Tag Archives: Industrial
Teradata Supports Volkswagen Industrial Cloud as Partner
SAN DIEGO and MUNICH, GERMANY – July 23, 2020 – Teradata (NYSE: TDC), the cloud data and analytics company, today announced that it will be a partner of the Volkswagen Industrial Cloud.
The Industrial Cloud – which is being developed by Volkswagen together with Amazon Web Services (AWS) – is an open IoT platform combining the data of all machines, plants and systems from all facilities of the Volkswagen Group. Teradata will support Volkswagen by providing cloud-based data analytics to optimize production processes and drive productivity increases in the plants.
“We are delighted to partner with Volkswagen as part of this open industry platform, joining other leading companies in shaping the future of digital production,” says Sascha Puljic, Vice President, Central Europe at Teradata. “Our cloud-based data and analytics solution provides comprehensive data intelligence and supports Volkswagen in fully leveraging the value of data in production to increase efficiency and quality.”
The aim of the Industrial Cloud is to integrate companies from the entire value chain of the automotive industry, creating a vast partner network that shares data and services so that all partners benefit – including suppliers, technology providers, system integrators, independent software vendors (ISVs), logistics providers and manufacturers.
In the future, all sensor data and other information will be integrated in the cloud-based platform and analyzed with intelligent algorithms. Relevant data will be made available to partners in the Industrial Cloud, creating enormous potential for optimizing production and logistics processes.
To this endeavor Teradata brings its extensive experience in industrial IoT data analysis, as well as its analytics platform – Teradata Vantage – which is available on AWS. Data analysis from the cloud is used throughout production at Volkswagen to, for example: optimize yield, implement data-based quality assurance and enable predictive maintenance.
Optimizing Production and Driving Productivity
One initial use case that showcases Teradata’s role in supporting Volkswagen’s efforts to improve production processes, is analyzing data from welding procedures in manufacturing:
- First, spot welding data and additional metadata is integrated and analyzed in Teradata Vantage.
- Using analytical models that include machine learning, Teradata classifies and refines the data based on various quality characteristics.
- Based on the results, the downstream inspection processes can be optimized, and required rework can be specifically assigned.
- The outcome is confident, documented quality of the manufactured parts as well as increased efficiency of the production and quality assurance processes.
Another example of Teradata’s future contribution comes from a set of preliminary projects being explored at the Volkswagen Group: Teradata was able to identify additional efficiency potential in production processes, using AI (artificial intelligence). The team analyzed sequence optimization for specific production plants, while also developing quality predictions for manufacturing processes and products.
Such use cases, driven by Teradata expertise in the manufacturing space, demonstrate the power of integrating data to elevate production processes and increase productivity.
About Volkswagen Group
The Group comprises twelve brands from seven European countries: Volkswagen Passenger Cars, Audi, SEAT, ŠKODA, Bentley, Bugatti, Lamborghini, Porsche, Ducati, Volkswagen Commercial Vehicles, Scania and MAN. In addition, the Volkswagen Group offers a wide range of financial services, including dealer and customer financing, leasing, banking and insurance activities, and fleet management.
What Manufacturers Can Learn From Formula One’s Industrial Optimization Model
Reading Time: 3 minutes
When it comes to optimizing manufacturing processes, no one is better at it than the Mercedes-AMG Petronas Formula One team. From the way the team collects data, to how it analyzes that data and optimizes its systems and processes, it serves as a model for manufacturers. Let’s take a look at what manufacturers can learn from Mercedes-AMG Petronas F1’s industrial optimization model in order to improve their own factories.
Data Collection
When it comes to data, like other manufacturers, the team produces a plethora of data that needs to be collected and analyzed. This translates to 45 terabytes of data produced during the course of a race week, comprised of 50,000 data points from over 300 sensors. Similarly, in a factory, production machines generate large volumes of data that need to be analyzed and quickly. For example, a CPG company can generate 5,000 data samples every 33 milliseconds. Manufacturers can learn from F1’s amazing ability to collect, analyze, and act on that tremendous amount of data in near real time.
Manufacturers can learn from F1’s amazing ability to collect, analyze, and act on that tremendous amount of data in near real time. Click To Tweet
Data Analysis
For the Mercedes-AMG Petronas F1 team, one of the ways data is collected is from a digital twin simulator, which tests overall car performance. There are billions of combinations of car set-ups that are possible, so the team needs to use analysis and experience to figure out the best ones to test.
