How are you going to innovate?

This year everyone appears to be talking about innovation. Many think it’s being driven in response to the pandemic. If that were so, all we would need to do is wait until the vaccine is delivered and we can forget about it and go back to the way it was. Almost no-one believes this to be true.

The commercial world is evolving, and the end state is not yet known. This means traditional budgeting, planning, efficiency drives and cost reduction will not be enough for success. Organisations must accelerate their innovation agenda – this is not about inventing something new; it’s about taking what you know, reconfiguring it to be relevant and continuing to adapt and evolve.

In the previous three posts I set out some of my thinking about the fourth industrial revolution because I think this model serves well to explain why we are experiencing change. As part of your innovation thinking you may want to consider seven fundamental factors that underpin the revolution. They may not have an immediate impact on today’s business but as Wayne Gretski almost said – it’s best to skate to where the puck is going, rather than where it is now.

It is hard to untangle these factors because they influence each other and form self-re-enforcing feedback loops (which accelerates change). I find it useful to use this when considering issues and deciding where to focus, I hope you do too.

1. information creation and connectivity

The ability to create, share and access information has implications across social, political, and industrial spheres. Whether as flash-mob revolutions, exposure of tax fraud, mob-trolling of celebrities or remote monitoring of industrial plant and machinery.

​Transparent information undermines authority by revealing the inconsistencies, lies and hypocrisy required to govern. Anonymous transmission of ideas on social media leads not only to emboldened action but also to misinformation and on-line bullying. Information is conflicting and unreliable and knowledge and certainly is displaced by opinion. The ability to sift and evaluate data and then apply rational analysis is not evenly distributed among populations.

​The cost and availability of creation, capture, and transmission equipment has reduced nearly to zero. It is ubiquitous. The creative idea, installation of capture equipment and the editing of results is rare and not free.  One cannot go back and measure the past, so value may be found in stored experience. If you can curate information and control its presentation, then there is power to influence perception.

​Commercial innovation is likely to arise from creative firsts, unique archives, collection networks, influencing curation, and low-cost data organisation, error-correction, and editing.

2. understanding and acting upon information

Advances in computing power have led to new ways to analyse information, methods to learn and infer meaning and procedures to decide how to act. This leads to automation – unattended service, purchase reccomendations, warehouse picking and self-driving vehicles.

​Too much data causes problems with human-led processing such as overload, decision biases and selective world-models. We have evolved to make binary conclusions “being decisive” and “acting with confidence” are perceived as star qualities. Leading based on flexible decisions resting on the probability afforded by analysing emerging information is uncommon. Motivating others to make swift progress in the face of uncertainty will require a new set of leadership skills.

​Commercial innovation is likely to arise from increased quality of service accurately targeted towards needs, as well as reduced cost of provision. Companies that can harness learn to direct activity and make progress under conditions of uncertainty will also benefit.

3. additive manufacture

This is not just 3D printing. Many things are traditionally created by removing material using techniques like cutting, drilling, thinning, and shaping. This wastes material, energy, and time. The materials we use – cement, steel, rubber, plastics are chosen because they lend themselves to these processes.

Additive manufacture will change the materials we pick, it will reduce waste in production and change the shapes we create and the material performance we obtain. It will not only impact factories but also it will change extraction industries and trade routes. It will be possible to email design files and create what’s needed on site without the need to ship raw materials, sub-assembled parts or finished goods.

We are seeing the rise of extrusions and laser-melted metal powders and will shortly embark on assembly at the molecular level. This will mean the same forces that change building materials will impact other wasteful processes including agriculture, slaughtering, drug formulation, paper making and paint manufacture. We can expect to also see different flow-processes with lower temperatures and pressures, lab-grown meat, structured drug design and smaller-batch runs. Additive manufacture principles will impact a diverse range of industries including specialist machine makers, house-hold construction, manufacturing, farming, and medicine.

Commercial innovation is likely to come from creative designs, disintermediating supply chains and creation of innovative not-possible-before shapes and material-performance. There will be insights for applying this technology to industries not considered before.

4. planet maintenance, collective responsibility

Some call this activism or environmentalism, but whatever you call it there are growing movements encouraging (and forcing) vested interests to consider the impact they have on the wider world. This encompasses the materials consumed, the energy used, and the waste products created.

​Fuelled by information and analysis governments have concluded that there is a climate emergency which calls for rapid decarbonisation. This is leading to energy transition, smart-grids and electric drive trains on the one hand, and examination of the energy intensity of industry and ways of living on the other. It has also given rise to the notion that resources on earth are finite which leads to the circular economy (where goods are recycled into new goods) on one hand, and the drive for mining of materials from asteroids and the seabed on the other.

​Commercial innovation is likely to occur around opportunities afforded by legislation – such as carbon pricing, outlawing of practices as well as the inclusion of sustainable methods and transparency of operation. Smart ways to redirect and reuse energy will become valuable.

5. organisation of labour

We now have remote working and video conferencing; people don’t need to go to the office. People don’t need to be in the same town or the same country.  The COVID crisis of 2020 saw mass adoption and made it normal to use.

