Part 5 in a 5-part series “Design-to-Operate with the Digital Supply Chain”
This blog series looks at the design-to-operate (D2O) product lifecycle, which spans the phases of design, plan, manufacture, deliver, and operate. The first four installments focused on design (by Thomas Ohnemus), planning (by David Vallejo), manufacturing (by Mike Lackey), and delivery (by Markus Rosemann). The focus here is on operations.
Beyond cost reduction
The operations phase of the D2O product lifecycle is ripe for transformation, poised to move away from its historical focus on reducing cost and standardizing maintenance operations. In its place, companies seek innovative new business processes powered by digital technologies like Industry 4.0 capable of improving the customer experience and increasing efficiencies.
Whatever the asset – pumps, compressors, or industrial robots – product differentiation alone is no longer sufficient. Customers want value-added services, applications, digital content, and products as a service, with a focus on outcomes that help them run their businesses better. Assets are not just a cost, they are smart, connected enablers of competitive advantage and better customer service.
Implications for companies across the D2O cycle
The implication for asset manufacturers is that now their focus needs to be extended downstream from design and manufacturing to the entire D2O product lifecycle. Similarly, asset operators want closer links with manufacturers to influence design, product improvements, and new services. The traditional boundaries are now blurring between D2O phases that have delineated which organization does what. From the earliest stages of design, through planning, manufacturing, installation, and operations, manufacturers and operators need closer collaboration. The heartbeat they share is the connected asset, providing both with new intelligence in real-time to drive collaborative processes.
Take, for instance, the following examples:
- Design: Industrial assets produced today are engineered using computer-aided design (CAD) tools. Historically, the data from CAD drawings has been confined to R&D. Today, companies are also making this data available downstream, turning it into fully functional digital twins of a product that can be rendered with 3D imaging and combined with contextual business data. This data brings together the entire history of the asset (manufacturing configuration, installation date, maintenance performed, serial numbers for replaced parts, performance data over time, and more). These digital twins can then be networked so that suppliers, service companies, and operators can optimize maintenance and operations by sharing relevant data and information, such as digital manuals, installation documents, and 3D instructions.
- Planning: The planning process typically starts with demand and ends with shipments where demand is pegged to customers. Today, planning can be extended to the asset itself, with new collaborative approaches and Internet of Things (IoT) technology that can help capture and analyze historical and future operational asset data to better integrate planning processes across both organizations. This can help reduce inventory costs for spare parts and improve planning for future product enhancements and upgrades.
- Manufacturing: Asset intelligence in the operations phase stems from connected assets that use IoT technology to transmit condition monitoring data on machine status, performance, and health. For manufacturing, this means outfitting assets with sensors to communicate with systems that ingest and analyze machine data in real-time. Increasingly, products also include software services and apps that need to be installed during manufacturing and updated throughout the lifecycle. Combined with IoT sensors, these services and apps offer incredible potential for value-added services, such as predictive maintenance or farm management (with connected agricultural machinery). In the past, aftermarket revenues were driven by spare parts. In the future, this will shift to digital services.
- Delivery and service: While the initial delivery and installation of the asset are important, even more critical throughout the operations phase is the logistics involved in ensuring that spare parts are delivered and made available as needed. Predictive maintenance scenarios, for example, can sync up with logistics such that parts designated for replacement automatically trigger replenishment and delivery processes. This requires the sharing of data between operations and logistics.
Ultimately, the aim of D2O is to break down silos within a company to better integrate business units. We have seen extensively in procurement, HR, and finance the idea of shared services to integrate and streamline processes. Maintenance can also benefit: instead of local teams working alone, a “shared service for assets” approach can help enable best practices and provide all stakeholders with the asset intelligence they need.
Collaboration and integration throughout the D2O product lifecycle, however, is not to be seen as an exclusively internal concern. Increasingly, the value chain from the customer’s perspective is made up of multiple organizations where different companies design, make, deliver, and maintain the asset. Hence, these shared services need to be network-enabled.
The product-as-a-service model is a good example. In this model, instead of selling an air compressor, the manufacturer provides the air compressor for free and charges the customer for outcomes (such as cubic meters of compressed air delivered) that are based on service level agreements and usage. For such a model to work, seamless collaboration across all phases of the D2O lifecycle is needed. Manufacturers have to stay connected to their assets operating at third-party locations, so IoT plays a critical role in ensuring asset performance and uptime for the supplier, who now acts as the operator under deep scrutiny of customers.
Moving forward with quick wins
Models such D2O do not require a global, enterprise-wide digital transformation initiative to realize value. A smarter approach is to think about practical steps, at least at first, that can lead to a bigger vision mid- or long-term.
Quick wins can serve as proof points for moving forward with more ambitious projects as desired. Some possibilities:
- Set up an asset intelligence repository: Consolidate data from the many sources of asset information and make it available to groups within the organization and partners on the outside. Serving as a foundation for improved asset intelligence, this repository can enable a “shared services” model for asset management enterprise-wide. It can also be expanded as new use cases are identified.
- Implement best practices for reliability-centered maintenance (RCM): Reduce risk and improve reliability with RCM processes that improve uptime by considering asset characteristics such as risk, reliability, and failure mode and effects analysis (FMEA).
- Connect assets for condition monitoring and predictive maintenance: With data from IoT sensors increasingly available for assets and modern analytics applications, companies can better assess machine condition in real-time, anticipate failure, and take action before they go down. Focusing on critical high-risk assets can be a quick start with significant benefits.
- Go mobile: Deliver asset intelligence directly to engineers in the field. Mobile delivery of work instructions and engineer feedback reduces administration and enables real-time visibility into work status and critical tasks. It improves productivity and safety by empowering engineers to access digital content for machines and even 3D instructions on how to address specific problems.
This wraps up our series on the D2O product lifecycle. Throughout, we’ve explored how each phase (design, plan, manufacture, deliver, and operate) can better intersect with one another to drive a holistic product lifecycle process that breaks down silos and helps organizations deliver better customers experiences overall.