Check out this article about Princess Auto’s Flowcasting journey from Supply Chain Management Review, authored by Mike Doherty and Sylvain Landry.
Also available on our Resources page.
Did you know that the iHome alarm clock, common in many hotels, shows a small PM when the time is after 12 noon? You wonder how many people fail to note the tiny ‘pm’ isn’t showing when they set the alarm, and miss their planned wake up. Seems a little complicated and unnecessary, wouldn’t you agree?
Did you also know that most microwaves also depict AM or PM? If you need the clock in the microwave to tell you whether it’s morning or night, somethings a tad wrong.
More data/information isn’t always better. In fact, in many cases, it’s a costly distraction or even provides the opportunity to get the important stuff wrong.
Contrary to current thinking, data isn’t free.
Unnecessary data is actually expensive.
If you’re like me, then your life is being subjected to lots of data and noise…unneeded and unwanted information that just confuses and adds complication.
Just think about shopping now for a moment. In a recent and instructive study sponsored by Oracle (see below), the disconnect between noise and what consumers really want is startling:
From the consumers view what this is telling us, and especially supply chain technology firms, we don’t seem to understand what’s noise and what’s actually relevant. I’d argue we’ve got big time noise issues in supply chain planning, especially when it relates to retail.
I’m talking about forecasting consumer sales at a retail store/webstore or point of consumption. If you understand retail and analyze actual sales you’ll discover something startling:
Many of the leading supply chain planning companies believe that the answer to forecasting and planning at store level is more data and more variables…in many cases, more noise. You’ll hear many of them proclaim that their solution takes hundreds of variables into account, simultaneously processing hundreds of millions of calculations to arrive at a forecast. A forecast, apparently, that is cloaked in beauty.
As an example, consider the weather. According to these companies not only can they forecast the weather, they can also determine the impact the weather forecast has on each store/item forecast.
Now, since you live in the real world with me, here’s a question for you: How often is the weather forecast (from the weather network that employs weather specialists and very sophisticated weather models) right? Half the time? Less? And that’s just trying to predict the next few days, let alone a long term forecast. Seems like noise, wouldn’t you agree?
Now, don’t get me wrong. I’m not saying the weather does not impact sales, especially for specific products. It does. What I’m saying is that people claiming to predict it with any degree of accuracy are really just adding noise to the forecast.
Weather. Facebook posts. Tweets. The price of tea in China. All noise, when trying to forecast sales by product at the retail store.
All this “information” needs to be sourced. Needs to be processed and interpreted somehow. And it complicates things for people as it’s difficult to understand how all these variables impact the forecast.
Let’s contrast that with a recent retail implementation of Flowcasting.
Our most recent retail implementation of Flowcasting factors none of these variables into the forecast and resulting plans. No weather forecasts, social media posts, or sentiment data is factored in at all.
None. Zip. Zilch. Nada. Heck, it’s so rudimentary that it doesn’t even use any artificial intelligence – I know, you’re aghast, right?
The secret sauce is an intuitive forecasting solution that produces integer forecasts over varying time periods (monthly, quarterly, semi-annually) and consumes these forecasts against actual sales. So, the forecasts and consumption could be considered like a probability. Think of it like someone managing a retail store. They can say fairly confidently that “I know this product will sell one this month, I just don’t know what day”!
The solution also includes simple replenishment logic to ensure all dependent plans are sensible and ordering for slow selling products is based on your opinion on how probable you think a sale is likely in the short term (i.e., orders are only triggered for a slow selling item if the probability of making a sale is high).
In addition to the simple, intuitive system capabilities above, the process also employs and utilizes a different kind of intelligence – human. Planners and category managers, since they are speaking the same language – sales – easily come to consensus for situations like promotions and new product introductions. Once the system is updated then the solution automatically translates and communicates the impact of these events for all partners.
So, what are the results of using such a simple, intuitive process and solution?
The process is delivering world class results in terms of in-stock, inventory performance and costs. Better results, from what I can tell, than what’s being promoted today by the more sophisticated solutions. And, importantly, enormously simpler, for obscenely less cost.
Noise is expensive.
The secret for delivering world class performance (supply chain or otherwise) is deceptively simple…
Strip away the noise.
Good intentions can often lead to unintended consequences. – Tim Walberg
Retailers with brick and mortar operations are always trying to keep the checkout lines moving and get customers out the door as quickly as possible. Many collect time stamps on their sales transactions in order to measure and reward their cashiers based on how quickly they can scan.
