Industrial Internet of Things to the Production Floor

Industrial Internet of Things to the Production Floor

Major IT companies have been scrambling to compete in the Industrial Internet of Things market. The control, instrumentation, and automation companies all talk about how this is all stuff they’ve been doing for years, or even decades, this is really quite new.

The first IT company people I talked with talked about selling boxes—gateways or edge computing. I’m thinking that there’s not enough money in that market. And, I was right. As the companies flesh out their strategies, the IoT group leadership keeps moving higher up the corporate ladder. And the vision broadens to include much of the portfolio of the companies enabling them to progressively enhance their competitive positions within their major customers.

Hitachi Vantara has recently been talking with me about their approach to the problem. I learned about Vantara and its focus initially through people I knew who landed new positions there. Life is always about serendipity. In the past, I’ve reported on the Lumada platform and the way the company is building modularly atop it. There was Maintenance Insights and then Video Insights. Now unveiled is Manufacturing Insights. I will get a deeper dive and talk to customers the second week of October when I attend its customer conference.

Note that these applications have more in common with MES than what you might think of as simply connecting devices with IIoT. In other words, the value proposition and integration into the customer grows.

Let’s discuss the latest addition to the Hitachi Lumada platform, Manufacturing Insights, which the company describes as a suite of industrial internet-of-things (IoT) solutions that empower the manufacturing industry to achieve transformative outcomes from data-driven insights. Using artificial intelligence (AI), machine learning (ML), and DataOps, Lumada Manufacturing Insights optimizes machine, production, and quality outcomes.

“Data and analytics have the power to modernize and transform manufacturing operations. But for too many manufacturers today, legacy infrastructure and disconnected software and processes slow innovation and impact competitive advantage,” said Brad Surak, chief product and strategy officer at Hitachi Vantara. “With Lumada Manufacturing Insights, customers can lay a foundation for digital innovation that works with the systems and software they have already to operationalize immediate gains in uptime, efficiency and quality and transform for the future.”

Accelerate Manufacturing Transformations

Lumada Manufacturing Insights applies data science rigor to drive continuous improvement opportunities based on predictive and prescriptive analytics. The solution integrates with existing applications and delivers actionable insights without the need for a rip-and-replace change of costly manufacturing equipment or applications. Lumada Manufacturing Insights supports a variety of deployment options and can run on-premises or in the cloud.

“With Hitachi Vantara, our customers benefit from our deep operational technology expertise and distinctive approach to co-creating with them to accelerate their digital journey,” said Bobby Soni, chief solutions and services officer at Hitachi Vantara. “With our proven methodologies and advanced tools, we can tailor solutions for our customers that enhance productivity, increase the speed of delivery, and ultimately deliver greater business outcomes.”

Providing machine, production and quality analytics, Lumada Manufacturing Insights drives transformational business outcomes by enabling customers to:

• Build on the intelligent manufacturing maturity model and empower the digital innovation foundation for continuous process improvement.

• Integrate data silos and stranded assets and augment data from video, lidar, and other advanced sensors to drive innovative new use cases for competitive advantage.

• Drive 4M (machine, man, material and methods) correlations for root-cause analysis at scale.

• Evaluate overall equipment effectiveness (OEE) and enhancement recommendations based on advanced AI and ML techniques.

• Evaluate scheduling efficiency and optimize for varying workloads, rates of production and workorder backlogs.

• Monitor and guide product quality with predictive and prescriptive insights.

• Improve precision of demand forecast and adherence to production plans and output.

Customer Comments

I hope to get more depth while I’m at the Next 2019 user conference Oct. 9-10. Here are some supplied quotes.

“Significant short-lead products have to be designed, prototyped and delivered to meet the demands of our customers and partners as we accelerate the product supply for 5G. Ericsson and Hitachi Vantara have collaborated to test Lumada Manufacturing Insights to gear up for an anticipated increase in new product introductions, establishing a digital innovation foundation for sustained gains,” said Shannon Lucas, head of customer unit emerging business for Ericsson North America. “We are leveraging the same solution that we will take to our joint customers in partnership with Hitachi Vantara, and will further expand IIoT use cases based on our 5G technologies.”

