How creating a red team helps you make better plans

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General Stanley McChrystal, a retired 4-star US General who commanded US and coalition forces in Afghanistan, creates a “red team” when planning an operation.

The idea is that when any group of people start to work on a plan, they think about certain ideas and strategies that they think will work.

As they do this, they start to focus on information that confirms what they believe and begin to discount what does not. It just seems to be the way the brain works – a confirmation bias.

In a military situation, this can result in the wrong decision – which can be fatal.

The job of the red team, which is made up of different people from those that did the planning, is to figure out how they would disrupt the plan.

Their job is to think creatively about the ways in which the plan could go wrong and what they would do to frustrate it.

This is also called ‘devil’s advocacy’, where one expert presents a plan and a second critiques it.

Importantly, the job of the job of the devil’s advocate is only to present flaws with the original plan, not to provide an alternative solution.

If they start to think about solving the problems they find, they start to introduce new biases.

In research by Richard Cozier and others from the 1970s onwards, they found that the use of a devil’s advocate significantly improved the prediction accuracy of strategic decisions.

Using a red team helps create a plan that is solid, rather than because the people who executed the plan were lucky.

We can still see how not doing this creates disastrous results now.

Theresa May’s result in the UK general election, where she managed to lose the conservative majority, is being blamed on how her chiefs of staff created an atmosphere where dissent was not tolerated.

This led to focusing on policies that lost them support and led to electing a UK parliment with a weakened government entering crucial negotiations with the European Union.

Some people may think that is actually a better result. A stronger government with an absolute mandate may have had the power to do what it wanted – resulting in a worse outcome.

The current parliment, with a stronger opposition critiquing the government’s plans, may result in a better outcome for the country in the coming years.

Why do fuel prices go up fast and down slow?

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Households in the UK spend between 12 and 27% of their disposable income on transport, of which a third can go on the cost of fuel.

People spent, on average, £72.70 on transport in 2016 and the cost of petrol and diesel was the biggest contributing factor.

Oil prices went up and down in 2016. At the start of the year, they were low and went lower on abundant supplies, with the spot price of crude oil heading towards $25 a barrel.

In the second and third quarter of 2016, producers responded with spending and production cuts, which helped prices head back towards $50 a barrel.

By the end of the year, OPEC’s decision to curb production and stick to quotas and an agreement from other countries to reduce output sent prices towards $55 a barrel.

So, in a market where global prices can double or halve in a year, why do these increases or decreases not show up in prices at the pump?

A litre of unleaded petrol in the UK went from around 102 pence per litre to 115 pence per litre by the end of the year.

We’ve all seen that when global oil prices fall, the reductions don’t seem to show at the pump. But when they rise, the price at the pump seems to go up straight away.

Why is this?

It’s not just imagination. It turns out there is a phenomenon, described in the industry as “Rockets and Feathers” that takes place.

In a commodity market, where prices are posted daily for all to see, as in the domestic fuel market, retailers know what each other is charging.

If oil prices go up, one retailer can raise prices in the knowledge that others in the area will see the increase, and feel like they can increase their price as well to benefit from the increased margin.

As everyone can see the posted price, this can even act as a signal to other producers – although there is no actual collusion taking place.

On the other hand, when global prices fall, each retailer can wait for someone else to take the first step.

Again, because they can see all the prices, there is no need to drop their price until someone else does first.

So there are different incentives when prices go up compared to when they go down.

This is why price go up fast, as one retailer raises its prices, the others notice and they raise theirs as well. On the way down, everyone waits for someone else to make the first price reduction.

And so, prices rocket up and drift slowly down.

How long will it take before we are all driving electric cars?

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777,497 electric vehicles were sold globally in 2016 while global car sales in total were 77.31 million, meaning that electric vehicles made up around 1% of sales.

Electric car sales are growing fast, although from a small base. They increased 41% in 2016 and have shown a 32% Compound Annual Growth Rate (CAGR) over the last four years.

Conventional cars, on the other hand are forecast to increase sales by 1.5%, with nearly 94 million units of light vehicles sold in 2017.

The last few years have seen a supportive environment in the US, Europe and China – all key markets for electric vehicles.