On-line retail, automation, self-driving cars, and additive manufacturing will reduce demand for labour in many sectors and, due to our global supply chains and clustering of industries, this is likely to create geographic areas where traditional work will become scarce.

The gig economy is at one end of a spectrum of employment that runs from employee, through contractor, project team into gig work. The quantum of work purchased is becoming smaller and pay is more related to outcome rather than time spent on a task. Bonds and exclusive service to one employer is becoming less common.

​Commercial innovation is likely to encompass ways to facilitate remote interactions, telepresence, and ways to build trust (both emotional and technical). Ways in which goods and people are transported will change leading to opportunities in non-traditional geographies and innovations are possible in the way labour is accessed, motivated, managed and rewarded.

6. culture, art, craft and beauty

The 4th industrial revolution moves us more towards a world where less human labour is needed to produce and distribute the goods, services, and energy we need. Other factors will come to the fore in determining what is more “valuable”.

​Where we are used to optimise for low-cost production, we will increasingly favour products, services and experiences that appeal on an emotional level. Emotions will become more important. This is occurring already via inclusion policies, social movements, and campaigns for various forms of justice. We can see on-line culture forming value through influencers and followers whose product is purely an experience and a connection between people with similar perceived values.

​How one spends time will become more important. Dedicating large amounts of time to an employer will seem less likely to determine level of “success”. This will lead people to choose to do more things that they like – leading to more artisan production.

​Commercial innovation may occur in the labour market by enabling people to find their vocation and navigating the changed expectations required to transition career thinking to match the 4th industrial age. The types of products and services sold, and the labour conditions required for workers will increasingly require taking account of design, beauty and evoke emotions, resonate with the values of buyers and be fun.

7. politics of wealth and power

This is likely to be the slowest area of the 4th Industrial revolution to mature. But it will be the most profound and biggest determinant of outcome. While it is tempting to ignore this because it does not lend itself to traditional commercial analysis, it is likely to prove one of the biggest source of disruption and should not be left unattended.

Changes in this factor are likely to occur in (possibly hotly debated) jumps because this deals with fundamental and, for many, unimaginable changes to basic principles of societal organisation. If labour is no longer in short supply this could lead to what used to be called mass unemployment.

I believe that we are less likely to tolerate wide-spread poverty such as that experienced when people moved from the land into the cities during the first industrial revolution. Perhaps we will find a way to allocate resources to people other than by labour, while still maintaining civil and ordered society. What was once called welfare may become a universal basic income.

Accepted definitions of wealth may change to include more than money. Because time is an immutable constraint, this may become a currency. How it’s spent may differentiate between rich and poor. Manners, deportment, compassion and popularity may be qualities that people will support to determine unequal reward for others. Honour and shame may become fashionalbe once more. In some socieites this may instead become enforced compliance. Human groups naturally form hierarchies. When traditional methods of determining who has more worth changes then so will our definition of who is more worthy. Some people want to be “top-dog” and will use every method to be so (or remain so) – not only by pulling themselves up, but also by pushing others down.

As information asymetry combines with confirmation bias, we are likely to see politics become more fractional. Groupings will emerge like sides on a battlefield. They may be wealthy industrialists with their capital and bankers, career politicians with their nationalistic tendencies, intellectually enlightened middle classes, disenfranchised and once-proud working classes and individuals who want to be made to feel special and better than their peers. These interests will come with different ideas about what to optimise for success and how to go about doing it.

Different factions with competing ideas, their votes, their followers, and their financial means will be pitted against each other. They will use new technologies, historic resources, traditional oratory, and brute force. They will use the structures and institutions of society – as well as whatever form of subterfuge is available – to further their conflicting objectives. Human history suggests that without acceptable compromise frustration will lead to anger, irrationality and even violence.

Conclusion

Commercial innovation here may be hard to achieve but being alert to the political and social dimensions will provide early warnings and adaptation may keep you on the right side of history.

For more information please see:

4th Industrial Revolution Implications parts 1-3

IR4 Part 1: Information and Communications LINK

IR Part 2: Work, Trade, Taxes and Government LINK

IR4 Part 3: Energy Transition LINK

Earlier thinking around the subject

Innovation and Productivity with the 4th Industrial Revolution LINK

Digital Disrtuption Landscape for Upstream Oil and Gas LINK

Get out of the way of digital Crhis LINK

6 Months into a 3 week crisis

I have lots of new ideas to share, but not the time to commit them to words.

I’ve not found time to update this blog for a while. To be honest I don’t think the uncertainty that comes with this crisis makes it wise to take too rigid a point of view. And, like many others I speak to, my days seem to be slipping past. I seem to be doing a lot of work, but I am finding less time to invest in new areas for the future and many discretionary tasks I no longer have the concentration to focus on.

Some of my friends and colleagues have noticed similar fatigue levels affecting performance in their businesses too. As one put it, we are now six months into a three week crisis.

