Creating value on the IoT – It ain’t about you

The Internet of Things is going to be driven by innovative business models as much as by innovative technology.  In order to ground the following discussion, I found it helpful to create this visual depiction of the IoT that defines and distinguishes the key elements that enter into these business models.  My simplified definition includes these six elements:

  1. The network backbone
  2. A server
  3. Smart devices, which I define as configurable, IP addressable devices permitting two-way communication
  4. Sensors, which although IP addressable, are not significantly configurable and allow for only one-way traffic back to the server
  5. That data generated by these elements which travels over the network
  6. Third party / cloud connections to the network; in other words - everything else


Business models involve both value creation and value extraction, and it is important to at least recognize that there will inevitably exist a category of “rent seeking” business models that create no reciprocal value.  These are largely the infrastructure components whose real value is primarily defined by ‘capacity’, such as network hubs / platforms, network pipe and switches, and the Last Mile, where they all seek to extract value from the IoT by virtue of their position as chokepoints.   While these may initially pass as viable business models, I expect most to eventually succumb to market and regulatory forces.

Having gotten that unpleasantness out of the way, let’s turn our attention to the business models that create value via their “Things”.   The fundamental case that kicks everything off is of course that of providing and owning the server, a device, and the data generated between them; a straight forward, one-to-one relationship.  After that, everything else flows through the Third Party / Cloud component:

  • How do I add value to the device, or to the server?
  • How do I add value to the data (i.e. Analytics)?
  • Can I connect additional devices that add value to the existing device / server?
  • How can this data add value to some third party business process?

That’s pretty much about all there is to the IoT.  Piece of cake, right?  There’s a lot more detail to be explored beneath each of these aspects of course , but this simple framework should at least provide you with a starting point for brainstorming where you might want to play in the future of the IoT.  Here are four great resources / articles for digging further into those details:

One obvious consideration is your ability to access the data and devices.  Can you get access to the data, and at what cost?  Can you get access to a configurable device, and if so, can you voluntarily reconfigure it?  The flip side of this is security - If you are a device/server/data owner, can you protect your data and your smart devices from involuntary reconfiguration (i.e. hacking)?

Beyond that, the salient fact that should jump out at you is that there are infinitely more ways to add value via the network / cloud / third parties / connections / additional devices than through the direct device-to-server connection.  I flirted with this point in this previous post, “The Value is in the Network”, and I would reinforce that the devices are not the endgame, the IoT is not the endgame, even the customers are not the endgame - the Ecosystem is the endgame.

My emphasis in that previous post was around monitoring the network and enhancing your data management / integration/ exchange capabilities across that network. The IoT raises the bar to from simply monitoring to “managing” your network, actively managing your ecosystem, cultivating partners whose devices, servers and data and add value to your own, and vice-versa.   On the IoT, the sum is greater than the parts.  If in your business model 1+1+1 only equals 3, you are quickly going to find yourself pushed aside by an ecosystem where the sum comes to 4 or 5.

For better or for worse, the smartphone is becoming our remote control for life.  But it’s just a remote.  The value is in the content, and the content is coming from all corners.  If you are an IoT player, it isn’t even 'remotely' about you anymore. But it is about you AND your friends.  Successful IoT business models will come down to playing well with others.  Rather than hunkering down behind your IoT firewall, get out there and make friends, starting with making it easy for potential friends to play with you.

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Diagnosis: Your data is not “normal”

“Let’s assume a normal distribution …”  Ugh!  That was your first mistake.  Why do we make this assumption?  It can’t be because we want to be able to mentally compute standard deviations, because we can’t and don’t it that way in practice.  No, we assume a normal distribution to simplify our decision making process – with it we can pretend to ignore the outliers and extremes, we can pretend that nothing significant happens very far from the mean.

Big mistake.

There are well over a hundred different statistical distributions other than “normal” available to characterize your data.  Let’s look at a few of those other major categories that describe much of the physical, biological, economic, social and psychological data that we may encounter as part of our business decision and management process.

Risk%20mgmtThe big one when it comes to its business impact is what is commonly known as the “fat tail” (or sometimes, “long tail”).  These are Nassim Taleb’s “Black Swans”.  In the real world, unlikely events don’t necessarily tail off quickly to a near-zero probability, but remain significant even in the extreme, and as Taleb points out, become not just likely over the longer term, but practically inevitable.  It is these fat tail events that leave us scratching our heads when our 95% confident plans go awry.

image63Next up are the bounded, or skewed distributions. Some things are more likely to happen in one direction than in the other.  Unlike with a normal distribution, the mode, median and mean of a skewed distribution are three different values.  ZERO represents a common left-hand bound, where variables cannot take on negative values.  Many production and quality issues have this bounded characteristic, where oversize is less common than undersize because you can always remove material but you can’t put it back on (additive manufacturing excepted).  Too large of a part will sometimes simply just not fit into the tool / jig, but you can grind that piece down to nothing if you’re not paying attention (I have a story about that best saved for another post).

scilab-examples-010Discrete or step-wise functions might describe a number of our business processes.  We make a lot of yes/no, binary, or all-or-nothing decisions in business, where the outcome becomes either A or B but not a lot in between.  In these cases, having a good handle on the limited range over which making an assumption of normality becomes important.


325px-Poisson_pmf_svgPoisson distributions.  These describe common fixed-time interval events such as the frequency of customers walking in the door, calls coming into the call center, or trucks arriving at the loading dock.  Understanding this behavior is critical to efficient resource allocation, otherwise you may either overstaff, influenced by the infrequent peaks, or understaff without the requisite flexibility to bring additional resources to bear when needed.


