It’s alive!! 5 ways buildings are taking advantage of the IoT

After spending a few days at TRIMax immersing myself in all things around facilities management and buildings of the future, one thing was clear. I need to take my building out for a coffee and pick its brain. Our buildings carry so much information about how they are used and we’ve only just begun to scratch the surface of the possibilities of what we can do with that data. It’s time to bring our buildings alive!

For many years, we’ve relied on our own interpretations of our observations. As we walk around a building, we may notice that our co-worker, Johnny, doesn’t come into the office very often even though he has his own corner office. We may get annoyed when we go to book a conference room but everything is taken – though they all appear empty most of the time. The frustration is real when you drag yourself out of bed at 6am to hit the hotel gym only to find all three machines are taken. What if we could improve that visibility and get the most accurate data possible?

Bringing your buildings alive

As we saw in the Kone video, it is possible for our buildings to either interrupt or improve the flow of our days. The applications span far beyond elevators and doors. Sensors can be infused in ice cream coolers, coffee machines, bathrooms, conference rooms, parking lots, and much more. Using the ice cream cooler as an example, consider the impact on a retailer if their ice cream cooler goes down on a hot day in July. That’s going to be a bad day for that retailer. But had they used IoT sensors and capabilities, this breakdown could have been predicted and prevented. In essence, we want our buildings to speak to us and the technology exists to make this possible.

“My sole focus is to bring your buildings alive” – John Smart, Program Director, Cognitive Building & Retail Solutions, IBM

Voice is the next big thing for smart buildings

“What if your building could listen and respond to your needs and wants? Voice is the next big thing in smart buildings.”  – John Smart, Program Director, Cognitive Building & Retail Solutions, IBM

Not only can our buildings talk to us, but we will also be able to talk to our buildings. Using the power of voice, we develop a whole new set of experiences for the employee. Imagine if you need a whiteboard for your meeting. You will be able to simply ask for it, your voice will be captured, and a service request is automatically generated.

Putting Watson in the walls

To fully optimize the capabilities of Watson, he really needs a place to stay. Since you will be optimizing your unused space, the only place left will be the walls. When we say Watson in the walls, we’re not talking about a new blockbuster hit, but rather the partnership we are working on with HARMAN.  IBM is working with Harman on My Personal Concierge, powered by Watson assistant. This is intended to optimize hotel stays and follow you wherever you are on your journey. Need to check if that elliptical in the gym is free without walking down? Check. Ran out of towels? Watson has your back.  Want to make a reservation for dinner at 7pm at the hotel lounge? No problem.

Update room vacancies in real-time

We’ve all been there. We need a conference room but everything is booked. We’re also all guilty of booking a conference room and then not needing it or using it. Do we take the time to cancel the room reservation? If you do, you’re a better person than I am. Most often, there is just a big discrepancy between what rooms are on hold and which are actually being used.

Watson Workplace Concierge can help update room vacancies in real-time. It uses the power of the device and the power of the IoT with little needed involvement by the employee. If you don’t show up to the room, you will get a notification on your phone asking if you still need the room. If you say no, it will automatically unlock the room for others to use. Similarly, if you leave a room mid-way through your reservation, Watson will ask if your meeting is complete and will free up the room.

Space occupancy tracking saves major headaches

The world of commercial real estate is very complex. There is a vast amount of space. In fact, there is 12B square feet in the U.S. alone; but only 67 percent is utilized.  How do we address this gap?

According to Susan Chace from Fidelity, having a strong space assessment depends on mobile tools and the ability to compare physical space to space information with TRIRIGA and make real-time updates as you walk the floor. Just walking around the building and observing is not enough and it is not accurate. Space needs can change daily and having technology in place to capture those changes can make all the difference in whether your space allocations are accurate.

Are you bringing your buildings to life?

The ability to capture key information from our buildings and use that data to optimize customer experiences, employee engagement, and your bottom line are very real. It was a hot topic at TRIMax this year and I look forward to seeing where 2018 will take us.

Don’t forget to catch up on all the activities you missed at TRIMax this year.

To see what others are doing with smart buildings, visit our buildings zone and facilities management hub.

