Sifting through mountains of irrelevant text just to locate important nuggets of information is as irritating as it is inefficient.
Yet, whether you’re a master’s student putting together your thesis or an engineer trying to categorize technical specifications, you’re going to have to do just that. Worst of all, these kinds of texts run rampant with ambiguities and unclear language making it unusable. Furthermore, they aren’t usually formatted with navigational purposes in mind.
Most of the time, extracting the important information out of text-dense documents is time sensitive, but how can 500 pages be analyzed for specific data in good time?
Improve Your Manual Process
Techniques such as speed reading can aid you tackles these large documents, but there are some faster, out of the box methods, that you can try out and which can help you tackle all that data.
If you’re a lawyer, engineer, economist, or any other highly-educated professional, you’ve had to make your way through undergraduate and graduate programs. One of the main principles of studying and learning is knowing what not to read when trudging through arduous texts such as scholarly and academic journals.
That principle never changes once you’re out of the realms of academia, and since every industry has deadlines you can’t waste time absorbing superfluous information.
Here are some tips on how to get to the meat of larger documents:
1) If a document contains an abstract, read the “findings” and “conclusions” sections first as they usually contain the pertinent information
2) Read the first and last sentence of every paragraph in order to get the gist
3) See if you can find a better-written source with similar information, it may make it easier to work through the more complex text
3) Understand the style format of text you’re reading so you know what to avoid
By mastering these more traditional methods of gathering necessary information, you will be well on your way to better efficiency but harnessing modern technologies can push you the extra mile.
Technology to Rescue
Modern advances in technologies, such as natural language processing, have given rise to a number of intelligent tools to help in different situations. Whether it be thousands of technical specifications, a large pile of CV’s or an uncontrollable amount of customer feedback, we can find tools that can help process the data and give extra insight.
Finding The Right Information In R&D
Collecting a reading list for a new problem takes industrial researchers an average of 3 weeks, with some of the material still being irrelevant for the project. Using artificial intelligence software, these mundane tasks can be sped up by using solutions such as Iris.ai’s exploration tool. Iris.ai builds an interdisciplinary research map based on a problem statement or research paper of your choice, and delivers a precise reading list in less than 2 days.
Accessing Emotional Insights
Many retailers need to understand what’s being said about them on social media. For those who’ve found themselves through various Twitter or Facebook wormholes, social media can present a vacuous hole of worthless text.
Even still, if you don’t assess endless social media posts about your products or services, you won’t be able to optimize your response to issues or fully grasp how your company is performing.
Companies such as Lumoa allow you to track all your customer experience insights from an online platform; finding what drives the customer experience, comparing it with KPI’s and understanding what you are doing well, and what you should be doing better.
A more do-it-yourself solution is also available from Amazon. Amazon Comprehend uses sentiment analysis to computationally determine whether a piece of writing is positive, negative, neutral, or mixed. It can be used as part of a serverless event driven architecture on Amazon Web Services.
Engineering Document Analysis
On the engineering side, tools such as Selko Analytics extract vital information from text-dense engineering specifications. Engineering departments can easily set up their own machine learning model through the tool and pre-process technical text to spot contractual risks, streamline procurement activities, and categorize tasks by groups or architectural levels.
The industry standards demand an influx of data in need of processing, analysis, and implementation, particularly given the complexities of safety and engineering requirements. Time is often of the essence and the engineering specifications are large written documents which need to be processed to get new projects underway.
Selko Analytics’s utilises its intelligent search features to locate items of text adhering to certain groups. Using its user-trained machine learning technology one can properly categorize text after analysing only 100 samples.
Requirements and regulations can be assessed with more efficiency and detail. All results can be verified by a person before export, and any changes made will be learned to produce better results next time.
Solutions Are Out There
Locating the important information in large documents with impenetrable walls of text is frustrating for any professional with a need to get projects and initiatives off the ground.
Thankfully there are plenty of methods to help you through this difficult process. Manual “scanning” methods employed by students are good tips that help with large documentation, but modern technology tools can also be utilised for improved efficiency and productivity.
Text based data can be hard to keep consistent and to navigate, and walls of pointless text can seem frustrating, but there are plenty of solutions abound to cut through the jargon and get right to the point.
