by Gary Mintchell | Nov 26, 2019 | Commentary, Software
Rockwell Automation through Blake Moret, chairman and CEO, invested $1 billion in PTC with Moret gaining a seat on the board. The public reason was really to get early information about ThinkWorx, the IIoT product.
The investment valued PTC, a company with $1 billion in sales, at approximately $17 billion. On the surface, we all pondered why.
Speeding up the time, I was able to spend a couple of hours with several people from PTC at last week’s Automation Fair event. This really opened my eyes to the depth and breadth of the ThingWorx offering. There is much technology and usefulness under the hood. This is powerful software.
Now, I understand. Beyond a relationship and most likely some preferential access to ThingWorx and other PTC technologies, I’m surmising that Rockwell Automation can also drop some visualization projects, cut development costs, and utilize the full value of the PTC software. That alone would be a good return on the investment.
Therefore, the most prominent branding at Automation Fair–Powered by PTC.
Revealing more of Rockwell’s piece-at-a-time partnering strategy, it is not using PTC’s CAD and PLM offerings for its digital twin development, but instead it is partnering with ANSYS.
Like I noted in my initial report on Automation Fair, partnering was the centerpiece of news from the event. Looks like it is also the centerpiece of product development. That is most likely financially prudent.
by Gary Mintchell | Nov 12, 2019 | Asset Performance Management, Data Management, Manufacturing IT, Operations Management, Software
Keynoters have a tough time with originality these Digital Days with everyone emphasizing Digital Transformation. Steve Lomholt-Thomson, chief revenue officer of AVEVA, took us on a Digital Journey this morning. Setting the tone of the three days of AVEVA World Congress (North America edition).
Three technology trends to watch: an IoT boom; cloud/empowered edge; and, AI / ML. The theme is digital. The Digital Organization discovers its Digital DNA, figures out how to build that Digital DNA through people who challenge the status quo; and then figures out how to track talent flow.
Which all starts us on our Digital Journey. On this journey, we unify end-to-end data, connect data silos taking an wholistic view of the business, and then visualize our assets and supply chain. I believe implied in all this is the company’s product AVEVA System Platform. The company touted six customer stories with at least five of them (and probably the sixth) all leveraging System Platform.
Oh, and the only time the “W” word was used referred to past tense.
Other areas of the company were highlighted:
Focus on assets–asset performance management including how to use machine learning (ML) and artificial intelligence (AI) for predictive analytics (predictive maintenance.
How to combine it all into a Digital Twin–bringing the design lifecycle and physical lifecycle into congruence.
Recently hired head of North America business, Christine Harding, interviewed customers from Campbell’s (soup/snacks), Quantum Solutions (integration project at St. Louis/Lambert airport), and Suncor (Canadian oil sands).
I have the rest of today and then tomorrow to take deeper dives into many of these topics. If there is anything you want me to ask, send a note.
by Gary Mintchell | Sep 5, 2019 | Internet of Things, Operations Management, Software
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.
by Gary Mintchell | Aug 14, 2019 | Asset Performance Management, Manufacturing IT, Software
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.
by Gary Mintchell | Jul 30, 2019 | Asset Performance Management, Manufacturing IT
I first met Quuppa and saw a demo of its real time locating system at the 2018 Hannover Messe. I have written about it here and here. The company has developed an interesting technology and application.
They wrote about industrial applications picking up, so I asked for an example. Below is a story about defining a problem, sourcing a solution, and then implementing it.
NGK Ceramics is a global specialist in the manufacturing of ceramic substrates used in catalytic converter applications for automotive, truck and off-road vehicles. The US manufacturing facility, located in Mooresville, North Carolina, covers more than 500k square feet with 365 days a year, twenty-four hours a day operations.