325px-Exponential_pdf_svgPower laws.  Would you think that the population of stars in the galaxy follows a normal distribution, with sort of an average sized star being the most common?  Not even close.  Small brown and white dwarfs are thousands of times more common than Sun-sized stars, which are tens of thousands of times more common than blue and red giants like Rigel and Betelgeuse.  Thank goodness things like earthquakes and tornados follow this pattern, known as a “power law”.

2000px-Barabasi-albert_model_degree_distribution_svgMuch of the natural world is governed by power laws, which look nothing at all like a normal distribution.  Smaller events are orders of magnitude more likely to occur than medium sized events, which in turn are orders of magnitude more likely than large ones.  Power laws grow exponentially in hockey stick fashion, but are typically displayed on a logarithmic scale, which converts the hockey stick into a straight line (left). Don’t let the linearity fool you, though – that vertical scale is growing by a factor of ten with each tick mark.

Brunswick stock price chart2That’s financial data over there to the right – can you tell without the axis labels if that’s monthly, hourly or per-minute price data?  Or, it could just as easily be your network traffic, again measured by the second or by the day.  This type of pattern is known as fractal, with the key property of self-similarity: it looks the same no matter what scale it is observed at.  Fractals conform to power laws, and therefore there are statistical approaches for dealing with them.

One piece of good news is that when it comes to forecasting, you don’t have to worry about normality - forecasting techniques do not depend on an assumption of normality. Knowing how to handle outliers, however, is crucial to forecast accuracy.  In some cases they can be thrown out as true aberrations / bad data, but in other cases they really do represent the normal flow of business and you ignore them at your peril.  In forecasting, outliers often represent discrete events, which can be isolated from the underlying pattern to improve the baseline forecast, but then deliberately reintroduced when appropriate, such as holidays or extreme weather conditions.

What we’ve just discussed above is called data characterization, and is standard operating procedure for your data analysts and scientists.  Analytics is a discipline. One of the first things your data experts will do will be to run statistics on the data to characterize it – tell us something about its underlying properties and behavior – as well as analyze the outliers, all part of the discipline or culture of analytics.

Economists like to assume the “rational economic man” – it permits them to sound as if they know what they are talking about.  Likewise, assuming a “rational consumer” (customer data is going to comprise a huge chunk of your Big Data) who behaves in a “normal” fashion is pushing things beyond the breaking point.  While plenty of data sets are normal (there are no humans ten times the average height, let alone even twice), don’t assume normality in your data or your business processes where it’s not warranted.

Soon enough we’ll probably drop the “big” from Big Data and just get on with it, but still, your future is going to have a LOT of data in it, and properly characterizing that data using descriptive analytics in order to effectively extract its latent value and insights will keep your Big Data exercise from turning into Big Trouble.

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Transformations – Personal and organizational

A new year, and with it comes reflection and resolutions.  While few resolutions are actually kept, change comes anyhow.

freytag2I was reminded recently of a conversation I once had with a high school classmate who I had hardly seen since graduation.  We were discussing a third person, and my friend’s comment to me was: “I didn’t know him very well.  And for that matter, I can’t say I know you very well now, either.”  She was of course making the point that, with time, we all change.

And thank goodness is all I can say.  Not only am I not the person she once knew when we were both 18 and on our way to college (that Leo had, shall we say, some developmental opportunities ahead of him), by my reckoning I am currently working on Leo version 7.0, counting from my first, stable, young adult personality at age 15, and am still a work in progress.

My first four versions came in fairly quick succession between the ages of 15 and 28, followed later by longer, more stable periods.  If I had to summarize my experience of these transformations, it would be:

  • A series of relatively impactful events and environmental changes occur (A, B, C, D, E, F, …)
  • Followed by a specific trigger event “X”.
  • The trigger event highlights certain previous life events and gives them significance. While Trigger event X might spotlight events A, B and C, a different Trigger Y would perhaps have selected events D, E and F as being the important precursors.
  • The transformation is not a single moment, but encompasses a period of time on either side of the Trigger, and is often not apparent until some time has passed for reflection and assessment.
  • The transformation is a response to environmental stress, and enhances your physical, psychological and financial competencies for survival in light of that stress.
  • The transformation requires facing fears and taking risks.
  • In retrospect, the transformation looks like a typical story/plot outline, starring you as the protagonist.

Over a period of several months I continually revised my assessment of my transformations. It took me a while to settle on not just seven, but these particular seven, relegating some previous Triggers to mere events while recognizing other events as being the true Triggers and accordingly shifting the time periods in question.

The criterion I settled on for defining a transformation was:  Would I now (or my previous personalities) be willing to go back to being that person?  For example, while I would have little consternation going back to the Leo I was four years ago, that is not the case for the self I was twelve years ago – too much has been learned since then to voluntarily give it back, no matter the price I may have paid for it.  (Not all transformations can be considered positive, but I’m going to leave retrograde motion out of this discussion).

While I might like to be able to claim that I reinvented myself six times over, that would not only be stretching the truth, but more like misremembering and misrepresenting the past.  While I did get better over time at re-engineering each new version, none of the seven Triggers or transformations were deliberate on my part, but merely reactions to changes in myself and my environment.  Life was forcing my hand, not the other way around.

This leads to my first proposal:   We all need to more proactively manage our lives and transformations, and to that end, a life or career coach or mentor is probably not a bad idea.  Someone objective, someone with a broader perspective on the world than we might have, someone to occasionally shake us out of our comfort zone, but as part of a proactive plan instead of a reactionary Trigger.  Considering the increasing pace of technological and cultural change, this is more necessary today than ever.

I had a coach early in my career, but I think her contribution was more in the direction of stability than transformation, which as a new parent was probably exactly what I and the new family needed at the time.  However, I do wish now that I had continued to work with her – there was no need for that fifth transformation to have waited 14 years to commence.

