Author Archives: Jeff Harrop

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Jeff Harrop

The Lead Time Conundrum

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Everything becomes buffering and buffering becomes everything. – Tom McCarthy

Managing on time performance (inbound or outbound) is a struggle for every retailer. Whenever there’s a delivery failure, somebody asks the inevitable question: “How do we prevent this from happening again?”

Sometimes the cause is known to be out of your control – a freak snowstorm leaves trucks stranded or there is temporary congestion at a facility. For these types of unpredictable reasons, it’s simply not possible to achieve 100% on-time delivery all the time.

But what about cases where a supplying location is chronically at 80%? What if it’s dozens of locations with this problem (which is not uncommon in retail)?

What we’ve seen is that, faced with this problem, many retailers have developed the bad habit of plastering over these issues by arbitrarily increasing their planning lead times. In many cases, they have even developed analytical approaches to calculate the demonstrated order-to-delivery times on historical orders and transfers, then automatically update the planning systems with increased lead times in an effort to boost on time delivery performance.

What often happens is that on time performance does improve (for a time), which tends to validate this approach. Then results start to slip again, triggering another wave of analysis or an increase in the frequency of lead time updates to ‘stay on top of things’.

The problem with this approach is that it assumes two things:

  1. That lead times are something that ‘just happens’ over which nobody has any control.
  2. That when on time delivery performance is failing, it must be because people aren’t being given enough time to do perform their tasks.

The actual order-to-delivery cycle time in a supply chain is the result of discrete processes (order management, picking, delivery and receiving). These processes must be routine, repeatable and – most importantly – designed to achieve the goal against which you’re measuring them.

The routine and repeatable part likely isn’t the issue in most cases. The amount of time it takes to pick an order or drive a fixed distance doesn’t have a lot of variation (especially when lead times are rounded up to the nearest day). Once you know what those standard times are, there really shouldn’t be any need to change them very frequently.

Yes, there are rare, infrequent events that will cause lateness, but those events can’t be blamed for chronically poor on-time performance.

More likely than not, the culprit is that one or more of the processes is not designed to achieve on time performance.

As an example, it’s commonplace for retailers to prioritize promotional shipments to stores that aren’t due to be shipped until next week ahead of regular shipments that are due to be shipped today. If the due date is not the primary prioritization criteria, then you can’t expect it to deliver high levels of on time performance.

It’s also commonplace for suppliers to prioritize their shipments based on the size of the customer order or the price being paid for the goods, neither of which has anything to do with when stock is required.

The problem here is that the processes involved do not respect dates – or at a minimum, don’t use the due date as the top prioritization criteria. How, exactly, does increasing lead times (which merely lengthens the amount of time between the order date and the scheduled ship date) solve that problem?

All it really does is erode the ability to adapt and react to changes and necessitates additional safety stock at the destination location to cover a longer frozen window.

Like anything in the supply chain, the key to solving chronic issues with on time delivery is to find the root cause of the problem and take the necessary steps to address them. To the extent that the processes are within your organization’s control, that can be relatively straightforward (assuming the intestinal fortitude exists to prioritize all shipments based on a due date, promotional or not).

If the issue is with a supplier, there needs to be a common understanding as to how their underlying processes work and what conditions are necessary to deliver consistently on time. This goes beyond using a scorecard to try to ‘shame them into submission’.

By adopting Flowcasting and sharing a time-phased schedule of their requirements, retailers can provide very valuable preparatory information to their suppliers that will give them visibility to shipping requirements weeks and months before the purchase order is ever placed.

Don’t fall into the bad habit of setting your lead times based on a data mining exercise. Look at them with a critical eye, identify where chronic failures are occurring and attack the root cause.

Ipso Facto

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If you don’t have the best of everything, make the best of everything you have. – Erk Russell

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Store in stock.

It’s one of the most critical measures of supply chain health in retail for which there is data readily available.

Unfortunately, your customers don’t care.

Think about it – the store in stock percentage represents the number of times the system inventory record for an item/store is above some minimum quantity (could be zero, could be the shelf facings, take your pick).