California, for example, accounts for more than half of electric vehicle sales in the US because of its zero-emission vehicle (ZEV) mandate that requires manufacturers to sell a certain percentage of electric vehicles.

People are nervous, however, about U.S policy under the new administration.

China has a reduced vehicle exise duty of 7.5% for qualifying vehicles that is expected to support auto sales in the world’s largest car market, with 28 million units expected to be sold in 2017.

Analysts at UBS predict that electric vehicles could reach cost parity with conventional vehicles as soon as 2018 because they will become cheaper to produce.

At present, the Tesla Model 3 is expected to lose $2,800 per car for the base version while GM loses $7,400 per car on every Chevy Bolt.

Car manufacturers need to achieve scale before they will start to break even.

While the running costs of electric cars are much cheaper than conventional vehicles when charged at home, around a sixth of the price at £2-4 per 100 miles, there are some things to watch out for.

Charging at rapid chargers away from home could cost as much or more than filling up with fuel.

Home charging systems add to the total cost of ownership and, as electric vehicles increase in number, will place strain on the grid in areas with high purchases.

The vehicle industry has long product cycles – cars are used for many years, and high capital investments.

This means that change is necessarily slow as the entire system adapts to a changing transport mix.

Oil is still expected to make up a third of European energy consumption due to transport demand.

If governments start banning sales of non-electric vehicles between 2025 and 2040 as many have indicated, we could all be driving electric vehicles by 2050-2060.

Where does the world’s LNG come from?

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Three quarters of the world’s natural gas is used in industrial applications and for power generation.

It burns more cleanly than oil or coal, which means that emissions from natural gas are lower.

As a result, governments around the world have policies that make using natural gas more attractive than the alternatives.

The IEA estimates that global gas consumption will grow from around 120 trillion cubic feet (TCF) in 2012 to 203 TCF by 2040.

So where does this gas come from?

The graphic above shows the top exporters of LNG by market share in 2016 according to the IGU World LNG Report 2017.

Australia now has the largest market share of LNG, going from 12% in 2015 to 44.3% in 2016, a huge increase.

Qatar remains an important source of gas, although the problems it is currently experiencing with its neighbours may have an impact on gas production this year.

Russia, despite its enormous gas reserves, is a relatively small player in the LNG export market.

The one to watch is the United States.

In 2016, the U.S. had a market share of 1.1%, making it the 16th on the list.

Over the next few years, however, it is expected to ramp up exports significantly.

In the next decade, Australia and the United States are expected to be the dominant exporters of LNG to the global market.

Who will win when it comes to developing clean energy technology?

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A series of high profile failures and strategic changes by cleantech companies raise questions about the whole sector.

Acquion energy, a maker of battery systems filed for bankruptcy in March 2017 after raising nearly $200 million from investors including Bill Gates.

Lightsail Energy, a startup co-founded by a charismatic prodigy, Danielle Fong, raised funds to develop compressed air storage energy systems, but is now changing tack to sell its containers to gas markets.

Solyandra a manufacturer of thin film solar cells, filed for bankruptcy in 2011, leaving the US Federal government liable for half a billion in a taxpayer funded loan.

An MIT study in 2016 found that more than half of the $25 billion invested in clean energy startups from 2006 to 2011 was lost, effectively result in a drying up of capital and investment to the sector.

What is going on here?

Many companies have still not figured out the economics of energy. The ones that will survive from now on will have to get their heads around some key factors.

1. Money

Cleantech companies often create new technologies, materials or processes.

These require investment in research and testing facilities, demonstration units and development installations or a track record in order to be accepted by consumers.

This means that they need a lot of money to invest in their infrastructure.

Many companies ran out of money before they created a sustainable income stream.

2. Time

Bringing a new cleantech product to market can takes months and years rather than days and weeks.

End user products such as battery packs have to go through rigorous testing, product certification and safety checks before they can be sold to the public.

The returns on individual technology projects for a customer are also likely to have paybacks that are longer than the typical corporate will accept: 5-6 years rather than 2 years.

As a result, the rates of return to investors in cleantech have been less than in other sectors traditionally backed by private / venture capital.

3: Competition

Electricity is a commodity. Makers of cleantech selling a system that creates electricity cannot control the price of the power from their systems.

