Journey to Industry 4.0 and Beyond with Cognitive Manufacturing -Taiwan computer assoc

Editor's Notes

• #2: ABB and IBM Partner in Industrial Artificial Intelligence Solutions https://youtu.be/-4CazFUHgDsIBM and SNCF: French Railways Operator Accelerates Innovation with Watson IoT https://youtu.be/25ec2HP45dI(Chinese caption ready)Schaeffler: Digital transformation to keep the world moving https://youtu.be/nx4dygJY_7U(Chinese caption ready)
• #3: Changing Organizational Knowledge Seeing into Manufacturing Quality Making Workers Safer Aiding the Elderly Changing Patient Experience Extending Research Dollars Inspiring Human Performance Improving Retailing Easing Regulatory Compliance Making Marketing Smarter
• #6: https://www.mediapost.com/publications/article/291358/90-of-todays-data-created-in-two-years.html
• #7: Reduce text on slide as part of your comfort level – delete the analytics pieces – addresses (set one); improves (set two)
• #9: Let’s drill into this a little bit. The establishment activities are about corpus and data development – connecting facts and bringing them together in a way you can study them more effectively. Having done so allows you to make the transition to understanding ACROSS processes and being able to deeply connect and configure entire systems, not simply machines
• #10: 70 Miles From Shore with Watson: Woodside Energy and IBM https://youtu.be/GFZ2IaTVkY8(Chinese caption ready) https://www.youtube.com/watch?v=GFZ2IaTVkY8 Key players and their roles: The executive sponsor of the project was the company’s senior vice president (SVP) of strategy, science and technology, who, as head of the internal data science department, has been a strong internal champion of big data within the enterprise. The project was funded by the company’s internal data science department. The IT organization played an extremely limited role in the deployment.Client question: What are the relative roles of the lines of business (LOBs) and IT in driving big data initiatives?  Driving change to achieve success: Although cognitive computing simplified the knowledge drill-down process, training still proved crucial in making the project a success. The training program was designed and conducted by a mix of company personnel and the IBM engagement team, with the majority of training delivered electronically through a series of step-by-step tutorials. Training involved hundreds of hours of domain subject matter experts (SMEs) comprising oil and gas engineering in addition to project personnel reviewing questions and potential answers. When SMEs disagreed, a third, more senior, SME made a decision. Client question: What are the primary training challenges of implementing self-service access to technical information?  Client insight: An important institutional goal of the project is to create more flexibility within the workforce. The basic goal is to make the company less dependent on narrow centers of expertise that threaten to constrain its growth. Expertise was still considered important. But by putting in place a cognitive front end to institutional knowledge, the company would allow employees, in essence, to “wear more hats.” One of the key cultural challenges of democratization of knowledge was to convince employees to venture outside their traditional comfort zones while ensuring the company still capitalizes on its SMEs. The cultural impact can be significant. It appears that some of the field engineers were not happy to embrace the new technologies, possibly because they feared the algorithm might replace them.Client question: What are the cultural and organizational challenges of “democratizing” access to technical and engineering data and lessening reliance on SMEs?
• #12: https://www.youtube.com/watch?v=S6AgywG5s6U&feature=youtu.be https://www.ibm.com/blogs/internet-of-things/cognitive-manufacturing/ The transportable robotic arm’s hardware wasn’t specially modified for the task, but was a standard industrial product shipped directly from the manufacturer. It had six joints and was able to move very precisely with very fine control. There was a small metal collar, like that for a drill bit, at the tip of the arm, and the brush was inserted into it and the collar screwed down to hold the brush tightly. Once the robotic arm was calibrated so the tip of the pen was set at the correct height above the table, the location, in three dimensions, of the tip of the brush could be precisely known and controlled. “The uniqueness was in the software,” Stanford-Clark observes.
• #13: https://www.youtube.com/watch?v=mhh0O_35POc
• #14: IBM Watson IoT: Cognitive Visual Inspection https://youtu.be/KLnqPuR3nWI
• #16: IBM and KONE: Watson IoT Gives Lift To Innovation In Smart Buildings https://youtu.be/EVbd3ejEXus(Chinese caption ready) https://www.youtube.com/watch?v=EVbd3ejEXus Kone is using the IBM Watson IoT Platform to connect, remotely monitor, and optimize its management of millions of elevators, escalators, doors, and turnstiles in buildings and cities worldwide. The system analyzes vast amounts of data from sensors embedded in equipment, helping to identify and predict issues, minimize downtime, and personalize the experience for users. Instead of having to call in a service engineer or stick to maintenance schedules, KONE can predict and respond to selected technical issues in real time, with the ability to run tests remotely and make commands over the cloud.