Have you ever wondered how services like Siri, Alexa, Cortana, and Google’s assistant work? Perhaps you’re content with a little mystery in your life, but if you want to learn about a new technology that is revolutionizing many industries, read on.
Natural Language Processing (NLP) is becoming increasingly ubiquitous across many devices with new uses emerging frequently. This article gives a quick look at the fundamentals of NLP, what it’s used for and different techniques that make it possible.
What is NLP?
NLP is the branch of artificial intelligence (AI) that is responsible for developing ways for machines to understand human language. Its development has been driven by the growth in big data, machine learning, computational linguistics, computer science and the desire to have more human to machine interaction.
A basic human-computer interaction with NLP may look like this:
• Human speaks to the computer
• The computer captures audio and converts it to text
• Computer processes “translated” text
• The computer converts it back to audio and “speaks” to human
Asimo robot communicating
With advances in technology, NLP is capable of analyzing large volumes of language-based data in a consistent way.
How is NLP Used?
Beyond requesting songs to be played or marked as your favorites, NLP can do much more. Some of the common ways NLP is currently used include:
• Chatbots to automate customer service and ordering
• Improve search results
• Make text processing faster
• Create advertisements
• Provide suggested responses to texts and emails
• Extract information from websites
• Answer complex questions
• Translation and sentiment analysis
At this stage of development, it’s likely that companies have only scratched the surface of what NLP is capable of. As machines and algorithms get more powerful and complex, it’s like that uses of NLP will expand beyond what we can imagine today.
What are Different Techniques of NLP?
While language and communication, in general, rely on syntax, semantics and pragmatics analysis, NLP needs slightly different techniques to carry out its impressive accomplishments.
This technique separates records into different groups or categories based on labels or codes.
Summarization is the process by which NLP can extract a key sentence or develop a short and accurate summary of a longer piece of text.
This technique organizes documents or records within a classification group. It creates clusters within the broader labels.
NLP also relies heavily on extracting data, keywords, keyphrases and other text.
This technique is utilized by search engines frequently as a way to match similar, duplicate or near-duplicate words or phrases. It’s a way NLP can be leveraged to find similarities between different records.
Sentiment analysis is the technique by which NLP can understand the nuance of and emotion behind what a human is trying to say.
This technique helps machines learn and understand contextual clues humans give when they speak.
Does Your Business Need NLP?
Most likely. If you’re not currently using AI in any capacity, it may be time to catch up.
Many functions within your business – sales, marketing, finance, operations, etc. – could benefit from the adoption of AI and natural language processing.
Selko is excited to be joining Slush 2018, since our experience at last year’s event was nothing short of amazing. Out of thousands of startups, we were chosen as a top 10 finalist in the Slush 100 Showcase (their pitching competition). To say we got the most out of attending Slush 2017 is an understatement.
This time around, as a seasoned attendee and more mature business overall, we can enjoy this year’s event even better than before. Slush started off as a 300 person event a few years ago and has now grown to 20,000 tech heads converging in Helsinki. This year we are focusing on meeting potential partners, industry reps and even possible recruits at the event. That kind of ambiance, energy and overall atmosphere not only brings great talent into one space but also provides a great environment to network and create great contacts.
Tuomas Ritola from Selko pitching on stage, Slush 2017
For More Than Inspiration
Tech events are known for impressive lineups with largely successful names. The great thing about Slush is that not only do they deliver just that, but you also get to dig into real world practices and hands-on advice from these great founders, leaders and startup pioneers. Slush is all about bringing true changemakers to the stage. Here are a few speakers from this year’s lineup:
• Dr. Werner Vogels, CTO at Amazon
• Bill Ready, COO at PayPal
• Julia Hartz, Co-Founder and CEO of Eventbrite
• Katarina Berg, Chief Human Resources Officer at Spotify
The Journey Behind Development
Slush not only knows how to create a great on-stage experience, but off stage there are ample opportunities for companies to share their ideas, products and even their stories. Startup District, Founders Mingle and even Speed Mentoring and Roundtables are just some of the amazing encounters that can happen from Dec 4-5.
And because of all the great contacts and leads from last year’s event, we’ve been in a great position to develop our shiny new MVP (minimum viable product), and we’re excited to show it off in Slush!
