Do you know which strategic play is right for you?


It’s conference season in the energy sector – and it’s a good chance to look around and see what companies are doing to position themselves as the industry and markets change around them.

For over 20 years now, we have seen battles between incumbents and innovators.

Innovators come along and try to get market share

The innovators – let’s call them the Red Team – see a market and believe they can do better.

They come along with a cheaper substitute and capture new, low-end customers.

For example, domestic users now have a choice of switching portals that make it much easier to compare offerings from suppliers and switch.

The portals are moving upmarket, targeting increasingly larger users and higher end customers – and compete with a number of other software and portal based systems that address the same market.

They also compete with various framework type structures that try to make it easier for users to decide between options.

This happens at every level of the market – in energy it’s being seen from how we buy energy, to how we use it and check the bills are right, and how we use our assets to make money.

The incumbents are slow to recognise how things are changing

Incumbent companies – the Blue Team – with market share and good profits don’t really want things to change.

They can ignore the innovators, but the ones that do that tend to find that by the time they are awake to the danger it’s too late.

A Blue Team that is on the lookout for this kind of competition has to either acquire the innovator or invest time and resource into creating a competing business that tackles the innovator head on.

In today’s digitalised markets, however, this article from the Harvard Business Review by Larry Downes and Paul Nunes says that things are a little different.

The fight just turned unfair

Downes and Nunes point to the emergence companies that land with a Big Bang and take market share suddenly and completely, with no warning – let’s call them Green Teams.

The military like this approach – the old adage says if you find yourself in a fair fight, then you didn’t plan your mission properly.

The big example here is how smartphones with free maps have upset the market for navigation devices.

The Green Teams, however, don’t operate like a military unit.

Instead, they’re often a group of people working on cool stuff that unexpectedly takes over a completely different industry from the one they’re in.

Products that come out of hackathons and experimental product launches have an effect beyond expectations because they turn out to be cheaper, more inventive and better integrated than the stuff that is out there right now.

Products like Twitter, Whatsap and WordPress changed the way we communicate and build stuff on the internet all at once – people signed up in massive numbers very quickly, leaving no time for incumbents to react.

Which team are you on?

In the energy business, the Blue Teams include traditional suppliers who are dealing with a changing energy system that is decarbonising.

Some are getting rid of traditional generation and becoming completely green generators.

The Red Teams include a host of new suppliers and players in the supply chain – from developers of platforms to technology.

The energy industry is notoriously slow to change – and this time around no company jumps out as being a clear Green Team leader, although many are trying to position themselves in this space.

The game goes on – the essence of strategy is knowing which team and play we’re going with.

Is information enough to spur action?


The domestic sector uses nearly 30% of the total energy used in the UK, and 80% of that is used for space and water heating.

Reducing energy use in this sector would clearly help reduce emissions and help the UK move towards its carbon targets.

Several approaches have been used to do this – from carrying out performance measurements using the Standard Assessment Procedure (SAP) and providing Energy Performance Certificates (EPCs) to dispensing written and face to face advice and information on how to save energy.

There are very few studies on whether any of these approaches actually result in savings.

A study published by the Behavioural Insights Team (BIT) in late 2017commissioned by NEST and Npower found that the predicted results from models such as the SAP varied widely from actual performance.

For example, the SAP predicted that savings from loft insulation in a medium home would be £120 and pay back in 2.5 years.

Real world data had a saving of £21, raising the payback period to 11 years.

On a day to day basis, however, the way in which people use the controls and settings in their homes has a greater impact on the amount of energy they use.

Does providing advice improve how they use their controls?

Another study in 2014 found that written information or advice in the home had no impact in the amount of energy used.

In some cases, showing people how to use their thermostats may have increased energy usage as they now used them to increase temperatures and get more comfortable.

This could be because of a number of reasons, and include the common problems with behaviour such as forgetting, ingrained habits and just not wanting to deal with the effort or hassle or doing something.

The purpose of the NEST and Npower commissioned study was to see if there was a statistically significant saving to be had from using a system like the NEST learning thermostat, which uses sensors and machine learning to optimise the heating schedule.

Once installed, NEST uses occupancy and weather data that is collected over time to figure out when it should turn the heating up to ensure comfort levels are maintained and when it can be reduced without impact.

Four studies – the most rigorous of their kind so far – showed that compared to homes having a programmable timer, thermostat and radiator valves, the NEST system could save 4.5 – 5% of total gas consumption.

Adding in an optional feature that does seasonal savings by tweaking winter use adds another 3.3% to the savings figure, taking the total to nearly 8%.

It can also nudge users – giving them leaves if they turn down the heating and act in an energy efficient way.

