Monday, March 25, 2013

Good news and then ... bad news

Cyprus seems to have done the sensible thing, it's not perfect by a long shot but it is far better than the original proposal providing protection for a reasonable threshold of 100,000 Euro. There will of course be consequences and the usual dire warnings of calamity as per Iceland. Care will need to be taken that routes aren't found to transfer money out of the country, the normal game of buying expensive consultancy services etc as a way of transferring cash.

Despite claims otherwise, there is a reasonable chance that the Cyprus model will be re-used and the market will know this. Hence, expect GBP and other non Euro currencies to strengthen over the next few weeks and months as some take flight from the Euro. Of course, a strengthening GBP will reduce inflation issues in the UK (an import led nation) but will also result in weakening FTSE and house prices (aka London, we live in a distorted market) as the cheap Foreign capital which has propped it up will take profits.

Overall, this wouldn't be bad for the UK as the real economy might start to get a chance to recover, alas the BoE which is fixated on propping up the FTSE 100, house prices and the notion that money supply will solve all evils is bound to go on another round of quantitative easing. Everyone knows this, which is another reason why Foreign capital will take flight and then be back again as GBP weakens.

So here is hoping the BoE does something surprising and doesn't embark on more QE? Before you scoff, Cyprus seems to have embarked on something relatively sound ... why not the BoE? Oh, if we push the boat out we could start thinking Keynesian economics, a bit of wealth tax, a bit of ... wishful thinking.

I know, I know and you don't have to tell me. The BoE will just go for more QE because despite its spectacular failure everywhere else it has been tried, why let a little bit of data get in the way of dogma?

[Update 27th March 2013]

Now it seems we find out that the controls were inadequate and the high deposit accounts have been allowed to flee the country. A terrible mistake.

Wednesday, March 20, 2013

Pushing it to the Nth Degree ...

I've been meaning to write something more on the whole Pioneer, Settler and Town Planner structure but things have got in the way.  Ok, first a big dose of warning.

Whilst writing out maps and using them as a visualise aid in determining strategy is something I've done for quite some time and is based upon a fairly solid ground, this next bit is pretty shaky i.e. it's based upon very few data samples.

However, it has worked effectively in overcoming many common business problems such as alignment etc. The problem of course is few data samples and the danger of backward causality i.e. company A does X which is successful therefore X must be applicable to company B.

So you need to take the next bit with a huge pinch of salt!

Let us assume you've gone through the exercise of mapping out value chains, identifying areas for efficiency, common components (services) and created some wicked strategy mixing all the goodness of ecosystems (models like ILC) and the various attacking, defensive and dark arts.

You now need to organise yourselves. 

Step 1 - Eat Pizza
Rather than the one size fits all approach or breaking down into silos based upon type of activities (i.e. finance, marketing, IT), I'd argue for use of a more cell based structure such as the Amazon Two Pizza approach of small teams connected by services.

If you've mapped out your environment, built a strategy around "why" and created a cell based structure you're in pretty good shape. You can always go and top up with more of the Next Generation practice.  Going further is a step into the unknown. So, I'll guide you with what I know but I can't guarantee the results.

Step 2 - AGAIN - Plunging into the Depths
Ok, first thing go and grab your maps and count up the number of activities, practices and data sets that you've mapped out at different stages of evolution. This is actually a good time to find and take out duplication and bias between those maps. Anyway, once you've counted up then ...

Step 3 - Plot the frequency
Plot a graph of the frequency of components i.e. number of components at each step of evolution / total number of components.  What you're aiming to build is a profile for your company. As a side note, if you've been evil and built maps for your competitors, you can build a profile for your industry as well.

Now, as things evolve (through competition) then the profile moves. However some industries are more weighted to commodity relative to genesis than others.

Step 4 - Prepare to run screaming
Ok, this bit is a huge leap of faith and the evidence for this is scant and certainly not in the hundreds of examples I'd prefer in order to show any statistical significance. However, nevertheless I've used it in the past and it has worked well but that statement means almost nothing (single example, backward causation etc).

Despite everyone telling you that you need to be an innovator or a company needs one culture, beyond the cultural bits about treating your staff decently I'm going to argue the general perception is wrong. In fact you need at least three different types of people and three different sub cultures to create a sustainable organisation. The groups, I'll refer to as Pioneers, Settlers and Town Planners. 

This is the structure I started to implement in Fotango about 2005. In its fullest implementation you need these three groups and a Chief Pioneer, Chief Settler and Chief Town Planner (though at that time I didn't call them that). Everything else is gone ... no finance department, no IT department, no silo structure based upon type of activities. 

Everything here is based upon evolution.

The balance between these three groups is determined by your profile  i.e. if your industry and the actions you are taking (i.e. activities in your maps) are weighted towards the more commodity activities, then you'll need a larger Town Planning group. Obviously you can decide to manipulate this by focusing on one area but the point is to get a relative balance.

So who are the Pioneers, Settlers and Town Planners and how does this work? 
First, the model of operation is based upon theft i.e. Pioneers create the novel and new (as in genesis), Settlers steal from the Pioneers and grow the market, Town Planners steal from the Settlers and commoditise it. This process forces the Pioneers to use components from the Town Planners in creating new higher order systems and hence a virtuous circle is created. You can do this with Ecosystems (i.e. the ILC model) and the whole point of chowing down on the ecosystem is to force it to move forward (genesis). This post is however an example of how to run this internally.

The characteristics of each group are different, so is the sub culture they will create and the people you need. I've added some details related to a more technology example below. Your profile will give you an idea of how many of each you will require.

Step 5 - Populate your cells
Ok, now you've got an idea of the type of people you're looking for and how you can create a virtuous circle of theft, now populate your cell based structure with the right type of people.