Slush 2015, photo by Jussi Hellsten
In Good Company
Of course, it’d be very remiss of us if we didn’t talk about the extensive list of innovative AI and machine learning startups that will be at Slush this year. Once again, we are in great company with a few notable mentions to include:
• Malls of Globe – World market offering smart retail platform as a service.
• BEAD – An AI system that analyzes, optimizes and operates a building’s energy management and operations by measuring real-time occupancy.
• Something Corporation – A personalized medicine company working on data-driven continuous care for chronic pain management.
• Selko Technologies – An AI based software that automates complicated requirements analysis in engineering, that can save years worth of expensive, repetitive expert work
Meet Us There
Slush is known for helping the next wave of tech entrepreneurs take their business to the next level. And with so many like-minded tech fanatics in one phenomenal atmosphere, it’s hard not to.
We’re looking forward to all the great new opportunities to connect and even possibly show off our shiny new MVP. We said it before but we’ll say it again: we’re excited to be joining Slush 2018! Hope to see you there!
Every industry is feeling the changes dealt by advancing technology. Everyone wants projects completed quickly, accurately, sustainably, and cost-efficiently. Engineering, procurement, and construction management companies are expected to meet these desires for other companies, ensuring projects are successfully finished on time and under budget.
To keep up with the demands of other companies, EPCM companies will need to embrace the technology that is the driving force behind change in their industry. Automation and AI tools can help EPCM companies meet the expectations of their clients, completing projects without errors and without missing the deadline.
Advancements in Technology Affecting the Industry
Many industries are beginning to rely on EPCM companies to help them with new projects. With a focus on efficiency, there is a demand for ever-increasing speed of project delivery. Earlier deadlines and faster turnaround requirements are forcing EPCM companies to move faster with less wiggle room in the budget.
Advances in technology are the driving force behind changes in the EPCM industry. Tasks can now be completed faster, with less errors, and sometimes even automated entirely. Robotics, Artificial Intelligence, the internet of things, and additive manufacturing are all impacting how business is conducted and will likely continue to change the business landscape.
For example, drones in a warehouse can monitor and track inventory much more efficiently than people. 3D printing can reduce the time it takes to create products.
Increasing efficiency and decreasing project timelines are goals that a company in any industry could demand. EPCM companies need to expect these demands and work to improve their own efficiency to stay ahead.
Sustainability is a growing concern felt through all industries as well. Construction produces carbon emissions and uses a large amount of resources. Finding a way to reduce emissions and use sustainable materials is a goal of many companies. These goals mean that the previous way of doing business could be changing. For example, cheap material suppliers may no longer be the best option if they are not using sustainable material. This alone can cause upheaval in vendor selection, with contracts and offer documents needing to be read closely for these new demands.
Text Data at the Core
Engineering, procurement, and construction management rely heavily on contract text, especially in procurement. EPCM companies have to go through the process of evaluating a selection of potential suppliers before deciding which ones will be the best choices for the project. They may receive multiple offer documents from different companies, and a decision needs to be made quickly for the project to continue.
These tender documents are often hundreds of pages long. When multiple companies submit offers, they all need to be read quickly and correctly to make the best decisions. If a section is missed or misunderstood, the supplier who is chosen may not really be the best candidate for the job and can result in a regulation or requirement not being met.
Because of the advances in technology, with automation and digitalization driving down the timeline of projects, EPCM companies need to implement automation and digitalization where they can to reduce their own project lengths.
There is so much data that is being created but not being analyzed or implemented to the extent that it could be. Companies that learn how to use the value their data possesses will continue to thrive. For EPCM companies, data can reveal information all through the value chain and can help improve efficiency.
Learning to harness new technologies can also open up new markets. Sending out an offer to a customer that was previous unattainable due to a large barrier to enter, could be minimized by modern tools.
Working across borders or in multiple jurisdictions can also create issues and bring the burden of extra text data to process.
Difference areas may have different regulations or requirements, which create a large amount of extra work before an offer could be made or a project could commence.
All in all, whether it is tenders in procurement, offers to customers or regulatory text, processing this text quickly and accurately is important. However, organizing and analyzing all of the technical text is a huge task. When dealing with very large documents, finding the important information and sharing sections with relevant engineers can be arduous, and in the worst case, vital information can be missed. Luckily, advancements in technology can also help EPCM companies move through project workflows without errors and without breaking the budget.
