IIoT Applications Deliver a Competitive Advantage to Process Industries

IIoT Applications Deliver a Competitive Advantage to Process Industries

This guest blog post was written by Deanna Johnson, global marketing communications manager at Emerson Process Management.

To some, the Industrial Internet of Things (IIoT) is just a new buzzword—but to the process industries, the IIoT is becoming a necessity to maintain competitiveness. Oil and gas companies, refineries, and other process industries are trying to cope with various market forces, many of which require improved plant performance.

The 650 major refineries globally are especially affected. Some of these plants are operating at peak performance, but many are not, causing a significant financial impact. Our calculations show the difference in operating costs associated with equipment reliability and energy efficiency between a well-run refinery and an average one is about $12.3 million per year for a typical 250,000 barrel-per-day facility. Assuming about 60 percent of refineries are not operating as well as they could, the overall worldwide financial impact runs to billions of dollars annually.

To increase reliability and efficiency, and to gain other operating benefits such as reduced maintenance and improved safety, many refineries and process plants are turning to the IIoT.

The IIoT essentially involves acquiring data from hundreds—if not thousands—of process and equipment sensors, and transmitting the data to central locations via wireless or hardwired networks. The goal is to sense anything, anywhere in a cost-effective manner.

Once the data arrives, it is stored in databases, historians, the cloud, and other locations where it can be accessed by software that analyzes and interprets the sensor information using “big data” techniques to diagnose conditions, detect equipment problems, and alert operations personnel. Such software can reside in the plant’s control system, a dedicated PC, or in a server half a world away.

The “Internet” part of IIoT refers to the fact that the Internet can be used to connect the various systems. In many instances, all the networking is done at the plant itself, with the Internet replaced by an internal intranet, but the basic principles still apply: huge amounts of data are gathered and analyzed to find and solve problems.

Space does not permit an exhaustive analysis of all the applications where the IIoT can save energy, reduce maintenance costs, and improve process efficiency. However, here is a short list of what is possible to monitor and analyze with these types of systems:

  • steam traps
  • pumps and compressors
  • heat exchangers
  • pressure relief valves
  • cooling towers
  • mobile workforces
  • safety showers and eye wash stations

Following are several examples of how the IIoT was used to improve efficiency and find problems at process plants worldwide.

Steam trap monitoring

Steam trap monitoring via wireless acoustic transmitters is a leading IIoT application. When traps fail open, high-pressure steam leaks out, so more steam has to be produced by boilers. Depending on the price of steam at a facility, a single failed-open steam trap can waste $30,000 worth of steam each year.

When traps fail closed, they do not remove water droplets from the steam. Accumulated water, moving through piping and equipment at a high rate of speed, can rupture steam lines and cause turbines to throw blades. Repairs are very expensive, and downtime is often significant.

Most plants monitor their steam traps manually via annual checks. This is very costly in terms of labor, misses many problems, and in the worst case can allow failed traps to operate for years.

Acoustic sensors and specialized software systems detect steam trap problems automatically and alert plant personnel so they can take action. In the past, these sensors were hardwired back to software systems, but the preferred modern method is to use wireless acoustic sensors connected back to software systems via a wireless mesh network, creating an IIoT.

Levaco Chemicals in Leverkusen, Germany, had to save energy to meet the June 2012 Energy Efficiency Directive required by the European Commission and ISO 50001. The plant determined that defective steam traps were causing loss of steam and inefficient heat transfer, and therefore wasting energy.

They installed 300 wireless steam trap monitors and three wireless gateways—one in each of three plant areas—on critical steam traps. The gateways connect to the wireless transmitters through the WirelessHART mesh network, and the gateways connect to the control system via hardwiring.

They also installed specialized data analysis software on a PC. The gateways connect to the PC via an Ethernet cable. This software analyzes real-time data from the steam trap acoustic monitors. These instruments measure the ultrasonic acoustic behavior and temperature of steam traps, and the software uses this data to identify existing and potential problems.

By repairing or replacing failed steam traps, the three plant areas immediately had substantial reductions in energy costs. Failed traps were no longer venting valuable steam, which lowered energy consumption to produce steam, and failed traps were no longer causing process shutdowns. The increased energy efficiency easily met the Energy Efficiency Directive and ISO 50001 requirements, and the plant was awarded a certificate of compliance in 2015.

Levaco calculated a return on investment of fewer than two years, thanks to savings in energy costs. It also reduced the number of process shutdowns because of steam trap failures, and eliminated the need for maintenance technicians to make regular rounds, resulting in further substantial savings.

In a similar application, a corn milling plant was experiencing a 15 percent annual steam trap failure rate, with 12.5 percent of the plant’s steam traps responsible for 38 percent of the steam loss. Steam trap issues were efficiently identified and addressed with the application of wireless steam trap acoustic sensors and accompanying analytics. The payback period was just a few months, and the annual savings were $301,108.