Here's the shocker. If past experience is anything to go by (which it might not, a normal caveat of too little data) :-
  • All three groups are valuable and you need equally brilliant Pioneers, Settlers and Town Planners.
  • You can accelerate your rate of genesis (creation of the novel and new) and efficiency at the same time.
  • People did seem to happier in this structure i.e. it seems to fit to some deep way of operating. There does appear to be people who like to focus on the well structured, well ordered and use their brilliance to improve. There are others who seem to enjoy the completely chaotic, failure prone and occasional areas of brilliance.
  • Business alignment is simply an artefact of our current organisational structures.
  • You can permanently eliminate inertia to change through this cycle of theft.
Now before you rush off and do this, remember the evidence for this is scant and you're talking handfuls of examples. It's a gamble. The results might well be a statistical fluke.

Tuesday, March 19, 2013

This is bound to upset some people ...

I'm not a fan of QE (a monetarist fantasy which only causes stagflation in import dependent countries), the bait and switch of private debt to the public purse nor the original proposed Cyprus wealth tax.

I am a fan of Government's acting more like pirates especially when they're the lender of last resort, defaulting on sovereign debt where necessary, forcing nationalisation when in the nations interest and letting organisations fail when it's not, use of Keynesian style investment approaches to overcome inertia in industry (a particularly problem in this phase of the economic cycle, otherwise I'd be suggesting a Hayekian approach), the deliberate gaming of the economic system to serve a nation's interests and the introduction of EU bonds for raising debt centrally and preventing financial markets pitting one country against another.

I am also a fan of introducing a one off wealth tax. In fact, I'm actually a fan of using more wealth tax longer term over income tax as a means of redistribution given that money gravitates towards money. There has never been a trickle down effect only a trickle up.

Ideally with a wealth tax you'd hit all asset classes (shares, property, deposits etc) and all legal persons (individuals, corporations etc). You need to protect the poorest with high minimum thresholds, you'd need to apply a progressive rate of taxation and you'd need to minimise the efforts of persons to shift assets around to avoid the tax.

In the case of Cyprus, I'm glad that they have rejected the existing proposal. However, this does not mean I believe that they should reject a one of wealth tax outright. They still have time before the banks re-open (since they control this) to reorganise this to a more targeted system.

Whilst the hitting of deposits alone (a single asset class) is blunt and will inevitably create unfair situations, the entire economic mess is going to cause some haircuts. It is better that those who can most afford the haircut are the ones who bear the brunt. One thing I did particularly like about the Cyprus approach was the suggestion that those hit by the wealth tax are given shares as compensation in the institutions which are at the heart of the problem. If it all recovers, you get your money back - or at least that is the the notional idea.

You'd have to run the numbers but let us say for sake of argument you create a minimum threshold of 200,000+ Euros with a starting rate of 5%, increasing to 15% for 1 Million Euros+, 30% for 2 Million Euros+, 50% for 5 Million Euros+ and 80% for 10 Million Euros+. You would in effect create the safety net protecting the poorest whilst creating a more balanced taxation on those who can afford it including companies, wealth funds and all organisations who under EU law are considered "legal" persons.

It's an imperfect system, one which should be followed up with a more general wealth tax on all assets balanced with a reduction in income tax. However, as a short term measure - this is one which in my view has merit across all of Europe and not just Cyprus.

[Update 23rd March 2013]

Apparently Cyprus is looking at 25% levy about 100,000 Euro. It's a good step in the right direction. According to the Guardian, Russia is sabre rattling - given that when banks open Russian capital is likely to flee, then Cyprus may as well introduce a more draconian regime i.e. up the threshold but increase the rate in a progressive manner (as above) penalising all accounts above 5 or 10 Million Euros with a tax rate of 80%. Actually, you might as well go the whole hog and impose a 100% rate above a certain level ... say 20 million Euro.

It's an imperfect system but Cyprus needs to hold their mettle and act like pirates.

[Update 27th March 2013]

In what has become a farce, it seems that capital controls were not applied effectively and many of the  high value deposits have been allowed to flee.

Basics ... repeated ... again ...

I thought I'd put a quick post on some very super simple basics to any software project ... scrub that ... anything you're planning to build (business to technology to whatever).

Step 1 - Needs
If you're going to try and build anything, start with asking questions like what does this thing need to do, how will its consumers interact with this, what will they expect or desire from this. If you haven't got a list of needs, then STOP ... go write one (Hint: Post-it Notes are brilliant for this). It can be pretty high level and yes, some of the more novel "needs" are bound to be wrong. This is ok.

Step 2 - Write down the Value Chain
Now you have a list of Needs, write down the value chain i.e. the components you're going to use to build something that meets the needs (Hint: Post-it Notes are brilliant for this). You need to think about activities (i.e. systems you might use), practices and data. You can even include things which have never been built before (e.g. cat mind reading system) but at least put the effort into writing it down and what you think you might need to build it (e.g. servers, data storage, a large number of cat CAT scans - geddit).

Step 3 - Evolution
Now everything (i.e. activities, practices and data) evolves. For activities that means from genesis to custom built to product to more commodity. Knowing how evolved something is matters otherwise in a world of utility computing infrastructure you might start thinking that a custom built server makes sense ... it rarely does unless you can build more efficient / commoditised environments than currently exist. To identify how evolved something is, ask yourself the questions of how widespread (ubiquitous) and how well defined & understood (certain) that thing is and use the figure below. Go through your value chain and determine how evolved each of the components are.

Step 4 - MAP it!
Now you know what the components in your value chain are and how evolved each component is, then put it down on a map (Hint: Massive whiteboards and Post-it notes are brilliant for this). Use one axis for value chain and one axis for evolution.

In the tradition of TV cookery programmes, here's one I baked earlier. In this case in 2005 - a simplified map of part of the value chain of an online photo service known as Fotango. (NB. back then, I used to use the term innovation rather than genesis but alas since innovation is widely used to mean many different things I've chosen genesis to mean the novel and new).

Step 5 - Check the Map for unmet needs
Take your map and look at it. The top (higher order systems) should contain all the components that provide the visible need (i.e. what the consumer uses) along with areas of differentiation (i.e. novel and new). Check to make sure you haven't got any unmet needs i.e. missing bits.

 Step 6 - Check the Map for areas of efficiency
Take your map and look at it, again. Many of the lower order components will be hidden from the consumer (i.e. they don't know what CRM system you are using) however you may well be treating those components incorrectly i.e. treating a customer relationship management (CRM) system as something novel and new when it's more of a commodity. Look through and identify any areas which you could treat more efficiently.