4castplus allows an entire team – engineers, project managers, project controls, and procurement – to work together and collaborate within a single system. It includes a suite of tools to keep EPCM companies on top of projects, with a full lifecycle procurement system, project controls, and customizable notifications based on system events.
Selko Analytics has developed Artificial Intelligence software that can automate the processing and categorizing of text data documents. Machine learning allows the software to be customized to a specific company’s needs, and the software will learn from any changes or corrections a user makes.
This will reduce the amount of time and people needed to read through technical text documents; they simply need to verify the software’s results at the end. Any corrections made are “learned” by the program and will ensure more accurate results with each use.
Aconex improves workflow processes by using one single platform for everyone involved on a project to collaborate. Automated workflows and a project archive will reduce time spent duplicating work, allowing personnel to dedicate their time and resources elsewhere. Integrated search tools allow users to find information quickly, and cost and schedule information ensure projects remain under budget.
There are many tools that can help engineering, procurement, and construction management companies meet the expectations of their clients. Technology is advancing so quickly, EPCM companies will need to embrace it and use technologies such as AI and automation to their advantage and do business faster, more accurately, sustainably, and cost-efficiently.
Aerospace is a growing, changing industry. More information is being collected, creating more data that needs to be analyzed and extrapolated to continue to innovate and advance a company in this industry. Companies want to create aircrafts that are more efficient, more customizable, better designed, faster, cheaper, safer, and more environmentally friendly. To do so, all of that information needs to be processed and understood.
Hundreds of pages of technical text can be difficult to read and organize, which could lead to an important requirement being missed. As the industry grows and changes, new solutions to the problem of text data are being created.
Changes, data and regulations
Sensors onboard aircraft are constantly collecting data. Information on the systems, equipment, and current and future weather conditions are sent from aircraft to pilots and ground operations. In the near future, planes could be able to minimize turbulence and maximize fuel efficiency by altering their routes using real-time data. Autonomous flight is getting closer to reality.
Using analytics to improve maintenance efficiency is increasing in popularity. Predictive maintenance uses analytics to determine when to replace certain parts of an aircraft. This helps maximize the lifespan of these parts, ensuring safety and minimizing equipment checks.
The space industry is getting bigger and more varied. Commercial flights and cubesats are providing a completely new business model and service design for the industry. New technologies are being introduced, making the engineering projects more complex.
Customer demand and the goal of achieving sustainable growth are factors for growth and change in every industry. In aerospace, there is a new focus on reducing emissions, innovation in cabin design, and a reduction in travel time. Production line efficiency is a way for companies in aerospace to achieve sustainable growth. These factors are reshaping the industry, changing how companies operate.
This increase in complexity is providing more and more data that needs to be processed, analyzed, and implemented. With a lot of safety critical aspects to take into account, text data for safety requirements, engineering requirements, and new regulations are expected to increase. With all of these changes, there will be even more text data to process and organize to ensure all regulations and requirements are being met.
Issues with processing vast amounts of text data
During procurement, time can be of the essence, and reading through hundreds of pages of technical text takes a lot of time. A large document may need to be read and understood very quickly so that a new project can get underway. Unfortunately, this can lead to some parts being misunderstood or missed entirely. Leaving room for error is a huge risk to the company, and can put them in breach of the contract they’ve signed, not to mention the potential for grievous errors in the finished project.
Engineering projects in the aerospace industry are based on a set of written specifications. These text documents are typically very large and can be difficult to navigate to the desired or applicable sections. They are also very complicated when the reader is trying to extract only the sections relevant to certain groups.
For each new space mission, a very large set of written requirements is passed down the supplier chain, all of which needs to be met and verified. Missing a relevant set of requirements can complicate projects and impact the project’s bottom line significantly.
With all of the advancements in technology, the aerospace industry is changing at an alarming rate. Devices are improving, becoming more complex, and increasing in popularity. All of these changes are creating more data in the form of requirements and regulations that need to be processed and analyzed before a project can be completed successfully. Trusting this job to a group of people who are assigned the task of reading through hundreds or thousands of pages of technical text can leave room for human error.
What would happen if a company missed a new regulation put forth by a governing body? Or if they didn’t catch an engineering requirement that directly contradicted a new safety requirement?
