Questions and Answers

Posted on November 6, 2018 by Mike Doherty

Did you know that most, if not all, organizations and innovations started with a question, or series of questions?

Reed Hastings concocted Netflix by asking a simple question to himself…”what if DVD’s could be rented through a subscription-type service, so no one ever had to pay late fees?” (Rumor was that this was just after he’d been hit with a $40 late fee).

Apple Computer was forged by Woz and Jobs asking, “Why aren’t computers small enough for people to have them in their homes and offices?”

In the 1940’s, the Polaroid instant camera was conceived based on the question of a three year old. Edwin H. Land’s daughter grew impatient after her father had taken a photo and asked, “Why do we have to wait for the picture, Daddy?”

Harvard child psychologist Dr. Paul Harris estimated that between the ages of two to five, a child asks about 40,000 questions. Yup, forty thousand!

Questions are pretty important. They lead to thinking, reflection, discovery and sometimes breakthrough ideas and businesses.

The problem is that we’re not five years old anymore and, as a result, we just don’t seem to ask enough questions – especially the “why” and “what if” kinds of questions. We should.

Turns out our quest for answers and solutions would be much better served by questions. To demonstrate the power of questions, let’s consider the evolution of solutions to develop a forward looking, time-phased forecast of consumer demand by item/store.

Early solutions realized that at item/store level a significant number of products sold at a very slow rate. Using just that items sales history, at that store, made it difficult to determine a selling pattern – how the forecasted demand would happen over the calendar year.

To solve this dilemma, many of the leading solutions used the concept of the “law of large numbers” – whereby they could aggregate a number of similar products into a grouping of those products to determine a sales pattern.

I won’t bore you with the details, but that is essentially the essence behind the thinking that, for the retail/store, the forecast pattern would need to be derived from a higher level forecast and then each individual store forecast would be that stores contribution to the forecast, spread across time using the higher level forecast’s selling pattern.

It’s the standard approach used by many solutions, one who’s even labelled it as multi-level forecasting. Most retail clients who are developing a time-phased forecast at item/store are using this approach.

Although the approach does produce a time-phased item/store forecast, it has glaring and significant problems – most notably in terms of complexity, manageability and reasonableness of using the same selling pattern for a product across a number of stores.

To help you understand, consider a can of pork and beans at a grocery retailer. At what level of aggregation would you pick so that the aggregate selling pattern could be used in every store for that product? If you think about it for a while, you’ll understand that two stores even with a few miles of each other could easily have very different selling patterns. Using the same pattern to spread each stores forecast would yield erroneous and poor results. And, in practice, they do.
Not only that, but you need to manage many different levels of a system calculated forecast and ensuring that these multi-level forecasts are synchronized amongst each level – which requires more system processing. Trying to determine the appropriate levels to forecast in order to account for the myriad of retail planning challenges has also been a big problem – which has tended to make the resulting implementations more complex.

As an example, for most of these implementations, it’s not uncommon to have 3 or more forecasting levels to “help” determine a selling pattern for the item/store. Adding to the issue is that as the multi-level implementation becomes more complex, it’s harder for planners to understand and manage.

Suffice it to say, this approach has not worked well. It’s taken a questioner, at heart, to figure out a better, simpler and more effective way.

Instead of the conventional wisdom, much like our 3 year old above, he asked some simple questions…

“What if I calculated a rolling, annual forecast first?” “Couldn’t I then spread that forecast into the weekly/daily selling pattern?”

As it turns out, he was right.

Then, another question…

“Why do I have to create a higher level forecast to determine a pattern?” Couldn’t I just aggregate sales history for like items, in the same store, to determine the selling pattern?”

Turns out, he could.

Finally, a last question…

“Couldn’t I then multiple the annual forecast by the selling pattern to get my time-phased, item/store forecast?”

Yes, indeed he could.

Now, the solution he developed also included some very simple and special thinking around slow selling items and using a varying time period to forecast them – fast sellers in weekly periods, slower sellers monthly and even slower sellers in quarterly or semi-annual periods.

The questions he asked himself were around the ideas of “Why does every item, at the retail store, need to be forecast in weekly time periods?”

Given the very slow rate of sales for most item/stores the answer is they don’t and shouldn’t.

The solution described above was arrived at by asking questions. It works beautifully and if you’re interested in learning more and perhaps asking a few questions of your own, you know how to find me.