Table 1 illustrates the savings possible in a large plant that has 8,000 steam traps, where 1,200 are considered critical. If the plant previously experienced a 15 percent failure rate per year, by preventing those failures with steam trap monitors the plant will save $3,279,960 per year.

Pump monitoring

It is estimated that pumps account for 7 percent of the total maintenance cost of a plant or refinery, and pump failures are responsible for 0.2 percent of lost production. Many pump failures can be predicted using IIoT, modern condition-based monitoring techniques, predictive technologies, and reliability-centered maintenance best practices.

Historically, the expense of installing a dedicated IIoT online monitoring system has prevented it from being used on anything beyond the most critical pumps. But with the relative ease of adding online pump condition monitoring with today’s wireless sensor technology, online monitoring can be installed quickly and inexpensively.

Today, wireless transmitters make it possible to monitor many pumps cost effectively.

Cavitation monitoring is needed on high-head multistage pumps, as they cannot tolerate this condition, even for a brief time. Although cavitation often happens when pumps operate outside their design ranges, it can also be caused by intermittent pump suction or discharge restrictions. Damage can occur before manual rounds discover the problem, but can be detected sooner by continuously monitoring the pump discharge pressure for fluctuations with a wireless pressure transmitter.

Vibration monitoring detects many common causes of pump failure. Excessive motor and pump vibration can be caused by a failing concrete foundation or metal frame, shaft misalignment, impeller damage, pump or motor bearing wear, or coupling wear and cavitation. Increasing vibration commonly leads to seal failure and can result in expensive repairs, process upsets, reduced throughput, fines if hazardous material is leaked, and fire if the leaked material is flammable.

Online vibration monitoring has been successful in detecting several root causes of pump degradation. A complete IIoT pump health monitoring system can pay for itself in months. At one refinery, for example, pump monitoring systems were installed on 80 pumps throughout the complex. The annual savings was more than $1.2 million after implementing the pump monitoring solution, resulting in a payback period of fewer than six months (table 2). The savings came from decreased maintenance costs of $360,000, and fewer losses from process shutdowns because of failed pumps, which were conservatively valued at $912,000.

Heat exchangers in many plants can be a major source of downtime, often causing considerable maintenance expenses, significant loss of production, and poor plant performance. Existing monitoring may involve manual spot measurements performed periodically. These types of measurements provide an inconsistent view of failures and are time consuming, with accurate assessment based upon technician expertise.

Many refiners are trying to maximize their use of low-cost crudes, but using this type of feedstock often presents significant processing challenges. Typically, crude unit preheat exchangers can foul unpredictably with changes in the crude blend and process conditions. As a result, energy efficiency is lost, and production can be limited. Adding additional wireless temperature measurements to exchanger banks provides increased data to process analytics software that can then alert operations to excessive fouling conditions and rates. Using WirelessHART technology, heat exchanger monitoring can be quickly automated and integrated with the existing automation system in a matter of days.

Wireless temperature transmitters and heat exchanger modeling software can determine when crude unit preheat exchangers need cleaning.

At one refinery, the #2 Crude Unit was subject to preheat train fouling. The refinery was unable to determine when to clean the heat exchanger for the greatest benefit. This lack of information prevented economic analysis planning, such as fouling degradation versus additional fired heater fuel required. An IIoT real-time temperature monitoring system was installed on the unit, which sent data to heat exchanger modeling software. Based on the analysis, the heat exchanger was cleaned on an as-needed basis, resulting in an estimated annual savings of $225,000 in maintenance costs, with further savings of $912,500 realized by preventing downtime (table 3).

More than a buzzword

The IIoT is more than a buzzword. It is here, and plants are using it to realize value from the hundreds of millions of connected sensors currently installed, and the millions more coming online each year. Many of these new sensors are wireless, because they can be installed quicker and at less cost than their wired equivalents, often with no required downtime. These low-cost wireless sensors and accompanying analytics can dramatically improve plant performance, increase safety, and pay for themselves within months.

About the Author
Deanna Johnson, global marketing communications manager at Emerson Process Management, focuses on Rosemount products and pervasive-sensing strategies. Her previous positions included development of integrated marketing communications programs for Emerson’s oil and gas and refining industries, as well as work on WirelessHART marketing. Johnson started her Emerson career in 1996. She has an MBA with a marketing focus.

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A version of this article originally was published at InTech magazine.

Connectivity, Productivity and Efficiency Benefits of IIoT Depend on Integrated Cybersecurity

Connectivity, Productivity and Efficiency Benefits of IIoT Depend on Integrated Cybersecurity

This article was written by Bill Lydon, chief editor at InTech magazine

 

I had a discussion with Gary Freburger, president of Schneider Electric’s process automation business, about the Industrial Internet of Things (IIoT). He framed the discussion by introducing a new concept, “intelligize.” Simply put, intelligize means establishing a method to sort, prioritize, and refine your data, to connect bits of data so they become meaningful information, and then to share that information with operators and other assets, ensuring that the most effective, valuable business and operating decisions and actions are taken.

