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How IoT enabled Predictive Maintenance can Transform Your Business?

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The internet of things (IoT) is transforming business across every industry, from healthcare to energy to retail, and the opportunities seem endless. In fact, by the year 2020, it’s estimated that manufacturers will invest $70 billion on IoT solutions, more than double their spending in 2015.

Here at Microsoft, we’re focused on helping our partners and customers in all industries seize IoT opportunities. That is why we’re hosting a new IoT in Action webinar series. Starting in January, we’ll tackle IoT topics from a different industry perspective each month. Our first delivery is all about IoT-enabled predictive maintenance for equipment manufacturers and how artificial intelligence (AI) technology can help you prevent equipment failures, boost product quality, and ultimately, drive profit. Attend the webinar on January 11, 2018 to learn more.


Benefits of IoT-predictive maintenance

Manufacturers that implement IoT and predictive maintenance garner a number of benefits, depending upon need and application. Benefits may include:

  • Reduced unscheduled downtime: Avoid costly equipment failures and unscheduled down time. Proactively address issues before they become problems that significantly impact operations.
  • Increased quality: Improve products and processes through machine-learning and detect maintenance issues early to increase customer satisfaction.
  • Decreased costs: Lower maintenance costs and extend equipment life.
  • Greater efficiency and output: Increase process efficiency, asset utilization, and production output.

Examples of IoT-enabled predictive maintenance

For a great example of how IoT-enabled predictive maintenance can transform business, let’s look at a steel manufacturer with multiple plants in India. Each plant has multiple arc furnaces that use water cooling panels for temperature control. However, leakages in the panels were causing safety issues as well as production losses. To resolve this issue, the manufacturer worked with Happiest Minds (a Microsoft partner) to build an Azure-based IoT solution that remotely monitors the panels, detects anomalies, and performs root-cause analysis. The implementation of predictive maintenance has prevented failures and production delays throughout the plants while helping ensure employee safety.

In another example, aircraft engine manufacturer Rolls-Royce implemented predictive maintenance on the Azure IoT platform to help their customers reduce costly flight delays caused by engine maintenance issues. Each of their 13,000 engines in operation worldwide has thousands of sensors that monitor engine components and deliver insights around fuel efficiency, engine performance, and operational efficiencies. These insights enable Rolls-Royce to anticipate maintenance needs and avoid costly, unscheduled delays.

Attend the IoT in Action webinar January 11, 2018

posted Jan 3, 2018 by Rahul Singh

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A Way To Optimization

Imagine a company has just suffered equipment failure. Aside from the costly setback and time-inducing repairs, the cause of the malfunction is largely unknown. It would be a further pain if the problem replicates itself in the future – the company and equipment management team would not have peace of mind.

Wouldn’t it be super if the next time around, there’d be an alert that the system sends to notify proper employees of the malfunction, prompting immediate action? Well, it’s better, but still not the ideal solution. You’d still have to suffer all the inconveniences that comes with inoperable equipment and resulting costs.

Now, what if your system recognizes a change in usual equipment behavior with the help of connected devices and sensors? This change could be an anomaly such as a temperature increase or a shift in an asset location. Your system would notify you of the anomaly detection and suggest you take action or suffer imminent equipment failure. Even a subtle shift in the temperature of a pipeline that is slowly heating up would allow enough data for the system to recognize that something is wrong. This process of data aggregation, analyzation, and visualization would then amount to something that is truly valuable for the enterprise.

Big Data for Smarter Analytics

Predictive maintenance is the early detection and recognition of equipment failure and the deployment of preventive solutions to deal with the problem. Over time, numerous forms of predictive maintenance have taken place to safeguard assets but not until the fourth industrial revolution has this form of preventive care been more efficient. Powered by sensor connectivity, IoT, big data analyzation, and you have the most efficient and modern take on predictive maintenance.

Not only would predictive maintenance save money for the enterprise, but big data analytics would result in potential automation that cuts down time and effort in equipment management. Going back to our overheating pipeline example – if the pipeline showed similar patterns of overheating over multiple instances, our system would not only suggest, but automate our decision making to ensure the appropriate responses are put in place. Both the cases of preventive care and automatized care are of course, made possible by data aggregation and analytics.

The evolution of equipment management started with reactive care, or simply put, reacting after the fact that the equipment is discovered to be faulty or damaged. Proactive care would be a more evolved step, and this would be where preventive maintenance is categorized. There would, of course, be differing gradations of these strategies. Like previously mentioned, automatized care would be a convenience add-on to the already-modernized preventive care.