So, if you’re a retailer and are using the complicated, hard-to-manage, multi-level forecasting approach outlined above, perhaps you should ask a question or two as well…

1. “Why are we doing it like this?”
2. “Who is using the new approach and how’s it working?”

They’re great questions and, as you now know, questions will lead you to the answers!

Accuracy or Precision?

Posted on October 3, 2018 by Jeff Harrop

It is the mark of an educated mind to rest satisfied with the degree of precision which the nature of the subject admits and not to seek exactness where only an approximation is possible. – Aristotle (384 BC – 322 BC)

My favourite part about writing these articles is finding just the right quote to introduce them. Before we get started, go back and read the quote from Aristotle above if you happened to skip past it – I think it both accurately and precisely summarizes my argument.

Now in the context of forecasting for the supply chain, let’s talk about what each of these terms mean:

Accuracy: Ability to hit the target (i.e. how close is the actual to the forecast?)

Precision: Size of the target you’re aiming at (i.e. specificity of product, place and timing of the forecast)

I’m sorry to be a total downer, but the reason this article is titled Accuracy or Precision is because you can’t have both. The upper right quadrant in the illustration above ain’t happening (a bit more on that later).

In the world of forecasting, people seem obsessed with accuracy and often ask questions like:

What level of forecast accuracy are you achieving?
How should we be benchmarking our forecast accuracy?
Are we accurate enough? How can we be more accurate?

The problem here is that any discussion about forecast accuracy that does not at the same time account for precision is a complete waste of time.

For example, one tried and true method for increasing forecast accuracy is by harnessing the mystical properties of The Law of Large Numbers.

To put it another way – by sacrificing precision.

Or to put it in the most cheeky way possible (many thanks to Richard Sherman for this gem, which I quote often):

Sherman’s Law:
Forecast accuracy improves in direct correlation to its distance from usefulness.

So how do we manage the tradeoff between precision and accuracy in forecasting?You must choose the level of precision that is required (and no more precise than that) and accept that in doing so, you may be sacrificing accuracy.

For a retailer, the only demand that is truly independent is customer demand at the point of sale. Customers choose specific items in specific locations on specific days. That’s how the retail business works.

This means that the precision of the forecasting process must be by item by location by day – full stop.

Would you be able to make a more accurate prediction by forecasting in aggregate for an item (or a group of items) across all locations by month? Without a doubt.

Will that help you figure out when you need to replenish stock for a 4 pack of 9.5 watt A19 LED light bulbs at store #1378 in Wichita, Kansas?

Nope. Useless.

I can almost see the wincing and hear the heart palpitations that this declaration will cause.

“Oh God! You’ll NEVER be able to get accurate forecasts at that level of precision!” To that I say two things:

It depends on what level of accuracy is actually required at that level of precision.
Too damn bad. That’s the requirement as per your customers’ expectation.

With regard to the first point, keep in mind that it’s not uncommon for an item in a retail store to sell fewer than 20 units per YEAR. On top of that, there are minimum display quantities and pack rounding that will ultimately dictate how much inventory will be available to customers to a much greater degree than the forecast.Forecasts by item/location/day are still necessary to plan and schedule the upstream supply chain properly, but it’s only necessary for forecasts at that level of precision to be reasonable, not accurate in the traditional sense of the word. This is especially true if you also replan daily with refreshed sales and inventory numbers for every item at every location.

There are those out there who would argue that my entire premise is flawed. That I’m not considering the fact that with advances in artificial intelligence, big data and machine learning, it will actually be possible to process trillions of data elements simultaneously to achieve both precision and accuracy. That I shouldn’t even be constraining my thinking to daily forecasting – soon, we’ll be able to forecast hourly.

Let’s go back to the example I mentioned earlier – an item that sells 20 units (give or take) in a location throughout the course of a year. Assuming that store is open for business 12 hours out of every day and closed 5 days per year for holidays, there are 4,320 hours in which those 20 units will sell. Are we to believe that collecting tons of noise (whoops, I meant “data”) from social media, weather forecasting services and the hourly movement of soybean prices (I mean, why not, right?) will actually be able to predict with accuracy the precise hour for each of those 20 units in that location over the next year? Out of 4,320 hours to choose from? Really?

(Let’s put aside the fact that no retailer that I’ve ever seen even measures how accurate their on hand records are right now, let alone thinking they can predict sales by hour).

I sometimes have a tendency to walk the middle line on these types of predictions. “I don’t see it happening anytime soon, but who knows? Maybe someday…”

Well, not this time.

