Kevin T. Higgins, Managing Editor, Food Processing
Feb 27, 2017
Copyright 2017 Putman Media
Syndication Source: Food Processing
Like a new pair of shoes that pinch until broken in, food manufacturers are slowly getting comfortable with the Industrial Internet of Things (IIoT).
Two years ago, mention of IIoT provoked blank stares or rolled eyes from the people who manage and run America’s food and beverage production facilities. Today, they are warming up to the concept. In a recent Food Processing-ABB joint survey, "What’s Driving Automation Investments in the Food and Beverage Industry" only 43 percent of food professionals indicated their plants weren’t engaged in any IIoT-related activities.
Some of the early stage work is driven by economics. Software that resides in a third-party server and essentially is rented is cheaper than licensing and maintaining the same software. Rented programs and the data they generate require third-party hosting, commonly referred to as cloud computing, and 22.4 percent of survey respondents say their organizations are engaged in cloud-based computing.
Data availability through mobile devices is described as IIoT’s second wave (connected PCs are the first), and progress is being made on that front. Whether it’s a portal to a machine’s controls that allows remote access or a web browser that connects a mobile phone to machine data through the aforementioned cloud, 20-25 percent of respondents report their companies are leveraging those capabilities.
Wireless connection of field devices to a database for real-time reporting of what is actually happening on the production line is an element of the IIoT infrastructure. It’s also the area with the most activity, with three in 10 food facilities utilizing wireless networks, the survey found.
Interest in further development of the IIoT infrastructure is building, a study commissioned last year by MESA International suggests. One-third of manufacturing professionals said their companies were investigating how the connected plant would benefit their organizations, up from one in five the previous year. Almost one in five said they see value in those investments, primarily because of the customer-service enhancements they could provide.
IIoT enthusiasm is greatest among IT professionals and top management, less so among engineers and operations personnel. Customers are clamoring for greater supply chain visibility, including status reports on order fulfillment and any quality issues. Less clear is how providing that information will positively impact production.
In the parlance of automation vendors, benefits will be laid bare by an “IT/OT convergence.” To the ears of seasoned operations personnel, that sounds suspiciously like a positive spin on the IT/engineering gap that emerged in the wake of Y2K spending. Food companies spent heavily on ERP systems in the run-up to the computer calamity that wasn’t at the dawn of the year 2000. Investments in plant automation suffered when capital expenditures focused on ERP implementations that were sold as helping manufacturing as well as the front office. As the new century got under way, engineers were asking, “Where’s the beef?”
Who’s your daddy?
Some of IIoT’s beef resides in the chop suey of Big Data. In the internet world, data is the new currency, and organizations like Google, Amazon Web Services, Oracle and Microsoft are trading hard currency for more data. “Companies are purchasing other companies just to get their data,” points out Rob Light, a research specialist with G2 Crowd (www.g2crowd.com), a Chicago-based business software review service.
Part of the promise of massive databases is a transformation of process analytics, first to predictive modeling and ultimately enabling adaptive change. That transformation likely is years away, however. It will require many more data scientists than are currently available, says Light, and higher education is just beginning to create the data science curricula needed to fill the skills gap.
A recent report by Harvard Business Review (HBR) Analytic Services decries the lack of business investment in Big Data development. Most Big Data projects are done on an ad hoc basis, the report notes, with fewer than one in five organizations pursuing them as part of a comprehensive strategy.
That may be more a reflection of most companies’ inability to invest millions in infrastructure development with long-term paybacks than a disinterest in the technology itself. Automation vendors’ poster child for IIoT investment in the food industry is Sugar Creek Packing Co., a pork processor that christened a new facility in Cambridge City, Ind., in 2015. Sugar Creek invested $6 million in networking hardware and software. The technology was a necessary foundation for a high-performance work team system that executive management introduced at the new facility. Eliminating workflow bottlenecks provided the return on investment.
Machine assets, not human assets, are the focus of most IIoT initiatives, however. For the process engineer, real-time data exchange between automated machines is the issue, with remote access through the internet to performance reports a residual benefit. The Big Data proxy for Google is the cloud or, possibly, OEMs in the role of Pretty Big Data.
Analyzing controls data from multiple machines that are identical or very similar can yield much more powerful information on machine condition and process performance than data from a single machine. If the database included hundreds of machines, regardless of ownership, in dozens of processing environments, it would be even more powerful.
The likelihood of that scenario, however, hovers around nil: machine performance data is jealously guarded. For years, OEMs and skid builders have provided remote diagnostic capabilities in advanced machine controls. If those diagnostics ran continuously in the background, the IIoT could be the conduit to vendor-supported alarms and analytics.
But the very idea of an internet portal to a plant’s Ethernet communications sends a shudder down the spine of IT. Instead of easier access from outside the plant, food manufacturers are placing more restrictions. “One word: firewalls,” summarizes Ola Wesstrom, senior industry manager-food and beverage for instrument supplier Endress+Hauser (www.us-endress.com), Greenwood, Ind. Expect more restrictions, not less, to allowing third parties to listen in on machine performance.
