The quest for more agile and efficient methodologies is centric to the psyche of the human race. Humans’ desire to accomplish tasks faster, easier, and at less cost is core to our species success. Businesses, by extension, do the same as a matter of survival, and now another evolutionary moment has arrived.
Process Industries are moving to a culture and business model in which decisions are based on analysis of operations and business process data. Throughout the organization, these companies employ software to collect, contextualize, visualize, and analyze data to gain new insights. The common question is, “How is the data going to help us?” Armed with new insights, organizations can anticipate changes and drive better business results. An information-driven culture encourages decisions based on quantifiable information and analysis.
The use of analytics in industrial companies is growing rapidly. For more than a decade, the information workhorse has been the business intelligence (BI) platform, supplemented by enterprise manufacturing intelligence (EMI) in the plant. These systems excelled at helping users discover and understand the underlying reasons and details about what happened and why. Now, with the industrial space becoming much more dynamic, the process industries are turning to advanced analytics and machine learning to support predictive and prescriptive solutions to optimize their organizations.
A modern approach built on data collection and analysis enables the process industries to develop new techniques that result in greater efficiencies, better yields, more consistent product quality, and increased production flexibility. It can also reduce time to market for new products. Together, analytics and Big Data provide multiple views of information that enable managers, operators, and engineers to collaborate and work together using real-time data and analysis in an information-driven environment.
When process industry professionals use the term “process optimization,” they typically refer to specific processes, avoiding a holistic approach. Traditionally, optimization refers to solutions termed advanced control. These solutions aim to maximize a handful of outcomes focusing on the operation of a small system of plant equipment. However, there are more “processes” that could be maximized within the process industries. With today’s technology, technology has the capacity to optimize systems of systems, for example, an entire unit like a hydrocracker or an entire plant site.
Companies have implemented optimization solutions for truck yards, the warehouse, energy and material management, and for asset utilization. They seek opportunities to optimize headcounts, projects, and production. However, since these types of site solutions tend to be approached as
“islands” within the larger organization, relevant data from adjacent processes could be missing. Additionally, optimization solutions have traditionally required significant effort to plan, budget, implement, and maintain.
As a result, most industrial companies still tend to make decisions based on habitual ways of doing things, tribal knowledge, rules-of-thumb, and the opinions of in-house experts. This approach has worked well enough until now, but it is becoming increasingly risky in today’s information-driven environment to continue to operate in this manner. Sometimes, the conventional wisdom about how processes work is wrong; but without proper examination and analysis, suboptimal performance will continue and new options won’t become apparent.
Market Pressures Demand More
Current optimization technologies have been around for a while and the process industries have been addressing the same things with similar technology. The “low hanging fruit” has been picked, but challenges remain. In fact, some challenges have only been partially addressed by existing optimization solutions.
For the process industries, today’s challenges include:
Responding quickly to changes in material costs and/or product demand.
Reducing operational expenses further
Increasing operational agility across the organization
Optimizing enterprise production per market conditions
Debottlenecking the plant further
Generating incremental production to increase profit
Reducing avoidable unscheduled downtime caused by human factors or equipment failure
Improving visibility into business health
Reducing the time to start production in new facilities
The pressure on businesses to compete successfully is stronger than ever. The process industries need to think beyond point solutions to find new methods to address their challenges. The Industrial Internet of Things (IIoT) offers substantial promise. Why? Because the solutions needed to move beyond the current remedies require more data from more data sources and more computing power.
The solutions making the news go beyond individual pieces of rotating equipment or even a single distillation column. These larger solutions look at the overall manufacturing process and predict production or maintenance issues for a system of equipment, like a process unit. Process licensors have begun offering cloud-based services leveraging their intimate knowledge and simulation of their processes to increase uptime, increase profits, and avert abnormal situations.
The vision is expanding even beyond the processing unit. Why stop there? Why not optimize an entire site, or the full enterprise? ERP providers have marketed this vision for quite some time, but computing power constraints and data flow roadblocks have thwarted implementation. ARC believes that IIoT-enabled Big Data analytics, plus associated cloud solutions can go a long way toward eliminating these constraints.
ARC sees potential to generate additional yield from the enterprise, increasing overall profit by expanding the scope of view. This may also create an enterprise that can adapt quickly to rapidly changing market conditions, and capitalize on new market opportunities. Complex plant optimization tools, including dynamic simulation that leverage high-fidelity models and predictive asset performance management (APM) solutions are not only possible, but easier to manage and deploy using cloud-based approaches.
Disruptive Technologies for Optimizing Plant Processes
In today’s business environment, real-time information is vital for enterprise-wide optimization. However, implementing the disruptive technologies often required to acquire this real-time information can, in
itself, cause radical changes to processes. So many companies still struggle with the problem of how to best collect, manage, and analyze data for process optimization. This is particularly true since those companies that have embraced some of the recent disruptive technologies, such as IIoT, and have seen a step change in the volume of data and analyzed information now at their disposal.
Creating a Solid Data Foundation
A major problem is that much of the data gathered today – whether from conventional process instrumentation and systems or less conventional IIoT-connected sensors and cloud-based applications -- still lacks the five attributes needed to ensure a solid foundation: accuracy, authenticity, integrity, context, and timeliness.