“As a progressive manufacturer, our focus was to accelerate transformative change, eliminate data silos and build a foundation for digital innovation that would accelerate our journey toward Manufacturing 4.0. “We leveraged the IIoT workshop to align our use cases with our business transformation priorities and have a roadmap for success with Lumada Manufacturing Insights,” said Vijay Kamineni, business transformation leader at Logan Aluminum. “The collaboration with Hitachi Vantara enables us to define business goals for each stage of our transformation, with clear outcomes that we believe will accelerate gains in productivity, quality, safety and sustainable manufacturing. “Hitachi Vantara brings a unique IT/OT advantage that will help us in the long run.”

“Humans and machines working together to deliver the vision of ‘digital drilling’ is driven by our ambition to achieve transformative outcomes, drilling our best wells every time and consistently achieving Target Zero for accidents. With Hitachi Vantara, we are realizing time to value with industrial analytics and the powerful Lumada platform to process more than 20,000 data streams per second per rig, providing actionable information to the right people at the right time and helping make optimal decisions. This drives our operational excellence and consequently our competitive advantage,” said Shuja Goraya, CTO at Precision Drilling Corporation. “We’re leveraging insights from video and lidar, integrating it with Lumada Manufacturing Insights to deliver business outcomes. It’s driving process optimization through effectively identifying improvement opportunities and shortening well delivery times for our customers. It’s all about effective use of data to make better decisions and then being able to consistently execute on these learnings. We are excited about our strategic partnership with Hitachi Vantara.”

Availability

Lumada Manufacturing Insights will be available worldwide Sept. 30, 2019.

Industrial Internet of Things to the Production Floor

Industrial Manufacturers Are Behind the Industrial IoT Innovation Curve

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.

Cloud-Based Enterprise Resource Management Optimized for EPC Market

Cloud-Based Enterprise Resource Management Optimized for EPC Market

I devoted a lot of time over several years working with an organization trying to construct a manufacturing IT platform that, using internationally adopted standards, would allow data to move seamlessly from engineering to construction to startup to operations and maintenance. Worley had key members on the team and provided time and effort to proof of concept work. The idea was to close the loop of as-designed to as-built to as-operated such that maintenance technicians could easily locate all necessary data about components and systems during startup and operations.

The project was under the umbrella of MIMOSA, of which I was chief marketing officer for a year. I still believe in the reason for the project, but for many reasons it just didn’t seem to take off. One reason was reluctance of major automation suppliers to sign on for a standards-based approach. With this announcement, it appears the work will be done through one supplier’s proprietary approach.

AVEVA announced that Worley has selected AVEVA’s Enterprise Resource Management solution as its preferred materials management platform. The partnership combines Worley’s Engineering, Procurement and Construction (EPC) knowledge with AVEVA’s industrial software expertise “to deliver the first cloud-based Enterprise Resource Management solution optimized for the EPC market.”

Like many businesses, today’s EPCs are challenged with reducing project costs while keeping pace with changing IT environments. However, as EPC projects operate as mini-enterprises, on-premises configuration and hosting of enterprise projects within private networks is not only costly, but restrictive and unsustainable in an industry undergoing mass consolidation. For global EPCs to remain competitive, the move from an on-premises infrastructure to cloud-based enterprise resource management is necessary.

Worley sought to help its customers find a way to streamline their materials management to deliver on these challenges while also creating process improvements, increased efficiency, ease-of-use and the ability to deliver in-house training. After reviewing AVEVA’s Enterprise Resource Management solution, which had historically been used in marine settings, Worley and AVEVA committed to developing the AVEVA solution to become the industry’s first cloud-based enterprise resource management platform purpose-built for EPCs.

“The EPC market is undergoing a period of change and our customers are looking to us to help them find solutions in this new world.  The advances in technology and digital disruption have provided us with an opportunity to rethink our approach to materials management. We needed to deliver an efficient, cloud-based solution customized for the nuances of our market,” said Andrew Wood, CEO Worley. “With AVEVA, we saw a commitment to developing this solution together to create something best-in-class for engineering. We believe the AVEVA Enterprise Resource Management solution marks a step forward for productivity, efficiency and effectiveness that will drive the EPC industry forward.”