This is utter BS. Unless all of the laws of statistics have been debunked recently without my noticing, degrees of freedom are still degrees of freedom.

Yes, I’m a loud and proud naysayer on this one and if anyone ever actually implements something like that and demonstrates the benefits they’re pitching, I will gleefully eat a wheelbarrow of live crickets when that time comes (assuming I’m not long dead).

In the meantime, I’m willing to bet my flying car, my personal jetpack and my timeshare on the moon colony (all of which were supposed to be ubiquitous by now) that this will eventually be exposed as total nonsense.

Fundamentals

Posted on July 4, 2018 by Mike Doherty

Imagine you’re a hotshot US high school basketball player during the late 1960’s. You’re all American and can pretty much choose any collegiate program you want, with an almost for certain scholarship attached and a better than decent chance that someday you’ll play professionally in the NBA.

You finally decide to join the famed UCLA program, under the tutelage of the acclaimed John Wooden, aptly named The Wizard of Westwood – an eventual winner of 10 NCAA titles in 12 seasons, including a still record 7 in a row.

I think it’s safe to say you’d be both nervous and excited.

First practice day arrives and you’re intrigued to see what you’ll learn from the Wizard. What techniques does he employ? What’s his secret sauce? How has he been so successful?

Wooden arrives, promptly introduces himself and his staff and new recruits begin to practice a drill that Wooden starts every season with…

…learning to put on your socks and tie your laces.

Astonished, your first practice with the top program in the country would include practicing, over and over, how to put on your socks and tie your shoe laces.

John Wooden understood how important fundamentals are. If the players got blisters from their socks or improperly laced shoes, they wouldn’t be able to move as well. If they couldn’t move as well, they couldn’t rebound or shoot as well and they’d miss shots. And if they missed rebounds and shots, they’d lose.

Master the fundamentals, Wooden realized, and the team would excel.

It’s impressive thinking and very insightful for all us working in supply chain planning.

Flowcasting is based on fundamentals. I remember talking recently to an Executive from a technology-focused and hyped firm who proclaimed to me, “Flowcasting is not new or innovative”. After all he proclaimed, “the concept has been around for more than 15 years and the book is 12 years old”.

Ok, so what’s that got to do with anything? Does something have to be new, exciting and, to date, unproven to add value? I think not and I know you do too.

Flowcasting is based on sound fundamentals that will stand the test of time. A forecast of consumer demand, at customer touchpoint only, is used to calculate and translate that only unknown into all resource requirements in the extended retail supply chain (or any industrial supply chain).

Joe Orlicky, one of the Godfathers of time-phased planning, MRP and DRP, said it best, almost 50 years ago when he proclaimed ”Never forecast what you can calculate”.

I’ve never met Dr. Orlicky or Dr. Wooden but I believe that they are kindred spirits. They understood the power and importance of fundamentals.

Now you do too.

A Symphony of Placid Beauty

Posted on May 2, 2018 by Mike Doherty

July 23, 1972. Reykjavík, Iceland. An event would occur that day that would rock the chess world and would continue to be talked about, even to this day. It was game 6 of the acclaimed world chess championship match between the World Champion, Boris Spassky of Russia, and the challenger, Bobby Fischer of the United States.

Fischer opened 1. C4, the English Opening – the first time in his entire career that he’d deviated from his beloved 1. E4 opening move. Spassky quickly transformed the opening into the Queen’s Gambit Declined, for which he was one of the world’s foremost experts in this line of defense.

What followed was a masterpiece. Fischer’s moves were new, exciting and novel on a variation that had been played by chess grandmasters for centuries. The moves were pure, clean and deceptively simple, yet powerful and profound.

When Spassky resigned after Fischer’s 41st move, not only did the crowd stand and applaud, so too did Spassky. They all knew that they’d witnessed a masterpiece. A game of such beauty and purity – still talked about to this day, as chess perfection.

Dr Anthony Saidy, a Fischer confidant and assistant described it magically when he proclaimed, “it was like a symphony of placid beauty”.

Fischer’s play in game 6 captures his signature style: crystalline – transparent but ingenious and incredibly profound and powerful. Nigel Short, the highest ranked British Grandmaster of all time sums up Fischer’s play nicely, “The thing that strikes me about Fischer’s chess,” he says, “is that it’s very clear. There are no mysterious rook moves or obscure manoeuvrings. There’s a great deal of logic to the chess. When you look at it you can understand it – afterwards. He just makes chess look very easy.”

