Category: Manufacturing
Why are Machine Learning Projects so Hard to Manage? – Lukas Biewald
I’ve watched lots of companies attempt to deploy machine learning — some succeed wildly and some fail spectacularly. One constant is that machine learning teams have a hard time setting goals and setting expectations. Why is this?
1. It’s really hard to tell in advance what’s hard and what’s easy.
Is it harder to beat Kasparov at chess or pick up and physically move the chess pieces? Computers beat the world champion chess player over twenty years ago, but reliably grasping and lifting objects is still an unsolved research problem. Humans are not good at evaluating what will be hard for AI and what will be easy. Even within a domain, performance can vary wildly. What’s good accuracy for predicting sentiment? On movie reviews, there is a lot of text and writers tend to be fairly clear about what they think and these days 90–95% accuracy is expected. On Twitter, two humans might only agree on the sentiment of a tweet 80% of the time. It might be possible to get 95% accuracy on the sentiment of tweets about certain airlines by just always predicting that the sentiment is going to be negative.
Metrics can also increase a lot in the early days of a project and then suddenly hit a wall. I once ran a Kaggle competition where thousands of people competed around the world to model my data. In the first week, the accuracy went from 35% to 65% percent but then over the next several months it never got above 68%. 68% accuracy was clearly the limit on the data with the best most up-to-date machine learning techniques. Those people competing in the Kaggle competition worked incredibly hard to get that 68% accuracy and I’m sure felt like it was a huge achievement. But for most use cases, 65% vs 68% is totally indistinguishable. If that had been an internal project, I would have definitely been disappointed by the outcome.
My friend Pete Skomoroch was recently telling me how frustrating it was to do engineering standups as a data scientist working on machine learning. Engineering projects generally move forward, but machine learning projects can completely stall. It’s possible, even common, for a week spent on modeling data to result in no improvement whatsoever.
2. Machine Learning is prone to fail in unexpected ways.
Machine learning generally works well as long as you have lots of training data *and* the data you’re running on in production looks a lot like your training data. Humans are so good at generalizing from training data that we have terrible intuitions about this. I built a little robot with a camera and a vision model trained on the millions of images of ImageNet which were taken off the web. I preprocessed the images on my robot camera to look like the images from the web but the accuracy was much worse than I expected. Why? Images off the web tend to frame the object in question. My robot wouldn’t necessarily look right at an object in the same way a human photographer would. Humans likely not even notice the difference but modern deep learning networks suffered a lot. There are ways to deal with this phenomenon, but I only noticed it because the degradation in performance was so jarring that I spent a lot of time debugging it.
Much more pernicious are the subtle differences that lead to degraded performance that are hard to spot. Language models trained on the New York Times don’t generalize well to social media texts. We might expect that. But apparently, models trained on text from 2017 experience degraded performance on text written in 2018. Upstream distributions shift over time in lots of ways. Fraud models break down completely as adversaries adapt to what the model is doing.
3. Machine Learning requires lots and lots of relevant training data.
Everyone knows this and yet it’s such a huge barrier. Computer vision can do amazing things, provided you are able to collect and label a massive amount of training data. For some use cases, the data is a free byproduct of some business process. This is where machine learning tends to work really well. For many other use cases, training data is incredibly expensive and challenging to collect. A lot of medical use cases seem perfect for machine learning — crucial decisions with lots of weak signals and clear outcomes — but the data is locked up due to important privacy issues or not collected consistently in the first place.
Many companies don’t know where to start in investing in collecting training data. It’s a significant effort and it’s hard to predict a priori how well the model will work.
What are the best practices to deal with these issues?
1. Pay a lot of attention to your training data.
Look at the cases where the algorithm is misclassifying data that it was trained on. These are almost always mislabels or strange edge cases. Either way you really want to know about them. Make everyone working on building models look at the training data and label some of the training data themselves. For many use cases, it’s very unlikely that a model will do better than the rate at which two independent humans agree.
2. Get something working end-to-end right away, then improve one thing at a time.
Start with the simplest thing that might work and get it deployed. You will learn a ton from doing this. Additional complexity at any stage in the process always improves models in research papers but it seldom improves models in the real world. Justify every additional piece of complexity.
Getting something into the hands of the end user helps you get an early read on how well the model is likely to work and it can bring up crucial issues like a disagreement between what the model is optimizing and what the end user wants. It also may make you reassess the kind of training data you are collecting. It’s much better to discover those issues quickly.
3. Look for graceful ways to handle the inevitable cases where the algorithm fails.
Nearly all machine learning models fail a fair amount of the time and how this is handled is absolutely crucial. Models often have a reliable confidence score that you can use. With batch processes, you can build human-in-the-loop systems that send low confidence predictions to an operator to make the system work reliably end to end and collect high-quality training data. With other use cases, you might be able to present low confident predictions in a way that potential errors are flagged or are less annoying to the end user.
What’s Next?
The original goal of machine learning was mostly around smart decision making, but more and more we are trying to put machine learning into products we use. As we start to rely more and more on machine learning algorithms, machine learning becomes an engineering discipline as much as a research topic. I’m incredibly excited about the opportunity to build completely new kinds of products but worried about the lack of tools and best practices. So much so that I started a company to help with this called Weights and Biases. If you’re interested in learning more, check out what we’re up to.
Source : https://medium.com/@l2k/why-are-machine-learning-projects-so-hard-to-manage-8e9b9cf49641
Industrial tech may not be sexy, but VCs are loving it – John Tough
There are nearly 300 industrial-focused companies within the Fortune 1,000. The medium revenue for these economy-anchoring firms is nearly $4.9 billion, resulting in over $9 trillion in market capitalization.
Due to the boring nature of some of these industrial verticals and the complexity of the value chain, venture-related events tend to get lost within our traditional VC news channels. But entrepreneurs (and VCs willing to fund them) are waking up to the potential rewards of gaining access to these markets.
Just how active is the sector now?
That’s right: Last year nearly $6 billion went into Series A, B & C startups within the industrial, engineering & construction, power, energy, mining & materials, and mobility segments. Venture capital dollars deployed to these sectors is growing at a 30 percent annual rate, up from ~$750 million in 2010.
And while $6 billion invested is notable due to the previous benchmarks, this early stage investment figure still only equates to ~0.2 percent of the revenue for the sector and ~1.2 percent of industry profits.
The number of deals in the space shows a similarly strong growth trajectory. But there are some interesting trends beginning to emerge: The capital deployed to the industrial technology market is growing at a faster clip than the number of deals. These differing growth trajectories mean that the average deal size has grown by 45 percent in the last eight years, from $18 to $26 million.
Detail by stage of financing
Median Series A deal size in 2018 was $11 million, representing a modest 8 percent increase in size versus 2012/2013. But Series A deal volume is up nearly 10x since then!
Median Series B deal size in 2018 was $20 million, an 83 percent growth over the past five years and deal volume is up about 4x.
Median Series C deal size in 2018 was $33 million, representing an enormous 113 percent growth over the past five years. But Series C deals have appeared to reach a plateau in the low 40s, so investors are becoming pickier in selecting the winners.
These graphs show that the Series A investors have stayed relatively consistent and that the overall 46 percent increase in sector deal size growth primarily originates from the Series B and Series C investment rounds. With bigger rounds, how are valuation levels adjusting?
Above: Growth in pre-money valuation particularly acute in later stage deals
The data shows that valuations have increased even faster than the round sizes have grown themselves. This means management teams are not feeling any incremental dilution by raising these larger rounds.
- The average Series A round now buys about 24 percent, slightly less than five years ago
- The average Series B round now buys about 22 percent of the company, down from 26 percent five years ago
- The average Series C round now buys approximately 20 percent, down from 23 percent five years ago.
Some conclusions
- Dollars invested as a portion of industry revenue and profit allows for further capital commitments.
- There is a growing appreciation for the industrial sales cycle. Investor willingness to wait for reduced risk to deploy even more capital in the perceived winners appears to be driving this trend.
- Entrepreneurs that can successfully de-risk their enterprise through revenue, partnerships, and industry hires will gain access to outsized capital pools. The winners in this market tend to compound as later customers look to early adopters
- Uncertainty still remains about exit opportunities for technology companies that serve these industries. While there are a few headline-grabbing acquisitions (PlanGrid, Kurion, OSIsoft), we are not hearing about a sizable exit from this market on a weekly or monthly cadence. This means we won’t know for a few years about the returns impact of these rising valuations. Grab your hard hat!
Source : https://venturebeat.com/2019/01/22/industrial-tech-may-not-be-sexy-but-vcs-are-loving-it/
Key to any successful industrial digitalisation project – Manufacturer
Intelligent use of real-time data is critical to successful industrial digitalisation. However, ensuring that data flows effectively is just as critical to success. Todd Gurela explains the importance of getting your manufacturing network right.
Industrial digitalisation, including the Industrial Internet of Things (IIoT), offers great promise for manufacturers looking to optimise business operations.
By bringing together the machines, processes, people and data on your plant floor through a secure Ethernet network, IIoT makes it possible to design, develop, and fabricate products faster, safer, and with less waste.
For example, one automotive parts supplier eliminated network downtime, saving around £750,000 in the process simply by deploying a new wireless network across the factory floor.
The time it took for the company to completely recoup their investment in the project? Just nine months.
The key to any successful industrial digitalisation project is factory data
Without data – extracted from multiple sources and delivered to the right application, at the right time – little optimisation can happen.
And there is a multitude of meaningful data held in factory equipment. Consider how real-time access to condition, performance, and quality data – across every machine on the floor – would help you make better business and production decisions.
Imagine the following. A machine sensor detects that volume is low for a particular part on your assembly line. Data analysis determines, based on real-time production speed and previous output totals, that the part needs to be re-stocked in one hour.