The facility was initially designed in 1988 to serve a limited geographical area in the US. However, with the business growing faster than expected and more areas being served by the same production plant, NGK faced a major challenge: how to grow the capacity of the North Carolina industrial plant. Efficiency was clearly the answer. As a first step, ASRS (Automated Storage and Retrieval Systems), together with AGV’s (Automated Guided Vehicles), were introduced to move pallets and materials in the shop floor without human intervention.
Even if this mitigated the problem, it was still not enough to manage high, yet variable, production demands in the long run. As a result, during production peaks, the pallets transporting both raw materials and semi-finished goods were temporarily stored all around the shop floor according to specific procedures. While this addressed the problem of lack of (ASRS) storage space, it introduced a significant new one, the additional time spent finding and moving pallets from one production phase to the next. At least two workers per shift were assigned to this task: just searching for and moving pallets.
In addition to this, at least once a year a complete plant inventory is required to verify all materials stored in the facility, but not yet shipped or sold. During this activity, the entire plant was surveyed, and all pallets were identified and verified against the data registered in the internal ERP system. This activity could take up to one week, with the slow down (if not interruption) of the production activities. Inevitably, any items lost or duplicated created an impact on the bottom line.
To deal with these issues, in 2017 NGK Ceramics decided to explore how solutions based on a Real Time Locating System (RTLS) could help by providing a Digital Twin of the manufacturing plant: the location of every pallet would be tracked continuously and that data would be synchronized with NGK’s MRP systems. This tracking of pallets provides a real-time view of where they are located in the industrial plant, with a number of supporting services to easily and rapidly search them and manage the production cycle.
TRACKING SOLUTION: REQUIREMENTS NGK Ceramics decided to evaluate a number of different scenarios for implementing a RTLS to track the progress of material and semi-finished goods throughout the flow of its manufacturing process. The key requirements to be addressed by the solution were:
• Configurable tracking accuracy: since the industrial plant covers a large area, with different uses of the spaces within the plant (production area vs. stocking areas vs. corridors), the ability to vary the location accuracy of asset tracking was important. In some areas, where the density of pallets is typically high (such as the warehouse) sub-meter accuracy is required in order to easily locate a specific pallet among the many stocked there. On the other hand, a 10 metre accuracy is sufficient in corridors or transit zones, where it is sufficient to track the presence of the pallet in the zone;
• Infrastructure cost: as NGK Ceramics facility is rather large, the number of RTLS antennas required to achieve the desired accuracy was clearly an important variable of the solution to be adopted. This impacted both the cost of the infrastructure as well as the costs related to the cabling (e.g., connectivity and power). Another factor was the cost of the tags to be attached to the pallets. This extended beyond the capital cost to also include the cost of replacing the batteries in the tags.
• Asset search and location functionality : NGK wanted this Digital Twin to be used in a variety of ways, from centralized systems to hand-held devices using a Google maps style red dot metaphor, so how the system was able to process the information and extract actionable knowledge for the final user (the worker in the shop floor) was important. This required addressing issues related to the usability and ergonomics of the system, Machine-2-Machine (M2M) application integration, while delivering on its intended use and the need to facilitate the searching and location of assets.
• Maturity of the solution: an enterprise-ready solution was requested. This refers to the support for active monitoring services of both the platform and the RTLS infrastructure. Any device or software component deployed in the facility needed to be monitored, with notifications sent in case of anomalies in the system. This includes the battery status of the devices/tags used for tracking the pallets.
DIGITISING THE PRODUCTION PROCESS NGK retained the services of Statler Consulting a specialist in the area of beacons and RTLS technologies, and issued a Request for Proposal (RFP) for a solution able to track in real-time the assets in their facility, and to deliver the necessary supporting services for the optimisation and real-time control of their production process. Among the many solutions proposed, ThinkIN was chosen as it proved to be the best match to the requirements identified by NGK. ThinkIN is an innovative IoT platform for real-time tracking, monitoring and control of assets and workforce in industrial environments.
