Tag: Operational Excellence

It’s not what you do, it’s the way that you do it……

This post is about competence, capability, and behaviour. Three words that many people are comfortable using but ones for which, when asked for an explanation of meaning, I have uncovered hundreds of different underlying concepts.

I’ve found that words really matter because they shape the way people think and behave. I’ve found that people can use the same words but mean different things. This gets in the way of organising group activity.

I pay more attention than many people I know to this. I take time to clarify and develop shared understanding. Maybe it’s because I’ve worked in many countries and cultures. Maybe it’s because I was trained in solution selling early in my career. Maybe I’m a pedant. I don’t know.

My roles in sales, marketing and as a consultant have presented me with opportunities to interact with hundreds of different companies across different continents and to observe their approaches to structuring work. I find it fascinating to uncover why things are the way they are, and how to make progress in different settings.

I find that people are often unaware of their own assumptions – what they believe to be objective truth is probably only so within an accepted framework, and that framework can sometimes be just an opinion. Maybe it isn’t accepted by others.

I have found that with careful choice of words it’s possible to influence individual performance and create improved group outcomes.

So here is my simple definition of competence, capability, and behaviour.

Competence

This is something that an individual person can do. They have a level of competency ranging from “incompetence” to “mastery”. An example might be “carpentry” – and may consist of sub-competencies such as “joint making”, “cutting to size”, and “veneering”. Competence is a combination of knowing what to do, the skill to do it, the number of times you’ve done it before (accumulated practice), and how recent the last time you did it was.

Capability

This is something that an organisation can do. In a one-person company it’s essentially the same as competence. It is strongly correlated with competency in a lone-wolf role such as rain-making sales. In other areas, capability relies on the successful organisation of different competencies brought by more than one person. In these circumstances an organisation can create capabilities that no single person is competent to perform on their own.

Behaviour

This is the “manner” in which work is performed. Are people polite to each other? Does a person have “presence” and “gravitas”? An organisation can exhibit collective behaviour – which is related to but not the same as culture, another word often understood differently. An individual can exhibit behaviour – which is related to but not the same as personality.

In both the case of capability and of competence it is possible for organisations and individuals to exhibit different behaviours but still be equally capable and competent. In this case they may well achieve different outcomes, especially if they must influence others.

What do you think?

What do you think of these definitions? How can you help improve them? Please comment here or email me directly.

To innovation and beyond – 2019+

My first post of the year – a look ahead for 2019 – was a bit tongue-in-cheek. Now The World Economic Forum (WEF) is meeting in Davos, Switzerland, I thought I would provide a more insightful analysis.

The WEF will be considering the implications of the 4th Industrial Revolution as the headline theme for their annual conference. If you’re new to all this here is a I4.0 primer from CNBC [Link]. 2019 is going to be a year where industrial innovation takes centre stage. 

The thinking from WEF is always good, detailed thorough. I think that some of the crucial themes for unlocking innovative value will be focussed around opportunities and risks. Here are some of my current favourites.

The Opportunities

  1. Using information and reconfigurable platforms to provide new solutions to stakeholder experience. This will establish new ways to create, deliver and consume the core outputs from industrial processes.
  2. Removing the idea of separation between “IT and the Business”. The two are now conjoined. Being good at tech will be a prerequisite of being good in business. Technology will be embedded in every way that work is done, products are created, consumed and delivered.
  3. Empowering the front-line will be crucial. The winners will be faster organisations where workers make autonomous decisions and are rewarded for outcomes. As an analogy think of Deliveroo drivers. For many reasons, more refined models of work-coordination are required but the core autonomous nature of the work is being previewed here. Decentralised decision-making and autonomous action guided by technology removes many of the tasks performed by middle management. I hope we will start to see teachers, dentists, doctors and nurses no longer filling in spreadsheets and working as relecutant automatons directed by ill-informed command-and-control resource-allocation systems.
  4. With power comes responsibility. Without middle management, new forms of controls (and motivation) will be needed to spot problems and reward behaviour. Surprisingly for some, I don’t believe it is the front-line worker, but middle management, that is most under threat from AI, visual computing and big-data. I hope the CFO won’t push progress only on AIQ but that marketing and talent managers will push the AEQ agenda. It’s important we understand not only economics but also pride, satisfaction and feelings of accomplishment.
  5. Innovation may not be in new forms of technology. The tech available to us now is far ahead of our application of it. Deployment options are already available but not used. Innovation will come from the application of existing technology to new areas of business. Those stuck with old infrastructure will not be able to reconfigure fast enough to keep up. Value will arise from designing new ways of working. Capturing the value will rest on finding ways to get the rest of us to work that way too.

