by Gary Mintchell | Jan 24, 2020 | Data Management, Manufacturing IT
Digitization is on everyone’s lips these days. If you have not taken steps to implement and improve digital data flow, you are probably already behind. I receive information regularly from PwC and here is a new report on how digitization is reshaping the manufacturing industry. The report takes a look at 8 companies and showcase how they improved their efficiency, productivity and customer experience by ensuring they have the right capabilities central to their operating model and by matching them with strong skill sets in analytics and IT.
Pressure from the consumer, new regulations and advances in information technology are all reasons that are pushing manufacturing organizations to digitize so they can avoid falling behind the new breed of market-leading ‘digital champions.’ The report identifies 4 significant changes CEOs must implement to maximize the benefits of digitization.
1. Drive organizational changes that address new digital capabilities and digitalized processes – e.g., product and process design and engineering, end-to-end procurement, supply chain/distribution and after-sales – right from the top, because these are so new and different
2. Hire more software and Internet of Things (IoT) engineers and data scientists, while training the wider workforce in digital skills
3. Learn from software businesses, which have the ability to develop use cases rapidly and turn them into software products
4. Extend digitalization beyond IT to include significant operational technologies (OT) such as track and trace solutions and digital twinning
From the report, “Already, digitally ‘smart’ manufacturers are gaining a competitive advantage by exploiting emerging technologies and trends such as digital twinning, predictive maintenance, track and trace, and modular design. These companies have dramatically improved their efficiency, productivity, and customer experience by ensuring these capabilities are central to their operating models and by matching them with strong skill sets in analytics and IT. “
During 2018 and early 2019, PwC conducted in-depth digitisation case studies of eight industrial and manufacturing organisations in Germany, the US, India, Japan and the Middle East. Drawing on discussions and interviews with CEOs and division heads, we explored the key triggers for change these companies faced, assessed how digital solutions are being implemented and how digitisation is affecting key aspects of their operating models. We also compared our eight organisations with other publicly cited digitisation case studies, and leveraged PwC’s 2018 study Digital Champions: How industry leaders build integrated operations ecosystems to deliver end-to-end customer solutions and other ongoing PwC research.
This paper is the result of ongoing collaboration between PwC and the Global Manufacturing and Industrialisation Summit (GMIS). GMIS provides a forum for industry leaders to interact with governments, technologists and academia in order to navigate the challenges and opportunities brought about by the digital technologies of the Fourth Industrial Revolution. PwC has been a knowledge partner with GMIS since 2016.
The eight case studies in this report make clear how far the role of digital technology goes beyond traditional IT systems. It also encompasses OT and data and analytics technologies. Full integration and linkage among these different technologies, and the ecosystems they are part of, are essential to a successful digital transformation. Yet success is impossible without a digitally smart workforce that is familiar with Industry 4.0 skills and tools.
These challenges are the subject of the second part of the report Digital Champions: How industry leaders build integrated operations ecosystems to deliver end-to-end customer solutions, which will be published in January 2020.
The report will elaborate further on the emerging theory of digital manufacturing and operations, in which successful, digitised industrial organisations will increasingly have to act like software companies in response to four key factors:
- The connected customer seeks a batch size of one, necessitating greater customisation of products and delivery time, improved customer experience, use of online channels and outcome-based business models.
- Digital operations require both engineering and software abilities to enable extensive data analysis and IoT-based integration, as well as digitisation of products and services.
- Organisations need augmented automation, in which machines become part of the organisation via closely connected machine–worker tasks and integrated IT and OT.
- Future employees will be ‘system-savvy craftspeople’ with the skills to use sensors in order to collect and analyse accurate data, as well as design and manage connected processes.
About the authors
Anil Khurana is PwC’s global industrial, manufacturing and automotive industry leader. He is a principal with PwC US.
Reinhard Geissbauer is a partner with PwC Germany based in Munich. He is the global lead for PwC’s Digital Operations Impact Center.
Steve Pillsbury is a principal with PwC US and the US lead for PwC’s Digital Operations Impact Center.
by Gary Mintchell | Jan 7, 2020 | Operations Management
I give up. To me, the end of the decade is next year figuring there was not a year 0, then the beginning of the new calendar was year 1 and the end of the first decade was year 10. Oh, well, mainstream media just can’t wait to jump into wrap-up frenzy. So, me, too.
The last 10 years in industrial technology was busy with new buzz words—heavier on marketing than on substance in many ways. We breezed through Industrie 4.0 with its cyber-physical systems. Then we had Internet of Things borrowed from the consumer, largely iPhone, space. But borrowing from GE advertising of the “Industrial Internet”, the “Industrial Internet of Things” became originally the European counterpoint to Germany’s Industry 4.0 and then grew into general adoption.
