Flipping your thinking

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


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


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

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

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

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

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

Them: <crickets>

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

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

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

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

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

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

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

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

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

This I have seen.

Prototyping the prototype

If someone asked me to summarize myself, I’d probably say that I’m a life-long student – an avid reader and someone who knows that things can always be made better – a lot better.

Some recent experiences have got me thinking about our approach to designing and implementing Flowcasting-like solutions for our clients.

First, what has made our approach so successful?  It’s really an obsession with simplicity and a deep understanding that instilling new behaviors is about people and process.

At the heart of the approach is what we call a process lab, or process prototype.

Think about how successful products are created.  They are designed.  Then a prototype is created.  Then it’s tested.  Then it’s revised.  Prototyped again.  Tested again, and the process continues until the product sees the light of day.

Why can’t a process change also be prototyped?

It can and we do.

We work with teams to design new processes and workflows on paper then build a lab-like environment and prototype the process with real end-users.  It helps people see and feel the process first-hand, and also provides us critical early feedback on the process – how it works, what people like, what they struggle with, where it can be improved, etc.

It’s all consistent with our belief about change – that people rarely believe what you tell them, but they always believe what they tell themselves.

On our recent implementation of Flowcasting for a hard goods retailer in Western Canada, I was fortunate to experience what could be described as rapid prototyping, with respect to the technology solution.

To set the stage, the Flowcasting solution we used was an early and immature solution.  However, the fundamental foundation and architecture, along with the retail focused functionality was second to none.

Of course, even though we designed and did our prototype lab work with the team and users, a number of things emerged that we needed to revisit as we made the journey.  As luck would have it, we were working with the actual architect of the solution and, over a couple of months, we made some important and elegant revisions to the solution that improved it considerably.

Essentially we did a series of small, software-focused prototypes (to support our process thinking, of course) that were quickly designed, tested then deployed.  It was one of the most exhilarating experiences I’ve been involved in and I’ve been doing project work since the dawn of civilization (at least it feels like that!).

The result was equally impressive.  Even though the RedPrairie Collaborative Flowcasting solution was already an excellent one, the changes that were prototyped and implemented transformed the solution into something special and unique.

In my professional and expert opinion the solution solves all the major retail planning challenges, is intuitive, scales like stink on a monkey and is so simple that even an adult can learn and use it.  Even. An. Adult.

Not long after this experience I read an interesting book called Scrum – about an approach for designing and implementing new technologies that relied on rapid prototyping.  The basic idea was to design things quickly, make the changes, test it and demonstrate it to people and adjust accordingly.  Then rinse and repeat.

Boom!  It was essentially the approach that we’d used so successfully to transform the Flowcasting solution.  And, it got me pondering.

Why wouldn’t we apply the same thinking to our implementation framework?  After all, the idea of a process prototype was not foreign to us – in fact, it’s a cornerstone of our approach.

Perhaps we should do more than one…just like this:

Prototype picture

The idea would be to do shorter bursts of design and lab work, then engage the users, get them to work with the process, and provide feedback and adjust.  I have no idea how many of these micro-prototypes we might need but the approach could be flexible to have as many as required – depending on the magnitude and scope of the change.

I really like the idea and hopefully will test it soon.  We’re working with a great bunch of folks at a Canadian retailer and hopefully we’ll get the opportunity to help them with the implementation.  If that happens, I’m sure we can leverage this thinking and incorporate it into our approach.

It will be like prototyping the prototype.

Respect the Fat Lady

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


When is it too late to update a forecast?

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

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

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

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

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

Au contraire.

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

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

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

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

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

Saying No

The folks that know me well, understand that I’m a pretty humble guy.  Today I’m going to toot my own horn a little, and share my deepest, closely guarded secret.  If bragging and self-indulgence offend you, then I suggest switching to a different channel.

I’ve had the good fortune to have led two of the most important and foundational planning implementations in retail.  I’ll give you a little background on each, then follow with what I believe are the secrets of success.

First up: Canadian Tire in the mid to late 1990’s.    While the design was essentially what we now call Flowcasting, the implementation focused on the introduction of integrated, time-phased planning from DC to suppliers and linking and collaborating with 1500+ suppliers who, to this day, receive a rolling 39-week projection of planned product purchases.

The implementation was a first in a couple of key areas.  It was the first complete implementation of DRP (Distribution Resource Planning) in retail and also the first widespread use and adoption of supplier scheduling across a retail vendor base.

The solution deployed was Manugistics (now part of JDA) and was also a very early implementation of client-server technology.

The implementation was very successful – improving a number of key metrics like service levels, inventory turns and supplier lead time and delivery performance.

