by Gary Mintchell | Feb 15, 2017 | Automation, Process Control
A long-time dream of enabling operators to see the profit impacts of process changes is a giant step closer to reality.
Much of my early career involved the intersection of engineering and profitability. No surprise that I valued my conversations with Peter Martin over the years. He has long been a proponent of just such technology and workflow.
Now at Schneider Electric (but still Foxboro), he has an organizational stability that may get the job done. Enter “EcoStruxure Profit Advisor.”
Developed through a partnership with Seeq, a leading provider of software and services that enable data-driven decision making, EcoStruxure Profit Advisor uses Big Data analytics to measure the financial performance of an industrial operation in real time, from the equipment asset level of a plant up to the process unit, plant area, plant site and enterprise levels. On-premise or cloud-enabled, it works seamlessly with any process historian to mine both historical and real-time data. It then processes that data through Schneider Electric’s proprietary segment-specific accounting algorithms to determine real-time operational profitability and potential savings.
Controlling Business Variables in Real Time
“While many companies are getting really good at controlling the efficiency of their operations in real time, they’re still managing their business month to month. That just doesn’t work anymore,” said Peter Martin, vice president of innovation, Schneider Electric Process Automation. “Business variables are changing so quickly—sometimes by the minute—that by the time companies receive updates from whatever enterprise resource planning systems they use, the information is no longer relevant to the business decisions they need to make or should have made. If they want to change the game, they need to control their other real-time business variables, including their safety, their reliability and especially their operational profitability. Profit Advisor allows them to do that.”
Because current cost accounting systems only measure the financial performance of the industrial operation at the overall plant level, it is difficult for companies to truly understand the financial impact—positive and negative—operational changes have on business performance. To address that need, Profit Advisor allows plant personnel to see and understand the ROI and business value their actions, activities and assets are contributing to the business in real time. It empowers the workforce to make better business decisions with a variety of data analytics, which can be displayed in various formats, to help drive operational profitability improvements, safely.
Innovating at Every Level to Deliver Value-focused IIoT
“Our customers are struggling with many issues, including the sheer speed of business and how to manage and use emerging technology to their advantage,” said Chris Lyden, senior vice president, Process Automation, Schneider Electric. “Everyone wants to talk about all this new technology without focusing on what value it can deliver. From our perspective, the digitization of industry is a real opportunity for our customers. We’re taking a value-focused approach to IIoT because we know our ability to innovate at every level can help our customers control their productivity and profitability in real time. That’s the only reason we should be talking about IIoT to begin with.”
Profit Advisor layers real-time accounting models onto the Seeq Workbench to become a scalable, repeatable and easy-to-implement solution for multiple segments, enabling customers to both measure and control their profitability. And because it can be integrated with Schneider Electric’s simulation and modelling software in a digital twin environment, users are further enabled to forecast profitability under different conditions or if changes to the operation are made.
Overall, the software provides
- Historical Data Review: Profit Advisor can evaluate the historical performance of the plant to assess its operational profitability, helping plant personnel analyze and understand how the
operation performed during different conditions. It enables the workforce to identify true performance-improving initiatives. And since it can be tied to individual pieces of equipment, it can provide that information down to even the smallest asset in the operation.
- Real Time Performance Indication: Profit Advisor can indicate current performance and inform plant personnel when their operating decisions are making the business more profitable. Actual ROI and return on improvements will be visible, enabling plant personnel to concentrate and refine their efforts to the actions that provide the greatest financial returns. It also enables plant personnel to determine which parts of operation are constraining operational profitability and accurately estimate the business value their decisions might actually create.
- Profit Planning: Profit Advisor empowers process engineers to predict the profitability of the changes they are proposing, which will substantially minimize project risk and help to eliminate waste.
Check out this YouTube video.
Control Advisor
Schneider Electric, the global specialist in energy management and automation, has added a new enterprise-wide IIoT plant performance and control optimization software to its PES and Foxboro Evo process automation systems and Foxboro I/A Series distributed control system. Leveraging Expertune PlantTriage technology, EcoStruxure ControlAdvisor, a native smart decision-support tool, provides plant personnel actionable real-time operating data and predictive analytics capabilities so they can monitor and adjust every control loop across
multiple plants and global sites 24/7. The software empowers them to optimize the real-time efficiency of the process throughout the plant lifecycle and to contribute directly to improved business
by Gary Mintchell | Dec 30, 2016 | Productivity
“Your imagination is your preview of life’s coming attractions.” — Albert Einstein
A good salesperson runs through the entire interview with her client in her mind while she’s still in the car.