Not finished with all this buzz, the industry discovered “digital”. We had digital twin (derived from cyber-physical systems). But these had to be connected with the digital thread. And all led into a digital transformation.
Let’s take a look at some specific topics.
Innovation
Much of the foundation was laid in the decade before. Maybe I should say decades. The industry started digitizing in the 1980s. It’s been building ever since. Through the first decade of this millennium great strides were made in control technology, usability, sensors (both sensitivity and communication), networks moving from analog to digital and through field buses to Ethernet.
In this decade, most companies grew by acquisition of smaller, innovative companies and start-ups. The remaining automation giants pieced together strategies based on visions of which companies to acquire and what customer solutions were required. Looking ahead, I’m considering what additional consolidation to anticipate. I think there will be more as the market does not seem to be growing dramatically.
Most innovation came in the realm of data. Decreasing costs of memory, networking stacks, and other silicon enabled leaps in ability to accumulate and communicate data. Borrowing software advances from IT, historians and relational databases grew more powerful along with new types of data handling and analysis coming from the “big data” and powerful analytics technologies.
Another IT innovation that finally hit industrial companies was adoption of “cloud” with the eventual development of edge. Instead of the Purdue Enterprise Reference Model of the control/automation equipment being the gateway of all data from the processes, companies began to go sensor to cloud, so to speak, breaking down the rigidity of PERA thinking.
Digital Everything
It is now old news that digital is everywhere. And, it is not a sudden development. It has been building for 30 years. Like all technology, it builds over time until it’s suddenly everywhere. The question is no longer what is becoming digital, nor is it speculating over marketing terms like digital transformation.
The question about digital everything is precisely how are we to use it to make things better for humans and society.
Sensors—At least by 2003, if not before, I was writing about the converging trends in silicon of smaller and less expensive networking, sensing, processing, and memory chips and stacks that would enable an explosion of ubiquitous sensing. It’s not only here; it is everywhere. Not only in manufacturing, but also in our homes and our palms.
Design—CAD, CAM, PLM have all progressed in power and usability. Most especially have been the development of data protocols that allow the digital data output of these applications to flow into operations and maintenance applications. Getting as-built and as-designed to align improves maintenance and reliability along with uptime and productivity. And not only in a single plant, but in an extended supply chain.
Networking—The emergence of fast, reliable, and standardized networking is the backbone of the new digital enterprise. It is here and proven.
Software—Emergence of more powerful databases, including even extension of historians, along with data conversion protocols and analysis tools provides information presented in an easily digestible form so that better decisions may be made throughout the extended enterprise.
IT/OT
Industry press have talked about IT/OT convergence until we are all sick of the phrase. Add to that stories of in-fighting between the organizations, and you have the making of good stories—but not of reality or providing a path to what works. As Operations Technology (OT) has become increasingly digital, it inevitably overlaps the Information Technology (IT) domain. Companies with good management have long since taken strides to foster better working relationships breaking the silos. Usually a simple step such as moving the respective manager’s offices close to each other to foster communication helps.
New Entrants
Speaking of IT and OT, the modification of the Purdue Enterprise Reference Modal to show data flowing from the plant/sensor level directly to the “cloud” for enterprise IT use has enticed new entrants into manufacturing technology.
If we are not forced to go through the control system to provide data for MES, MOM, ERP, CRS, and the like, then perhaps the IT companies such as Dell Technologies, Hewlett Packard Enterprise, and Hitachi Vantara can develop their compute platforms, partnerships, and software to provide that gateway between plant floor and enterprise without disturbing the control platform.
Therefore we are witnessing proliferating partnerships among IT and OT automation suppliers in order to provide complete solutions to customers.
Strategy
Remember—it is all meaningless unless it gets translated into intelligent action to make the manufacturing supply chain more productive with better quality and more humane.
by Gary Mintchell | Sep 25, 2019 | Data Management, Internet of Things, Manufacturing IT
DataOps—a phrase I had not heard before. Now I know. Last week while I was in California I ran into John Harrington, who along with other former Kepware leaders Tony Paine and Torey Penrod-Cambra, had left Kepware following its acquisition by PTC to found a new company in the DataOps for Industry market. The news he told me about went live yesterday. HighByte announced that its beta program for HighByte Intelligence Hub is now live. More than a dozen manufacturers, distributors, and system integrators from the United States, Europe, and Asia have already been accepted into the program and granted early access to the software in a exchange for their feedback.
Intelligence Hub
HighByte Intelligence Hub will be the company’s first product to market since incorporating in August 2018. HighByte launched the beta program as part of its Agile approach to software design and development. The aim of the program is to improve performance, features, functionality, and user experience of the product prior to its commercial launch later this year.