All the emergency measures we put in place are all still there, the system is starting to creak and it no longer seems temporary. And it doesn’t really work for the long-run. We have learned new ways to use technology and have become expert in the tools for remote working. What we must now do is rethink our processes and routines to take advantage of these while making space to grow and learn.

Ken & Mark from AGM transitions, and I have been working on turning our small guides into a book. It’s now available from Amazon here: [LINK] – I hope that the practical advice and structure are something that will help you through this stage of lock down.

Here is the link to the original post: [LINK]

Watch out, they are comming for you

The cost of innovation is going down, barriers to entry are falling

Keeping it special

If you work in heavy industry and are near technology, you will know that there are some very robust pieces of kit out there. What I’ve always been surprised at is:

1. how simple many of the devices are in terms of functionality; and

2. how “special” they are in terms of obfuscating the obvious.

The effects of these two factors has been, for years, to reduce competition. By making it difficult to get hold of units (via price) and creating a jargon around the obvious configuration/deployment it has promoted a closed shop approach.

Keeping up standards

In some ways keeping out the riff-raff can be promoted as a good thing – it provides assurances around quality and safety. But it slows down innovation. You might say that perhaps this is good. Maybe you don’t want to be too innovative around safety and compliance systems. Afterall making mistakes is expensive and dangerous.

Keep up!

One of the aspects of the 4th industrial revolution that will challenge that thinking is simulation. I used to think that digital twins, virtual worlds and simulation would help reduce the cost of maintenance, let the experts create new ways to work and basically bring down the operating costs for the incumbents.

What if it leads to a whole new raft of competitors? What if anyone can have low-cost access to a virtual oil rig, or virtual power station, or virtual chemical plant? Not only will they learn how it’s supposed to work, they can try things and see what happens – learn by doing, learn by breaking, but do it virtually. Perhaps this will lead to:  

  1. they might come up with much better ways to operate it that you do; and
  2. train themselves to operate it before you hired them

Result: Better ways of working, access to more talent, incumbents get beaten.

If you have ever witnessed teenagers playing fortnite, you will know how fast their thinking can become and how fast their brain-hand connetion is. Imagine how quickly they will be able to react to real-world situations and think through the information being thrown at them.

Examples

I’ll provide two examples of where “public access” and “new ways of working” are already influencing established hierarchies. It won’t be long before these mechanisms appear in heavy industry.

Don’t expect today’s engineers to enter the workforce unprepared nor unwilling to take on the establishment. Watch out for competition from smart people who are not part of the established hierarchy. Don’t think the way you work today, will be the way you work tomorrow.

Example 1: Team Huub-Watt bike

I was lucky enough to see this cycle team win gold at the Track Cycling World Cup in December 2019. The team is comprised soley of amateur racers and they ran a completely novel strategy calculated using simulations and software. Their budget is £15,000 per year. They beat Team GB who have the best coaches, facilities and trainers available – and a budget this year of £26m. That’s over 1,000 fold decrease in cost and substatially BETTER performance.

Response from the establishment was to change the rules, enforce the status quo. This may not work forever. It probably won’t work for you.

https://www.tri247.com/triathlon-features/interviews/huub-wattbike-uci-interview

They were not, however, afraid to make use of the technology for their own ends. Zwift is a cycle simulator that people can use at home and join in real-time cycle events and ride-outs while collecting performance statistics. It is now being used by pro-teams to identify and recruit talent.

https://www.cyclingweekly.com/news/latest-news/i-want-to-ride-in-the-worldtour-how-british-cycling-are-using-zwift-to-help-identify-young-talent-454806

Example 2: British Touring Car Championship

In the gentleman’s toilet at the Royal Automobile Club in Pall Mall – in the heart of establilshment London – there are a series of framed caricatures of some of motor racing’s greats from the last 100 years. These include W.O. Bentley and Mike Hawthorn. Motor racing is glamourous. And costly. The money needed to race in formula 1 are legendary, but even the karting in a 125cc class will likely cost you the best part of £50K a season. Developing cars, tracks and drivers costs money.

So what do you think will be the outcome of last weekends win for James Baldwin in the first of the British GT Touring Car championship races? It’s a pretty big series, and winning a race is not easy.

Especially if it’s your first race you’ve ever competed in.

James honed his skill as a driver in a simulator he set up at home for under £1,000. And his talent was found when he entered a competition in an “E-Sports” event.

Turns out that the simulation prepared him surprisingly well.

https://www.goodwood.com/grr/race/modern/2020/8/worlds-fastest-gamer-wins-on-british-gt-debut/

https://www.bbc.co.uk/news/newsbeat-53554245

2020 Vision

Sorry for the title. It’s not very original. Everyone’s been using that for the last decade, but still it seems appropriate. Every January I’ve made a post predicting the year ahead. I normally write this in December and publish it at the beginning of the year. It normally makes a few tongue in cheek exaggerations to in order to raise a smile. I stole this idea from Old Knights Almanac that used to appear each year in the RETRA magazine [Link ]

Today is the day we leave the European Union. My advice is to ignore this and go and buy today’s FT. It has many stories that summarise the transition we’ve witnessed and sets out the stall for next year. Below I’ve taken extracts and headlines and they tell the story. The one thing not mentioned is the UK Government’s industrial strategy, more on that in another post. Oh, and my watch phrase for this decade is “Society 5.0” – I think we’ll be hearing more about this in the comming while.