Like F1, in a factory, Industrial Internet of Things (IIoT) data must be analyzed in real time to understand how a process is performing in order to detect anomalies. Digital twins are also used in factories to reduce waste and improve product quality; a faulty product can lead to increased costs, rework, and unhappy customers, in addition to hefty fines and business closures. Digital twins are able to achieve this by mimicking real-world processes by utilizing sensors data in real-time to hone in and predict the key elements and attributes to optimize production, or prevent unnecessary failures.
Optimization
When everything is properly optimized, the Mercedes-AMG Petronas F1 team sees the most benefit at the track. After careful analysis of the data, the team is able to find the optimum car setup in rapidly changing circumstances leading to significant gains in performance. Other examples include a reduction in anomalies in gearbox changes, resulting in great track performance improvements, helping ensure the best race and qualifying lap times.
Imagine what that kind of time-saving that type of optimization could do for your company.
In fact, without proper manufacturing optimization, manufacturers face unplanned outages, which translate in a lower Overall Equipment Effectiveness (OEE). However, when optimized, manufacturers see increased performance and higher quality products.
Looking Ahead
In the coming decade, many manufacturers are going to be switching their smart factory strategy from one that was focused on technology implementation to one that is focused on process-change management. This will result in manufacturers treating their own IIoT assets like internal customers, reducing downtime, equipment failures, and diagnosing and resolving issues. Manufacturers will increasingly leverage digital twins driven by IIoT and machine learning in order to save operational expenses and optimize supply chains.
In the coming decade, many manufacturers are going to be switching their smart factory strategy from one focused on technology implementation to one that is focused on process-change management. Click To Tweet
While Mercedes-AMG Petronas F1 pioneered the modern industrial optimization model, manufacturers are starting to implement these best practices into their own factories. From data collection, data analysis, and optimization, manufacturers have an opportunity for greater industrial optimization going forward. When utilized properly and with the right technology, factories can increase performance, reduce costs, and produce higher quality products.
Download this infographic to see in greater detail what manufacturers can learn from Mercedes-AMG Petronas F1’s industrial optimization model. And, to learn more about how TIBCO gives the team a competitive advantage, visit our partnership page.
The Intelligent Industrial Manufacturing Enterprise: Five Ways Forward
Gone are the days when the industrial manufacturing value proposition was relatively simple: industrial manufacturers made products and delivered them.
Now, the industrial manufacturer’s job does not end with delivery. Driven by ever-more-demanding customers and supported by the widespread uptake of the Internet of Things (IoT) and the emerging power of machine learning and artificial intelligence, industrial manufacturers are developing new capabilities to track huge volumes of data generated by thousands of devices and are adjusting their service depending on the circumstances.
The goal is to be more responsive, always-on, and highly adaptable. Industrial manufacturers seek to collaborate more fully with their customers from discovery through design, service, and beyond. Wherever possible, they aim to deliver the kind of experiences and outcomes that customers reward with loyalty and ongoing business.
But how do companies move forward? Successful ones are focusing on the following five strategic priorities:
Be customer-centric
Industrial manufacturers are looking for ways to maintain customer-for-life relationships based on a 360-degree understanding of their customers. The starting point is a holistic view of their customers’ business processes – ending with the knowledge of how those customers use the products in their daily operations.
To get there, industrial manufacturers are moving toward omnichannel models for managing customers’ interactions across channels (Web, direct sales, IoT, and more). The goal is seamless interactions with customers, the ability to quickly see all products bought, and real-time visibility into how products are performing. This will help companies position the customer’s point of view at the center of every decision.
Serve the segment of one
Increasingly, industrial manufacturers will be able to deliver completely customized products, services, and solutions that precisely fit the needs of individual customers based on sophisticated platform, configuration, and mass-customization strategies.
Industrial manufacturers are moving toward this goal by rationalizing existing product options using machine learning to understand what really sells and what doesn’t. In addition, organizations are pulling in customer experience data to better understand requirements – and then using this data to inform requirements in product configurators that let customers define their own products on the fly.
Embrace digital smart products and solutions
Industrial manufacturers are shifting to products with more digital functionality, allowing even more flexible configuration of products. Thus, software-based features are on the rise with connectivity to enable remote access and monitoring.