Step 7 - Break existing model (if necessary)
In some cases you're dealing with an existing system which may be treated as though it's one thing. This creates all sorts of one size fits all problems as the characteristics of activities, practices and data change as they evolve. If you've got a large complex value chain which you're planning to outsource for example, break the model ... there's a better way.

Step 8 - Treat as Small Units
Break down large complex systems into small units because the way you treat each unit may vary (more on that latter). Ideally you can even organise around this e.g. copy Amazon's two pizza structure.


Step 9 - Apply the right techniques / methodologies
Now you've broken down the value chain into sensible component chunks start by making sure you apply the right techniques to the right stage of evolution. If you've got a management consultant in telling you that you can become an "Agile" shop or a "Six Sigma" shop then consider letting them go. There are extremely rare exceptions where "one size" works but for the majority you'll need to use both agile and six sigma and alter which technique you choose based upon how evolved something is.

Step 10 - Manage the Chaotic (the uncharted)
For example, the creation of novel and new (as in Genesis, Custom Built) should be done in-house with use of Agile techniques because of the chaotic nature of what is being managed (constantly changing, rare, highly uncertain, deviating from the past, unpredictable etc).  These activities are all about experimentation, gambling, failing (lots of this) and potential future value. If you can't afford to do this in-house, then hire a contract developer and ask yourself why are you doing this?

Step 11 - Manage the Transitional
The transitional activities (more evolved than chaotic, less evolved than linear) which are represented by products and rental services need to leverage the outside ecosystem i.e. buy products, listen to customers etc. These activities are ones which are becoming more widespread and well defined, they are becoming predictable, customer feedback and data is key etc. From a consumption point of view, try to use open means (source, data, process etc) where it exists and look for more open standards (saves future headaches).

Step 12  - Manage the Linear (the industrialised)
The more industrialised activities which are represented by commodity and utility services are ones which are  widespread, well defined, predictable and are all about efficiency and consistency. Use highly structured methods, outsourced to tightly defined contracts or even better utility services. Ideally you want a competitive market with switching so insist on open standards and open methods (source, data etc) where possible.

Step 13 - Shared Components
Once you've create one map of a value chain for your business then go create more. Once you have enough maps, start looking for shared components and start consolidating them.

Step 14 - Update your maps
Evolution is a consequence of competition (consumer and supplier) and it's continuous. So update your maps and make sure you know your needs, where you differentiate, the value chain, areas of efficiency, shared components and that you treat components with the right techniques. Writing a map for a value chain should only take a short time once you get good at (i.e. varies from 20 minutes to an hour or so). Updating is even quicker.

Step 15 -  Now you're ready
Once you've done the basics, you can now start thinking about how to exploit ecosystems or open mechanisms or dark arts to manipulate the map. You can look at competitors value chains, consumers and your own suppliers and look at the impacts of changing the environment. You can look to push something more open, encourage efficiency, drive development of higher order systems, create new sources of worth, undermine competitors barriers to entry, run a tower and moat play ... oh, there's a long list. You can start to predict obvious changes in the industry, competitor moves, deal with inertia, game your culture, create structures designed to cope with change, create centres of gravity ... oh, there's a long list.

However, most importantly you're in a position to discuss this, to talk about strategy in some form of meaningful sense, to talk about the why of action. All the previous 14 steps are about getting you into a position of talking about why.

Now in practice, you don't have to write out maps, you just need to ask equivalent questions. A mental model is fine as long as you can articulate the value chain, how evolved its components are, realise that you'll need multiple techniques and can play the game that way. I happen to draw it out because my memory sucks and the visualisation helps me explain to others what I'm thinking of doing. The steps above are of course a simplification, I didn't want to make the post too long but it's good enough.

Which brings me to the point of what not to do. Don't come and ask me a question about whether this or that strategy make sense unless you have done the above or something similar. I'm simply going to ask you "why" you're running this strategy? If you don't understand your value chain and how evolved the components are, then any strategy you have will either be shooting in the dark or because everyone else is doing this. 

If you want to really get up my nostrils you might tell me that your strategy is to be agile, efficient, nimble or innovative OR alternatively give me a long list of how, what and whens such as purchasing choices, implementation details, operational decisions or tactical choices OR you might just throw in a couple of buzz words like cloud, big data and social media to jazz it up. It'll be a very short conversation.

Situational awareness is essential to any form of competitive environment. Mapping is an extremely useful tool in this regard.

What workforces need ...

Every now and then I read an article which is more like a physical workout consisting of twisted facial expressions and face palms. Today's pleasure is the Guardian article on "Worforces need innovators and operators for business success" and I can't help but quickly write this response.

"Innovation comes in many shapes and sizes from big and radical to small and incremental. It is not just about inventing new products but increasingly about service innovation, process innovation, business model innovation and social innovation."

Yes, Innovation is a completely useless word as it used to describe many entirely different things. In other words, as activities evolve they go through different domains (see figure 1) - genesis, custom built, product and utility but we have an annoying habit of calling each of these innovation as though the creation (genesis) of the first phone was somehow the same as the addition of a new feature on a modern phone.

Figure 1 - Evolution

"Let us think a little about what the 'default' mode of organisations is. If a crisis hits, profits fall and a competitor gains market share, what is the company's first reaction? It is usually to reduce costs and improve efficiency to get the company into shape."

There isn't really a default mode but instead a mode which is related to the style of competition the company is used to (peace, war, wonder - see figure 2). For example, take computing infrastructure and the last 30 years of relatively peaceful competition in which computing infrastructure is provided as a product, where sustaining change has exceeded disruptive change and the focus has been on maximising profitability.

The shift from product to utility (nee cloud) is simply the process of evolution and is highly predictable. It has changed the style of competition from peace to war, with the introduction of new players, a rapid shift, a fight for survival and where disruptive change now exceeds sustaining. It's is perfectly normal for companies to fall back on there previous way of operating (though this is fatal), to deny the shift and to succumb to inertia. All organisations will exhibit these characteristics, inertia due to past success is actually perfectly healthy in most circumstances just fatal in this one.

