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Pushing IIoT predictive maintenance forward: two challenges to overcome

Enabled by wireless technology and connected devices, communication between machines and human technicians is fueling a shift from preventative to predictive maintenance. To push IIoT predictive maintenance technologies up the slope of enlightenment and spark mainstream adoption and success, two major challenges must be overcome: the challenge to obtain high quality data from industrial machines, and that to fuse sensor data with maintenance activities.

An article in Reliabilityweb offers solutions ranging from deep learning algorithms to tapping into the intuitive human capacity of sound-based diagnosis.

Read more about ways to overcome IIoT maintenance challenges and combine deep learning and human input: http://reliabilityweb.com/articles/entry/pushing-iiot-predictive-maintenance-forward-two-challenges-to-overcome

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Via Reliabilityweb

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How the Internet of Things impacts supply chains

Enterprise resource planning and supply chain management (SCM) have gone hand-in-hand for quite some time, but the IoT revolution will allow those solutions to be enhanced by intelligently connecting people, processes, data, and things via devices and sensors.

“Think of it as SCM 2.0,” writes Udaya Shankar, Vice President and Head of Internet of Things for Xchanging, a business process service provider for the global insurance industry. According to Shankar’s article in Inbound Logistics, this deeper intelligence can come to life in many different ways when it comes to supply chain data and intelligence – from the automation of the manufacturing process to improved visibility within the warehouse.

One area that Shankar believes will play a prominent role in the future supply chain, as it’s impacted by IoT, is in-transit visibility. “The logistics ecosystem has many players, and thus, many moving parts. Products are handled and transferred between the manufacturer, suppliers, the distribution center, retailer, and customer.”

Read more about how IoT can help supply chain professionals at:
http://www.inboundlogistics.com/cms/article/how-the-internet-of-things-impacts-supply-chains/

Image credit: Lightspring / Shutterstock.com

Via Inbound Logistics

2 Comments

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  • Bhanwar Singh Rathore 22.07.2017 16:53
  • Bhanwar Singh Rathore 22.07.2017 16:40

    Transit visibility has improved assurance level in planning and customer satisfaction level

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The deciding factor – how to utilize IoT data analytics for business intelligence

To make the most of data, it has to be transformed into information, which then has to be transformed into intelligence. As companies seek to leverage data – whether it’s internal, external, structured, or unstructured – to improve profitability or boost operational efficiency, analytics makes it possible to gain insights on business areas that were previously out of reach. Alun Jones, Data Scientist at Konecranes, talks about how organizations can best use IoT data analytics to arrive at more impactful business decisions.

According to McKinsey, the potential economic impact of the IoT could reach $11 trillion per year in 2025. That figure is equivalent to around 11 percent of the global economy. Turning that possibility into reality depends on how effectively IoT data analytics is used to drive better decision-making. The technology research firm Gartner identified IoT data analytics as one of the key IoT-related technologies that should be on every organization’s radar in 2017 and 2018, second only to security.

To identify target-rich, high-value data that can be used to generate business intelligence, the following steps should be taken.

1. Be aware of what you already have. It makes sense to know if some of that information is already available or accessible even if it isn’t immediately apparent. If you don’t know, then find out: Build a data map for your enterprise.

2. Think like a custodian, not an owner. The term “data owner” can be misleading as it appears to imply not only ultimate responsibility, but also the ability to utilize data for one’s own purposes. Both are not necessarily true of data use within a business. A data custodian, meanwhile, is responsible for the technical environment and controls around data.

3. Every action is part of the value chain. The siloed approach to data access makes unifying the analytics layer a challenge. To generate scalability and real-time performance, however, all types of analytics – descriptive, diagnostics, predictive and prescriptive – must be brought together into a single engine.

The role of cloud analytics platforms

In terms of using cloud analytics platforms to derive value from IoT data, it’s important to remember that not all data is created equal. Companies should think of ways to get data from a device into a position where it can be analyzed; the priorities of that data need to be determined as well.