People can misunderstand requirements or fail to catch contradictory requirements when given the task of analyzing vast amounts of text data. Human error is not always something that we can afford. Fortunately, however, there are tools that can help alleviate these risks.
Solutions to help with processing text data
Where Ansys Esterel helps with critical system code, there are a set of tools that can also relief complexity on the text data and requirement side.
Skywise, by Airbus, aims to connect all aspects of the aviation sector, and eventually all of aerospace, by allowing companies to improve efficiency through the use of one digital platform to integrate all data sources. Compiling shareable information on maintenance, safety, and other workflows allow for greater transparency and increased communication and efficiency.
Selko Analytics uses AI to help locate specifications specific to certain groups, using “Intelligent Search” for a fast analysis of the text data, or user-trained machine learning that requires only 100 samples.
Using Artificial Intelligence to categorize and analyze vast amounts of data ensures that a person can spot risks, important requirements and regulations. All results can be verified by a person before export, and any changes made will be learned to produce better results next time.
Surrounded by today’s complex technology, it is important to stay on top of the game. Solutions such as these helps reduce risks and help manage all that text information. They help companies optimize their efficiency in an industry that is very quickly changing.
Selko team present at the SEAnnovation area hosted by Starburst at this year’s Euronaval in Paris. The first day has flown by with Selko’s CEO Tuomas Ritola pitching at the startup stage, and great conversations with European engineering giants.
We are still here tomorrow, so come visit us at stand 14 C10/E17!
Maritime engineering often involves large development project, and safety critical aspects are often at the center of consideration when developing something new. This results in large amount of technical requirement text that needs to be processed and understood.
Looking at the expected changes in the industry in the next few years, it is safe to say there will be an even larger amount of regulation to deal with. Engineers already spend a lot of time processing this text, and this is likely to increase in the future.
Regulations, standards, customer requirements…
Depending on the project and the field, developing something new, or simply replying to a customer request in a new location, can include a lot of extra work when regulations and standards are involved.
Regulations change from region to region, and the International Maritime Organization (IMO) works with governmental regulatory bodies to oversee collaboration in the field of shipping and international trade. IEC and ISO also play a role with several field related standards, such as ISO/TC8, ISO/TC10, IEC TC 18 (and many more depending on the field), which are considered together with regulatory bodies to avoid duplication. For ships, an extra layer of complexity can also come from being able to conform to a certain class.
Engineers have been battling with regulations and technical standards for years, but an important source of textual requirements comes also from customers and stakeholders. When selling a complicated system, or when purchasing them, there is a lot of text data that needs to be processes by the experts. This can be a matter of working through received tenders, or separating pages upon pages of customer requirements to the right teams. Simply put, the more complicated the system, the more data is often involved.
Complex in nature, large in amount
With large amount of data comes also large amounts of difficulties. Communication between different teams in a projects can be hard if the information is vague or ambiguous. All requirements need to be verified, but with thousand of pages, it is easy to miss the important detail. Information coming from many different stakeholders can cause contradictions in the analysis and it can be hard to spot these issues before it is too late. If the text corresponds to a longer project, any issues not solved in the beginning of the project, can have exponential costs down the line.
New technology showing the way
The amount of data is only going to increase. There is an incomprehensible amount of data generated at any given time; by 2020 this is expected to increase by 4,300%. This is not all without cause. Looking only at the maritime industry, several radical changes are taking place; autonomous ships are being introduced, sensors are being applied more than ever, robotics are helping with complex tasks and communication systems are expanding.
With new technology come new regulatory issues. Safety critical systems are getting more common and this will cause an increase in requirement text and need for verification.
Taking advantage of this huge amount of data requires a different way of thinking: How data is built and used? How much of it we can handle? How fast we can process and analyse it? And where and how are decisions made?
How to deal with all the information?
Luckily there also exist technology to help deal with this. For managing technical requirements solutions such as IBM Door, Polarion and Jira can offer a platform for controlling that information.
On the procurement side, large integrated tools such as SAS Ariba can help control a large set of suppliers and improve supply chain management.
Artificial intelligence can also be utilised, and tools such as Selko Analytics can automate some of the tasks in processing large quantities of text.
And finally, to keep it all together, protocols such as the Shipdex Protocol can help keep everyone in sync.