Accuracy means free from error. It also means that the data conforms exactly to the applicable truth or standard. Authenticity means that the origin of the data cannot be disputed. Integrity means that the data set or packet remains complete as it undergoes a number of operations such as capture, analysis, storage, retrieval, update, and transfer. In context data is relevant to a particular person or function. Finally, timeliness means that the data is delivered at the optimum time needed to support either manual, guided, or automated operations. The process industries must ensure a solid foundation for their data to avoid erroneous analysis resulting in poor decisions, which could do more harm than good.
The good news for today’s process industries, is that established process control and information hardware and software technologies – combined with newer disruptive technologies – can work together to help companies develop a solid foundation for their data and thus support process, plant, and enterprise optimization initiatives.
Process measurement devices continue to become smarter, smaller, less expensive, and incorporate more powerful communications capabilities. Advanced process control, including multi-variate and model-based predictive control, is being deployed at a growing number of manufacturing sites due to it its ability to improve and calculate ROI to support the investment.
At the same time, disruptive technologies such as IIoT-enabled remote sensing and Big Data analytics and cloud computing are gradually gaining acceptance helping industrial organizations achieve their optimization goals.
Both private and third-party cloud providers offer solutions than can help improve process operations in a cost-effective manner. Manufacturing functions, such as material and energy procurement, product quality, and production management, are provided through software-as-a service (SaaS) by third-party providers. Virtualization technologies are reducing computing hardware, software, energy and IT support costs.
The process industries are taking advantage of Big Data analytics to support optimization. With IT/OT convergence, enterprise-level IT providers are offering integrated families of business applications that capture and analyze transactional data in real time using in-memory platforms. And with the process industries allowing employees to “bring your own device” (BYOD), many operators, supervisors, and managers are now using their own mobile devices to monitor plant- and factory-floor performance. Finally, social networks are being used for virtual user groups within the plant, between plants, and between the plants and their automation and enterprise suppliers.
Late Adopters Face Increased Risk
Many traditionally risk-averse industrial enterprises have been slow to innovate until new technology has matured. Today, however, the risk of being a late adopter exceeds the risk of being an early adopter. This is especially true for IIoT. Successful adoption of the latest IIoT-enabled automation and information technologies can challenge traditional manufacturing enterprises. It is not unusual for big corporations to dismiss the value of a disruptive technology because it does not reinforce the current company business model. However, it they are to succeed in optimizing critical processes and the enterprise as a whole, today’s industrial enterprises cannot afford to wait to adapt their manufacturing and business processes to take advantage of the latest control and information innovations.
IIoT-enabled solutions enable the deployment of data-driven decision making and tapping the business transformation possibilities. Though it may seem daunting, organizations should integrate cloud-based solutions into normal operating practices. Despite the noise and confusion in the market, getting started doesn’t need to be hard. It’s a matter of understanding a few key issues as companies look at the solutions available, choosing a manageable starting point, and realizing they can start modestly and grow the capabilities over time.
Based on ARC research and analysis, we recommend the following:
The process industries must start thinking about how the next wave of disruptive technologies is going to affect their businesses and how they could be leveraged to optimize their processes. Artificial intelligence, machine learning, augmented reality, virtual reality, and other disruptive technologies are already making their way into the consumer world. It is just a matter of time before they also become mainstream technologies in manufacturing.
The process industries must strive to be “disruptive ready,” embracing, rather than fearing disruptive technologies and planning how these could be best leveraged to optimize their processes, their plants, and their businesses in an uber-competitive flat world. Companies should share their goal and expect support from their suppliers.
Consider your organization’s goals and vision. This problem of how to best leverage and utilize this abundance of IIoT-driven data and analyzed information for optimization is partly because most business models are still designed around sustaining technologies; rather than disruptive technologies. Companies must first determine the problems they are trying to solve before defining their specific data and analyzed information requirements. Only then can they create paths to map out where that data and analyzed information can go to best optimize their operations. Solutions of this nature involve several different departments and require senior level support.
Invest in a simple use case. Cloud solutions lend themselves well to proof of concept versus traditional solutions, such as system upgrades. Solution providers are relatively comfortable with quickly ingesting a limited data set, identifying patterns, and then working with its cus-tomers to demonstrate a reasonable ROI. This will allow you to better understand the process and adapt the organization accordingly, as well as gain a tangible understanding of the benefits.
Be realistic with your expectations on the human element. As a core capability, advanced analytics involves a mix of subject matter expertise, analytics, and technology. Some aspects of advanced analytics are quite straightforward and will require less human intervention. Yet, analytics is an exercise in continual process improvement, not a one-time change management effort. The more you engage in the process (and have the right knowledge, skills and abilities to do so), the more value you derive.
Insist on transparency throughout the entire engagement process with the solution provider. This will help “de-mystify” the solution process and understand the context of the results.
Realize that, in time, you will want to own your discovery. Nobody knows your business like you do. Over time, your subject matter experts are best positioned to understand what to look for, what it means, and what to do with the results. The benefit to the company overall, is this expertise becomes institutionalized and available throughout the organization.
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Keywords: Process Optimization, Cloud, Analytics, Maintenance, IIoT, Digitalization, ARC Advisory Group.