Figure 2 - Evolution and competitive states

Fortunately, this is where the role of the CEO becomes so important as it is their job to see the clearly visible and predictable storm and move the organisation out of the way. Their job is to steer the Titanic out of the path of the huge iceberg. Lucky for us, in this case the iceberg has been known about for over a decade and is clearly visible with big neon signs saying "Iceberg here!"

The fact that a company fails to change course and instead ploughs into the iceberg is not the fault of the company, they are behaving normally. Companies crash because the one job the CEO really needs to do ... their CEO failed to do. Of course, there'll be endless whinging about the "Innovator's dilemma" except most of the disruptive changes we see (such as cloud computing) have been entirely predictable for over a decade and they are not unexpected in any shape or form. They should have been planned for, no excuses.

"As London Business School professors Costas Markides and Paul Geroski say in one of their articles: 'The skills, mindsets and competencies needed for discovery and innovation are not only different from those needed for mass market optimisation, they conflict …'"

This is known as the Salaman and Storey Innovation Paradox, from  Managers’ Theories about the process of innovation, Management Studies, 2002. It is a well known and understood phenomenon caused by the changing characteristics of activities (see figure 3). As activities, practices and data evolve (from more Chaotic to Linear) then their characteristics change and the techniques we need to deal with genesis (i.e. the novel and new) are polar opposite to the skills we need to deal with the linear.

Figure 3 - Example company map and change of characteristics as activities evolve.

"An operational excellence mindset is well suited for cost cutting and improving efficiencies, focusing on numbers and details, eliminating risk and ambiguity, as well as analysis and evidence. Contrast that with a mindset thriving on innovation, where there is a preference for visual language and the big picture, being comfortable with risk and ambiguity, as well as trusting intuition and dreams."

Here we fall into the innovation trap as "cost cutting and improving efficiencies" can be seen as operational innovation or process improvement etc. It is so important to define the terms you're talking about. The assumption that different techniques are needed depending upon whether we're talking about Chaotic (i.e. creation of the novel and new) or Linear (commodity and utility) is well known (see figure 4)

Figure 4 - Different Techniques are needed at different stages of Evolution

Then the article continues to go on about these two different groups. Ok, a word of warning. The article is pretty smart if it was written in the 1990s. Today, it's pretty poor. No discussion on next generation practices, use of cell based structure (two pizza model) and hence nothing which moves the topic forward beyond early 2000s.

Yes, there are two extremes but you need three groups to connect it all together. I've seen many examples of companies who've focused on the two extremes (chaotic and linear) and not the flow (the constant process of evolution) between the extremes and what happens is you get two isolated groups. In defence of the article, well, at least it has moved beyond "one size fits all" and attempts to make everything homogenous. That's not much of a saving grace but I thought I'd at least give it that.

Anyway, enough of this - the article is giving me shivers and it is forcing me to make face palm gestures.  More on structure here.

Monday, March 18, 2013

Mapping in one slide

Asked, to describe mapping in one slide ...

That basically sums it up. Oh and before someone asks what "real" strategy is - it's the type of strategy which has a clearly articulated why and doesn't boil down to randomly shooting in the dark. Alas, most strategy I come across is firmly in the "shooting in the dark" corner often with extensive how, what and whens of shooting in the dark and vague whys or more commonly a why of "because everyone else is doing this".

On Cyprus

There are many things I don't like, for example the use of Quantitative Easing (a purely monetarist fantasy of increasing money supply) which has the effect of weakening the internal economy and increasing inflation in import led countries. However, the Cyprus decision for a one of wealth tax on depositors  to support a Euro Bailout has to take the biscuit.

There are many different options, from a default on debts and raising future funds through a Euro Bond (preventing markets playing a game of isolating one country from another) to a more Keynesian style investment but if we're going to create a tax on wealth why hit an asset class which can disproportionately hit the poorest?

If you're going to have a one off wealth tax then hit all asset classes and protect the most vulnerable i.e. set a minimum level of wealth (e.g. 200K+ euro), include all asset classes (cash, shares, property etc) and hit all legal persons (i.e. real people and also companies which are defined under European law as legal persons).

If you're going to insist on only depositors then at the very least have a minimum threshold (e.g. 100K+ Euro) and make sure you hit all legal persons (i.e. company deposits including the banks). The problem of course is that companies will shift money rapidly into other asset classes, there are always ways to avoid a tax which hits one asset class. So make sure you apply the tax on dates that have already past (i.e. take an average of four dates over the past year or use the end of the last financial year or better still apply it immediately) rather than some forward date. There will be plenty of appeals, lobbyist efforts, financial engineering and attempts to shift deposits into other classes by the wealthy. Amongst all this sham and acts of self interest, there will be some actual cases of unfairness e.g. the couple who sold their house on the wrong day.

Cyprus has a large financial and shipping industry along with huge reserves of natural gas and extensive deposits by international banks. There are many ways to skin a cat, hitting your average depositor is not the way to do this, so at the very least protect the poorest.

Thursday, March 14, 2013

Being Tardy ...

I haven't published any videos recently of the many (and I mean many e.g. 60+) talks that I've given over the last two years.

Obviously, I need to get on top of that. In the meantime, here's a shortened version of a recent presentation covering mapping, evolution, commoditisation, innovation, ecosystems, componentisation, strategy and all that jazz. I've added some greyed speech bubbles to give it a bit more flow.

It's all pretty basic stuff but just in case ...

Grumble, Grumble

There's a long and growing list of words I dislike ...

In its purest form it is the creation (or genesis) of an activity or practice or data which by definition is rare, uncertain, constantly changing, experimental and deviating from the past. Alas, it is commonly used to described a feature difference on an existing product to a different mechanism of provision (e.g. utility) for a pre-existing activity. The first phone, a better phone and rental model for phones are all described as "innovation" despite them being entirely different in nature. It is therefore, a fairly useless word which forces the normal response of - "What sort of innovation are you talking about?"

Is the process by which an activity evolves from genesis to becoming more of a commodity and it represents a shift from imperfect to more perfect market competition. Alas, it is also confused with commodification (the process by which something with social value obtains financial value) thereby rendering it fairly useless and forcing the normal response of - "What sort of commoditisation are you talking about?"