Maritime engineering is a fascinating field, that is facing some exciting changes in the coming years. People and organisations will interact differently and processes can be improved, leading to better and more rewarding work. Being able to harness this data and get the most out of it is going to be more important than ever, but also present a great amount of opportunities.
We are talking today with Paul Beecher from the World Economic Forum.
We will be talking about trends in production, how new technologies are shaping the world, who the key stakeholders are, and how policy can be used to aid less developed countries grow.
Paul has been researching policy and manufacturing, first at the Institute for Manufacturing at the University of Cambridge, and now at the World Economic Forum in Geneva. Paul was recently a project lead for the “Shaping the Future of Production” System Initiative.
Paul, you’ve been working with the Institute for Manufacturing at Cambridge and the World Economic Forum so could you tell us a little bit about your background and how you got into this?
Well, I started off as a research engineer back in the early 2000’s, mostly in nanoscience. Researching novel nanomaterials for electronics, be it metal nanocrystals, carbon nanotubes, organic semiconductors, inorganic semiconducting nanomaterials, graphene, etc. And I suppose a time came when I had an interest in having a broader purview.
I was interested in how strategic decisions get made, I was interested in this notion that in, advanced western economies especially, the knowledge economy is very important, and that led to my current role here in Geneva with the World Economic Forum where there is an initiative called Shaping the Future of Production and I’ve been working on that since late 2016. And we take quite a broad view on what production means within that effort.
So you’ve basically been looking at policy from a higher level and tried to understand the global themes…
Yes, exactly, and the initiative now has more than 26 countries that account for, I think, something on the order of 80-85% of world manufacturing output. So the idea, the ethos here is that we try to get the views of multiple stakeholders, the people that look at this through different lenses – CEOs, ministers, union leaders, academics and so on – and typically try to incubate public-private efforts to solve the challenges of today and prepare the ground for the future.
What do you think are the challenges today? What are people trying to solve at the moment?
I would say the impact of technology in all its forms – there is an impact on business, there’s a societal impact, and then there’s also implications for the environment. So we try and take a view across those three main pillars.
We have projects on technology and innovation, one on what we call the future production workforce which deals with skills challenges looking ahead over the next few decades, and also accelerating sustainable production. How can we get production worldwide to align with UN Sustainable Development Goals?
So in your blog you were talking about production, and you have quite a specific definition for it, how would you explain it?
Well, as I said, we take a very broad view of what production is. A full value chain approach. So that includes all the way back to initial design, the sourcing of materials, then onto the shop floor, then through the supply chain all the way to the end user, and then there’s end of life issues to consider. So everything that comes under that umbrella is something that we regard as being relevant to production. And as such we have quite a diverse group of companies and organisations that engage with us.
And are there any global trends you’ve identified within that field, or production in general?
Yes, I think there are a few things we can say across those main pillars. I think what you find is that technology is heavily impacting business. I think it’s almost becoming an unavoidable, existential requirement for companies to embrace technology these days. And one of the challenges around that is capturing value from new technologies, including robotics, artificial intelligence, internet of things, additive manufacturing. How are these technologies coming together? Is there potential in using them in combination to obtain greater value capture? So these are the questions we are trying to tease out.
What you often find is that there are different sectors have embraced technologies at different rates and within each sector you will often have champion companies, companies that are sort of in the middle of the road, and then, for want of a better word, follower, laggard companies. And from a policy perspective you want to raise everybody up, as it were, especially SME’s who might not have the resources, whether it is human resources, or the capital to invest in and take advantage of these technologies. There’s a lot of support required to lift everybody up. So that’s one of the things we’re looking at.
Then there’s the impact on what’s happening not just on the shop floor but across the value chain, and what we’re observing from the employment point of view is that I don’t think we can necessarily predict the future in terms of whether there is going to be net job gains or losses, but I think but it is fair to say that some segments in the value chain will contract and others will expand. The eventual picture may not be a clear one, certain tasks will be fully automated, partially automated, etc., but overall it will entail requirement for other kinds of skills. So that’s not a settled picture and it’s one of the things we’re also very keen on investigating further.
They have said that technology has actually increased the amount of jobs than gotten rid of them…
Exactly. Every previous industrial revolution, going back 200 years, has in the long run created more employment and better employment. But then there is contemporary debate about whether this time it’s different.