The thermostat is around £280 installed, with a payback of 6.5 to 11 years.

And this is still where the problem lies.

Even at a relatively low capital cost, the payback is going to be on the order of 10 years.

And that makes it hard to create a simple business case for change – especially for operators of large portfolios that may quickly have to spend hundreds of thousands of pounds to retrofit a few thousand homes.

New homes will probably get systems like NEST fitted as standard and, when we do a major refurb, it will be a small part of the overall cost and easy to justify.

But, in summary, the evidence shows that we get better results when we automate how choices are made rather than if we ask people to change.

How to think critically


Thinking critically about something is not the same as criticising it – just like making an argument is very different from having an argument.

In day-to-day usage it appears negative or destructive – but from an academic point it’s simply a common sense approach.

How can we approach a new situation, idea or information with our eyes open and do something meaningful as a result?

This starts with being able to consider the situation critically – questioning rather than blindly accepting the things that are put in front of us.

This is especially important in a world where we face complex choices – from whether we should act quickly or slowly on our own contribution to climate change to enthusiastically adopt the latest technology fad.

Quite often we default to doing nothing – and that may be the worst of all outcomes. That leads to atrophy and failure.

John Mingers identified four aspects of critical thinking that act as a useful checklist for us.

The first is to be wary of rhetoric.

Is the argument fair, balanced and logical or is the speaker using language in a way that could appeal to emotions or mislead us?

Is it a sales pitch rather than an insight?

It’s not always easy to tell, because we can be swayed by passionate people who believe in what they are saying – but we need to try.

The second aspect is to question tradition.

Tradition can involve unquestioned assumptions that are made by people or the culture and practices that have sprung up around an idea.

This can be a difficult thing to approach as the existing position, or status quo, is something people will cling strongly to and resist changing.

It’s easier to go along with them – but that might not be the right thing to do.

The third aspect is not to accept authority unthinkingly.

Arthur C. Clarke wrote – If an elderly but distinguished scientist says that something is possible, he is almost certainly right; but if he says that it is impossible, he is very probably wrong.

Things change – and sometimes people that have built their reputation on a particular set of ideas find it difficult or impossible to accept that their contributions could now be overturned.

If they have power, they can direct resources and attention to other areas instead.

We see examples of this everywhere – most notably in politics across the world.

The final aspect is to question the objectivity of the people involved.

Robert Pirsig in Zen and the art of motorcycle maintenance writes about how scientific work can be like sorting grains of sand on the beach into piles.

The piles represent related ideas, concepts, theories. It’s the way that we approach and classify the world.

The thing we cannot forget is that the piles do not exist on their own.

There is a person kneeling there on the beach making them.

And we need to consider how value-free and objective that person is about the issue.

For example, we would not give a news report from a state that routinely censors information the same weight as a report from a respected investigative reporter.

So, in summary, critical thinking is not about criticising.

It’s about not blindly following persuasive, traditional, authoritarian or seemingly objective points of view.

When should you get really interested in new technology?


The pace at which technology is developing appears to be speeding up all around us – so what should we do in such an environment?

Technology developers and evangelists can have any number of brilliant ideas and solutions, but as buyers and users that may have to use the technology for a number of years, we need to be careful.

The Gartner hype cycle is a popular way of looking at different technology and says that they tend to pass through five phases:

  1. A new technology is created.
  2. We expect too much from it.
  3. We’re disappointed when it doesn’t meet those expectations.
  4. We learn and change and figure out how to use it properly.
  5. It helps to be more productive up to a point, and then the effects level off.

It’s a nice graph, but there is little evidence to say that is actually works or has any science behind it.

It’s more a picture of how industry insiders collectively think about technologies at a point in time than an accurate reflection of the journey technologies take from creation to mass adoption.

A more useful indicator, used widely in academia, is the idea of Technology Readiness Levels or TRLs.

TRLs originated in the aviation industry, where pre-flight checks are common before taking off – a process called flight readiness reviews.

Having a checklist to go through and check and double check critical elements is a major contributor to flight safety.

NASA took this one step further, and came up with the idea of checking whether technologies were ready to start being used in programmes – a technology readiness review.

This led to the idea of readiness levels – and a formal version of these are used by many organisations.

For those of us that need to make a decision about a specific technology option, the picture above shows an adapted version of the TRL framework that may be useful.

In essence, we go from low TRL levels, where a technology progresses from basic principles to a model that works in the laboratory between levels 1 and 4.

At the other end, at 9, we have systems that are proven, work in the field and are probably widely deployed – but that are also mature and perhaps need changing

The interesting stuff is happening between 5 and 8, and this is where we should focus our attention.