The cloud-enabled solution from AVEVA and Worley is the first of its kind and will be fully optimized for the EPC market. By embedding Worley’s subject matter expertise in EPC supply chain management, major updates to the AVEVA Enterprise Resource Management solution for EPCs includes:

  • Project-specific functionality: Enabling EPCs to view and work on projects in AVEVA Enterprise Resource Management as standalone entities
  • Updated catalogs and specifications module: Migration of Worley’s legacy corporate catalog and specifications to create a robust, easy-to-use model for EPCs
  • Training solution: Allowing EPCs to streamline internal training on the new solution

In April 2019, Worley and AVEVA kicked off the final stage of a four-phase program to develop the AVEVA Enterprise Resource Management solution for EPCs. Phase one included design, while phase two incorporated the solution build, moving onto phase three integrations and catalog readiness, and now into phase four—project go-live and decommissioning of Worley’s legacy solution.

As part of the program, AVEVA Enterprise Resource Management reduced training time across engineering, procurement and project controls by 23%. Participants noted the solution was easy to use, provided quality training materials and the right functionality for EPC projects.

“The construct of the co-managed project team exceeded all expectations. We set up stringent delivery benchmarks and executed the project in phases to ensure alignment between the teams remained in place. A transparent and open working relationship with a keen focus on the success of the initiative played a crucial role in adjusting to all project challenges, and this solution is something we are proud to have delivered together,” commented Craig Hayman, CEO AVEVA.

The first official project roll-out for Worley on the AVEVA Enterprise Resource Management solution for EPCs will begin this month. Worley will use AVEVA Enterprise Resource Management and AVEVA Everything3D innovative plant project execution software in tandem, and the two companies have agreed to work to continually mature enterprise resource management for the EPC market.

Podcast 194 Beware Hype

Podcast 194 Beware Hype

Podcast 194 of my long-running series—Beware Hype of OT and IT

Platforms come and go–sometimes quickly with turns in technology. IoT platforms were all the rage. Just like IT/OT Convergence and other hyped tech. But engineers are quietly working together to apply the technologies to solve business and industrial problems. Don’t watch the hype. Notice when everyone is using it.

This podcast is sponsored by Ignition from Inductive Automation.

Smart Factory Transition

Smart Factory Transition

The short take: ADVICS and Macnica Networks, Inc. deploy FogHorn Edge Computing Software in Smart Factory Transition. We talk endlessly about IoT, digital transformation, and now Smart Factory Transition. Do these terms mean anything? I think we are seeing people do actual work by using digital technologies that they mostly already have pieces of. Then marketers come along and christen it with a name. We are witnessing real progress improving manufacturing and production with modern thinking and tech.

In this case according to the press release, a $5B automotive brake system manufacturer deploys FogHorn Lightning Edge Computing Software Platform for real-time data processing, machine learning and AI. Note: machine learning is usually considered a subset of AI.

ADVICS Co. Ltd., working with Macnica Networks Inc., has deployed FogHorn Lightning Edge Computing Software to provide onsite data processing, real-time analytics, and ultimately machine learning AI in its smart factory transition.

ADVICS supplies advanced, high-quality automotive brake systems and components globally. ADVICS partnered with Macnica Networks to digitize its manufacturing sites and integrate varied equipment data to enable edge-based real-time visualization and analytics of its manufacturing. The digital transformation has allowed ADVICS to identify production issues immediately and quickly determine the root cause therefore improving manufacturing efficiencies. Manual workloads surrounding data acquisition have also been significantly reduced, enabling operation leaders to spend more time on managing production.

“ADVICS digital transformation to a smart factory reflects their mission to contribute to the reliability of society by pursuing a better safety, environment and comfort through products that delight customers,” said Yuta Endo, vice president, general manager of business development and head of APAC operations at FogHorn. “We are excited to work with our partner, Macnica Networks, to help ADVICS enhance manufacturing efficiency. FogHorn Lightning is uniquely positioned to help companies transform streaming data into actionable, predictive insights right at the edge, providing real-time monitoring and diagnostics, streaming analytics, machine learning and operations optimization.”

FogHorn’s Lightning product portfolio embeds edge computing software locally, as close to the source of streaming sensor data as possible. FogHorn Lightning Edge platform delivers low latency for onsite data processing and real-time analytics in addition to its machine learning and artificial intelligence (AI) capabilities.

ADVICS is one of the 13 major Aisin Group companies. The main business is the development, production and sales of automotive brake systems and parts that make up these systems.