Why would it be different?
That’s a very good question. I think maybe people feel like this revolution, these technologies that are coming on stream now, have the potential to make more people redundant. I don’t know, and nobody fully knows yet. In my work I encounter scepticism, pessimism, optimism, and every other kind of viewpoint, so it’s something we’re still trying to figure out.
So coming back to AI. They do say it will change the world more than the original industrial revolution, I mean I don’t know if you agree with this, but how do you think it will affect production?
I think it’s already affecting production significantly.
I think it’s probably the technology we have on our radar above all others. I think computing power’s reached a certain level that is enabling lots of other developments to emerge from it, and it’s getting more difficult to keep pace.
I think there is a sense of untapped potential at the moment, that there is so much data already being generated that companies don’t know how to use. The figure could be as low as one or two percent. So it’s almost as we’ve reached so far but we’re lacking in certain capabilities to fully capture the value.
I think there could be quite a profound change over the next fifteen to twenty years. And I think the companies that thrive will be those that learn how to take advantage and how to make sense of the data they generate, whether that’s on the shop floor or whether it’s in their consumer relations or whether it’s elsewhere in the value chain. The companies that get a handle on that will be the ones that will prosper more.
So I think it’s significant in the sense that I think it’s an unavoidable change, and it’s an unavoidable challenge for businesses large and small.
And which businesses, or which industries, do you think are currently leading this, or are most advanced?
Often when you’re dealing with a high value added product, I think automotive has embraced robotics quite a lot. I think their production lines are very sophisticated now and I think they tend to be at the forefront.
At the other end of the scale you might have a sector such as textiles, and we deal with countries that have significant textile industries. Here there can be an ambivalence about technology, because in these countries they see that this sector provides a lot of employment, maybe not very good employment, but it’s employment, and they’re sometimes reluctant to fully embrace technology for fear of creating a disgruntled population. And of course clothing is less sophisticated than a car. So you get this range of adoption. But I think the trend in all sectors is that it’s becoming more and more ubiquitous.
And how is this actually changing policy? I mean can you give as a concrete example of something that would change on the policy side?
I think on the policy side, going back to the issue of ASEAN, these countries fear the so called middle income trap. It’s one of these issues that development economists, who we work with, debate over. Is leap-frogging something that can be viably done? Can you jump from lower levels of development to higher levels of economic development by embracing technology? To do so will will likely involve huge upskilling.
Do we have any examples? Has that happened?
The classic example is mobile communications in some parts of the world that would traditionally have had limited landline communications technology, and in some cases never invested, because they jumped straight to mobile phones.
The challenge with this so called Fourth Industrial Revolution, and the amount of data generated in it, is that it requires significantly greater investment than mobile communication because the volume of data requires optical fibres, significant infrastructural investments, and so on. And that capital requirement is a challenge for countries that are trying to promote themselves to higher levels of development, and create an infrastructure and a platform for their companies to really take advantage of these new technologies. So that’s one of the debates that we’re having, and I think also on the issue of infrastructure, one of the other challenges is to think about not just the next five years but the next 50-100 years.
This comes back to the sustainability question as well, that the investment needs to be smart from that perspective as well. The work we do in sustainability is to do with demonstrating how technologies can be beneficial for supporting the environment, and we’re creating a policy toolkit to support this. Not descriptive as such, but informing the policy perspective to help meet the UN Sustainable Development Goals, and showing how technology in production can support that.
We have reviewed several sectors now, including food and beverage, textiles, electronics and automotive. And we have a further project focused on policy, what we term readiness for the future production and that is where we have analysed countries across many economic drivers, and looked at their institutions, and tried to determine how ready they are for the future of production. Again, not prescriptive, but a set of sort of policy tools that may be adotped according to the destination a country wants to reach, mindful that not every country aspires to being a high value added leading production nation.
At the moment we are creating what a so called Transition Framework that any country can look at and maybe think, okay, we’ve got an educated workforce but we don’t have a history of being a strong technological nation, what can we do if we wanted more high value manufacturing, what would we need to do, etc. etc.?
So basically we can use policy as a guideline or something to help us get somewhere rather than you know something to restrict us.