Is the art of manipulating an environment to gain a desirable outcome. It requires in-depth awareness of the environment (i.e. yours and competitors value chains and how this is evolving), the toolsets available (i.e. mechanisms to manipulate the environment) and experience in doing this. It is commonly confused with tactical choices, operational details, purchasing decisions, implementation details, vague aspirational statements (being innovative, being nimble etc) and other irrelevant information on how, what and when. Hence whenever I'm presented with a strategy, I'm inevitably forced to ask the question of - "Explain the why?"

Disruptive Innovation
Ignoring the issue that the word "Innovation" will immediately force me to ask the question above, the term disruption is often applied to situations where no-one is actually being disrupted. In some cases it is more of an aspirational goal. Guaranteed to send me into howls of laughter is the inevitable - "Our strategy is to disrupt this market with our innovation" - which normally translates to - "everyone else is doing this, so we're hoping our version will be better than others" - the latter statement having nothing to do with strategy, innovation or disruption.

Anyway, even if the term is used in the original sense of Christensen's work, the problem is that most disruptive change is not caused by an unexpected market change but instead an entirely predictable one such as the evolution of an activity from product to utility. In such circumstances the issue of inertia to change can be resolved with planning and the disruption that does occur is often entirely unnecessary and a consequence of executive failure to plan for the inevitable. 

Still, such issues are mainly ignored and "disruptive innovation" is used to describe all manner of situations including ones where no-one is actually disrupted or the change is entirely predictable and preventable.  Hence, I'm inevitably forced to ask the question of - "Do you know what those words mean?"

Enough grumbling for today. 

You can tell I'm reading a piece of work which talks about strategy, innovation, commoditisation and disruption and it is quite clear that in my mind the author has no clue what any of those words actually mean. I'm sure it'll soon talk about :-
  • cloud computing - using some rather mangled definition as opposed to a simple shift from product to utility
  • big data - with endless drivel on numerous Vs rather than describing the normal historical process of rapid increases in un-modeled data which over time become modeled and why the key is the models.
  • new forms of organisation couched in terms of some amazing and "innovative" change and without reference to the normal process of co-evolution of practice which occurs with any of the standard historical cycles of change leading to organisations such as American System, Fordism ... all the way to Web 2.0 etc.
Of course it'll be littered with dreadful concepts like unstructured data (as if the data doesn't haven't structure which we just don't know about yet) or paradigm shifting or one of today's favourite "one size fits all" solutions which ignores the normal evolutionary flow from chaotic to more linear order and the consequential yoyo between extremes (agile vs six sigma, push vs pull marketing, network vs hierarchical structure, innovation vs efficiency etc) which can be avoided by adopting both.

Alternatively, I can stop reading ... hmmm ... I think that one wins my vote.

Tuesday, March 12, 2013

Notes for draft version

I'm in the middle of writing a book, so I thought I'd put my draft notes up (which is what these recent blog posts on strategy have been derived from) on Scribd.

The final version of the work will be radically different in terms of structure, cases and general presentation. However, I thought the rough and ready notes might be of general interest.

You'll have to forgive the wide number of spelling and grammatical errors, it's draft notes after all. However, in general it's readable and contains the main points covering commoditisation, evolution, ecosystems, new forms of organisations, common economic cycles, co-evolution of practice, componentisation, strategy, dark arts, prediction and a host of other things.

If you've not seen any of my presentations or work before, it'll probably not make a great deal of sense. It's not a general public piece, more for people who have already taken an interest in the subject and want a few more details.


In this final section, I wish to use all that we have discussed to examine the future to see if we cannot make some reasonable guesses as to what will happen. This will also serve as a reference point to test the predictive capabilities of the work.

There are two main areas I wish to discuss: - 
  • A highly predictable change in terms of “What” i.e. the commoditization of a pre-existing activity. For this I will use 3D printing.
  • A highly predictable change in terms of “When” i.e. the impact of cloud computing in enabling an age of wonder and the development of intelligent agents.
I will avoid the general discussion on the genesis of the novel and new which is both unpredictable in terms of what and when. I tend to cheat with my predictions, using pre-existing trends.

What not When
I have had a long history with 3D printing, having written my first research report on the subject in 2001. I’m not going to concern myself with the mechanics of additive manufacturing techniques and the potential for hybrid printing of both physical and electronic structure, as there are now many works that cover this.  I will instead concentrate on the consequences of this change.

First, 3D printing is part of a range of techniques that cover the commoditization of the manufacturing process itself i.e. factories are shifting from being custom built environments that consume large numbers of products to more digital environments that use increasingly commoditized methods to create objects. 3D printing is best thought of as part of the digital factory and in some cases this will be used in the home and in others in large scale manufacturing environments.

The impacts of 3D printing (and the related technologies such as printed electronics, hybrid printing) will be the same as the commoditization of any activity. Hence we should see a standard set of consequences that I first wrote about in 2006.