With this information, your team can arrange for replacement parts to arrive before you run out, and avoid a production stoppage.
This scenario may be a theoretical, but it illustrates a genuine truth. Manufacturers need reliable, scalable, secure factory networks so they can focus on their most important task: making whatever they make more efficiently, at higher quality levels, and at lower costs.
At the heart of this truth is the factory network. So, while the key to a successful Industry 4.0 project is data, the key to meaningful, accurate data is the network. And manufacturers need to plan carefully to ensure their network can deliver on their needs.
Five key network characteristics
There are five characteristics manufacturers should look for in a factory network before selecting a vendor.
In no particular order, they are:
Interoperability – this ability allows for the ‘flattening’ of the industrial network to improve data sharing, and usually includes Ethernet as a standard.
Automation – for ‘plug and play’ network deployment to streamline processes and drive productivity.
Simplicity – the network infrastructure should be simple, as should the management.
Security – your network should be secure and provide visibility into and control of your data to reduce risk, protect intellectual property, and ensure production integrity.
Intelligence – you need a network that makes it possible to analyse data, and take action quickly, even at the network edge.
Manufacturers need solutions with these features to help aggregate, visualise, and analyse data from connected machines and equipment, and to assure the reliable, rapid, and secure delivery of data. Anything less will leave them wanting, and with subpar results.
These five characteristics are explained in more detail below, along with a real-world case study of a British manufacturer who recently modernised its network and is now expanding globally.
1. Interoperability
Network interoperability allows manufacturers to seamlessly pull data from anywhere in their facility. An emerging standard in this area is Time Sensitive Networking (TSN).
Although not yet widely adopted, TSN provides a common communications pathway for your machines. With TSN, the future of industrial networks will be a single, open Ethernet network across the factory floor that enables manufacturers to access data with ease and efficiency.
Most important, TSN opens up critical control applications such as robot control, drive control, and vision systems to the Industrial Internet of Things (IIoT), making it possible for manufacturers to identify areas for optimisation and cost reduction.
Also, with the OPC-UA protocol now running over TSN, it also becomes possible to have standard and secure communication from sensor to cloud. In fact, TSN fills an important gap in standard networking by protecting critical traffic.
How so? Automation and control applications require consistent delivery of data from sensors, to controllers and actuators.
TSN ensures that critical traffic flows promptly, securing bandwidth and time in the network infrastructure for critical applications, while supporting all other forms of traffic.
And because TSN is delivered over standard Industrial Ethernet, control networks can take advantage of the security built into the technology.
TSN eliminates network silos that block reachability to critical plant areas, so that you can extract real-time data for analytics and business insights.
This is key to the future of factory networks, as TSN will drive the interoperability required for manufacturers to maximise the value from Industry 4.0 projects.
One leading manufacturer estimated that unscheduled downtime cost them more than £16,000/minute in lost profits and productivity. That’s almost £1m per hour if production stops. Could your organisation survive a stoppage like that?
2. Automation
Network automation is critical for manufacturers who have growing network demands. This includes needing to add new machines, or integrate operational controls, to existing infrastructure as well as net-new deployments.
Network uptime becomes increasingly important as the network expands. Ask yourself whether your network and its supporting tools have the capability for ‘plug and play’ network deployments that greatly reduce downtime if – and when – failure occurs.
It’s essential that factories leverage networks that automate certain tasks – to automatically set correct switch settings, for example – to meet Industry 4.0 objectives. The task is too overwhelming otherwise.
3. Simplicity
Like automation, network simplicity is an essential component of the factory network. Choosing a single network infrastructure, capable of handling TSN, Ethernet IP, Profinet, and CCLink traffic can significantly simplify installation, reduce maintenance expense, and reduce downtime.
It also makes it possible to get all your machine controls, from any of the top worldwide automation vendors, to talk through the same network hardware.
Consider also that you want a network that can be managed by operations and IT professionals. Avoid solutions that are too IT-centric and look for user-friendly tools that operations can use to troubleshoot network issues quickly.
Tools that visualise the network topology for operations professionals can be especially useful in this regard.
For example, knowing which PLC (including firmware data) is connected to which port, and which I/O is connected to the same switch, can help speed commissioning and troubleshooting.
Last, validated network designs are essential to factory success. These designs help manufacturers quickly roll out new network deployments and maintain the performance of automation equipment. Make sure this is part of the service your network vendor can provide.
4. Security
Cybersecurity is critically important on the factory floor. As manufacturing networks grow, so does the attack surface, or vectors, for malicious activity such as a ransomware attack.
According to the Cisco 2017 Midyear Cybersecurity Report, nearly 50% of manufacturers use six or more security vendors in their facilities. This mix and match of security products and vendors can be difficult to manage for even the most seasoned security expert.
No single product, technology or methodology can fully secure industrial operations. However, there are vendors that can provide comprehensive network security solutions in their plant network infrastructure that include simple protections for physical assets, such as blocking access to ports in unmanaged switches or using managed switches.
Protecting critical manufacturing assets requires a holistic defence-in-depth security approach that uses multiple layers of defence to address different types of threats. It also requires a network design that leverages industrial security best practices such as ‘Demilitarized Zones’ (DMZs) to provide pervasive security across the entire plant.
5. Intelligence
Consider for a moment how professional athletes react to their surroundings. They interpret what is happening in real-time, and make split-second decisions based on what is going on around them.
Part of what makes those decisions possible is how the players have been coached to react in certain situations. If players needed to ask their coach for advice before taking every shot, tackling the opposition, or sprinting for victory…well, the results wouldn’t be very good.
Just as a team’s performance improves when players can take in their surroundings and perform an appropriate action, the factory performs better when certain network data can be processed and actioned upon immediately – without needing to travel to the data centre first.
Processing data in this way is called ‘edge’, or ‘fog’, computing. It entails running applications right on your network hardware to make more intelligent, faster decisions.
Manufacturers need to access information quickly, filter it in real-time, then use that data to better understand processes and areas for improvement.
Processing data at the edge is key to unlocking networking intelligence, so it’s important to ask yourself whether your factory network can support edge applications before beginning a project. And if it can’t, it’s time to consider a new network.
A final note on network intelligence. Once you deploy edge applications, make sure you have the tools to manage and implement them with confidence, at scale. Managing massive amounts of data can quickly become a problem, so you’ll need systems that can extract, compute, and move data to the right places at the right time.
The opportunity for manufacturers who invest in Industry 4.0 solutions is massive (and it’s time that leaders from the top floor and shop floor realised it). But before any Industry 4.0 project can get off the ground, the right foundation needs to be in place.
The factory (or industrial) network is that foundation… and manufacturers owe it to themselves to select the best one available.
Case Study:
SAS International is a leading British manufacturer of quality metal ceilings and bespoke architectural metalwork. Installed in iconic, landmark buildings worldwide, SAS products lead through innovation, cutting-edge design and technical acoustic expertise.
Their success is built on continued investment in manufacturing and achieving value for clients through world-class engineered solutions.
In the UK, SAS operates factories in Bridgend, Birmingham and Maybole, with headquarters and warehouse facilities in Reading. The company has recently expanded its export markets and employs nearly 1,000 staff internationally.
However, the IT infrastructure was operating on ageing equipment with connectivity, visibility and security constraints.
The company’s IT team recently modernised its network, upgrading from commercial-grade wireless to a new network solution with a unified dashboard that allows them to remotely manage distributed sites.
They now have instant visibility and control over the network devices, as well as the mobile devices used by employees daily.
Results
During the initial deployment, the IT team was able to identify cabling issues that previously they would not have been alerted to or been able to investigate.
With upcoming projects and continually working to optimise solutions, like cloud storage, the network is now robust enough and reliable enough to support future IT needs.
SAS is retrofitting numerous manufacturing machines with computers. This retrofit, partnered with the new network, allows remote communications between the machines and the designers without having to manually input data at the machines themselves.
The robust wireless infrastructure is changing the manual printing and checking of stock by enabling handheld scanners and creating a more efficient and cost-effective product flow.
Fault mitigation and anomaly detection have been huge benefits of the solution. For example, the IT team was able to quickly identify a bandwidth issue when a phenomenal amount of data was generated from an automated transfer to a shop machine.
They were able to spot the issue, identify the machine, and fix the problem. Before, they would merely have seen there was a network slowdown, but wouldn’t have been able to identify or resolve the problem.
The SAS team will continue to benefit from the included firmware updates and new feature releases that are integrated into the solution, providing them with a future-proof solution as they expand to global sites in the future.
Source : https://www.themanufacturer.com/articles/the-key-to-any-successful-industrial-digitalisation-project/
Digital Transformation of Business and Society: Challenges and Opportunities by 2020 – Frank Diana
At a recent KPMG Robotic Innovations event, Futurist and friend Gerd Leonhard delivered a keynote titled “The Digital Transformation of Business and Society: Challenges and Opportunities by 2020”. I highly recommend viewing the Video of his presentation. As Gerd describes, he is a Futurist focused on foresight and observations — not predicting the future. We are at a point in history where every company needs a Gerd Leonhard. For many of the reasons presented in the video, future thinking is rapidly growing in importance. As Gerd so rightly points out, we are still vastly under-estimating the sheer velocity of change.
With regard to future thinking, Gerd used my future scenario slide to describe both the exponential and combinatorial nature of future scenarios — not only do we need to think exponentially, but we also need to think in a combinatorial manner. Gerd mentioned Tesla as a company that really knows how to do this.