Take blockchain technologies, for instance.

The idea of the blockchain and demonstrators with code have been around for a while.

Bitcoin, arguably the first proper prototype that began operating on the web in anger, has been around for nearly a decade.

We are now in a situation where there are a number of prototypes being operated – we can create apps on ethereum now and test them out – but there are still challenges that need to be solved around scaling and power usage.

So, we might score blockchain a 7 with strong potential to go onto 8.

That might suggest that a good time to get involved is right now.

How to analyse the future


Thinking about the future is not easy.

As humans we fall prey to biases, and two in particular are important.

The first is hindsight bias where, looking back, we think that things that have happened were far more inevitable than they actually were.

For example a Trump victory seems like it was pre-ordained now – Hillary never stood a chance against the Twitter machine.

At the time, however, not many around the world seriously thought Trump would win.

The second is foresight bias – we believe some things are more likely to happen than others and so bet on them more heavily.

We need tools and methods to guard against these biases and reason about the future more effectively – and the military and intelligence establishments are a good source of information on these.

For example, this guide sets out a detailed approach to counterfactual reasoning, one of the tools every analyst should be able to use.

When we think about the future we often do one of two things.

1. We look at trends

We see trends and infer outcomes that result from those trends – a technique called forecasting.

For example, we might see a trend towards decentralised currencies with bitcoin or a trend towards widescale adoption of solar photovoltaic and distributed generation.

We forecast an outcome based on these trends – the end of traditional banking or energy firms.

2. We create possible futures

We do futuring when we look at drivers and come up with possible scenarios that might result.

For example, the widespread use of mobile phones will make desktop or offline services less relevant for things like getting media, checking mail and reading the news.

Counterfactual reasoning

Counterfactual means counter to the facts, and we reason that way by asking questions like “What if” or “If we”.

We can look at a problem in terms of antecedents and precedents – or before and after a fact.

Approaching a problem in this way has two benefits – it helps us explore cause and effects and it lets us be more creative.

For example, take a statement like the fall in the price of solar panels means that we will have widespread adoption in residential neighbourhoods.

That seems like a perfectly reasonable statement – but what happens if we break it down?

Should we start a solar panels sales business right now?

The before bit is a fall in the price of solar panels – which we see happening right now.

Cheap solar panels clearly lead to cheaper costs for the equipment.

But, does that alone justify the conclusion about what comes after – widespread adoption in residential neighbourhoods?

It does not – because we haven’t looked at the components in detail.

First, we need to examine why prices are low. Is it because the technology is getting better and cheaper, or is it because massive capacity increases in China are resulting in panels being dumped on the world market?

Then we need to think about the in-between – what may happen if what we predict takes place.

Low prices for panels don’t get around other problems – such as the connection constraints in neighbourhood, the other costs of installation such as scaffolding, and the possibility that high demand for installations coupled with low numbers of qualified tradespeople after BREXIT may result in bumping up the costs overall.

Then there is the after – new homes are very likely to have panels fitted – they can be designed in.

But will there be a rush by homeowners to retrofit panels or will they be put off by the up front cost and possible impact on sale prices?

If existing homes are slow to change, the overall rate of change will be slow because existing housing stock stays in place for decades so for everything to be replaced with new energy-efficient housing could take a century.


We can jump very quickly from what we see now to what we think will happen in the future.

The purpose of using analytic methods in a structured way is to help slow us down and examine the situation in more detail, coming to a more considered view on what may happen.

The conclusions we come to as a result may help us make better decisions.

What we should do before investing in technology


We often think of technology as a good thing – surely having the latest version of something is obviously the best way and people who do that win?

Perhaps not.

Charlie Munger said – The great lesson in microeconomics is to discriminate between when technology is going to help you and when it’s going to kill you.

Most companies would benefit from new production technology that is more efficient and so uses less energy.

The deciding factor, however, is what that technology does for the business

Does it help it create more products, for example.

In a commodity business, being able to push more product out means that the market has more supply and so prices go down.

The cost and energy savings made by the more efficient technology is wiped out by the reduction in prices to customers.

All of the benefit goes the customer, with little staying with the manufacturer.

The Japanese are well-known for having slow upgrade cycles, using older equipment for much longer.

This is because changing things adds complexity and could reduce the amount of time the factory actually operates.

In addition, changes often introduce new problems, and Japanese companies value stability and continuity.

They invest in systems that help them reduce defects, by continually monitoring a number of parameters and warning them when things are going wrong.

This helps them maintain quality.