A great college football running back viewed video of his best plays and then ran the back in his mind.
A speaker visualizes his performance while off stage before anything begins.
People make lists of New Year’s Resolutions and then file them away–undone. Years ago I gained a shred of wisdom when I realized I was just copying last year’s resolutions and reprinting them in the front of my planning diary (before it was all electronic). Why go through the exercise only to feel guilt at the end of the year? Or the first of February?
Albert Einstein made his mark in physics not through his knowledge of math but through his imagination. He imagined gravitational pull on planets and stars, and light traveling through time. That told him which equations to work out and how to work them.
Instead of lists (which I love for remembering things to do or for brainstorming) why not try imagination? Imagine what your year could be like and what sort of person you will be.
- Imagine joining a group that promotes a cause you admire. See yourself there. Then call someone next month.
- See yourself reading two books a month for personal growth. Then download several books for your tablet app. Or visit a bookstore and buy a few books. Put them in a visible place. Read for an hour every morning or evening. You’ll be amazed.
- Visualize time with the family.
- See yourself at the gym every morning or evening. See the entire process of getting there, your workout, the sauna, the shower, feeling refreshed.
- What can you imagine for yourself? There are no limits in imagination. Let it loose and follow it where it goes.
Who sees the irony of my making a list of suggestions? 😉
Happy New Year.
PS: I have mostly taken the week off for thinking and imagining. So my December stats will suck. I’ll be back at it next week with more connected manufacturing coverage, leadership thoughts, and occasional marketing tips.
by Gary Mintchell | Jul 28, 2016 | Manufacturing IT, Operations Management, Software
A manufacturing software supplier must go beyond where they are to keep pace with today’s needs. GE Digital just announced such an extension–to offer decision support capabilities. The new GE HMI/SCADA software offers “comprehensive and best-in-class monitoring and visualization capabilities,” as well as work process management, analytics, and mobility. Based on ISA high performance design principles, this solution enables companies to troubleshoot faster, reduce waste and increase productivity.
“Most SCADA systems are still configured as HMIs – simply a display to indicate status,” said Matthew Wells, General Manager Automation Software for GE Digital. “In developing this new generation solution, we have combined industry standards, GE research and Industrial Internet technologies to exceed traditional HMI/SCADA, increasing operational efficiency and delivering on business outcomes.”
Context-driven navigation and situational awareness
The new GE software features a context-rich HMI that changes as the user moves through the system. Navigation is derived from a structured asset model. Using the model, the software always can provide operators with the most relevant information – in context – and minimize time to response. Additionally, the structured asset model mapped to the SCADA database significantly speeds configuration. Modern technologies such as HTML5 and Web HMI allow for centralized development and deployment, as well as accessibility anywhere in multiple form factors.
“With high performance HMI/SCADA, operators are able to quickly determine an abnormal situation and get to the root causes of many issues,” said Sergio Chavez, Automation Engineer with Los Angeles Dept. of Water and Power. “We help operators visualize a process and make alarms very visible. We’re shaving the time it takes for operators to act on a situation.”
To help engineers create the right user experience, GE also provides predefined smart objects and templates designed using efficient HMI concepts. Standard layouts and cards – such as trends, alarms, alarm summaries, and KPIs – are available out of the box, speeding configuration and improving user situational awareness.
Task management and mobility
Additionally, GE’s fourth generation HMI/SCADA portfolio has task management capabilities, triggering the right actions, at the right time, by the right person, in the right place based on alarms or other events. GE’s new Workflow 2.5 and Mobile 2.0 solutions extend the capabilities of Decision Support HMI/SCADA further, helping companies achieve their critical business outcomes with integrated workflows and intelligent alarming, available anytime and anywhere.
“Operator effectiveness allows operators the opportunity to grow professionally,” according to Bill Fritz, Director of Public Works, Waterford Township, Michigan. “They can reinvent themselves and gain new value-added skills. They can take on new roles.”
GE’s Wells explained, “Use technology to improve the operator experience and manage operations for greater efficiency. With just a quick look, operators today should be able to recognize which information requires their attention and what it indicates – which speeds response and drives to business outcomes.”
by Gary Mintchell | Mar 10, 2016 | News
A new study by the MAPI Foundation (Manufacturers Alliance for Productivity and Innovation) analyzes productivity growth in manufacturing over the past 25 years and provides “compelling statistical evidence on the importance that capital investment and educated labor have on productivity performance.”
