The industrial Internet of Things is breathing a new life into product lifecycle (PLM) practices and PLM software itself.
From its early days, the mantra and promise of product lifecycle management was anchored in the ability to harmonize all product lifecycle activities and frontload complex design and manufacturing. The promised benefits centered on accelerating design and manufacturing ramp up and reducing associated cost by identifying mistakes and resolving conflicts early on, when the cost of design change is still low. Additionally, a centralized repository of reusable designs, best practices, compliance procedures and other objects fosters reuse of enterprise knowledge and experience. These, in turn, reduce the number of design iterations, lower the cost of engineering change orders (ECOs), improve product quality, reduce warranty costs and accelerate time to market.
Frontloading Design Decisions
The notion of frontloading decisions sounds promising but can be difficult to articulate. Two examples that illustrate the process and benefits of frontloading key decisions are design for service (DFS) and design for manufacturing (DFM).
Design for Service
One of the chief requirements for practically every industrial product is reliability and availability: the measured ability of the newly designed equipment to operate continually and in full capacity to fulfill its intended business outcome. To meet availability goals, the engineering team will focus on improving reliability and reducing the system’s failure rate. But in many instances, the focus on reliability causes the engineers to neglect other factors in the service chain and the ability of the field service team to maintain and repair the equipment cost-effectively, such as easy and safe access to replaceable units.
Design for service entails considerations such as hardware modularity and spare parts, ergonomics and the design of special tools to make repair faster and safer. These cannot be incorporated late in the design cycle; they must be designed-in from product inception.
Design for Manufacturing
Another product lifecycle activity that product companies often wrestle with is manufacturing engineering. All too often, the engineering team specifies a part is difficult to manufacture cost effectively due to a number of factors that include raw material type and manufacturing process, dimensional tolerances, quality assurance and secondary processing such as finishing.
Design for manufacturing practice allows engineers, in collaboration with manufacturing engineers and supply chain experts, to conceptualize and simulate the manufacturing and assembly process, reevaluate geometries, choice of materials and manufacturing processes, and make recommendations to optimize cost, quality and time to market, all before any irreversible design decisions have been made.
Yet, New Products Continue to Fail
The benefits of early design decisions are very rewarding and seemingly easy to attain. Yet, new products continue to disappoint. From complete and utter failures to lackluster performance, the vast majority of new products do not meet the expectations of marketers and designers alike. Even new products that eventually become a market success often struggle on the way to market because a large number of preventable design changes that result in lengthy and costly production ramp up.
What happened to the promise of PLM?
The concept of frontloading design decisions is, of course, sound.
But the potential benefit of frontloading design decisions is often confronted by the insufficient understanding of individual team members about other functional domains and the far-reaching ramifications of decisions they make on both upstream and downstream product lifecycle activities.
Poor visibility to product lifecycle information and, in particular, lack of data and analytic tools that provide accurate and unbiased insight into downstream processes limit the organization’s ability to harmonize design, manufacturing and service activities and excel in lifecycle cost, quality and compliance optimization.
Connectivity Enables Better and Faster Product Lifecycle Decisions
Today, the combination of industrial Internet of Things that connects smart devices and connected enterprise data repositories gives product organizations rich information about product operations and user interactions, in real time if necessary, and provides deep insight and decision-making context that were previously unattainable.
But is this enough? Is more data all that is needed to make better product design decisions? Can product teams handle the torrents of data and turns complex multi-domain information into effective decisions?
While IoT data is undoubtedly very valuable, it isn’t sufficient.
Even when equipped with accurate data, some team members will have difficulties consolidating and analyzing it effectively because of gaps in domain knowledge and process understanding. They need a centralized repository of multidisciplinary information supported by multiple analytic tools such as design for manufacturing analytical tools in order to understand how their design decisions impact the ability of downstream activities to meet their operational goals and constraints.
The Return of the Real-time Enterprise
The real-time enterprise is a data-driven business systems design concept seeking to improve organizational situational awareness and responsiveness. Appearing under different name variations such as sense-and-respond networks and on-demand enterprises, the idea was popular during the first decade of the 21st century. While well-received, many organizations found the implementation impractical.
Today, the Internet of Things and its digital thread counterpart give organizations an unprecedent ability to improve visibility to product lifecycle events. Product teams across all functions, from marketing to field service, have access to information captured downstream to gain visibility, in real time and in context, to inform and guide decision-making and implement evidence-based closed-loop control mechanisms.
Connected PLM is not about IoT connectivity. It is a product development process that leverages data form connected devices and enterprise systems to curate rich context for advanced analytics and enhanced decision-making processes.
This post was sponsored by HCL DFMPro.