IoT is finally being recognized and flexibly integrated as an enterprise enabler, particularly for end-to-end product lifecycle management (PLM).
Keith Higgins, VP of Digital Transformation, Rockwell Automation,
Dave Duncan, Vice President of Product Management – PLM, Digital Engineering Solutions, PTC
The marriage between physical and digital worlds now is more apparent than ever. Phones can make purchases at real-world cash registers thanks to digital wallets, watches can track fitness and workout progress by using biometrics and GPS triangulation, and cars can automatically report real-world collisions and traffic data. However, the marriage between digital and physical doesn’t stop with consumers. Many industries, from manufacturing to medical to virtually any industry, are realizing the advantages of merging physical and digital realities– specifically, those provided by internet of things (IoT) technology.
The Endress+Hauser Process Training Unit (PTU) on May 21 and May 22, 2018 in Greenwood, IN.
Early IoT concepts and capabilities were discussed and put into action as early as 1982. However, the adoption of IoT on an industry scale wasn’t truly considered as a feasible option until around 2008, due to limitations on internet availability and the cost and scale of the sensors needed. Thanks to recent advances in web-based technology and applications, as well as the reduction in cost and size of IoT sensors, IoT is finally being recognized and flexibly integrated as an enterprise enabler, particularly for end-to-end product lifecycle management (PLM). In fact, McKinsey estimates the impact of IoT technology on the global economy will be as high as $6.2 trillion before 2025.
How IoT improves PLM
The broad range of uses for IoT technology concentrates around collecting data and information from the real world and transferring it to the digital world for research and analytics purposes. Data collected by IoT sensors can provide information surrounding the machine or system on which the sensor is placed. Over time, the collective insights pulled from multiple different IoT sensors can be analyzed to uncover larger insights about the organization as a whole, such as where improvements must be made and operations must be adjusted. Just as an athlete’s individual statistics can provide insights into the ways their strengths can be used to the overall team’s advantage, IoT sensor data can provide granular insights into the role and performance of a particular machine or system within the context of overall operations.
Not only does IoT data provide insight on where the product needs more investment to improve quality, but it also provides cost-saving opportunities. For example, a part designed for a 1,000 kg load may in fact only bear a maximum 500 kg load, offering significant part cost savings with an engineering change order. When used from a PLM perspective, IoT provides the data organizations need to continuously improve products and/or processes, while saving costs.
Designing for connectivity
Connected products have limitations on the amount and types of data collected. While sensors can and have been retroactively applied to machines and systems to generate valuable data-driven insights, limitations still exist on the capabilities of IoT sensors. In addition, once pre-existing sensors are connected, they can be creatively leveraged to provide new information. For example, machines can flag issues without needing a worker to check on it, allowing problems to be addressed in real time before unplanned downtime or a full fledge break down. It’s more difficult for an organization to fully realize the value of IoT when the systems being measured were never designed with IoT functionality in mind.
Designing products considering the data they need to collect and the analytics organizations want to obtain enables end-to-end product lifecycle management along with a host of added benefits. This is done by recognizing the unique nature of smart connected products, expanding the roles and departments leveraging the data provided by connected products, and taking a systems engineering approach to PLM.
Connected products can provide a continuous stream of data when connected to an application built to communicate with other systems and applications. By developing and deploying effective smart connected products, organizations will have tackled two significant challenges:
First, the connected product will adequately carry out its real-world purpose in the field and/or on the shop floor; and
Second, the organization has a monitoring tool to track the performance of machinery and provide data to be analyzed for other tangential areas of improvement within the organization.
In keeping with the metaphorical comparison of the athlete, this would be equivalent to a professional sports team drafting a first-round pick that can also coach themselves and systematically analyze their own performance. Further, it would even be as if the athlete could not only track their own performance, but offer suggestions regarding how the rest of the team could improve based on their singular experience. The data provided by smart connected products holds immense value, not only for the host organization, but also the organization’s network of trading partners and suppliers.
When developing and designing connected products, all internal stakeholders affected by the product must be involved. All organizational departments and leaders must be involved in the design if they are to reap the full benefits of connected products. The stakeholders will be the ones defining the IoT strategy for the product to ensure it serves the needs of the business and will be needed to adequately customize the data streams needed to support their selected strategy. With the IoT strategy in mind, the stakeholders must define the product’s subsystems, interfaces and desired data streams prior to beginning the product design process. This requires each affected department – from marketing to sales to finance to product management to quality and compliance – to work with one another. In reality, no stakeholder, department or role exists in isolation when it comes to connected products. When developing smart connected products, a systems engineering approach is necessary because so many systems and departments depend on the product’s capabilities to provide valuable information.
By using model-based system engineering (MBSE), organizations can leverage an industry-standard framework to organize their initial ideas surrounding the most effective ways to drive value from smart connected products. The predetermined means of value will inform the product’s design. IoT sensors must be properly integrated into the design process from the beginning. This allows IoT sensors to be strategically placed and leveraged for maximum added value and to power digital threads throughout the organization.
PLM as a collaboration platform for concurrent engineering
In designing products optimized for IoT connectivity, organizations no longer need to rely on manual services to identify potential issues. IoT provides a holistic view of real-time operating conditions and performance. This helps enable a digital thread that allows for company-wide access to data which improves speed, agility and product/operational efficiency (versus siloed legacy approaches).
The digital thread enables enterprises to anticipate and effectively communicate bi-directionally up and down stream of where the product is in its lifecycle, ensuring all participants use the most current data and can react quickly to changes or new insights. Organizations can develop digital models of physical products, operational processes, or a person’s task by leveraging business system data from virtual manufacturing simulations (digital twin), fed with real-time insights from IoT sensors. Connected products also enable engineers and product managers to improve end-to-end PLM by connecting products to networks throughout their lifecycle.
Gone are the days when product teams would have to wait until a maintenance service request is received to identify long-term product issues and failures. Thanks to network connectivity, machines can be monitored in real time – enabling product teams and engineers to not only quickly identify root causes for field or production line failures, but continuously improve design processes to combat unexpected failures with predictive and preventative maintenance. These maintenance insights add another opportunity for added value, as they can be shared with customers and partners.
The benefits of connectivity also extend to the end-user. Analyzing usage data can enhance how customers experience and use a product. Understanding how, why, when and where a product is being used is equally as important as simply understanding how, why, when and where a product breaks. This allows businesses to understand how customers use products and continuously improve them based on real-world feedback and insights collected directly from the product. By replacing design assumptions with facts collected by connected products, organizations can better meet the specific needs of their customers.
When an organization succeeds in designing products for connectivity and deploying them to the field to automatically collect data to inform future product engineering decisions, they have successfully achieved end-to-end PLM. By designing connected products optimized for IoT, organizations can accurately track their product throughout its lifecycle – enabling them to collect valuable information surrounding the product’s performance and use cases. In addition, having access to IoT data and advanced analytics allows organizations to leverage connected products at lower service costs, maximize uptime and improve product development.
Rockwell Automation
www.rockwellautomation.com
PTC
www.ptc.com
Filed Under: IoT • IIoT • Internet of things • Industry 4.0, Commentaries • insights • Technical thinking, DIGITAL TRANSFORMATION (DX)