What customers truly care about is on shelf availability. In other words, when they’re standing there in the aisle, is the product present on the shelf for them to buy it?

What if the system inventory record for an item says there is 5 on hand, but the store is physically out of stock?

What if the system record matches what’s physically in the four walls of the store, but the product is stuck in the back room (or some other customer inaccessible location)?

What if the system record matches the physical quantity in the store and the product is displayed in multiple locations on the sales floor, half of which are empty?

All of those scenarios represent in-stock successes, but on shelf availability failures.

Inevitably, item level RFID tagging is going to be as ubiquitous as item level barcoding is today. Problem is that nobody’s really talking about it anymore, so it’s going a lot slower than we would like. Even when it does come to pass, there will be significant capital investment required at store level to get to the point where stock can be precisely counted and located in real time.

At some point, it will become possible to truly measure on-shelf availability – but it’s going to take years.

Do we really want to wait that long?

If the physical count in the store more closely matches the system record and if the supply chain (including the back of the store) is aligned to flow product directly to the shelf as quickly as possible, then in stock will more closely resemble on shelf availability.

The good news is that there are things retailers can be doing today to make this happen before ‘self-counting shelves’ are a reality.

Make On Hand Accuracy a Store KPI

Management of the on hand balance at the store is often viewed as a necessary evil and it can seem overwhelming. It’s common practice for retailers to count store stock annually and pat themselves on the back for achieving a low shrink percentage measured in dollars.

The problem is that this measure is for accountants, not customers. To the customer, the physical quantity of each item in each store on each day is what’s important. Practices that degrade the accuracy of item level counts should be reviewed and corrected, such as:

  • – Scanning items under a ‘dummy’ product number if the bar code is missing from the tag.
  • – Blind receiving shipments from the DC to get product into the store faster (unless the DCs are consistently demonstrating very high levels of picking accuracy).
  • – ‘Pencil whipping’ the on hand balance, rather than thoroughly investigating and searching when the system record is significantly different from what’s immediately visible.

Only by instituting an on hand accuracy measurement program (and using the results to identify and fix flawed processes) can you have confidence that store on hand system records match what’s actually in the store.

Eliminate the Back Room

I’m not suggesting something as drastic as knocking down walls, but the back room should only exist to hold product that doesn’t physically fit on the shelf at the time of receipt. An easy way to eliminate the back room is to make changes in the supply chain that support that goal:

  • – Minimum shipment quantities from the DC should be aligned to the planogram of the smallest store that it services. If the shelf in the smallest store only holds 6 units of an item, then you’re guaranteeing backroom stock if the DC ships in cases of 12. Maybe the DC should be shipping that item in onesies. Will that increase DC handling costs? Probably. But just think of how much labour is being consumed across dozens (hundreds? thousands?) of stores each day rummaging through the back room to find product to keep the shelves full.
  • – Ship more frequently to the store, thereby reducing the shipment quantities (assuming ship packs have been ‘right sized’). See my argument above about considering the cost of store labour by not providing them with shelf-ready shipments.
  • – Appropriately staff the stores such that a truck can be received and the product put onto the sales floor within 2 shifts. That way, there should never be any question as to where the product is in the store – if it’s in the on hand, it’s out on the floor.

The Supply Chain, Merchandising and Distribution home office operations have to do their part here. They have all of the data they need to set up the stores for success in this regard – they just need to be co-ordinated.

Institute Plain Old Good Shopkeeping

In a retail store, there are generally two ways of doing things – the easy way or the right way. As with most things, taking the ‘easy’ shortcut now tends to make your life more difficult down the road, while expending a little extra effort to do what we know is right pays handsome future dividends:

  • – Don’t just jam product wherever there’s available overhead space to get it off your checklist. At least try to find a spot near the product’s home. If there isn’t a spot, then make a spot. Much better to reorganize when the opportunity presents itself, rather than going on a scavenger hunt when a customer is tapping her foot waiting for you to find the product.
  • – When using backroom storage is unavoidable, keep it organized. There’s nothing wrong with using masking tape and black markers as a stock locator system if you don’t have anything more sophisticated at your disposal.
  • – Walk the aisles at least once a day. When product has been put in the wrong spot, it will stick out like a sore thumb to an experienced retail associate. Put it back in the home (or at least collect it into a central area where the restocking crew can deal with it when their shift begins).