To learn more about IBM TRIRIGA, visit the IBM Marketplace.

 

The post It’s alive!! 5 ways buildings are taking advantage of the IoT appeared first on Internet of Things blog.

Internet of Things blog

Sustaining Advantage With Transitory Technologies

Relentless technology change can feel like a rollercoaster — simultaneously exhilarating and exhausting. Just as we reorient from one twist, we’re thrust into the next one.

For managers, one difficulty with new technology is that it typically must integrate with the old. Managers working with data generated by technology rarely have the luxury of a single version of data. Instead, the analysis must incorporate multiple generations that — by definition — differ from each other.

The rise of embedded devices for data collection makes this situation far, far worse. Consider the industrial equipment market: The long life of this hardware combined with the rapid evolution of sensors is difficult to manage.

Siemens AG provides an excellent example both of this difficulty and the opportunity it presents. Gerhard Kress, director of mobility data services, describes the data challenge the situation creates:

“Industrial equipment has time spans that are so much longer than IT time spans. That is a huge issue because, for example, you cannot just shut down the control center of a power plant to upgrade. Or given that the average life span of a rolling stock vehicle is about 30 to 40 years, you have a large, installed base that simply does not have all the modern functionalities in it yet.”

Instead, his group must be ready to handle the latest technology (for new products) as well as quite old technology (for their installed products). Siemens, for instance, still services a water turbine that was installed 105 years ago. Lifetimes for heavy equipment “easily last 20 to 40 years.” While it’s a challenge, Siemens is able to create advantage from this difficulty in several ways.

Turning Legacy Into Advantage

Using its data platform, Siemens differentiates through compatibility. Kress notes that: “One of Siemen’s advantages, and a barrier to entry for the other [nonrail] players … is that [we] understand very old rail data formats. Siemens can read data formats from 30 years ago; we have to read them.” While someone else could develop systems to read these, it would take significant time to build. Kress notes that documentation is rarely a priority and that “a lot of the documentation was only through people, and [gathering information] required talking to the experts who designed it.” Reverse engineering takes considerable time and effort for others, but organizations that already understand these legacy formats have an advantage.

Understanding How Data Collectors Evolve

Beyond just the old data formats, Siemens has also built up considerable expertise in tracking changes in the data-providing sensors themselves. Kress describes that with sensor data, “I cannot assume sensors to be always correct, because they might have a life span of 10 years but are installed on equipment that is still in use. Some begin generating errors.”

As a result, Siemens spends considerable effort validating data from its equipment and has a rich understanding of how the data reliability changes over time and the kinds of errors that begin to creep in. In an industrial setting, Kress describes how misinterpreting this data can be critical, “because imagine you take a train out of operation and evacuate all the passengers because you believe something is broken, but it turns out it’s a sensor.” An organization with a deep history of data creates advantage in that it will understand how to interpret this data correctly.

Creating New Algorithms for Old Data

The interpretation process can take the form of tacit knowledge in the organization. But it can also manifest itself in explicit and differentiating algorithms. Kress describes that “we’ve also spent [an] enormous amount of effort in the last three years in researching new methods, because industrial data behaves very different than, let’s say, clickstream data from the internet.”

Siemens uses standard machine learning but has also developed new methods when needed. Kress notes, “We’ve filed about 30 patents on new mathematics, because we couldn’t find what we needed.” These advances provide further opportunity for advantage, since “it took us a while to learn and to find out that these structural differences in the data that cause so many problems.” Organizations that work with data from old and new equipment can learn more about the shortcomings that modern techniques have in this context and can gain advantage in developing tools that no one else has.

Perhaps ideally, organizations would always be able to have all of their data-providing equipment on the latest and greatest platforms. In many IT contexts, this may be close to possible. For example, the IT “Gang of Four” (Amazon, Apple, Facebook, and Google) can often deploy changes relatively quickly across their technology infrastructure; historical data is likely different, but current data is homogeneous. Or companies can ask their eager consumers to embark on yet another buying spree of adapters and peripherals to be compatible with their latest gizmo that has rendered existing products obsolete.