First here is an extract from this story (https://www.ft.com/content/b64b692e-4387-11ea-abea-0c7a29cd66fe).

This caught my eye because it illustrates the emerging tech leadership that is flowing from a very entrepreneurial and exceedingly smart China, the comming tech trade-wars and how there is a shift in earnings among tech players reflective of the shift in tech approaches – showing even when you are the innovator you have to keep innovating!

BT has said the cost of implementing the UK government’s cap on the use of Huawei equipment will cost it £500m over the next five years as it reported its third quarter figures.

[…]

There’s a bumper crop of earnings to report: Microsoft reported a 14 per cent advance in revenues, to $36.9bn, helped by cloud revenues which grew 39 per cent to $12.5bn, Tesla has notched up its first-ever back-to-back quarterly net profits. The electric car pioneer called 2019 “a turning point”. AT&T’s entertainment business WarnerMedia revealed a $1.2bn hit due to costly investments in its upcoming streaming service to rival Netflix. Nintendo’s quarterly operating profit rose 6 per cent to $1.5bn, missing expectations. Samsung Electronics confirmed its fifth straight quarterly decline in profits but said it expected memory market conditions to improve in 2020.

To avoid the risk of plagiarism I am going to direct you to today’s FT (go buy a copy or have Amazon deliver you one). The headlines from these stories paint the picture and tell the story all by themselves.

Why Microsoft and Tesla are the decade’s big disrupters

https://www.ft.com/content/b3e659fc-4380-11ea-a43a-c4b328d9061c

Ginni Rometty steps down as IBM tackles cloud era

https://www.ft.com/content/aabee59a-43aa-11ea-abea-0c7a29cd66fe

Rich and famous turn to ‘personal cyber security’ to protect phones

https://www.ft.com/content/96c79040-40ea-11ea-bdb5-169ba7be433d 

The Apple effect: Germany fears being left behind by Big Tech

https://www.ft.com/content/6f69433a-40f0-11ea-a047-eae9bd51ceba

Elon Musk jolted by German protests over Tesla factory plan

https://www.ft.com/content/8b10555e-4345-11ea-abea-0c7a29cd66fe 

The UK’s employment and productivity puzzle

https://www.ft.com/content/a470b09a-4276-11ea-a43a-c4b328d9061c 

For today’s oil market the real threat is to demand, not supply

https://www.ft.com/content/5bf49cb0-41cb-11ea-bdb5-169ba7be433d 

Shell to slow investor payouts after earnings fall 50%

https://www.ft.com/content/4e1fa700-4334-11ea-a43a-c4b328d9061

Orsted/offshore wind: Go-Greta:

(Henrik Poulsen has turned a national oil company into the world’s largest offshore wind builder and green energy champion)

https://www.ft.com/content/719dd81d-2527-4b83-8aed-e6624476c191

Competition rules stymie co-operation on climate goals

https://www.ft.com/content/b3e0da9c-3eba-11ea-b84f-a62c46f39bc2 

I wish you a healthy, hearty,happy and prosperous 2020.

While we were sleeping – Oil 1.4 and Solar

It’s been very busy since the Network Dinner in September. I will post an update on the discussion later this month.

In the mean time I’ve been busy working on innovation – more of that later – but I recently came across two interesting items that I think might be worth sharing.

Firstly the FT ran a special issue talking about Oil and Gas 4.0 [Link]. It’s good to see that this term is being widely applied – and a big change from when I started to talk about it a few years ago.

I wrote an article in March 2016 when I claimed that Oil and Gas were really at 2.5 while industry was going 4.0 [Link] I was concerned about the lack of urgency and technology progress. I also called out the contribution of Collette Cohen as being one of the few that seemed to get technology. She is now director of the Oil and Gas Technology Centre.

The OGTC were referred to in this article [Link]

In October, the non-profit Oil & Gas Technology Centre (OGTC) in Aberdeen in Scotland, announced the next phase in its autonomous robots project with Total of France, which is developing what it calls the world’s first offshore work-class robot. The first phase of the work saw Austrian firm Taurob create a robot to conduct visual inspections at Total’s Shetland gas plant and the Alwyn gas platform in the North Sea. A second-generation version will have a stronger chassis and a heavy-duty arm that will lift objects and turn valves. It will be tested by Total and Equinor of Norway, the research initiative’s new partner.

“A lot of our work on hazardous environments focuses on whether we can avoid sending people into those areas in the first place,” says Stephen Ashley, head of OGTC’s digital transformation solution centre.

Another article coined the phrase Oil and Gas 1.4 which is a clever take on the combination of an old-age industrial organisation embracing new digital technologies within its core business. I think I like this term better than my 2.5 one.