I suppose you could wait until self-counting shelves come along instead, but guess what? You’ll still need to do everything described above to have the physical product properly presented to the consumer anyhow.

Why not start working on it now?

Marketplace Perceptions Rarely Reflect Reality (at least in this case)

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What’s wrong with this picture?

Back in 2014, Lora Cecere (a well-regarded supply chain consultant, researcher and blogger) wrote a post called Preparing for the Third Act. She said “JDA has used the maintenance stream from customers as an annuity income base with very little innovation into manufacturing applications. While there has been some funding of retail applications, customers are disappointed.”

Our experience is consistent with Lora’s assessment. We’ve been working in the trenches on this for the last 20 years and that opinion is also shared by many of our colleagues in the consulting ranks.

In fact, we have first-hand experience with customers who have been disappointed and with customers who are delighted. That puts us in a unique position.

What’s going on now makes no sense. You can buy the software that disappoints, but you can’t buy the software that does not disappoint. To make things even more absurd, the same company (JDA) has both software packages in its stable.

Retailers agree that planning all of their inventory and supply chain resources based on a forecast of sales at the retail shelf makes perfect sense. Additionally, manufacturers agree that getting time-phased replenishment schedules based on those plans from their retail customers provides significant additional value across the extended supply chain.

But because you can only buy the software that disappoints, most of the implementations will likewise be disappointing. Consequently, the perception of these systems in the marketplace is fairly negative.

It shouldn’t be. These systems can work very well.

First, a brief history of where this all started and how we got where we are today.

Initially, retailers had a choice between time-phased planning software designed for the manufacturing/distribution market or software designed for retail that couldn’t do time-phased planning.

Later, software was developed specifically for the mission: time-phased planning at store level. It could handle gigantic data volumes economically, is easy to use and easy to implement. It’s suitable for a small company (a handful of stores) and has been tested with volumes up to 450 million item/store combinations on inexpensive hardware.

Unsurprisingly, a square peg forced into a round hole (systems initially designed for use in manufacturing plants and distribution centres being applied to store level) yielded the disappointing results.

Also unsurprisingly, a system designed specifically to plan from store level back to manufacturers works just fine. In fact, our client (a mid-market Canadian hard goods retailer) is now planning every item at every store and DC, sharing schedules with suppliers, managing capacities and achieving extraordinary business results.

No doubt, the problem has been solved.

So what’s the path forward?

It’s in everybody’s best interest to work together.

From JDA’s perspective, this is a new wide-open market for them – and it’s enormous. But it won’t be developed if the marketplace perceives that implementing these systems delivers disappointing results.

In the event that JDA were to develop a new system with new technology and features appropriate to a retail business, they still need to build it, sell it to some early adopters, get it working well and rack up a few unequivocal success stories before they can begin to overcome the current level of customer disappointment.

How long will all of that take? An optimistic estimate would be 2 years. A realistic one is more like 3-5 years. Will there still be a market then?

From the retailer’s and manufacturer’s perspective, they can be saving tens to hundreds of millions of dollars per year (depending on size) and providing a superior consumer experience.

From the consultant’s point of view (the people who recommend and implement these systems for a living), having the ability to implement the software that isn’t being sold – but has been proven to work – will increase the number of implementations with outstanding results (rather than disappointing results). The net effect of this is that JDA will have more revenue and more success than if they continue to keep this software off the market. JDA has made this type of arrangement with other partners to their mutual benefit, without head-butting or causing confusion in the marketplace.

It could be that JDA is too close to the problem to see this as a solution.

Maybe Blackstone can look at situations like this more objectively and without bias, unencumbered from all that’s transpired to date.