But realistically, many organizations operate in a context where this rapid cutover is nowhere near possible. As more and more companies become technology companies and combine technology in their products, this difficulty will become much more widespread. However, rather than a disadvantage, organizations can create advantage by effectively managing the complexities that mismatched physical and virtual time horizons create.


MIT Sloan Management Review

Can machine learning secure your competitive advantage?

machine learning

Business dynamics are evolving with every passing second. There is no doubt that the competition in today’s business world is much more intense than it was a decade ago. Companies are fighting to hold on to any advantages.

Digitalization and the introduction of machine learning into day-to-day business processes have created a prominent structural shift in the last decade. The algorithms have continuously improved and developed.

Every idea that has completely transformed our lives was initially met with criticism. Acceptance is always followed by skepticism, and only when the idea becomes reality does the mainstream truly accept it. At first, data integration, data visualization, and data analytics were no different.

See also: How to start incorporating machine learning into enterprise

Incorporating data structures into business processes to reach a valuable conclusion is not a new practice. The methods, however, have continuously improved. Initially, such data was only available to the government, where they used it to make defense strategies. Ever heard of Enigma?

In the modern day, continuous development and improvement in data structures, along with the introduction of open source cloud-based platforms, has made it possible for everyone to access data. The commercialization of data has minimized public criticism and skepticism.

Companies now realize that data is knowledge and knowledge is power. Data is probably the most important asset a company owns. Businesses go to great lengths to obtain more information, improve the processes of data analytics and protect that data from potential theft. This is because nearly anything about a business can be revealed by crunching the right data.

It is impossible to reap the maximum benefit from data integration without incorporating the right kind of data structure. The foundation of a data-driven organization is laid on four pillars. It becomes increasingly difficult for any organization to thrive if it lacks any of the following features.

Four key elements

Here are the four key elements of a comprehensive data management system:

  • Hybrid data management
  • Unified governance
  • Data science and machine learning
  • Data analytics and visualization

Hybrid data management refers to the accessibility and repeated usage of the data. The primary step for incorporating a data-driven structure in your organization is to ensure that the data is available. Then you proceed by bringing all the departments within the business on board. The primary data structure unifies all the individual departments in a company and streamlines the flow of information between those departments.

If there is a communication gap between the departments, it will hinder the flow of information. Mismanagement of communication will result in chaos and havoc instead of increasing the efficiency of business operations.

Initially, strict rules and regulations governed data and restricted people from accessing it. The new form of data governance makes data accessible, but it also ensures security and protection. You can learn more about the new European Union General Data Protection Regulation (GDPR) law and unified data governance over here in Rob Thomas’ GDPR session.

The other two aspects of data management are concerned with data engineering. A spreadsheet full of numbers is of no use if it cannot be tailored to deduce some useful insights about business operations. This requires analytical skills to filter out irrelevant information. There are various visualization technologies that make it possible and easier for people to handle and comprehend data.

Want to learn more about the topic? Register now to join me at the live session with Hilary Mason, Dez Blanchfield, Rob Thomas, Kate Silverton, Seth Dobrin and Marc Altshuller.

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The post Can machine learning secure your competitive advantage? appeared first on ReadWrite.

ReadWrite

Take advantage of preventive maintenance in the Cognitive Era

Providing essential asset health management and asset maintenance for any organization’s critical assets requires a fine balance. As you are aware, under-maintenance can result in low productivity, expedited costs and even safety risks. At the same time, excessive maintenance of assets can be wasteful – resulting in unnecessary downtime, higher labor costs, and could even create tension between line-managers and maintenance staff.

Over-investing in maintenance can be detrimental to operational and fiscal efficiency. By squandering precious resources – time, budget, and staff – you could be completing work which is not only unnecessary, but expensive as well. The dilemma begs the question: Is there an optimum balance between operations and maintenance, and, if so, how can it be achieved?

IoT-generated data can provide an in-depth view into asset health

The Internet of Things is proving to be one of the greatest technology catalysts for transformation. Through the use of sensors and instrumentation, it’s now possible to use real-time data analysis, cloud and edge computing to gain deeper insights into assets health and performance. By combining advanced analytics with new cognitive capabilities, organizations can now infuse intelligence into physical objects to gain deeper insights into assets health and performance.