I guess what this study highlights are factors that should have already been well known. Plus the study was financed by Rockwell Automation—a technology developer and supplier—which is an interesting caveat. MAPI is an organization composed of manufacturers and suppliers. I’d really see one of the follow-ups discuss what manner of investment makes the most difference.
The research explores the drivers of productivity performance on subsectors. In particular, the study looks for ways that manufacturers who have already invested in capital equipment can increase productivity and innovation.
Productivity Series
The report is the first in a series on productivity that the MAPI Foundation is producing this year. Cliff Waldman, director of economic studies at the MAPI Foundation, produced the study using well-accepted theory and regression analysis of several decades’ worth of data. The study reveals evidence that innovation and capital investment play a significant role in driving multifactor productivity growth (i.e., output per unit of a combined set of inputs including labor, materials, and capital) in a wide range of manufacturing subsectors. Capital investment is the mechanism by which productivity-enhancing innovation spreads through companies, supply chains, and the broad economy.
“In the manufacturing sector, strong productivity performance is needed to meet the globally driven challenges of cost pressures and competitiveness,” Waldman observes. “For both manufacturing and the economy as a whole, the recent slowdown in productivity causes concern, because it contributes to both slow output and wage growth.”
“Isolating the critical investments required to improve productivity performance is an important foundational element in the MAPI Foundation’s first study,” added Joe Kann, vice president of global business development at Rockwell Automation. “We look forward to the conclusions regarding industry-specific productivity drivers that will be identified in the remaining studies.”
Educated Labor
Waldman’s research finds that another key link to productivity performance is the labor force participation rate of the population holding a B.A. degree or higher, in effect the economy’s supply of educated labor.
The manufacturing sector, a traditional driver of overall productivity, has seen its pace of productivity growth slow over the last 15 years. As Waldman notes, part of this is due to slowing productivity growth in the computer and electronic products industry, which has played an outsized role in driving manufacturing productivity growth in recent decades.
According to the study, industry subsectors that have experienced relative improvements in productivity performance since 1993 include machinery, transportation equipment, and printing. But their growth has not been enough on an absolute basis to replace the decline in computer subsector productivity. Industries with a noticeable drop since 1993 in their relative pace of productivity growth include primary metals and petroleum and coal products.
Sector Correlations
The paper reveals strong cross-subsector correlations for both labor productivity growth and multifactor productivity growth. The apparent interconnectedness of productivity performance across industries, says Waldman, is likely the result of supply chain linkages, innovation spillovers, cluster impacts, and trade channels. Such evidence suggests that, where investments in any one industry lead to faster productivity growth, such expenditures can have impacts that extend to other subsectors as well.
Waldman concludes that a beneficial policy response must consist of a coordinated program that stimulates manufacturing equipment investment as well as innovation investment and increases the supply of educated labor in the broad economy. The MAPI Foundation’s next study on productivity builds on this work and will reveal the findings of a national survey on technology and automation investment that was conducted to determine the drivers and pace of change in various manufacturing industries.
Summary of major findings include:
- While the computer and electronic products subsector has historically played an outsized role in the relatively strong productivity performance of the broader manufacturing sector, productivity growth in the information technology space has slowed dramatically in recent years. This has happened as the high-impact innovation that led to persistent and rapid increases in computer processing speeds, which are necessarily accounted for in the calculation of computer-sector productivity growth, naturally reached physical limits. This is reducing manufacturing’s rate of productivity growth.
- Though the machinery and transportation equipment subsectors have shown notable improvement in their productivity performance over the past 15 years, it has not been enough on an absolute basis to make up for diminishing computer subsector productivity; overall manufacturing productivity growth is therefore languishing at historically weak rates.
- More than two decades’ worth of government statistics and regression analysis demonstrate that innovation and capital investment are directly correlated to and thus play a significant role in driving multifactor productivity growth in a wide range of manufacturing subsectors.
- An increase in the labor force participation rate of those with a B.A. degree and higher correlates to faster labor productivity growth in multiple industries. The supply of educated labor plays a definitive role in driving labor productivity growth across diverse subsectors.
