager starts seeing shelf gaps? With store inventory accuracy hovering in the 50-60% range, I’m going to say “probably a lot”. 

I would argue that it’s probably the single biggest “supply issue” in retail, but the subject is so universally ignored and not measured that there’s no way of actually proving me right or wrong on that. But it sure feels like I may be right.

Regardless, lack of incontrovertible evidence aside, an informal quorum of people seem to agree with me anyhow, even if they may not know it. 

Why do I say that?

Whenever we talk to retailers about fully integrated planning from supplier to shelf and explain the process in detail, someone inevitably connects these dots:

  • Store on hands are a critical input to the planning process – TRUE!
  • Store on hands aren’t particularly accurate – TRUE!
  • Therefore, you can’t properly plan anything until inventory accuracy is under control – hmmm, well…

Here’s the thing about that last point: Inaccurate inventory has been a problem in retail for time immemorial. Computer assisted ordering, stock checking for customers, online order pickup in store – these are all common practices in retail today and have delivered significant benefits in terms of growth, efficiency and customer service. The most critical input for all of these processes is store inventory balances, which everybody knows are not accurate. Yet I haven’t seen any retailers shutting down automated ordering or buy online/pick up in store programs until their store inventory accuracy is up to snuff. 

Could greater benefits be achieved if store inventory records were more accurate? Duh! But that doesn’t change the fact that significant benefits can be (and have been) achieved in spite of inaccurate inventories. Implementing an end-to-end planning approach that relies on store inventory balances is no different in that regard. There is no hard dependency on some arbitrary level of inventory accuracy to start building or improving upon planning capabilities.

Transforming a retailer from a reactionary firefighter to an integrated planning organization takes hard work and discipline and delivers enoromous benefit.

Sustainably improving inventory accuracy in stores also takes hard work and discipline and also delivers enormous benefit.

Either of these initiatives can appear daunting individually, so – except for the most courageous among us – creating a false dependency (i.e. “we can’t plan without accurate inventory”) is a surefire way to ensure that nothing gets done about either of them.

As John F. Kennedy famously popularized: “The rising tide lifts all the boats.”

A retailer with so-so inventory accuracy will be made better with improved planning capabilities. A retailer with poor (or virtually nonexistent) planning capabilities will be made better with improved inventory accuracy.

So work on planning and shelve inventory accuracy for awhile.
Or work on inventory accuracy and shelve planning for awhile. Or be extra brave and work on both at the same time.

But work on something.

Remember Warren Buffet’s addendum: “A rising tide may lift all boats, but only when the tide goes out do you realize who’s been swimming naked.”