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A Rather Unassuming Approach

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

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

Why?

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

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

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

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

The store replenishment problem is relatively straightforward:

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

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

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

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

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

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

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

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

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

The result?

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

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

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

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

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

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

Secret Formulas of Implementation Success

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As someone who’s been doing project work for decades, I must admit, it’s always cool and rewarding when you implement something. Shipping your work and having it exposed to reality instead of theory is the essence of innovation – taking an idea, or a design, and making it real.

But implementation work is hard, especially for a business process like Flowcasting since it touches, interacts and changes a large part of a retail business and extended eco-system.

I’ve been very lucky over my career to have either led, or co-led, three successful implementations in retail of Flowcasting or major elements of the concept. As an implementer at heart, over the years, what’s emerged are some mechanisms I’ve used that I believe are instrumental in success.

What I’d call my secret formulas.

For a key one, we’ll turn the clocks back to the mid-to-late 1990s. At the time I was the leader of a team for a national, Canadian hardgoods retailer, who’s mandate was to design and implement new processes and supporting technology to improve the planning and flow of inventory from supplier to store shelf.

The team had essentially designed what we now call Flowcasting and had selected technology to support the process. While we all understood that planning from the store level back was technically infeasible, we decided to forecast DC-level demand, and calculate and share forward looking supply projections with our merchandise vendors – in the process instilling the concept of supplier scheduling in retail. I won’t bore you with the details, but the project was quite successful and helped cement some of the principles of Flowcasting in retail, including supplier scheduling and working to a single set of numbers.

For a project of this size, like most larger scale transformations, we had a cross-functional governance team established – essentially like a steering committee – that would help guide the project and provide advice and suggestions to the implementation team. And to be honest, they did a good job.

However, inevitably, when a group of that size and functional diversity is tasked with guiding and asking questions of the leader (in this case me), there are bound to be some dumb asks and even dumber suggestions.

That was the input for me to develop my “Rule of 3”, which I/we used successfully on this implementation, and I’ve used ever since.

It works like this. If the ask/suggestion from the steering committee or large governance group sounded mental to me, I’d note it down and tell everyone I’d think about it. Then, I’d go back to the team and see what they thought. If they agreed it was mental, I’d ignore the ask/suggestion. And I’d continue to ignore it until the group had asked a third time – at which time I/we’d develop a response.

The beautiful thing about this approach is that seldom does the request ever get asked again, let alone a third time. It’s forgotten and therefore requires no cycles of thought or response from me and the team. I’m not exactly sure why but my thinking is that in larger groups people tend to like to hear themselves talk – they want to make suggestions/contributions, so they can’t help themselves and sometimes make a dumb suggestion or ask. Then, by the time the next session comes around, they completely forget about their initial request.

As an example, when I was working with our Winnipeg-based retail client designing and ultimately implementing Flowcasting, me and the team leader had to regularly present to a large cross functional group about Flowcasting – how it would work, the benefits, the implementation approach, etc.

I remember at one large, cross-project session a participant asking something like “How will the new process factor in social media sentiment into the demand planning process, to potentially revise the forecast of that item and others?” My response was, “Not sure yet, but we’ll think about it”.

I remember the team leader asking me after, “what are we going to do?”. My answer was simple: “Nothing. We’re going to ignore that and see if it’s ever asked again”. It wasn’t and the rest is history.

Now, not to brag or anything, but this client was able to improve daily in-stock from about 92% to 98%, while reducing both DC and store inventories, all while completely ignoring social media sentiment (whatever that is). Thanks to the Rule of 3.

Now, don’t get me wrong. I’m not saying that most of the suggestions from steering committees and cross functional groups are/were dumb – they’re not. I’m saying that a certain percentage will be and you, as an implementer, need a mechanism to ignore them and/or say “No” nicely, so you can stay focused on what matters.

For me, it’s The Rule of 3. It has been a loyal friend to me, over many years and implementations, and I hope you can use it – or something like it – as well.

It’s one of my secret formulas of implementation success.

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.

Repetito est mater studiorum

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“Repetition is the mother of learning, the father of action, which makes it the architect of accomplishment.” – Zig Ziglar

When I was growing up, I was a very competitive dude, particularly in sports. When it came to sports, for me, winning was everything. Basically, I was an asshole. Many of my friends and foes called me by a different name. What’s the name they used? Oh yeah, a cunt. It’s OK, it was the truth and they often called me that to my face.

Here’s a story, from high school, that helps confirm my then status. 

In the basement of our school, down near the gym, there was a ping pong table. And, during lunch and breaks, students would play. The rule was simple: the winner stays on the table, until someone beats them, and then they take over. So, a very good player could play for quite a while.