Data Integration for the Enterprise

Big data analytics is only adopted by 53% of companies in 2017, with undoubtedly less companies using this data to deploy predictive maintenance. For some companies, especially smaller ones, their budget don’t allow for this adoption. However, other businesses are potentially self-encumbered by a trust and familiarity in legacy management systems.

The other ~47% of businesses also hesitate to make the technological leap because of the growing complexity of technology and IoT capabilities. However, the hypothetical ramp to make this leap could propel businesses to new and greater heights of efficient budgeting through preventive care.

If you or your company is a part of this percentage, or if you’d rather invest in streamlined analytics and preventive care, check out ONE Tech’s page – featuring a video, white paper, and demonstration on predictive analytics.

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Contemporary fashion is undergoing a transformation.

We are increasingly witnessing all industry sectors shift towards the digital. And fashion is no stranger to technology.

Counterfeiting has become a global menace. We have already discussed how IoT is helping companies like HP fight counterfeiting.

But the fashion industry is trickier.

Counterfeiting and fake products manufacturing are higher in the fashion industry given the availability of knock-offs of big brands at cheaper prices. As a result, apparel gets even more challenging to authenticate thanks to the increasing sophistication of manufacturing products that can pass off as originals. There are many people donning fake LVs (Louis Vuitton) and MKs (Michael Kors) all around the world. Some customers even get duped and end up purchasing fake products for very real prices. Moreover, the rise of e-commerce websites has multiplied the scale of the problem.

As per the OECD, 2.5 percent of all imports account for counterfeited products, out of which, the US, Italian and French brands are hit the worst. Worth nearly half a trillion dollars per year, profits recovered from the counterfeiting of goods are further used to organize other crimes according to the report by OECD and the EU’s Intellectual Property Office.

With efforts taken by industry leaders to trademark products, many are now integrating technology to further help customers and retailers authenticate products.

Chanel and LV Authentication Labels

The rise in fake products and counterfeiting has provoked big brands to fight back. Chanel places hologram stickers with unique serial numbers in the lining of its handbags while Louis Vuitton has ‘date codes’ to validate authentic LV products.


Succumbing to authentication methods pertaining to the 70’s and 80’s may not be feasible in a world where technology allows sophisticated counterfeiting and manufacture of fake products.


What is required is an IoT approach towards authenticating these merchandising products.

Toronto based Authentic or Not believes every product needs an ID. By embedding their microchips in apparel and other merchandising brands, Authentic or Not claims to bridge the gap between technology and fashion. Their microchips are designed specifically to withstand washing and dry-cleaning conditions, integrating fashion and IoT. And hovering a smartphone in front of microchips-embedded clothes can verify their authenticity.

IoT Enabled Clothing can help authenticate products and Fight Counterfeiting (Source: Authentic or Not)

This is only one of the use-cases for incorporating IoT into fashion. What is interesting to note here, is that products do not necessarily need a ‘microchip’ to participate in the Internet of Things. They can do so without one.

When we talked about ‘Everyday Shirts on the Internet’, we explained the relevance of ‘pseudo-connected’ devices and things that can also be a part of this trend. By allocating a unique identifier to products that cannot directly connect to the internet, they become eligible to participate in the Internet of Products.

QR codes, RFIDs, and other unique product identifiers to build a brand’s product directory such as barcodes or other two-dimensional code labels can be used. These technologies are also not very expensive to deploy on all products, whereas a microchip on every product can be more feasible for higher value luxury fashion products.

Scannable and readable physical product markers can potentially IoT enable clothing and other merchandising like handbags, sunglasses, watches, shoes etc. Maintaining a digital record of fashion products allows customers and retailers to quickly run a check on the internet against these authentication labels.

These physical markers can be checked against the brands’ product data directory or centralized product IoT inventory. Brands can utilize and leverage this user-product data to enable other features like warranty management as well.

As a result, it makes it tougher for counterfeits to replicate and sell fake products.

IoT Platforms have the potential to drastically intervene and transform the billion-dollar counterfeit industry. With lack of Intellectual property rights to safeguard products, high-end fashion and merchandising brands need to deploy IoT technologies internally. Brands can implement similar technologies to fight back counterfeiting and fake products by building ‘smart products or by simply connecting them to the internet.