Manufacturing Simulation Software Competitive Assessment

Manufacturing Simulation Software Competitive Assessment

Industrial Internet of Things plays the starring role in the new digital transformation theater, but digital twin is the supporting actress without whom there would be no drama. Simulation comprises an important element of this whole digital enterprise scene. ABI Research has been releasing some interesting research reports, and this one just hit my inbox that is quite interesting.

The Manufacturing Simulation Software Competitive Assessment analyzed and ranked seven major vendors in the industry – Siemens, Dassault Systèmes, Arena (Rockwell Automation), AnyLogic, FlexSim, Simio, and Simul8 – using ABI Research’s unbiased innovation/implementation criteria framework. For this competitive assessment, innovation scores examined the technical capabilities of the vendor’s software and implementation scores focused on the vendor’s commercial ability to deliver their solution around the world across a variety of manufacturing verticals.

Ranked as the top manufacturing simulation software vendor, Siemens scored highest in implementation and topped four of the ten scoring criteria. Dassault Systèmes came in a close second, having scored the highest in innovation and topped three of the ten criteria.

A key judgment criterion within the innovation category was digital twin capability, the software’s ability to align end-to-end physical processes with a dynamic digital representation that provides two-way feedback and ongoing optimization. Vendors were also judged according to data ingestion, the software’s ability to utilize high volumes of real-time data from a variety of sources, including industrial equipment and sensors on the factory floor. Further assessment included UX, data modeling and analytics, and virtual commissioning capabilities.

ABI Research chose these vendors for the assessment due to their simulation capabilities in discrete manufacturing specifically, where software is used to simulate physical processes digitally to optimize engineering, planning, and operations on the factory floor.

Siemens scored strongest overall due to its ability to integrate simulation with the widest range of adjacent industrial software and hardware. This integration provides the most robust end-to-end product offering to manufacturers. Another major strength of Siemens is virtual commissioning, delivered through its Simcenter and PLC Sim Advance tools. These tools allow simulation capabilities to extend to the machine control level, where individual machines can be virtualized and modeled to improve equipment efficiency and reduce failure rates. Dassault Systèmes very closely followed Siemens and topped the innovation category due to outstanding digital twin capabilities and analytics performance via the company’s impressive 3DExperience platform. These two companies stood out from the field and were therefore named Leaders in the report.

“It is no coincidence that the two companies with the strongest end-to-end software offerings across the smart manufacturing value chain have emerged as Leaders in this report,” said Ryan Martin, Principal Analyst at ABI Research. “Siemens and Dassault Systèmes can leverage their broad service offerings and industrial expertise to feed innovation and to implement complete solutions that equate to powerful and reliable simulations in discrete manufacturing.”

Three companies- Arena (Rockwell), AnyLogic and FlexSim- were named as Followers in the report. While these companies lack the full range of simulation capabilities of the Leaders, especially at the machine and equipment level, they have strong modeling and analytics capabilities. They, therefore, provide effective solutions for simulating factory floor layouts to optimize discrete manufacturing performance according to key metrics such as product throughput, machine downtime, capacity, and inventory levels. Arena, owned by Rockwell Automation, topped the Followers category due to strong performance in data modelling and analytics. Arena’s complex variability modeling capabilities and its strong installed base within the market contributed to a strong score in implementation.

“Ultimately the companies that scored best in the ranking can go beyond high-level factory layout simulation by also accurately modeling and commissioning industrial equipment on the factory floor and incorporating product design into the simulation environment. This means the way machines behave and how they are used to manufacture actual products is considered more comprehensively, a key factor in generating more reliable simulations. For this reason, Siemens and Dassault Systèmes stand out as market leaders in discrete manufacturing simulation software,” concludes Martin.

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.

Improve IIoT Deployment

Improve IIoT Deployment

The Industrial Internet of Things by definition is all about connections. Connecting hundreds of devices which often have differing protocols is a huge challenge. In an attempt to facilitate IIoT deployments, ioTium has announced an alliance with Telit. The agreement allows Telit deviceWISE gateway technology on the ioTium Edge App Store for single-click deployment.

After wading through a couple of paragraphs of marketing generalities, I found the best explanation with this quote. “With the cooperation of Telit, customers can now rapidly connect different communications protocols like BACnet, OPC, Modbus or even proprietary protocols to various IoT cloud offerings such as Azure IoT, Siemens MindSphere or private cloud end points,” said Sri Rajagopal, CTO, ioTium. “All commissioning, data mapping, and contextualization can now be done remotely, dramatically reducing the time and cost of flying technicians and data scientists to the site to remediate in person.”

