Product lifecycle management has traditionally followed a linear path from design to production. Design, manufacturing, asset management, and the supply chain operated mostly in siloed domains that consisted of largely disconnected processes, a framework that was entirely sufficient when engineering definitions remained relatively static, or when feedback loops linking a finished product to design were measured by months or years instead of by minutes or days.

But many factors in our increasingly digital world have conspired to upend this familiar dynamic. Mass connectivity results in an unprecedented amount of unstructured data flowing into the enterprise. Sensor data obtained through Internet of Things (IoT)-connected devices, combined with new technologies such as 3D printing, make the previously linear path untenable. In just the past few years, the playing field has changed so dramatically as to be almost unrecognizable, requiring companies to turn to new tools to meet demands for immediacy and individualization in the production and delivery of goods and services.

Much of what is driving the urgent need for discrete manufacturers and even process industries to rethink how they create and deliver goods and services to customers is this unprecedented and growing customer demand for customization and immediacy. When a car can be built from an online menu of options as though placing an order at a restaurant, and the vehicle can be delivered to your door a week after you hit “send,” that has a significant downstream effect on planning, ordering, design, production, and delivery. Such scenarios encourage manufacturers to adopt a lot-size-of-one production environment in which customized products can be built on the same shop floor with little disruption to a production line. Moreover, they require that manufacturing align with customers to meet their expectations. To do that, formerly siloed domains and processes must come closer together and become agile enough to meet individual demands.

The Digital Reinvention of Manufacturing

Customization, however, is only a part of what is driving manufacturers to reinvent themselves in a digital framework that encompasses a connected, end-to-end supply chain. In a digital enterprise, design, manufacturing, asset management, and finished products all come together in an uninterrupted, real-time loop. In this environment, manufacturers capture individual customer requirements and automate and deliver on those requirements, despite having a shrinking window to fulfill the demand for immediacy.

A digital supply chain must meet the needs of engineering-to-order and configure-to-order models that have sprung up in response to these new production demands. In the former, customers now collaborate with suppliers to engineer to an order of one. In the latter, customers are now presented with nearly unlimited options to create personalized products. In both cases, the challenge for manufacturers is building to those individual specifications.

As the traditional build-to-stock model diminishes, the importance of leveraging IoT technology as a tool to help manufacturing meet this challenge builds. First, using sensor data on machines on the manufacturing floor in a lot-size-of-one production model is needed to fulfill individual orders. Second, analyzing sensor data from a product in the field — a car, for example — affects design for future iterations; improvements based on real-time performance data are then reflected for the next set of customers configuring a product.

Underlying this evolving design and production model is a need to ensure that the assets themselves are running optimally, and here too sensor data becomes critically important to ensure that a machine on a production line, or a machine in the field with output that is sold as a service, does not experience unintended downtime.

This article explores in depth the ways in which design, manufacturing, and asset management are changing, and how by combining IoT sensor data and other unstructured source data with business data, organizations can begin to make real-time decisions based on current conditions either on the shop floor or in the field to satisfy changing customer needs.

Selling a Service and Expanding the Role of IoT

While we’ve touched on how design and production models are changing to reflect evolving customer expectations, the transition to a digital supply chain and digital manufacturing is also driven by a significant change in how consumers purchase products, with a shift toward purchasing items as a service. Organizations are interested not in goods, but in outcomes. A construction company that traditionally budgeted for the purchase of excavation equipment might be more interested in a contract in which it pays by the tons of earth moved. The reasons for this should be familiar to any enterprise that has explored a transition to the cloud: lower total cost of ownership (TCO), more agility, and resource reallocation with a focus on delivering value. This shift toward outcomes is also true from the consumer standpoint. A consumer building a customized car is just as likely to be interested in receiving real-time service updates for as long as they own the vehicle as they are in the car’s fuel economy. Instead of an engine light that lets them know the oil is low, they want a dashboard monitor with GPS coordinates to the nearest mechanic and an offer to schedule an appointment.

Meeting these demands requires retooling some long-entrenched business processes because selling services creates the opportunity and the need to design a new product. A backhoe’s boom and bucket would need strategically placed sensors to measure output, and this information would ultimately need to be connected to a back-end system so that billing can create invoices. Equipment uptime becomes critically important as well, which changes service and maintenance parameters.

These examples are typical of the challenges organizations face in how they must deliver goods and services in a digital framework for a digitally savvy consumer, and they show why many of the traditional ways of thinking about the design and manufacture of products, and how to meet demand, are no longer relevant.

