About Jeff Harrop

Jeff Harrop

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.

Jimmy Crack Corn

 

Science may have found a cure for most evils; but it has found no remedy for the worst of them all – the apathy of human beings. – Helen Keller (1880-1968)

apathy-i-dont-care

On hand accuracy.

It has been a problem ever since retailers started using barcode scanning to maintain stock records in their stores.

It’s certainly not the first time we’ve written on this topic, nor is it likely to be the last.

The real question is: Why is this such a pervasive problem?

I think I may have the answer: Nobody cares.

Okay, maybe that’s a little harsh. It’s probably more fair to say that there is a long list of things that retailers care about more than the accuracy of their on hands.

I’m not being judgmental, nor am I trying to invoke shame. I’m just making a dispassionate observation based on 25 years experience working in retail.

Whatever you think of the axiom “what gets measured gets managed” (NOT a quote from Peter Drucker), I would argue that it is largely true.

By that yardstick, I have yet to come across a single retailer who routinely measures the accuracy of their on hands as a KPI, even though – if you think about it – it wouldn’t be that difficult to do. Just send out a count list of a random sample of SKUs each month to every store and have them do a detailed count. Either the system record matches what’s physically there or it doesn’t.

Measuring forecast accuracy (the ability to predict an unknown future) seems to take up a lot more time and attention than inventory accuracy (the ability to keep a stock record in synch with a quantity that exists in the physical world right now), but the accuracy of on hand records has a much greater influence on the customer experience than forecast accuracy – by a very wide margin.

And on hand accuracy will only become more important as retailers expand customer delivery options to include click and collect and ship from store. Even “old school” shoppers (those who just want to go to the store to buy something and leave) will be expecting to check online to see how much a store has in stock before getting in their cars.

It’s quite clear that retailers should care about this more, so why don’t they?

Conflating Accuracy and Shrink

After a physical stock count, positive and negative on hand variances are costed and summed up. If the value of the system on hand drops by less than 2% of sales after the count adjustments are made, this is deemed to be a good result when compared to the industry as a whole. The conclusion is drawn that the management of inventory must therefore be under control and that on hand records must not be that far off. The problem with shrink is that the positive and negative errors can still be large in magnitude, but they cancel each other out, thereby hiding significant issues with on hand record accuracy (by item/location, which is what the customer cares about). Shrink is a measure for accountants, not customers.

Store Replenishment is Manual Anyhow

It’s still common practice for many retailers to use visual shelf reviews for store replenishment. Department managers walk through the aisles with RF scanning guns, scan the shelf tags for items they want to order and use an app on the gun to place replenishment orders. Most often, this process is used when perpetual inventory capabilities don’t exist at store level, but it’s not uncommon to see it also being used even if stores have system calculated on hand balances. Why? Because there isn’t enough trust in the accuracy of the on hands to use them for automated replenishment. Hmmm…

It’s Perceived to be an Overwhelming Problem

It’s certainly true that the number of item/store inventory pools that need to be kept accurate can get quite large. The predominant thinking in retail is that the only way to make inventory records more accurate is to count each item more frequently. Do the math on that and many retailers conclude that the labour costs to maintain accurate inventory records will drive them into bankruptcy.

The problem with this viewpoint is that frequent counting and correcting isn’t really maintaining accurate records – it’s continuously fixing inaccurate records. A different way to look at it is not by the sheer volume of item/location records to be managed, but rather by the number of potential process failure points that could affect any given item in any given location.

Think about an auto assembly line where every finished car that rolls off  has a 2 inch scratch on the right front fender. One option to address this problem is to set up an additional station at the end of the line to buff out the scratch on every single car that rolls through. This is analogous to the “count and correct” approach to managing inventory records – highly labour intensive and only addresses the problem after it has already occurred.

Another option would be to trace back through the process until you find the where the scratch is occurring and why. Maybe there’s a bolt sticking out from a pass-through point that’s causing the scratch. Cut off the end of the bolt, no more scratches. Addressing this one point of process failure permanently resolves the root cause of the defect for every car that passes through the process.

Going back to our store on hand accuracy example, a retailer may have thousands or millions of item/store combinations, but the number of processes (potential points of failure) that change on hand balances is limited:

  • DC picking
  • Store receiving
  • Stock writedowns for damage or waste
  • Counts
  • Sales and saleable returns

For retailers who have implemented store perpetual inventory, each of these processes that affect the movement of physical stock have a corresponding transaction that changes the on hand balance accordingly. How carefully are those transactions being recorded for accuracy (versus speed)?