There are a lot of parallels to Fischer’s chess, particularly game 6 from 1972, and Flowcasting.

Flowcasting, as you know, seamlessly connects the supply chain from the consumer to the factory in a natural and logical way. That’s easy to understand and most people seem to get that.

However, like analyzing Fischer’s moves, the nuances of the process are deceptively powerful and profound. I’ll outline a couple of important ones, though there are others and they follow the Fischer-like mantra – deceptively easy to understand, simple, yet profound.

The forecasting approach used by the leading Flowcasting solutions does not use any sophisticated algorithms. Instead it uses and builds on profile based forecasting techniques that have been around for decades – the subtle improvement (like some of Fischer’s novelties in game 6) is the use of differing forecast time periods by SKU, converting them to integer forecasts for slow selling items and then consuming these forecasts as actual sales happen.

Why is that placid-like beauty? Because if you study retail, and real sales history, you’ll uncover that the vast majority of products sell less than 26 units per year per store for virtually any retailer. Trying to find a fancy algorithm that can predict when these sales will occur is a fool’s game.

The simple combination of integer forecasting, consumption and daily re-planning simplifies the solution to deliver results. And, once explained to planners, makes sense to them and is easy to understand and manage. Much like Fischer’s moves, the ideas and concepts are deceptively simple and profound.

Consider also the simple and profound concept within Flowcasting of daily, net change re-planning. At our most recent implementation the solution works like this: for any product that had a sale or change that occurred yesterday, then the entire supply chain is re-forecast and re-planned from consumer to factory for that item.

In retail, on a daily basis, only about 5-15% of the products experience a change daily. Only these are re-planned daily, adjusting the future flows of inventory to ensure you remain in-stock and your inventory is productive. All projections are easily and simply converted into the language of the business so that the entire organization is working to a single set of numbers. One other benefit of net change, daily re-planning is that it also dramatically reduces system processing requirements.

Flowcasting is an easy concept and solution to understand and most people wholeheartedly agree with the premise. The trick to making it successful is to understand the nuances, embrace its simplicity and instill, over time, that kind of thinking in your planning organization.

Once you do, then, from experience, your supply chain and indeed your company will work like a Symphony of Placid Beauty!

Ungrain

Posted on January 3, 2018 by Mike Doherty

In 1983 Benjamin Libet, researcher in the Department of Physiology at the University of California, performed one of the most famous and controversial experiments in the history of neuroscience.

In simple terms, Libet’s experiment measured and timed the response of the neural circuitry of the brain, based on some very basic commands – like moving your left wrist, followed by your right wrist. What he discovered is that there is a time lapse between the decisions our neural circuitry makes for us and our awareness of the situation.

What that means, in a nutshell, is that for basic operations and requests the brain has already been hardwired, or ingrained, into a conditioned response, basically without thought. The brain has seen this movie (or ask) so many times that the response is automatic.

For us folks who are working on changing people’s behaviors and habits, we can relate. People become ingrained in current practices, processes and ways of thinking and it usually takes considerable time and effort to change – the thinking and the response.

Libet, however, didn’t stop there. Further work, research and experiments concluded that there were generally only two ways to change the history of the brain as it relates to a specific ask or task. They are asking WHY and making a JOKE of the situation.

Let’s look at an important supply chain planning example and focus on the WHY.

To date, most retail planners, consultants and solution providers have firmly cemented and ingrained the thinking that to systemically create a forward looking time-phased forecast by item/store (or webstore) requires that you forecast at multiple levels and then spread the higher level forecasts down to the lower (store) level.

Initially the thinking was that the aggregate level forecast would be more accurate, and that is usually the case. But some people realized that the higher level forecast was of no value – it’s the lowest level of forecast that drives the integrated supply chain.

Asking and wondering WHY enough times eventually surfaced that the higher level forecast was really only helpful in determining a selling pattern, especially for slower selling products where a pattern was difficult to detect.

Our colleague, Darryl, not only understood but asked WHY it was necessary to forecast at a higher level. Couldn’t the pattern be determined, at the selling location, without the need and complexity of forecasting at a higher level.