The product life cycle that has traditionally started with design and ended with production now extends to analyzing the real-time performance of a finished product in the field. Earlier, we looked at how sensor data can be used to collaborate on design changes and improve products; with non-static products, sensor data is also important from a service and maintenance perspective. And here we see how IoT fundamentally changes long-standing, usage-based maintenance strategies. In the Industrial IoT or Industry 4.0 realm especially, sensor data is being used to predict asset failure, including manufacturing equipment, connected fleets, and other assets in the field. This is an obvious yet important use case for how IoT changes the very nature of manufacturing and enables the product life cycle of the future.

Despite tremendous changes in how organizations manage and maintain assets, the requirements remain the same in many important ways — organizations must balance cost, risk, and performance in accordance with the latest regulations and management standards. In this light, the use of IoT presents opportunities beyond forming new maintenance strategies. With real-time insight into an asset’s performance, organizations can make accurate and insightful decisions that minimize risk and cost while maintaining peak performance in ways that were never before possible. The massive amounts of data generated by assets every second become currency; mining this data can significantly reduce maintenance costs, increase asset uptime, and lower operational risk.

Let’s say, for example, that a manufacturer produces a piece of construction equipment that has a hydraulic system with a capacity to lift 1,000 pounds. If the manufacturer, in selling the outcome as a service by monitoring tonnage, sees that no client lifts more than 800 pounds at a time, the manufacturer can adjust the design requirements during the next redesign process to reduce manufacturing costs, lower maintenance costs, and increase performance.

Using IoT Data to Create a Digital Twin for End-to-End Visibility

Further applications of IoT data can be seen in the use of digital twins, where sensor data and real-time operational insights integrate with business data. In the previous example of the hydraulic lift, a digital representation of the asset would be instrumental for developing business processes to track and manage the asset, enabling operations to visualize what is happening in the field, and connecting all this information with business data in real time. This digital twin is important not just for the after-market side of manufacturing, but also for the management of manufacturing processes themselves. In today’s production environments, it is critically important for plant-level engineers and operators to have full visibility and awareness of production assets and to likewise use sensor data to drive new processes. To gain this visibility, many manufacturers use SAP Digital Manufacturing Insights to create a process control environment in which digital twins empower engineers and operators to manage production assets based on what is happening on the shop floor in real time.

A digital representation also enables the reinvention of business models and business processes because it provides real-time visibility into all product stakeholders from the design stage to the product’s real-time performance. A digital representation provides decision makers with the right information at the right time, creating an end-to-end picture of how connected products or assets are working together.

This type of end-to-end picture is also relevant for organizations that wish to reinvent their products by pushing software updates directly to consumers. This could be a car manufacturer, for example, making enhancements to a dashboard display and delivering these enhancements over a wireless network. In use cases such as this and other direct field updates, a connected product or asset represents more than a visualization or digital representation — it is, in fact, a direct line to design. This is one more example of how in today’s hyper-connected enterprise environment, information transparency throughout the entire asset life cycle and within an ecosystem of operators, equipment manufacturers, and service providers has a material impact on design and engineering.

Combining Two Worlds of Data

The concept of using sensor data to manage connected products and assets, and to materially alter design requirements and manufacturing processes to drive innovation, is not new; many companies are well on their way to using IoT to reinvent business processes and create new business models. The challenge of undertaking this reinvention, however, lies in establishing a system of record defined by the seamless integration of IoT and unstructured data with mission-critical business data.

IoT amps up the challenge of amalgamating various data sources, which was far easier when manufacturers were tracking and managing standalone products and assets. When that was the case, information from individual assets was recorded in a back-end system of record, and the information — such as when a machine failed, how often it was down, and its repair history and cost — was used reactively. Collecting real-world operational sensor data to use along with business data in real time is a significant change and a critical step in generating actionable intelligence for reinventing your manufacturing or design processes.

Combining the two worlds — that is, combining IoT and unstructured data with business data — is what Gartner refers to as bimodal IT, a model in which master data and basic transactional data resides in a stable system of record on the business application side while sensor data, sentiment data, and other unstructured data sources reside on a system of innovation or intelligence, which also includes collaboration networks and edge applications for bringing big data to the enterprise.

The question is how to effectively combine these two modes to support corporate objectives — how to layer business context on top of big data to create a new digital system of record where IoT is indistinguishable from mission-critical processes. For SAP users, the strategic path to successful bimodal IT is to leverage SAP S/4HANA as the digital system of record and SAP Leonardo as the system of intelligence.

A System of Intelligence

The world of IoT will enable and accelerate business process change, but only if the enterprise can manage how devices are connected, capture what the sensor data is saying, and understand the ramifications for the business. SAP Leonardo and SAP S/4HANA make this possible.