• #18: Schaeffler: Digital transformation to keep the world moving https://youtu.be/nx4dygJY_7U
• #19: https://www.youtube.com/watch?v=sfaI1ym5otM http://ecc.ibm.com/case-study/us-en/ECCF-WWC12366USEN Business challenge story Analytics and track cycling: a match made in heaven If you were to ask a group of statisticians what makes a football, hockey or basketball team successful, the debate might last for hours. Even in a sport like baseball, which is relatively amenable to statistical analysis, the complexity of the factors that contribute to a team’s performance mean that analytics is as much an art as it is a science. “Of all the technology projects we’ve worked on this year, the IBM jStart project has made the biggest contribution to achieving our goals.” —Andy Sparks, Director of Track Programs, US Cycling Track cycling, by contrast, is a statistician’s dream sport. Races are held indoors in a velodrome, on a flat, smooth track with no weather conditions to take into account; and the bikes have a single fixed gear, so there is no need to worry about optimal gear ratios or gear changes. As a result, there are very few significant variables that determine whether a rider or a team wins or loses, and performance can be measured using a relatively simple set of equations. Andy Sparks, Director of Track Programs for USA Cycling, explains: “The single most important factor in winning a race is the power that the riders are able to exert on the pedals. The bikes we use have a power meter on the crank that measures the power generated in Watts. The introduction of the power meter has totally transformed competitive cycling, because it lets us quantify exactly what riders need to do to achieve a certain time in events like the Women’s Team Pursuit.” Team Pursuit is a four-person cycling event in which the riders aim to complete 16 laps of a 250 meter track in the shortest possible time. Each rider takes turns to assume the “pull” position at the front of the team, while the other three members ride in their slipstream. The slipstream effect is so significant that it requires approximately 30 percent less effort to follow than it does to pull. When a rider’s pull comes to an end, they need to move to the back of the group as efficiently as possible. Even a smooth exchange slows the team down by around 0.5 seconds, and a poorly executed exchange can be very costly: if a rider falls too far back, they need to exert a significant amount of energy to catch up and get back into the slipstream. Such errors are known as “matches burned”. In a sport that is often decided by fractions of a second—for example, the USA team’s victory over Australia in the semi-finals at the London 2012 Olympics was by less than one tenth of a second—it is critical to plan and execute the pulls and exchanges to perfection. Deciding who should pull when and for how long is critical to get the best possible performance from each rider and help the team become greater than the sum of its parts. Andy Sparks explains: “The ability to measure the key factors that drive race performance gives us the ability to set targets for what we want to achieve. Actually achieving those targets is a different matter. We aim at improving our overall race times by three percent per year, of which one percent should come from the athletes’ physiological improvement, one percent from team and staff fortification, and the final one percent from technological innovation.” Neal Henderson of APEX Coaching & Consulting works for USA Cycling as a High Performance Consultant. He comments: “From the technology side, one of the most important things we do is analyze the data that the bikes’ power meters capture during our training sessions. This helps us see the wattage that each rider was producing at every stage of the race, and also calculate W-prime depletion—a measurement of how much of their anaerobic muscle capacity each rider has used up during the session and how long it takes to regenerate.” However, gathering and analyzing the data from these sensors has always been a challenge. After each training session finished, Neal Henderson had to plug the head unit of each bike into his PC, download the data, manually slice it into half-second intervals, match those intervals to the events that took place during the session (for example, when each rider was pulling, versus when they were exchanging or pursuing), and then calculate a variety of key metrics. This took at least one hour per rider—and even when all four riders’ data was ready, there was still the task of collating and comparing individual results to get a 360-degree view of team performance. Andy Sparks comments: “Neal would regularly be staying up past midnight each day during a training camp. He’s an incredibly valuable and talented coach, and we share him with several other cycling teams—so while he’s with us, we want him to be interacting with our riders, rather than spending all his time crunching the numbers on a computer.” Neal Henderson adds: “The time taken to get the data also had an impact on how effective the analysis was as a coaching tool. If you’re talking to a rider about what they did on the track yesterday, that’s much less immediate and powerful than if you can talk to them while their legs are still burning from the session!” Transformation story Finding a better solution with the Internet of Things Working with IBM jStart, the Women’s Team Pursuit team is now harnessing emerging technologies