Are DC shipments regularly audited for accuracy? Do stores “blind receive” shipments only from highly reliable sources? Are there nightly procedures to scan out damaged or unsaleable goods? Is the store well organized so that all units of a particular item can be easily found before a physical count is done? is every sale being properly scanned at the checkout?

Of course, the elephant (or maybe scapegoat?) in the room is theft. After all, there is no corresponding transaction for those stock movements. While there are certainly things that can be done to reduce theft, I consider it to be a self evident fact that it won’t be eliminated completely anytime soon.

But before you assume that every negative stock adjustment “must have been theft”, are you totally certain that all of the other processes are being transacted properly?

Does it seem reasonable to assume that for every single unique product whose on hand balance decreases after a physical count (typically 20-30% of all products in a store) all of those units were stolen since the last count?

And if we do assume that theft is the culprit in the vast majority of those cases, then what are we to assume about products whose on hand balances increase after being counted (typically 10-20% of all products in a store)? Are customers or employees sneaking items into the store, leaving them on the shelves and secretly leaving without being detected?

Setting theft aside, there’s still plenty that can be done by thoroughly examining and addressing the potential points of process failure that cause on hands to become inaccurate in the first place, while at the same time reducing the amount of time and money being spent on “counting and correcting”.

What’s Step 1 on this path?

You need to care.

What’s Good for the Goose

 

What’s good for the goose is good for the gander – Popular Idiom

ruledoesntapply

Thinking in retail supply chain management is still evolving.

Which is a nicer way of saying that it’s not very evolved.

Don’t get me wrong here. It wasn’t that long ago that virtually no retailer even had a Supply Chain function. When I first started my career, retailers were just beginning to use the word “logistics” – a military term, fancy that! – in their job descriptions and org charts. At the time it was an acknowledgement that sourcing, inbound transportation, distribution and outbound transportation were all interrelated activities, not stand alone functions.

A positive development, but “logistics” was really all about shipping containers, warehouses and trucks – the mission ended at the store receiving bay.

Time passed and barcode scanning at the checkouts became ubiquitous.

More time passed and many (but by no means a large majority) of medium to large sized retailers implemented scan based receiving and perpetual inventory balances at stores in a centralized system. This was followed quickly by computer assisted store ordering and with that came the notion that store replenishment could be a highly automated, centralized function.

Shortly thereafter, retailers began to recognize that they needed more than just operational logistics, but true supply chain management – covering all of the planning and execution processes that move product from the point of manufacture to the retail shelf.

In theory, at least.

I say that, because even though most retailers of size have adopted the supply chain management vernacular and have added Supply Chain VP roles to their org structures, over the years I’ve heard some dubious “supply chain” discussions that tend to suggest that thinking hasn’t fully evolved past “trucks and warehouses”. Some of you reading this now my find yourselves falling into this train of thought without even realizing it.

So how do you know if your thinking is drifting away from holistic supply chain thinking toward myopic logistics centric thinking?

An approach that we use is to apply the Goose and Gander Rule to these situations. If you find yourself advocating behaviour in the middle of the supply chain that seems nonsensical if applied upstream or downstream, then you’re not thinking holistically.

Here are a few examples:


The warehouse is overstocked. We can’t sell it from there, so let’s push it out to the stores.


At a very superficial level, this argument makes some sense. It is true that product can’t sell if it’s sitting in the warehouse (setting aside the fact that using this approach to transfer overstock from warehouses to stores generally doesn’t make it sell any faster).

Now suppose that a supplier unexpectedly shipped a truckload of product that you didn’t need to your distribution centre because they were overstocked. Would you just receive it and scramble to find a place to store it? Because that’s what happens when you push product to stores.

Or how would you feel if you were out shopping and as you were approaching the checkout, a member of the store staff started filling your cart with items that the store didn’t want to stock any more? Would you just pay for it with a shrug and leave?

I hate to break the news, but there is no such thing as “push” when you’re thinking of the retail supply chain holistically. The only way to liquidate excess inventory is to encourage a “pull” by dropping the price or negotiating a return. All pushing does is add more cost to the product and transfer the operational issues downstream.


If we increase DC to store lead times, we can have store orders locked in further in advance and optimize our operations.


Planning with certainty is definitely easier than planning with uncertainty, but where does it end? Do you increase store lead times by 2 days? 2 weeks? 2 months? Why not lock in store orders for the next full year?

Increasing lead times does nothing but make the supply chain less responsive and that helps precisely no one. And, like the “push” scenario described above, stores are forced to hold more inventory, so you’re improving efficiency at one DC, but degrading it in dozens of stores served by that DC.

Again, would you be okay with suppliers arbitrarily increasing order lead times to improve their operational efficiency at your expense?