Princess Auto presenting at SCMA in Toronto on October 21st

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In October, our client Princess Auto will be speaking at the Supply Chain Management Association Annual Conference in Toronto. They are the first retailer in the world to have fully integrated their supply chain from the point of consumption to their supplier base – every product in every store with a rolling 52 week planning horizon. Fast sellers, slow sellers, limited supply items, highly seasonal, promotions – the whole shebang. Not to be missed!

Facts and Principles

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The truth is more important than the facts. – Frank Lloyd Wright (1869-1959)

facts_truth

‘Our decision making needs to be fact based!’

Not many people would argue with that statement. But I will.

While I wouldn’t recommend making decisions devoid of all fact, we need to be careful not to assume that facts, figures and analysis are the only requirements to make good decisions. More importantly, we must never use facts as a cop-out to allow ourselves to make decisions that we know are bad. As obvious as this sounds, doing the wrong thing for the sake of political expediency and ‘keeping the peace’ happens all too frequently in business today.

As a case in point, many economic studies have used facts and figures to argue that a major catalyst to economic growth in the United States in the 1800s was the widespread use of slave labour in agriculture. Some have even gone so far to suggest that America would not be the economic superpower it is today without the slave trade.

In a presidential election year, there is much hand wringing about the state of the U.S. economy and there has never been an election in which this hasn’t been a key voting issue. So here’s my question: If ‘the facts’ show that slave labour was historically a key contributor to economic growth, why isn’t anyone suggesting a return to slavery as part of their platform?

The first problem is that facts are rarely, if ever, complete. The second problem is that humans have a tendency to dismiss facts that don’t support their preconceptions.

The fact is (no pun intended) that the really big and important decisions can often be made on principle (as in the slavery example) without having to bother doing a full blown cost benefit analysis to tell you the answer.

Data analysis is great, but it must be used to support and measure decisions made on principle, not to make the decisions themselves. As an example, we are often lambasted for our long standing criticism of pre-distributed cross dock as a retail distribution channel. After all, it reduces picking volume and frees up pick slots in the DC, decreases ‘touches’ in the supply chain and takes advantage of the existing outbound network to get product to the stores. What could be wrong with that?

While those are certainly facts about cross-dock, so are these:

  • It shifts the burden of picking store orders from a facility that was designed for that purpose (the retail DC) to a facility that was not (the supplier’s DC), lessening efficiency and increasing cost.
  • It requires stores to lock in orders further in advance, resulting in decreased agility when demand changes and higher inventories in the stores.
  • It reduces transport cube utilization, as pallets must be built with only the handful of products that are shipped by the supplier, not the thousands of products that are shipped by the retail DC.

So how do we use these conflicting facts (along with dozens of others that I didn’t mention) to determine whether or not cross-docking is a wise distribution strategy?

You don’t.

Retail is about customer service. Customers can walk into any store at any time to get any product. Their expectation is that the product they want will be there on the shelf when they show up to get it.

Postponement (i.e. committing to decisions at the last possible moment) is a timeless supply chain principle that maximizes service while minimizing costs.

By its nature, the cross-dock channel increases commit times at the point where the customer is demanding the product without notice and builds inventory at the point in the supply chain where it is fully costed and can’t easily be redirected.

That’s not to say that there is never a scenario whereby cross-docking doesn’t make sense, but violation of a core supply chain principle should at least give you pause before pursuing it in a big way.

No facts required.

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.

Flowcasting the…

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What’s in a name? That which we call a rose by any other name would smell as sweet. – William Shakespeare, Romeo and Juliet

It’s hard to believe that nearly 10 years have gone by since Flowcasting the Retail Supply Chain was first published. Mike, Andre and I had the manuscript nearly complete before we turned our attention to figuring out the title. I figured it would end up being something boring like ‘Retail Resource Planning’.

Then, Andre got us both on a conference call to tell us that he came up with a title for the book: ‘Flowcasting’. For reasons I can no longer remember (or maybe perhaps because it wasn’t my idea), I immediately disliked it. We went back and forth on it for awhile and as time went on, the name ‘Flowcasting’ grew on me.