to solve its analytics challenge. Instead of manually extracting the data from the power meters and sensors after each training session, the data is automatically collected by an Android phone in the rider’s pocket, and transmitted to the cloud, where it is stored and analyzed as soon as the session finishes. Within seconds, the results are then sent back from the cloud to the coaches’ tablets, in the form of a summary dashboard that presents metrics such as W-prime depletion and matches burned in an intuitive graphical format. From a technical perspective, IBM Watson™ Internet of Things Platform acts as a cloud integration hub, receiving the data and directing it to other components of the solution. For example, the raw data from the sensors passes through a Node-RED storage flow to an IBM Cloudant® database, which is used to supply the summary dashboard, and also feed a Jupyter Notebook for more complex analysis by the team’s data scientists. Other components, which will be coming online soon, include the use of IBM Analytics for Apache Spark to calculate metrics in real time. This will allow the team’s coaches to monitor performance not only after the training session, but while it is still in progress—for example, during each exchange, the coaches will be able to see whether a match was burned. The team even plans to introduce smart glasses, which will provide a personalized head-up display of whichever key metrics are most useful for each of the riders, while they are actually on the track. Andy Sparks comments: “We always had a vision that this kind of thing was possible, and IBM jStart is helping us turn it into a reality. We have been so impressed with the jStart team’s ability to orchestrate all of these emerging technologies to build a solution that delivers exactly what we need, in seconds.” Neal Henderson comments: “The ability to get hold of the data immediately after the training session has finished has completely changed my relationship with the team. I’m spending much more time with the riders and the other coaches, and because we can all see the data instantly, it’s much easier to identify problems, make adjustments, and reinforce winning behaviors that they can take into the next session.” Results story On track for victory The team began using the solution shortly before its victory at the 2016 World Championships in London, and will continue using it at training camps in the lead up to the 2016 Olympic Games in Rio. “This year has already been one of the most successful in our history—at the World Championships, we won the qualifier by four seconds, beat the previous US record by six seconds, and took the gold medal in the final,” says Andy Sparks. “Although we only started using the IBM solution a few weeks before the event, we immediately saw its potential to help us identify and fast-track tactical and technical improvements. It’s the most important technology project we have worked on this year, and we see it as a key tool in our preparations for Rio.” Neal Henderson adds: “We always aim to train as hard as possible, to make racing as easy as possible. The solution helps us show our riders exactly how effectively they’re working, so they can see that what we’re asking them to do in competition isn’t impossible—it’s what they’ve trained for, and what they’ve achieved 100 times before in training. “The ability to instantly quantify and reinforce the positive gains made during each session really helps lower the stress of competing in a big race, and helps the riders focus on executing the performance that they already know they are capable of.” Although certain elements of the solution cannot be used during competitive races, USA Cycling believes that the dashboards could become even more valuable during the intense time-pressure of a competitive event. Neal Henderson says: “When you’re at an event, there’s only a very short window between the races—there just isn’t time to spend four or five hours pulling data together, and even if there were, the riders need some downtime instead of worrying about what happened in the last race. The big advantage of instant analytics is that we will be able to give the riders a quick debrief after the first race, advise them on tactics for the next one, and then just let them relax and recover.” Andy Sparks concludes: “The whole engagement with IBM jStart has been in line with our culture of excellence at USA Cycling. The jStart team have the same principles—everything they do is delivered to the highest possible standard, and we’re proud to be working with them to push the boundaries of what is possible in cycling technology. “Compared to other big cycling nations, our budget is very tight—we rely 100 percent on sponsorship, whereas many teams receive large amounts of government funding. Yet we’re showing that we can compete successfully at international level. IBM deserves credit for helping us train smarter and free our riders to execute successfully at the major events. Of all the technology projects we’ve worked on this year, the IBM jStart project has made the biggest contribution to achieving our goals.”
• #20: https://www.youtube.com/watch?v=S6C37zmxE6o
• #24: Value Drivers and Detractors Core Processes Desired Insights Desired Outcomes Data Inputs and Quality Metric Targets Prioritization Detail
• #27: Value Drivers and Detractors Core Processes Desired Insights Desired Outcomes Data Inputs and Quality Metric Targets Prioritization Detail