These include: -
  • Commoditization of the manufacturing process will result in an explosion of new activities (higher order systems) as the means of manufacture become ubiquitous. This will create a time of wonder and new industries but what those new things are and what new industries form, well we don't know yet because they're uncertain.
  • The manufacturing industry will shift from a state of "peace" where relative competition exists and sustaining change tends to exceed disruptive change to a state of "war". It will become a fight for survival, where many past giants who have created inertia to change due to past success fail to adapt and subsequently collapse. During this "war" disruptive change will exceed sustaining change and new entrants (not encumbered by past business models) will lead the charge.
  • We will see a flood of capital from the past manufacturing industries to these new industries.
  • As the activity of manufacturing changes (from custom built factories to ubiquitous 3D printers) then practices in manufacturing will change. Practices often co-evolve with activities. The growth in new practices of more “Agile Manufacturing” will result in a yo-yo between more unstructured and structured techniques, a manufacturing equivalent of Agile vs Six Sigma.
  • The new practices will result in new forms of organization as per electrical age (Fordism), Internet age (Web 2.0), Cloud (Next gen) and every other age.
  • At the height of this change concerns will be raised how commoditization of the manufacturing process will lead to mass unemployment etc. This will overlook every other example of the same phenomenon (Hawkins and the electrical age) and how each time we fail to anticipate the new activities (higher order systems) and related industries that will form.
  • In a desperate attempt to save existing business models, past industries will promote the importance of physical DRM to prevent people stealing copyrighted ideas or making dangerous items. Security and the threat of people being able to manufacture items like guns will be used to explain why this change is dangerous for us all and must be stopped. Furthermore Government officials pushed on by lobbyists will start to talk about the needs for certification of designers, architects and the like on the grounds of "consumer benefit". 
  • Along with disruption of past giants many secondary industries will discover that their industries will be disrupted due to rapid reduction in barriers to entry.
  • Because of the reduced barriers to entry there will be a rise in competitors that were once former consumers 
  • The trickle of adoption to these new manufacturing techniques and practices will become a flood as the combined forces of efficiency (through commoditization of the activity), increased agility in building higher order system (componentization) and future sources of worth (Schumpeter) kick in. This will take everyone in the industry by "surprise" due to the previous “peaceful” phases of competition.
  • As commoditization of the manufacturing process spreads and the explosion in higher order systems and new industries accelerates there will be a corresponding explosion in un-modeled data. This is data which we don't know how to model yet but will eventually be modeled as we understand it more. 
  • As hardware becomes more malleable like software, it will be realised that the function of a device consists of digital and physical elements both of which can be described by code and hence a new language will form. In this new language you will describe the function of the thing you want and a compiler works out which bits should be code and which bits should be bits to be printed. 
  • The open meme (e.g. open source, open hardware, open data, open APIs) will happily continue its symbiotic relationship with the Internet and grow rapidly in this space. The patent system will become hopelessly outmatched for this world and will become as harmful for manufacturing as it has been in the software industry.
  • Google, Amazon and other “players” are likely to adapt quickly to the change and attack with their normal two factor market and platforms plays to grab the developing ecosystems in this space. Many manufacturing, architectural and construction companies will find themselves now competing with the likes of Amazon backed by huge ecosystems of companies selling designs for direct printing.

The above “what” is highly predictable though the exact “when” depends upon the actions of individual actors and is hence unpredictable. However, we can assume the above changes will significantly hit the manufacturing industry between 2020-2030 based upon the original predictions in 2006.

In the last seven years, we have already seen the start of this process gather momentum including some of the dark arts such as concerns over printing dangerous items.

When not What
In this section I wish to cover a change that we know will happen (in terms of time) but just not what it will entail. This change relates to the commoditization of IT to components provided as utility services and how it will enable the rapid creation (nee Genesis) of higher order systems.

We know that our industry is going to experience a time of wonder but due to uncertainty we don’t know what those new higher order systems will be. 

We can however make a reasonable guess by looking at the common components that are now provided as more ubiquitous and well-defined utility services and extrapolate what possible higher order systems can form. By reasonable guess, I mean we can confine our focus to a specific area but it should be noted that this time of wonder is highly uncertain and so we don’t actually know.

I’m going to focus on a piece of work I presented in 2005 called “Any Given Tuesday”. This work described a future with more intelligent software agents that understood both my network of things, my routine, my continual “exhaust” of data and my relationship between other networks. The work highlighted two scenarios: -

In the first scenario, I wake up at 6:45 am, spend 10 minutes trying to find my watch, leave the house at 7:15 am, drive like a madman to the station, spend 30 minutes waiting for a train due to cancellation, get to London bridge at 9:15 am, get soaked because it is raining, rush to work missing my coffee, arrive at work 9:35, discover my CFO has been trying to call but I've left my phone at home, realise I have football today but no boots as I threw them away last week, my partner calls to remind me it's Mother's day and my Sister's birthday tomorrow - both of which I've done nothing about. 

Overall: I'm wet, late, had no coffee, I've annoyed my CFO, I'll miss out on football and I've still got to work out what to do about Mother's day and my sister. I'm hardly in the best mindset for work.

In the second scenario, I wake up fifteen minutes earlier at 6.30 am, pick up my watch and phone that are on the kitchen table, leave the house at 6.50 grabbing an umbrella from beside the door, arrive at the train station at 7:10 am and catch the 7:15 am, arrive at Canon Street at 8:20 am, pick up pre-ordered football boots from the sports store, grab a coffee, walk to work putting up my umbrella when it starts to rain, arrive at work 9:15 am, tell my CFO the reports been done and when my partner calls explain that I've sent my Mother flowers and my sister has a new ipod which will be delivered tomorrow as her last one broke. 

Overall: On time, dry, plus coffee, I'll be able to play football, mother and sister's presents are sorted and reports done. I'm in the right mindset for work.

Ok, so what happened between the two scenarios? Did I become Mr. Organized or learn the twenty-seven secrets of successful people? No, it's all done with technology and asking a few simple questions.

First, everything is tagged, everything is online and everything is a network. My network of things knows it's a Tuesday, how long it takes me to get ready and what things I need for work. It knows where those things are and the weather forecast. It can interrogate the train stations network to get times and cancellation information and it knows where I need to be. It knows I play football on Tuesday and that I threw my boots away. It knows I like to drink coffee and that its Mothers day tomorrow and that I bought flowers last year. It can ask my Mother's network what sort of flowers she has and whether anyone is buying her flowers? It knows it's my Sister's birthday tomorrow, it can ask her network for suggestions. It now knows she broke her ipod (remember this was written in 2005), that she hasn't replaced it and what her favourite songs are and where she will be tomorrow. My network knows my CFO was in a meeting where they discussed a new way of analyzing value from users.

My network of things can now, find a shop with the boots I need and pre-order, find a coffee shop and calculate a route to work to pick up both. Calculate time for journeys and dynamically deal with cancellations. Sort out ipod and flowers. Analyze the CFO meeting and determine most probable research to be collated and who to contact.