HighByte Intelligence Hub belongs to a new classification of software in the industrial market known as DataOps solutions. HighByte Intelligence Hub was developed to solve data integration and security problems for industrial businesses. It is the only solution on the market that combines edge operations, advanced data contextualization, and the ability to deliver secure, application-specific information. Other approaches are highly customized and require extensive scripting and manual manipulation, which cannot scale beyond initial requirements and are not viable solutions for long-term digital transformation.
“We recognized a major problem in the market,” said Tony Paine, Co-Founder & CEO of HighByte. “Industrial companies are drowning in data, but they are unable to use it. The data is in the wrong place; it is in the wrong format; it has no context; and it lacks consistency. We are looking to solve this problem with HighByte Intelligence Hub.”
The company’s R&D efforts have been fueled by two non-equity grants awarded by the Maine Technology Institute (MTI) in 2019. “We are excited to join HighByte on their journey to building a great product and a great company here in Maine,” said Lou Simms, Investment Officer at MTI. “HighByte was awarded these grants because of the experience and track record of their founding team, large addressable market, and ability to meet business and product milestones.”
To further accelerate product development and go-to-market activities, HighByte is actively raising a seed investment round. For more information, please contact [email protected].
Learn more about the HighByte founding team —All people I’ve know for many years in the data connectivity business.
Background
From Wikipedia: DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations.
DataOps incorporates the Agile methodology to shorten the cycle time of analytics development in alignment with business goals.
DataOps is not tied to a particular technology, architecture, tool, language or framework. Tools that support DataOps promote collaboration, orchestration, quality, security, access and ease of use.
From Oracle, DataOps, or data operations, is the latest agile operations methodology to spring from the collective consciousness of IT and big data professionals. It focuses on cultivating data management practices and processes that improve the speed and accuracy of analytics, including data access, quality control, automation, integration, and, ultimately, model deployment and management.
At its core, DataOps is about aligning the way you manage your data with the goals you have for that data. If you want to, say, reduce your customer churn rate, you could leverage your customer data to build a recommendation engine that surfaces products that are relevant to your customers — which would keep them buying longer. But that’s only possible if your data science team has access to the data they need to build that system and the tools to deploy it, and can integrate it with your website, continually feed it new data, monitor performance, etc., an ongoing process that will likely include input from your engineering, IT, and business teams.
Conclusion
As we move further along the Digital Transformation path of leveraging digital data to its utmost, this looks to be a good tool in the utility belt.
by Gary Mintchell | Sep 10, 2019 | Internet of Things, Manufacturing IT
Sean Riley, Global Director of Manufacturing and Transportation at Software AG, discussed Industrial IoT (IIoT) implementation in industry with me a couple of weeks ago. Now, a survey sponsored by Software AG has been released revealing that manufacturers are not scaling IIoT across the enterprise due to failure to invest in predictive analytics and innovative integration strategies.
The shocking thing to me about the survey is that it mirrors survey results over the past three or four years. Executives and managers recognize a problem further even acknowledging that this is something that could cost them competitively against the market even putting them out of business. Yet, they cannot figure out how to do it right. They whine about how tough it is.
Sounds to me like a new crop of leadership is needed.
There are good practices taught some 40 years ago when I took a deep dive while implementing my first IT project. Things like understanding the system first. Bringing all the departments in on the plans, work to be done, and benefits we all would get. Some recommendations from Software AG sound that familiar—breaking silos, bringing IT and OT organizations closer together (a management problem, not a technical one), transparency in the project roll out.
The survey of over 125 North American manufacturers primarily in the heavy industry and automotive sectors revealed inability to scale IIoT investments across their enterprises results in losing millions of dollars in potential profits.
The survey also revealed that the vast majority of manufacturers queried report that their IIoT investments are limited – locked in one small department or sector of their company – preventing these organizations from sharing the power of IIoT across their enterprises.
Other key findings include:
- 80% of all survey respondents agree that processes around IIoT platforms need to be optimized or they will face a competitive disadvantage but very few are doing this
- IT-OT integration is considered one of the most difficult tasks – with 57% of automotive manufacturers stating that this has prevented them from realizing full ROI from their IIoT investments
- 84% of automotive and heavy industry manufacturers agree that the most important area of IIoT is “monetization of product-as-a-service-revenue.” However, optimizing production is still important with 58% of heavy industry and 50% of automotive manufacturers agreeing with that statement
- Curiously, defining threshold-based rules is considered almost as difficult as leveraging predictive analytics to scale IIoT. More than 60% of respondents stated that defining threshold-based rules was as difficult as integrating IT systems and IoT sensors into existing control systems.
“Manufacturers place a high value on IIoT, but they are encountering serious difficulties in unlocking the complete intended value to unleash their innovation across their organizations,” said Riley. “Fortunately, there is a way for them to quickly and easily resolve this problem. By investing in the right IT-OT integration strategy that leverages sensors, predictive analytics, machine learning, control applications, and product quality control, manufacturers can fix this problem in less than 6-12 months while realizing other key benefits, namely extended equipment lifetime, reduced equipment maintenance costs and accessing more accurate data for production-quality improvements.”