“While all industry is chomping at the bit to realize the promise and rewards of IIoT,” Freburger noted, “all that connectivity and proposed productivity and efficiency won’t matter if the culture, systems, or plants are not inherently safe and secure. Before deploying IIoT, it is important to understand not only the implications for your business, but also the implications for overall safety and security.” In short, “a cornerstone of an effective industrial automation system is integrated cybersecurity.”

It is critically important to think about all the opportunities IIoT presents before connecting a large volume of sensors, solutions, and automation and control systems. The prospect of connecting billions of devices to industrial automation systems begs two really important questions.

First, how do we keep systems and information secure? Adding more devices creates a broader attack surface, which increases cybersecurity risks. In Freburger’s view, there must be a balance between adding intelligence, securing the devices, and protecting the data. Collecting data just for the sake of having more data might not create any additional value at all. More data has the potential to cause more operator confusion and increase the cyberattack risk.

Second, what do we do with the data and information? “You need a process to figure out what it means and what it is telling you,” he said. “There are a lot of options for using data, including trending, exception reporting, alarming, and other functions. But there needs to be a reason to collect all this data. It’s what we call an operational intelligence approach, which relies on optimizing automation and control, remote management, and predictive maintenance to enable managed services, advanced analytics, and the generation of actionable information that drive better, more informed decision making.”

Improving operational efficiency and reliability can be better accomplished by providing the intelligent data for operators to make the better decisions that optimize production. Freburger used an interesting analogy to make his point. “If you connect your washing machine to the Internet, what do you really want to know? Do you want to know when the water turns on, the soap dispenses, the drying cycle time, the rinse cycle time, the spin cycle duration and RPMs? That’s a lot of data. But is it valuable and worth extending your risk of a cyberincursion? And what would you do with the data anyway? In all practicality, all you probably want to know is when the washer turned on, when it’s complete, and if there is a potential problem. Just because I can connect my washing machine to the Internet doesn’t mean I should, unless it makes sense and unless I can do something valuable with the information.”

“What’s interesting to me from our perspective, with a lot of feedback from users, is that control systems have become complicated,” he told me. “We’ve come to the realization that we need to simplify the data and make it easier for users. This includes standardization in a number of areas to make things simpler—for example, standards that define the meaning of operator display colors for consistency. But ‘simpler’ and connecting another 5,000 devices don’t quite go together. The important thing is deciding how to intelligize the data, deciding what you really want to accomplish, how to use the data to do that, how to bring it into the systems, and how to keep it and your systems secure.”

“The Industrial Internet of Things is a wonderful advancement, and a real opportunity to increase ROI [return on investment] and asset value. When it comes to process automation, we should be using IIoT capabilities to push control further toward the device layer, which means making instrumentation much smarter. This should allow you to simplify the control architecture to match the topology, so that we are reducing time, cost, and effort to configure systems.”

Distinguishing the data you really need from the available data is important in system design. For Freburger, this simply means applying lean design concepts to improve operations, efficiency, and productivity. “The IIoT strengthens our capabilities so we are better able to help customers extend the life of their assets, enhance decision-making, and create a smart enterprise control system that drives improved financial performance for the business. But it has to be inherently cybersecure first.”

 

Bill LydonAbout the Author
Bill Lydon is chief editor of InTech magazine. Lydon has been active in manufacturing automation for more than 25 years. He started his career as a designer of computer-based machine tool controls; in other positions, he applied programmable logic controllers and process control technology. In addition to experience at various large companies, he co-founded and was president of a venture-capital-funded industrial automation software company. Lydon believes the success factors in manufacturing are changing, making it imperative to apply automation as a strategic tool to compete.
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A version of this article originally was published at InTech magazine

Internet of Things: Creating Customer Value from Home to Industry

Internet of Things: Creating Customer Value from Home to Industry

This post was written by Prabhu Soundarrajan, global marketing director for Honeywell Analytics

 

The Industrial Internet of Things (IIoT) has been a very popular topic. Several startup companies emerged, and major corporations introduced initiatives throughout the year. Most of us are wondering where IIoT will go within our industry. I want to offer a personal account of how I have been affected by the trend and share my thoughts on how it may impact our industry.

Having spent the past 15 years in the commercialization of innovative technology, I took it upon myself to understand the economic value created by this new technology.

Connected home

My home is now connected. Throughout the year, I piled up a number of “smart home” devices to enable my house for IoT. It started with a smart thermostat, camera, lighting, shower, security system, home theater, TV, and car. By definition everything in my home has connectivity to enable new value in comfort and flexibility. So is IoT for the connected home really new? It is really an evolution of what I am used to, with a slight premium for technology and convenience. I was happy to pay for it and enjoy the benefits of a connected home. I made the transition within a year without really noticing it. Every major retailer has IoT-enabled products and offers incentives to encourage consumers to buy IoT-enabled products.