Next, it’s also essential to gather data from numerous sources as interoperability is key in a heterogeneous environment. Last, it is advisable to have distributed data sources so that the cloud is there by default. Cloud simply means off premises; there will be distance between individual data sources and the computer power performing the processing. If you are uncomfortable with the cloud then find out why, and work to alleviate those anxieties. Processing IoT data close to the source results in less network delay than transferring it to the cloud, processing it there, and sending back the actionable result, such as computing and analysis at the edge.

“Harnessing IoT data analytics for business intelligence is not a one-time exercise, but a continuous process.”

Delivering value

As far as the barriers to widespread IoT value delivery are concerned, these could be overcome in two ways. First is technical. This covers everything from data gathering and low power or no power devices. (At the moment, for example, sensors and devices need power to drive them or the transmission of data, and in the future there will be a need to have devices that have lower power requirements.) Data architecture and cost of hardware should likewise be considered.

Second is the people aspect. Gatekeepers need to change. Management must improve its ability to understand and interpret the output from analytics. Individuals need to collaborate, even with those outside their respective enterprises. Normal business practices mean that things are driven on short-term departmental measures – this must be reconsidered as well. Do you design your plant to be cheaper to build, or more efficient and flexible to run?

Overall, harnessing IoT data analytics for business intelligence is not a one-time exercise. Rather, it’s a continuous process. Bear in mind that not everything is going to work. Optimizing what you do today is not enough either.

In addition, look at how to change the business model in a way that fits the market. For instance, GE builds airplane engines. They innovate by fitting sensors to gather that data and transfer it back to the factory. This is adopted over time so all engines send data about themselves. Over time, this is optimized so that GE knows the state of each asset and is able to predict when parts are likely to fail. This reduces downtime, making maintenance more efficient. Once the asset behavior is understood and de-risked, the business is transformed from building engines to offering engines as a service. GE’s software platform is now the key element in their business model. Cranes are a little behind but are catching up fast as the platforms needed to support such devices are already being built.

Alun Jones works as a Data Scientist at Konecranes. He is participating in several panel discussions at the IoT Tech Expo Europe event in Berlin on June 1-2, 2017.

Interview w/ Alun Jones

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Combining mixed data – unlocking the real value of IoT

Most companies are at a design and test phase in terms of Industrial Internet solutions. Integration into larger, complex systems remains somewhere on the horizon. The full potential of the gathered data will only be truly realized once comprehensive integration into these complex systems becomes a prominent trend. Niall O’Doherty, Director of Business Development Emerging Industries Team at Teradata Corporation, hopes that within five years, the technology necessary for such integration will be commonplace. The question then becomes — will corporate philosophies match the capabilities of these technologies?

Data environments are being inherently redefined due to developments across IoT and IIoT. To do away with detached data “pockets” – which is to say, with data that remains unintegrated into systems or with other data – an overall process of synthesis is necessary. Key to such a synthesis, and subsequent realization of the true value of IoT and IIoT, will be the integration of the already widespread use of sensor data.

“To get to the real transformational value, more of these systems must be put into place. In order for that to happen, sensor data needs to be integrated with product data, customer data, ERP (Enterprise Resource Planning) data and other traditional data. For many organizations, bringing sensor data together with traditional data – and making sense of it all – is still a major challenge,” states Niall O’Doherty.

“I hope that in the next five years we will be able to regard sensor data connected to communications infrastructure as a common feature of business,” he continues.

The increasing flow and current of data across organizations and systems naturally raises pertinent questions about data ownership. The fact that once data enters ecosystems, no single organization, agency or equipment manufacturer is going to have exclusive control of the data and its distribution, casts doubts over the approach of companies and – according to O’Doherty – over the attitudes of individuals.

“Are people going to be willing to share all this information? Are they going to be willing to take the output of their particular optimized process, and put it into the input of another, so that we can build a better understanding of what’s going on in a complex manufacturing environment? I think that a lot these commercial and cultural issues will need to be resolved, otherwise they can really trip up organizations.”