Industrial manufacturers are extending original (physical) products with digital services that augment and extend product functionality. Combining insights into the end-user experience and the relevance and value of digital capabilities, manufacturers will extend the lifecycle of the product and increase lifetime revenue. With a direct feedback loop from the product back to the manufacturer, product enhancements and future developments will be based on the actual usage and experience of the product, from first interaction to product retirement.
Implement the digital supply chain and smart factory
Supply chains and manufacturing networks are becoming modular and flexible to allow the seamless execution of different manufacturing strategies. Industrial manufacturers, accordingly, are using Industry 4.0 philosophies and new digital technologies to implement “shop-floor-to-top-floor” connectivity for real-time visibility.
Subsequent steps will increase machine-to-machine connectivity and collaboration, allowing autonomous decisions based on sensor data and machine learning algorithms. Industrial manufacturers will combine feedback from connected stakeholders (customers, workers, suppliers) and associated processes to further improve overall manufacturing and supply chain performance. Intelligently connecting manufacturing, logistics, and supply chains enables companies to quickly address short-term demand impulses, supply fluctuations, and changes to customer orders, allowing a truly modular production process. This production flexibility enables industrial manufacturers to produce higher-quality individualized goods at lower costs.
Develop service-based business models
As revenues are increasingly linked to services that are based on and built around smart products, more industrial manufacturers will offer products as a service based on the value delivered to the end customer.
Remote condition monitoring of assets is critical to success with such models – enabling manufacturers to identify and provide additional value-added services. Based on the data collected, organizations can get better insight into how products are used. This enables them to offer pay-for-outcome models where the risk and long-term value of each customer is clearly understood.
The future is bright
Ultimately, the winners in the industrial manufacturing industry will be those companies that successfully transform themselves into fully customer-centric companies. Ahead for the industry is unprecedented change at unprecedented speed – but our industry is positioned to be a driver of progress. Together, we can lead the way.
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How Are Industrial Manufacturers Looking At Digitalization?
Part 1 of the “Digitalization of the IM&C Industry” series
Digitalization is an ongoing journey. As innovative technologies become more mainstream, the pace of innovation and transformation continues to increase. This is a journey that all companies are embracing in one way or another.
Take it from Georg Kube, global vice president of Industrial Machinery and Components (IM&C) at SAP:
“Companies are looking for new ways to deliver value to their customers using digital channels and creating personalized, digitally enabled products across all sectors, from consumer products to heavy equipment and machinery.”
This is fundamental for industrial machinery and components companies, as they need to not only innovate their business processes but also provide intelligent machines and equipment to help their customers innovate their businesses.
A July 2018 commissioned study conducted by Forrester Consulting on behalf of SAP found that 88% of innovative manufacturers have started or completed their digital transformation, compared to 54% of other companies. Those manufacturers that considered themselves to be innovative rated themselves highly across the board for innovation in strategy, technology, people, process, and culture.
Digital transformation is achieving real business value for manufacturing companies:
“Companies with 50% or more of their revenues from digital ecosystems achieve 32% higher revenue growth and 27% higher profit margin.”
– MIT Sloan Management Review, Thriving in an Increasingly Digital Ecosystem.
Industrial manufacturers can realize opportunity in two areas: Top-line revenue growth through better and more intelligent and differentiated products, and bottom-line cost savings through more efficient and more effective innovative processes using the digital capabilities of the equipment they use.
Transformation is not new to industrial machinery and component companies. Since the 1990s, manufacturers have been looking at process automation and techniques such as lean manufacturing. In the early 2000s, their focus shifted to becoming more responsive, and they started looking at flexible manufacturing processes, integrating the supply chain, and of course, further automation.
The current evolution is to build intelligence into the processes so each industrial manufacturer becomes more event- or insight-driven. This means using sensor data from all parts of the process and external data to drive operational decisions. It also means ensuring that the customer experience is more digital and consistent across any channel and that you have a complete understanding and visibility of your customer—finally going beyond basic automation by applying machine learning algorithms to improve performance and efficiency.