Eventually, he arrived at a simple, sensible solution. In a previous newsletter, I outlined the key elements of the approach but the key elements of the approach are:

An annual forecast is calculated, along with a decimal forecast (by day and week) for the 52 weeks that comprise the annual forecast
A category or department level selling pattern is calculated at the store location (or other’s if needed)
Simple user forecast thresholds are applied against the annual forecast to determine the forecast time period and how to determine the selling pattern – including using the category/department level pattern from above for slow sellers (to get the sales pattern)
The same thresholds determine whether to convert the decimal forecast to integers
For the forecasts that will be converted to integers, a random number between 0 and 1 is calculated, then the small decimal forecasts are added from there and once the cumulative forecast hits 1, then an integer forecast is 1 is used in that period, and the counter and randomizer starts again…this logic is applied to the 52 week forward looking forecast

Now, while the above is tougher to write to help understand, our experience in outlining this to people is that they not only understand, but it makes intuitive sense to them.

This solution was originally key functionality of the RedPrairie Collaborative Flowcasting solution and is now available within the JDA solution set, aptly named JDA Slow Mover Forecasting and Replenishment.

Yes, but does it work?

The graph below outlines the sales forecasts of our recent implementation of Flowcasting at a Canadian hardgoods retailer, using this exact approach:

As you can see, a significant number of products are slow or very slow sellers (54% sell less than one unit per month at a store). However, using this approach the company was able to improve in-stock by 6%, while also reducing and improving inventory performance.

Having an integer-like forecast for all these item/store combinations is important since it allows them to calculate time-phased DC and vendor replenishment plans, along with complete capacity and financial projections – allowing them to work to a single set of numbers.

In addition, the solution is so much simpler in terms of understanding, flexibility and processing requirements.

Given the above, people should embrace this solution full tilt. This should be a no-brainer, right?

Nope, wrong.

Our old villain, ingrained, has helped cement the view of higher level forecasting in retail.

It’s ironic, and a little sad, that a number of people and companies who advise and help companies change and learn new and presumably better ways have not embraced this approach, and instead are still pushing old, tired and ineffective solutions.

They need to ungrain their thinking (ungrain is the opposite of ingrain and yes, I made this word up!).

My advice is simple: if you’re a retailer who is forecasting at a higher level, or you’re someone who’s pushing this approach, please stop.

Learn. Understand. See it yourself. Ask WHY. And, importantly…

Ungrain the old and begin to ingrain the new.

A beautiful mind

Posted on November 1, 2017 by Mike Doherty

Do you remember the movie “A Beautiful Mind”?

The film is based on mathematician Dr. John Nash’s life, and, during one part, attempts to explain how Nash got the idea for his equilibrium theory as a part of game theory. In the scene Dr. Nash is at a bar with three pals, and they are all enraptured by a beautiful blond woman who walks in with her friends.

While his friends banter about which of them would successfully woo the woman, Dr. Nash concludes they should do the opposite – Ignore her. “If we all go for her,” he says, “we block each other and not a single one of us is going to get her. That’s the only way we win.” That’s the moment when he formulated his idea.

The idea that pops into Dr. Nash’s head at that moment is very instructive in the innovation process. Often, real innovation happens because you are in a situation and you’re paying attention, or listening, and you just connect the dots. It’s the subconscious mind at work, finally coming to grips with something you’ve likely been pondering for a while.

It’s a great film and a beautiful story.

Here’s another beautiful story of essentially the same approach that was used to create the breakthrough thinking and solution in demand planning at store level – which, as we know, drives the entire Flowcasting process.

In retail, forecasting at store level, systemically, has been a major challenge for a long time. Not only do most retailers have millions of store/item combinations, they also need to deal with virtually every imaginable sales pattern. But, by far, the largest challenge, is the large number of slow selling items – accounting for 50%+ of virtually any retailers assortment.

The main issues with slow selling items is twofold: finding a selling pattern amongst sparse data, and ensuring that the forecast reflected the somewhat random nature of the actual sales.

The hero in our true story is named Darryl. Darryl is the architect of the RedPrairie Flowcasting solution (now part of JDA) and, specifically, the profile-based, randomized integer forecasting approach that has simplified retail store level forecasting to a beautiful, elegant, intuitive process that does something incredible – it works and is very low touch.