Similarly, being able to receive quickly at the back of the store is seen as critical to customer service – product only sells off the shelf, not from the receiving bay or the back of a truck.
This focus on speed has led to many in-store transactional “efficiencies”:
These time saving measures can certainly delight “the customer of this moment”, but there can also be consequences.
In the “mult key” example, the 12 cans scanned could be across 6 different flavours of juice. The customer may not care since they’re paying the same price, but the inventory records for 6 different SKUs have just been fouled up for the sake of saving a few seconds. To the extent that the system on hand balances are used to make automated replenishment decisions, this one action could be inconveniencing countless customers for several more days, weeks or even months before the lie is exposed.
The smile on a customer’s face because you saved her 5 seconds at the checkout or the cashier speed rankings board in the break room might be tangible signs of “great customer service”, but the not-so-easy-to-see costs of stockouts and lost sales that arise from this practice over time is extremely costly.
Similarly with skipping code checks or “pencil whipping” back door receipts. Is sacrificing accuracy for the sake of speed really good customer service policy?
A recent article published in Canadian Grocer magazine begins with the following sentence:
“A lack of open checkouts and crowded aisles may be annoying to grocery shoppers, but their biggest frustration is finding a desired product is out of stock, according to new research from Field Agent.”
According to the article, out of stocks are costing Canadian grocers $63 billion per year in sales. While better store level planning and replenishment can drive system reported in-stocks close to 100%, the benefits are muted if the replenishment system thinks the store has 5 units when they actually have none.
Not only does this affect the experience of a walk-in customer looking at an empty shelf, but it’s actually even more serious in an omnichannel world where the expectation is that retailers will publish store inventories on their public websites (gulp!). An empty shelf is one thing, but publishing an inaccurate on hand on your website is tantamount to lying right to your customers’ faces.
We’ve seen firsthand that it’s not uncommon for retailers to have a store on hand accuracy percentage in the low 60s (meaning that almost 40% of the time, the system on hand record differs from the counted quantity by more than 5% at item/location level). Furthermore, we’ve found that on the day of an inventory count, the actual in stock is several points lower than the reported in stock on average.
Suffice it to say that inaccurate on hand records are a big part of the out of stocks problem.
Nothing I’ve said above is particularly revolutionary or insightful. The real question is why has it been allowed to continue?
In my view, there are 3 key reasons:
It’s true that overcoming inertia on this will not be easy.
Your customers’ expectations will continue to rise regardless.
It’s 1971 and Bill Fernandez would do something that would change the course of history. On that fateful day, Bill decided to go for a nice stroll with his good friend, Steve Jobs. As luck would have it, their walk took them pass the house of another of Bill’s pals, Steve Wozniak.
Luckily, Woz’s car was dirty and he was outside, washing it. Bill introduced the two Steve’s and they instantly hit it off. They both shared a passion for technology and practical jokes. Soon after, they started hanging out, collaborating and eventually working together to form Apple. The rest is history.
It’s incredible, in life and business, how powerful and important Luck is.
People who know me well, know that I’m an avid reader and one of the authors that’s influenced my thinking the most is the legendary Tom Peters – you know, of In Search of Excellence fame, among many other brilliant works.
Tom’s also a big believer in Luck. In fact, he believes it’s the most important factor in anyone’s success. I think he’s right. As he correctly points out in his ditty below, you make your own luck and, when you do, you just get luckier and luckier – which is an ongoing philosophy that helps you learn, change, grow and deliver.
So, today, I’m celebrating and counting my lucky stars. I know that luck is THE factor in any success (and failures) that I’ve had. Just consider…
Years ago, I started my career fresh from school at a prestigious consulting firm in downtown Toronto. As luck would have it, one of my Partners, Gus, gave me some brilliant advice. He said to me, “Mike you don’t know shit. The only way to learn is to read. Tons. I’ll make a deal with you. For every business related book you read, the firm will pay for it”. Luckily, I took the advice of Gus and this propelled me into life-long reading and learning.
Roughly 20 years ago, another massive jolt of luck helped me considerably. I was leading a team at a large Canadian retailer who would eventually design what we now call Flowcasting, along with delivering the first full scale implementation of integrated time-phased planning and supplier scheduling in retail.