My second proposal is that, following this model, organizations are probably in a better position to proactively trigger transformations than are individuals.  Organizations are much better suited to develop and compartmentalize the capability to objectively analyze itself, and then provide the incitement to change.  If not internally, this capability can also be readily acquired externally via change management consultants.

An entirely reasonable organizational approach to change would be to replicate the individual process by deliberately creating the preparatory, foundational precursor events A, B and C (the ‘rising action’), then instigating a Trigger (the ‘crisis’), followed by events D, E and F (the ‘denouement’) which completes the story of the transformation and becomes the new context in which the organizations understands itself and its mission.

Two factors are primarily responsible for the lack of both organizational and personal transformation.  The first is the lack of a vision, the lack of the transformative storyline / myth / context that I proposed above.  In an organization this is the job of the CEO; as for an individual – this is why the use of a career/life coach or mentor can be so beneficial.

The second factor is fear and risk.  For an individual the risk is typically emotional or financial.  For an organization not in financial straits, the analog to the individual’s psychological risk would be the lack of a well-defined strategy.  You know you need to be on the opposite river bank, and that the only bridge is weak and deteriorating and won’t be there much longer, but you hesitate because the other side is unknown territory.

One approach some organizations take is to spin-off their fearless, agile component and let them lead the way without the baggage of the larger organization.  Another approach is to hire a CEO or other talent with experience on the other side.  Or, you could scout the new territory, often with the help of outside consultants who have experience in that terrain, or utilize insights gleaned from your current business intelligence database.

Lastly there is the approach I discussed some time ago (“Having a strategy versus being strategic”) of simply making the commitment, crossing that river first and allowing your strategy to develop over time once you’re there and can make refinements based on real data rather than speculation.  As I admitted in that previous post, I am not necessarily comfortable with the idea of strategy as simply the sum of my tactics, but sometimes that approach may be just what’s called for.  If your future is on the other side, whether that be the love of your life and future spouse, or because technology is making your industry / market / business model rapidly obsolete, sometimes you just need to face your fears and make the leap.  On a personal level this is similar to the behaviorist approach of inverting the "Beliefs ---> Attitudes ---> Behaviors" model, and simply changing your behavior and letting your beliefs and attitudes catch up later.

Regardless of how you get there, personally or organizationally, eventually you ARE going to end up on the other side of that river, with many more rivers to cross in your future after that.  The question is:  Will you cross unwillingly and unexpectedly because the bridge is burning or the ground you’re standing on has given way, or will your transformation be a more deliberate affair, part of a purposeful journey or quest rather than a flight of necessity?

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Getting started with Supply Chain Segmentation

All unsuccessful segmented supply chains are alike; each successful supply chain is successful in its own way.” ― Leo Tolstoy Sadovy

Segmentation is the new big thing in supply chain management, or at least it’s an old big thing made new again.  It was the keynote topic at last month’s IE Group Supply Chain Summit in Chicago, and is typically addressed by at least a couple of speakers at every supply chain conference I’ve seen lately.

segmentation12The complexity of customer expectations and service levels, your product portfolio, the global supply chain, varied distribution channels, coupled with the internet and social media, makes moving from an undifferentiated to a segmented supply chain almost an imperative, even though doing so adds a layer of complexity that many manufacturing companies are not ready for.  To read the recent literature on the topic, when you start trying to combine segmentation based on your products with segmentation based on your customers, it goes from merely complicated to overly complex in a heartbeat.

Here’s a short list of just a few of the various segmentation strategies and permutations to consider:

  • Product-driven segmentation:
    • Large volume, long production runs, standardized operations
    • Limited editions, fluctuating demand
    • Made-to-order, low volume, short runs, high margin (high cost-to-serve?)
  • A volume / variability 2x2 matrix
    • High volume commodities
    • High volume seasonal or promotional items
    • Low volume, predictable
    • Low volume specialty or custom orders
  • A typical three-segment retail-oriented model:
    • Regular replenishment
    • Seasonal, but predictable demand (swimwear, lawn fertilizer)
    • Volatile, one-off demand (fashion, new products, promotions)
  • Customer-based segmentation – many ways to do this:
    • Standard, higher quality, or premium service / customization
    • By channel
    • By lead-time service level (build-to-stock, configure-to-order, build-to-order)
    • By customer size, volume or value
    • Other customer characteristics, such as vendor managed inventory, level of data and forecast/POS integration / collaboration, SLA penalties or geography
  • Risk-oriented segmentation, based on political, environmental or economic risk/disruption factors, and on product lifecycle stage considerations

I am a practical sort, concerned primarily with execution.  I want to make Pareto’s Law work for me and go after the low-hanging 80% that only requires 20% of the effort, and I want that first demonstrable success.  Lastly, I would be well advised to dust off the old adage – keep it simple, stupid – and that list of possible segmentation models above looks anything but simple.

The conference keynote case study mentioned above concerned a multinational alcoholic beverage company that was trying to balance the production needs of large volume, stable, established brands with the flexibility needed in a surprisingly innovative market that sees several hundred new products introduced every year.  Their big breakthrough was to move from a one-plant/one brand, one-line/one-product practice (largely inherited via multiple acquisitions over the years) to an agile approach where each line in each plant could handle any combination of product, bottle, label or packaging.  For example, before the changeover, there were some labels that had to be spun on clockwise, and other labels counterclockwise, which just by itself cut the number of available production lines in half.

With that in mind, and based on the success stories and key takeaways I’ve seen presented or in print, I think I’d approach my first supply chain segmentation project in the following manner:

  1. Get a good understanding of my cost-to-serve.
  2. Employ analytic forecasting.
  3. Take a product-oriented approach to the supply chain segmentation.
  4. Deal with my customer segmentation opportunities via inventory and service policy.

