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.
So last week I shared an update on Schneider Electric from the ARC Forum–mostly on cybersecurity. A helpful marketing person guided me to the press release with all the data that updated the software side of the week’s news–specifically asset performance management. For the most part the discussion did not center on product updates but on “increasing momentum surrounding customer adoption”. In other words, Schneider wanted to highlight an area of software not often brought to center stage and show that it is a growth area.
Kim Custeau (I misspelled her name in my last post, I believe–thank you autocorrect), Asset Performance Management Business Lead, shared how investments in the cloud, advanced machine learning, and augmented reality, coupled with new partnerships, have empowered customers.
“Defining and executing an asset performance strategy is a critical component to improving productivity while safeguarding business continuity,” she said. “We have been delivering proven, industry leading asset performance solutions for nearly 30 years, and continue to invest in a long-term strategy to drive innovation in this area. Our focus is to provide real value to our customers by empowering them to maximize return on capital investment and improve profitability. We are proud to see our customer results speak for themselves with significant savings.”
Machine learning and prescriptive analytics:
- Duke Energy prevented an estimated $35 million cost from early warning detection of a steam turbine problem
- Ascend Performance Materials now responds faster to alerts saving an estimated $2 million through avoided plant shutdowns
- BASF is implementing AR to improve asset performance, reliability, and utilization while increasing production efficiency and safety because technicians leverage an augmented digital representation of the asset.
Cloud and Hybrid Deployment:
- WaterForce partnered with Schneider Electric to develop and IIoT remote monitoring and control system in the cloud that allows farmers to operate irrigation pivots with greater agility, efficiency, and sustainability.
- MaxGrip and Schneider Electric announced a partnership to expand APM consulting and add Risk-based Maintenance capabilities. The APM Assessment is a first step for industrial companies to evaluate asset reliability and digital transformation strategy.
- Schneider Electric and Accenture completed development of a Digital Services Factory to rapidly build and scale new predictive maintenance, asset monitoring, and energy optimization offerings. As a result, a large food and beverage company saved over $1 million in maintenance costs
The afternoon stream I moderated at the Industry of Things World conference focused on connected vehicles—Construction Equipment, Trucks, Airplanes. I also interviewed a farm equipment manufacturer about some perhaps surprising uses of data-driven decision making in agriculture.
But first, a thought from another keynote address:
From a NASA study—If you want to employ a creative genius, you’ll have 98% success employing a 3-5 year old; if you hire an adult, you probability of success drops to 2%.
I caught up with Alexander Purdy of John Deere between sessions. He’s not an engineer or IT manager like many of the attendees and speakers (he had a later keynote). He on the business end. How can John Deere grab a competitive advantage and serve customers through connected data? After a career as a consultant, he loves actually doing things.
His group deals in guidance systems and digital solutions. Guidance systems essentially link a GPS to large farm equipment. Not only does this ensure the rows of corn are nice and straight, the digital decision making increases coverage and yield.
Deere’s digital solutions include online JDLink, JDonline, and an ops center. A farmer can sit in her office and plot out planting regimes setting up everything before going into the field. There is even a way to collaborate on methods and local information.
Let’s take seeding for example. Sensors connected back to the system can feedback soil conditions. This helps the planter decide for each seed in a cornfield the optimum x, y, and z (yes, they measure depth of planted seed). The idea is to get each plant to grow at about the same rate.
Connected Construction Equipment
Kjell Jespersen, Caterpillar, spoke on huge construction equipment. Customers have been using the large amount of data generated by construction equipment mainly for improving maintenance. However, they crave better productivity data to manage their business. Developing the systems for gathering and analyzing all the data will become crucial as a competitive advantage—or failure to do so could force a company to exit the business.
Turning to the long-haul trucking business, companies are turning to truck suppliers such as Volvo and Mack Trucks to provide connected vehicle technology to provide data for improved customer support. According to Evandro Silva, Manager Connected Vehicle Services, Volvo Group Trucks North America, a telematics solution was used to develop a connected service that enables quick diagnosis of issues, proactive scheduling for repairs, and confirmation that needed parts are in stock and ready to install—all while the truck is still on the job.
Airplane Digital Twin
Robert Rencher, Senior Systems Engineer and Associate Technical Fellow, The Boeing Company, took the discussion to a new level—literally. From equipment that stays firmly on the ground, Rencher discussed the role of a “digital twin” throughout the lifecycle of an aircraft. A digital twin is defined within a system representing the characteristics of the object and the virtual environment in which the digital representation of objects and their physical equivalent, vice versa, are represented digitally and co-exist such that the object’s past, current, and future capabilities and can be assessed and evaluated in real-time. As an object progresses through each phase of its lifecycle, various systems interface with the digital twin.
Look for information about next year’s conference and all the other conferences. Next year will be about the same time of year in San Diego. Details are still being worked out. Check out twitter conversations at #IoTClan