The Shadow knows
Data security is the obsession of IT and, to some extent, executive management. Engineers and operations personnel, on the other hand, are more receptive to IIoT collaboration.
In the Food Processing-ABB automation survey, plant operations professionals were twice as likely to view remote access to machine controls favorably as C-suite executives. They also had a much more favorable view of vendor access to controls data and the connection of field devices to a wireless network.
A possible workaround to security concerns is creation of a parallel controls network, a system of “shadow sensors,” in the words of Rob McGreevy, vice president-operations, information & asset management at Schneider Electric (software.schneider-electric.com/), Andover, Mass.
“Low-cost sensing and other technologies allow engineers to enhance performance monitoring in a fraction of the time and cost,” he explains.
“Shadow sensors costing $200 or $300 each could sit on top of the high-fidelity, deterministic controls needed for high-speed machines.”
A handful of these wireless devices would communicate via Bluetooth to the cloud and monitor machine condition, product quality and other factors. “It’s not that complicated and doesn’t require pulling wires and costly systems integration,” he adds.
Another avenue to better process control and improvement in yield and product quality passes through in-line inspection systems. The performance of upstream machinery is inferred in the products being inspected. While inspection equipment is only required to render a pass/fail decision, it often has the computing power to do much more.
An example is the software suite that Key Technology Inc. (www.key.net), Walla Walla, Wash., began embedding in high-speed optical sorters two years ago. The immediate benefits relate to the quality of raw materials, but there also is potential for improved process control and machine performance.
“Optical sorters could be looked at as digital information centers,” observes Marco Azzaretti, who oversees Key’s advanced inspection systems. Images of each item moving down the line are captured, and real-time processing of the captured data can be used to adjust the process.
Potato processors are at the top of the sophistication hierarchy of fruit, vegetable and nut processors who use Key’s sorters. Their lines run 24/7 up to three weeks between cleaning and sanitation shutdowns. When sorters are placed at multiple points on the line, they tell a story of how the processes between them impacted the product. They also provide clues about upstream machine performance and the state of the sorter itself — a malfunctioning ejector, for example, or a sensor window in need of cleaning.
Machine performance also is an indicator of component wear, and analyzing the performance of multiple sorters in a company’s manufacturing network enhances predictive maintenance. The information would be even more powerful if it consolidated data from comparable sorters at McCain Foods, Simplot and other potato processors, although security concerns pre-empt that possibility.
In food manufacturing, such a database is a pipedream. On the other hand, simulation models built on integrated data from multiple sources might one day help resolve bottlenecks and lead to process improvements for every potato processor.
“Big Data can be a significant enabler,” Azzaretti allows, “but it goes hand in hand with the ability to capture data in real time.” When throughput is measure in tons per hour, the faster the response to change, the less rework and waste.
“We work with leading companies, with sophisticated engineering departments that are on the bleeding edge of capturing processing line data to optimize processes,” he adds. “But in the last three years, we’ve noticed widespread interest in this kind of information, in different industry segments and company sizes.” Some want the data delivered off line for analysis, others are using it to drive line performance, and the most highly automated want real-time data fed directly to SCADA systems or MES line-management software for integration with other machine data.
“The crux of the issue now is how all this data is going to come together,” he says. The machine-to-machine protocol of choice in Key’s customer base is OPC UA, while other industry segments favor EtherNet/IP, Profinet and other protocols.
Change is a’comin
If IIoT is to be more than a catch phrase, it will have to deliver more than vague promises of transformational change and provide a business model with clear returns on investment. To date, no such model has emerged in food manufacturing.
An evaluation of offshore oil rigs by McKinsey & Co. concluded that those rigs are equipped with as many as 30,000 sensors, but less than 1 percent of the data generated is used in decision making. Uploading all that sensor data to a cloud surely would fill many servers, but would it help ExxonMobil or BP improve efficiency or pump one more gallon to justify the effort?
Some observers trace the origins of the IIoT to the trucking industry, where GPS devices and wireless sensors relay data via the internet for improved fleet management. In general business, an IIoT case is being made for assistance in product development and improved customer service — priorities for executive management, to be sure, but goals that don’t improve operations within the four walls of the factory.
“Creating the IIoT infrastructure is expensive and time consuming,” G2 Crowd’s Light understates. “Huge growth was expected in 2016, but it didn’t happen and it may not in 2017, either. It’s still a few years away.”
Nonetheless, proponents remain upbeat. “The IT/OT convergence is a reality,” maintains Schneider’s McGreevy. “The IT folks used to be seated at the table with OT half the time. Now they’re at the table 90-100 percent of the time.”
A few examples of the transformational changes that proponents promise would go a long way to jump starting some food company initiatives. Until then, firms will continue breaking in their IIoT shoes and figuring out which suits they go with best.
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