And now the risks

  1. Innovation will come from networks. Big companies will look to small companies for ideas, small companies will be formed from collaborative networks of individuals. Ideas will be mashed-up to cross-fertilise creativity. Guards must be in place to avoid exploitative situations – if they arise unchecked it will mean that the small-guys can’t and won’t play for long. Without them, brilliant ideas will never be used. Rights management is crucial for the distribution of the value created. In the way that song-writing credits generate performance fees for artists. Licenses for ways-of-working are needed to stimulate innovation, and society needs to enable easy access to legal enforcement to uphold claims against copying without permission.
  2. Massive generalisation follows: Young people are frustrated by old-people’s inability to embrace new ways of working. Technology savvy folks are orders of magnitude more productive than their peers. They are quicker to make decisions and to multi-task. This leads to not only high-productivity but also to high-error rates. Iterative short-cycle experimentation and learning-by-doing is the hall-mark of agile strategy. This is not an approach that has been adapted to high-risk industrial work-settings. This leads to a clash of culture and an inability to attract and retain talent.
  3. Innovative individuals will continue to pursue independent careers in increasing numbers. Old industries will die, vested interests will be disenfranchised. The world of work, taxation, social contracts, pensions and access to finance will have to evolve to cope with this. To create a consensus and establish a sense of fairness new-politicians will need not only wisdom but also to deploy the old-tools of oratory and persuasion. There will be big disagreements across society and between nations. It will be necessary to create hope for those who fear being disenfranchised. They will not go quietly into that good night.
  4. Politics of property will come to the fore – the control of assets will be important. Whether that is physical real-estate where low-paid important workers are unable to afford to live where the people who need them reside; property from an accumulation of historical data that provides an unassailable lead and monopoly positions; or the “IDEA” that one person has spent 10 years creating that is exploited by a large corporation without reward. Society will need to find ways to address the control and distribution of property in a world where labour and working-time may not function as a distribution & motivation method.

I will spend time exploring these themes during the year – I have a number of initiatives already kicking off for the year and I hope that you’ll be able to help.

Industry 4.0 – Are we there yet?

Something’s up. I was reviewing my web-stats for this site and I found that since Jan 2018 one post has been read 1000’s of more times than any other one. This one: Innovation and Productivity with the Fourth Industrial Revolution.

About 3 years ago,  I started to talk about how relevant to the Oil and gas industry were things I had been studying about the 4th Industrial Revolution. At that time no one had a clue what I was talking about. Some thought I may have gone mad. I’m glad to say that things have (mostly) changed.

Who’s talking about it?

Since 2018 I’ve seen the term being used by a number of my peers in Oil and Gas, and when you search on this and combine with the term “Digitalisation” It becomes obvious that there is a major movement underway.

Steve Ashley uses the term in his article: Digital, data and taking control of our own destiny and its also used in this press release from Petrotechnics.

Today this announcement from CapGemini helping Statoil with a 3 year project to create a digital roadmap.

How to assess a digital project

There are still a few fundamental questions that every digitalisation project should be able to answer. Below is your starter for ten, and – yes – you may confer:

Examine the project value:

  • What is the primary business driver;
  • Does this project fulfil the agenda of the CIO or the COO;
  • What is the net cost of this project;
  • When will I see value (hint: the choice is either this year, or 3-5+ years out); and
  • Is this a platform investment or can it ride a set of network effects?