Then the obligatory quote from the partner. I’ve talked with Fred Yentz for many years about connecting data. Here’s his thought on this announcement. “Our alliance with ioTium establishes a best-in-class approach for digital connectivity in the industrial world,” said Fred Yentz, president Strategic Partnerships, Telit. “Together, we are providing industrial enterprise customers a secure, plug-and-play way to connect any machine to cloud-based applications to capitalize on the benefits of Industry 4.0.”

Solving this problem is mainly what the various platforms are attempting. I would be interested in hearing what is actually working out in the field. Comment or send me an email. Something is working, because engineers are doing this.

Manufacturing Simulation Software Competitive Assessment

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.

Advice for Managing and Assessing Trustworthiness for IIoT

Advice for Managing and Assessing Trustworthiness for IIoT

The spread of connected devices with the resultant flow of data throughout the industrial enterprise spurs concern for security and trustworthiness of that data. The Industrial Internet Consortium (IIC) and its members recognize this problem / challenge.

I normally have a conversation with the authors of the IIC papers to get a context and sense of all the work involved in their development. In this particular case, I ran out of time. Many of you know that I am up to my eyes in soccer activities at this time of year. I just finished leading a class of new referees while I am at one of my peak times for assigning referees to games. Sometimes, I just don’t have enough hours. I bet you have never felt that…

So, IIC has published the Managing and Assessing Trustworthiness for IIoT in Practice white paper. The paper serves as an introductory guide to trustworthiness in IIoT, which is driven by the convergence of IT with OT, and includes a definition of trustworthiness, examples and a best-practice approach to managing trustworthiness in IIoT systems.

Confidence is essential to business, including confidence that the consequences of decisions and processes are acceptable and that business information is handled properly. The advent of IIoT means that confidence is also now required in technologies, physical components, and systems in addition to confidence in individuals, organizations and processes.

“The fact is that it is possible to have ‘too much’ trustworthiness,” said Jim Morrish, co-Chair of the IIC Business Strategy and Solution Lifecycle Working Group. “Trustworthiness costs, in terms of the costs of devices and associated software, and also often in terms of user experience and functionality. A trustworthiness solution for a nuclear processing plant would be an unnecessary hindrance to the day-to-day operations of a peanut butter manufacturer.”

The white paper’s best-practice approach to managing trustworthiness is comprised of four phases: baselining the system, analyzing potential trustworthiness events, implementing trustworthiness targets and governance, and iterating and maintaining the resulting trustworthiness model.

“This whitepaper demonstrates that trustworthiness is more than just another academic phrase to describe expectations of stakeholders, operators and users of an IIoT system,” said Marcellus Buchheit, President and CEO of Wibu-Systems USA, cofounder of Wibu-Systems AG in Germany and co-chair of the IIC Trustworthiness Task Group. “This paper presents several models that show how trustworthiness can be practically used in business decisions to increase trust in an IIoT system under the impact of business reality and constraints.”

The white paper also highlights that trustworthiness is not a static concept. “An IIoT system must address trustworthiness requirements throughout the lifecycle of the system. This means that industrial IoT trustworthiness is not a project with a finite start and a finite end. It is a journey that must be powered by an established program,” said Bassam Zarkout, founder of IGnPower and co-author of the paper.

“Security is already recognized as one of the most important considerations when designing an IIoT system,” said Frederick Hirsch who is a Standards Manager at Fujitsu, and also co-chair of the IIC Trustworthiness Task Group. “This white paper expands on that thinking by recognizing that safety, privacy, reliability and resilience need to be considered in conjunction with security to establish trust that IIoT systems will not only be functional but also will not harm people, the environment or society.”

The white paper discusses a live example of an IIoT system analysed from a trustworthiness perspective. Fujitsu’s Factory Operation Visibility & Intelligence (FOVI) system (and IIC testbed) has the primary goal of bringing more visibility of operations to plant managers in near-real time. The goal is to reduce human errors, bring more predictability to product assembly and delivery, and optimize production all while ensuring a sufficient level of trustworthiness.

“FOVI highlights how the different aspects of trustworthiness can impact business performance,” said Jacques Durand, Director of Engineering and Standards at Fujitsu, co-Chair of the IIC Business Strategy and Solution Lifecycle Working Group and also a member of the IIC Steering Committee. “For instance slowing down a production line can reduce costs associated with stress on machinery and machine operators, but such a course of action may also adversely impact productivity or lead time. In the white paper we highlight the need to understand trade-offs and to use metrics in a data-driven and intelligent manner.”

The Managing and Assessing Trustworthiness for IIoT in Practice white paper sets the stage for further work that the IIC will undertake focusing on trustworthiness.

The full IIC Managing and Assessing Trustworthiness for IIoT in Practice white paper and a list of IIC members who contributed can be found on the IIC website.