The original design was enthusiastically supported by our team, but did not have the blessings of Senior Management. In fact, the VP at the time (my boss) indicated that this would not work, we’d better change it, or I’d be fired.
Luckily one of the IT folks, John, then said to me something like “this is just like DRP at store level. You should call Andre Martin and see what he thinks”. To which I replied, “Who’s Andre Martin and what is DRP?”. The next day John brought me copy of Andre’s book, Distribution Resource Planing. I read it (luckily I’m a reader you know) and agreed. I called Andre the next day and eventually he and his colleague, Darryl, helped us convince Senior Management the design was solid – which led to a very successful implementation and helped change the paradigm of retail planning.
As luck would have it, my director on that initial project would later become CEO of Princess Auto Ltd (PAL) – as you know, an early adopter of the Flowcasting process and solution. Given his understanding of the potential of planning and connecting the supply chain from consumption to supply, it was not surprising that we were called to help. Luck had played an important role again.
Luck also played a significant role in the successful implementation of Flowcasting at PAL. The Executive Sponsor, Ken, and the Team Lead, Kim, were people that:
We were lucky that the three of us had very similar views and philosophy regarding change – focusing on changing the mental model, and less on spewing what I’d call Corporate Mayonnaise.
In addition to being like-minded, the project team at PAL were lucky in that they used a software solution that was designed for the job. The RedPrairie Collaborative Flowcasting solution was designed for purpose – a simple, elegant, low-touch, intuitive system that is easy to use and even easier to implement.
We were very lucky that as an early adopter, we were given the opportunity to use the solution to prove the concept, at scale. As a result, our implementation focused mainly on changing minds and behaviors rather than the typical system and integration issues that plague these implementations when a solution not fit for purpose is deployed.
So, my advice to you is simple. When you get the chance, jot down all the luck you’ve had in your career and life so far. If you’re honest, you’ll realize that luck has played a huge role in your success and who you are today.
And, by all means, you should continue to welcome and encourage more luck into your life.
Thank you and Good Luck!
It requires a very unusual mind to undertake the analysis of the obvious. – Alfred North Whitehead (1861-1947)
My doctor told me that I need to reduce the amount of salt, fat and sugar in my diet. So I immediately increased the frequency of oil changes for my car.
I don’t blame you. That’s how I felt after I read a recent survey about the adoption of artificial intelligence (AI) in retail.
Note that I’m not criticizing the survey itself. It’s a summary of collected thoughts and opinions of retail C-level executives (pretty evenly split among hardlines/softlines/grocery on the format dimension and large/medium/small on the size dimension), so by definition it can’t be “wrong”. I just found some of the responses to be revealing – and bewildering.
On the “makes sense” side of the ledger, the retail executives surveyed intend to significantly expand customer delivery options for purchases made online over the next 24 months, specifically:
This supports my (not particularly original) view that the physical store affords traditional brick and mortar retailers a competitive advantage over online retailers like Amazon, at least in the short to medium term.
However, the next part of the survey is where we start to see trouble (the title of this section is “Retailers Everywhere Aren’t Ready for the Anywhere Shelf”):
What’s worse is that there is no mention at all about inventory accuracy. I submit that the other 45% and 22% respectively may have inventory visibility capabilities, but are they certain that their store level inventory records are accurate? Do they actually measure store on hand accuracy (by item by location in units, which is what a customer sees) as a KPI?
The title of the next slide is “Customer Experience and Supply Chain Maturity Demands Edge Technologies”. Okay… Sure… I guess.
The slide after that concludes that retail C-suite executives believe that the top technologies “having the broadest business impact on productivity, operational efficiency and customer experience” are as follows:
Towards the end, it was revealed that “The C-suite is planning a 5X increase in artificial intelligence adoption over the next 2 years”. And that 50% of those executives see AI as an emerging technology that will have a significant impact on “sharpening inventory levels” (whatever that actually means).
So just to recap:
I’m reminded of the scene in Die Hard 2 (careful before you click – the language is not suitable for a work environment or if small children are nearby) where terrorists take over Dulles International Airport during a zero visibility snowstorm and crash a passenger jet simply by transmitting a false altitude reading to the cockpit of the plane.
Even in 1990, passenger aircraft were quite technologically advanced and loaded with systems that could meet the definition of “artificial intelligence“. What happens when one piece of critical data fed into the system is wrong? Catastrophe.