“I hope that in the next five years we will be able to regard sensor data connected to communications infrastructure as a common feature of business.”

Making sense of sensors

With the capacity to extract data from vast processes becoming more prominent, complex analytics must process data in ways that allow for more than simply deciphering averages and statistics. According to O’Doherty, this is particularly imperative for industrial and manufacturing companies.

“With the volumes that sensor data is generating, especially in the industrial world, coupled with the complexity of analytics, you really need to bring the analytics and algorithms to the data. In order to do that, you need a scalable IoT platform.”

In the material handling industry, such a platform could facilitate anything from predictive analytics to looking at how employees move on a factory floor, thus optimizing operations accordingly. O’Doherty uses the enhanced oil industry as an example. By putting highly instrumented equipment on rigs and sensors on the ocean-floor, the Industrial Internet has greatly aided in efficiency and optimizations of complex processes and systems. “What’s innovative for them is how they are now using vast amounts of data to understand the subsurface a lot better,” O’Doherty says.

Same products, new services

For O’Doherty, the creation of new business models via sensor data is not necessarily at the crux of Industrial Internet developments. Instead, he sees business models created for existing products as reaping the benefits of the Industrial Internet in the future.

“I see the power of sensor data and the Industrial Internet in allowing organizations to implement a scale for different business models. Those models may already exist, but as a result of this new data, they can be made more profitable and customer-oriented. It’s about understanding and mitigating risk so that you can potentially implement multiple models for the same products: to different markets, companies or customers.” This also increases the likelihood of new services emerging indirectly from existing products.

The notion of selling services, as opposed to products, is a concrete example of how the evolution of the Industrial Internet allows for the modification of, or experimentation with, existing business models. “For example, the notion of Power by the Hour – meaning a company won’t sell their customers an engine or a train, but instead the power needed to run them – was in fact coined in the 1960s by Bristol Siddeley. So, it’s not necessarily a new business model,” O’Doherty notes. Interestingly, later that decade Bristol Siddeley was bought out by Rolls Royce, currently one of the forerunners in embracing the Industrial Internet.

In order for the rest of the manufacturing world to keep up with the likes of Rolls Royce, O’Doherty reminds CIOs and CEOs of their roles as “enablers,” who first and foremost allow for businesses to change the way they approach products and services in general. “My advice – to a CIO in particular – would be to ensure you build the right infrastructure and environment to allow people in your company to access the data they need, and add the analysis they want,” O’Doherty concludes.

Niall O’Doherty works as Director of Business Development Emerging Industries Team at Teradata Corporation

Image credit: alphaspirit / Shutterstock.com

Interview w/ Niall O’Doherty

2 Comments

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  • Marko Yli-Pietila 06.07.2017 12:57

    It seems that companies are willing to share their data if they see the sharing valuable to them. One good example of successful data exchange is Braincube, https://braincube.com/.

  • Mohamed Sharaf 02.06.2017 17:15

    Are people going to be willing to share all this information? ???

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An IoT platform for material handling

The IoT is all about using sensor data to make better decisions. For decades, warehouse management systems have relied on scans from barcode scanners to confirm floor level activities have occurred correctly. In the last ten years, other automatic identification technologies have achieved the same types of process reliability.

According to an article in Forbes, material handling systems are a natural generator of sensor data. Contributor Steve Banker cites SensorThink, a digital platform for the connected warehouse. Making its debut at ProMat, the leading material handling conference in North America, this platform includes a warehouse control system, a digital platform for capturing IoT data, and Cloud analytics for analyzing the data.

“The digital platform collects the IoT data, cleanses it and harmonizes it. The data can come from material handling systems, lift truck sensors, building automation systems – which control the temperature and humidity of buildings, and security systems,” Banker writes. SensorThink compresses massive amounts of data by only collecting change of state data.

Read more about new potential for analytics and optimization in warehousing at: https://www.forbes.com/sites/stevebanker/2017/04/05/an-iot-platform-for-material-handling/#77581e9b1182

Image credit: zhu difeng/ Shutterstock.com

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