Tag Archives: Forecasting

Fundamentals

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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

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Game6-FinalPosition

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

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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:

Slow-sellers2

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

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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:

FcstApproach2

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:

  1. Determine what time period to use to forecast in (weeks, months, quarters, semi-annual)
  2. Determine which level of already pre-aggregated history to use to spread the annual forecast in the time period
  3. 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

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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

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“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

missouri

“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

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

opera

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

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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)

best-archery-return-policy

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.

 

Measuring Forecast Performance

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Never compare your inside with somebody else’s outside – Hugh Macleod

appleorange

I’m aware that this topic has been covered ad nauseum, but first a brief word on the subject of benchmarking your forecast accuracy against competitors or industry peers:

Don’t.

Does any company in the world have the exact same product mix that you do? The same market presence? The same merchandising and promotional strategies?

If your answer to all three of the above questions is ‘yes’, then you have a lot more to worry about than your forecast accuracy.

For the rest of you, you’re probably wondering to yourself: “How do I know if we’re doing a good job of forecasting?”

Should you measure MAPE? MAD/Mean? Weighted MAPE? Symmetric MAPE? Comparison to a naïve method? Should you be using different methods depending on volume?

Yes! Wait, no! Okay, maybe…

The problem here is that if you’re looking for some arithmetic equation to definitively tell you whether or not your forecasting process is working, you’re tilting at windmills.

It’s easy to measure on time performance: Either the shipment arrived on time or it didn’t. In cases where it didn’t, you can pinpoint where the failure occurred.

It’s easy to measure inventory record accuracy: Either the physical count matches the computer record or it doesn’t. In cases where it doesn’t, the number of variables that can contribute to the error is limited.

In both of the above cases (and most other supply chain performance metrics), near-perfection is an achievable goal if you have the resources and motivation to attack the underlying problems. You can always rank your performance in terms of ‘closeness to 100%’.

Demand forecast accuracy is an entirely different animal. Demand is a function of human behaviour (which is often, but not always rational), weather, the actions of your competitors and completely unforeseen events whose impact on demand only makes sense through hindsight.

So is measuring forecast accuracy pointless?

Of course not, so long as you acknowledge that the goal is continuous improvement, not ‘closeness to 100%’ or ‘at least as good as our competitors’. And, for God’s sake, don’t rank and reward (or punish) your demand planners based solely on how accurate their forecasts are!

Always remember that a forecast is the result of a process and that people’s performance and accountability should be measured on things that they can directly control.

Also, reasonableness is what you’re ultimately striving for, not some arbitrary accuracy measurement. As a case in point, item/store level demand can be extremely low for the majority of items in any retail enterprise. If a forecast is 1 unit for a week and you sell 3, that’s a 67% error rate – but was it really a bad forecast?

A much better way to think of forecast performance is in terms of tolerance. For products that sell 10-20 units per year at a store, a MAPE of 70% might be quite tolerable. But for items that sell 100-200 units per week a MAPE of 30% might be unacceptable.

Start by just setting a sliding scale based on volume, using whatever level of error you’re currently achieving for each volume level as a benchmark ‘tolerance’. It doesn’t matter so much where you set the tolerances – it only matters that the tolerances you set are grounded in reasonableness.

Your overall forecast performance is a simple ratio: Number of Forecasts Outside Tolerance / Number of Forecasts Produced * 100%.

Whenever your error rate exceeds tolerance (for that item’s volume level), you need to figure out what caused the error to be abnormally high and, more importantly, if any change to the process could have prevented that error from occurring.

Perhaps your promotional forecasts are always biased to the high side. Does everyone involved in the process understand that the goal is to rationally predict the demand, not provide an aspirational target?

Perhaps demand at a particular store is skyrocketing for a group of items because a nearby competitor closed up shop. Do you have a process whereby the people in the field can communicate this information to the demand planning group?

Perhaps sales of a seasonal line is in the doldrums because spring is breaking late in a large swath of the country. Have your seasonal demand planners been watching the Weather Channel?

Not every out of tolerance forecast result has an explanation. And not every out of tolerance forecast with an explanation has a remedy.

But some do.

Working your errors in this fashion is where demand insight comes from. Over time, your forecasts out of tolerance will drop and your understanding of demand drivers will increase. Then you can tighten the tolerances a little and start the cycle again.

Overly Sophistimicated

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There are many methods for predicting the future. For example, you can read horoscopes, tea leaves, tarot cards or crystal balls. Collectively, these are known as ‘nutty methods’. Or you can put well researched facts into sophisticated computer models, more commonly known as ‘a complete waste of time.’ – Scott Adams

If you have your driver’s license, you can get into virtually any automobile in any country in the world and drive it. Not only that, but you can drive any car made between 1908 and today.

You want to make a left turn? Rotate the steering wheel counter-clockwise.
Right turn? Clockwise.
Speed up? Press your foot down on the accelerator pedal.
Slow down? Remove your foot from the accelerator pedal.
Come to a stop? Press your foot down on the brake pedal.

Think all of the advances in automotive technology – from the Ford Model T in 1908 to the Tesla Model S in 2016… Over 100 years and countless technological leaps, yet the ‘user interface’ has remained the same (and universally applied) for all this time.

This is what makes the skill of driving easy to learn and transferable from one car to the next. And all of the complexities of road design, elevation and traffic can be solved by making the decisions on the part of the driver in any scenario very simple: speed up, slow down, stop or turn. Heck, even the lunar rover used the same user interface to deal with extraterrestrial terrain!

Not only that, but because the interface is simple and control on the part of the driver is absolute, there is built in accountability for the result. If the car is travelling faster than the speed limit, it’s because the driver made it so, manufacturing defects (most often caused by ‘over sophistimication’) notwithstanding.

While supply chain forecasting software hasn’t been around since the early 1900s, it’s been around long enough that it doesn’t seem unreasonable to expect some level of uniformity in the user interface by now.

Yet, while a semi-experienced driver can walk up to an Avis counter and be off cruising in a car model that they’ve never driven before within minutes, it would take weeks (if not months) for an experienced forecaster to become proficient in a software tool that they’ve never used before.

The difference, in my opinion, is that the automobile was designed from the start to be used by any person. Advanced degrees in chemistry, physics and engineering are needed to build a car, not operate it.

While no one expects that ‘any person’ can be a professional forecaster, it should not be necessary (nor is it economically feasible) for every person accountable for predicting demand to have a PhD in statistics to understand how to operate a forecasting system. The less understood the methods are for calculating forecasts, the easier it is for people on the front line of the process to avail themselves of accountability for the results. Police hand out speeding tickets to drivers, not passengers.

Obviously, not all cars are alike. They compete on features, gadgets, styling, horsepower and price. But whatever new gizmos car manufacturers dream up, they can’t escape the simple, intuitive user interface that has been in place for over 100 years.

While I’m sure it’s an enriching intellectual exercise to fill pages with clouds of Greek symbols in the quest to develop the most sophisticated forecasting algorithm, wouldn’t it be nice if managing a demand forecast was as easy as driving a car?