He then described our current pivot point of exponential change: a point in history where humanity will change more in the next twenty years than in the previous 300. With that as a backdrop, he encouraged the audience to look five years into the future and spend 3 to 5% of their time focused on foresight. He quoted Peter Drucker (“In times of change the greatest danger is to act with yesterday’s logic”) and stated that leaders must shift from a focus on what is, to a focus on what could be. Gerd added that “wait and see” means “wait and die” (love that by the way). He urged leaders to focus on 2020 and build a plan to participate in that future, emphasizing the question is no longer what-if, but what-when. We are entering an era where the impossible is doable, and the headline for that era is: exponential, convergent, combinatorial, and inter-dependent — words that should be a key part of the leadership lexicon going forward. Here are some snapshots from his presentation:
- Because of exponential progression, it is difficult to imagine the world in 5 years, and although the industrial era was impactful, it will not compare to what lies ahead. The danger of vastly under-estimating the sheer velocity of change is real. For example, in just three months, the projection for the number of autonomous vehicles sold in 2035 went from 100 million to 1.5 billion
- Six years ago Gerd advised a German auto company about the driverless car and the implications of a sharing economy — and they laughed. Think of what’s happened in just six years — can’t imagine anyone is laughing now. He witnessed something similar as a veteran of the music business where he tried to guide the industry through digital disruption; an industry that shifted from selling $20 CDs to making a fraction of a penny per play. Gerd’s experience in the music business is a lesson we should learn from: you can’t stop people who see value from extracting that value. Protectionist behavior did not work, as the industry lost 71% of their revenue in 12 years. Streaming music will be huge, but the winners are not traditional players. The winners are Spotify, Apple, Facebook, Google, etc. This scenario likely plays out across every industry, as new businesses are emerging, but traditional companies are not running them. Gerd stressed that we can’t let this happen across these other industries
- Anything that can be automated will be automated: truck drivers and pilots go away, as robots don’t need unions. There is just too much to be gained not to automate. For example, 60% of the cost in the system could be eliminated by interconnecting logistics, possibly realized via a Logistics Internet as described by economist Jeremy Rifkin. But the drive towards automation will have unintended consequences and some science fiction scenarios could play out. Humanity and technology are indeed intertwining, but technology does not have ethics. A self-driving car would need ethics, as we make difficult decisions while driving all the time. How does a car decide to hit a frog versus swerving and hitting a mother and her child? Speaking of science fiction scenarios, Gerd predicts that when these things come together, humans and machines will have converged:
- Gerd has been using the term “Hellven” to represent the two paths technology can take. Is it 90% heaven and 10% hell (unintended consequences), or can this equation flip? He asks the question: Where are we trying to go with this? He used the real example of Drones used to benefit society (heaven), but people buying guns to shoot them down (hell). As we pursue exponential technologies, we must do it in a way that avoids negative consequences. Will we allow humanity to move down a path where by 2030, we will all be human-machine hybrids? Will hacking drive chaos, as hackers gain control of a vehicle? A recent Jeep recall of 1.4 million jeeps underscores the possibility. A world of super intelligence requires super humanity — technology does not have ethics, but society depends on it. Is this Ray Kurzweil vision what we want?
- Is society truly ready for human-machine hybrids, or even advancements like the driverless car that may be closer to realization? Gerd used a very effective Video to make the point
- Followers of my Blog know I’m a big believer in the coming shift to value ecosystems. Gerd described this as a move away from Egosystems, where large companies are running large things, to interdependent Ecosystems. I’ve talked about the blurring of industry boundaries and the movement towards ecosystems. We may ultimately move away from the industry construct and end up with a handful of ecosystems like: mobility, shelter, resources, wellness, growth, money, maker, and comfort
- Our kids will live to 90 or 100 as the default. We are gaining 8 hours of longevity per day — one third of a year per year. Genetic engineering is likely to eradicate disease, impacting longevity and global population. DNA editing is becoming a real possibility in the next 10 years, and at least 50 Silicon Valley companies are focused on ending aging and eliminating death. One such company is Human Longevity Inc., which was co-founded by Peter Diamandis of Singularity University. Gerd used a quote from Peter to help the audience understand the motivation: “Today there are six to seven trillion dollars a year spent on healthcare, half of which goes to people over the age of 65. In addition, people over the age of 65 hold something on the order of $60 trillion in wealth. And the question is what would people pay for an extra 10, 20, 30, 40 years of healthy life. It’s a huge opportunity”
- Gerd described the growing need to focus on the right side of our brain. He believes that algorithms can only go so far. Our right brain characteristics cannot be replicated by an algorithm, making a human-algorithm combination — or humarithm as Gerd calls it — a better path. The right brain characteristics that grow in importance and drive future hiring profiles are:
- Google is on the way to becoming the global operating system — an Artificial Intelligence enterprise. In the future, you won’t search, because as a digital assistant, Google will already know what you want. Gerd quotes Ray Kurzweil in saying that by 2027, the capacity of one computer will equal that of the human brain — at which point we shift from an artificial narrow intelligence, to an artificial general intelligence. In thinking about AI, Gerd flips the paradigm to IA or intelligent Assistant. For example, Schwab already has an intelligent portfolio. He indicated that every bank is investing in intelligent portfolios that deal with simple investments that robots can handle. This leads to a 50% replacement of financial advisors by robots and AI
- This intelligent assistant race has just begun, as Siri, Google Now, Facebook MoneyPenny, and Amazon Echo vie for intelligent assistant positioning. Intelligent assistants could eliminate the need for actual assistants in five years, and creep into countless scenarios over time. Police departments are already capable of determining who is likely to commit a crime in the next month, and there are examples of police taking preventative measures. Augmentation adds another dimension, as an officer wearing glasses can identify you upon seeing you and have your records displayed in front of them. There are over 100 companies focused on augmentation, and a number of intelligent assistant examples surrounding IBM Watson; the most discussed being the effectiveness of doctor assistance. An intelligent assistant is the likely first role in the autonomous vehicle transition, as cars step in to provide a number of valuable services without completely taking over. There are countless Examples emerging
- Gerd took two polls during his keynote. Here is the first: how do you feel about the rise of intelligent digital assistants? Answers 1 and 2 below received the lion share of the votes
- Collectively, automation, robotics, intelligent assistants, and artificial intelligence will reframe business, commerce, culture, and society. This is perhaps the key take away from a discussion like this. We are at an inflection point where reframing begins to drive real structural change. How many leaders are ready for true structural change?
- Gerd likes to refer to the 7-ations: Digitization, De-Materialization, Automation, Virtualization, Optimization, Augmentation, and Robotization. Consequences of the exponential and combinatorial growth of these seven include dependency, job displacement, and abundance. Whereas these seven are tools for dramatic cost reduction, they also lead to abundance. Examples are everywhere, from the 16 million songs available through Spotify, to the 3D printed cars that require only 50 parts. As supply exceeds demand in category after category, we reach abundance. As Gerd put it, in five years’ time, genome sequencing will be cheaper than flushing the toilet and abundant energy will be available by 2035 (2015 will be the first year that a major oil company will leave the oil business to enter the abundance of the renewable business). Other things to consider regarding abundance:
- Efficiency and business improvement is a path not a destination. Gerd estimates that total efficiency will be reached in 5 to 10 years, creating value through productivity gains along the way. However, after total efficiency is met, value comes from purpose. Purpose-driven companies have an aspirational purpose that aims to transform the planet; referred to as a massive transformative purpose in a recent book on exponential organizations. When you consider the value that the millennial generation places on purpose, it is clear that successful organizations must excel at both technology and humanity. If we allow technology to trump humanity, business would have no purpose
- In the first phase, the value lies in the automation itself (productivity, cost savings). In the second phase, the value lies in those things that cannot be automated. Anything that is human about your company cannot be automated: purpose, design, and brand become more powerful. Companies must invent new things that are only possible because of automation
- Technological unemployment is real this time — and exponential. Gerd talked to a recent study by the Economist that describes how robotics and artificial intelligence will increasingly be used in place of humans to perform repetitive tasks. On the other side of the spectrum is a demand for better customer service and greater skills in innovation driven by globalization and falling barriers to market entry. Therefore, creativity and social intelligence will become crucial differentiators for many businesses; jobs will increasingly demand skills in creative problem-solving and constructive interaction with others
- Gerd described a basic income guarantee that may be necessary if some of these unemployment scenarios play out. Something like this is already on the ballot in Switzerland, and it is not the first time this has been talked about:
- In the world of automation, experience becomes extremely valuable — and you can’t, nor should attempt to — automate experiences. We clearly see an intense focus on customer experience, and we had a great discussion on the topic on an August 26th Game Changers broadcast. Innovation is critical to both the service economy and experience economy. Gerd used a visual to describe the progression of economic value:
Source: B. Joseph Pine II and James Gilmore: The Experience Economy
- Gerd used a second poll to sense how people would feel about humans becoming artificially intelligent. Here again, the audience leaned towards the first two possible answers:
Gerd then summarized the session as follows:
The future is exponential, combinatorial, and interdependent: the sooner we can adjust our thinking (lateral) the better we will be at designing our future.
My take: Gerd hits on a key point. Leaders must think differently. There is very little in a leader’s collective experience that can guide them through the type of change ahead — it requires us all to think differently
When looking at AI, consider trying IA first (intelligent assistance / augmentation).
My take: These considerations allow us to create the future in a way that avoids unintended consequences. Technology as a supplement, not a replacement
Efficiency and cost reduction based on automation, AI/IA and Robotization are good stories but not the final destination: we need to go beyond the 7-ations and inevitable abundance to create new value that cannot be easily automated.
My take: Future thinking is critical for us to be effective here. We have to have a sense as to where all of this is heading, if we are to effectively create new sources of value
We won’t just need better algorithms — we also need stronger humarithms i.e. values, ethics, standards, principles and social contracts.