They are, instead, forced to compete with existing alternatives in a commodity market.

Even in a cleantech market such as that for solar panels, new technologies struggle to compete against silicon panels.

This is not because the new technologies are not better. It’s just that the massive investment in silicon fabrication facilities worldwide has made the cost of silicon panels fall much faster than alternatives.

4: Policy

A huge amount of momentum in cleantech is driven by government policy.

Over several years, clean energy in Europe and the UK has been driven by subsidies.

In the US, tax treatment for energy from wind has resulted in large-scale developments by the likes of MidAmerican energy.

As we go forward, however, the new Trump administration wants a renaissance in oil and coal and will change policy to support those industries.

5: Buyers

Buyers and investors in cleantech companies are more likely to be existing utilities now rather than VC investors.

This is because the incumbents can add new technologies to their portfolio of existing assets rather than having to depend entirely on the new technology for income.

The energy sector has been around for a long time and change is slow. You need deep pockets to hang around

Summary

In summary, cleantech companies that have a core proposition built around a technology or process may struggle to create a sustainable income stream.

Larger systems are more economic. Scale succeeds.

TEsla has succeeded by going big fast, and its latest thing is to build the world’s biggest battery facility.

The sector will continue to need a supportive policy environment to move ahead, and we will need to wait and see what happens.

This is especially important in the US, given its size and innovative capacity.

Ultimately, the energy sector will be driven by large scale projects and policy – much like it has always been.

The maths of success

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How can you become more successful?

One answer was inside a TED talk by Damian Kulash, the lead singer and director of OK Go, an American rock band known for its elaborate and quirky videos.

Damian explained that in one of their videos, a giant Rube Goldberg machine, there were 130 sequences that had to take place one after the other where each sequence triggered the next.

In each sequence, something fairly simple takes place. For example, a ball rolls down an incline, a counterweight lowers an object or an object something swivels on its axis and hits something else.

If each sequence works 9 out of 10 times then its probability of working correctly is 90% or 0.9.

How likely is it the band will be successful at filming the entire set of sequences in one take?

The maths of probability is used to work this out. In the equation P^n, P stands for probability and n is the number of events.

Plugging this into the equation, the probability of each sequence working is P = 0.9 and the number of sequences is n = 130.

0.9 raised to the power of 130 is 0.000001125.

That means there is literally one chance in a million that the series of sequences will work.

In life and work, we often have to do things that follow a process, where one thing needs to be done after another.

If we aim to be quite good at each thing – and get it right 9 out of 10 times, then the more things we have to do, the less often we will be successful.

For example, if an operation in a business takes 10 steps and you are 90% sucessful at each step, the probability of success is 0.9^10= 35%.

That means two-thirds of your customers are likely to be unhappy with what they get from you.

You can improve your chances of success by doing two things.

First, increase P. Get better at doing each thing.

If you get things right 99% of the time, 0.99^10 = 0.9, which means your customers are happy 90% of the time.

Only 1 out of 10 walks away unhappy. Still not great.

Second, reduce n, the number of things you have to do.

If n was 4 rather than 10, then you would get 0.99^10 = 96%.

Now 96% of your customers are happy.

In summary, the maths of success says work on optimising the formula P^n so that the answer tends to 1.

In other words: Do less, and do it better.

The Chasm: Every innovator’s challenge

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Why do some innovative products and services succeed while others fail?

The answer may lie in how they navigate the chasm, an idea talked about in Crossing the Chasm by Geoffrey Moore, first published in 1991 but still relevant today.

The basic roadmap for bringing a new product or service to market follows a lifecycle.

First, innovators get excited about a new opportunity and create the first version of the product.

Then visionaries, the people who see how this could be different, useful, life changing – they get involved and the innovator starts to make early sales.

This is good because it validates the product and shows there is a market for it – however small.

The product develops, gets better, gets case studies and a small customer base happy to recommend it to others. At this point, the early majority start to get interested.

The product continues to be used and developed. With a strong base of customers, the rest of the market sees this as a strong option or alternative to existing solutions.

Sales take off and the company enters a period of hyper-growth.

Now the product is considered safe, reliable and the first choice. It’s now a mass market product.