Would you shop at a store that only allows customers in the door who placed their orders two days in advance?

Customers buy what they want when they want. There are things that can be done to influence their behaviour, but it can’t be fully controlled in such a way that you can schedule your supply chain flow to be a flat line, day in and day out.


We sell a lot of slow moving dogs. We should segregate those items in the DC and just pick and deliver them to the stores once a month.


The first problem with this line of thinking is that “slow moving” doesn’t necessarily mean “not important to the assortment”.

Also, aren’t you sending 1 or 2 (or more) shipments a week to the same stores from the same building anyhow?

When’s the last time you went shopping for groceries and were told by store staff that, even though you need mushroom soup today, they only sell mushroom soup on alternate Thursdays?

Listen, I’m not arguing that retailers’ logistics operations shouldn’t be run as efficiently as possible. You just need to do it without cheating.

We need to remember that the SKU count, inventory and staff levels across the store network is many times greater than the logistics operations. Employing tactics that hurt the stores in order to improve KPIs in the DCs or Transport operations is tantamount to cutting of your nose to spite your face.

Managing the Long Tail

If you don’t mind haunting the margins, I think there is more freedom there. – Colin Firth

long-tail

 

A couple of months ago, I wrote a piece called Employing the Law of Large Numbers in Bottom Up Forecasting. The morals of that story were fourfold:

  1. That when sales at item/store level are intermittent (fewer than 52 units per year), a proper sales pattern at that level can’t be properly determined from the demand data at that level.
  2. That any retailer has a sufficient percentage of slow selling item/store combinations that the problem simply can’t be ignored in the planning process.
  3. That using a multi level, top-down approach to developing properly shaped forecasts in a retail context is fundamentally flawed.
  4. That the Law of Large Numbers can be used in a store centric fashion by aggregating sales across similar items at a store only for the purpose of determining the shape of the curve, thereby eliminating the need to create any forecasts above item/store level.

A high level explanation of the Profile Based Forecasting approach developed by Darryl Landvater (but not dissimilar to what many retailers were doing for years with systems like INFOREM and various home grown solutions) was presented as the antidote to this problem. Oh and by the way, it works fabulously well, even with such a low level of “sophistication” (i.e. unnecessary complexity).

But being able to shape a forecast for intermittent demands without using top-down forecasting is only one aspect of the slow seller problem. The objective of this piece is to look more closely at the implications of intermittent demands on replenishment.

The Bunching Problem

Regardless of how you provide a shape to an item/store forecast for a slow selling item (using either Profile Based Forecasting or the far more cumbersome and deeply flawed top-down method), you are still left with a forecasted stream of small decimal numbers.

In the example below, the shape of the sales curve cannot be determined using only sales history from two years ago (blue line) and the most recent year (orange line), so the pattern for the forecast (green dashed line) was derived from an aggregation of sales of similar items at the same store and multiplied through the selling rate of the item/store itself (in this case 13.5 units per year):

You can see that the forecast indeed has a defined shape – it’s not merely a flat line that would be calculated from intermittent demand data with most forecasting approaches. However, when you multiply the shape by a low rate of sale, you don’t actually have a realistic demand forecast. In reality, what you have is a forecast of the probability that a sale will occur.

Having values to the right of the decimal in a forecast is not a problem in and of itself. But when the value to the left of the decimal is a zero, it can create a huge problem in replenishment.

Why?

Because replenishment calculations always operate in discrete units and don’t know the difference between a forecast of true demand and a forecast of a probability of a sale.

Using the first 8 weeks of the forecast calculated above, you can see how time-phased replenishment logic will behave:

The store sells 13 to 14 units per year, has a safety stock of 2 units and 2 units in stock (a little less than 2 months of supply). By all accounts, this store is in good shape and doesn’t need any more inventory right now.

However, the replenishment calculation is being told that 0.185 units will be deducted from inventory in the first week, which will drive the on hand below the safety stock. An immediate requirement of 1 unit is triggered to ensure that doesn’t happen.

Think of what that means. Suppose you have 100 stores in which the item is slow selling and the on hand level is currently sitting at the safety stock (not an uncommon scenario in retail). Because of small decimal forecasts triggering immediate requirements at all of those stores, the DC needs to ship out 100 pieces to support sales of fewer than 20 pieces at store level – demand has been distorted 500%.

Now, further suppose that this isn’t a break-pack item and the ship multiple to the store is an inner pack of 4 pieces – instead of 100 pieces, the immediate requirement would be 400 pieces and demand would be distorted by 2,000%!

The Antidote to Bunching – Integer Forecasts

What’s needed to prevent bunching from occurring is to convert the forecast of small decimals (the probability of a sale occurring) into a realistic forecast of demand, while still retaining the proper shape of the curve.