All it now needs to do is ask me some basic questions: -
  • Do I want to buy some new boots for £35 so I can play football tomorrow?
  • Do I want to send my Mother flowers for Mother's day?
  • My Sister's ipod is broken, do I want to buy her a new one for her birthday?
  • The CFO is after an examination of user spending patterns vs latency on the site, do you want me to prepare an initial analysis with the latest research on the subject?

Then it needs to calculate my journey and wake me up when I need to be woken up. This is what I called augmented intelligence and it likely to be provided through intelligent software agents that ask the questions and take care of the details. The agents will depend upon commoditized means of providing computing infrastructure, large-scale data analytic systems, extensive and modeled search and voice analysis, ubiquitous sensors and networks. Back in 2005, the capabilities were already there but it was economically unfeasible, the recent round of commoditization in IT is bringing many of these into reach.

In 2005, we could be sure that IT would enter an age of wonder (after the war) and intelligent software agents was a reasonable guess as to one of the higher order systems that were created.  Today, we have MindMeld, Google Now, SIRI and EVI.

The Cycle of Change
The above demonstrates how examination in terms of evolution is not only useful for creating maps and competing with others but also for providing some level of forecasting over the future. Alas, such forecasting is limited to “What” just not “When” or “When” just not “What”. However, the use of weak signals and componentization can help narrow down the uncertainty to likely areas of probability.

We can already see what the impact of 3D printing will be (probably sometime around 2020-2030) and how a time of wonder will occur in IT over the next five years (probably involving some form of intelligent software agents). 

Those newly created activities in the time of wonder will themselves evolve and eventually become a commodity initiating a new cycle of change in that industry. So, we can use these cycles to predict probable areas of interest well in advance.

However, the cycles of changes themselves are not linear but impacted by commoditization of the various means of communication. Whilst the evolution from Archimedes screw to standard nuts and bolt (brought on by Maudslay’s screw cutting lathe) took 2,000 years, the Parthian battery to utility provision of electricity was 1,400 years, the evolution of the first phone to telephony itself being a commodity was over one hundred years and computing infrastructure took a mere sixty years.

Whilst it is doubtful that we have become more “innovative” as a species (i.e. our ability to create the novel and new has accelerated), the speed at which things commoditize and the next cycle starts certainly appears to be accelerating. The printing press, postage stamps, telephony and the Internet have all accelerated the general rate of evolution of all other activities by increasing communication and participation.

So it is probably reasonable to say that along with being able to predict “What” not “When” or “When” not “What”, the one other thing that evolution teaches us is the cycle of change is likely to become more rapid.

Hopefully by now the reader is fully aware that things aren't quite as random as people make out and the importance of understanding evolution and mapping in competition. If so, well, I've done my job.



Post 27 on Management and Strategy series.

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Falsifiability and Secondary Predictions

The work I have presented here is no more than weak hypothesis. In this section I wish to describe what I mean by this and some of the limits of the work.

Scientific knowledge evolves through a similar pattern of concept (Genesis) to hypothesis to theory to universally accepted (widespread, well defined and well understood).  The characteristics of the knowledge change as it evolves with the results becoming more predictable and models of understanding more “modeled”. 

Concepts themselves are simply ideas with little or no supporting data. By the time something becomes a hypothesis you would expect to see significant sets of data, correlation and causation and some predictive capability. The evolution graph itself (from which the rest of this body of work is derived from) has correlation over thousands of data points and causation through consumer and supply competition. It is however not directly predictable over time and one of the axis is certainty.

The certainty axis creates a problem as we can define something as a commodity with a high degree of certainty but in early stages it is more difficult to determine what something is e.g. is this a custom built system or an early stage product for an activity that will become a commodity?

Hence the data collected has been applied retroactively i.e. the past history of today’s commodities is what has been used to generate the evolution graph.  Prior to something becoming a commodity we can only guestimate what it is e.g. use aggregated expert opinion. The more evolved something is (i.e. the more certain) then the more accurate our guesses are likely to be.

When something is novel and new, we cannot determine whether it or something else will become the commodity for that activity. Whilst we know that the highest value is created in the product stage and therefore a system may represent a source of future value, our certainty over this is very low. As the system evolves then our certainty increases but also the future value declines as it becomes more ubiquitous.

There is unfortunately an inverse relationship between future value and certainty and we have no way of peering into the future. We have no crystal balls and certainty acts as an information barrier to the future (see figure 58)

Figure 58 – Future Value, Evolution and Certainty

You can however may secondary predictions, so as something evolves then we should see co-evolved practices, new forms of organization which appear and then diffuse along with changing styles of competition (from “peace” to “war” to “wonder”). Hence along with correlation, causation and past historical data then we have also been able to conduct some secondary prediction tests. So far we haven’t broken the model. It is however only weak hypothesis but albeit an apparently useful one.

Expectation curves
The model was also used to examine the change of expectation over time. By looking at the differential benefit created by some novel thing minus the costs associated with it, we plotted a benefit curve over the evolution curve (see figure 59).

From this curve, something novel and new tends to incur costs (Research & Development) that exceed initial benefit, as the activity matures the benefit rapidly increases until a peak is reached. Then the benefit declines reach some form of plateau (minimal selling price).

Figure 59 – Differential Benefit over Evolution

However, what’s interesting is to view the delta in expectation of the benefits for the consumer. For example, the consumer (as in business consumer) will often read some whitepaper describing the system and how it is changing things but there is a lag between when the paper is written, the decision to purchase and implementation. What was relatively novel when the whitepaper was written maybe commonplace by the time a company implements.

This creates a delta in expectation, as a company may believe that such a system will create a “differential” only to discover that “everyone now has it”. This delta in expectation was modeled over time to provide a generalized average “shape” (see figure 60).  

Figure 60 – Expectation over Time.

The expectation curve starts from a low base, has a peak of expectation, a trough of low expectation and then plateau’s to a level. This is similar to Gartner’s Hype Cycle (see figure 61).

Figure 61 – Expectation vs Hype Cycle

So, what does this tell us about the Hype Cycle? 

Well, a number of things if we assume that the expectation curve and the hype cycle are the same phenomenon and the evolution curve represents change.