My take: Gerd is an evangelist for creating our future in a way that avoids hellish outcomes — and kudos to him for being that voice
“The best way to predict the future is to create it” (Alan Kay).
My Take: our context when we think about the future puts it years away, and that is just not the case anymore. What we think will take ten years is likely to happen in two. We can’t create the future if we don’t focus on it through an exponential lens
Source : https://medium.com/@frankdiana/digital-transformation-of-business-and-society-5d9286e39dbf
Robots for Rent – Why RaaS Works – RIA
Renting robots as temp labor? Not a new idea. But it’s certainly one that is gaining followers.
Rising labor shortages, tightly contested global markets, and growing interest in automation are tightening the screws on traditional business models. A broader spectrum of users are seeking flexible automation solutions. More suppliers are adopting new-age rental or lease options to satisfy the demand. Some are mature companies answering the call, others are startups blazing a path for the rest of the industry. Robotics as a Service (RaaS) is an emerging trend whose time has come.
Steel Collar Associates may have been ahead of its time when RIA spoke with its owner in 2013 about his “Humanoids for Hire” – aka Yaskawa dual-arm robots for rent. Already several years into his venture at the time, Bill Higgins was having little success contracting out his robo-employees. Back then, industry was barely warming up to the idea of cage-free robots rubbing elbows with their human coworkers. Now every major robot manufacturer has a collaborative robot on its roster. And a slew of startups have joined the fray.
Just like human-robot collaboration is helping democratize robotics, RaaS will help bring robots to the masses. And cobots aren’t the only robots for rent.
Whether you have a short-term need, want to try before you buy, forgo a capital expenditure, or lower your cost of entry to robotic automation, RaaS is worth a closer look. It’s robots on demand, when and where you want them.
Robots on Demand
Out-of-the-box solutions like those offered by READY Robotics, which are easy to use and easy to deploy, are making RaaS a reality. Your next, or perhaps first, robotic solution may be a Johnny-on-the-spot – on wheels.
“The TaskMate is a ready-to-use, on-demand robot worker that is specifically designed to come out of its shipping crate ready to be deployed to the production line,” says READY Robotics CEO Ben Gibbs, noting that manufacturers without the time to undertake custom robot integration are looking for an out-of-the box automation solution. Rental options make the foray easier.
“Time is their most precious resource. They want something like the TaskMate that is essentially ready to go out of the box,” says Gibbs. “They may have to do a little fixturing or put together a parts presentation hopper. Besides that, it’s something they can deploy pretty quickly. We’re driving towards providing a solution that’s as easy to use as your personal computer.”
The system consists of a collaborative robot arm mounted on a stand with casters, so you can wheel it into position anywhere on the production floor. The ease of portability makes it ideal for high-mix, low-volume production where it can be quickly relocated to different manufacturing cells. Nicknamed the “Swiss Army Knife” of robots, the TaskMate performs a variety of automation tasks from machine tending to pick-and-place applications, to parts inspection.
The TaskMate comes in two varieties, the 5-kg payload R5 and 10-kg payload R10 (pictured). Both systems use robot arms from collaborative robot maker Universal Robots. The UR arm is equipped with a force sensor and a universal interface called the TEACHMATE that allows different robot grippers to be hot-swapped onto the end of the arm. Supported end effector brands include SCHUNK, Robotiq and Piab.
Contributing to the system’s ease of use is READY’s proprietary operating system, the FORGE/OS software. A simple flowchart interface (pictured) controls the robot arm, end-of-arm tooling and other peripherals. No coding is required.
For those tasks requiring a higher payload, reach, or cycle time than is capable with the power-and-force limiting cobot included with the TaskMate R5 and R10 systems, READY also offers its FORGE controller (formerly called the TaskMate Kit). Running the intuitive FORGE/OS software, the controller provides the same easy programming interface but is designed as a standalone system for ABB, FANUC, UR and Yaskawa robots.
“For example, if you plug the FORGE controller into a FANUC robot, you no longer have to program in Karel (the robot OEM’s proprietary programming language),” explains Gibbs. “On the teach pendant, you can use FORGE/OS to program the robot directly, so you have the same programming experience on the controller as you do on the TaskMate.
“We started primarily with smaller six degree-of-freedom robot arms, like the FANUC LR Mate and GP7 from Yaskawa,” continues Gibbs. “We have started to integrate some of the larger robots as well, like the FANUC M-710iC/50. Ultimately, we’re driving toward a ubiquitous programming experience regardless of what robot arm or robot manufacturer you’re using.”
In the Cloud
A common element in the RaaS rental model is cloud robotics. READY offers customers the ability to remotely monitor the TaskMate or other robotic systems hooked up to the FORGE controller.
“We can set them up with alerts, so when the production cycle is completed or the robot enters an unexpected error state, they can receive an email notifying the floor manager or line operator to check the system,” says Gibbs.
You can also save and back up programs to the cloud, and deploy them from one robot to another. If an operator were to inadvertently lose a program, rather than rewrite it from scratch, you can just drop the backup version from the cloud onto the system and be up and running again in minutes.
The TaskMate systems and FORGE controller are available for both purchase and rental.
“We provide a menu to our customers of how they might want to consume our products and services,” says Gibbs. “That may be all the way from a traditional CapEx (capital expenditure) purchase if they want to buy one of our TaskMates upfront, to the other end of the spectrum where they can rent the system with no contract for however long or short of a duration they want.”
For an additional charge, READY can manage the entire asset for the customer.
“We set it up, we program it, and we remotely monitor it to make sure it’s maximizing its uptime. We can come in and tweak the program if it’s running into unexpected errors. All of the systems are equipped with cell modems, so they can update the software over the air. We handle all of the maintenance or it’s handled by our channel partners.”
No-Term Rental
Gibbs says flexibility is the biggest advantage to their rental option. READY offers a 3-month trial rental. But customers are not required to keep it for that full term.
“We have a no-term rental. That’s even more appealing because it can come entirely out of your OpEx (operating expenditure) budget. Instead of going through a lengthy CapEx approval process, we’ve had some customers just run their corporate credit card, because the rental is below their approval level for an OpEx purchase. They can easily set up the system and use it for a few months. That alone provides them with a much stronger justification for moving forward with CapEx if they want, or just continue to expand their rental.
“At the end of the first month, if they decide that it’s not working out, just like any incompetent worker, they can fire it and send it back.”
If the customer chooses to continue renting, Gibbs says it’s more cost-effective to sign a contract. This reduces the risk for everyone, so there’s usually a financial incentive.
“The primary way we differentiate ourselves is that we offer that no-term rental with a fixed monthly fee, which allows these factories to capture the traditional value of automation. We don’t have a meter running that says you ran it 22 hours this day, so you owe us for 22 hours of work. We encourage them to run it as long as they want. The expectation is the longer you run it, the cheaper it should be.”
Flexibility for High-Mix, Low-Volume
READY’s target customers range from small job shops to large multinationals and Fortune 500 companies.
“Attwood is a great example of the type of high-mix, low-volume production environment where the flexibility of the TaskMate really shines,” says Gibbs.
Attwood Marine in Lowell, Michigan, is one of the world’s largest producers of boat parts, accessories and supplies. If it’s on your boat, there’s a good chance this century-old company made it. They make thousands of different parts, but cater to a relatively small marine market. The challenges of high-mix, low-volume production in a highly competitive market had them looking for an automation solution.
The flexibility of the TaskMate to quickly deploy and redeploy depending on Attwood’s short- or long-term needs was a deciding factor. With only a couple hundred employees and no dedicated robotics programmer on staff, the customer appreciates the FORGE software’s ease of use. Plus the ability to rent the system plays to the seasonal nature of Attwood’s business and lowers the cost of their first foray into robotic automation.
Attwood has deployed the TaskMate R10 to a half-dozen cells on the production floor performing CNC machine tending, pick-and-place tasks like palletizing, loading/unloading conveyors and case packing, and even repetitive testing. You need to actuate a switch or pull a cord 250,000 times? That’s a job for flexible automation.
By deploying one robot system to multiple production cells, Attwood was able to spread their ROI across multiple product lines and realize up to a 30 percent reduction in overall manufacturing costs. Watch the TaskMate on the job at Attwood Marine.
Small to midsized businesses aren’t the only ones benefiting. Large multinationals like tools manufacturer Stanley Black & Decker use the TaskMate R10 for machine tending CNC lathes.
“Multinationals may have robot programmers on staff, but usually not enough of them,” says Gibbs. “Automation engineers are in high demand and very difficult to come by. Any technology that makes it faster and easier for people to set up robots is a tremendous value. Even with large multinationals, some like to be asset-light and do a rental, but everyone loves the ease of programming we offer through FORGE.”
Forged in the Lab
READY’s portable plug-and-play solution is a technology spinoff from Professor Greg Hager’s research in human-machine collaborative systems at Johns Hopkins University. Gibbs, an alumnus, was working in the university’s technology ventures office helping researchers like Prof. Hager develop commercialization strategies for their new technologies. Hager, along with Gibbs, and fellow alum CTO Kelleher Guerin cofounded the startup in October 2015. Another cofounder, Drew Greenblatt, President of Marlin Steel Wire Products (an SME in the Know), offered up his nearby Baltimore, Maryland-based custom metal forms factory as a prototype test site for the TaskMate. The system was officially launched in July 2017.
Prof. Hager is now an advisor to the company. Distinguished robotics researcher, Henrik Christensen, is Chairman of the Board of Advisors. In December 2017, the startup secured $15 million in Series A funding led by Drive Capital.