- Statistical analysis shows a strong interconnectedness of productivity performance across subsectors. This evidence supports the hypothesis that because of supply chain linkages, innovation spillovers, cluster impacts, and trade channels, productivity determination is not independent across manufacturing industries. When changes are made in one industry that promote productivity, these can affect productivity performance in other industries as well.
by Gary Mintchell | Jan 20, 2016 | Automation, Internet of Things, Networking, Operations Management
Everyone is in a rush to get an opinion or observation published about the Internet of Things. Evidently it gets lots of page views. Recently other analysts have been publishing thought pieces on IoT in Industry. It appears they have reached the same conclusion that I first broached a couple of years ago. The IoT is not a “thing.” To make any sense of it and use it for any strategy, it must be thought of as an ecosystem encompassing a variety of technologies.
Here is an article that appeared in the Sloan MIT Management Review. Since I am a subscriber, I don’t know if you can see the article at this link.
The writer is Sam Ransbotham is an associate professor of information systems at the Carroll School of Management at Boston College and the MIT Sloan Management Review Guest Editor for the Data and Analytics Big Idea Initiative. He suggests, “The Internet of Things will bring huge changes to the way markets and businesses work — and it could get messy.”
Here is a bromide that I’ve read a thousand times, “Most businesses aren’t ready for the changes to the marketplace that the Internet of Things will bring. But the time to prepare for them is now.”
Actually most business adapt. Some are visionary and will develop new products, processes, and services–and make a lot of money. Others will adapt and survive. Still others will wonder what happened and die. That is the way of business for at least 5,000 years.
Use Case for Internet of Things
“Yes, the potential insights from IoT are enticing. For example, it’s fun to think about the potential personal and even societal benefits from self-driving cars, such as fewer accidents, no problems with parking, more productivity while traveling, car sharing, greater infrastructure efficiency, etc. But perhaps a more profound implication is the data that they can collect. These cars will also be widely distributed “things,” gathering performance data that can help manufacturers diagnose problems, operational data that can help mechanics prevent failures, driver data that can help insurers understand risk, road data that can help cities improve infrastructure, etc. These kinds of insights, we’re ready for.”
But there are a lot more changes coming with the IoT transformation than many people may recognize.
Ransbothem looks into information technology as a model for what will happen in IoT. “About a decade ago, advances in information technology converged to fuel a boom in corporate use of analytics. First, widespread implementation of information systems captured unprecedented amount of data in ways that could be used in isolation or combined. Second, tools and technologies allowed the inexpensive storage and processing. Third, savvy analytical innovators creatively combined these to show everyone else what could be done.”
We have seen all this play out in industrial systems. There remains more to be done, here, though.
He proceeds to look at Internet of Things. “First, the cost and physical size of sensor technology have dropped such that they can be incorporated into most items. Second, widespread communications infrastructure is in place to allow these distributed components to coordinate. Third, once again, savvy innovators are showing the rest of us the possibilities from the data they collect.”
Manufacturing and production are not only poised to exploit these technologies and strategies, they have already been implementing to one degree or another. But his point is valid. IoT needs the ecosystem of sensing devices, networking, communication technology, databases, analytics, and visualization.
Ransbothem identifies four areas of change. Of these, I direct your attention to the last–process changes. I think everything feeds into process changes–not just the processes to make things, but also the information technology, supply chain, and human processes that must not only adapt but thrive with the new information awareness.
- Market Power: IoT should provide a greater amount and a greater value of data, but are companies ready to align their interests in obtaining value from this data with the multiple other companies and end users who create, own, and service the products that originate the data? In the driverless car example, it is easy to see how multiple stakeholders could make use of the data from cars; the same is true for other devices. But it may not be clear who owns what data and how it can be used.
- Complexity: Few organizations are prepared to be hardware and software development companies. But that’s what the Internet of Things will enable. As products are built with embedded sensors, the component mix increases in complexity. As a result, manufacturing systems and supply chains will become more elaborate. Software embedded in products will need to be updateable when the inevitable shortcomings are found.
- Security: If we believe data is valuable, then we need to be ready for people to want to take it from us — why would data be any different than any other precious item? The IoT context intensifies the need for security requirements; for example, sensors or software that allow physical control of the product make attacks easier.
- Process Changes: Many business processes continue to be “pull” oriented. Information is gathered, then analyzed, then decisions are made. This works when change is slow. But with the IoT transition, data will stream in constantly, defying routine reporting and normal working hours. Flooding data from IoT devices will give opportunities for quick reaction, but only if organizations can develop the capacity needed to take advantage of it. Few mainstream large companies are ready for this, much less small- to medium-sized companies that lack the resources of their larger corporate brethren.
The Internet of Things is bringing and will continue to bring advances in how we do business. How well will executives, managers, and engineers execute on this vision? That is key.