Applications such as automatic climate control, energy management, and 24/7 security drove my willingness to pay. I paid a few hundred dollars to make my home “connected,” and my wife (boss) already appreciates it. I look like a rock star to her.

Connected industrial safety

Companies are leveraging IoT connectivity so customers can have the same level of connectivity in their workplace as I have in my home (e.g., an IoT-enabled connected safety solution for industrial workers). Industrial work environments are challenging in terms of safety hazards, compliance requirements, and exposure to risks. Unexpected events can lead to a major accident, causing downtime for several days, which in turn affects the productivity of the enterprise. Connected, safety-enabled IIoT has 24/7 real-time monitoring to provide situational awareness for the worker, supervisor, plant manager, and whoever else needs to be in the know. Gas detectors and personal protective equipment are some of the “things” in the safety space that give tremendous value when connected.

Connected workplace

The benefits already proven by connected safety solutions are tremendous. An ethanol plant in the state of Washington could detect a small leak in a storage column when a worker was doing a regular inspection. The worker transmitted information about a gas leak, which the control room operator translated as a product leak after the first few hours of system installation. This helped the plant to change work procedures and process optimization that saved more than $250,000 for the enterprise. The return on investment for a connected safety solution was only a few months.

A major petroleum refiner in Texas embraced connected safety solutions to develop a new emergency management process. It transmitted the hazard data map from gas detectors monitoring the perimeter of the facility, along with a wealth of new data, to the control room three kilometers away. An oil-processing plant in California correlated the personal exposure data for worker health and developed comprehensive work procedures for confined space entry, resulting in greater compliance with environmental regulations. This company shared best practices across its global sites securely over the cloud to enhance a culture of safety. These positive developments were not possible in the past when edge devices were not connected, but with connectivity new value streams were identified for the end user.

The practical applications of IIoT and connected safety solutions are driving three major value propositions for the enterprise:

  • Safety: End users can now transition from reactive to proactive safety procedures and plan and manage the entire safety life cycle of the enterprise.
  • Compliance: Work rules management has transformed from “trust” to “verification” to reduce liabilities across the enterprise.
  • Productivity: Real-time, 24/7 safety data increases operational efficiency by reducing or eliminating labor-intensive procedures.

The IIoT is creating value in both home and industrial environments. The final say for the technology is in the incremental and differentiated value created for customers.

About the Author
Prabhu Soundarrajan is global marketing director for Honeywell Analytics. He has served in a number of capacities as a volunteer leader, including director of ISA’s chemical and petroleum industries division (ChemPID).

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A version of this article originally was published at InTech magazine

How IIoT Will Revolutionize Real-Time Plant Maintenance and Analytics

How IIoT Will Revolutionize Real-Time Plant Maintenance and Analytics

This article was written by Mary Bunzel, general manager of the Manufacturing Industry Solutions Group at Intel Corporation.

With all the hype in the press about the “new” Internet of Things (IoT) and what it offers industry, it is challenging to decide which pieces are best for an organization and how to get started. The fact is, what is best for your organization is probably at least a bit different from what is best for another organization.

Systems and processes no longer exist in and of themselves. With connectivity and visibility throughout the enterprise, the interrelationship between all parts of an organization is becoming obvious. This provides new opportunities for improvement and for leveraging technology to achieve more efficient operations.

 

Production processes, maintenance protocols, safety initiatives, training content (or lack of it), staffing, and schedules affect each other in ways we could never have foreseen even 10 years ago. Yes, we understood that interconnected components were potentially valuable, but now we have technology in place to better define, measure, and act on these interrelationships.

After the business challenges of the 2008 global financial crisis, most of us have harvested the low-hanging fruit that keeps the doors open and allows us to continue improving. The next step is to take advantage of the tools of IoT. IoT has been part of our lives for quite a while, whether we know it or not. Our cars, assets, and even our Amazon accounts use huge numbers of data streams to protect us, improve performance, and drive our buying habits. Perhaps IoT should be called “things we can communicate with,” but TWCCW does not quite roll off the tongue.

What is the difference between now and 10 years ago? Analytic methods and software have been refined. They are easier to use and available on demand with cloud services. In the past, we could see most of the obvious correlations. Those that were less obvious were only identified by really smart subject-matter experts using spreadsheets, really geeky math, and big mainframe computers such as the IBM 360.

Analytic engines give us the capability to work many of these concepts much more quickly. Just as the PC distributed processing from the mainframe to the desktop, analytic engines drive the processing the same direction. We can process thousands of data streams, and discover—or let the machine discover—hidden relationships and correlations. Then, we can use these relationships to validate changes to systems or processes.

For example, weather conditions, such as temperature, humidity, and pending storms, affect how assets perform. Understanding what is “normal” is predicated on the kind of process running, the quality of the raw materials, and the quality of the energy delivered by the provider. Seeking correlations between these influencers is not optional anymore; this capability is considered a base requirement for operations and maintenance.