Riley outlined five best practices for manufacturers to follow when looking to scale their IIoT investments across their enterprises and realize immediate profits and competitive advantage. Those best practices are:
1. Ensure clear collaboration between IT and the business by leveraging a step by step approach that starts focused and has clear near term and long- term objectives to scale
2. Create a transparent roll out process and don’t let other plants or departments move ahead outside of it
3. Give IT the ability to connect at speed with a digital production platform that is proven to be successful
4. Leverage a GUI driven, consistent platform to enable an ecosystem of IT associates, business users and partners around the platform
5. Enable the plant or field service workers to work autonomously without continual support from IT through GUI driven analytics, centralized management and easy, batch device connectivity and management
Riley also stated that it is critically important for manufacturers to select the best possible IIoT integration platform supported by key enabling technologies like streaming analytics, machine learning, predictive analytics and a larger ecosystem. Software AG’s Cumulocity IoT platform recently received the highest use case scores from Gartner Group in the brand new “Critical Capabilities for Industrial IoT Platforms” report which included Monitoring Use Case, Predictive Analytics for Equipment Use and Connected Industrial Assets Use Case for its IoT.
The Software AG IIoT Implementation survey was completed in Q2 2019 by Software AG and an independent third-party research house. The survey queried nearly 200 respondents at large manufacturing companies across automotive, heavy industry, high-technology, electronics, pharmaceutical and medical device industries. The respondents were primarily senior executives leading Manufacturing or Information Technology with the breakdown of 50% Managers, 38% Directors and 13% Vice Presidents or higher.
Software AG product
The press release contained some information about the company’s IoT platform—Cumulocity.
Being device and protocol agnostic allows it to connect, manage, and control any “thing” over any network. Cumulocity IoT is open and independent, letting customers connect to millions of devices without being locked into one single vendor.
by Gary Mintchell | May 30, 2019 | Manufacturing IT, News, Security
Cyber Security got a shout-out during the Siemens Spotlight on Innovation forum in Orlando last week. Leo Simonovich, VP and Global Head, Industrial Cyber and Digital Security at Siemens Gas and Power, and Mike Wiacek, co-founder & CSO of Chronicle (an Alphabet company) took the stage discussing their newly signed cyber security agreement.
Key phrase—“customers can own their environment”. Perhaps the most interesting conversation I had during the networking event was with a Chronicle tech person who gave me a deep dive into the product. This is security unlike everything else I investigate in the OT realm. This isn’t a network monitoring app. Nor is it a device that acts as a firewall for industrial control devices. It builds a huge database and adds analytics (which is “in our DNA”). The solution has two parts—visibility and context. It bridges IT and OT worlds with the intent to “democratize security for the success of the digital economy”; that is, make it accessible to customers, simple, affordable, easy-to-use.
Through a unified approach that will leverage Chronicle’s Backstory platform and Siemens’ strength in industrial cyber security, the combined offering gives energy customers unparalleled visibility across information technology (IT) and operational technology (OT) to provide operational insights and confidentially act on threats.
The energy industry has historically been unable to centrally apply analytics to process data streams, cost-effectively store and secure data, and identify malicious threats within OT systems. Research conducted by Siemens and Ponemon Institute found that while 60 percent of energy companies want to leverage analytics, only 20 percent are utilizing any analytics to do security monitoring in the OT environment. Small and medium enterprises are particularly vulnerable to security breaches as they frequently do not have the internal expertise to manage and address increasingly sophisticated attacks.
“The innovative partnership between Siemens and Chronicle demonstrates a new frontier in applying the power of security analytics to critical infrastructure that is increasingly dependent on digital technology,” said Simonovich. “Cyber-attacks targeting energy companies have reached unprecedented speeds, and our cutting-edge managed service unlocks the analytics ecosystem offers a new level of protection from potential operational, business and safety losses.”
“Energy infrastructure is an obvious example of cyber-attacks affecting the physical world and directly impacting people’s lives,” said Ansh Patnaik, Chief Product Officer, Chronicle. “Backstory’s security telemetry processing capabilities, combined with Siemens’ deep expertise, gives customers new options for protecting their operations.”
The partnership between Siemens and Chronicle will help energy companies securely and cost-effectively leverage the cloud to store and categorize data, while applying analytics, artificial intelligence, and machine learning to OT systems that can identify patterns, anomalies, and cyber threats. Chronicle’s Backstory, a global security telemetry platform for investigation and threat hunting, will be the backbone of Siemens managed service for industrial cyber monitoring, including in both hybrid and cloud environments. This combined solution enables security across the industry’s operating environment – from energy exploration and extraction to power generation and delivery.