Breaking these down a bit further:

  1. Cost-to-serve. Before I do anything, I want accurate product, process, customer and channel costs on which to base my decisions, informed by a cost and profitability management solution that gives me cost output I can trust.
  2. Analytic forecasting. Because it all starts with the forecast. It can only get worse from there. Start higher in order to finish higher.
  3. Product-oriented approach. Yes, it’s inside-out thinking, but it seems to be where all the successful segmentation projects started from. It’s easier to understand and control than either working back from the customer or trying to bite off the entire holistic supply chain in one go.
  4. I’m still going to have to deal with customer and channel differences. What if a high-value customer wants a low-value product? We all know how that story ends – Lola gets what Lola wants. I need to accommodate my premium customers through some post-production combination of inventory policy, customer service/care, and order allocation/commitment process.

I can, however, imagine several scenarios where I might have to start from the customer and work backwards, such as having the federal government as a customer (where mil-spec products might necessitate a holistic supply chain approach all the way back to the farthest tier-n supplier), or when you have significantly different classes of customers who buy through distinctly separate channels. But for all practical purposes, you aren’t going to get one specific segmentation scheme that meets both all of your operational priorities and all your high-priority customer needs (and mitigates all your major supply chain risks).

One final bit of advice from the experts can be summed up as:  One physical supply chain with multiple virtual segmented supply chains running against it.  These virtual supply chains are distinguished by policy, not by brick-and-mortar – inventory, sourcing, production, fulfillment, logistics and service policies.  Because it’s easier to change policy than to change concrete and steel.

As nearly every supply chain expert stresses, one size does not fit all.  You need to select a segmentation strategy that’s right for your business.  But please do select just one appropriate strategy, not some unworkable hybrid. Unsuccessful supply chains are alike in that they tend to be more complex than they have to.

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Big Silos: The dark side of Big Data

big-data-image3The bigness of your data is likely not its most important characteristic. In fact, it probably doesn’t even rank among the Top 3 most important data issues you have to deal with.  Data quality, the integration of data silos, and handling and extracting value from unstructured data are still the most fertile fields for making your data work for you.  [And if I were to list a fourth data management priority it would be, as I described in this previous post (“External data: Radar for your business”), the integration of external data sources into your business decision support process]

Data Quality:  The bigger the data, the bigger the garbage-in problem, which scales linearly with data volume.  Before you can extract value from the bigness of the data, you need to address the quality of the data itself.  If you haven’t been employing robust, scalable data quality tools, now would be the time.

Have we gotten any better at data quality? My personal, one sample survey would indicate that we have not.  With a relatively unusual last name, Sadovy, although only six letters, I’ve seen it misspelled over two dozen different ways in my life, and I thought I’d seen them all by my mid 40’s.  But once my three children became college-aged and started receiving daily credit card offers in the mail, several new ways to misspell my name came to light, a credit to the creativity of today’s automated processing systems.  Even being a Smith/Smythe or Jones/Joens doesn’t leave you immune to a misplaced bit or byte.

Without a focus on data quality, big data just gives you that many more customer names to get wrong.

Data Integration:  If you’ve got a data silo problem, and who doesn’t, then all big data contributes to the process is to make those silos bigger.  Which makes the eventual data integration exercise that much more of a challenge.

Enterprise big data comes at you from a dizzying array of directions – from mainframes and ERP systems, from transactional and BI databases, from sensors and social media, from customers and suppliers. To make matters worse, each of these various sources and applications has its own, sometimes proprietary, data model.

And we’re still not finished with the complexities of this issue yet, because enterprise data has one more endearing quality that makes integration difficult – it’s decentralized and distributed. Extracting value from its bigness by creating one humungous centralized, homogeneous data warehouse is simply out of the question.  If Sartre had been a philosopher of data science he might have said, “Integration precedes value extraction”.

Unstructured Data:  Depending on what study you prefer, it’s claimed that 70 to 90 percent of all data generated is unstructured.  This unstructured bigness doesn’t readily fit into predefined columns, rows, data entry or relational database fields.  Customer feedback, emails, contracts, Web documents, blogs, Twitter feeds, warranty claims, surveys, research studies, client notes, competitive intelligence, often in different languages and dialects … the list goes on. Who has the time to read all this, let alone find an efficient way to extract the latent value from it?

Unstructured data may be both big and bad, but again, with the right tools, it’s not unmanageable. Text mining, sentiment analysis, contextual analysis – there are automated machine learning and natural language processing techniques available today to deal with the volume and ferret out the insights.

Big Data’ is of course a relative term, but when I think ‘big data’ one of the following three data categories seems to be in play:

  • High transaction volumes: Millions of customers, billions of transactions (i.e. ATMs or POS), or tens of thousands of SKUs crossed with other attributes such as retail locations, cost and/or service levels.
  • Temporally dense: Sensor data, audio.
  • Spatially dense: Video, satellite imagery.

The business issue becomes – what do you want to do with all this data? And the place to start is not with the data, or with its bigness, but with the business problems you want to solve, the business insights you want to gain, and the business decisions you want to support.  Starting from there and working backwards to the data means running squarely into the issues of data quality, data integration and unstructured text analytics.  It’s only after you get a handle on this trio of capabilities that you can begin to effectively tap the big data spigots.

Extracting tangible value and insights from high-quality, integrated data, no matter its volume, velocity or variety, is where the payoff lies. Getting to this payoff in an environment where your data is growing exponentially in all dimensions requires an investment in robust data management tools. The consumers of this data, the business users, don’t know or care about its bigness – they just want the right data applicable to their particular business problem, and they want to be able to trust that data. Trust, access and insights – it’s got “quality” and “integration” and “analytics” written all over it.