The baseline forecasting process works like this:

In Darryl’s approach, unlike that of other attempts, he first calculates an annual forecast by item/store. Then simple user defined sales thresholds automatically doing the following:

Determine what time period to use to forecast in (weeks, months, quarters, semi-annual)
Determine which level of already pre-aggregated history to use to spread the annual forecast in the time period
Determine whether to convert the forecast into integers – which he randomizes by store/item, ensuring that the same item across many stores will not have an integer forecast in the same week

How did he think of this? Well, similar to Dr. Nash, he found himself in a situation where someone said something very interesting and it sparked his thinking and helped him connect the dots.

Rumour has it that Darryl was walking around a Canadian Tire store years ago and was talking to the owner of the store. They approached a section of the store and the owner grabbed a particular product and said something like, “I don’t know when we’ll sell these, all I know is that we’ll sell two every quarter”!

BOOM! The idea for a different time period for forecasting by item/store popped into Darryl’s head and this event triggered the thinking and eventual development of the baseline forecasting process.

This is a significant development – so much so that it has been patented and is now available with the JDA product solution set. What it has done is obsolete the need for multi-level forecasting approaches that, to date, have been the norm in attempting to create store/item level forecasts.

This approach is simple, intuitive, elegant and is computationally blazingly fast – another key requirement in retail store level forecasting.

Oh, and it also works. We implemented this exact approach during our very successful implementation of Flowcasting at Princess Auto. The solution is forecasting items in all varying time periods and is creating store/item forecasts for products that sell from 1 unit a year at store level, to over 25,000 units a year.

Even more important is that the people that would become demand planners (with no prior knowledge or experience in demand planning) would understand and become proficient using this approach. Just another benefit of simplicity.

John Nash looks like any other bloke. But, without a doubt, he’s got a beautiful mind.

The hero in our story, Darryl is just like John. If you met him, you’d immediately think he’s another Vermont farmer who’s good with hydraulics. But behind those coveralls and hay-stained hands is…

A beautiful mind.

Flipping your thinking

Posted on May 3, 2017 by Mike Doherty

When students at Segerstrom High School in California attend calculus class, they’ve already learned the day’s lesson beforehand — having watched it on a short online video prepared by their teacher, the night before.

So without a lecture delivered by a teacher, students spend class time doing practice problems in small groups, taking snap quizzes, explaining concepts to the class, and sometimes making their own videos while the teacher moves from student to student to help kids who are having problems.

It’s a new form of learning called Flip – because the idea has flipped traditional education on its head – homework is for the lecture, while the classroom, traditionally reserved for the lecture, is for practice and deeper learning and collaboration.

Flipped learning is catching on in a number of schools across North America, as a younger, more tech-savvy student population – including teachers – now make up the typical classroom.

When it comes to supply chain planning, the concept of flipping applies nicely and most people, and most companies, could benefit greatly by flipping their thinking.

Let’s take CPG manufacturers. When it comes to demand planning, they have it difficult. Trying to forecast what their retail and other customers are going to do and want is difficult and it’s not getting any easier. The empowered consumer, changing and dynamic retailer-led strategies are just two examples of shifts that are making it almost impossible to predict the demand, with any level of reasonableness. The result? Additional inventory and buffer stock required to respond, “just in case”.

There are a number of studies that prove this point. Forecast accuracy has not improved and, in most cases, it’s getting worse.

Supply chain practitioners and experts are responding in the typical fashion. We need better algorithms, fancier formulas, maybe even artificial intelligence and some big data sprinkled on top in order to find a better forecasting engine.

Sorry folks, that’s not working and as consumers and customers become more demanding and expectations rise, it’s going to get worse. What’s needed is to flip the thinking and to change the paradigm.

CPG manufacturers, for the most part, are forecasting what should be calculated. The demand plan they are trying to predict for their customer, should be provided to them in the form of a supplier schedule. And that schedule should reflect the latest knowledge about the consumer, and any and all associated strategies and tactics that will entice the consumer’s buying patterns and/or product flows.

Forecasting consumer demand is, as has been proven, simpler and easier that trying to predict dependent demand – that is, the resulting demand on DC’s and plants based on ordering rules, lead times, and other constraints that tend to “pollute” the dependent demand plan.

When it comes to demand planning, Joe Orlicky had it right some 40 years ago: you should never forecast what can be calculated.

Of course, what we’re talking about is a retailer using the Flowcasting process to plan all flows from supplier to consumer – factoring in any and all constraints that translate the consumer forecast into the purchase projection from retailer to supplier.

Why is this so much better than the traditional approaches? First, the entire retail supply chain (or any industrial supply chain) is driven by only one forecast – consumer demand. All other demands can and should be calculated. The effect is to dramatically simplify planning. The retailer and manufacturer are working to a single, shared forecast of what’s expected to sell.