SAP Leonardo, SAP’s new digital innovation system, enables rapid innovation to help SAP customers reimagine their businesses. With its portfolio of capabilities — including SAP Leonardo IoT, SAP Leonardo Machine Learning, SAP Leonardo Analytics, SAP Leonardo Big Data, and SAP Leonardo Blockchain — SAP Leonardo powers a digital approach to manufacturing, both on premise using the SAP HANA platform and in the cloud using SAP Cloud Platform, SAP’s cloud-based development platform. SAP Leonardo ties together a connected network of people, processes, and things, and applies business context to the real-time insight generated from this network.

To envision how SAP Leonardo can help a business tie everything together, think of the use cases that can be derived from a fleet of delivery trucks moving through a city. Vehicle insights generated from sensor analysis of how the fleet moves safely from A to B in the most efficient and economical way can influence business decisions pertaining to shipping, but also those pertaining to planning, design, acquisition, operation, and maintenance, all the way through to the decommissioning of an asset. This is all part of the recognition that the parameters for deciding what and how to build something shift daily.

SAP S/4HANA, the digital core natively built on SAP HANA, enables real-time, analytics-driven decision making. SAP S/4HANA not only uses the SAP HANA platform as an in-memory database and data platform, but also leverages its large set of capabilities, such as geospatial, search, text analytics, predictive analytics, and machine learning functionalities, to provide a system of record that supports digital business. 

With SAP HANA in addition to these solutions, manufacturers can easily combine business and product data and use that data to proactively drive better outcomes. This is essential for capitalizing on the changing dynamics arising from increasing customization and the growing focus on service-oriented business. For example, organizations that decide to sell their products as a service must contend with a host of new dimensions beyond a traditional material master — new dimensions that need the power of SAP HANA to be effectively managed.

Turning big data into insight involves more than just implementing SAP S/4HANA and SAP Leonardo, however — it involves deriving analytic insights from that combination as well as other SAP solutions. The analytic capabilities of SAP Digital Manufacturing Insights use this concept of big data analytics on the manufacturing floor. Based on SAP Cloud Platform, the scalable SAP Digital Manufacturing Insights allows manufacturers to generate data that is easily digested and acted on at all levels — from operators to management to the C-suite. Rather than working off intuition or emotion, executives can tap real-time analytics based on pre-defined key performance indicators (KPIs) — quality, cost, delivery, productivity, and energy use — to identify any problems or lagging areas across their global operations. With everyone working off the same data, plant management and on-site operators can focus on addressing the problem, rather than making their own guesses on what will work better.

SAP Digital Manufacturing Insights integrates directly with SAP customers’ existing SAP ERP or SAP S/4HANA systems, meaning better, more efficient data use that provides even greater context for improving managerial decisions. And it leverages predictive and machine learning algorithms to increase its effectiveness, meaning that in addition to being able to onboard users quickly and give them the information they need, companies will see iterative improvements in process quality and execution over their global operations.

Managing an Intelligent Network

The end result of these transformations is a growing network of connected assets and products. To help customers manage this network, SAP provides solutions such as SAP Asset Intelligence Network and the SAP S/4HANA Cloud solution for intelligent product design.

SAP Asset Intelligence Network, which runs on SAP Cloud Platform and integrates natively with SAP S/4HANA, uses IoT technology to serve as a central repository for information related to assets connected to the network. Using this network, stakeholders such as operators and manufacturers can share operation, maintenance, performance, analytics, and best practices information about these assets, enabling a collaborative and transparent approach to asset management.

The SAP S/4HANA Cloud solution for intelligent product design supports product research and development, including requirements management, functional design structures, and embedded software — essentially, all the things design never had to worry about in the old, siloed paradigm. This solution recognizes the impact that more localized manufacturing operations, as well as bringing formerly siloed domains together in a more collaborative internal network, has on the way people work. It also recognizes that this approach facilitates collaboration both internally and externally for how design, manufacturing, and operations share insights generated from SAP Leonardo and use those insights to shape business decisions. The solution is another way to introduce transparency throughout the entire asset life cycle and within the ecosystem of designers, equipment manufacturers, operators, and service providers.

A Seamless Integration of Core Systems and Big Data

A next-generation system of record does not mean organizations must undertake a painful rip-and-replace or turn their back on key infrastructure investments. A system of record with a bimodal IT framework is, at a basic level, an avenue for organizations to seamlessly integrate core systems with the world of big data. Its final form, however, is whatever the business deems most effective in achieving corporate goals for bringing IoT into the business, including involving planning, simulation, and prediction in its analytics and forecasting efforts. Exactly how you disrupt the traditional product life cycle, integrate IoT with business data, and obtain more efficient business outcomes depends on the path you choose — SAP Leonardo simply helps you follow it.

For more information, visit www.sap.com/products/supply-chain-iot/scm.html and www.sap.com/products/leonardo.html.