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Customer Relations by Walking Around

Const1Perhaps nowhere is the saying “time is money” more true than in the construction industry.  There is no better indicator of project cost and budget over/underrun than the number of days on-site.  Reducing that number has a near 1:1 relationship with cost cutting, so it’s no wonder that days on-site is the most watched project metric.

Further complicating matters, the construction industry is well-behind the 3D adoption curve, still relying primarily on 2D blueprints when most other manufacturers have long since moved to 3D CAD-CAM design and production systems, despite the obvious benefits of the application of 3D systems to the construction of 3D physical structures.

Stepping into this breach is Nancy Novak, Vice President of Operations for Balfour Beatty Construction services, a speaker at the IE Group's Manufacturing Analytics Summit earlier this year.  Nancy specializes in applying off-site manufacturing (OSM) techniques to large commercial and industrial projects – one of the most innovative process to recently emerge in this industry.

Or maybe I shouldn’t call it a process, as much as OSM’s intent is to productize the construction industry, to allow it to standardize and reap the benefits of common manufacturing techniques and processes that have been around for decades.  The benefits of OSM include:

  • Faster – Fewer days on-site with a more predictable schedule  (i.e. lower cost)
  • Safer – Less on-site labor, better site logistics
  • Better quality, with a more predictable product

This is the story that Nancy brings to her potential clients.  With each project, she explores with the construction team the possibilities for modular systems that can be assembled off-site and then integrated into the larger structure on-site, whole and in working order, such as bathrooms, kitchen facilities, elevators and staircases, office space, HVAC, interior and exterior walls, and even entire living suites for apartment complexes.

Perhaps the most surprising aspect of her work is how often the client informs her that she is the first person who ever proposed such an approach to them, how often she is the first person to suggest that they take a walk through a current project to assess what improvements might be able to be incorporated into the next one.  Not so much management-by-walking-around as sales, or customer relationships, by walking around.

This is an easy lesson to apply to your own highly-competitive manufacturing business.  Are you tired of the price wars?  Are you looking for a differentiator other than features, functions and performance in a largely mature market?  Are you interested in taking need-based, consultative selling to the next logical level?  Then instead of making the focus of your next customer visit your own products and services, simply ask for the opportunity to walk around their business environment and ask “what if”?

Many your customers will of course have well-defined problems with straight forward solutions, where the the only obstacle is budget, but it’s more likely that their needs and problems are much more nebulous or even completely hidden.  As Henry Ford once famously quipped, “If I had asked people what they wanted, they would have said faster horses.”  Often they are looking for you to be the expert, or, as we often say here in the world of SAS analytics, “tell me something I don’t know”.  To get to the answer, first you need the insight.

I couldn’t possibly list here all the insights you and your customer might uncover, but just to give you a flavor for the types of questions to ask:

  • What can we do regarding custom packaging / logistics that would better suit how you use our product?
  • What services might better be provided on-site or mid-stream rather than all before or after delivery?
  • What if we could manufacture the product in multiple components (or singularly) for easier installation / service?
  • What integration could we be doing with your other suppliers before our product ever gets to you?

If all goes well, this inevitably leads to a discussion around where BOTH parties are making changes to their products and processes to reduce the total overall cost and/or to otherwise make the total end product more competitive. Not just, “What can I do for you?”, but “What can we do together?” The proverbial yet rarely seen "win/win".  Getting to this level of conversation is the best differentiator you could ever have.  You are no longer just a vendor, nor even a ‘strategic supplier’, but a real business partner.  You are no longer competing on price against a dozen other contenders, but are now critical to making your client more competitive in THEIR market.

So what are you waiting for?  Go for a walk – it will be good for you, … and your customer, …and their customer.

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Analogies, mind-mapping and New Product Forecasting

There are two ways you can react to a “Hey – that was my idea” situation.  The first would be to throw a pity party and lament about how unfair life is – if only the car hadn’t broken down and I didn’t have grass to mow and laundry to do I could have filed a patent and been a millionaire by now.  The other is to recognize that you were never going to do anything of the sort under any conditions anyhow and simply take the experience as confirmation bias of how brilliant you are.

1363280485Hofstadter-Surfaces_andWhen I came across Pulitzer Prize-winning author Douglas Hofstadter’s latest work, “Surfaces and Essences: Analogy as the Fuel and Fire of Thinking”, I chose the latter course.  His core theme is that analogies lie at the heart of how we develop concepts, how we construct language, how we understand the world, how we think – something I not only heartily agree with, but a concept I considered myself decades before Hofstadter’s book.

Among other things, I fancy myself an amateur student of language.  You see, as a parent, by necessity you become an amateur student of a whole host of subjects that previously may have never interested you.  For example, I find that parents are three times more likely than non-parents to know that Michael Crichton got it wrong: T-Rex is from the Cretaceous, not the Jurassic, and I have the short video, “Cretaceous Park”, to prove it, created by my then five year-old son, who produced it in order to set the record straight among his kindergarten classmates.

Likewise, as a parent you also quickly become an expert in the field of linguistics as you watch in amazement the literal explosion of language once your children have mastered a basic vocabulary.  They start with what they have in their linguistic toolkit and build on it, making telltale mistakes along the way that shows how their mind is working, such as undressing a banana, or cooking water, or breaking their book, before they’ve learned the verbs peel, boil and tear.