This article [link] makes the point that the new technology is prevalent in some areas of the business, but that the new frontier for production might be the application of technology to find economic ways of enabling enhanced oil recovery. 

Unmanned rigs are now commonplace, complex operations are monitored from a single control room, leaks and emissions of greenhouse gases can be identified by drones and satellites, removing much of the need for direct human inspection. Numerous technologies are being applied in ways that can reduce cost and improve productivity.

The key question, however, is whether the digital revolution can answer the sector’s biggest challenge: how to secure future production. Oil demand is not falling. There may be 7m electric vehicles on the world’s roads but there are also 1.2bn vehicles with internal combustion engines.

[…]

One answer must be for companies to make the most of assets they already hold. Across the world the typical recovery rate from a conventional oil or gasfield is only 35 per cent.Even after decades of production giant fields such as Prudhoe Bay in Alaska or Ghawar in Saudi Arabia still contain billions of barrels of oil. Recovery rates have slowly risen and provinces such as the North Sea, originally expected to close at the end of the last century, continue to produce oil and gas. In Norway recovery rates are typically 50 per cent — well above the world average but still leaving half the resource base undeveloped.

The point at which recovery becomes uneconomic, ie when the cost of enhanced recovery is greater than the value of the oil, is a serious constraint.

What I’ve found really interesting this year is how irrelevant the oil and gas industry seems to have become down here in London. What I mean by that is that Oil and Gas seemed to be at the crux of things in a way that, say, copper mining and concrete production wasn’t. It used to be a cool place to play with technology, travel the world and to make a bunch of money. I think those days may be over (though some predict a spike in prices around 2025). Now no-one here cares about Oil and Gas at all.

Where I am seeing a lot of action and excitement is around Solar and Wind. I thought for a while it was just me becoming aware, but now I’m onvinced that it was a sea change and it really is picking up. And the cost-curve of Solar is particularly striking.

I urge you to have a look at Tim Harford’s article on Solar [link]. As always he has an ability to grasp the implications of what he sees in ways that other’s don’t. He looks a PV cells – how in 1980 Solar was about $100 per watt ($10.000 to light a light bulb). It is now already below $0.25 / watt and falling. Utility scale production is now looking to provide generation at below $0.015 per KW/h. [link]

The thing about Solar Panels is that they are a pure manufacturing play. Once created they just sit there and make energy. No moving parts, no plat to really operated as such. We have been, and continue to be, very good at manufacturing standard products in standard factories.

Sometimes the learning curve is shallow and sometimes it is steep, but it always seems to be there.

In the case of PV cells, it’s quite steep: for every doubling of output, cost falls by over 20%.

And this matters because output is increasing so fast: between 2010 and 2016 the world produced 100 times more solar cells than it had before 2010.

Batteries – an important parallel technology for solar PV – are also marching along a steep learning curve.

The learning curve creates a feedback loop that makes it harder to predict technological change. Popular products become cheap and cheaper products become popular.

And any new product needs somehow to get through the expensive early stages. Solar PV cells needed to be heavily subsidised at first – as they were in Germany for environmental reasons.

More recently China seems to have been willing to manufacture large quantities in order to master the technology.

Watch this space, it’s just getting cheaper, better and faster. This is where the action is – I just don’t know how to play the opportunity yet.

 

Machine Learning – more learning….

As an addendum to last week’s post about machine learning – here is an article by the BBC : https://www.bbc.co.uk/news/technology-48825761. This describes a story about an amazon employee who built a cat-flap. Fed up with receiving “presents”, he made this clever device to recognise whether his cat has come home with prey in its jaws. If it has, it refuses to open and the cat must stay outside.

I thought it neatly encapsulated some problems of machine learning I am finding, and also pointed to some possible features where this technology could be applied to generate more value.

The training problem and need for clean data

It took 23,000 photos to train the algorithm. Each had to be hand sorted to determine whether there was a cat, a cat with prey etc. etc.

This is like the oil and gas industry in that it needs a lot of clean training data that may not be available without a lot of manual input.

The Bayesian stats can work against you

The frequency of event-occurrence in this case is once every 10 days. The maker ran a trial for 5 weeks (so should have seen 3-4 instances in that trial, though in this case it’s stated as seven). There was one false positive and one false negative – giving an error rate which may be around 20% (though there are not enough samples to have a lot of confidence). If the algorithm continues to learn and say that the cat uses the flap 5 times a day on average. This means, in a year it will have 1,825 additional samples from which 35 will be positives of which 7 will be false and 1790 negatives of which 358 will be false-positives. Manual correction will be required 365 times (i.e. per day on average) and the learning rate will take about 12 years to duplicate the original training set. I don’t know, but I suspect there are diminishing returns on adding new data so how much smarter it will be in 12 years I’ve no idea.

Disclaimer – a good statistician will know my maths above is not quite right, but the principle is.

Do you trust the alarm? Do you take notice?