Then I came up with the idea that we should be more descriptive about the process. It’s a new concept for managing the retail supply chain, right? So, we should call the book ‘Flowcasting the Retail Supply Chain’!

Now I wish I had just listened to Andre and left well enough alone.

Over the years, we’ve written and talked at length about the types of major changes a process like Flowcasting brings about in the supply chain areas for retailers and their trading partners:

  • Managing all supply chain resources (inventory, labour, capacities and spend) using a single set of numbers based on a forecast of consumer demand at the shelf
  • Collapsing lead times between retailers and their trading partners by unlocking the power of the Supplier Schedule
  • Improving supply chain operational performance by providing 52 weeks of future visibility from the store shelf back to the factory and giving manufacturers the opportunity to shift their operations from ‘make to stock’ to ‘make to order’
  • And so on and so forth

To be sure, the supply chain operational and planning changes are significant – but that’s just the beginning. To make Flowcasting successful, the mindset changes in other areas of the retail organization can be just as revolutionary.

The Merchandising Organization

Historically, the buyers in a retail organization were (and in most cases still are) just that: ‘people who buy stuff’. Their primary accountability is growing sales in their categories, and the conventional wisdom is that the best way to increase sales is to have a lot of inventory. If you think you’re going to sell 10,000 units on an ad, then you buy 20,000.

Flowcasting turns this notion on its head. Because it is always accounting for all inventories in the supply chain and replanning every day based on the sales forecast, ‘Buyers’ must learn to become ‘Category Managers’ who focus on their key accountability: generating demand and providing input to the sales forecast. The change management implications of this change cannot be overstated, as this can be viewed as taking away their control of a key input to their overall success.

Similarly, buyers are accountable for maximizing gross margin on their lines. When negotiating case packs and ordering minimums, they may be inclined to choose the option that gets them the lowest overall cost per unit. But this can be very costly to the business overall if these constraints make it impossible to flow product to the store shelf, particularly for lower sales volume stores. High gross margin doesn’t necessarily equate to high profitability for the business as a whole if folks in the merchandising organization haven’t been given the tools and accountability to think holistically about the supply chain.

Store Operations

Two decades ago, the retail supply chain was distribution centres and trucks. A few years ago, the thinking began to evolve to include the retail store as part of the supply chain. Now, we are finally starting to think of the supply chain as linking the factory to the customer’s hands.

Most retailers measure store ‘in stock’, but what’s meaningful to the customer is on shelf availability (meaning that if a store has 10 units in inventory, but it’s stuck in the back room somewhere, it’s an ‘in stock success’, but a failure to the customer).

One of the biggest questions retailers face is ‘How much autonomy do we give to the store?’ While it’s true that each individual store is closer to the market they serve than the folks at home office, does this mean that giving every store manager a stock ordering screen to use at their discretion will automatically increase customer service? In my experience, the only things it increases with regularity is inventory and confusion.

Even though Flowcasting would generally be a centrally managed process for most retailers, it’s driven from individual store level sales patterns and constraints that may not be known to a store manager. The entire process works much better when people are accountable and measured only for the things that are within their control. For a retail store, that means two things:

  • Receive the product quickly and get it straight to the shelf
  • Keep the inventory records accurate

If stores are focusing their energy in these areas, it doesn’t guarantee perfect on shelf availability for the customer, but it makes it easier to trace back where the process is failing and make corrections when there are fewer fingers in the pie.

Human Resources

Flowcasting isn’t just a different calculation for coming up with an order recommendation. It’s a fundamentally different way of operating a retail business. As such, it requires a different skill set and mindset to manage it.

While it might be tempting to fill an open replenishment position with someone with replenishment experience at another organization, you could be trying to fit a square peg in a round hole if her prior experience is with traditional reorder point or push methods. In fact, you may have to invest more time with ‘untraining’ than you do with training.

To be successful with Flowcasting, a person needs to be organized and possess decent problem solving skills. Other than that, no specific ‘experience’ is required – in point of fact, it could actually prove to be a detriment.

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?