I need some help understanding the thought process here. How exactly will AI solve the inventory visibility/accuracy problem? Are we talking about every retailer having shelf scanning robots running around in every store 2 years from now? What does “sharpen inventory levels” mean and how is AI expected to achieve that (very nebulous sounding) goal?
I’m seriously asking.
Read an awesome report from Bob Phibbs (What do shoppers really want?) about what customers really want in retail. The key finding is that it’s not necessarily about technology, it’s about something more basic – more human. They want a streamlined, smooth shopping experience and they want help from people…someone to answer questions and make them feel important.
The authors conclude that “When customers have an emotional connection to the brand, they feel more confident buying and that can’t be created by just putting a robot in your store; you need a branded shopping experience”.
My colleagues and friends from the LEVEL5 Strategy Group not only understand, but they can help. They have the capability to assess and measure, in depth, how customers “feel” emotionally about a retail brand. This always uncovers revelations and insights that were previously missed or unknown. From that, they help craft winning brand strategies to amplify the positives and address the negatives.
Business is not about algorithms. It’s about people. It’s social. And it’s emotional. Winning strategies always start within the hearts of people. Win the heart and the mind will follow.
It’s an altercation that’s stuck with me for decades.
Roughly twenty years ago I was leading a retail team that would eventually design what we now call Flowcasting. We were an eclectic team, full of passion and dedicated to designing and implementing something new, and much better.
After a particularly explosive team session – that saw tensions and ideas run hot – everyone went back to their workstations to let sleeping dogs lie. One business team member, who’d really gotten into it with one of the IT associates, could not contain his passion. He promptly walked over to the team member’s cubicle and said…
“Oh, one more thing…F**k You!!”
Like most of the team, I was a little startled. I went over and talked to the team member and we had a good chat about how inappropriate his actions were. Luckily the IT team member was one cool dude and he didn’t take offence to it – the event just rolled off his back. To his credit, the next day my team member formally apologized and all was forgiven.
Now, please don’t think I’m condoning this type of action. I’m not. However, as a student of business, change and innovation I’ve been actively learning and trying to understand what really seeds innovation and, in particular, what types of people seem to be able to make change happen.
And, during my research and studies, I keep coming back to this event. It’s evidence of what seems to be a key trait and characteristic of innovative teams and people. They are what many refer to as…
If I think back to that team from two decades ago, we were definitely unvarnished. We called a spade a spade. Had little to no respect to the company hierarchy and even less for the status quo. And, as a team, we were brutally honest with each other and everyone on the team felt very comfortable letting me know when I was full of shit – which was, and continues to be, often.
But that team moved, as Steve Jobs would say, mountains – not only designing what would later morph into Flowcasting, but implementing a significant portion of the concept and, as a result, changing the mental model of retail planning.
I had no idea at the time, but being unvarnished was the key trait we had. Franseca Gino has extensively studied what makes great teams and penned a brilliant book about her learnings, entitled “Rebel Talent”.
She dedicates consider time to unvarnishment and quotes extensively from Ed Catmull, famed leader of Pixar Animation Studios who’s worked brilliantly with another member of the unvarnished hall of fame – Steve Jobs.
According to Catmull, “a hallmark of creative cultures is that people feel free to share ideas, opinions and criticisms. When the group draws on the unvarnished perspectives of all its members, the collective knowledge and decision making benefits.”
According to Catmull, and others (including me), “Candor is the key to constructive collaboration”. The KEY to disruptive innovation.
Here’s another example to prove my point. When I was consulting at a national western Canadian retailer, our team was lucky to have an Executive Sponsor who was, as I now understand, unvarnished as well.
As the project unfolded I was amazed how he operated and the way he encouraged and responded to what I’d call dissent. Most leaders of teams absolutely abhor dissent – having been unfortunately schooled over time that company hierarchy was there for a reason and was the tie-breaker on decision making and direction setting.
Our Sponsor openly encouraged people to dissent with him and readily and openly changed his mind whenever required. I vividly remember a very tense and rough session around job design and rollout in which he was at loggerheads with the team, including me. When I think back, it was amazing to see how “safe” team members felt disagreeing with him – and, in this case, very passionately.
As it turned out, over the next few days, we continued the dialogue and he changed his opinion 180 degrees – eventually agreeing with his direct report.
