The Internet of Things (IoT) has emerged as something of a phenomenon in the last few years. The concept has become so popular that its impact is being felt across many industries. The healthcare industry is no exception. It is playing a leading role in this transformative process. Five years ago, the medical connectivity market was largely insignificant but has caught up rapidly. The market is expected to grow an astounding CAGR of 38% until 2020 by adopting the capabilities of the IoT. This intense growth is due to the increasing connectivity of medical devices and personal health tracking devices on the market, leading to an explosion in healthcare big data. Medical equipment, personal health and fitness trackers collect terabytes of data each day, most of which goes unutilized. In the future, the application of advanced analytics to healthcare ‘big data’ will have far-reaching implications on the industry overall, with the global healthcare analytics market expected to reach $20.8 billion by 2020.
Read More →Your Assets Are Talking; Are You Listening?
The data analytics revolution has the potential to transform the way companies organize, operate, develop talent, create value, and service their customers. The momentum is building in many companies, but only a few are reaping major rewards from their data. Companies are doing a great job connecting assets, equipment, and devices, as well as gathering all that information into distributed databases. However, the challenge lies in finding ways to listen to what the equipment is telling us and responding in useful, actionable, and intelligent ways that improve business operations and reduce costs. Effective listening enables companies to move away from reactive maintenance activities and move towards predictive maintenance activities. True success will come from breaking down the walls of silence between employees and the assets they are managing. Unfortunately, many asset management solutions gather and report data with little thought on how to support the change from reactive maintenance to predictive maintenance.
Read More →Case Study: Cyient Uses Predictive Analytics to Predict Failures in Ultra Low Temperature Freezers
Client Problem Cyient‘s client, a leading laboratory equipment manufacturing company, collects data from their Ultra Low Temperature (ULT) freezers deployed across the globe using multiple sensors. They faced challenges in managing this massive data and could therefore barely use it to identify critical failures in the freezers so that immediate maintenance action could be taken. In spite of their huge effort, maintenance was always reactive in nature and the company failed to keep the system up all the time. While the data collected from freezers gave indications of past failures and potential issues, often the delay in extracting these issues from the data resulted in increased gravity of failures. The company thus contended with greater maintenance effort and cost. The client targeted early detection of issues and the ability to predict critical failures in advance to significantly improve the reliability of these equipment and customer satisfaction.
Read More →Fog Computing and Industrial IoT
The Internet of Things (IoT) promises to enable many new business models. In industrial markets, the concepts of IoT, or Industrial IoT (IIoT), are already gaining traction. At the simplest level, for example, IIoT provides the ability to more closely monitor a production line in near real time, or manage buildings and their associated plants more efficiently. IIoT also offers the ability to radically change your whole business approach. While it is easy to grasp the basic concepts behind any IIoT implementation, if you dig a little deeper there are a significant number of decisions to make in the way it will operate. For example, a cloud platform might well be able to establish trend lines on how often a motor is switched on/off to activate a conveyor belt, but does it need to rely on the cloud platform to tell it when to turn on or off?