But manufacturers need to be vigilant as digitalization opens the playing field for companies that are not traditional manufacturers but that have expertise in these technologies from other industries. To be ready for these challenges it is essential for industrial manufacturers to focus on the right strategic priorities to drive digitalization. In conjunction with our customers, SAP has identified 5 key digital priorities:
1. Customer-centricity
Putting the end customers’ point of view at the center of every decision is essential for success in the digital age. This does not only apply to the sales department; it should also apply to what products are built and what services are offered.
2. Serving the segment of one
Providing solutions that precisely fit the needs of one single customer has been commonplace in traditional engineer-to-order environments. Now that same level of personalization must be applied to everyone in a cost-effective way. This requires the ability to capture customer requirements effectively and enable mass customization to give customers exactly what they want.
3. Digital smart products
Product differentiation and specificity stems from the digital capabilities and value-added services that are bundled with physical products. Delivering and using digital capabilities like self-awareness of technical health and operational status or business system connectivity helps industrial manufacturers differentiate themselves.
4. Digital supply chain and smart factory
Digital technology on the shop floor and in the supply chain is not new. What is new is the way that production and logistics can be intelligently connected to the rest of the business and to handle external impulses like short-term demand and supply fluctuations or changes in the configuration of a customer order on the fly.
5. Servitization and new business models
As traditional products are commoditized, IM&C companies are shifting from selling products to providing complete solutions, charging for outcomes, or even monetizing asset data. Generating more revenue from services is a goal for manufacturers who are looking for higher profit margins and increased customer intimacy.
These priorities impact every line of business across the value chain. The digital poster shows how each line of business will need different capabilities to achieve these priorities.
The commissioned study by Forrester Consulting on behalf of SAP showed that these digital priorities resonated well with manufacturers, which felt that these digital priorities were important for their business. Interestingly, those manufacturers that considered themselves innovative as had a much higher focus on those digital priorities than did other companies.
In my next blog, I will cover how innovative manufacturers see the cloud as a key foundation for digital transformation.
The Fourth Industrial Revolution For Finance
Part 1 in the 3-part “Finance and Intelligent Technology” series by Tony Klimas of EY and Joel Bernstein of SAP.
The “Fourth Industrial Revolution” is the idea that intelligent technologies such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and many more are pulling the global economy into a new post-computing age digital era. In this “transformative age,” industry disruption is common, and transformation to become an intelligent enterprise is the road to business survival.
The order of the day in the Fourth Industrial Revolution is adaptability. “Agile” is on the lips of corporate leaders everywhere. But more than ever, being agile as an enterprise means enabling agility across organizational units in a way that creates adaptability, insight, and cost-effective operation. All lines of business need to come together and work in tandem to deliver the kinds of experiences customers expect in the digital economy.
For no line of business is this truer than for finance – which by its very nature touches so many parts of any business. Take payables and receivables, for example. Traditionally, accounts payable/receivable is a back-office function. But in the digital economy, how you pay out and collect money is integral to the way customers and partners experience their interactions with you. Ride-sharing companies can link to your credit card so you don’t have to exchange money with your driver. Insurance companies can use telematics to bill you according to actual driving performance rather than actuarial tables based on “people like you.” These are just a couple of examples.
Finance as a change facilitator
Recently, I had a discussion with Joel Bernstein, who leads finance for global customer operations at SAP. According to Joel, part of making SAP more agile is finding ways to unlock the value of its data to build an intelligent finance organization that acts as a facilitator of change rather than a bottleneck that impedes change.
The moves made by the finance team at SAP in this regard are well-documented. Today, finance supports a growing cloud-based business where SAP customers pay according to a subscription model. The finance team has also moved to a shared services model where financial services are delivered out of regional centers following streamlined processes, assisted by automation wherever possible.
However, this transformation has not stopped there. More recently, the finance team has implemented a new “Deal Health Index” tool. “Let’s say a salesperson is preparing a quote,” Joel told me. “The deal is complex, including multiple product and service components in different geographical locations all priced separately.”
In the past, the salesperson would rely on a mixture of on-the-job expertise, information stored in price books, and the assistance of the finance team to craft quotes that adequately preserved margin. As SAP expands, however, this is hardly a scalable solution. “One thing the Deal Health index does,” Joel said, “is track the margin of any quote as it’s being built. If your deal health is green, you’re okay. If it starts to turn red, you’ll want to take a look at things before the quote is flagged for finance review.”