Neuroscience refers to this as being able to work with “psychological safety” – which is a fancier way of saying people are free to be unvarnished. To say what they believe, why and to whom with no consequences whatsoever.
Without question, as I’ve been thinking and studying great teams and innovation I realize just how brilliant this Sponsor was and the environment he helped to foster.
How many Executives, Leaders or teams are really working in an unvarnished environment – with complete psychological safety? I think you’d agree, not many.
If you, your company and your supply chain is going to compete and continually evolve and improve, won’t ongoing innovation need to become a way of life? And that means people need to collaborate better, disrupt faster and feel completely comfortable challenging and destroying the status quo.
Now, I’m not saying that when you don’t agree with someone to tell them to go F-themselves.
What I am saying – and other folks who are a lot smarter than me – is that hiring, promoting, encouraging and fostering people and a working environment that is unvarnished will be a crucial!
So here’s to being unvarnished. To being and working in safety. To real collaboration and candor.
And to looking your status quo in the eye and saying…”F**k you!”
There is no shame in not knowing; the shame lies in not finding out. – Russian Proverb
Customer expectations of brick & mortar retailers are changing.
Most retailers are failing miserably at meeting those expectations with regard to providing information about stock availability at their stores online.
I’m not talking about whether or not they have sufficient stock to meet customer demand – it’s even more basic than that. When a customer is looking to visit your store can you even properly tell him/her what your stock status actually is?
Recently, I decided to anecdotally put one particular store to the test on this. I chose this store for the following reasons:
On the day of my “study”, I only had 2 items I needed. Before leaving, I called up the pages for those items on my iPhone and went to the store. When I got there, I refreshed the pages to retrieve the most up-to-date stock information and compared that number to what I actually found on the shelf. After that, I wandered around the aisles and picked a few other items at random and did the same thing.
Now before I share the results, there are some rather significant caveats that I need to mention:
The first item on my list was a carbon dioxide cylinder for our SodaStream. Note that I’ve attempted to crop out any information that would reveal who the retailer is (logos, shelf tags, product identifiers, etc.). This won’t stop some of you from recognizing them, but I can’t do much about that.
Okay, back to the SodaStream cylinder. When I reached the shelf and refreshed the page on my phone, here’s what I got:
Wow, 337 units in stock! (As an aside, this retailer almost always shows the aisle number in the store where the product can be found, which is stellar – not sure why it’s not shown in this case, but it’s a product I buy often, so I knew exactly where to go).
Now here’s the shelf:
You can’t see them all in this image, but the actual count was 18 units, far short of 337. Obviously this is either a massive inventory record error or there’s a pallet of them on a secondary display or in the back room. So long as they sell fewer than 18 per day, buyers of this item will be happy.
The second item on my list was a large, bark deterring dog collar for my mother-in-law’s dog (it uses vibration or noise to deter barking, not electric shocks, so don’t judge me!). As you’ll see below, my phone told me to go to aisle 56 to find 1 unit:
Unfortunately when I got to the aisle, there was none to be found. I spent a few minutes searching all of the overheads, pegs and bins in this aisle and one aisle over in each direction and couldn’t find it.
While in aisle 56, I picked another random item (mulberry scented dog shampoo) and looked it up on my phone:
And here is the shelf:
6 units – right on the nose.
Now, how about this Bissell Little Green pet stain remover?
This item is on promotion for $25.00 off and I found an end aisle display with 12 units:
…and one more unit in the home in aisle 60:
So that’s 13 on the shelf vs 32 units reported on hand. But because this item is promoted, there is almost certainly more in the back room to replenish the shelves.
On to aisle 17 to check out the Stanley chalk line reels.
Hoping to find 5…
…and 5 it is.
You get the picture (no pun intended). I also documented a few other items in the same way, but I’ll spare you the photographic evidence:
So what was the point of all this and why did I choose “Concealing Your Shame” as the title? Am I trying to shame this retailer for what (anecdotally and with all of my previous caveats applied) looks like imperfect performance?
Store on hand accuracy is not easy to achieve and this retailer is to be highly commended for their confidence and willingness to be as transparent to customers as possible.
No, the shame is reserved for those retailers who have on hand balances readily available in their systems but choose not to share it. I guess the thinking is that you can’t fail if you don’t try.
I say it again: customer expectations are changing.
If you’re afraid to share your on hand balances with your customers, I have 2 questions:
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!
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:
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):
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?
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:
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.