How do you get started? Pick one area, one piece of equipment, or a high-tech process to focus on. Cloud computing offers easy access to business analytic models (see IBM.com/IoT for more information) that you can experiment with. Connecting a data stream is as easy as deploying an app. Let existing models show you correlations you did not know about—explore and expand on these as signposts to early successes.

Recognizing that effective use of data is dependent on an understanding of what you already have, determine where decision-support data lives and bring it all into a single framework. Only then can you move toward more forward-looking possibilities.

Where do you start? Unless you are living under a wet rock, it really does not matter what your role in the enterprise is; we can all see an opportunity. Grab a small piece, and get started.

Reactive to predictive

Technology development is expanding the tools available to increase the effectiveness of maintenance to dramatically improved uptime and equipment availability. Reactive maintenance, which waits for machines and other equipment to break down and then fixes them, is a costly method, affecting production efficiency and manufacturing quality. This practice also has a big impact on increased life-cycle costs, often shortening the useful life of equipment.

Preventive maintenance based on calendar time improves equipment effectiveness. However, lacking a link between equipment use and wear, this method has not proven to be reliable, and it requires a significant commitment of labor resources. Much of the work and materials are overkill. Condition-based maintenance using real-time monitoring to constantly assess the condition of assets can dramatically improve availability and limit downtime. The big next step in maintenance is enabled by IoT technology and cloud computing. Companies identify and correlate patterns in variables that, taken as a whole, affect equipment performance to determine actions that can prevent failures. The application of predictive methods can significantly improve maintenance strategy and the ability to anticipate performance issues and mitigate them before they impact operations and cause unscheduled downtime.

Exploiting asset data

More and more intelligence is built into sensors on equipment every day. Automation systems linked to these intelligent sensors deliver insights into real-time performance data. With the application of Internet of Things technology, these terabytes of data turn into actionable information. The opportunity is for a much clearer fact-based understanding of asset performance and efficiencies to lower maintenance costs, improve production uptime (lower downtime), improve product quality, improve production yield, reduce unplanned downtime, and optimize maintenance labor resources. This data can also be used to justify replacement of existing equipment and verify performance of new production processes and recently installed equipment.

Newer and easier-to-use analytic modeling software is becoming available due to the demands of customers whose appetites are whetted by compelling results and who drive the need for more and more insight into their business operations. Analytic models are bringing high-hanging fruit in reach; maintenance and operational improvement directly affects the bottom line, and that is why large enterprises are so interested in leveraging these technologies.

Exploring potential worth

Data from automation and monitoring systems, leveraged with analytics, monitoring, and reporting, creates the basis for a real-time maintenance program. The potential impact of employing predictive maintenance is significant, as illustrated by a Nucleus Research analysis of potential improvements:

  • Reduction of annual unplanned downtime: 60–90 percent
  • Reduction of excess capacity required to compensate for unplanned downtime: up to 90 percent
  • Scrap or rework reduction: up to 50 percent
  • Asset life extension improving lifetime return on assets: 5–15 percent

Identify and prioritize needs

A valuable analysis is to identify and prioritize your situation considering three factors relative to analytic use cases.

  • Operational and organizational readiness: Are you ready, or do people need more information and training?
  • Business and strategy alignment: Is this in line with your company’s goals and objectives?
  • Risk and return value: For your operations, what is the economic potential?
How real-time maintenance and analytics affect an operation depends on the organizational characteristics
Areas of improvement         Organizational characteristics with highest value return
Asset quality yield improvement from predictive analytics impacting production and manufacturing processes Complex discrete and process manufacturing
Asset quality yield improvement for higher levels of quality of finished goods and services Complex discrete and process manufacturing
Process-driven root-cause detection and diagnosis and prognosis for quick resolution of complex problems High-risk industry, multisite operations
Process-driven predictive tool calibration for improved throughput, uptime, and accuracy to maintain tolerance accuracy Precision manufacturing
Reducing recalls and warranty exposure based on predictive, early alert, field-asset problem determination Competitive markets, costly product development cycles
Asset track and trace to detect and predict asset movement and location (supply-chain management) Collaborative partners for subcomponents, expensive assets, outsourced maintenance
Reducing scrap due to improvements in production process analytics and root-cause analysis High cost of raw materials, fixed cost for processed goods
Early warning and predictive parameter modeling for early and precise problem determination Precision manufacturing
Product service improvements as a result of defect detection and prevention results in customer loyalty Competitive markets, expensive recalls, risk to company reputation
Asset monitoring and analytics for regulatory compliance warranty or recall Highly regulated industry, high cost of noncompliance
About the Author
Mary Bunzel is the general manager of the Manufacturing Industry Solutions Group at Intel Corporation. Previouysly she was with IBM and brings more than 30 years of experience in best practices for manufacturing industries, with a special focus on the automotive, industrial products, and food and pharmaceutical industries. Before joining IBM, Bunzel spent 10 years working for MRO Software (PSDI), where her role was Maximo’s strategic accounts manager for General Mills, J&J, Cargill, ADM, Ford, and General Motors. Bunzel serves as IBM’s voice to the market, to customers, and to analyst groups on the state of the manufacturing market as it relates to Maximo asset management offerings.
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A version of this article originally was published at InTech magazine

What Are the Benefits of Connected Manufacturing?