Metaphors come next and get incorporated into the very meanings of words: tables have legs, bellies have buttons, and airplanes get tails.  The analogies get more complex over time, as we encounter windows of opportunity, haunting melodies and watertight reasoning.  These later develop into idioms where sometimes the analogy is still clear, as in ‘bend over backwards’, ‘between a rock and a hard place’ or ‘stacked the deck’, and others where the root is barely discernable, such as ‘kick the bucket’, ‘egg someone on’ or to ‘give someone short shrift’.

One thing I instinctively knew about myself at a young age was that my preferred learning style was by analogy and via storytelling.  Rather than feverishly trying to scribe every single detail into my notes as the teacher or professor spoke, I saved those for later (an especially useful approach in today's Google-age) and focused on relating the main and secondary concepts with each other and with what I already knew, working them into my existing knowledge framework and creating a new, expanded or more complex story about the subject for myself.  I was mind mapping, or concept mapping as I thought of it, way before it became a thing.

This concept of analogies is what lies behind SAS’ New Product Forecasting solution.  New product forecasting (NPF) can be a recurring challenge for consumer goods and other manufacturers and retailers. The lack of product history or an uncertain product life cycle can dampen the hopes of getting an accurate statistically-based forecast.  Here are some of NPF situations you might encounter:

  • Entirely new types of products.
  • New markets for existing products (such as expanding a regional brand nationally or globally).
  • Refinements of existing products (such as new and improved versions or packaging changes).

SAS’ patent-pending structured judgment methodology helps you automate the evaluation and selection of candidate analogous products, facilitates the review and clustering of previous new product introductions, and generates statistical forecasts. This structured judgment approach uses product attributes from prior and new products, along with historical sales, to create analogies.

The use of analogies is a common NPF practice. You can see it, for example, in the real estate market, where an agent will prepare a list of “comps” – similar houses in the area that are on the market or have recently sold – and use this to suggest a selling price.

The structured analogy approach requires two types of data – product attributes (for prior and new products) and historical sales (for prior products). Product attributes can include:

  • Product type (toy, music, clothing, shirts, etc.).
  • Season of introduction (summer item, winter item, etc.).
  • Financial (target price, competitor price, etc.).
  • Target market demographic (gender, age, income, postal code etc.).
  • Physical characteristics (style, color, size, etc.).

The statistical forecast is then built using a structured process based on defining and selecting candidate surrogate products and models.  Furthermore, you can combine this with data visualization to study previous new product introductions to gain a better sense of the associated risks and uncertainties.

Candidate products2

Using analogies to improve your forecasting should not seem at all foreign – you’ve been using analogies since you were a toddler to expand your knowledge base by connecting and building on what you already know.  To find out more, check out this white paper, “Combining Analytics and Structured Judgment: A Step-By-Step Guide for New Product Forecasting”, and learn the details of getting from A to B, of getting from the product history you know to the new product forecast you don’t.  You might call it mind-mapping for your new product forecast; See - analogies are everywhere!

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CaaS – Crime-as-a-Service: Murder on the Internet of Things

Europol, the law enforcement agency of the European Union, in its recently released 2014 Internet Organized Crime Threat Assessment (iOCTA), cited a report by U.S. security firm IID that predicts that the first “online murder” will occur by year end, based on the number of computer security system flaws discovered by hackers.

pacemaker2While there have been no reported cases of hacking-related deaths so far, former vice president Dick Cheney has had the wireless function on his implanted defibrillator disabled in order to prevent potential hackers from remotely accessing his device. Just such a scenario was played out fictionally in the political TV thriller Homeland, in which his counterpart was murdered by terrorists who were able to hack into the (fictional) vice president’s pacemaker.  In an interview last year, Cheney said, “I was aware of the danger that existed and found it credible – [the scene in Homeland] was an accurate portrayal of what was possible.”

Cheney’s fears are not unfounded in the least.  2012 research from security vendor IOActive regarding the security shortcomings in the 4.5+ million pacemakers sold between 2006 and 2011 in the U.S turned up the following:

  • Until recently, pacemakers were reprogrammed by medical staff using a wand that had to pass within a couple of meters of a patient, which flips a software switch that allows it to accept new instructions.
  • But the trend is to go wireless. Several medical manufacturers are now selling bedside transmitters that replace the wand, with a range of up to 30 to 50 feet.  With such a range, remote attacks become more feasible.  For example, devices have been found to cough up their serial number and model number with a special command, making it is possible to reprogram the firmware of a pacemaker in a person's body.  Other problems with the devices include the fact they often contain personal data about patients, such as their name and their doctor. The devices also have "backdoors," or ways that programmers can get access to them without the standard authentication - backdoors available for more nefarious uses.
  • Just as your laptop scans the local environment searching for available WiFi networks, there is software out there that allows a user to scan for medical devices within range. A list will appear, and a user can select a device, such as a pacemaker, which can then be shut off or configured to deliver a shock if direct access can be obtained.
  • As if this wasn't bad enough, it is possible to upload specially-crafted firmware to a company's servers that would infect multiple pacemakers, spreading through their systems like a real virus - we are potentially looking at a worm with the ability to commit mass murder.

Can it get worse?  By now you’ve heard of SaaS (software-as-a-service) and PaaS (platform-as-a-service), but how about CaaS – Crime-as-a-Service?  From the Europol report: “A service-based criminal industry is developing, in which specialists in the virtual underground economy develop products and services for use by other criminals. This 'Crime-as-a-Service' business model drives innovation and sophistication, and provides access to a wide range of services that facilitate almost any type of cybercrime. As a consequence, entry barriers into cybercrime are being lowered, allowing those lacking technical expertise - including traditional organized crime groups - to venture into cybercrime by purchasing the skills and tools they lack.”