Read More →4 Essential Ingredients that Go Into Successful IoT Asset Tracking and Workflow Optimization Solutions
Do any of the following statements describe your situation? I don’t have a way to efficiently orchestrate our complex workflows of materials, tools, and people I can’t track the location or condition of my assets, or prevent them from getting lost or stolen I have no idea how to leverage our partner relationships and tie into their inventory systems I don’t have a good way of keeping my operational costs in check Many enterprises have implemented IoT solutions simply for technology’s sake and, after the novelty wore off, operators were left with yet one more system to learn, maintain, and pay off. Many of these solutions added unnecessary operational complexity that forced enterprises to shift focus from their core competencies. Choose an IoT tracking and workflow optimization solution that incorporates the following 4 essential ingredients: Robust and battle-tested tech; True cross-boundary asset and workflow management; Industry-centricity engineered into the solution; A goal-driven approach. Read more, via Cloudleaf: http://bit.ly/2p8xDDi
Read More →Top 3 Ways IIoT Enables Business Outcomes in Manufacturing
According to IoT solutions company relayr, the manufacturing industry, as a whole, is the least digitally advanced industry today. It is also the industry with the most to gain from the Industrial Internet of Things (IIoT). With shrinking margins and increasing pressure to deliver better, faster, and cheaper, manufacturers are looking for ways to improve efficiencies, reduce costs, and open up new revenue streams, none of which can be accomplished without embracing the IIoT. The key areas in which the IIoT is poised to help manufacturers achieve their target business outcomes are: Asset Services – the continual monitoring and analysis of production line and/or supply chain data points that are most influential to a manufacturing enterprise. This data can be used to trigger automated responses in the machines, alert employees, track production and inventory in real time, and even enable custom product manufacturing Predictive Maintenance – the ability to conduct “just in time” maintenance, drastically reducing costs related to unplanned downtime as well as unnecessary preventative maintenance visits for healthy equipment. Through advanced analytics and anomaly detection, predictive maintenance capabilities only grow smarter over time through AI capabilities, to continually refine and improve manufacturers’ maintenance performance. Device Management – the functionality delivered by the ability to connect, retrieve data, and take action at the device (equipment) level. This interoperability is only achievable through a solid foundation of an IIoT middleware platform, connected hardware, and a solid device management software component. …
Read More →The Adaptive Machine: Cultivating a ‘green patch’ in your brownfield
Despite the well-documented advantages of digital transformation and smart factories, it can be overwhelming to know where to begin. Cultivating a green patch in your brownfield can be a good way to start. A green patch is bigger than a pilot project, because it is a full-scale production system, but it is a small investment than a greenfield. Typically, the project scope would be one line or cell. We talked with John Kowal, Director of Business Development at B&R Industrial Automation, a global supplier of advanced machine control solutions, about adaptive machines and how to cultivate a green patch within a brown field.
Read More →IoT Enables Predictive Maintenance
Over the past ten years or so there have been many smart manufacturing initiatives that have promoted the need for production facilities to become much more efficient and effective through the use of technology. The concept of the connected factory, achieved through the Industrial Internet of Things (IIoT), is being implemented, which is significantly driving up operational effectiveness, resource utilization and a digital visibility of production and process metrics never before thought possible.
Read More →IBM: Be ready for Industry 4.0 with Cognitive Manufacturing
Is your business ready for industry 4.0? Automation and the need for an exchange and flow of data to create meaningful insights is growing, and with it is the need for businesses to prepare themselves for Industry 4.0. Cognitive manufacturing can help prepare manufacturers with Industry 4.0, as it can be used to bring insight across the entire value chain by connecting data points across systems, equipments and processes. It can help businesses “gain the visibility, flexibility and agility to grow, evolve and succeed in the digital age.” Cognitive manufacturing allows leaders to see the greater picture developed by data collected in real-time. Decision makers can then make choices based on the facts and data presented to them in a timely manner, find areas of weaknesses and opportunities to improve in the supply chain. Learn how Cognitive Manufacturing can help your business get ready for Industry 4.0 in IBM’s ebook: ibm.biz/BeReadyeBook!
Read More →KONE Improves “People Flow” in 1.1 Million Elevators with IBM Watson IoT
KONE Corp, a leading elevator and escalator manufacturer and service provider, identified an opportunity for improvement in “people flow”— a term KONE defines as the pressure to keep people moving smoothly. Every day, billions of people use elevators and escalators, and it takes just one malfunction for a user to be inconvenienced and for business to be lost as a result of an obstacle in “people flow.” To mitigate these malfunctions and to deal with unanticipated breakdowns and service interruptions, KONE has decided to go digital. KONE chose IBM Watson IoT, running on the IBM Cloud, to optimize connectivity and real-time data. The platform can connect, remotely monitor and optimize the management of over a million elevators and escalators worldwide. KONE’s decision to use IBM Watson IoT resulted in 1.1 million elevators connected to a cloud-based system that predicts maintenance needs Improves equipment availability through reduced downtime, fewer faults, and more detailed performance information Foster third-party innovation through application development to improve customer service and user experience By going digital and connecting to the cloud, the system can use sensor data from its equipment to help identify and predict issues, minimize downtime and personalize and improve the user experience. Learn more about how IBM Watson IoT improved KONE’s “people flow” and optimized their processes in their case study, http://ibm.biz/BdjMHS.
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