One day, me and my pals sauntered down and watched as a girl named Dana beat all comers. Then, at the insistence of my mates, it was my turn to challenge her. She crushed me. My buddies, of course, knowing how competitive I was, absolutely shamed me that day and for many weeks after.

So, what did I do?

I did what any red-blooded, super competitive dude would do. I bought my own racket and a ping pong table.  I was determined to win.

Ping pong tables fold in the middle where the net is so that, when folded, the other side of the table is upright. It allows a single player to hit the ball, over the net, against the other side of the table and it pretty much guarantees that the ball will be returned. So, I set up the folded table in my parents’ basement and, every chance I got, would go down and pound balls against the returning wall.

Over and over. Harder and faster. Learning how to put overspin on both a forehand and backhand. Learning how to smash and return a smash. Repetition after repetition – for hours and days on end.  

I would soon get to challenge Dana again. And, with my buddies watching, I would demolish her and would become not only the ping pong champion of my school, but also the best high school player in the county.

The moral of this true story isn’t to confirm that I was an asshole. The moral of the story is highlighting the importance of repetition.

There’s an old Latin saying, “repetito est mater studiorum” which means repetition is the mother of learning.

When it comes to instilling new ways of working, turns out repetition really is the mother of learning.

Implementing a new planning approach like Flowcasting in retail benefits greatly from repetition. You’re essentially teaching the planners and the wider organization (including suppliers) how to think differently about integrated demand and supply planning, so the more often people are exposed to the idea, the better. 

I recently read a great book about change called The Human Element.  In it they outline one of the most important strategies for instilling change is to “Acclimate the Idea” through repetition and repeated exposure (i.e., give people time to think and internalize the idea/change)

In a recent implementation of Flowcasting, the idea of repetition was leveraged extensively to help people make the change journey, including:

  • An ongoing education program which started with a cascade from the CEO and delivered repeated educational sessions to help people internalize the change in thinking and underlying principles of the new process
  • Process prototypes where the Buying Teams (Merch and Supply Chain) would execute a day in the life scenario, with company-specific data for every major planning scenario – like product life cycle, promotions planning, seasonal planning, etc.
  • A supplier education & training program to teach suppliers and the Buying Teams the new approach to collaboration
  • Training sessions to demonstrate how people would execute the new ways of working
  • Coaching sessions and ongoing coaching with job aides to help people transition from the old to the new

What do all these activities do? 

They constantly repeat and demonstrate to people the underlying change and principles of the new process. As an example, in each of the process prototypes, the Buying Teams could see what was meant by a valid simulation of reality, what the supplier would see in their supplier schedules, why postponing creating a purchase order for promotional volume was better for everyone, plus many other learnings. Repetition, with real scenarios, helped them instill new thinking and helped acclimate the ideas.

Getting good at anything (Flowcasting or ping pong) requires learning.  And learning needs repetition.

After all, she really is the mother of learning.

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.

A dynamic, digital twin

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Frank Gehry is widely acclaimed as one of the world’s greatest architects. His most famous and celebrated building, the Guggenheim Museum in Bilbao, is the design and subsequent construction that elevated him to superstardom.

The story of how Gehry designs and the technologies he used to develop this, and subsequent masterpieces, is instructive and very relevant for supply chain planning and management.

Gehry usually begins by sketching ideas on paper with scrawls that would mystify most folks. Then he mostly works with models – usually working with wooden building blocks of different sizes that he stacks, and restacks, always looking for something that might be functional and is visually appealing.

Until recently, he’s worked with these types of models his whole life. His studio is filled with them – the culmination of decades of model building. He usually starts at one scale, then tries another and then another to see the project from varying perspectives. He zeroes in on some aspects of the design in his model, zooming in and out until he better understands the design from many different viewpoints and angles. He’s always trying new ideas, reviewing his designs with his team and client, eventually deciding on what works or doesn’t. Eventually he settles on the design and then they get on with it.

After landing the project to design the Guggenheim Bilbao, he and his team spent the better part of two years working through these iterative models, using the decidedly analog world of building blocks and cardboard to visualize the result.

Then, our old friend technology made a house call and changed his design capability forever.

Gehry would be introduced to computer simulation software called CATIA, allowing Gehry to build his designs on a computer. Originally the software was built to help design jets but was modified to allow buildings to be designed – on a computer – in three dimensions. Early in his career his designs were mostly straight lines and box-like shapes, but this technology would allow him to design curves and spirals that would be beautiful and aesthetically pleasing.

CATIA’s capabilities proved incredible. Gehry and his team could alter the design quickly, change curves or shapes, and the system would instantly calculate the implications for the entire design – from structural integrity to electrical/plumbing requirements, to overall cost. They could iterate new ideas and concepts on the computer, simulate the results, then rinse and repeat, and only then, once happy, begin construction.