This problem has been solved (likewise by Darryl Landvater) using simple accumulator logic with a random seed to convert a forecast of small decimals into a forecast of integers.

It works like this:

  • Start with a random number between 0 and 1
  • Add this random number to the decimal forecast of the first period
  • Continue to add forecasts for subsequent periods to the accumulation until the value to the right of the decimal in the accumulation “tips over” to the next integer – place a forecast of 1 unit at each of these “tip-over” points

Here’s our small decimal forecast converted to integers in this fashion:

Because a random seed is being used for each item/store, the timing of the first integer forecast will vary by each item/store.

And because the accumulator uses the shaped decimal forecast, the shape of the curve is preserved. In faster selling periods, the accumulator will tip over more frequently and the integer forecasts will likewise be more frequent. In slower periods, the opposite is true.

Below is our original forecast after it has been converted from decimals to integers using this logic:

And when the requirements across multiple stores are placed back on the DC, they are not “bunched” and a more realistic shipment schedule results:

Stabilizing the Plans – Variable Consumption Periods

Just to stay grounded in reality, none of what has been described above (or, for that matter, in the previous piece Employing the Law of Large Numbers in Bottom Up Forecasting) improves forecast accuracy in the traditional sense. This is because, quite frankly, it’s not possible to predict with a high degree of accuracy the exact quantity and timing of 13 units of sales over a 52 week forecast horizon.

The goal here is not pinpoint accuracy (the logic does start with a random number after all), but reasonableness, consistency and ease of use. It allows for long tail items to have the same multi-echelon planning approach as fast selling items without having separate processes “on the side” to deal with them.

For fast selling items with continuous demand, it is common to forecast in weekly buckets, spread the weekly forecast into days for replenishment using a traffic profile for that location and consume the forecast against actuals to date for the current week:

In the example above, the total forecast for Week 1 is 100 units. By end of day Wednesday, the posted actuals to date totalled 29 units, but the original forecast for those 3 days was 24 units. The difference of -5 units is spread proportionally to the remainder of the week such as to keep the total forecast for the week at 100 units. The assumption being used is that you have higher confidence in the weekly total of 100 units than you have in the exact daily timing as to when those 100 units will actually sell.

For slow moving items, we would not even have confidence in the weekly forecasts, so consuming forecast against actual for a week makes no sense. However, there would still be a need to keep the forecast stable in the very likely event that the timing and magnitude of the actuals don’t match the original forecast. In this case, we would consume forecast against actuals on a less frequent basis:

The logic is the same, but the consumption period is longer to reflect the appropriate level of confidence in the forecast timing.

Controlling Store Inventory – Selective Order Release

Let’s assume for a moment a 1 week lead time from DC to store. In the example below, a shipment is planned in Week 2, which means that in order to get this shipment in Week 2, the store needs to trigger a firm replenishment right now:

Using standard replenishment rules that you would use for fast moving items, this planned shipment would automatically trigger as a store transfer in Week 1 to be delivered in Week 2. But this replenishment requirement is being calculated based on a forecast in Week 2 and as previously mentioned, we do not have confidence that this specific quantity will be sold in this specific week at this specific store.

When that shipment of 1 unit arrives at the store (bringing the on hand up to 3 units), it’s quite possible that you won’t actually sell it for several more weeks. And the overstock situation would be further exacerbated if the order multiple is greater than 1 unit.

This is where having the original decimal forecast is useful. Remember that, as a practical matter, the small decimals represent the probability of a sale in a particular week. This allows us to calculate a tradeoff between firming this shipment now or waiting for the sale to materialize first.

Let’s assume that choosing to forgo the shipment in Week 2 today means that the next opportunity for a shipment is in Week 3. In the example below, we can see that there is a 67.8% chance (0.185 + 0.185 + 0.308) that we will sell 1 unit and drop the on hand below safety stock between now and the next available ship date:

Based on this probability, would you release the shipment or not? The threshold for this decision could be determined based on any number of factors such as product size, cost, etc. For example, if an item is small and cheap, you might use a low probability threshold to trigger a shipment. If another slow selling item is very large and expensive, you might set the threshold very high to ensure that this product is only replenished after a sale drives the on hand below the safety stock.

Remember, the probabilities themselves follow the sales curve, so an order has a higher probability of triggering in a higher selling period than in a lower selling period, which would be the desired behaviour.

The point of all of this is that the same principles of Flowcasting (forecast only at the point of consumption, every item has a 52 week forecast and plan, only order at the lead time, etc.) can still apply to items on the long tail, so long as the planning logic you use incorporates these elements.