Assistance on demand
This kind of on-demand, in-the-moment assistance is critical for supporting a growing and constantly changing organization that demands agility. Traditional training – where you learn a process in a multi-day workshop and then forget about 90% of it – does little to help organizations keep pace in the digital economy.
That’s why Joel’s team is planning to go even further with the help of chatbots and machine learning. When a salesperson is in the middle of building a quote and has a question about the process, a chatbot can pop up to provide in-context assistance. These chatbots, furthermore, will be undergirded by machine learning. Algorithms can analyze processes for usage patterns, identify where the problems occur, and provide insight on where to modify a process or proactively add assistance. Conceivably, these chatbots could put themselves out of business: the more they learn, the simpler processes can get.
This kind of an approach helps put finance organizations out in front of challenges and opportunities. “If you’re going to be an effective finance organization,” Joel told me, “you can’t put yourself in the position of catching the results from the rest of the business. You have to be present at the beginning of the process. Otherwise, if there’s anything broken along the way, you’ll be in a bad place. Whether it’s paying vendors, building deals, collecting cash, or whatever – you need to build in transparency, efficiency, and scalability from step one.”
A foundation of trusted data
All of this, of course, is predicated on a solid foundation of trusted data. Shared services at SAP, for example, are made possible with a single source of truth for the processes they support. The same can be said for the Deal Health Index and the chatbots to come. Only with a solid data platform backed by solid data governance can lines of business within organizations effectively align with one another to drive change and deliver the kinds of experiences customers expect as a result of the Fourth Industrial Revolution.
The next blog in this series explores cloud computing as the foundation for intelligent technologies.
Want to hear more how finance leaders are harnessing the power of technology innovations to transform their operations? Register today to attend the first-ever, complimentary online SAP Finance and Risk Management Virtual Event Tuesday, Feb. 5 for an insightful experience of customers, experts, partners, and SAP executives discussing today’s pressing challenges and opportunities.
The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organization or its member firms.
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Winning Strategies for the Fourth Industrial Revolution
- Data is the modern commercial playing field, information dominance is your goal, those that can “act and with speed” have the advantage over those which cannot.
- Advantages in the speed of data-driven decision-making, automation, robotic process automation will dictate the winners of tomorrow.
- Complexity is the enemy of agility, and acts as poison from the past.
- It takes an optimized Information Logistics Systems (OILS) to support real-time digital interactions.
- Faster operational tempos and information logistics systems – open up a plethora of new business opportunities and business models.
- Precision and real-time data beats estimates and conjecture.
- Digital interactions require real-time business operational tempos.
- Bad data will make the smartest systems dumb.
- Competitive advantages from new technologies depreciate quickly – so act fast.
- Situational awareness enables management to focus on the knowns, rather than the unknowns.
- Demand for real-time digital customer interactions increases the need for contextually relevant and personalized user experiences and digital transformation across all systems and processes.
- Data has a shelf life, and the economic value of data diminishes quickly over time, and the more data that is collected analyzed and used, the greater the economic value it produces in aggregate. In addition, the economic value of data multiplies when combined with context and right time delivery.
- Digits can be changed faster than the human mindset.
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I invite you to watch my latest short video on digital technology trends and strategies:
Measuring the Pace of Change in the Fourth Industrial Revolution
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- Report: 40 Months of Hyper-Digital Transformation
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- Technology Must Disappear in 2017
- Merging Humans with AI and Machine Learning Systems
- In Defense of the Human Experience in a Digital World
- Profits that Kill in the Age of Digital Transformation
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- Digital Hope and Redemption in the Digital Age
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- Jettison the Heavy Baggage and Digitally Transform
- Digital Transformation – The Dark Side
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- 15 Rules for Winning in Digital Transformation
- The End Goal of Digital Transformation
- Digital Transformation and the Ignorance Penalty
- Surviving the Three Ages of Digital Transformation
- The Advantages of an Advantage in Digital Transformation
- From Digital to Hyper-Transformation
- Believers, Non-Believers and Digital Transformation
- Forces Driving the Digital Transformation Era
- Digital Transformation Requires Agility and Energy Measurement
- A Doctrine for Digital Transformation is Required
- Digital Transformation and Its Role in Mobility and Competition
- Digital Transformation – A Revolution in Precision Through IoT, Analytics and Mobility
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- Mobile and IoT Technologies are Inside the Curve of Human Time
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Reporting on Industrial IoT Platforms and AI from MWC17
As part of SAP’s IoT Influencer program, I had the honor of interviewing Hitachi’s Rob Tiffany on Industrial IoT platforms, mobility platforms and the role of artificial intelligence at Mobile World Congress 2017.