Examine the project risks:

  • What things will prevent me from realising the planned benefits;
  • How much change will be required in the way we work;
  • If I do this, does it prevent me from doing other things;
  • How likely is it to require more money than I’ve budgeted for; and
  • What happens if I wait and do it later?

These criteria (and others) should be considered for every project.

Enterprise level

At the enterprise level there are a couple more  things that need to be considered including:

  • How does this project fit into my portfolio of digitiallisation initiatives;
  • As my business gets more digitalised, how am I addressing digital integrity; and
  • Should my HSSE function be renamed HSSED?

Further reading

I wrote an article for ITProportal for some of this and here is a note on project portfolio prioritisation that I wrote a few years back – Introduction to Prioritisation V 1.0.

Where’s the Delta?

On Sunday August 7th 2016 Delta airlines suffered an IT outage. Earlier in the summer SouthWest airlines suffered similar.

Delta cancelled at least 740 flights by Monday (I am sure there will be more) and Southwest cancelled 2,300 flights.  (Reference link , BBC link)

My calculation, at the bottom of the post, suggests that this cost DELTA at least 60 Million USD in lost capacity –  not counting the damage to the brand and additional costs associated with handling customer enquiries.

IT and the business are inseparable in the 4th industrial revolution. For many years there have been moves to outsource IT, drive down its cost and to make it standard and commoditised.  For utility IT this made sense. It was a cost of doing business. It was a necessary qualifier attribute but conveyed no competitive advantage.  If your copy of Microsoft Word was slightly faster than mine, it was unlikely that you’d capture more business or be able to charge more.

The outsourcing movement was used to drive this process, often awarding contracts to low-cost service centres in Eastern Europe or India.

In my view cloud based services – such as Microsoft 365 and Salesforce.com will become the norm for utility IT services and remove most of this responsibility from the IT department which will, as a consequence, go the way of the typing pool. In 25 years new entrants to the workforce will scratch their heads wondering what the point of the IT department was.

The world is, however, changing and changing rapidly. IT is becoming embedded into the core operational process of business. And executives that don’t understand IT will not succeed for long. Any company that perpetuates the phrase “IT and the Business” or any IT department that talks of “The Business” as if it were something separate from the IT function will go the way of the dinosaurs.

I don’t know if Delta outsourced its IT, or what the cause of the issue was. But it is clear that the situation was mismanaged before the outage (Reliability and resilience: no hot-backup, or hot disaster recovery ). This calls into question either the competence of those charged with planning operations or the business decision to not invest in technology and systems. Either way this is a failure of management to grasp the importance of IT in the primary operations of the business. Just because you don’t understand it doesn’t make it simple, or mean the problem can be ignored. Airlines are pretty good at maintaining aircraft, there are international standards for how it should be done and inspection audits making sure it is. Perhaps we need something similar for IT?

On this occasion it was a risk management failure where the loss of IT functionality impacted availability and utilisation of assets. My guess would be though that there are many areas where IT could be applied to the primary business to drive increases in efficiency and reliability. If management is unable to understand the business case or appoint competent management for IT resilience it is unlikely that they are exploring these more nuanced applications of IT.

The airline industry is not alone.

Put this into business context

In 2015 Delta operated an average of 5,400 flights per day, so about 15% of flights were grounded on Monday. Assuming that these planes were now in the wrong position some of them would have to reposition empty (let’s say 50%). Passengers rebooked who were scheduled to fly on a grounded flight (and Delta allowed all passengers to rebook any flight scheduled for Monday). Let’s say 30% of all Monday’s passengers (those on grounded flights and a similar number who took the precaution) took the place of fare paying passengers on later days.

Then we have a utilisation impact on aircraft of:

15% of one day capacity for cancellations

7.5% of one day capacity for repositioning

30% of one day capacity for rebooking.