READY maintains an office in Baltimore, while its headquarters is in Columbus, Ohio. They are a FANUC Authorized System Integrator. Gibbs says they are in the process of building a channel partner network of integrators and distributors to support future growth.
Pay As You Go
Business models under the RaaS umbrella vary widely, and are evolving. Startups like Hirebotics and Kindred leverage cloud robotics more intensely to monitor robot uptime, collect data, and enhance performance using AI. They charge by the hour, or even by the second. You pay for only what you use. Each service model has its advantages.
Some RaaS advocates offer subscription-based models. Some took a page from the sharing economy. Think Airbnb, Lyft, TaskRabbit, Poshmark. Share an abode, a car or clothes. Skip the overhead, the infrastructure and the long-term commitment. Pay as you go for a robot on the run.
Mobile Robots for Hire
Autonomous mobile robots (AMRs) are no strangers to the RaaS model, either. RIA members Aethon and Savioke lease their mobile robots for various applications in healthcare, hospitality and manufacturing. Startup inVia Robotics offers a subscription-based RaaS solution for its warehouse “Picker” robots.
We first explored the emergence of AMRs in the Always-On Supply Chain. It’s startling how much the logistics robot market has changed in just a couple of years. Since then, prototypes and beta deployments have turned into full product lines with significant investor funding. Major users like DHL, Walmart and Kroger, not to mention early adopter Amazon, are doubling down on their mobile fleets.
After triple-digit revenue growth in Europe, Mobile Industrial Robots (MiR) was just breaking onto the North American scene two years ago. Now, as they celebrate comparable growth on this side of the pond, MiR prepares to launch a new lease program in January.
MiR is another prodigy of Denmark’s booming robotics cluster. They join Danish cousin Universal Robots on the list of Teradyne’s smart robotics acquisitions. Odense must have the Midas touch.
Go Big or Go Home
Responding to customer demands for larger payloads, MiR introduced its 500-kg mobile platform at Automatica in June. The MiR500 (pictured) comes with a pallet transport system that automatically lifts pallets off a rack and delivers them autonomously. Watch it in action on the production floor of this agricultural machine manufacturer.
“Everybody we deal with today is making a big push to eliminate forklift traffic from the inner aisleways of production lines,” says Ed Mullen, Vice President of Sales – Americas for MiR in Holbrook, New York. “That’s really driving the whole launch of the MiR500. We’ve gone through some epic growth here in my division.”
Mullen’s division is responsible for supporting MiR’s extensive distributor network in all markets between Canada and Brazil. Right now, the Americas account for about a third of the global business.
“We’re seeing applications in industrial automation, warehouses and distribution centers,” says Mullen. “Electronics, semiconductor and a lot of the tier automotive companies, like Faurecia, Visteon and Magna, have all invested in our platforms and are scaling the business. We see this being implemented across all industries, which is really adding to our excitement.”
Lease Options
Although Mullen says they’ve seen tremendous success with the current buy model, MiR is trying to make it even easier to work with this emerging technology. That drove them to the RaaS model.
“We think a leasing option will allow companies that are still trying to understand the use cases for the technology to get in quicker, and then slowly scale the business up as they learn how to apply it and what the sweet spots are for autonomous mobile robots. The lease option is intended to reduce the cost of entry. Today it’s mainly the bigger multinationals that are buying, but we believe by providing options for lower entry points, this will make the use cases in the small-to-midsized companies come to light.”
He says a third-party company will handle all the leases. MiR’s distributor network will engage with the third-party company to put together lease programs for customers.
MiR has also implemented a Preferred System Integrator (PSI) program to augment the existing network of distribution partners. Two and a half years ago, it was mainly large companies investing in these mobile platforms. They were purchasing in volumes of one to five robots. Today, they’re seeing investments of 20, 30, or even more than 50 robots.
“When you get into these bigger deployments, it’s more critical to have companies that are equipped to handle them. Our distribution partners are set up as a sales channel. Although most of them have integration capabilities, they don’t want to invest in deploying hundreds of robots at one time. They rather hand that off to a company that’s able to properly support large-scale deployments.”
Over the last couple of years, MiR had been focused on bringing more efficiency to the manufacturing process; not necessarily replacing existing AGVs and forklifts.
“For example, you have a guy that gets paid a healthy salary to sit in front of a machine tool and use his skills to do a certain task. That’s what makes the company money. But when he has to get up and carry a tray of parts to the next phase in the production cycle, that’s inefficient. That’s what we’ve been focusing on, at least with our MiR100 and MiR200 (pictured).”
Technologies, an Indiana-based company specializing in custom plastic injection molding and mold tooling. The mobile robot loops the shop floor, autonomously transporting finished product from the presses to quality inspection. This frees up personnel for more high-value tasks and eliminates material flow bottlenecks.
“With the new MiR500, we’re going after heavier loads and palletizer loads. That’s replacing standard AGVs and forklifts. We’re also starting to see big conveyor companies like Simplimatic Automation and FlexLink move to a more flexible type of platform with autonomous mobile robots.
“Parallel to the hardware is our software. A key part of our company is the way we develop the software, the way we allow people to interface with the product. We’re continuously making it more intuitive and easier to use.”
MiR offers two software packages, the operating system that comes with the robot and the fleet management software that manages two or more robots. The latter is not a requirement, but Mullen says most companies are investing in it to get additional functionality when interfacing with their enterprise system. The newest fleet system is moving to a cloud-based option.
Hardware and software updates are all handled through MiR’s distribution channel and Mullen doesn’t think any of that will change under the lease option.
“The support model will stay the same. Our distributors are all trained on hardware updates, preventative maintenance and troubleshooting. I firmly believe the major component to our success today is our distribution model.”
Mullen says he’s looking forward to new products coming out in 2019. MiR is also hiring. They expect to double their employee count in the Americas and globally.
High-Tech, Short-Term Need
It’s many of these feisty startups that we’re seeing adopt nontraditional models like RaaS. But stalwarts are coming on board, too.
Established in 1992, RobotWorx is part of SCOTT Technology Ltd., a century-old New Zealand-based company specializing in automated production, robotics and process machinery. RobotWorx joined the SCOTT family of international companies in 2014 and recently completed a rigorous audit process to become an RIA Certified Robot Integrator.
RobotWorx buys, reconditions and sells used robots, along with maintaining an inventory of new robotic systems and offering full robot integration and training services. Rentals are nothing new to them. They’ve been renting robots for several years, before it was a trend. But in response to the upswing in industry requests of late, RobotWorx rolled out a major push on their rental program this past spring.
“We’ve done a lot with the TV and film industry,” says Tom Fischer, Operations Manager for RobotWorx in Marion, Ohio. “If you’ve seen the latest AT&T commercial, there are blue and orange robots in it. We rented those out for a week.”
Dubbed “Bruce” and “Linda” on strips of tape along their outstretched arms, these brightly colored robots have a starring role in this AT&T Business commercial promoting Edge-to-Edge Intelligence? solutions. Fischer says companies in this industry usually select a particular size of robot, typically either a long-reach or large-payload material handling robot, like the Yaskawa Motoman long-reach robots in this AT&T commercial.
Ever wonder if the robots in commercials are just there for effect? It turns out, not always. Fischer says these are fully functioning robots. AT&T’s ad agency must have a robot wrangler off camera to keep Bruce and Linda in line. However, the other robots in the background are the result of TV magic.
“We basically just sent them the robots,” says Fischer. “They did what they wanted to do with them and then sent them back.”
For quick gigs like this commercial, or maybe a movie cameo or even a tradeshow display, rental robots make sense. But how do you know when it’s better to rent or buy?
“We’ll do a cost analysis with the customer,” says Fischer. “We have an ROI calculator on our website if they want to see what their long-term commitment capital investment would be. (Check out RIA’s Robot ROI Calculator). We also look at it from the standpoint that if they have a long-term contract with somebody, their return on investment is going to be a lot better with a purchase. If they think they’re only going to use the robot for six months, it doesn’t make sense for them to buy it.”
Rent-A-Cell
RobotWorx rents robots by the week, month or year. A week is the minimum, but there’s no long-term commitment required. A rental includes a robot, the robot controller, teach pendant and end-of-arm tooling (EOAT). Robot brands available include ABB, FANUC, KUKA, Universal Robots, and Yaskawa Motoman.
They also rent entire ready-to-ship robot cells for welding or material handling. The most popular systems are the RWZero (pictured) and RW950 cells.
“The RWZero cell is very basic,” says Fischer. “You have a widget and you need 5,000 of them. Rent this cell and you have a production line instantly.”
The RW950 is more portable. Fisher calls it a “pallet platform.” The robot, controller, operator station and workpiece positioner all share a common base, which is basically a large steel structure that can be moved around with a forklift whenever needed. See the RW950 Welding Workcell in action.
“We’ve done a lot of the small weld cells,” he says. “We always have a couple on hand so we can supply those on demand. We’ve done larger material handling cells, as well.
“We have a third-party company that does the financing if you need it. A lot of people just end up paying it upfront. If they were to purchase the robot after they’ve rented it, we apply that towards the purchase as well.”
Fischer says 20 percent of the rental price is credited to the purchase if a customer decides to keep the robot. All the robots and robotic cells are up to date on maintenance before they leave the RobotWorx floor and shouldn’t require any major maintenance for at least a year. He says most customers end up buying the robot if their rental period exceeds a year.