What Are the Benefits of Connected Manufacturing?

This article was written by David Parrish, global senior director of industrial machinery and components marketing for SAP. | @parrish_denver

More and more customers today are looking for highly customized products. At the same time, manufacturers are under pressure to produce efficiently at mass-production costs. One way organizations can meet this challenge is by integrating business and manufacturing systems with automation technology. Traditionally, these systems have run independently of each other, but the factories of the future will have a fully integrated system from sales orders to manufacturing orders to delivery confirmations. Known as connected manufacturing or Industry 4.0, this integrated approach can help manufacturers operate more efficiently using a variety of data sources from both operations and enterprise systems. More specifically, companies are able to turn massive amounts of data into action by using advanced analytics to identify bottlenecks, troubleshoot issues, understand asset interdependencies, and reduce costs.

To understand the true potential of integrating business and manufacturing systems from the top floor to the shop floor, it is helpful to review the situation many companies operate in today. Typically, traditional shop management processes are manual and time intensive. Getting manufacturing performance data to the top executives is a slow process, subject to human error and interpretation. Often it begins with plant managers creating spreadsheets from system-generated reports. Executives review these spreadsheets at the end of each week and use the data to create visual dashboards for higher-level management. Unfortunately, complicated graphs and spreadsheets do not give enough actionable items to help until after the fact. Another example is shop-floor supervisors physically dispatching job packets. Operators receive a hand-delivered stack of papers with their jobs for the day and verbally report back completion to their supervisors.

It is fairly obvious to most manufacturers that paperless systems improve efficiency by reducing manual work. But imagine what could be accomplished if a company could use technology to create real-time connections among the automated manufacturing floor and all other business systems, both internal and external. Not only that, but what if the data was instantly converted into visuals, so all employees could easily analyze and understand the information?

With information at their fingertips, managers can identify and resolve issues up and down the supply chain to increase efficiencies, improve profitability, and raise customer satisfaction. In fact, the seamless integration of plant information with business systems presents so many opportunities, companies are just scratching the surface of what is possible. Below are a few examples of existing processes being transformed by connected technology:

  • Plant production planning: Smart planning, forecasting, and clear visibility into operations and finances are essential to maximizing plant resources, especially when adding assets from an often dizzying array of mergers or acquisitions. Using an integrated system allows managers to easily assign which plant manufactures which products for a given order and strengthens on-time delivery performance.
  • Employee performance: People on the plant floor get real-time feedback on how their actual work compares to planned output. They can immediately see the value they are contributing to the whole business and the impact their work has on customers.
  • Product traceability: Connected manufacturing systems enable transparency and collaboration across the entire supply network, starting within the four walls of a manufacturing plant and extending across global supply chains.
  • Production warehouse: Connecting the shop floor to other business systems allows decision makers to optimize material movements between warehousing and production. By integrating real-time material availability with real-time manufacturing capacity, days-in-inventory reductions of 15 percent or more are common.
  • Manufacturing accountability: Workers at each production station are now guided by visual displays of standard work instructions, elapsed assembly times, and customer-specific requirements for various features and options. As a result, manufacturing efficiency increases, and production cycle times are compressed significantly.
  • Sourcing: By making strategic sourcing a key component of an overall strategy to cut costs and maintain a competitive edge, companies with connected systems have increased productivity by finding and qualifying new suppliers faster. Additionally, they are able to gather feedback on suppliers from all departments, so they can evaluate a wide range of performance metrics, such as price, quality, and on-time delivery performance.

Real-time, connected manufacturers enjoy many benefits not available to companies still working within a more traditional environment, which typically has misaligned departmental silos, manual processes, and disconnected systems. Putting the right data in the hands of decision makers when they need it allows companies to take immediate action in response to changing market conditions. Companies report more effective communication and consistent oversight when the factory floor and the front and back offices share a single powerful database. Also, sharing information helps encourage the development of ideas or better solutions from all parts of the organization. Finally, integration between the shop floor and the top floor can reduce manufacturing costs by increasing overall equipment effectiveness and minimizing unplanned downtime.

Although there are many practical applications of an integrated technology solution, one of the biggest advantages is that it allows everyone from the machine operator to the CEO to have a voice. Placing the power of information in the hands of all employees and supply-chain partners can create a collaborative environment in which everyone plays an important role in solving problems. In this way, companies can thrive, enjoying a continuous stream of innovative ideas that can quickly become reality.