Having good monitoring systems lets workers manage more systems and machines each – while good working practices, maintenance regimes and stable technology let operations carry on without crisis or constant intervention.

All too often, we look for a silver bullet – a new technology solution that will solve all our problems.

We should start, however, by making sure that we are using what we already have well – and good monitoring systems are our eyes and ears into the operations.

It’s simple really – we need to do the basic things a little better, every day.

And that starts with looking and improving what is in place before buying something new.

What type of service model do you have?


The UK economy is dominated by the service sector, which makes up more than 80% of GDP.

Many industrialised countries are in a similar position, moving away from raw material extraction and manufacturing to an economy based on service and, increasingly, knowledge based activities.

How should we think about service businesses?

We often start by thinking of a service as something people do for other people but this doesn’t capture the full picture.

In 1978 Dan R.E. Thomas, writing in the Harvard Business Review, suggested that we need to ask two questions to understand the model used in a given service business:

  1. How is the service rendered?
  2. What equipment or people render the service?

Matching services with business models

Although the article is old, it can be adapted into a framework to help match services with business models.

On one axis, we can think of people and their skills, ranging from relatively unskilled to professionals with extensive qualifications.

On the other, we set out how they use equipment and whether it needs to be operated, monitored or can be automated.

Services that require a human operator range from mowing a lawn, which can be done by someone relatively unskilled with a mower, to heart surgery, which requires a team of professionals with specialised equipment and facilities.

Monitored services can range from overseeing equipment, such as a car wash to more complex plant operations and consulting services.

In these situations the people don’t need to get physically involved but use systems to keep track of operations and change settings as needed.

Automated services range from vending machines at one extreme that have a fairly straightforward task of dispensing products to expert systems such as a health website that allows us to diagnose ourselves and decide whether we need to go to a hospital or not.

Why knowing the kind of service model you have is important

The kind of service model we operate decides how we scale the business.

If a business depends on one person’s time to succeed, then scale can only happen by adding more similar people.

Think lawyers, accountants and management consultants.

There is a reason why most professional practices are small.

They can only grow by putting in more capital and pushing up their fixed costs base which, if revenue fails to grow as expected, means they eventually slide into failure.

There aren’t that many ways to get around this. A common solution is to find patrons – small or big.

Scaling equipment, on the other hand, may be an easier option.

As more of the service is automated, the same number of professionals can deliver a better service to customers.

Unless it spills over into self-service.

There is a crucial difference between service automation that makes things better and cheaper for users and service automation that makes things better and cheaper for providers.

Getting users to do more of the work can easily fall into the latter category.

The right blend of service and equipment

A good service business, it would seem, has a core of people with appropriate skills and scales by adding technology and automation that improves service quality to customers before adding more people.

As with most things, that’s easy to say, but not simple to do.

How to create the conditions for complex outcomes


The natural world is teeming with creatures perfectly adapted to their environment – that have ways of walking, swimming and flying, live alone or in social groups and participate in an ecosystem with their own unique niche and capabilities?

Where do we begin trying to understand how they do it?

We start by breaking things down into parts that we can understand.

Like blind people touching parts of an elephant, we find pieces – a snake-like tail, a fan-like ear, a tree-like leg.

If we bolted a snake, a log and a fan together with the other bits that we identified, would we get an elephant?

The answer is clearly no – but we persist in trying to build complicated things from simpler pieces.

Take most systems, for example.

An organisation is a system made up of people in roles.

There are some at the top who see themselves as the brains and controllers of the outfit and many people who do work.

Organisations are often designed – made up of structures and hierarchies and reporting lines – held together and moved in a particular direction by incentives, punishments and guidance.

Does organisational behaviour come from the particular arrangement and positioning of people?

Or does it emerge from somewhere else?

The study of emergence looks at how complex behaviour arises from the interaction between simpler elements.

There is a difference between complex and complicated.

Complicated may be something like a steam train – with lots of moving parts. When the parts move in the way they should, we get something complicated like a moving train.

An example of a complex thing is a flock of birds flying in the sky together. Each bird maintains its distance from another – and the whole flock can swoop and move like a single living thing – but there is no one bird that plans or controls the action.

The complex thing that we can relate to easily is the Internet.

We are all connected by a vast decentralised network that has only a few simple rules about pages and links – but is so much more than that now.

Emergence is sometimes seen as the border between order and chaos.

In an ordered world, everything has its place – we put a rock on top of another rock and eventually we can get a building.

A chaotic world is dynamic – as elements combine randomly with feedback to create new conditions that – and range from the weather to swirls in a coffee mug.

As we move from order to chaos – we pass through emergence – and that is where life and the behaviour we see in the natural world seems to be.