Just take a moment to let that sink in: Lowering barriers to entry, criminal innovation, CaaS as a business model.  It really shouldn’t surprise us, though - criminal enterprises have been adapting the principles of sound business management from the early days of organized crime.  Did you know that the illegal drug market is a $2.5-trillion dollar industry? Not merely a billion dollar industry, it’s a TRILLION dollar industry, employing standard business school tactics such as quality control, freemium pricing models, upselling, risk management and branding, not to mention the ever changing supply chain and logistics challenges.

Cyber criminals are at least, if not more, sophisticated than the typical drug trade.

Lest you think that cyber security is primarily the province of the big banks and retailers, how your products will integrate with the Internet of Things (IoT) should make you think twice.

I went over twenty years with the same credit card number - now I have to get a new one pretty much every year because someone got hacked, and I’m guessing that your experience hasn’t been much different.  And remember, these breaches are occurring at large enterprises already employing a significantly sized staff of cybersecurity experts.

If your device is going to be on the internet, security will need to be baked into the design from the very beginning.  Again, from the Europol report:  “"The Internet of Things represents a whole new attack vector that we believe criminals will already be looking for ways to exploit. The IoT is inevitable. We must expect a rapidly growing number of devices to be rendered 'smart' and thence to become interconnected. Unfortunately, we feel that it is equally inevitable that many of these devices will leave vulnerabilities via which access to networks can be gained by criminals.”

Rod Rasmussen, the president of IID - the source of the murder prediction mentioned at the beginning of this post - had this to say: "There's already this huge quasi-underground market where you can buy and sell vulnerabilities that have been discovered. Although the first ever reported internet murder is yet to happen, ‘death by internet’ is already a reality as seen from a number of suicides linked to personally-targeted online attacks.”

While it’s unlikely that anyone will die from a stolen credit card number, that’s not going to be the case for many of the tens of billions of devices attached to the internet, from medical devices to wearables to the connected car.  As a manufacturer of current and potential IoT devices, you may not be aware of SAS’ dominant presence in the fraud detection/prevention and cybersecurity field.  When you get a call from your bank freezing your credit card and questioning that $3,500 purchase at a shopping mall in Altoona, it was likely SAS analytics behind the scenes that identified and flagged the fraud.

If CaaS is going to be part of the criminal elements’ business model, cybersecurity will need to be part of your product design and IoT business model, and SAS can help.  While your brand may survive a variety of production quality problems, it won't survive a murder on the IoT.


[You can also learn more about cybersecurity from Ray Boisvert, CEO and founder of I-Sec Integrated Strategies, at his presentation, “The Threat Landscape: Cyber Tools and Methods Transforming the Business Environment,” at SAS' Premier Business Leadership Series, Wednesday, Oct. 22, from 2:15 to 3 p.m. Boisvert sees cybersecurity as a task for analytics that can help organizations tease out the proverbial signal from the massive internet “noise” around serious threats. The challenge is to identify the right threat vector related to the most valued elements an organization holds dear. The organization will only be successful if it has technology to quickly digest huge streams of data, in real time, so that it may begin to see patterns that can thwart further attacks.]

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East meets North: Integrated Business Planning for both efficiency and alignment

Sales and Operations Planning (S&OP) started out with big aspirations.  As initially conceived, S&OP was to cover the entire domain now called Integrated Business Planning (IBP).  As S&OP process implementations rolled out during the 1980’s, this broad scope turned out to be a bit much to attempt in one bite.  S&OP instead settled effectively into a more focused and limited role, and it would be another decade before a new attempt, and a new name, IBP, re-emerged to tackle the larger picture.

So what is the difference between the two, and does it matter?

Briefly, S&OP is the balancing act between supply and demand.  It sets the production plan for the upcoming period based on the unconstrained sales/demand forecast but informed and adjusted for other supply chain constraints such as capacity, material supply, inventory levels, logistics, and customer lead-time requirements.


S&OP is a process focused on the EFFICIENCY of the production process.  In terms of Treacy and Wiersema’s three Value Disciplines, S&OP is concerned with the efficiency and effectiveness of the horizontal OPERATIONAL EXCELLENCE value discipline.   Note that while the prescription of the Value Disciplines is that organizations should focus on achieving excellence in primarily only one of the three, all three are always present, so even if your chosen value discipline is Customer Intimacy or Innovation, you still have an operational aspect to your business that needs to be optimized.

What does IBP bring to the party that S&OP lacks?  Alignment.  Financial and strategic alignment.

A couple of years ago I wrote this post (“The Nine-Foot Aviator”) about what first steps to take when attempting to institute an IBP process.  I had to admit then that, despite the brilliant description of east-west versus north-south processes by Gartner’s Noha Tohamy, I and much of the audience that heard her presentation still seemed confused over definitions and boundaries and roles and functions when it came to differentiating S&OP from IBP.  It was all a bit fuzzy, although you could see hints of the resolution in the IBP calendar shared at the IE Group conference by Verso Paper’s Michael Partridge – review sessions that included finance, risk and general management in addition to the usual production, demand and supply suspects.

However, if you approach the two processes from the perspective of the Value Disciplines, the distinction becomes obvious (see "The Sound and the Fury" for the connection between the value disciplines and your value-creation business processes).  As I mentioned above, S&OP operates primarily within the horizontal, east-west, operational discipline, with the aim of improving the efficiency and effectiveness of that discipline.  IBP, however, takes a broader, north-south perspective – the ALIGNMENT of S&OP and the Operational Excellence discipline with the organization’s financial and strategic objectives.  What is optimal for the horizontal production and supply chain process might not be optimal for the business as a whole.  Examples include:

  • Does the S&OP plan meet cash flow, earnings, revenue and margin objectives?
  • Does it meet the company’s risk appetite / profile, and are the identified risk mitigation plans acceptable?
  • Does it comply with safety and sustainability requirements?
  • Does it appropriately support marketing, new product/territory expansion and commercial initiatives?
  • Does it align with other strategic objectives, such as quality or customer retention?