Cybersecurity risks need to be assessed and mitigated as industrial plants find value in communicating more data internally and externally to improve performance and maintenance. The ISA99 standards on industrial automation and control systems (IACS) security provide a framework for analysis and protection.

About the Author
David Parrish, global senior director of industrial machinery and components marketing for SAP, previously held various product and industry marketing positions with J.D. Edwards, PeopleSoft, and QAD. Parrish has a BS in advertising from the University of Illinois-Urbana, an MBA in transportation management from the University of Colorado-Boulder, and a CPIM from the American Production and Inventory Control Society (APICS).
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A version of this article originally was published at InTech magazine

Separating Hype from the True Value of Industrial Big Data Analytics

Separating Hype from the True Value of Industrial Big Data Analytics

This article was written by Matthew Littlefield, president and principal analyst of LNS Research.

In many ways I liken the current Industrial Internet of Things (IIoT) hype to the original dot-com bubble in the late 1990s. Although short-term expectations well exceeded short-term gains, no one can deny the long-term transformative power of the Internet. The IIoT may not bring immediate transformation, but by 2020 or 2025, when we take a step back to see how far we have come, I believe the resounding response will be that the revolution was real.

In the industrial sector, we have the benefit and curse of technology refresh rates typically measured in decades, and technology selection processes typically measured in months or years. This is a benefit, because the technology we buy is built to last and provides a return on investment (ROI) well beyond that of other industries. It is also a curse. Since we do not get new technology very often, when we get the chance we often bite off more than we can chew, are paralyzed by fear of the unknown, or seek out the new shiny toy.

Perhaps no other new technology comes with more promise to manufacturing executives than big data analytics. It is a new technology that captures the imagination and offers the ability to find previously unfindable data correlations that will return untold benefits in cost reduction, quality improvement, supply chain efficiency, and more. In the face of such promise, it is critical that executives do not get caught up in this hype and instead focus on solving business problems, not acquiring a new shiny tool.

Same old, same old?

For the most part, industrial organizations are still trying to solve the same problems that have been an issue for the past few decades or more. Quality, cost, and customer responsiveness are the goals, and disparate data sources, legacy systems, and no clear ROI are the challenges. The fact that these same issues have proved so intractable in the past is one of the main reasons big data analytics is gaining so much attention today. Companies are frustrated with the lack of progress being made with legacy tools and see big data analytics as a new approach.

That is not to say that some industrial companies today do not view big data analytics as a path toward solving new problems with new technology. The skills gap is well documented in the industrial sector, and enabling new models for knowledge capture and remote service is one of the relatively new industry challenges being addressed with big data analytics. And although it is still a small minority, 5 percent of manufacturers or less, some companies also see big data analytics as a way to move from being not only an industrial company, but also a digital company. Companies use these new capabilities to offer either new value-added services, such as zero downtime or performance benchmarking, or new business models, such as uptime as a service or power by the hour. Examples of companies already having made this transition include Heidelberg Press (large printing presses), Konecranes (large cranes and lifting equipment), Fanuc (robotics), and Trane (industrial heating, ventilation, air conditioning equipment and service).

Big data analytics in manufacturing

Visualization and analytics are not new to the industrial space. Traditionally these capabilities were delivered by either pure play enterprise manufacturing intelligence (EMI) vendors or as EMI products from enterprise resource planning (ERP), manufacturing operations management (MOM), or automation vendors. The types of data that can be analyzed are structured and semistructured data from application databases or data historians and, in some cases, machine data. The types of information that are then presented include production performance data and analysis, such as an asset’s overall equipment effectiveness (OEE), the location of the production line’s bottleneck, or the quality of the product being produced and the financial impact of package overfills and variation. In the case of statistical process control, recommendations can even be made to operators to proactively improve future performance. In every one of these use cases, there is a clear and demonstrable ROI, and in many cases benchmark data has shown these technologies can improve performance in OEE by 10 percent or more.

However, these products and use cases all come up short on the definition and value proposition of industrial big data analytics. First and foremost, big data is about more than just data volume—petabytes alone are not enough. Instead, big data analytics should be thought of in the context of the three Vs: volume, velocity, and variety. Most traditional EMI vendors get high marks on volume, middling marks on velocity, and low marks on variety.

To handle volume, velocity, and variety, companies need to deploy a full big data or, more likely, an IIoT platform that has drivers, gateways, data transport, and edge analytics capabilities to quickly and efficiently capture data. They also need a hybrid (cloud and on-premise) computing environment with a traditional relational database, traditional data historian, in-memory database, and Hadoop capabilities. And finally, they need a data schema that quickly and efficiency brings together different data types like financial transactions, operational transactions, product engineering, simulation, machine data, web data, and geo-spatial data, alongside algorithms that provide traditional manufacturing analytical capabilities and new data science capabilities like neural networks and machine learning.