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Oppo To Spend USD216 Million For Industrial Park In India
Chinese mobile phone manufacturer Oppo plans to invest USD216 million to build an industrial park in India’s Greater Noida, and this project will include a manufacturing plant.
According to Sky Li, Oppo’s vice president and president for its India branch, the company’s industrial park will cover an area of 1,000 acres and the construction will be divided into stages. The entire project is expected to be completed in two or three years. Its initial annual output is expected to be 50 million phones and its ultimate goal is to produce 100 million phones every year.
At present, Oppo already has one factory in Greater Noida.
Li is very confident about Oppo’s performance in the Indian smartphone market. He said Oppo will gain market share by providing outstanding user experience. Li also said that they are not after high shipments or prompt company scale expansion. Instead, the company will focus on providing high-quality products to users.
Statistics from GSMA show that in 2016 India exceeded America and became the world’s second largest smartphone market. The association also predicted that by 2020, the number of India’s mobile phone users will reach one billion. By June 2016, this number was 616 million.
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Recommended article: The Guardian’s Summary of Julian Assange’s Interview Went Viral and Was Completely False.
Companies capitalize on industrial IoT data analysis
As sensors are attached to more and more industrial machines, these connected devices create a massive glut of data. But to unleash the true value of Internet of things, or IoT, data analysis, businesses need to have a clear understanding of its strengths and weaknesses.
In a panel discussion at the IoT Data Analytics and Visualization conference in Palo Alto, Calif., several speakers said the most important thing to keep in mind is that the industrial Internet should be about improving business processes, not just about implementing cool new technology.
Nauman Sheikh, founder and CEO of analytics consulting firm Asrym, Inc., said he recently worked with a large public utility to put sensors on maintenance trucks to do predictive maintenance. Sheikh built the predictive models that analyzed the data coming from the sensors to identify vibration patterns and other signs that a truck might be at risk of breaking down.
But when he pitched the idea to the utility’s management team, he didn’t talk about the algorithms he would build or the sensor and networking technology required. He said the key to getting the project off the ground was to identify with the needs of the business team and “speak their language.”
“The lesson learned was, if I was pitching to them some nice tools or fancy technology, there would have been no interest,” Sheikh said. “If you can connect to their pain the value IoT can bring to the table, the adoption will be fast and very effective.”
Similarly, Prakash Iyer, vice president of software architecture and strategy at Trimble Navigation, said businesses interested in IoT data analysis should focus on the ultimate goal, which should be automating industrial processes. Trimble makes GPS positioning software and connected hardware for industries such as agriculture and construction.
The point of any IoT or industrial Internet project, Iyer said, is to develop new insights from machinery into which businesses previously had little visibility. By analyzing data coming from machinery, either using simple visualizations or more complex machine learning algorithms, it may be possible to automate industrial processes by developing sets of business rules that kick in whenever an event occurs. Once businesses understand industrial processes, they can develop rules to automate them. But if an IoT data analysis project fails to deliver actionable insights, then the organization should question its investment, Iyer said.
If you can connect to their pain the value IoT can bring to the table, the adoption will be fast and very effective. Nauman Sheikhfounder and CEO of Asrym, Inc.
“The most important thing is not the visualization, but how you make it actionable,” Iyer said. “We need to be able to automate the next step.”
For Sudhi Ranjan Sinha, vice president of product development, building technology and services at Johnson Controls, the key to realizing business value from industrial Internet analytics projects is planning for the future by being flexible. He said in his company’s industry of heating and ventilation, legislated uses of refrigerants and customer expectations of efficiency are likely to change over time. But most of the equipment they produce has a life expectancy of around 25 years.
So Sinha said when he and his team build predictive models that measure efficiency and identify potential problems in connected heating and ventilation units, it’s important to keep in mind that things will change and to build flexibility into the models by not relying on efficiency assumptions made when the unit is produced.
“There is no permanency in this space,” Sinha said. “Every model we create has to be adaptive.”
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