If the assumptions are correct then: -
  • From the benefit curve we can determine the period of highest differential and future value is in the technology trigger / peak of inflated expectations where the act is highly uncertain. The slope of enlightenment and plateau of productivity is where little or no differential or future value exists.
  • If the Gartner curve is based upon physical measurement, we should be able to reverse the process to identify where something is accurately on the evolution curve i.e. we should be able to say where something that is uncertain is on the certainty axis. This, of course, by definition is impossible. Hence the Gartner curve cannot be based upon physical measurement but must instead be aggregated subjective opinion.

The evolutionary model used throughout is simply a model, not the truth. It is weak hypothesis at best but has some supporting data with causation, correlation and secondary prediction tests. It does appear to be useful and makes some interesting observations on current management tools (e.g. hype cycles). It is falsifiable but as yet we haven’t managed to break it. As it currently stands, it is the best model we have to explain regularly observed phenomenon.


Post 26 on Management and Strategy series.

Next post in series ... The Future

Previous post in series ... Outside in the Distance

Beginning of the Management and Strategy series ... There must be some way out of here

Monday, March 11, 2013

Outside in the distance

The game of business is not played alone but against others. Predicting changes in the market along with competitors move is an important part of this game. The use of maps is simply a visual tool that helps enable this but it is important to understand the limits of what can and what cannot be predicted. 

Genesis (Unpredictable)
The genesis of the novel and new is, by its very nature, highly uncertain and hence unpredictable. You can make reasonable guesses by looking at components that are commoditizing and how they may be used to create other things but this is more about narrowing down a potential wide field to what is more likely. For example, today with ubiquitous and well-defined components for computer infrastructure, data capture through devices, voice recognition and complex search capabilities, predictive algorithms and ... then you could guess that “intelligent agents” will arise. In fact this is already happening with tools such as Google Now or MindMeld

In general genesis is extremely hard to predict. It’s like being able to predict the rise of television when Westinghouse introduced utility provision of electricity. The odds are not in your favour.

Solution to the Genesis Problem
Other than normal means of experimentation with fail quick, the only effective way of solving this problem is to get others to create the novel and new for you. The approach of using an ecosystem under an ILC model is specifically focused on this.

Substitution (Unpredictable)
Many products provide for numerous needs of consumers. Such products often have complex value chains with many high level visible components. Competing products also often differentiate with novel and new components. In some cases one product will simply be beaten by a better product (i.e. iPhone vs Blackberry). In some cases, the substitution of one product with another (e.g. cable vs hydraulic excavators) occurs because of changes in the value chain such as a new component or characteristic becoming important. This is extremely hard to predict and the competitor often develops in underserved markets first. Unfortunately the change is also compounded by inertia caused by pre-existing business models that sell one form of the product to established customers (e.g. cable excavators). The market is seen to move in an “unpredictable” or “unexpected” manner with existing vendors that are suffering from inertia likely to be “disrupted”. This is a classic case of the Innovator’s Dilemma.

Solution to Substitution
Protecting yourself against such “unexpected” market change is extremely difficult. Given some new characteristic or a change in the value chain then the opponent can often establish themselves in other underserved markets before attacking the main market. Being able to see that hydraulic will replace cable excavators requires incredible foresight. The best option is access to information (as per the ecosystem model) combined with a willingness to understand that existing models can be replaced. Often a method of buying up smaller companies that are growing and which could provide alternative approaches is relatively sound but the innovator’s dilemma is an extremely hard nut to crack.

Evolution (Predictable)
Unlike the creation of the novel and new (genesis) or the substitution of one value chain with another, the evolution of any activity, practice or data is highly predictable. For example, the shift from genesis to custom built to products (and rental services) to commodity (and utility services) is a well-trodden path.

The process of evolution is governed by competition between all actors (consumers and suppliers) however the timing depends upon the actions of individual actors. Hence whilst evolution (the “what”) is predictable, “when” it will occur is not. 

An example of this is cloud computing and the shift towards utility services. This was a highly predictable change in terms of “what” which was first described in 1966 in Douglas Parkhill’s book the challenge of the computer utility. The precise “when” however depended upon individual actors and hence was not predictable.

The difference between changes that result from the interaction of all actors (e.g. the “what” of evolution) vs. changes that result from individual actors  (e.g. the “when” of evolution) is essential in understanding many broad economic effects. For example, whilst Cloud Computing was an inevitable consequence of evolution, the question of centralization vs decentralization very much depends upon the game play between the individual actors in the market.

Solution to Evolution
The issues caused by evolution, such as disruption of past business models and inertia to change can be prepared for well in advance due to its highly predictable nature. However, the “when” of action needs to be resolved by looking for weak signals such as consumer dissatisfaction with the cost associated with an activity. Changes such as cloud computing are often associated with the Innovator’s dilemma however given this class of change is neither “unexpected” nor “unpredictable” there exist little reason why any corporation should be disrupted by such a change other than through executive failure.

Economic Cycles (Predictable)
Since evolution is predictable, the corresponding economic phases of peace, war and wonder are also predictable. This means the style of competition and types of approaches needed for each phase can also be prepared for e.g:-

  • The “war” phase is all about operational efficiency, volume operations and provision of good enough components. 
  • The “wonder” phase is all about experimentation, the rush to become established and rapid rates of genesis. Though “what” is created is unpredictable, the “when” is predictable i.e. during this phase, novel and new creations will appear which will dominate our society over time. 
  • The “peace” phase is all about maintenance of profitability and the status quo.

Solution to Economic Cycle
The solution is simply to understand the style of competition, type of leadership and the methods that are appropriate change with evolution of any activity, data or practice.

Mapping is a useful technique in visualizing the environment onto which the above rules can be applied. In Boyd’s OODA loop (observe, orient, decide, act) then mapping is focused on the first step of observation and hence increasing situational awareness. Without this any action or strategy is akin to shooting in the dark. In such environments, companies are reduced to simply observing what others are doing. Symptoms of this include many decisions being based upon backward causality i.e. if successful Company A does X then by doing X we will become successful. Copying, use of hype-cycles, analyst reports on the market become rife.

Assuming the reader has an understanding of mapping, then I find the following table useful when dealing with prediction: -


Post 25 on Management and Strategy series.

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