Exactly, that’s how we view it. We’ve already published white papers across each of these topics and they’re on our website. Those were published for this year’s Annual Meeting in January, and we’re already preparing publication of the latest findings for the next Annual Meeting in January.
Okay brilliant, that sounds all very exciting. So where do you see this all heading, I mean we’ve kind of been talking about it already but what’s the next big disruptor in your mind?
Well, that’s an interesting question. I think the question of sustainability in all its forms is the long term aspiration. So it’s about finding solutions and there’s no one silver bullet, but rather many actions required to sustainably live within planetary boundaries. But also sustainability from societal and economic standpoints, acknowledging the myriad contexts that exist in different countries and different sectors, that’s ultimately what we need to work towards. I think that involves strong levels of good employment.
Production, including manufacturing, is responsible for a lot of natural resource use, a lot of carbon emissions, etc., so there is sort of an onus to come up with a greener way of making things. From the economic standpoint, one of its stats that I perhaps should have mentioned earlier is that there is only at best about 30% of the companies that are really taking advantage of the technologies that are out there. A lot of companies are trapped in so called pilot purgatory where they try something as a pilot but never really go through with overhauling their operations, or else not try at all for whatever reason.
So it’s partly about trying to encourage greater embrace of technology, and I think largely speaking we are advocates, because it opens up the possibility of new product opportunities. That it allows us to tackleproblems that were not solvable before, while acknowledging there’s potential for unintended consequences as well, and trying to anticipate and mitigate those.
That’s a great way to think about it.
Returning to your question about the next big disruptor, I’m a bit reluctant to use the word disruptor, but I think there are incremental solutions out there, and some of them reveal considerable promise.
Over the last few years, we’ve tried to come up with what we call a Vision for the Future of Production, something that all of these different people that we work with can agree on.
We hear things that are kind of, I don’t know if the word is intimidating, but fairly daunting, such as that industry requirements are changing faster than the length of a college degree. How do you design education such that when you come out of college, you have an essential base of core skills, but then there’s also this notion that soft skills are going to become more important for technical people.
There are things like standardisation that are becoming more important, especially when you’ve got a lot of cross border data, so when you have that, when you work across jurisdictions, that’s where the soft skills come in when you have to work with a team from another company, from another country in another culture. For example, one of the challenges about additive manufacturing, given that you can produce a product anywhere, is that it brings up notions of IP and ownership. They can be a bit knotty to disentangle.
So standardisation is something that we’re probably going to become more involved in because we’re one of those organisations that do talk to people from around the world. Harmonisation of standards can be hard to get towards, it entails a lot of work and a lot of trust. One of the other things you also mentioned is this so called reshoring phenomenon, and it’s linked to this notion of labour arbitrage.
Labour arbitrage is to do with low wage countries that are often happy to stay as low wage countries, and I kind of talked a little bit about that earlier. Some experts think that there might only be a ten year window where they can continue to stay on that course.
You have this idea that for the consumer, products become more personalised and customised, and it might eventually become cost effective to reshore production in so called high value countries.
That is not an entirely settled consensus. Some of the people researching this think that we’re not really there yet, but I think it’s something that could happen. And that is a challenge for an emerging economy, you know, if they lose those external markets, what do they then do to lift themselves? So this is an example of the big policy conundrums for the years ahead.
Also, one of the interesting stats about production is that two third of R&D investment in a country is focused on manufacturing in one way or another. So the making of things is where most of research and development spend is directed.
Okay interesting, and that’s an average across the world?
Yes, and that’s repeated in many countries in various geographies at various stages of development. It’s one of the things that makes this topic so fascinating..
Selko is among the first 37 companies to join the Ethics in AI Initiative run by the Ministry of Economic Affairs and Employment, Finland. In the kick off event on Friday, the 5th of October, Selko represented deep-tech startups among multiple industry giants.
In this first-in-the-world initiative, companies working in the field of artificial intelligence, or using it internally, are challenged to discuss ethics around AI and to come up with a set of principles that encourage ethical use of data, the creation of algorithms and the implementation of models.
K-Group, Stora Enso and OP were leading the discussion on Friday and presented the work they had done internally. The conversation continued around political streamlining and issues around equality and working on project based AI.
As part of the initiative, Selko will soon be releasing its own set of principles and best practices.