So in this example, quite like oil and gas operations, the issue was getting hold of clean training data. The event being detected was comparatively rare meaning that a lot of false positives are likely. In the example above an “alarm” would have sounded 365 times and 28 occasions it would have been real. With the sparsity of events the this means that the algorithm will not learn very fast and I think the alarms will be ignored.

So where can the applications be better?

Distributed learning

Parallel learning helps build better predictive models. if we had the same cat-flap for every cat, and every owner corrected the false signals and right algorithm could learn quickly and disseminate the results to all, this would speed the learning process. Self-driving cars are a good example of where this is possible, and google-search is great example of the power of parallel experience.

Products and services impossible for a human

Situations where there is so much data that manual processing is impossible. Here I don’t mean that you can collect so much data on a manual operation that is ongoing that you cannot analyse all the extra information. What I do mean is that there is intrinsically so much information it would be impossible to analyse by hand and never has been. So an ML approach is the only one possible. For instance looking at real-time changes patterns in data networks.

Simple situations where it’s expensive and boring for a human

Automated first-line help systems, call screening, password resetting etc. These are all tasks where humans can do them, but they are simple tasks which are too boring for smart people to do, where automated help can often provide a better experience. And where “sorry I didn’t get that, did you say yes?” is only mildly irritating not the cause of major corporate loss.

Conclusion

There are places that machine learning will be revolutionary, but I suspect that much of ML will be either embedded to make normal tasks a bit easier – such as auto-spell checking, voice recognition etc. Or they will tackle classes of problem such as IT security, or on-line shopping behaviour where there is inherently a lot of fast-moving data and manual monitoring is simply not feasible nor fast enough to work.

Machine Learning — Data First

With all the attention on Machine Learning (ML) that I encountered at London Tech Week, I thought I better find out a bit more about it. I wanted to verify my view that it won’t have a dramatic impact on the Oil and Gas industry and see if this was actually true.

My findings, so far, is that ML might:

  • Speed up some analysis
  • Change the spread-sheet paradigm (for better or for worse)
  • Enable people with the right level of expertise to create predictive analysis (whether this will be at all valuable is a different matter)
  • In a myriad of small ways, change minor tasks (removing annoyance, reduce low-value add data & reporting work, change interfaces)

None of these applications are dramatic in themselves, but over time they may add incremental benefits that provide a moving improvement-front. A bit like a six-sigma or Kanban.

What will you need in order to drive broad-value from ML

You’ll only be able to take advantage of that if four things are true:

  1. You have clean, historical data available
  2. You can access and combine quality-controlled, time-dependant data in near real-time (ideally from multiple sources)
  3. You have wide-spread knowledge of how to apply the new analysis tools – like those based on “R”. (think how many people can use Excel today for collecting, analysing, querying and reporting on data – varying degrees of proficiency, but who do you know that can use “R”)
  4. You are prepared to reorganise the way work is performed to take advantage of the new possibilities created by: data analysis leading to demonstrated-fact-based / probability-assessed management decisions & employee actions.

Low cost hardware is the trigger

The interest in machine learning is spawned from the dramatic drop in the cost of hardware and software required to perform the number crunching required. Because of this, not only has the complexity of the addressable problem increased but also the inefficiency of code that can be supported increases the usability of tools and techniques leading to their application by practitioners outside pure decision sciences.

If you attend any of the IoT conferences – or speak to the large vendors of real-time industrial data, you’ll hear a lot about how edge-computing and Machine-Learning will change things for industry. “Edge” means placing computing power in the field with low-power and small costs.

Putting this alongside the sensor enables pre-processing information to send back only the results. This helps to reduce data bandwidths and increase responses.

For a view on how cheap this type of technology now is – and how undramatic the applications of ML really are I invite you to have a look at this video (from the hobbyist market) showing what can be done for less than $100. Listen out for the references to “TensorFlow” one day that will be important, there are also some passing references to cloud-based resources that may be of interest.

What I learned at London Tech Week

Big Data, Artificial intelligence, Machine Learning and Computer Vision

Turns out that Computer Vision requires Machine Learning, and Machine Learning requires Artificial intelligence. Artificial intelligence is of most use when there is large amounts of data to process. Hence in a way, AI relies on big-data (though not always). The cognoscenti use the abbreviations CV, ML, AI and err…. “big data” to refer to these technologies.

Some simple definitions

These are what are called HORIZONTAL TECHNOLOGIES, because they are general and can be applied to a range of problems across industries. They are already having an impact in some areas but they are not a panacea.

Big Data relates to the collection and storage of large quantities of data and being able to access and manipulate this quickly – using high speed networks, special search algorithms, parallel processing etc. It gives rise to a whole world of shared resources, cloud computing and specialised storage schemes. When real-time information is included (such as sensor data) then this is the world of IoT, time-series data and edge processing.

Ai is a way to analyse the information contained the big-data very quickly. Creating inferences between data signals and looking for patterns, sometimes used to predict outcomes and reduce uncertainty. AI is very narrow in its applicability, even Bill Gates says you wouldn’t trust it to order your inbox for you, so it’s ability to make judgements is limited. A lot of what we talk about being AI is a form of linear regression, mass computational power enabling the quick processing of data and crunching of large amounts of data. AI can give the illusion of being smart, when in fact it can be easily fooled.