ThinkIN technology is based on Quuppa4 RTLS for the high precision location of assets in the shop floor. Quuppa utilizes a unique combination of Bluetooth Low Energy (BLE) and the Angle of Arrival (AoA) methodology, as well as advanced location algorithms that have been developed over the course of more than 15 years, to calculate highly accurate, real-time indoor positioning, even in the most demanding environments, including inside warehouses and manufacturing facilities. The low-power system is a reliable, highly-customizable, scalable and costefficient solution for providing an accurate “dot on the map.”
ThinkIN platform provides a comprehensive set of services ranging from real-time support (e.g, asset search and location, alerts and geo-fencing, etc.), to Industrial IoT analytics. It also includes a number of tools to support the active monitoring of the infrastructure (both hardware and software) and a comprehensive set of user interfaces to explore the data collected and used to locate assets in real-time in the shop floor. In terms of tracking technology, Quuppa RTLS provided an optimal trade-off in terms of location accuracy, number of antennas required to cover the NGK facility and maturity of technology.
Overall 95 antennas are used to cover the complete NGK facility, with a location accuracy of approximately one meter in the areas of interest and approximately 5 meters in other areas. Different tag form factors were evaluated. Eventually, a custom Bluetooth Low Energy tag with a slim badge form factor was designed and manufactured in order to optimally align with NGK’s existing manufacturing process. The tag ensures 4+ years of life without battery replacement. Pallets, carrying products or semi-finished goods, are identified by means of their Product Travel Ticket (PTT), which includes all the necessary information about the kind of product manufactured, together with information on production stage ( e.g. forming line, firing in kilns, etc.). At the very beginning of the production process, an RTLS TAG is associated with the pallet Travel Ticket through a mobile application running on a scanner.
The application allows the scanning of both the QR code present on the PTT and the QR code on the TAG. This association creates a Digital Twin of the pallet, which is now tracked in real-time throughout its manufacturing process. The pallets can now be easily located through the ThinkIN mobile service. Additionally, plant-level views allow staff to monitor the status of the pallets across the entire facility, maintaining an always up-to-date inventory of all pallets stocked or moving in the facility.
Starting from ThinkIN open APIs, a dedicated mobile interface was created for an optimal utilisation of data over the shop-floor and to facilitate the work of employees in the search and location of pallets with a specific Travel Ticket. Figure 3: Tracking of assets in NGK facility Additional services delivered through the ThinkIN platform enable the quality control of pallets depending on their production stage, with alerts being triggered if the pallet moves into areas not allowed. To prevent this, a specific geo-localised workflow is imposed on the travel path of pallets depending on their production process. Warnings are raised when the specific workflow is not adhered to.
LOOKING AHEAD
The project started in 2017 with an initial pilot phase, and is now scaling up to the entire production plant with a possible extension in the coming years to other NGK manufacturing sites. NGK is planning to obtain a return on their investment in a 2 year time frame. Today we are in year two and ThinkIN solution is integrated with the production control system adding value to the manufacturing process by making the pallet searching process more effective.
ThinkIN’s platform has allowed NGK to digitize the shop floor by recreating the plant on screens accessible to all workers. Thanks to the data collected by tags and devices, workers can use the interface to find pallets around the manufacturing plant based on the information of the goods transported by the pallets, such as product type, bench number, kill cycle, and other key criteria for the production routing.
The efficiency of the shop floor was significantly increased thanks to ThinkIN for Industry. In the first year, NGK Ceramics reduced the costs of the wasted time searching for pallets and of the time spent doing the annual inventory. Thanks to the new solution, the inventory is constantly up-to-date. Moreover, the accuracy in tracking reduced the risk of accidents caused by the movement of pallets with forklifts in the shop floor searching for the needed pallet. ThinkIN for Industry, therefore, is a location intelligence technology that by capturing data from the shop floor in a digital platform offers the chance to automate the real world in new ways that can enhance and optimise workflows in the shop floor.