Second, the entire supply chain can be re-planned quickly and effortlessly – making the supply chain agile and dynamic. Changes are and can be viewed almost in real-time and the changes are automatically translated for all partners in the supply chain – in units, cube, weight, dollars, capacity or any language needed throughout the supply chain. The result is that the entire supply chain is working to a single set of numbers.

Third, when you embrace the idea of Flowcasting as it relates to planning, you get so much more than a better forecast. Unlike traditional approaches that are trying to mathematically predict the demand, the supplier schedules that are a resultant of the Flowcasting process, calculate the demand by aggregating product flows.

Therefore, trading partners can see, well into the future, projected product flows between any two locations and this provides tremendous insight and flexibility to improve and smooth flows, as well as proactively put in place solutions to potential flow issues before they happen. The retailer and manufacturer can actually work, using the same system and process, as if they were one company – all oriented to delight and deliver to the consumer, in the most profitable manner possible.

Finally, in addition to providing product flows the approach also produces projections of sales, inventory, purchases, receipts and, as mentioned, flows in any language of the business – units, cube, weight and capacities for operations folks and dollars for financial folks and Management in order to get better control of the business and ensure that plans stay on track.

If you’re planning the retail supply chain, you get so much more when you forecast less.

So, what is the path forward for manufacturers?

They need to flip their thinking and understand that they are trying to forecast what should be calculated – and that this practice will soon be obsolete.

Next, they should engage and work with their key retail and other customers to help educate their customers that a process like Flowcasting not only helps them (in the form of a supplier schedule and complete visibility), it provides even more value to the retail customer. In fact, to date, it’s the only planning approach that consistently delivers in-stock levels of 98%+, even during promotions – crushing the industry averages of around 92%.

Once they are successful, a CPG manufacturer, over time, can be working with their top retail customers and receiving valid, up-to-date, supplier schedules that in most companies account for 70-85% of their volume. The additional demands can then be forecasted using the latest approaches – demand sensing, etc.

Imagine, for a moment, what that would mean to the retail industry and the CPG manufacturers in general. The impact would be enormous – from increased sales and profits, to significant reductions in inventory and working capital. Not to mention the impact to consumers and customer loyalty.

Is all this possible?

Sure, but to make it happen the first step is to flip your thinking.

I’m From Missouri

Posted on April 5, 2017 by Jeff Harrop

“I am from a state that raises corn and cotton and cockleburs and Democrats, and frothy eloquence neither convinces nor satisfies me. I am from Missouri. You have got to show me.” – William Duncan Vandiver, US Congressman, speech at 1899 naval banquet

“How are you going to incorporate Big Data into your supply chain planning processes?”

It’s a question we hear often (mostly from fellow consultants).

Our typical response is: “I’m not sure. What are you talking about?”

Them: “You know, accessing social media and weather data to detect demand trends and then incorporating the results into your sales forecasting process.”

Us: “Wow, that sounds pretty awesome. Can you put me in touch with a retailer who has actually done this successfully and is achieving benefit from it?”

Them: <crickets>

I’m not trying to be cheeky here. On the face of it, this seems to make some sense. We know that changes in the weather can affect demand for certain items. But sales happen on specific items at specific stores.

It seems to me that for weather data to be of value, we must be able to accurately predict temperature and precipitation far enough out into the future to be able to respond. Not only that, but these accurate predictions need to also be very geographically specific – markets 10 miles from each other can experience very different weather on different days.

Seems a bit of a stretch, but let’s suppose that’s possible. Now, you need to be able to quantify the impact those weather predictions will have on each specific item sold in each specific store in order for the upstream supply chain to respond.

Is that even possible? Maybe. But I’ve never seen it, nor have I even seen a plausible explanation as to how it could be achieved.

With regard to social media and browsing data, I have to say that I’m even more skeptical. I get that clicks that result in purchases are clear signals of demand, but if a discussion about a product is trending on Twitter or getting a high number of page views on your e-commerce site (without a corresponding purchase), how exactly do you update your forecasts for specific items in specific locations once you have visibility to this information?

If you were somehow able to track how many customers in a brick and mortar store pick up a product, read the label, then place it back on the shelf, would that change your future sales expectation?

Clearly there’s a lot about Big Data that I don’t know.