The second implementation was the recently completed implementation of the Flowcasting process at Princess Auto Ltd.  This implementation is a world first.  They are the first retailer to plan their entire, integrated supply chain based on a forecast of consumer demand.  The business is being managed using a single set of numbers and they have achieved the vision outlined in our book (http://www.flowcastingbook.com/).

In terms of results, store in-stock has risen nicely and they consistently have store in-stocks of 97%+, even for promoted products.  Inventories are more productive and the entire business is using the Flowcasting projections to manage to a single set of numbers.

The solution deployed was the Collaborative Flowcasting solution, a joint venture with RedPraire and the Retail Pipeline Integration Group, now also part of JDA.

Obviously, I’m pretty proud of these implementations. While there are a number of things we could have improved upon during the implementations, these two implementations have pushed the thinking and practice of retail supply chain planning and integration.

Now, lean in, real close, for I’m about to share the real secrets of success.

The success of these implementations is a direct result of being able to say…   No.

Great innovations and implementations are, in my opinion, largely a result of being able to say no to suggestions and ideas that don’t support the vision.  Steve Jobs described it as “saying no to a thousand things”.

The same principle applies in supply chain planning and especially the introduction of supporting technologies.

My approach is to build simple designs and only use technology and code where a computer algorithm can do a better job than a human.  Instead of trying to algorithm our way to greatness, the focus is on changing the process and thinking rather than changing computer code.

For example, in my Flowcasting world, I would never allow us to go down the path of ordering product from a supplier at two different lead times – one for regular and one for promotional volumes.   I have consistently said no to this inevitable request – instead, helping the design team understand the complexity of this thinking and, then orienting them to educate and work with suppliers so they can plan and deliver to a single lead time.

This, of course, is just one of many situations where I happily get to say No.  Over time, people begin to realize the impact of saying yes to everything…the design and solution is too cumbersome, too heavy, hard to implement and manage and often collapses under its own weight.

Now, of course I’m not saying to say No to everything suggested.  Usually the suggestions are grounded in decent thinking and needs.  The art is to understand what people need and to deliver the needs, but not necessarily in the manner they have suggested – which, inevitably they do.

Imagine if planning system architects subscribed to the Doherty/Jobs doctrine?

We’d have elegant, simple and intuitive planning solutions – not the complicated, rigid solutions that tend to dominate the market today.

One notable exception was the Flowcasting solution we used during the Princess Auto implementation.  It’s a solution that was designed for Flowcasting from the store/shelf and the architect is also a master at “saying no to a thousand things”.

As a proof point, the initial implementation of the Flowcasting solution was rolled out company-wide using a single business consultant (yours truly) and less than a third of one person’s time from the technology provider, in an elapsed time of 18 months.

Furthermore, the solution was cloud-based and integrated with their existing ERP system using 5 simple integration points (i.e., interfaces) and with No customizations and No system workarounds.

There’s that word No again.

It really is the secret to success.

At least mine.

The Point of No Return


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


Tedious. Banal. Tiresome.

These are all worthy adjectives to describe this topic.

So why am I even discussing it?

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

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

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

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

So which is correct?

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

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

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

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

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

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

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

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


Flowcasting at HEC Montreal

On December 2nd, we were honoured to speak at HEC Montreal with our longtime friend and colleague Andre Martin. Mike and I shared the success story of our client, Princess Auto, the first retailer in the world to fully integrate their supply chain from the store shelf to the supplier base. It was very well received and there was lots of engaging discussion afterword. Our sincere thanks to Professors Sylvain Landry and Jacques Roy for having us!

Flowcasting Implementation - Lessons Learned

As loyal readers know, we’ve recently completed the implementation of the Flowcasting process for the Canadian hard goods retailer, Princess Auto. As a reminder, at a high level, here’s how their planning process works using the Flowcasting process and solution:

  • A single, time-phased forecast of consumer demand, at item/store level drives all planning activity from store to supplier
  • Long term visibility is calculated using the concept of dependent demand, shared throughout the organization and automatically translated into the language of the business (units, cube, weight, dollars)
  • Everyone is working and planning to a single set of numbers and a true model of the business exists inside the Flowcasting planning system
  • Transfers and orders are planned in advance, but are only committed to at a single lead time, regardless of the volume (i.e., promotions orders are created at the same lead time as regular orders)
  • Each day, based on any change, the entire supply chain is re-synchronized so everyone is working with the latest information

Recently, I had the pleasure to give a talk with them at the Supply Chain Management Association Ontario annual conference, and it afforded me the opportunity to grab a couple of beers with Ken Larson and to talk about lessons learned.  Mr. Larson was the Business Sponsor of Flowcasting during the design and implementation, and played a key role in its success.   Given a number of retailers are likely to embark on a Flowcasting journey, here then, are our lessons learned.

