How to Optimize Asset Management with IIoT
In this white paper, we explore the benefits of IIoT-connected service monitoring frameworks to enhance the reliability and extend the functionality of industrial equipment. We also look at the implementation challenges that companies face as the mandate for increased digital services grows stronger. Finally, we look at the importance of analytics for enhancing time-to-value of asset performance data and for gaining actionable insights to upgrade management levels.
Rise of Connected Ecosystems in Manufacturing
Improved asset management through IIoT-generated data can provide benefits up and down the supply chain. These include relaying critical supplier data, remote asset monitoring to increase performance, or improving product transportation and distribution. As one example, companies can analyze data accrued through sensors to boost time-to-repair metrics. Manufacturers can also leverage connected product-centric ecosystems to support overall operational excellence as well as to design and develop products that better align with customer needs and preferences.
As you consider your move toward increased digital services, it’s critical to recognize how fundamental the IIoT is to connected products, connected supply chains, and smart manufacturing. Based on a recent study by Cognizant’s Center for the Future of Work, the business-to-business industrial equipment space is among the most active areas for smart product development, with 58% of respondents stating that their companies are already developing products.
Improved asset management is one benefit of increased smart systems adoption. However, the benefits of IIoT connectivity are not limited to manufacturers. Large distributed-asset-based industries, such as rail and utilities, are also transitioning toward integrated adoption of asset management frameworks. They’re making use of the enhanced capabilities of both improved networking and centralized data processing.
In addition to improving the functionality of diverse assets (shop floor machinery, remote field equipment, transport vehicles, etc.), greater use of the IIoT can help to optimize and lower operational costs, reduce resource consumption, and increase equipment/machinery uptime. This is based on using quantifiable performance data to drive incremental improvements. These real-time, data-rich insights are derived from point of origination sensors (at machine device level). Further, by applying data analytics, companies can use these insights to more accurately predict peak performance levels and adjust online/offline schedules to optimize production.
Benefits of IIoT for Asset Management
Across all industries, smart systems are helping to make significant advances in design, engineering, production, and transportation. For example, in the automotive industry telematics can relay useful performance information. This data can influence future designs and in turn speed up production cycles to bring the latest models to market faster. These smart systems comprise a number of consistent elements: IoT-networked sensors, Machine-to-Machine (M2M) communications, intelligent automation, interoperability, and security protocols.
The same holds true for advanced asset management in manufacturing. In contrast to traditional forms of asset management that are grounded in a reactive and static approach, IIoT-based operational data can bolster more proactive, predictive, and responsive methods for asset maintenance and renewal. Most connected ecosystems that employ the IIoT include these five components:
An important aspect of industrial hardware is the ability to apply sensors to an asset. These provide the means by which machinery, heavy equipment, or transport can transmit data. In an effort to increase the return on assets (ROA), manufacturers are using their digital capabilities to develop remote service monitoring frameworks. These further enhance both the servicing and reliability of all IIoT-connected industrial assets. Moreover, the intelligence levels and data granularity of these manufacturing sensors continue to grow. That’s due to advances in data gathering, storage, processing, networking, and communications.
In the manufacturing sector, Machine-to-Machine (M2M) communication has been a mainstay for decades. In certain respects, the IIoT simply represents a more sophisticated version of that technology. As manufacturing plants adopt increasing numbers of wireless networks, these interconnected ecosystems offer an effective means for relaying asset information so that operations can instantly respond as necessary using mobile-based tools.
3. Data Management
Discrete silos of information have long posed challenges to manufacturing companies. With increased digital capabilities, these organizations are moving away from traditional relational databases responsible for those divisions. Instead, operations teams are relying on big data-oriented and in-memory databases to more easily store, share, and act on information quickly, thus improving overall asset management.
4. Intelligence and Analysis
Analytics tools are evolving to address different types of intelligence requirements. That’s partly due to the fact that no single tool can meet all the conditions within different business and operational contexts. Improved processing, diverse platforms (proprietary, open source, etc.), and the ability to analyze ever-increasing amounts of unstructured data enables manufacturers to generate insights across every aspect of production and to optimize asset performance.
Increased mobility on the shop floor enables operations to gain access to a wealth of asset-related information and to act quickly on that data in real-time, especially in terms of maintenance and repair. Technicians thus have the capabilities to receive alerts and notifications, access knowledge bases, and capture data to improve asset oversight and control. Such capabilities also increase an organization’s competitive edge through improved decision-making and more streamlined production processes.