52.5% of one-day’s capacity utilisation (in the height of busy season) was lost due to a systems outage.

Assuming 350 flying days this is then 0.525/350 = 0.15% capacity hit for this outage.

Last year’s revenue for Delta was 40Bln USD.

My “back of the envelope” calculation suggests that this systems outage cost DELTA $60Million USD in lost utilisation.

The brand has been impaired so future passenger numbers are likely to be lower than they would have been (at least for a while, especially as they could not even take bookings on Monday). Add to this the additional cost of media relations and customer complaint handling and we’re looking at a $100m problem.

Oh and if you are a European Union passenger you are entitled to 600 Euro’s in compensation too.

image credit Link

Innovation and productivity with 4th Industrial Revolution

This is a long post. There is a lot to understand on this topic and this is the primer you’ll need. Please do follow the links. It will take a while but it’ll be worth it.

There is much being discussed around the acceleration of technology and how exponential development in multiple areas is converging and how this will impact on industrial production. About time too really because productivity is failing to pick-up, interest rates at or below zero, cash being hoarded by companies and investment rates are low [link]. This exponential increase is actually not that new – Moore’s law’s been around since… well since Moore launched Intel, and it was a one-way flow before even Schottky was on the scene. These trends are not new and if you don’t believe me here’s a great talk from 11 years ago by Ray Kurzweil with lots of evidence and predictions [Link]. Even Bill Gates saw this in a book he published in 1999 called Business @ the Speed of Thought [link], though these days that speed would be seen as a little too slow.

In my opinion the Oil industry harnessed many of the aspects of this movement in upstream exploration during the 1990’s. It was an early adopter in the process of FINDING reserves. Then the process of adoption stopped. Productivity per geoscientist and the complexity of the information they deal with is orders of magnitude better than it was during the last oil-bust of 1986, so much so that we now have more fields and deposits than we know what to do with. We’re pretty good at finding the stuff. But we’re quite rubbish at developing and operating it at low cost – especially small deposits, which we are so good at spotting now. Development, Operations & Maintenance has ossified – contracts and work practices are stuck. From an operator approach, the production of hydrocarbons has barely moved since the early 90’s (FPSO concepts aside). In my opinion through outsourcing, procurement, short-termism and misalignment of incentives it has become positively petrified.

If the Fourth Industrial Revolution is really going to have an impact we’ll need to address development on four fronts: Economic, Social, Political, and Technical

Economics

If this is going to happen then it has got to make sense for the bottom line. That means productivity: outputs, inputs and the cost of technology. McKinsey, recently cited in Industries of the Future, by Alec Ross [Link], suggested that the manufacturing sector could raise productivity by 2.5% to 5% and save more than $1Trillion in cost annually.

In one example McKinsey says “To capture the potential, manufacturers can consider three moves. Primarily, companies can gather more information and make better use of it. An oil-exploration company collected more than 30,000 pieces of data from each of its drilling rigs—yet 99 percent of that data was lost due to problems of data transmission, storage, and architecture. The tiny trickle of data it did capture was incredibly useful for managers. But so much more can be done. The executives we surveyed said that correcting these data inefficiencies should improve productivity by about 25 percent. [Link]”

Of course some traditional economists think we’re doomed to no innovation and permanent low growth – such as Robert Gordon [Link]. There are many in the old-guard of Oil and Gas that would agree. Most of them have their secretaries print their emails out for them, and refuse to carry a smart phone. Good luck chaps, I think you’ll find the millennials don’t care what you think anymore. Others say differently [Link]

There are many big hitters with some very big numbers, they’re all pointing in the same direction. I’m backing the future, not the past. And I think that Industry 4.0 will feature in the future of Oil and Gas. There are challenges but the prize is big enough that we will overcome them.