It’s about Behavior Change
Flowcasting is first and foremost about people, process and changing behaviors – or as Ken likes to refer to it, “the mental model”.  While you cannot implement the process without a system, paradoxically the more time and effort you spend addressing people and process, the better.

The approach we used was an approach that we’ve honed over the years and has its seeds originating from the Proven Path approach that’s been successfully applied thousands of times by the Oliver Wight Group.  Over the years, we’ve improved on this approach and added details and activities to help ensure that people come to the conclusion themselves that the change is a better way of working.

It’s an approach that requires lots of time and effort on education, process labs, pilots and coaching.  I can still remember Ken initially questioning the amount of education we were planning.  As we were finishing up the rollout out he said to me, “you know, you can’t do too much education”.

Big Leaps aren’t that difficult
Flowcasting looks like a massive change and leap of faith for any retailer.  While it’s true, there is a lot to change for most retailers, our experience is that it’s not nearly as difficult as some might think.   That’s because the process and change are intuitive and natural.

After all, the Flowcasting process plans the way we do business.  We create the forecast only at the store level, because that’s where sales happen.  The DC’s demand and plans are to satisfy the store.  The supplier schedule is to satisfy what the DC’s need.   The lesson here is that even a big change on the surface doesn’t have to be that difficult, especially if it is natural and makes sense.

Process harmony is more important than collaboration
Read any literature regarding supply chain planning and it’s very likely collaboration will surface.  One of the most counter-intuitive lessons learned during the implementation was that, for a retailer utilizing the Flowcasting approach, collaboration was not nearly as important as process harmony and integration.

First, let’s address collaboration and its cousin, consensus.  Since the planning process is driven by a forecast of consumer demand, by item/store, the ability for different groups to improve the forecast by adding valuable knowledge is quite limited.  The retailer holds all the information that are the essential drivers of consumer demand and what we determined is that other groups, particularly the manufacturers, either had some of the same information, or were missing large pieces of information entirely.  It would be almost impossible for them to improve the consumer demand forecast.

Now, suppose we decided to go through the effort to share with them all the same information – POS history and all the attributes that drove promotional sales, for example.  Why would the supplier derive any better forecast using the same information?  The same thinking largely held true internally between the Demand Planner and the Buying Team.  There’s an old saying “in business when two people always agree one of them is unnecessary”.  We think a similar axiom holds true in forecasting consumer demand.

What we discovered, and believe it will be true for most retailers, is that a retail demand planner has the information and knowledge to determine the consumer demand forecast.  Sure, in some cases for things like limited history promotions, the demand planner may need to collaborate and come to consensus internally with the Buying/Merchandising/Category Teams, but this is proving to be an exception rather than the norm.

What’s more important is what we call process harmony and integration.  The demand and supply planning processes do not work in isolation.  They need to integrate closely with key Merchandising processes like Assortment Planning/Line Review and Promotions Planning, to name two.   These processes need to work in harmony and to a heartbeat that works for the business.

For example, new products cannot be introduced and planned violating lead times required to get the product on the shelf.  The same is true for promotions planning as this process needs to be performed far enough into the future so that all parties can plan to fulfill the demand.

Harmony and integration are achieved by co-designing the Flowcasting process with these processes and ensuring that the entire company is educated and understands these key process linkages and timelines.

Software is important and should be largely invisible
As mentioned above, it really is all about people and process.  That of course, is only true as long as you have software capable of supporting the Flowcasting process.   Using the wrong software will be the kiss of death to your efforts, as most of the time will be spent jury-rigging software not suited to the job.

As an example, I know of a number of retail companies that have attempted to enable the Flowcasting process starting, of course, with a forecast of consumer demand at item/store level.    However, since the solutions didn’t have the capability to forecast at the item/store level, attempts were made to forecast at a higher level and then spread this forecast down to each store.  Unfortunately this hasn’t worked well and it dramatically complicates the forecasting process.

The solution we used was designed for the job and as a result the focus of the implementation was a polar opposite to most implementations.  We spent most of our time on process, people, educating, and changing the mental models of people, rather than on the software and integrating it to their legacy ERP system.  And, isn’t that the way a Flowcasting implementation, or any behavior/change implementation, should be?

Our lesson is simple.  Find a solution that works for the task at hand and then the implementation can be largely focused where it should be. What we found is that the software was largely invisible to the effort – that is, not a lot of effort, focus, discussion or issues were as a result of the software.

I’d like to thank Ken Larson and the entire Team for their work, commitment and sponsorship to help make the Flowcasting implementation at Princess Auto a success.  We’re very hopeful that you take these lessons to heart as you embark on a Flowcasting journey, or any change for that matter.

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


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.