IoT and Manufacturing: You’ve Got the Data…

IoT and Manufacturing: You’ve Got the Data…

May 5, 2016

“IoT and Manufacturing: You’ve Got the Data…Now What Do You Do with it?” By Shawn Kaul, Lean Synergy International I doubt there’s a manufacturer in the industrialized world who hasn’t heard about the Internet of Things and how it’s going to drive the next industrial revolution. And it certainly could. But whether the IoT actually delivers on its promised value has little to do with how well the various sensors collect and transmit data; it has everything to do with how we use that data. And it starts with deciding which data to use. Machinery that is equipped with sensors and connected to the IoT generates mountains of data. One GE wind turbine, for example, is equipped with 20,000 sensors that generate 400 data points per second. That works out to more than one million data points per hour – just for one piece of machinery. If you think about that happening all day long, all across your factory floor, it’s easy to see how the sheer volume of data being collected could render the whole thing useless if you’re not careful. Nobody can work with and act on that much data, and a lot of people simply don’t know where to start. But it’s critically important to give careful thought to which data you’re going to pay attention to. To get the most benefit out of the IoT, ignore any data (for now, at least) that doesn’t meet these three criteria: relevant, actionable, and strategic. Relevant If you took logic or statistics courses in college, you might remember having discussions about “correlation vs. causation”. In a nutshell, it means that just because two data points are related, that doesn’t mean either one causes the other. Here are a couple of examples: The divorce rate in Maine has a 99.26% correlation with per capita consumption of margarine. And there’s a 94.7% correlation between per capita cheese consumption and the number of people who die by getting tangled in their bed sheets. Obviously, it would be absurd to suggest that there’s a causative relationship in either of those scenarios – despite the fact that, when graphed, the trends match each other almost perfectly. Your factory...

Top 3 U.S. Manufacturing Challenges (and Opportunities)…

Top 3 U.S. Manufacturing Challenges (and Opportunities)…

Jan 20, 2016

“Top 3 U.S. Manufacturing Challenges (and Opportunities) in 2016” By Scott Stone, ThomasNet Is the glass half empty or half full? It’s an age old question, and there’s no right answer; it’s all about perspective. How about this question: Does the U.S. manufacturing industry have challenges or opportunities? Once again, there’s no right answer; it’s all about perspective. American manufacturing stands at a critical point in its long history – and how industry leaders view the changing landscape will influence its future. As technology, demographics, and economic climate shift, manufacturers must innovate in order to stay competitive. Manufacturing in particular is positioned for domestic growth and, more than ever, industry leaders must guide their organizations strategically. Let’s take a look at some of the issues facing the industry through the lens of both challenges and opportunities: Manufacturing Skills Gap Challenge:Over the past several years, the skills gap has been top of mind for U.S. manufacturers. In Accenture’s 2014 Manufacturing Skills and Training Study, more than 75 percent of manufacturing respondents reported a shortage of skilled workers, particularly in positions requiring more than a high school diploma, but less than a four-year college degree. This issue isn’t going anywhere—it’s probably going to get worse over the next decade. Research from the Manufacturing Institute and Deloitte predicts there could be as many as 2 million unfilled manufacturing jobs by 2025, up from initial estimates of 600,000. Baby Boomers are aging and retiring and there are simply not enough skilled workers to fill the positions they’re leaving. Manufacturers are challenged to find a solution to keep up with demand and are turning to automation, developing partnerships with local trade schools and colleges, and implementing in-house mentoring and internship programs. Opportunity: One thing is for certain: Manufacturers can no longer afford to think about their systems, processes, and labor force in the same way. It’s time to innovate and figure out new (better!) ways of getting the job done. Whether it’s optimizing the production line, balancing work flows and labor, or incorporating new technology, it’s an opportunity to build the future of not only an organization, but the industry as a whole. As individuals and as a collective, there is the prospect...

The New Reality Of IoT For Distributors And Manufacturers

The New Reality Of IoT For Distributors And Manufacturers

Jan 11, 2016

By Ranga Bodla, Manufacturing Business Technology The emergence of intelligent, networked devices — aka the Internet of Things (IoT) — promises major change in many aspects of business. IoT is already being applied in manufacturing, distribution and logistics to do such things as monitor the environmental condition of products during shipment, send an alert when factory equipment needs maintenance, and track the speed, safety, and fuel efficiency of trucks and their drivers. Manufacturers and other businesses that aren’t paying attention today will face tough competition from competitors tomorrow. “Every day more sensors are being deployed in factories on motors to check vibrations and temperature, or on the factory line to measure production and product quality,” said Howard Heppelman, vice president and general manager for Connected Product Management at PTC, which makes an IoT platform. Heppelman, who spoke in a webinar, Creating a Winning IoT Strategy for Manufacturers, Logistics, and Service Providers, predicts that more advanced IoT applications will emerge — such as an augmented reality application that would let a factory manager walk the floor with glasses that overlays alerts and readings onto a 3D view of the same factory. In his report, The IoT Impact: Finding Your Company’s Role in the New Smart Connected World, Bill McBeath, chief research officer for ChainLink Research, called the emergence of IoT technology a “tectonic” change that will alter many aspects of business, including the very structure of industries. “We’re entering the era of the IoT, which is enabling things like remote diagnostics or predictive diagnostics,” McBeath said when he presented his findings in the Creating a Winning IoT Strategy webinar. “IoT is more than sensors. It includes substantial software components, not just in the device but up in the cloud. Internet connectivity takes things to a new level.” He foresees IoT applications that provide very precise tracking of orders and shipments, with alerts sent to the recipient if there are weather or traffic delays, or that monitor products’ temperature, vibration, or shock during storage or shipping and assigning risk factors to determine damage and liability. Maintenance may be conducted via the network as well, with fixes sent as software updates rather than technicians with a toolbox. IoT devices...