Social

The way that many of us work is going to have to change. Luckily the Millennials are already preparing for this shift with their search for meaningful work, emphasis on creativity and individuality; and understanding that they can blend their work and leisure time in ways that the crumbly generation see as slacking and entitled. Forbes have a top ten ways in which the work place will be influenced [Link] and Linda Grattan has her views here [Link]. For me it just seems an obvious way to work. But then I’ve never been very good at dealing with routine, structure and command-and-control. It’ll be interesting to see how we can blend the command-and-control requirements of operations with the caffeine fuelled micro-attention span of people even more “wired” than me.

We’re also seeing cyber-social developments such as the creative commons movement [Link] and open source projects like the Arduino [Link] all of which are fuelling exponential cross-fertilisation of ideas. We are witnessing the rise of the sharing economy [link] and temporary configurations of people who move about often. These are all challenging assumptions about ownership and permanence that are at odds with our current ownership-model for resources.

Political

The Guardian in Nov 2015 reported that ” this revolution could leave up to 35% of all workers in the UK, and 47% of those in the US, at risk of being displaced by technology over the next 20 years, according to Oxford University research cited in the report, with job losses likely to be concentrated at the bottom of the income scale.” [link]

With modern communications and the ability to mobilise quickly we’ve already seen massive changes in the way the people (or, in Greek, demos) interact with conventional democratic systems and capitalism. This is very thoughtful piece by Yanis Varoufakis the recently deposed Greek finance minister [link]. Whether that’s the Arab spring, so-called ISIS, Brexit, the mass-migration of populations or the astonishing rise of Donald Trump, things are getting decidedly odd in traditional politics. There’s a lot of complaint and not a lot of traditional power that can be exercised in public anymore [link]. Just take a look at the mass-mobilisation of a Brazilian flash-mob to protest graft allegations levied against the establishment [link]

Cyber-politics is a whole new dimension. Whether cyber aggression is aimed at accessing private information, denying or altering the dissemination of information or compromising the physical integrity of machine-based systems the ability of people to alter the course of events through “hacking” has never been so great. China has its infamous PLA unit 61398 [Link] one of over 20 cyber-military units it controls, North Korea doesn’t like Sony much as the 2014 hack showed [Link], Iran might be the land of the rising Shamoon that hit Aramco [Link], Ukraine has got on the wrong side of Russian Hackers who shut off their power grid [Link], and who knows who might have written Stuxnet that took out the Iranian centrifuges while telling the control room all was normal [Link]? Now the actors are not only nation-states, but also corporations and little boys alone in their bedrooms [link]

We have the Geneva convention that is supposed to stop states shooting the red cross, bombing civilians, gassing troops and firing mercury-filled dumb-dumbs. We have the international court in The Hague (funded by Andrew Carnegie incidentally [link]) that prosecutes war criminals. I’m not sure who I should call if North Korea invades my X-Box or steals my Bitcoins. And if you are a corporation with cross-border operations you don’t either.

Technical

There are a number of technologies that are developing exponentially at the moment and they’re feeding into changed ways-of-working that will bring about the fourth industrial revolution. Ultimately this will help you plan to build better plant and it will help you operate what you have better. Optimising operations is a sense-and-respond problem. Prepare for the future, know what’s going on right now and do things to make it better. Technology that helps falls into four areas that increase:

  • learning about what’s possible;
  • what’s going on right now and situational awareness;
  • knowledge of interdependence, decision options and consequence; and
  • ability to execute quickly and accurately

Increased learning about what is possible

Big data has gained traction in the last decade. Grab lots of data from everywhere, apply some Bayesian stats, set a base-level and determine the probability of correlation. Works really well when you buy a book from Amazon and it suggests that you might want to buy some reading glasses to go with it. Works pretty well in finding potential hidden relationships and developing predictive algorithms for equipment failure too [link] [link]

Like a lot of developments, this area is moving fast. How do you know what’s even possible these days? It’s so hard to keep up. Data overload, over-stimulation, who even has time to read this stuff?

