Learn key strategies that can help maximize successful, scalable networked deployments, including how to manage data and achieve holistic security.
As the Industrial Internet of Things (IIoT) shifts from a buzzword to a business priority, many companies are increasingly eager to learn how it is being used in tangible ways. They also want to know how the latest technologies can help them make the most of the IIoT in their own operations.
Today, organizations are progressing from pilot or proof-of-concept IIoT projects to scalable IIoT deployments, according to the Global IoT Decision Maker Survey from the International Data Corporation (IDC). About one-third (31%) of those surveyed said they have already launched IIoT solutions, while another 43% said they are looking to deploy solutions in the next 12 months.
To this end, here are some key strategies to help improve the success of IIoT deployments.
Answers Hiding in Analytics
The number of IIoT devices in industrial control systems continues to grow at a rapid pace. With this growth in networked devices comes a significant increase in the volume of data that industrial companies must be able to manage and leverage for business outcomes.
Scalable, flexible analytics can contextualize your information and deliver value incrementally in devices, the plant and the enterprise.
We are learning when it makes the most sense to analyze the data in real-time at the source or store it in the cloud for more long-term examination. Conditioning raw data into contextualized data, preferably at the source, is becoming an increasingly valuable best practice.
Local maintenance analytics, for example, can use device-level data to produce real-time alerts about critical device and machine health. This can help implement faster decision-making closer to the process, where time is critical.
Machine-level or plant-level analytics implemented in edge devices such as controllers and plant-floor servers can be used to optimize machines, processes and plants. They can also be used to implement predictive-maintenance strategies.
Enterprise-level analytics integrate plant-floor information with business intelligence. This can help improve operational productivity or compliance efforts across several sites.
Security Must Be Holistic
The top IIoT challenge cited by respondents in the IDC survey is security (26%).
This is not surprising. Security can seem like an overwhelming burden given the challenges faced, from legacy equipment that was not designed for security to more easily accessible information that can be vulnerable to both malicious and non-malicious threats.
To face the challenges, taking a holistic approach to industrial security can put your organization in line with best industry practices for protecting intellectual property and other assets.
A holistic security approach begins with conducting a security assessment to identify risk areas and potential threats using free security assessment tools. Upon completing of the assessment, you should understand your security posture and the specific mitigation techniques needed to bring your operation to an acceptable risk state.
From there, your industrial security program should adopt a defense-in-depth (DiD) security approach. DiD security adheres to the principle that any single point of protection can and probably will be defeated. It uses physical, electronic and procedural safeguards to create multiple layers of protection throughout your enterprise.
Industrial firewalls, for example, should be implemented at the cell/area zone level to help detect, prevent and respond to potentially malicious traffic between devices. However, these should only be one part of a multifaceted security program. Companies today are utilizing reference architectures, an ecosystem of partners and industry best practices to implement secure IoT systems.
Finally, make a point to only work with trusted vendors. Request their security policies and practices, and make sure they help — not hurt — your ability to meet your security goals.
Driving on the Edge: Using Digital Transformation for Greater Profitability
Tire producers are blessed with a healthy market outlook. Unfortunately they face unprecedented challenges too.
In fact, global tire demand is expected to increase about four percent each year through 2019. At the same time, manufacturers increasingly compete in a global marketplace – and must produce more variations to address the automotive landscape that includes internal combustion, hybrid and full-electric vehicles.
How Can Tire Makers Best Address Market Conditions?
Dominque Scheider, strategic account team leader at Rockwell Automation explains, “To ramp-up productivity and agility, leading manufacturers have embraced smarter plant-floor technology. But many are challenged to create an information-enabled production environment from the patchwork of digital assets on their manufacturing floor.”
True digital transformation requires not only collecting relevant data from intelligent assets. It also includes converting that data into information that enables people at all levels of the organization to work smarter and more productively.
Expanding Beyond Conventional Cloud-Based Platforms
As smart assets began to proliferate on plant floors in recent years, so did cloud-based analytics platforms designed to transform generated data into useful intelligence.
Conventional cloud-based platforms can aggregate data from multiple sources. However, gaining manufacturing and business insights from that data takes time – and may require data architects, business intelligence engineers and other in-house data management expertise.
In addition, sending data to the cloud and back is not practical for every application. A traditional cloud-based approach simply cannot provide contextualized information quickly enough to immediately impact plant processes – and people as they go about their work each day.
A Cost-Effective, Pragmatic Approach to Distributed Intelligence
A scalable analytics platform can provide a cost-effective and pragmatic way to distribute actionable intelligence across all levels of the organization – on the edge, on-premises or in the cloud.
This can have an immediate impact in many areas of tire manufacturing. Moreover, will become increasingly important in complex applications like mixing and curing, where machine learning can have a dramatic effect on product quality, manufacturing agility and energy efficiency.
But that’s just part of the solution. The scalable platform extends beyond the edge to compute engines and tools that automatically orchestrate data from multiple sources – and allow users to see commonalities and gain operational and business insights faster.
The bottom line? A scalable approach enables tire producers to more deeply engage with each level of the organization in optimizing its manufacturing process – from local engineering and maintenance to the top levels of the company. And from the device to the cloud.
Learn From Our Experience
At Rockwell Automation, we don't just talk about IIoT: We brought The Connected Enterprise to life in our facilities, helping to boost our bottom line.
In recent years, we have converged disparate IT and OT systems, and used IIoT technologies to create a Connected Enterprise. This has helped us improve our agility and productivity, and achieve faster, smarter decision-making. As a result, we've improved control in our processes and implemented a standardized approach across our global facilities.
Learn more about The Connected Enterprise from Rockwell Automation.