Time is not always the deciding factor under the RaaS model. As robotic systems become easier to deploy and redeploy, the idea of robots as a service will gain more permanence as a long-term solution. In the future, robotics in our workplaces and homes will be as ubiquitous as the Internet. In the meantime, we’ll keep our eyes on RaaS as it gets ready for primetime
Source : https://www.robotics.org/content-detail.cfm/Industrial-Robotics-Industry-Insights/Robots-for-Rent-Why-RaaS-Works/content_id/7665
Here Are the Top Five Questions CEOs Ask About AI – CIO
Recently in a risk management meeting, I watched a data scientist explain to a group of executives why convolutional neural networks were the algorithm of choice to help discover fraudulent transactions. The executives—all of whom agreed that the company needed to invest in artificial intelligence—seemed baffled by the need for so much detail. “How will we know if it’s working?” asked a senior director to the visible relief of his colleagues.
Although they believe AI’s value, many executives are still wondering about its adoption. The following five questions are boardroom staples:
1. “What’s the reporting structure for an AI team?”
Organizational issues are never far from the minds of executives looking to accelerate efficiencies and drive growth. And, while this question isn’t new, the answer might be.
Captivated by the idea of data scientists analyzing potentially competitively-differentiating data, managers often advocate formalizing a data science team as a corporate service. Others assume that AI will fall within an existing analytics or data center-of-excellence (COE).
AI positioning depends on incumbent practices. A retailer’s customer service department designated a group of AI experts to develop “follow the sun chatbots” that would serve the retailer’s increasingly global customer base. Conversely a regional bank considered AI more of an enterprise service, centralizing statisticians and machine learning developers into a separate team reporting to the CIO.
These decisions were vastly different, but they were both the right ones for their respective companies.
Considerations:
- How unique (e.g., competitively differentiating) is the expected outcome? If the proposed AI effort is seen as strategic, it might be better to create team of subject matter experts and developers with its own budget, headcount, and skills so as not distract from or siphon resources from existing projects.
- To what extent are internal skills available? If data scientists and AI developers are already clustered within a COE, it might be better to leave the team as-is, hiring additional experts as demand grows.
- How important will it be to package and brand the results of an AI effort? If AI outcome is a new product or service, it might be better to create a dedicated team that can deliver the product and assume maintenance and enhancement duties as it continues to innovate.
2. “Should we launch our AI effort using some sort of solution, or will coding from scratch distinguish our offering?”
When people hear the term AI they conjure thoughts of smart Menlo Park hipsters stationed at standing desks wearing ear buds in their pierced ears and writing custom code late into the night. Indeed, some version of this scenario is how AI has taken shape in many companies.
Executives tend to romanticize AI development as an intense, heads-down enterprise, forgetting that development planning, market research, data knowledge, and training should also be part of the mix. Coding from scratch might actually prolong AI delivery, especially with the emerging crop of developer toolkits (Amazon Sagemaker and Google Cloud AI are two) that bundle open source routines, APIs, and notebooks into packaged frameworks.
These packages can accelerate productivity, carving weeks or even months off development schedules. Or they can exacerbate collaboration efforts.
Considerations:
- Is time-to-delivery a success metric? In other words, is there lower tolerance for research or so-called “skunkworks” projects where timeframes and outcomes could be vague?
- Is there a discrete budget for an AI project? This could make it easier to procure developer SDKs or other productivity tools.
- How much research will developer toolboxes require? Depending on your company’s level of skill, in the time it takes to research, obtain approval for, procure, and learn an AI developer toolkit your team could have delivered important new functionality.
3. “Do we need a business case for AI?”
It’s all about perspective. AI might be positioned as edgy and disruptive with its own internal brand, signaling a fresh commitment to innovation. Or it could represent the evolution of analytics, the inevitable culmination of past efforts that laid the groundwork for AI.
I’ve noticed that AI projects are considered successful when they are deployed incrementally, when they further an agreed-upon goal, when they deliver something the competition hasn’t done yet, and when they support existing cultural norms.
Considerations:
- Do other strategic projects require business cases? If they do, decide whether you want AI to be part of the standard cadre of successful strategic initiatives, or to stand on its own.
- Are business cases generally required for capital expenditures? If so, would bucking the norm make you an innovative disruptor, or an obstinate rule-breaker?
- How formal is the initiative approval process? The absence of a business case might signal a lack of rigor, jeopardizing funding.
- What will be sacrificed if you don’t build a business case? Budget? Headcount? Visibility? Prestige?
4. “We’ve had an executive sponsor for nearly every high-profile project. What about AI?”
Incumbent norms once again matter here. But when it comes to AI the level of disruption is often directly proportional to the need for a sponsor.
A senior AI specialist at a health care network decided to take the time to discuss possible AI use cases (medication compliance, readmission reduction, and deep learning diagnostics) with executives “so that they’d know what they’d be in for.” More importantly she knew that the executives who expressed the most interest in the candidate AI undertakings would be the likeliest to promote her new project. “This is a company where you absolutely need someone powerful in your corner,” she explained.
Considerations:
- Does the company’s funding model require an executive sponsor? Challenging that rule might cost you time, not to mention allies.
- Have high-impact projects with no executive sponsor failed? You might not want your AI project to be the first.
- Is the proposed AI effort specific to a line of business? In this case enlisting an executive sponsor familiar with the business problem AI is slated to solve can be an effective insurance policy.
5. “What practical advice do you have for teams just getting started?”
If you’re new to AI you’ll need to be careful about departing from norms, since this might attract undue attention and distract from promising outcomes. Remember Peter Drucker’s quote about culture eating strategy for breakfast? Going rogue is risky.
On the other hand, positioning AI as disruptive and evolutionary can do wonders for both the external brand as well as internal employee morale, assuring constituents that the company is committed to innovation, and considers emerging tech to be strategic.
Either way, the most important success measures for AI are setting accurate expectations, sharing them often, and addressing questions and concerns without delay.
Considerations:
- Distribute a high-level delivery schedule. An unbounded research project is not enough. Be sure you’re building something—AI experts agree that execution matters—and be clear about the delivery plan.
- Help colleagues envision the benefits. Does AI promise first mover advantage? Significant cost reductions? Brand awareness?
- Explain enough to color in the goal. Building a convolutional neural network to diagnose skin lesions via image scans is a world away from using unsupervised learning to discover unanticipated correlations between customer segments. As one of my clients says, “Don’t let the vague in.”
These days AI has mojo. Companies are getting serious about it in a way they haven’t been before. And the more your executives understand about how it will be deployed—and why—the better the chances for delivering ongoing value.
Source : https://www.cio.com/article/3318639/artificial-intelligence/5-questions-ceos-are-asking-about-ai.html
Augmented reality , the state of art in the industry- Miscible
Miscible.io attended The Augmented World Expo in Europe / Munich , October 2018, here is my report.
What a great #AWE2018 show in Munich, with a strong focus on the industry usage and, of course , the german automotive industry was well represented. Some new , simple but efficient, AR devices , and plenty of good use cases with a confirmed ROI. This edition was PRAGMATIC.
Here are my six take aways from this edition. Enjoy it !
1 – The return of investment of the AR solutions
The use of XR by automotive companies, big pharma, and teachers confirmed some good ROI with some “ready to use” solutions, especially in this domains :
- Better Remote guidance/collaboration with simple AR tools : Vuforia Chalk, Xmreality, Reflekt , Bridge , dynamic 365 and many more.
- More efficient quality control or mounting process with an AR assistance ( For an easy approach, the Vuzix Blade glasses and the Skylight AR platform is a good combination)
- Cost efficient and better quality of trainings with the AR contents (Reflekt use case with Bosch )
2 – This is still the firstfruits of AR and some improvements are expected for drawbacks
- Hardware : field of view, contrast/brigtness , 3D asset resolutions
- Some AR headset are heavy to wear, it can have some consequences on the operator confort and security.
- Accuracy between virtual and reality overlay / recognition
- Automation process from Authoring software to build an end user solution.
3 – Challenge of the Authoring
To create specific and advanced AR Apps, there is still some challenges with the content authoring and with the integration to the legacy systems to retrieve master data and 3D assets. Automotized and integrated AR app need some ingenious developments.
An interesting use case from Boeing ( using hololens to assist the mounting of cables) shows how they did to get an integrated and automatized AR app. Their AR solution architecture in 4 blocks :
- A web service to design the new AR app (UX and workflow)
- A call to legacy systems to collect Master Data and 3D data / assets
- Creation of an integrated Packaged data = asset bundle for the AR
- Creation of the specific AR app (Vuforia / Unity) , to be transfered to the stand alone system, the Hololens glass.
4 – concept of 3D asset as a master data
The usage of AR and VR becomes more important in many domains : From conception to maintenance and sales (configurator, catalogs …)
The consequence is that original CAD files can be transformed and used in different processes of your company, where it becomes a challenge to use high polygon from CAD applications into other 3D / VR / AR applications, where there is a need of lighter 3D assets, also with some needs of texture and rendering adjustment.
gIFT can be a solution , glTF defines an extensible, common publishing format for 3D content tools and services that streamlines authoring workflows and enables interoperable use of content across the industry.
The main challenge is to implement a good centralised and integrated 3D asset management strategy, considering them as important as your other key master data.
5 – service company and expert to design advanced AR / VR solutions , integrated in the enterprise information system.
The conception of advanced and integrated AR solutions for large companies needs some new expert combining knowlegde in 3D apps and experience in system integration.
This projects need new types of information system architecture taking in account the AR technologies.
PTC looks like a leader in providing efficient and scalable tools for large companies. PTC, owner of Vuforia is also exceling with other 3D / PLM management solutions like windchill , to smoothly integrate 3D management in all the processes and IT of the enterprise.
Sopra Steria , the french IS integration company, is also taking this role , bringing his system integration experience into the new AR /VR usages in the industry.