With this full set of capabilities, industrial companies have access to the full suite of analytics: descriptive, diagnostic, predictive, and prescriptive. All are a big part of enabling smart, connected operations and moving from real-time operations to predictive operations and ultimately to autonomous operations.

Keeping pace with the consumer

The hype created by platforms like IBM’s Watson beating U.S. game show Jeopardy champions is undeniable. It clearly shows the power of new computing. Watson is a question-answering computer system capable of answering questions posed in natural language that IBM’s research team developed in its DeepQA project. As time passes, however, big data analytics will become part of the next-generation applications that span system hierarchy and value chain pillars, just as business intelligence became part of ERP and EMI became part of MOM.

One of the promises of big data analytics and the IIoT is how it will transform traditional hierarchical system models deployed in industrial settings, most often typified by the Purdue or ISA-95 standards model. For a number of reasons, the traditional hierarchical model has not led to the shop floor to top floor connectivity that many predicted. Continued adoption of homegrown systems at different layers in the model, different plant versus enterprise networks and security standards, differing data models, and more, all limit the long-term viability of the current architecture.

As legacy and next-generation applications build on top of IIoT platforms, the new applications will be held to a higher standard than ever before in the industrial space. In fact, vendors will not be competing with traditional industry apps, but instead with the usability of consumer-grade apps Uber, Waze, and Amazon. To win in this race, applications will have to take a mobile-first mentality and build social, collaboration, and search tools and intuitive big data analytics into the fabric of the applications. These applications will also likely be targeted at specific use cases that span the system hierarchy described above as well as the different pillars of the value chain. Examples of these types of applications include asset reliability and benchmarking, traceability and genealogy, energy and emissions optimization, and flexible manufacturing or lot size of one.

Operational excellence and industrial transformation

To help executives, operational leaders, and entire organizations capture the value of big data analytics, it is critical to start with strategic initiatives or endeavors, not the desire for technology. Leading companies are building these initiatives around the core capabilities of people, process, and technology. They are using frameworks such operational excellence, which has been developed by many leading process and discrete manufacturing companies for cross-functional collaboration between quality, environmental health and safety, manufacturing operations, asset performance management, and more to bring structure to organization goals.

Using proven models like operational excellence as a foundation, companies can bring together areas of the business like energy, environmental health and safety, quality, manufacturing operations, and asset performance, to begin thinking about industrial transformation. This construct also brings together business leaders and information technology (IT) leaders, often even paving the way for the creation of a cross-functional manufacturing systems group. IT leaders are an invaluable resource in these endeavors.

Born out of necessity and experience, IT leaders are often more focused on avoiding technology adoption for technology’s sake than anyone else in the organization. They are often dogmatic about defining business process transformation, ROI, and risk prior to technology projects. These IT leaders have been through and blamed for more than a few boil-the-ocean ERP projects that were over budget and over time without a clear business case justification other than an executive directive that “we need that to manage the business.”

With a solid foundation for cross-functional collaboration that includes IT and automation leadership, companies should move quickly to start adopting platform tools for big data analytics. When starting, it is important to note that it is impossible to know upfront all of the data that will be needed, which will be valuable, and which will prove unusable. It is also impossible to know or predict all of the potential benefits that will be uncovered. Given this uncertainty, start incrementally with a lean startup approach. Do not be scared of failure, learn fast, adapt fast, and show value fast. This is the opposite of what has been traditionally deployed in manufacturing, with monolithic applications. It is also why so many technology projects have stalled or failed. It is also the reason to ensure that the chosen big data analytics and IIoT platform tools are flexible and adaptable. It cannot take weeks or months to clean data or bring in new data types to the model; by the time the data is ready the question has changed. Data cleansing and data analysis has to happen easily and quickly, think minutes and hours.

The final piece of the puzzle as your company starts down the big data analytics journey is people and training. It is important to start the endeavor with the understanding that your organization will not be able to hire enough data scientists to support the thirst of the organization once it gets started. Instead, organizations will have to “home grow” data science teams and teach manufacturing subject-matter experts just enough data science to be effective and efficient, not vice versus.

Cisco, for example, has a homegrown team of more than 80 data scientists who came from the supply chain organization and were given two years of training. Luckily, industrial companies are not starting from scratch; they have an existing template of how to build programs for teaching manufacturing experts adjacent tools sets. Just as 20 years ago, Six Sigma took over the manufacturing world and taught folks just enough finance and statistics to drive cost and variability out of the system, the industry will need to start a new big data analytics skills revolution. Companies will have to invest in employees, and employees will also have to invest in themselves to move the whole industry forward and make big data analytics a reality.

About the Author
Matthew Littlefield is president and principal analyst of LNS Research, an industry analyst firm focused on operational excellence and industrial transformation. His research spans the industrial value chain and examines how next-generation technologies like the IIoT are delivering business benefits.
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A version of this article originally was published at InTech magazine

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