I remember Schlumberger creating an amazing “portal” called the hub [link], Other companies did similar [link]. Initiatives were started to capture the learnings from each employee and make them available to all other employees. I even heard a talk once describing the use of retiree mentors to help existing employees [link]. People were planning for “The Big Crew Change” when the aging workers retire and new low people come on board [link]. This all tied into concepts like “Hive Minds” which were popular in the 90’s [link].

Well “The Big Crew Change” became the “The Big Layoff” when oil prices crashed in 2014. All that experience and knowledge was not on the balance sheet but was on the P&L. So it was fired without financial impairment and write-off. But the fundamental problem remained, and probably got worse. So much to know, so much to learn and no time to do it. Welcome to one of the drivers that will build demand for machine learning.

Machines can analyse masses of information much quicker than humans can. Up until recently, however, doing that in context and to derive meaning from them has been hard. Development has been showcased by game-playing computers such as Deep Blue for Chess [link], then Watson for Jeopardy [link], and most recently a Google built machine – AlphaGo for GO [Link].

Combine learning algorithms with connected systems, however, and things get really interesting. Learning requires teaching. Unlike programming in Fortran, learning machines construct their own programs by being taught and from the situations they encounter. Distributed and cloud-connected learning is exponential, one machine learns something somewhere and every other machine knows it. Forever. Perhaps we should blow the dust off those long-forgotten “portal” promises around knowledge bases, institutional learning and corporate memory?

Here is a great TED Talk on machine learning [link]. Of course it doesn’t always go well as Microsoft found out with it’s recent “Hitler loving Sex-Robot” [link]

What’s going on right now and situational awareness

Machines that learn what matters and suggest how to respond can eliminate operator overload by removing the trivial and hiding noise. Automated actions can be taken to keep things running. I recently heard an analogy about the difference between the information received by a pilot of a typhoon (arguably the worlds most advanced fighting machine) and a world-war 2 Spitfire. The Spitfire pilot had dials telling him the airspeed, engine speed etc. All just data. The Typhoon pilot, however, could not possibly cope with all the data available. So this data is assembled to show him only what he needs to know in the current situation and that depends on context.

Paul Smith, former UK RAF Pilot says “Bring all these [sensor and interpretation] elements together and it becomes clear why we talk about Eurofighter Typhoon operators having the ‘Combat Edge’ – the situational awareness and a suite of flexible weapons options that offer pilots a real advantage in the battlespace.” [Link]

Building on this, if the aircraft systems detect a heat-seeking missile closing, it launches flares automatically and tells the pilot afterwards – no point in raising an alarm and waiting! Same for the oil and gas sector, why do automatic fault development detection systems write a report and wait. Why don’t they just order the parts, consolidate shipments and schedule engineers for the next maintenance activity? It’s a small example but Amazon is already letting washing machines re-order soap powder [link], Imagine what Amazon-like logistics would do for the Oil and Gas industry.

In order to know what’s going on right now requires a lot of sensors talking to each other and reporting back. Too much detail for a human system to ever cope with properly. The data needs to be reporting to systems that learn what’s important, what actions it should take and how it should present its findings to its operators. The system needs to learn how to behave. These systems need to be aware of the situation and act accordingly. Cloud computing is also an important vector this mix, where systems are connected to each other through internet, and keep each other in synch sharing learning and preparing information so that it can be shared widely, securely and scalably. Google are letting developers play with their learning platform [link]. This is an area where we will see rapid innovation that Oil and Gas can benefit from.

Of course there are some very boring building blocks that will be needed. Connecting systems together will of course require a lot of plumbing – don’t underestimate the size of this problem, here is an example of the type of architecture you might need [link]. Companies like Eigen (www.eigen.co), Tibco (www.tibco.co.uk), BEA (www.bea.com) are active in this area. And it’s important that we really know that our data is correct – as in this case when a demolition crew targeted the wrong house and blamed it on google maps [Link]. So companies like datum360 (www.datum360.com) and Informatica (link).