If you don’t want to invest in this kind of complex projects, for a first step in AR/VR or for some quick wins at a low budget , new content authoring solutions exist to build your AR app with some simple user interfaces and workflows : skylight by Upskill , worklink by Scope AR
6 – The need for an open AR Cloud
“A real time 3D (or spatial) map of the world, the AR cloud, will be the single most important software infrastructure in computing. Far more valuable than facebook social graph, or google page rank index” say Ori Inbar, Co-Founder and CEO of Augmented Reality.ORG. A promising prediction.
The AR cloud provide a persistant , multiuser and cross device AR landscape. It allows people to share experiences and collaborate. The most known AR cloud experience so far is the famous Pokemon Go game.
So far the AR map works using GPS or image recognition, or local point of cloud for a limited space / a building. The dream will be to copy the world as a point of cloud, for a global AR cloud landscape. A real time systems that could be used by robots, drones etc…
The AWE exhibition presented some interesting AR cloud initiative :
- The Open AR Cloud Initiative launched at the event and had its first working session.
- Some good SDK are now available to build your own local AR clouds : Wikitude an Immersal
Source : https://www.linkedin.com/pulse/augmented-reality-state-art-industry-fr%C3%A9d%C3%A9ric-niederberger/
Edge Computing Emerges as Megatrend in Automation – Design News
Edge computing technology is quickly becoming a megatrend in industrial control, offering a wide range of benefits for factory automation applications. While the major cloud suppliers are expanding, new communications hardware and software technology are beginning to provide new solutions compared to the previous offerings used in factory automation.
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| A future application possibility that illustrates both the general concept and potential impact of edge computing in automation and control is edge data being visualized on a tablet in a brownfield application. (Image source: B&R Industrial Automation) |
“The most important benefit [compared to existing solutions] will be interoperability—from the device level to the cloud,” John Kowal, director of business development for B&R Industrial Automation, told Design News. “So it’s very important that communications be standards-based, as you see with OPC UA TSN. ‘Flavors’ of Ethernet including ‘flavors’ of TSN should not be considered as providing interoperable edge communications, although they will function perfectly well in a closed system. Interoperability is one of the primary differences between previous solutions. OPC UA TSN is critical to connecting the edge device to everything else.”
Emerging Technology Solutions
Sari Germanos of B&R added that these comments about edge computing can also be equally applied to the cloud. “With edge, you are using fog instead of cloud with a gateway. Edge controllers need things like redundancy and backup, while cloud services do that for you automatically,” Germanos said. He also noted that cloud computing generally makes data readily accessible from anywhere in the world, while the choice of serious cloud providers for industrial production applications is limited. Edge controllers are likely to have more local features and functions, though the responsibility for tasks like maintenance and backup falls on the user.
Factory Automation Applications
Kowal noted that you could say that any automation application would benefit from collecting and analyzing data at the edge. But the key is what kind of data, what aspects of operations, and what are the expectations of analytics that can deliver actionable productivity improvements? “If your goal is uptime, then you will want to collect data on machine health, such as bearing frequencies, temperatures, lubrication and coolant levels, increased friction on mechanical systems, gauging, and metrology,” he said.
Some of the same logic applies to product quality. Machine wear and tear leads to reduced yield which can, in turn, be defined in terms of OEE data gathering that may already be taking place, but will not be captured at shorter intervals and automatically communicated and analyzed.
Capturing Production Capacity as well as Machine and Materials Availability
Beyond the maintenance and production efficiency aspects, Kowal said that users should consider capturing production capacity, machine and raw material availability, and constraint and output data. These will be needed to schedule smaller batch sizes, tier more effectively into ordering and production scheduling systems, and ultimately improve delivery times to customers.
Edge control technology also offers benefits compared to IoT gateway products. Kowal said that he’s never been big on splitting hairs with technology definitions—at least not from the perspective of results. But fundamentally, brownfield operators tend to want gateways to translate between their installed base of equipment, which may not even be currently networked, and the cloud. Typically, these are boxes equipped with legacy communications interfaces that act as a gateway to get data from the control system without a controls retrofit, which can be costly, risky, and even ineffective.
“We have done some work in this space, though B&R’s primary market is in new equipment,” Kowal added. “In that case, you have many options how to implement edge computing on a new machine or production line. You can use smart sensors and other devices direct to cloud or to an edge controller. The edge controller or computing resource can take many form factors. It can be a machine controller, an industrial PC that’s also used for other tasks like HMI or cell control, a small PLC used within the machine, or a standalone dedicated edge controller.”
Boosted Memory, Processing, and Connections
Germanos noted that industrial controllers were not designed to be edge controllers; they are typically designed to control one machine versus a complete production line. Edge controllers have built-in redundancy to maintain production line operation.
“If I was designing a new machine, cell, line, or facility, I would set up the machine controllers as the edge controller/computers rather than add another piece of control hardware or gateway,” Germanos said. “Today, you can get machine controllers with plenty of memory, processing power, and network connections. I would not select a control platform unless it supports OPC UA, and I would strongly urge selecting a technology provider that supports the OPC UA TSN movement known as “The Shapers,” so that as this new standard for Industrial Ethernet evolves, I would be free from the ‘flavors’ of Ethernet.”
His recommendation is to use a platform that runs a real-time operating system for the machinery on one core or, using a Hypervisor, whatever other OS might be appropriate for any additional applications that run on Windows or Linux.
Source : https://www.designnews.com/automation-motion-control/edge-computing-emerges-megatrend-automation/27888481159634
Steps to Successful Machine Shop Digitalization – Modern Machine Shop
Industry 4.0. Industrial Internet of Things (IIoT). Smart manufacturing. The buzzwords abound, but what does this technology really mean for your shop?
“Whether your business is small or large, you should know that digital manufacturing is coming, and it’s coming really quickly,” says Sean Holt, president of Sandvik Coromant for the Americas. “You have to assess how it’s going to affect the sustainability of your business, what are its risks, its benefits, and most importantly, how to take the first steps towards digitalization.”
“Who cares?” you might be thinking. “I just want to make good parts, on-time, and for a fair price. That’s what’s most important to me.”
Consider this: any seasoned machinist or programmer can walk up to a machine and know instantly if something is awry. There’s just one problem: finding those qualified people is increasingly arduous, and most shops need their operators to manage multiple machines. It would be a huge advantage to have another way to know that the parts being made on Machine #5 right now are about to go out of tolerance, and further, that the spindle bearings on VMC #2 will fail in three weeks.
The path to those capabilities is data; cutting tool data, machine data, quality data, operator productivity data. It may sound simplistic, but that’s the essence of Industry 4.0 and the Internet of Things: the collection and analysis of data, followed by better decision-making as a result of these data-related efforts. To the shop of the future, data will be everything.
That’s why many equipment builders, and now tooling suppliers, are making their products “smart,” giving manufacturers the ability to “listen” to what the shop floor is telling them with data that is easy to understand.
In a controlled machining process where decisions and actions are based on real-time information and facts, manufacturers are able to optimize their processes and significantly reduce their manufacturing-related waste. CoroPlus® ProcessControl monitoring system—including hardware, software, installation and support—increases the overall in-cut process stability and security, ultimately enabling increased productivity and profitability.
Shops that embrace digitalization will have much better information with which to operate their businesses because:
- Their machines and cutting tools will be able to identify chatter (even when it can’t be heard), and part quality, and tool life will be better.
- Understanding what’s going on inside spindle bearings, axis motors and other machine components will prevent unexpected equipment failures.
- Knowing exactly how hard to push cutting tools will increase production levels.
- The ability to more easily spot trends in part quality and tool wear will help machinists and engineers develop better processes.
Start Small
Getting started is easier and less expensive than you probably expect—you won’t need a big budget ($1,000 or so should do), or the technical skills of a computer scientist. “I tell people to start small,” says Andy Henderson, vice-president of engineering at industrial technology firm company Praemo. “Hook up one machine, start collecting some data, and then let the value you’re receiving from that machine pay for the next one, and the next, scaling upwards as you go.”
At the very least, getting your machine tools “connected” will let you check production status from anywhere. Taken to the next level, you can gather hundreds or even thousands of data points from a modern machine tool, including in-process metrology data, machine maintenance information, production output, scrap levels, cutting tool usage, job status…the list goes on and on.
But don’t do that. At least, not yet. Better to pick a pilot machine, choose one or two of whichever production values or machine metrics are most important to you, and start watching the data flow in. You’ll soon spot causes of downtime that are expensive to the shop but simple to cure. Areas for continuous improvement will become abundantly clear. Unexpected failures will eventually become a thing of the past.
Additional benefits accrue as shops move up the scale of digitalization. Simply finding out why your equipment isn’t running is a good start. Ultimately, it lets shops focus sharply on the best value-adding activities reaping the highest returns on equipment and people.
Worried about the cost? Don’t be. According to Will Sobel, co-founder and chief strategy officer of advanced manufacturing analytics software company VIMANA, the ROI can be “amazingly ridiculous,” sometimes as short as a few weeks. “If you look at a typical manufacturing processes in a typical shop, equipment utilization is often around 30-percent,” he points out. “It doesn’t take much to improve that figure.”
Move Rapidly
Stas Mylek, Mastercam developer, CNC Software’s director of product management, says those looking for a quick win should consider purchasing monitoring software. “There are plenty of applications out there that you can get for minimal investment and that make the traditional green, yellow, and red indicator lights obsolete,” he says. “Using such an application to collate and make sense of data allows you to better understand your processes and where each machine is making money.”
A connected shop provides actionable intelligence to all levels of the enterprise on
how to manufacture more efficiently.
Having good monitoring software is one thing; acting on that information is another. And shops will be well served by appointing a data evangelist (or team, depending on the size of the company) to chase down improvement opportunities. This person will work with suppliers, report back to management, and work to spread the good word of digitalization throughout the organization.