Finally, the people who have been holding off, the sceptics and laggards turn up and start to use the product.

Where companies fail is in making the move from selling to visionaries to selling to the early majority.

This is the chasm, a point where the company is running out of visionaries to approach – the people with the early interest but the early majority are not ready to commit because the product is still relatively new, immature and not widely used.

Existing providers, threatened by the new product, can use fear, uncertainty and doubt (FUD) to protect their own businesses.

The chasm is the most challenging period for a new product or service.

There are no easy answers to get across it.

The point, however, is that ignoring the chasm will most likely lead to failure.

A winning strategy will think about ways to bridge the chasm – and go from a product that visionaries will use because they can see the benefits to ones that the early majority will use because you have removed the risks.

This shift in thinking – moving from selling the benefits to removing the risks is likely to be the deciding factor in getting from one side of the chasm to the other and entering the next stage of growth.

In summary, sucess or failure for companies and products is often a result of how well they cross the chasm.

Why forecasting is for cows

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McKinsey talked what they called one of the ugliest and most common charts in strategy at the start of the year – the hairy back.

At the start of any process – a startup pitch, a sales plan, a production forecast, you need to show a chart where, from a standing start, forecasts go confidently upwards – the “Hockey stick” graph.

In reality, the forecasts are not achieved. Year after year, as actual performance stays flat or even drops, the forecasts create a series of lines that go up – and the hockey stick turns into a “hairy back” – like the ones cows have…

Why does this happen? Why do smart people get it wrong year after year?

There are a number of reasons, and they all come down to the psychology of business.

Targets are set that are not connected to the underlying business

A target is easy to set. 50% growth. 20% increase in production. 80% reduction in staff turnover.

If someone important in a business sets a target, then everyone else starts to work to try and meet that target.

Proposals are worked on to make sure the numbers reach the target rate, especially if everyone is competing for resources and the only way to get your budget for the year is to make sure you reach the target.

This is called “gaming” and a big part of good strategic planning is minimising the opportunities for gaming behaviour in pursuit of a target.

As Warren Buffet writes, if your business is based on “making the numbers”, you can well end up with a situation where you “make up” the numbers.

Targets should be based on a realistic assessment of the capacity of the business and the resources it has in place and how they create value.

Human biases get in the way

Once someone decides that a particular approach is a good idea, confirmation bias kicks in.

That person now looks for information and data that confirms their point of view, and discounts or ignores information that disagrees with it.

People believe that all you need is a goal and optimism and you will do anything with whatever you have.

In reality, what you achieve is often determined by mundane things like whether you have the resources and time to do fairly basic tasks well day after day.

If it eventually turns out that the idea doesn’t work, attribution bias helps the person explain it away by blaming whatever seems convenient.

Ultimately, although forecast setting seems very scientific, there is an emotional dimension to almost everything we do, and that causes us to use shortcuts, see patterns where none exist, tend to believe what we would like to be true, explain everything and feel like we operate more logically than we really do.

How to create better forecasts

There are two ways to improve forecasting.

First, put better decision making processes in place. Use techniques to generate ideas, encourage dissenting views and create real discussion, debate and challenge about options and what they mean for you.

Second, listen to the voice of the business. Targets need to be connected to the business, and there will be lots of data that shows you what is happening.

The trick is to convert this data into insight, separate out what is signal from the noise and connect decisions to data.

A roundup of BREXIT risks: What does it mean for you?

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Theresa May invoked Article 50 of the Lisbon Treaty on 29th March, giving the UK two years to leave the EU – on Friday 29th March 2019.

The government’s focus over the period is on laws, transferring EU law into UK law and deciding what to keep and what to scrap.

What are the really big issues from the UK’s point of view?

There are 3 things that come out of the withdrawal letter sent to the EU.

  1. If the UK leaves the EU without an agreement, trade will continue on default World Trade Organisation (WTO) terms but it would be good to have a free trade agreement.
  2. We have to work out what happens to citizens currently living and working in different countries.
  3. It would help to have an implementation period to avoid a cliff edge.

The negotiations between the UK and the EU need to unpick over 40 years of processes and go through thousands of points, but these are probably the most crucial overall.

What about individuals – what could happen to us?