Decision making & Interdependence

Knowing what’s going on is great, but what do you need to do to make your situation better? That’s the question that quickly arises once teams get sight of data and information in context. Firstly it’s important to know what the options could be – but also how choices in one system effect another.

Simulation is one of the keys to understanding the consequences of decisions – that’s why chess computers work out 100’s of moves ahead and choose the best one to use now. To simulate the decisions on a plant requires a digital model of the plant and its behaviour against which to run tests. The digital model is sometimes called a “Digital Twin” and this allows you to make a change or react to a fault condition and see what the knock-on consequences of selected actions will be in the future. This can be used to test options and optimise outcomes. Hit the model with a series of possible actions in an automated way and it’s possible to uncover the best sequence of actions and back-calculate why, rather than the normal forward progression. It’s very powerful – here are some articles discussing simulation of plant [link]

Integrated planning enables you to make a decision about sequencing events in such a way as to minimise down-time by running jobs in parallel within real-world constraints. This might mean being prepared and ready-to-act when an opportunity unexpectedly arises. Understanding system-wide effects is the key to getting this right, and with more complex interconnected systems with cross-ownership (like present in the UK sector of the North Sea) it not easy – and the owner stakes in oil fields can lead to misalignment of financial interests. Bain has a good article on integrated planning [link].

So far it seems that human + machine combination provides the best mix for solving problems. The creativity of the human is key and augmented decision making with rapid feed-back loops from simulation enables optimisation of decisions. From the first simple spreadsheets that appeared in business the testing of “what-if” scenarios has meant that we have been able to tune procedures across many areas of operations. The combination is not a new concept, here is a very relevant paper from Carnegie Mellon 1998 [link]

Increased speed and accuracy of execution

One of the issues that I’ve come across is the “precision” approach of some operators in the field. The best plans are of no use if they are not executed properly, if parts aren’t damaged and if the wrong parts were not fitted. It happens. Sometimes, of course, the instructions make no sense and the field have to modify them to make them work. That modification of instruction is rarely fed back into the system so little learning takes place. Sometimes the plant is updated and records not updated. All this leads to mismatch between what is recorded and what the plant operators “know”.

Sometimes the physical effort required to perform an inspection means that it cannot be done as often as you’d like, or perhaps is skipped by a crew unwilling or unable to schedule. Autonomous vehicles are in use for inspection activities firstly replacing deep divers and latterly, as costs have gone down they are found in inspections roles as Drones taking cameras into inaccessible places. Perhaps it won’t be long until we have small UAV’s mapping plant and equipment in huge detail. Here is a TED talk that demonstrates what’s already possible [link]

On-site machining of parts may soon be replaced by on-site manufacturing. Additive manufacturing (a broader term than 3D printing) is finding its way not only into printing of small intricate parts but emerging are the start of large-scale construction. It’s not there yet, but imagine what this would mean for logistics or construction in hostile environments. Here is an example of a team in Amsterdam who are in the process of printing a Steel bridge over a canal. That could change some of our approaches to Maintenance and Modification one day. [link]

And, of course, there will always be people involved. But multi-skilled and informed. Augmented reality displays – identifying parts, performing on-the-fly risk assessments and acting as advisors. This will change the way that operators will be able to apply basic skills augmented with real-time instruction and feedback. Meron Gribetz demonstrates here a virtual reality system that could revolutionise the on-shore-off-shore interface, as well as providing just-in-time information. Here is his TED talk [link]

And if you don’t think a Robot can replace people on platforms – have a look at this [link]

 

 

Digitally disrupted operations

I have already said that I believe the time is now for O&G operations to become digital. Radically different cost models are going to be needed and digital is one way they will be achieved.