“There goes the budget,” you may say. And while it’s true that taking your IIoT data collection pilot project to the next level will cost the company some cash—in infrastructure, hardware and software, and additional labor costs—it’s important to remember that the additional visibility to production and machine tool data, and the benefits derived from both, will greatly outweigh any investment costs.
That’s not to say that the appointed data evangelist should pound people on the head with his or her findings. For one thing, this person will typically have less manufacturing skill and experience than the machinists, programmers, and engineers responsible for part production each day—a talented but technically-oriented machine operator is a good choice for such a role, one able to communicate effectively while recognizing that the people he or she is working with may be reluctant to change their ways.
And there is still a vital role for your experienced people to play. Software doesn’t make great choices when comparing different solutions. That’s why humans will always be better (for the foreseeable future, at least) about when to shut a machine down, for example, or the best way to adjust feeds and speeds when chatter occurs. This is why involvement from the entire manufacturing team is crucial to any Industry 4.0 implementation.
Ready to pull the trigger? All it takes is a connected machine tool, a little data and willingness to change. Properly implemented, the results will be greater throughput and higher profit margins. Get going. Industry 4.0 is waiting
Source : https://www.mmsonline.com/articles/steps-to-successful-machine-shop-digitalization-think-big
VB – Tech startups raise big bucks to help companies cut waste
Society has become somewhat accustomed to disposable goods, be it cheap garments, budget phones, or plastic packaging.
But with Earth facing untold apocalyptic catastrophes in the decades to come, there has been a growing push to do something — anything — to counter the predicted cataclysmic events that await us.
A few months back, Seattle became the first major U.S. city to ban single-use disposable straws, while England could become the first country to ban them next year. Starbucks, meanwhile, will usher out plastic straws across all its stores globally by 2020.
These are small measures by anyone’s standard, but they feed into a broader trend that’s striving to cut waste and reduce our dependency on disposable goods.
Food, in particular, is one area where we’re seeing this trend amplified, with big-name investors lining up for their piece of the waste-cutting pie.
Food for thought
Earlier this month, Santa Barbara-based Apeel Sciences raised a whopping $70 million from U.S. hedge fund Viking Global Investors, Andreessen Horowitz, Upfront Ventures, and others. This took the company’s total funding to $110 million.
So what is Apeel doing to reduce food waste, exactly? Well, it essentially applies a second layer of skin to fruit and vegetables to reinforce protection and prolong their shelf life by reducing water loss and oxidation. The company said that produce that has been given the Apeel treatment typically stays fresher for up to three times longer.
Above: Apeel Sciences applies a second layer of skin to fruit and vegetables to prolong their shelf life.
The funding came just a few months after Apeel Sciences commercialized its product via avocados at Costco and Harps Food Stores in the U.S., which it said led to a 65 percentage-point margin increase and a 10 percent sales increase in Hass avocados.
“As Apeel products continue to hit the shelves, the retail world is now beginning to experience what was clear from day one, which is that Apeel is a product with the potential to change the world,” said Yves Sisteron, founder and managing partner at Upfront Ventures, which first invested in Apeel Sciences as part of its $5.8 million series A round back in 2014.
Earlier this week Swedish startup Karma raised $12 million for a marketplace that helps restaurants and supermarkets cut food waste by selling their surplus goods at a discount. Investors included Swedish investment firm Kinnevik, with participation from Bessemer Venture Partners (one of the oldest venture capital firms in the U.S.), Electrolux, and E.ventures.
The premise behind Karma is simple. The consumer creates an account and can see what’s available in their area — the offerings are whatever food outlets have an excess of, so there won’t necessarily be a consistent choice of goods each day. But if you’re not fussy and all you’re looking for is a good discount, then you may find cakes, bread, sandwiches, freshly squeezed lemonade, and pretty much anything else.
Above: Karma lunch offerings in London.
The problem that Karma is looking to fix is this: Roughly one third of food produced globally each year never reaches a human mouth, according to the United Nations’ Food and Agriculture Organization. That’s $1 trillion worth of edible food ending up in a landfill.
Karma is available across Sweden, while it also recently launched in London, its first international market. But with a fresh $12 million in the coffers, it’s planning to launch into more international markets across Europe and the U.S.
Both these startups show that while ethical concepts are attractive to investors, you’re not going to get anywhere on altruism alone: Your idea and execution needs to be underpinned by a solid business.
“While the Karma team is really going after a good cause, we share a very fundamental belief with the founders: to have a lasting and meaningful impact, companies around sustainability need to be for-profit and have an attractive business model,” said E.ventures partner Jonathan Becker.
Elsewhere in the culinary realm, Full Harvest this week raised $8.5 million in a series A round of funding led by Spark Capital. The San Francisco-based startup offers a B2B marketplace that helps farmers sell surplus and imperfect goods to food and beverage companies.
Above: A… deformed apple?
Up to 40 percent of food in the U.S. goes uneaten each year, according to the Natural Resources Defense Council, and a big part of this problem is that grocery stores and supermarkets don’t want to buy ugly fruit and vegetables because, well, consumers don’t want to buy them either. But a wonky apple is every bit as nutritious as an aesthetically pleasing apple, which is how Full Harvest manages to find a market that connects farms with food buyers. The company already works with a number of U.S. food and drink companies, and it has claimed that it helped one U.S. farm grow its profits by 12 percent per acre.
“A ReFed report has stated that $10 billion invested into solving food waste will bring $100 billion of value to society due to true cost accounting across the entire supply chain, economy and environment,” the company said in a statement. “We also have sold close to 7 million pounds of produce that would otherwise have gone to waste, which is equivalent of preventing 430 million gallons of water from going to waste — enough to provide drinking water for 8 million people for a year — and 2.5 million kilograms of CO2e emissions from being produced.”
But it’s not just the food industry that’s seeking traction in the waste-cutting world.
Factory reset
Last week, London-based Unmade raised a modest $4 million in a round of funding led by Felix Capital. The Techstars London alumnus develops a software platform for fashion brands to offer customizable clothes directly to consumers.
Above: Unmade: Customizing a sweater on Farfetch’s website.
The on-demand model not only allows fashionistas to fine-tune patterns or mix colors around, but it also promises a more sustainable business. Clothes are not created in bulk before demand is established — each item is effectively made-to-order, thus cutting down on waste. The company said that it now works with three of the top fashion brands in the U.S.
This kind of manufacturing potentially has a big future — last year Amazon was awarded a patent for a similar system that’s capable of producing products, including garments, after an order is placed.
On-demand product manufacturing means that supply meets demand rather than surpasses it, and ensures a bunch of perfectly wearable goods do not end up in the trash.
“By conservative estimates, 10 percent to 25 percent of all clothes made each season are never sold and go to landfill or are burned — after travelling through a network of stores and discount retailers,” added Unmade cofounder and chief product officer Ben Alun-Jones. “When you think the fashion industry today is at least $2.4 trillion, this is a huge environmental issue. Our mission is to transform the current business model of the fashion industry, one that frequently leads to significant overproduction and waste, and start to create clothing that is either tailored by or made for the consumer.”
The made-to-order model could also have an impact on return rates, thus reducing waste even further — if a customer has played an active role in designing their garment, they may feel more attached to it when it arrives.
“With our current customers, we have also seen a big reduction in return rates once they start using our platform,” Alun-Jones continued.
Last month, Atomico — the VC firm founded by Skype co-creator Niklas Zennström — led a $10 million investment in Oden Technologies. The London-based company serves up the hardware and software for manufacturers to track faults and establish patterns that may affect their factory equipment performance. It’s all about analytics and big data.
While the chief driving force behind Oden Technologies centers on improving efficiency and cutting costs, firmly embedded in this business model is the need to reduce waste. Manufacturing facilities can waste millions of dollars worth of materials each year due to factors such as “variation and imprecise specifications,” something that Oden says it solves.
By improving operational processes on the factory floor, Oden claims it can detect inefficiencies and issues “up to 95 percent faster” and cut waste by “hundreds of thousands of dollars” each year.
And this is what major VC firms like Atomico are investing in: “Industry 4.0,” which includes big data, artificial intelligence, and robotics. It’s about digitizing the $12 trillion manufacturing industry to make it more efficient — which means less waste.
“We believe that the global manufacturing industry is on the brink of a new machine age; one in which the industrial Internet of Things and cloud analytics, coupled with machine learning and artificial intelligence, are set to transform existing production processes, slash waste, drive incredible efficiencies and increase output,” Atomico said at the time of its investment.
We’re seeing shifts in this direction across the industrial spectrum. While the world prepares for self-driving cars to infiltrate its highways, some industries are already embracing autonomous driving technology.
Sugarcane is among the largest crop globally in terms of quantity produced. During collection, large trucks normally drive beside the harvester at low speed to take the sugarcane off-site. However, up to four percent can be lost as the truck tramples fledgling crops, which is due to driver error. As such, Volvo revealed last year that it was trialing self-steering trucks to help sugarcane farmers improve crop yield — drivers don’t have to worry about keeping the truck in a straight line as the steering is all automated.
Above: Volvo self-steering truck.
Whether it’s moving away from disposable straws, ensuring food doesn’t end up in a landfill, confirming every piece of clothing has a buyer before it’s made, or minimizing factories’ excess material burn rate, the goal is the same: Cutting waste will play a major part in the future of our planet. Crucially, this also means improving a company’s bottom line, which will be pivotal in garnering buy-in from more companies.
“Waste inherently means something of no value or of no use,” Unmade’s Alun-Jones added. “Removing that from manufacturing makes good business sense and is clearly of financial value. Because the scale of waste in some very large industries is so massive, this is clearly a big opportunity for startups.”
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