Harness the power of preventive and predictive models

Most equipment manufactured today ships with a spectrum of built-in sensors designed to provide reams of real-time data to industrial control systems such as Supervisory Control and Data Acquisition (SCADA) systems, Building Management Systems (BMS) or Programmable Logic Controllers (PLCs). For older equipment, there are simple ways to retrofit assets with sensors too. As a result, operations teams can make use of streaming data from assets to monitor equipment performance and watch for early warning signs of failure – provided the right tools and dashboards are in place to turn that data into actionable insight.

Use case: reducing unnecessary preventive maintenance

As much as 70 percent of an organization’s investment in preventive maintenance has no effect on uptime metrics.

Operating routine maintenance without insight can be costly. Take the instance of a tier one automotive supplier operating a production line with 300 paint guns. In this particular shop, routine maintenance on paint guns is done on a time-based rotation – regardless of use or performance, a paint gun is taken off the line for maintenance every 900 hours.

In our example, the established rotation schedule for the PM is not based on any insight into the performance of the equipment – whether the pressure is off, or if the spray is uneven – it’s based solely on the number of hours the gun is used. In our scenario, the gun has never been run to failure. The conditions of that failure have not been measured. The data from the failure conditions is not being monitored, recorded, or modeled in any way that could help create a better maintenance program – one which kicks-in when a condition of failure is likely to occur. What if you could figure out when a paint gun is likely to fail, and only then have to take that piece of equipment off line for maintenance?

Breaking the digital blindness cycle

By running a piece of equipment to failure, you can establish benchmarks. By first establishing a base line, you are able to make more accurate assessments of when maintenance is actually needed, before a machine breaks.

Whatever the failure criteria is – less pressure, uneven spray – monitoring the data points surrounding these factors will help you see at failure what the readings are. From there, you can build this information into a calculation or model to identify what happens when the equipment reaches that point of failure – and, only at that time, do the PM.

This is a great example of what happens when things are done without insight. In this case, assuming the PM needs to be done without understanding the conditions of failure, your organization ends up wasting money on PMs that don’t need to happen. If you are operating a production line with 300 guns, taking a piece of equipment offline every 900 hours at a cost of 5K a PM means your organization is spending a lot of money unnecessarily.

Having visibility into the health of your assets will take you steps closer to operational efficiency. Understanding when maintenance is needed, for what duration, and at what impact to the organization are critical factors. Tapping advances in technology – analytics, IoT data, cognitive APIs like visual and voice recognition, combined with powerful computer processing – can help organizations gain better insight into their asset health.

Three ways to improve operational effectiveness and ROI

Here are three ways to improve operational efficiency and increase return-on-investment (ROI) using the data gathered from asset management solutions:

  1. Out-of-the-box technologies collect, filter and map real-time data from equipment and make it available to reliability engineers and maintenance professionals for optimizing preventive maintenance.
  2. Cloud technologies provide a cost-effective way to aggregate, store and use advanced analytics against massive amounts of data coming from equipment, in combination with other sources.
  3. Analytics technologies are more powerful in not only capturing tribal knowledge of engineers, but also for uncovering new, hidden patterns that can be used to predict failures. They are also becoming simpler to use, even by those without advanced statistical knowledge. Today’s flexible analytic technologies work with data from multiple sources and in different formats. They even make sense of non-traditional, unstructured data formats such as video.

For the first time, this new world of cutting-edge machine learning and real-time analytics is converging with leading enterprise asset management platforms like IBM Maximo. Together, IoT, EAM, and Analytics provide asset-intensive organizations a complete view of critical assets, enabling on-time maintenance with sharp analytics and cognitive capabilities that infuse intelligence to processes.

