Author Archives: Jeff Harrop

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Jeff Harrop

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.” 

Retail Stock Management: The Cycle of Insanity

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The object in life is not to be on the side of the majority, but to escape finding oneself in the ranks of the insane. – Marcus Aurelius Antoninus (121AD – 180AD)

Is this a comedy of errors or a tragedy?

1. INT. RETAIL STORE (AISLE 6) – MONDAY, 10:34AM

RICK – a retail department manager – sees a confused looking CUSTOMER in front of one of the shelves he manages. He approaches to see if he can help.

RICK
Is there something I can help you with?

CUSTOMER
I need one of these, but I can’t find any.

RICK scans the shelf tag (Item #542317) and the system shows an on hand balance of 24 units.

RICK
That’s weird. I better check the back room. Give me a few minutes.

RICK walks back to the stockroom and opens the door.

STOCKROOM

Upon opening the door, RICK is confronted with a disorganized mess of cartons – some already opened – haphazardly packed onto shelves and pallets of stock on the floor stacked 3 deep against the wall.

RICK

I’m never going to find it. I’ll just write it off and the system will order more.

He takes out his trusty handheld device and, with a few keystrokes, wipes out the 24 on hand units in the system. He heads back to the CUSTOMER who is patiently waiting for him in Aisle 6.

BACK IN AISLE 6

RICK
I couldn’t find any back there, but we should have some more by the end of the week.

CUSTOMER
(disappointed)
Oh, okay.

2. INT. RETAIL STORE (RECEIVING) – MONDAY, 4:55PM

SALLY – the receiving manager – is looking at her daily reports and sees that there are quite a few trucks coming over the next few days. The mayhem in the stockroom has caused inventory to pile up in her receiving area and she anticipates disaster by week’s end. She leaves a note to the night crew asking them to get as much stock out of the stockroom onto the sales floor as possible.

3. INT. HOME OFFICE (SERVER ROOM) – TUESDAY, 2:25AM

Lights are blinking in the dark room as the computer assisted ordering system (CAO) is combing through the stock records for RICK’s store. CAO comes across a zero balance for Item #542317.

CAO
They’re out of stock! I had better send them another case!

CAO sends a replenishment request to the DC for 24 units of Item #542317, due into the store on their Thursday shipment.

4. INT. RETAIL STORE (RICK’S DEPARTMENT) – TUESDAY, 5:55AM

RICK arrives to see that the night crew has been very busy. There are pallets of stock in every aisle waiting to be put on the shelves. He gets to work.

AISLE 6

As RICK is working his stock, he comes across a case of 24 units of Item #542317 that was somewhere in the stockroom.

RICK
There you are! I’ve been looking for you! Well, not really.

He fills up the shelf with stock and admires his work. He brings back all of the stock he couldn’t fit on the shelf back to the stockroom. It’s still messy in there, but at least there’s some room to move around. He doesn’t update the on hand balance to reverse the writeoff he processed on Monday.

5. INT. RETAIL STORE (RECEIVING) – THURSDAY, 8:51PM

The Thursday shipment has arrived. SALLY has been struggling to keep up all week and the stockroom is really getting jammed again. Before leaving for the day, she approaches PHIL, the night crew manager.

SALLY
PHIL, I really need you to clear out Receiving tonight! I have more trucks coming tomorrow to get us through the weekend and I don’t have any room to bring them in!

PHIL
No problem, will do!

6. INT. RETAIL STORE (RICK’S DEPARTMENT) – FRIDAY, 5:53AM

RICK arrives and sees that there are lots of pallets of stock in his aisles that need to be put away. It’s the weekend, so he isn’t surprised. He gets to work cutting open cartons and filling shelves.

AISLE 6

RICK picks up a case of 24 units of Item #542317 that CAO requested in the wee hours of Tuesday morning. A few units have sold since Tuesday, but the shelf is still mostly full and the new stock won’t fit.

RICK
(frustrated)
Stupid replenishment system! Either I’m out of stock or I have way too much!

He sets the case back down onto the pallet to take back to the stockroom after he’s done filling the shelves.

STOCKROOM

RICK starts shoving his excess stock wherever it can fit, including the case of Item #542317. He puts in on a shelf, slides it to the back and then puts a couple boxes of other stock in front of it. It’s been a rough morning so he heads to the break room to get something to eat. The writeoff he processed on Monday is now a distant memory. Even though there are 40+ pieces of Item #542317 in the store, the system thinks there are only 24. Hopefully it will get corrected when they do the physical inventory after Christmas.

7. INT. RETAIL STORE (AISLE 6) – 3 WEEKS LATER

RICK sees a confused looking CUSTOMER in front of one of the shelves he manages. He approaches to see if he can help.

RICK
Is there something I can help you with?

CUSTOMER
I need one of these, but I can’t find any.

RICK scans the shelf tag (Item #542317) and the system shows an on hand balance of 24 units.

RICK
That’s weird. I better check the back room. Give me a few minutes…

FADE TO BLACK

What Demand Planners Really Need

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

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

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

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

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

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

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

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

Comprehension

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

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

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

Control

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Bread and Butter

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

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

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

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

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

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

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

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

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

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

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

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

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

There’s your real bread and butter.