Macnica Networks is a member of the Macnica Group, a growing global technology distributor. The company has over 20 years of experience in product localization, sales, and technical support of computer network equipment. It supplies a full line of leading-edge network appliances, software, telecom solutions to its customers, and consistently brings innovative new products to their portfolio.

FogHorn is a developer of edge computing software for industrial and commercial IoT application solutions.

Smart Factory Transition

OSIsoft Discusses Digital Twin

The concept of digital twins was born from the marriage known as cyber-physical systems. The cyber representation of a product or process was often held digitally within CAD/CAM or PLM systems. These became linked to the physical object through a feedback loop that kept the two in sync.

Digital Twin has moved from the esoteric to mainstream within industrial culture. And digital no longer is consigned to drawing databases, as my recent conversation with Michael Kanellos and Perry Zalvesey of OSIsoft reveals.

They described the process this way, “From devices all the way to buildings and factories, we’re now living in a world where everything is connected. And as these operations become more connected, it’s increasingly important to identify the strongest solution to monitor them. With the introduction of IoT, sensor and even AI technology to industrial operators, there’s been a surge of unfamiliar digital strategies – the latest being digital twins.”

OSIsoft prefers to consider digital twin as a loose term, as it can be either a complete network doppelganger or just a copy of key data streams to narrow in on specific issues. Everyone has their own preference and iteration.

OSIsoft named its digital twin technology the Asset Framework, which allows companies to take a project-by-project approach, creating solutions for each need on a rolling basis.

When one of its customers, DCP Midstream, began deploying OSIsoft’s AF tool it rolled out 12 AF based applications in two months, experiencing a $20-$25 million one-year return.

Application of OSIsoft’s Asset Framework has been strong in the water industry. Zalvesey says that his first work in the area was with modeling processes that were only static models. Today’s digital twins are dynamic. Designers can model the facility and objects within it. Each object has attributes that data are then associated with. Where originally there was a pump object—say we define “Pump 12” and associate data such as temperature and pressure and more. Now with Asset Framework, designers can create a template class “pump” and be able to replicate for as many pumps as a facility contains.

1. Asset Framework is the core digital twin offering. It’s as a relational layer on top of PI that combines all the data streams (temp, pressure, vibration) of an asset into one screen. A lot of people get fancy with the digital twin term but to us it’s a simulation combined with live data.

2. A simple AF template for a pump probably takes a half an hour to build. It can then be replicated ad inifinitum. It’s a drag and drop process. AF is part of PI Server (it was a separate product years ago but combined into it.) Complex ones can take months. Element, a company that OSIsoft helped incubate (and has since culled investment from Kleiner Perkins, GE and others) has built a service called AF accelerator. Basically, they parachute a team of data scientists to study your large assets and then develop automated ways to build AF templates for complete mines or offshore oil platforms. It still takes two months or so but they can streamline a lot of the coding tasks. BP used them.

3. Examples:

  • DCP. In 2017, the company launched an effort to digitize operations. One of the first steps was using PI to collect the data and use AF to create simple and complex digital twins. DCP has 61 gas plants for instance. Each one has been modeled with AF. Plant managers are show a live feed of current production, idealized production, and the differential in terms of gas produced and revenue. DCP discovered that it could increase production per plant on average $2000-$5000 per day, or millions a year, by giving the plant managers better visibility into current production and market pricing. In year one, it saved $20=$25 million, paying off the entire project (including the cost of building a centralized control center in Colorado and staffing it.) The next year (2018) it saved another $20 million.
  • MOL. One of the largest uses of AF. MOL tracks 400,000 data streams and has 21,000+ AF instances based on 300 templates (a single template can be replicated several times.) MOL says that it has added $1 billion EBITDA since 2010 by using its data better. With AF, for instance, they figured out why hydrogen corrosion was exceeding the norm. In some instances, they’ve used advanced analytics—an experiment to see if it could use high sulfur crudes required deep analytics—but most of the time MOL has made its improvements by creating AF templates, studying the phenomena and taking action.
  • Colorado Springs. Complete opposite end of big. It’s a small, regional utility.
  • Heineken uses AF to model its plants to reduce energy. Aurelian Metals used it to boost gold extraction from ore from 75% to 89%. Michelin saved $4 million because AF let them recover more quickly from a previous outage. Deschutes Brewery meanwhile boosted production by $450K and delayed a plant (per our 2018 meeting.