The Guggenheim building was first fully designed on a computer.

In a moment of foreshadowing, the design and digital design process was labelled a “digital twin”. Once the digital twin was finalized and agreed to, only then did construction begin.

The term “digital twin” has become somewhat fashionable and, to be honest, quite important in supply chain. And what do people mean by the term “digital twin”, when thinking about the supply chain? Here’s one definition…

A digital twin is a digital replica of a physical supply chain. It helps organizations recreate their real supply chain in a virtual world so they can test scenarios, model different nodes, modes, flows, and policies and understand how decisions and disruptions will impact network operations.

For most supply chain folks, the digital twin is relatively static and represents the current state, or outlines a snapshot of the supply chain, as of today – for example, what’s happening in the supply chain, as of right now.

But, like Gehry’s ability to dynamically change design elements and immediately see the impact overall, wouldn’t the best digital twin for supply chains also be dynamic, complete, and forward-looking?

It would.

And isn’t that what Flowcasting is?

It’s a future-dated, up-to-date, complete model of the business. It depicts all current and projected demand, supply, inventory, and financial flows and resource requirements, based on the strategies and tactics that are driving a retailer and their trading partners. If something changes, then the dynamic model re-calculates the projections – so the forward-looking digital twin is always current. Everyone can see the projections in their respective language of the business (e.g., units, cases, dollars, capacities, resources) and work to a single set of numbers.

The architectural “digital twin” was a breakthrough approach for Frank Gehry and architecture in general.

The forward-looking, dynamic “digital twin” – that is, Flowcasting – is a similar breakthrough approach for supply chain planning.

New Model for Retailer-Supplier Collaboration

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Thank you to Supply Chain Management Review for publishing our latest article, “A New Model for Retailer-Supplier Collaboration” in the March/April edition of their always excellent magazine.

Check it out here: https://bt.e-ditionsbyfry.com/publication/?m=24891&i=816750&p=34&ver=html5

The article outlines a new approach to collaborative inventory planning, based on the profound insight of Dr. Joseph Orlicky and the ideas and concepts developed and implemented by our long-time colleague, Andre Martin.

We’d also like to thank two forward-looking companies, Princess Auto Ltd and Watson Gloves, for agreeing to be featured in the article, demonstrating that the approach works and benefits consumers and both retailer and supplier.

It turns out that Joe and Andre are right – you should never forecast what you can calculate!

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

Subtract

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“Life can be improved by adding, or by subtracting. The world pushes us to add because that benefits them. But the secret is to focus on subtracting.”

                    - Derek Sivers

People don’t subtract.

Our minds add before even considering taking away.

Don’t believe me?

Leidy Klotz is a Behavioral Science Professor at the University of Virginia and a student of “less”. He conducted a series of experiments that demonstrate people think “more” instead of “less”.

Consider the following diagram and the ask.

Thousands of participants were asked to make the patterns on the left and right side of the dark middle vertical line match each other, with the least number of changes.

There are two best answers. One is to add four shaded blocks on the left and the other is to subtract four shaded blocks on the right.

Only about 15 percent of participants chose to subtract.

Intrigued, Professor Klotz and his research assistants concocted numerous additional experiments to test whether people would add or subtract. They all produced the same result and conclusion – people are addicted to and inclined to add. It wasn’t close.

Big fucking deal, right?

Not so fast. Unfortunately, adding almost always makes things more complicated, polluted, and worse. You’d be better off subtracting.

A great example in supply chain is demand planning.

Demand planning, according to many, is becoming the poster child of adding. Let’s factor in more variables to produce an even more beautiful and voluptuous forecast. Are you sure you all these additional variables will improve the demand plan?

I doubt it.

First, many companies are forecasting what should be calculated. It’s been proven that the farther away from end consumption you’re trying to forecast, the more variables you’ll try to add. And the resulting forecast usually gets worse the more you add – since you’re often adding noise.

We have a retail client that is forecasting consumer demand at the item/store level only and calculating all inventory flows from store to supplier – what we call Flowcasting. Their demand planning process only considers two variables to calculate the baseline forecast:
• the sales history in units
• an indication if the sales was influenced by something abnormal (e.g., like promotions, clearance, out of stock, etc.)

All “other” variables that the “experts” say should be included have been subtracted.

Yet their planning process consistently delivers industry leading daily in-stocks and inventory flows to the store shelf.

The idea is simple, profound, and extremely difficult for us all. For process and solution designs, and pretty much everything, you need to remove what’s unnecessary.

You need to subtract.

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.