In other words, IBP does two things that S&OP does not:

  • It aligns one value discipline, operations, with the other two – innovation and customer relationship management.
  • It aligns the Operational Excellence value discipline with the broader, high-level strategic objectives of the organization as a whole.

In order to fulfill the promise of IBP, the key takeaway from this would be to move beyond just the alignment of S&OP and Operations and generalize the intent and scope of IBP to include all three value disciplines.   In most companies the product development and customer relationship value disciplines have their own internal efficiency and effectiveness processes , maybe not as complex as S&OP but every bit as critical, especially if one of them is the organization’s chosen strategic focus.  It should not be sacrilegious to expect for R&D to regularly check its alignment with the company’s marketing direction or customer service performance, nor for marketing to likewise understand product development roadmaps and for sales to proactively be aware of the ever changing array of production and development constraints that could impact client relations.

IBP as a concept has wider applications than just policing S&OP, and you can use the process as applied broadly to manage your business holistically, complementing horizontal business-process efficiency with vertical and strategic alignment.  Via S&OP, East has already met West – with IBP, East gets introduced to North and South as well.

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The Cloud and other forces – Climate change, or just the weather?

SD thunderstormI’ve been having trouble getting a handle on the relationships between the nexus of forces / third platform themes  of social media, mobility, big data, analytics, and the cloud, and it made me feel better that someone like Geoffrey Moore, world-renowned author of “Crossing the Chasm”, seems to be in the same boat.  If you haven’t run across it yet, Geoffrey Moore is an official LinkedIn “InFluencer”, and you can 'follow' him on LinkedIn and read up on some of his recent insights on his author page.   Moore has been tackling these topics for several years now, and you can watch especially how his opinion of the Cloud has evolved over time.

In sorting these themes out, I wanted to assess them outside the influence of those parties who have a stake in how the subject gets framed, and also from the point of view of, “So what? – What’s in it for me?”

Mobility” seems to be the easiest to classify, but the repercussions are going to be monumental.  No need for a ‘third platform’, mobility fits neatly into the client-server model, or maybe it should now be called ‘device-server’. Or rather, there are a range of devices in the client role that run the continuum from thick client to sensor.  In between are smart devices of all flavors, such as phones, in-car GPS and home appliances.  Far from being just an IT problem (i.e. BYOD), mobility impacts everyone from marketing to operations, as everything from people to products goes mobile.

Big Data’ is of course a relative term, but when I think ‘big data’ one of the following three data categories seems to be in play:

  • High transaction volumes: Millions of customers, billions of transactions (i.e. ATMs or POS), or tens of thousands of SKUs crossed with other attributes such as retail locations, cost and/or service levels.
  • Temporally dense:  Sensor data, audio.
  • Spatially dense:  Video, satellite imagery.

The business issue becomes – what do you want to do with all this data?  Is it just a matter of storage, in which case Hadoop might be called for, or does the value come from real-time event stream processing, or does the data serve as the foundation for the further application of analytics and the extraction of metadata?  But does Big Data constitute a fundamental building block for a new computing platform?  By itself, I don’t think so – evolutionary rather than revolutionary.

Analytics’ always has the potential for revolution, because it is in the unique position of being able to respond to the requirement, “Tell me something I don’t know”.  How much risk is in that forecast?  What’s the optimal product mix given certain production constraints?  What’s the next best offer to make to that customer in the store or on the web?  What are our customers saying about us on social media?  Insights like that are more than a system, platform or architecture.

The Cloud.  Is it just an outsourcing model / just another business model, or is it going to be as disruptive as its proponents advocate?  For the time being, the answer seems to depend on which side of the equation you find yourself.  If you are in the IT business, the potential for disruption, either by yourself or your competitors, is keeping you awake at night.  Those on the receiving end, however, currently seem to view the Cloud primarily from a cost basis, from the motivation to cut the costs and risks of hardware acquisition, maintenance, and software migrations.

While this might be a good foot in the door (or sticking your head in the cloud?), it would be best not to dismiss too quickly what the cloud visionaries have envisioned.  Two potentially important cloud applications to keep in mind are:

  • SaaS as a way to acquire capabilities you could never support in-house, especially niche applications that could add considerable value but don’t currently generate enough internal critical mass.  Watch this space – SaaS as a business model will enable a plethora of new applications that were previously barely imaginable.
  • PaaS as an internal IT business model.  You won’t be outsourcing everything, there will still be mission critical enterprise apps that you manage in-house, but in order to meet your internal business client needs you are likely going to have to be more cost competitive and more flexible with your IT resources.  As an effective business model, PaaS needn’t remain the sole domain of the big boys.

That leaves us with Social Media.  Honestly, I don’t know how to classify it.  It’s a game changer, most certainly.  As I’ve mentioned in a previous post, I’m quite the fan of Marshall McLuhan and his observations on the media.  If “the medium is the message”, what is the message of Social Media?  The implications of “social” as both a medium and a message are likely to be both subtle and far reaching.  This isn’t about “digital” at all – this is about brand reputations and networks and experiences and influencers and chaos.  Your current struggles with big data or with BYOD security are just a taste of what’s to come in the social arena.

If I had to put a label on them, Big Data feels like weather, like a cold front passing through.  Mobility is the storm itself.  For now, the Cloud is like the seasons – winter if you are in one hemisphere but summer if you are in the other.  Analytics is a longer trend, an El Niño with global consequences.  And social media? Climate change for sure – but whether it’s a runaway greenhouse or the next ice age remains to be seen.

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