Try IBM Maximo Asset Health Insights solution for better insights for preventive maintenance

The IBM Maximo Asset Health Insights solution comprehensively manages asset health for an organization’s entire asset portfolio. The process begins by gathering data streaming from sensors using the Watson IoT Platform to illustrate real-time condition data. This data is then combined with historical data in IBM Maximo, where engineers can define baseline health for each asset or for a class of assets across their operations. The streaming asset health data is used to monitor the health of assets against the pre-defined rules. It is then scored and visualized to easily understand potential problem areas and accelerate preventive maintenance.

Learn more

Read the paper to learn more about how technology is evolving to augment human intelligence and monitor asset health more accurately. Discover the benefits of asset health management, including greater ROI with IoT and cognitive capabilities for asset health insights.

  • Gain operational effectiveness and ROI with the Internet of Things (IoT) and business analytics.
  • Infuse your asset management system with new cognitive capabilities for immediate insight into asset health and optimal preventive maintenance scheduling.
  • Manage your full asset portfolio with a single, integrated solution including Asset Health Insights, IBM Maximo and Watson IoT Platform.

Visit our website to further explore how you IBM enterprise asset management solutions can help you achieve greater operational efficiency.

 

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Internet of Things blog

How a pharmaceutical supply chain company is taking advantage of the Internet of Things

In 2014, during a routine check from the Ministry of Health in the U.S., it was found that only 55 percent of vaccines were stored and transported in the temperature conditions that ensured the medication maintained its quality. To put that into perspective, every baby born receives vaccines to prevent diseases such as small pox and measles. If only 55 percent of those vaccinations maintain safety requirements, that creates a situation where a majority of babies don’t get the quality dosage and medication they need to protect them from diseases.

To overcome this challenge, organizations are turning to technology. More specifically, the Internet of Things (IoT) is making it possible to ensure the safer transportation and delivery of medications. Dutch pharmaceutical services company, AntTail, is paving the way for building innovative IoT applications that more effectively track the conditions of medications while in transit.  

The team at AntTail built an IoT application using the Mendix low-code application development platform. The application collects sensor data from medication shipments to provide information on temperature, as well as send push notifications to patients with reminders on when to take the medication.

One of the barriers for creating IoT apps is the requirement of many disparate technologies. AntTail uses a central router as a hub for all of the sensors, collecting the data when there is a connection and storing the data when there is no connection to ensure that no data is lost. The Router uses Vodafone’s Managed IoT Connectivity Platform as a way to connect to AWS, and has a Java service running that puts the data into Hadoop.

Hadoop is a means to store all of the data, but it is not complete without the context needed to make smart business decisions. AntTail uses Mendix to add context to the data by assigning roles to each sensor. The app takes into account where the sensor is being used in order to determine the role and assign a trigger.

For example, some sensors are assigned the role of “Last Mile” because they travel from the pharmacy to the patient. These sensors monitor not only the temperature the medication is being stored in, but also the adherence, making sure the patient takes the medication. The sensor is triggered when the patient opens the package and deactivates itself.

Other sensors are placed in warehouses and must be up and running 24/7; if no data is being collected and the sensor is offline for more than 30 minutes, an alarm profile is set up to notify the caretaker. Basic shipments also carry sensors that start at point A and deactivate when they get to point B in order to trigger a notification that the shipment has made it to its destination.

The app can visualize all of these sensors and evaluate the data for any triggers. AntTail uses the REST services module from the Mendix app store to access the full power of JSON-based REST APIs. The module serves three goals: consuming services, publishing services and synchronizing data between apps by combining consume and publish. By using the native REST service, AntTail’s customers can access the data quickly to make important business decisions in real-time.

“It’s lightning fast; I get 10,000 records in less than a second,” says Mark Roemers, CEO and Co-Founder of AntTail.

As a result, there has been a 99 percent success rate in tracking and alarming, keeping the medicine at proper temperatures and patients taking the medicine at the prescribed intervals due to reminders from the app.

Mark says that the next steps for AntTail are to take the app mobile. They are already in the process of building out their mobile apps in order to provide proactive notifications 24/7. For the warehouse manager, this means that even when he is not in front of his computer he will be able to receive notifications if a sensor reports an excursion and can act immediately. With the mobile solution, patients, pharmacists and logistics customers can access and interpret the sensors’ data from anywhere at any time.

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