“When assessing the implications, consider the fact that that new digital business models are the principal reason why just over half of the names of companies on the Fortune 500 have disappeared since the year 2000. And yet, we are only at the beginning of what the World Economic Forum calls the “Fourth Industrial Revolution,” characterized not only by mass adoption of digital technologies but by innovations in everything from energy to biosciences.” Pierre Nanterme – Accenture CEO [Link]

For me this revolution started with a computer programme called Mosaic, the first internet browser – which I discovered in 1993 while goofing around using Kermit, WAIS, Gopher, FTP and downloading cool stuff from GNU. I was being paid to generally muck-about and call it work. Since that moment I have witnessed a massive rise in computing power, information storage and interconnectivity that has left me gawping in awe. The chart below, from The New Machine Age, illustrates the trend.

Five Phases of Disruption

I model this disruption in 4 overlapping phases that are well established (each relying on the ones before it to progress) – and we’re about to see the fifth phase make itself felt.

Phase 1: Pure Information Industries

This was the first to be disrupted. It started with libraries, newspapers and advertising. As technology progressed this then disrupted industries requiring higher information capacity (bandwidth & storage) such as music and radio, and is now doing the same for television and cable companies. Bi-directional communication led to the X-Factor, the Huffington Post and any number of citizen journalists and bloggers.

Phase 2: Customer Engagement

As more people started to have access to and use the internet it was a small extension to make commercial transactions and shopping. As this ramped up customer experience of retail, customer-service departments and opened up access to a vast array of diverse products that could never be held in stock on the high-street. Now there are very few consumer engagements that do not have to integrate a digital channel into their offerings. Coffee and haircuts can’t be online – just about everything else can. Even there Starbucks is integrating a digital offering into their coffee order-to-pay process.

Phase 3: Co-ordination and logistics

It started with on-line parcel tracking, cross-docking and behind-the-scenes scheduling algorithms. Adding mobile GPS and mobile data allowed supply chain and logistics to start its transformation. Firstly on the containerisation and automatic freight and now down to warehouse location, stock control and soon perhaps delivery by dedicated drones [Link]. Phases 1, 2 & 3 have combined to give me my Occado delivery today at 12:30 (sharp).

Phase 4: Asset and resource sharing

This phase is still young and we’re seeing it play out in the consumer space first – a reversal I’ll elaborate on later. Companies like AirBNB, Uber, ZIPCar and others. In general this is the idea that Assets are not fully utilised by their owners all the time, and spare capacity can be made available through a brokering and booking service – and then scheduled and delivered.

Phase 5: Machine-optimised operations

Remote sensing, predictive algorithms, human-machine teaming – integrated with maintenance planning (plus all the attributes in phases 1-3) should lead to more reliable plant constantly optimised and operated by fewer people. This phase is being referred to as The Internet of Things.

“The Internet of Things (IoT) is changing manufacturing as we know it. Factories and plants that are connected to the Internet are more efficient, productive and smarter than their non-connected counterparts. In a marketplace where companies increasingly need to do whatever they can to survive, those that don’t take advantage of connectivity are lagging behind.”  Forbes Magazine [Link]

The reversing order of adoption

Sometime between 1992 and now a reversal in adoption sequence occurred. Prior to Mosaic the sequence of adoption was: Military, Big Business, Small Business, and Consumer. There was also a geographic sequence that meant technologies emerging in California took a few years (5+?) to make it to Europe and the same again to make it to Asia. The order has now reversed and the spread of ideas is both bi-directional and super-fast. For instance we’re going to see individuals install HIVE before most plant install remote operations. So I think we can already see the new technologies and ways-of working being successfully deployed for consumers – the question is how will the Oil and Gas industry adapt them for its use?

How could real-time sharing of Oil and Gas assets and equipment be made to work? How could we create an “Oil-Uber” for self-employed drilling engineers? How can we scale-up technology like HIVE, algorithms for maintenance diagnostics, combined with the GPS on a tag like that in my £100 Garmin watch attached to and despatch the most available uber-spare-part.

Of course, innovations will sneak up on us through lots, and lots, of small changes but the effect will dramatic – looking back we will see the change, but it will happen gradually with the companies that use more efficient technologies buying assets from those that don’t – or, more accurately, buying assets from their officially appointed receivers.