The Internet of Things Will Make Manufacturing Smarter

The Internet of Things Will Make Manufacturing Smarter

Aug 26, 2015

By Kevin O’Marah, Industry Week Manufacturing worldwide is on the cusp of a revolution. New information technologies are suddenly offering not only to make the management of manufacturing more effective, as we saw with early versions of plant and enterprise software, but the work itself smarter. Technologies based on the Internet of Things have the potential to radically improve visibility in manufacturing to the point where each unit of production can be “seen” at each step in the production process. Batch-level visibility is being replaced by unit-level visibility. This is the dawn of smart manufacturing. The transformation that it implies is huge. SCM World’s recent field survey on smart manufacturing and the Internet of Things finds that while one in five today admit their factory operations are offline completely, this will drop to near zero in five years. In fact, according to our Visibility Maturity Model (Figure 1), half of all manufacturing executives surveyed expect to have visibility across the supply chain at this unit level. Only 10% predict they’ll still be limited to single factory-level insights and control—a drop of 75% from today’s state of affairs. Smart manufacturing is about creating an environment where all available information—from within the plant floor and from along the supply chain—is captured in real-time, made visible and turned into actionable insights. Smart manufacturing comprises all aspects of business, blurring the boundaries among plant operations, supply chain, product design and demand management. Enabling virtual tracking of capital assets, processes, resources and products, smart manufacturing gives enterprises full visibility which in turn supports streamlining business processes and optimizing supply and demand. In essence, smart manufacturing is a decision-making environment. Very importantly, smart manufacturing includes proactive and autonomic analytics capabilities, making smart manufacturing an intelligent and self-healing environment. With smart manufacturing organizations can predictively meet business needs through intelligent and automated actions driven by previously inaccessible insights from the physical world. Smart manufacturing transforms businesses into proactive, autonomic organizations that predict and fix potentially disruptive issues, evolve operations and delight customers, all while increasing the bottom line. A number of leading global manufacturers—including the likes of Bosch, Cisco, FCA (Fiat Chrysler Automobiles), GE, General Mills, Harley-Davidson and Siemens—are early adopters...

Using Big Data From The IoT To Predict Machine Failure

Using Big Data From The IoT To Predict Machine Failure

Jan 7, 2015

By Aurimas Adomavicius, Manufacturing Business Technology Leveraging Big Data to predict downtime on the manufacturing floor is something we’ve been spending a lot of time on in our labs. In reality, interpreting Big Data in manufacturing is a highly complex process that involves an interconnected fleet of data producing machines, overwhelming amounts of data, transformation of data for digestion, interpretation and automation of strategic decisions to maximize uptime and profitability. Collecting Big Data The Internet of Things — a term used to describe the interconnectivity of all smart devices — in this case applies to the machines on a manufacturing floor, the computers, smartphones and tablets on desks or pockets, and everything in between. A connection and a standardized interface between all of these devices allow for the transmission and collection of large amounts of data. The standard that makes this possible in manufacturing is called MTConnect. MTConnect is an open channel of communication that provides plug-and-play interconnectivity to facilitate real-time data exchange between machines and devices using XML and HTTP. Using MTConnect’s OpenSource format, manufacturers can continuously retrieve valuable operational data from their own fleet of machines. The sort of data that is received and ultimately analyzed through MTConnect includes temperatures, revolutions per minutes on spindles and machine activity, among other things. Collecting and transforming this information is step one, but you can see where this data – and the ability to interpret this data– is going to be highly valuable from an operational viewpoint. Interpreting Big Data As it stands, the sheer amount of events and the way the data is collected makes it difficult for us to do anything with it. The industry has responded with software that helps analyze and simplify the data, allowing us to digest and contextualize what we’re seeing. After all, without context and knowing what to look for the data is largely meaningless. We’re talking about incredibly large amounts of data; a large shop with 100 machines or tools can produce terabytes of monitoring data annually. Using visualization tools similar to, for example, Kibana, which is a dynamic interface for making sense of data on an ElasticSearch database, predictive analytics can be built. With this information, you can pinpoint and forecast machine...