The Challenges of IIoT Implementation
While establishing IIoT-connected ecosystems offers a wealth of opportunities, companies can also face implementation challenges. This is partly due to the need for architectures that can handle scale, interoperability, and offer long-term viability. In general, a lack of industry standards and well-defined best practices present barriers as companies consider their best approach. These concerns extend not only to ensuring security, but they also include data gathering/storage issues and acquiring the right connectivity networks.
Adopting an IIoT ecosystem requires significant collaboration across an organization and must be driven by executive leadership. Moreover, a single internal, cross-functional group should be responsible for directing the connected agenda. As manufacturers add more sensors and wireless capabilities to establish greater IIoT connectivity, they’re encountering certain challenges in these areas:
Safeguards need to be applied to communications pathways, data capture processes, and storage. In addition to ensuring that governance and compliance policies are in place, frequent security testing is essential. Recent cyber incursions, such as the 2016 Mirai Distributed Denial of Service (DDoS) IoT botnet attach, have renewed focus on the importance of security as C-suite executives strategize greater IIoT adoption.
An IIoT architecture consists of many heterogeneous networks connecting devices and things via standard and proprietary protocols, including secure TLS and DTLS. Adding unique authentication keys and identifiers to large numbers of manufacturing assets vastly increases complexity. Since there is no plug-in, off-the-shelf approach to securing IIoT communications at the protocol level, companies face the difficult task of balancing security as it relates to bandwidth, sensor power supplies, network communications, and processing. To ensure resilient and secure IIoT solutions, governance, risk, and compliance policies need to be in place along with regular security updates.
• Data Gathering
For manufacturing companies, the selection of device and data management platforms depends on degrees of scalability, flexibility, ease of device and data management, and application support. Moreover, companies must address privacy concerns for all data that is captured, stored, processed, and accessed, regardless of the device used (sensors, mobile, etc.).
Choosing the right data gathering technology depends on a unique combination of IIoT infrastructure capability and maturity, perceived customer value (has asset management improved? has it produced results?) and the business model selected. The key with IIoT-connected asset management is to capture actionable data that addresses the specific problems to be solved in terms of asset management and increases efficiencies.
• IIoT Connectivity
Within a manufacturing environment, an IIoT-connected network includes hardware devices (sensors, controllers, gateways) communications protocols, analytics tools, and device and data management platforms. This represents a complex interchange of technologies lacking standards and common protocols. Regardless, manufacturers are moving forward quickly to capitalize on the IIoT. For example, research from IDC predicts that by 2018, 40 percent of the top one hundred discrete manufacturers will include IP connectivity to provide products as a service.
This trend, termed servitization, extends the value of product offerings by supplementing additional services. Remote diagnostics and maintenance can be performed based on accrued product data. The same model can be applied to shop floor or remote asset management. Ultimately, IIoT systems and the connectivity through which they relay data must be adaptive and scalable. This is possible primarily through software or added functionality that integrates with the overall IIoT solution.
The Role of Analytics
As more data is generated, manufacturers are increasingly inundated with information. Research firm McKinsey estimates the value created by IoT applications will rise to $3.7 trillion in 2025. This will be due in part to improved inventory assessment and operations management as well as predictive maintenance—all key aspects of effective asset management.
In general, gaining the most current status information is critical to effective asset management. Edge analytic processes (i.e., at the edge of the network) provide fresh, actionable data analyzed in real time to help reduce service latency, improve asset response times, and draw locally usable management insights to make predictions on asset maintenance and upkeep.
Diverse industry sectors that rely on edge analytics for asset management include: Transportation (cargo/container tracking); Heavy Equipment and OEMs (asset diagnostics and usage), and Energy (operational technology (OT) planning). A key aspect to keep in mind regarding edge analytics is its ability to deliver results for specific asset-related problem solving and to eliminate extraneous data.
Compiling different sensor readings, edge analytics stores data locally for anomaly detection, sending only problematic data to the cloud where it triggers alerts and reports for operations teams. This both ensures privacy and data security as well as reduces overall storage and bandwidth costs for companies. Higher asset management maturity through edge analytics assures that uptime, asset longevity, cost control, safety, and quality meet the production objectives set by the C-suite executive team.
Increasing numbers of manufacturing companies are moving toward digital services adoption. And the IIoT represents a vital link to a product-centric ecosystem of connected customers, connected supply chains, and smart manufacturing. These organizations are also relying on the IIoT to raise the levels of asset management to new heights. Technology innovations related to sensors, networking, and software are making possible real-time, data-rich insights across a diverse ecosystem in which mobile-based operations teams can respond instantly. While companies do encounter implementation challenges, the potential returns from better-managed assets are too impressive to dismiss. As manufacturing organizations adopt the IIoT along with edge analytics, they’re gaining highly mature processes for predictive and prescriptive maintenance.