But how can we use this in daily life or business?

With knowledge work in particular, a strict rules based approach is unlikely to create anything particularly interesting or innovative.

Instead, its the interaction between people with capabilities working together that creates output from the organisation that is “greater than the sum of its parts”.

Managers should try and do just a few things.

  1. Find good people.
  2. Remove as many barriers as possible that stop them working together.
  3. Set a few working practices
  4. Get out of their way.

Then, wait to see what emerges.

Why we need to get tactile with data


We often put a lot of effort into creating a good looking dashboard or reporting system – but then what happens?

All too often it simply becomes background noise.

We get it every day and ignore or simply tune out – going blind to it while we get on with whatever is latest and loudest.

We tend to fall into patterns and things like data and information displays can simply become pretty pictures rather than being used for reflection and action.

Unless you work in a lean organisation, that is.

The Japanese have a word for a big room – Obeya – that they use to call a dedicated space that is where people come together, can see what is going on and collaborate.

A way to think about this is like a command centre, a war room or the bridge of a ship.

Japanese companies like Toyota use this every day – or even several times a day.

It starts with a simple idea – we have to hit certain targets every day.

We get together during the day and go through the numbers and see if we are on track or not.

If not, we can make changes and correct our course. Being in the same space helps with having that conversation.

The space doesn’t have to be physical – it can be a digital space where we can get together, share and modify information.

But the important bit is that we need to engage with information we get.

At Toyota they make updating information a manual activity – writing numbers by hand, drawing charts and updating status indicators.

It’s the process of interacting with the data and information we have in front of us – of trying to touch and feel it – that transforms it from being a pretty picture on the wall to the source of our next action.

We are more engaged when we understand what is in front of us – and that makes for better conversations and more useful collaboration between colleagues.

It’s interesting that as we have more and more powerful ways to dissect and distill the data around us we humans become the bottlenecks in being able to use the information and insights more effectively.

And that’s no bad thing – constraints are good for innovation.

If anything – we need to slow down even more.

We need to look beyond the dashboard as a result and focus on the end result – what are we trying to achieve each day?

We should get our fancy algorithms and computers to do the number crunching they are good at and give us the figures we need – that’s not work people should do.

Our job to use our time to get a feel for the numbers – getting tactile with them.

That frees us up to use our creative ability to come up with solutions – and we need all the creative time we can get.

Because there are a lot of interesting problems out there to solve.

How to solve real-world management problems


We know we have to change things – but how?

A naive approach is to think that there is an engineering solution to everything.

Buy new software, change machinery, institute a new form or process and everything will fall into place and get better.

That approach assumes that business systems fit together like Lego blocks and swapping a new one in or moving them around will fix the problem.

It’s simply a question of knowing what systems we want.

We can then list out the options we have, model alternatives and then select the best one based on objective criteria – simple.

There’s just one problem.

The chances are that the people involved want different things.

The thing that the accountants see as an issue is different from how engineers would put the situation which is vastly different from the vision of the CEO and shareholders.

And, because its much easier to see things from our point of view, the decisions that get made say more about power and influence than impact and improvement.

Soft systems methodology (SSM), a programme of action research carried out by Professor Peter Checkland at Lancaster University, is an approach that helps us cut through such situations and have a structured debate about the problem situation and what we can do to improve it.

The principle that underlies SSM is that the one thing that we see in all real-world problems is that people are trying to take purposeful action.

Take carbon reductions, for example.

No one in any organisation these days would argue that it’s not better to be sustainable, reduce energy and emissions, and act in a more socially responsible way.

Why don’t we do more then?

The engineers blame the accountants for stopping projects.

The accountants point the finger at the CEO’s decision to limit costs.

The CEO points to the priorities of shareholders and the desperate need to meet quarterly numbers and support the stock price.

There are no solutions to such problem situations.

Instead, SSM teaches that there are accommodations – things we can do that reconcile conflicting interests.

It does not mean that everyone is satisfied – but it is a position that we can all come to that allows us to move forward and take action.

And that’s done through a structured learning cycle as shown in the figure.

We take a real-world situation, create models that describe a set of activities that enable purposeful action in that situation and compare them.

We use these models to talk to each other and see different points of view and figure out what accommodations we can make.

Then – we can take action to improve the situation.

But it doesn’t end there. This is a learning cycle – which means we can go around the loop forever, or at least as long as we’re still in business.

And it’s more complex – because we still need to think about culture and politics – that adds more layers to our analysis.

The thing is that real-life problems that involve people, their opinions and time aren’t simple to solve.

Let’s not pretend that they are – then we might start to make some real progress.