Things will cost more

The fall in the value of the pound has made imported goods more expensive. People expect the currency to stay low for the long term now, so prices could increase rather than decrease.

Travelling could involve more checks

At the moment, there is visa free travel across the EU. In some countries, UK citizens can stay for up to 90 days without needing a visa, and that sort of arrangement would help with tourism.

If you are planning to stay for longer, that may require visas in the future.

Common services could change

There will be an impact on areas like health cards, insurance, car number plates and so on where the outcome of the negotiations will determine where you buy them and what it means for you.

What will happen to businesses?

The risks for businesses fall into three broad categories:

  1. Labour
  2. Trade
  3. Finance

A number of industries depend on international labour

Sectors such as Accomodation and Food Services, Manufacturing and Transport have between 20 and 30% of their workforce made up of non-UK nationals.

These businesses will have to prepare for the cost of replacing or recruiting workers.

A trade deal is crucial for exporters and importers

Many products involve supply chains that criss cross the EU.

The Airbus supply chain, for example, has parts built in several different countries. Could the parts built in the UK have to pay additional tariffs? Or, could those manufacturing tasks be moved to Europe.

Four-fifths of cars made in the UK are exported and more than half of these go to the EU.

The financial sector is exposed to similar risks. Activities carried out in London may move to Hamburg or Paris.

The costs of doing business don’t look like they will go down

If the exchange rate remains low, companies that import will have problems while those that export could see increases in sales.

Wages, however, will probably rise if there are fewer workers available and the value of the remaining pool goes up.

Agriculture, in particular, could suffer both from a shortage of workers and from the loss of EU subsidies.

Summary – a model for assessing risks

As a starting point, it makes sense to fall back on Michael Porter’s value chain.

What are the things you do to deliver a product or service to your market?

How will you BREXIT affect you in each of these areas:

  1. Your infrastructure – office locations, equipment, materials.
  2. Your workforce.
  3. The technology you use.
  4. Procurement – how you buy everything you need.
  5. Inbound logistics.
  6. Operations.
  7. Outbound logistics.
  8. Marketing and sales.
  9. Service.

Will BREXIT increase costs or decrease costs for you in these areas?

Will it create opportunities or result in lost business?

With less than two years to go, it is time to get busy planning for the future.

Note

Drawing – inspired by the work of Chaz Hutton

Why is UK productivity flatlining?

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The ONS released its quarterly bulletin on labour productivity covering the first quarter of 2017 on 5th July 2017.

The index of productivity, measured by gross value added (GVA) per hour normalised fell from 102.4 to 101.8, a drop of 0.59%.

The BBC’s headline for this was “Today’s productivity figures are bad to the point of shocking.

Really?

It’s worth going over a couple of points from Understanding Variation: The key to managing chaos.

Comparing one number with another for a previous period is often used when reporting figures in the press. If the number is up, great. If it down, the sky is falling.

While this is easy to do, such comparisons are weak and lead to sensationalist headlines with little insight. They are limited because they use less data than there is available and because they don’t take variation that is in real world data into account.

If we look at the underlying data over a slightly longer period a few things jump out at us.

First, gross value added (GVA) seems to increase over time fairly linearly. Apart from a brief hiccup around the time of the financial crisis in 2008 the line pretty much trends upwards.

That takes care of the numerator in the productivity equation.

The denominator seems more complicated. Looking at the ONS data, its hard to find something that is a simple measure of hours worked.

For example, this page has some material, but its hard to work out which particular set or trend should be used.

Looking at a website called trading economics, it looks like average hours worked came down from 1995 to 2010 and then started rising agan.

So, we have two charts. One with steadily rising numbers and another with numbers that first fall, and then start to rise again.

When you put the two together, the impact is a chart that first increases and then flatlines as the rate of change of GVA matches the rate of change of hours worked.

The reasons for this in the press point towards the number and type of jobs in the economy as the problem.

There are more jobs than before, so there are more people working than before. This means that total hours worked is increasing faster than the rate at which output from those hours is increasing because the jobs being worked are not as high value as they were previously.

To change the trend, you need to either increase GVA – do more valuable work, or reduce hours – do the same work with less people.

Simple to say, but not easy to do.