But here’s something I do know. A retailer who recently implemented Flowcasting is currently achieving sustained daily in-stock levels between 97% and 98% (it was at 91% previously – right around the industry average). This is an ‘all in’ number, meaning that it encompasses all actively replenished products across all stores, including seasonal items and items on promotion.

With some continuous improvement efforts and maybe some operational changes, I have no doubt that they can get to be sustainably above 98% in stock. They are not currently using any weather or social media Big Data.

This I have seen.

Respect the Fat Lady

Posted on February 1, 2017 by Jeff Harrop

It ain’t over ’til the fat lady sings. – Modern Proverb

When is it too late to update a forecast?

Here’s a theoretical scenario. You’re a retailer who sells barbecue charcoal. The July 4th is approaching and a large spike in sales is predicted for that week.

Time marches on and now you’re at the beginning of the week in which the holiday is going to happen. For a large swath of the country, a large storm front is passing through and there’s no way in hell that people will be out barbecuing in their usual numbers.

Remember, the holiday is only a few days away now. Chances are that the stores have already received (or have en route) a large amount of charcoal based on the forecast that was in force when outbound shipments were being committed to the stores.

So, we’re already within the week of the forecasted event and most (if not all) of the product has already been shipped to support a sales forecast that is way too high. Nothing can be done at this point to change that outcome.

So changing the forecast to reflect the expected downturn in sales is basically pointless, right?

Au contraire.

When the entire supply chain is linked to the sales forecast at the store shelf, then the purpose of the forecast goes far beyond just replenishing the store.

The store sales forecast drives the store’s replenishment needs and the store replenishment needs drive the DC’s replenishment needs, and so on. All of this happens on a continuum that really has nothing to do with what’s already been committed and what hasn’t.

If your sales forecast for charcoal in the affected stores is 5,000 units over the next 5 days, but you know with a pretty high degree of certainty that you will only sell about 2,000 because of the weather, then why would you delay the process of realigning the entire supply chain to this new reality by several days just because you can’t affect the immediate outcome in the stores right now?

The point here is that while the supply chain is constrained, the sales forecast that drives it is not. It may not be possible for a forecast update to change orders that are already en route, but it is always possible to change the next planned order based on the new reality. In that way, you already have a plan in place that is starting to get you out of trouble before the impact of the problem has even fully materialized. In other words, bad news early is better than bad news late.

If you have information that you think will materially impact sales, then the only time it’s too late to update the forecast is after it’s already happened.

The Point of No Return

Posted on December 7, 2016 by Jeff Harrop

Events in the past may be roughly divided into those which probably never happened and those which do not matter. – William Ralph Inge (1860-1954)

Tedious. Banal. Tiresome.

These are all worthy adjectives to describe this topic.

So why am I even discussing it?

Because, for some reason I’m unable to explain, the question of how to deal with saleable merchandise returns in the sales forecasting process often seems to take on the same gravity as a discussion of Roe v. Wade or the existence of intelligent extraterrestrial life.

Point of sale data, imperfect as it is, is really the only information we have to build an historical proxy of customer demand. However, the POS data contains both sales and merchandise returns, so the existential question becomes: Do we build our history using gross sales or net sales?

The main argument on the ‘gross sales’ side of the debate is that a return is an unpredictable inventory event, not a true indicator of ‘negative demand’.

On the ‘net sales’ side, the main argument is that constructing a forecast using gross sales data overstates demand and will ultimately lead to excess inventory.

So which is correct?

Gross sales and here’s why: Demand has two dimensions – quantity and time.

Once a day has gone into the past, whatever happened, happened. Although most retailers have transaction ID numbers on receipts that allow for returns to be associated with the original purchase, we must assume that the customer intended to keep the item on the day it was purchased.

The fact that there was negative demand a few days (or weeks) later doesn’t change the fact that there was positive demand on the day of the original purchase.

Whenever I’m at a client who starts thinking about this too hard, I like to use the following example:

Suppose that you know with 100% certainty that you will sell 10 units of Product X on a particular day. Further suppose that you know with 100% certainty that 4 units of Product X will be returned in a saleable state on that same day.

You don’t know exactly when the sales will happen throughout the day, nor do you know exactly when the returns will happen. At the beginning of that day, what is the minimum number of units of Product X you would want to have on the shelf?

If your answer is 10 units, then that means you want to plan with gross sales.

If your answer is less than 10 units, then that means you’re not very serious about customer service.