Machine Learning is the ability for an algorithm/analysis to change over time by examining a changing stream of input information and comparing computed outcome with desired outcome and tuning. Neural networks. Machine learning is an application of AI.

Computer vision is an application of both AI and ML which is used to process images (still and moving), one of the most controversial applications of this is with facial recognition and the automatic tracking of people.

Some of the take-aways from London Tech-week

Big Data

Everyone that spoke (and I mean everyone) – said that their biggest issue of applying any form of advanced analysis fell down on the quality of the data. The meaning of information collected and the way it is labelled is so inconsistent. There were some semantics companies working with different ways of expressing ideas in language which may hold a key to explaining the differences between the labelling of data items. Until this takes off then 75%-90% of your AI budget is going to be spent cleaning up data and sorting out the meaning of feeds. Trouble is you will spend this 90% of your budget with no tangible change in outcome as you can’t get started until it’s sorted out.

AI

I saw what I thought was a brilliant example of this from a Swedish company called Spacemaker (https://spacemaker.ai/ ). This company works alongside architects to help them choose between the complex trade-offs required when selecting the layout of buildings. Trade-offs between natural light, housing density, noise exposure, energy efficiency etc. By providing optimisation inputs the computer very quickly generates possible layouts which works alongside the architect freeing them from the mundane, but complex, calculations and predictions of weather patterns, seasons etc. The result is much better buildings but not taking away the artistic judgements of the architects.

In Oil and Gas I can see a similar “advisor” system working alongside production engineers, economic planners, maintenance engineers, planners and schedulers helping to provide scenarios based on optimisation parameters enabling them to choose the best configuration to implement.

Machine Learning

I saw an example from a company called Dark Trace (https://www.darktrace.com/en/ ). As well as being a simply brilliant commercial success with enough financing to direct sufficient money dedicated to PR, marketing, sales and distribution (as well as well researched and implemented tech). Said quickly their system sits at the network hubs in your organisation and reads each packet of information (sometimes understanding the content, but often only it’s source and destination). It uses ML to work out what normal looks like for you (and evolves), and if it sees something abnormal start it can raise questions. It can also take action by IP spoofing to intercept traffic and block comms. One of their success stories is the NHS trusts that installed them and contained the WannaCry attack (https://www.bbc.co.uk/news/health-43795001 )

This might be applicable to Oil and Gas by monitoring all the signals in the real-time stack and learning what normal operation looks like and then being able to spot abnormalities as they start to occur. It would be more complex because the relationships are more complex than network traffic but still, got to be worth a shot.

Computer vision

I found this very interesting. I spoke to a company Winnow which has applied computer vision to the problem of cutting food waste from restaurants. (https://venturebeat.com/2019/03/21/winnow-uses-computer-vision-to-help-commercial-kitchens-cut-food-waste/ ). By using a camera it captures images of what the chef is throwing away and through a series of algorithms works out the amount and value of the waste. For instance, perhaps a chef orders the same amount of broccoli every day, but really only uses most of it on Fridays, Wednesdays and Saturdays. Or maybe the ordering changes with the weather – but either way, if you can reduce the over ordering then everyone wins.

People are already applying technology like this in industrial settings to check that people use their safety equipment (like glasses, harnesses etc.), you could also start to put cameras covering manual controls to create records of change and current settings – it would be a cheap way to retro-fit instrumentation.

Conclusion

That’s all for now but some of the other things I found out about included “The Ostrich Problem”, the real-world applications of AR/VR, more on cyber security and what 5G will really mean. Other things that are hot right now are Commercial adoption, future of work, small-scale adaptive robotics, AI-Ethics and Decarbonisation. More of this later.

London Tech Week

Last week was London tech-week. I guess a bit like London fashion week, only much larger.

There were events all over London – this has grown from a week of borrowed conference rooms and underground gatherings into a large series of happenings. Monday-Wednesday saw the COG-X show near the google campus north of Kings Cross. At this event there were 10 stages giving parallel presentation sessions over the full three days, an Expo, start-up section and various corporate networking events.

Wednesday and Thursday (yes overlapping) saw the TechXLR8 exhibition at the Excel Centre, this was a large expo event with presentation 6 stages running all day.

Alongside these two there was also 5G Europe and Identity Management conferences both of substantial size.

Have a look at the website here https://londontechweek.com/events – there are 18 pages of events with 7 events per page. Tech-XLR8 above is only one of these.

This blog previously covered the launch of the UK’s industrial strategy (at Jodrell Bank) and the lack of coverage of this in the main stream media [Link]. Well, despite there being still no interest from the media. The UK industrial strategy was evident everywhere – with announcements from the various bodies, challenges, and funding opportunities. Have a look at this if you haven’t already : https://www.gov.uk/government/publications/industrial-strategy-the-grand-challenges

And did you know there is an “Office for Artificial Intelligence” ? https://www.gov.uk/government/organisations/office-for-artificial-intelligence

I’ll write more about some of the events in due course but here are the highlights:

  • There were 1000’s of under 40, very intelligent, eager advocates of tech everywhere. Very diverse in terms of sex, ethnicity, country of origin you name it, very much in contrast to this [Link]
  •  AI, IoT, ML, CV, AR, VR were the flavours of the moment (and I learnt some really interesting new insights here, more later)
  •  AI Ethics is a huge deal, and lots of people are thinking about this.
  •  Energy tracks focused on decarbonisation, distributed grid and combining sensor technology with predictive algorithms to reduce consumption. Oil and Gas didn’t feature once.
  •  Interesting to see the traditional tech players (with notable exceptions) were looking dated and pushing out platitudes about the new tech and the business impact it should have (but with no concrete examples). Meanwhile there were (really) hundreds of well-funded small companies that had real-world use-cases for niche solutions that had demonstrated value (though most had not had to pass a business-case hurdle to get going).

What struck me most was the vibrancy of the arenas, the buzz of conversation and the high-energy engagement between participants – problem solving and exchanging ideas. It was very refreshing to see. There was also a willingness by all sorts of industries to try new solutions and approaches – knowing that not everything will work but understanding the need to learn and push the envelope forward. The pace of change is amazing.

I was lucky enough to have the chance to try a VR simulation made by Linconshire Fire Brigade to train their officers in fire investigation. On with a VR head-set and into a virtual world. It was very, very realistic.

Oh and everyone was talking about “Digital Disruption”

Next year London Tech-Week should be one for your diary.

Looking for inspiration

I like to look across sources for analogy and stimulating ideas. A couple of things have recently caught my eye.

I find it amazing how hard it is for people (including me) to see the implications of new technologies and ways of working. In retrospect, once a change has happened, it’s obvious what the outcome would have to be. But when the change is happening it’s not so clear.

Going up

Ground floor
Perfumery, stationary, and leather goods, wigs and haberdashery, kitchenware and food. Going up…

Can you remember the theme tune to Are You Being Served?

I’m old enough to remember the lift operators in Aberdeen’s E&M and Watt & Grant department stores. They were replaced by automated lifts in about 1980. The stores have both succumbed – one to the shopping mall, the other a victim to digital retail.

Being a lift operator was a skilled profession, making sure that you stopped the elevator car level with the floor and opening the concertina iron-work doors with the brass handles.  Apparently New York’s last lift operator was only made redundant in 2009 Link

The Economist 1843 magazine just ran a story making the connection between the elevator operators strike and the adoption of self-driving cars. We could probably do the same with roles in the oil field.

The elevator strikes in 1945-47 crippled the city, and led to calls to redesign the city so that only low-rise development was permitted – to reduce the power of unions.

Of course, the answer was – as we know – automated elevators. But a lot of change management was required before people started to use them. Innovations such as emergency stop buttons, telephones for help and recorded announcements all came about in this time.

I’ll wager that we will look back at some of the manual ways of operating an oilfield we use today in the same way was we look back at the anachronism of the elevator operator.

Electricity – who’d want that?

Another story that I picked up on and found illustrated a point was this one [Link]. It’s written by the BBC’s Tim Harford. He asked and answered the question why did it take so long for electricity to displace steam in the factories in the North of England. It was decades after the invention that it was fully adopted.

He explained that it required a redesign of factories before the economics made enough sense for people to abandon centrally powered manufacturing and move to individually powered machines. We’ll see the same adoption economics in oil field operations and technologies such as 3D printing.

Digital Marketing – a lesson for oil and gas?

Today I found another article that resonated. This one is from Marketing Week [Link]

Mark Ritson makes the case that the separation between Digital Marketing teams and Traditional Marketing is ridiculous. What I think he’s saying echoes my point that there should be no separation between “IT” and “The Business”, because IT needs to be just how things are done around here. It’s true in Marketing, it’s true in Oil and Gas too.

“… On the one hand you need to avoid being precious about your digital creds. Signal early you are entirely comfortable losing the D prefix from your title and, for good measure, add something re-assuring like ‘I do not even know what digital means anymore’ or ‘isn’t everything digital now?’.

The merger process means that anyone who is a member of the extreme digerati will be the victim of the new regime. You know the type: obsessed with AI, convinced in the long-term value of VR, boastful that they don’t own a TV. They will be the first to go when the revolution comes.

Digital experience is a prerequisite

But make no mistake, it’s no good proclaiming that digital is wank and it’s time to get back to basics, pull all the money from Facebook and get it back into ‘proper’ media. The post-digital era cuts both ways.

While idiot digerati will be exposed, so too will those who aren’t open to the potential of all the new research and media options that have appeared over the past decade. When Alastair Pegg, the leading marketer at Co-op Bank, noted that that there was “no such thing as digital marketing” he followed up with the corollary that “all marketing is digital marketing”.

I think I can see the parallels between what he’s saying is happening in Marketing now, and what will overtake the world of Oil and Gas operations in the next 3-5 years. What do you think?