An enterprise approach to IoT modeling and applications

The total economic potential of the Internet of Things (IoT) has been valued at $11.1 trillion a year by 2025. This value is spread across a spectrum of applications from different sectors. This diversity creates challenges in how to implement and employ the range of IoT applications, both in costs and

in time to market but moreover, how to effectively integrate them into the business environments of any and all enterprises. The current predominating practice of building IoT applications is to start with a single problem related to the “thing” or a machine and build a bespoke solution for it.

However, the traditional bottom-up Operational Technology (OT) problem approach to an IoT solution benefits from being complemented with a top-down enterprise business process approach. Why is this? It’s because the visualization and modeling of real-world concepts, entities, and requirements—across enterprise organizations—can help close the gap between the enterprise business process and technology perspectives.

What does it mean to take an enterprise (business) process perspective towards IoT?

Taking an enterprise process perspective means that we start with the challenges or needs of an end user (this can be a person, an organization, a department in an enterprise, or a role in an enterprise), and articulate them in the end user's terms. This is as opposed to starting bottom up from technology and an isolated problem with the associated risk of resulting in a stove pipe solution.

We model them according to how the end user sees their reality. For example, the person responsible for the production line in a factory is interested in efficiency, low costs, ensuring uptime of machines, the timely delivery of what is produced, maximizing production given the resources, and so on.

So, what are the benefits?

From an enterprise perspective, starting with modeling user needs and refining them further by digging deeper through the enterprise, IT, and OT layers, down to the machines and production lines, means that the process begins with the needs of those not familiar with IoT as a technology. By capturing a holistic picture of how the enterprise operates and what the needs are in a formal way, it provides the baseline for realizing the IoT solutions.

Figure 1 shows a breakdown of our approach to an enterprise-centric perspective to IoT applications:

Figure 1: Our approach to an enterprise-centric perspective to IoT applications.

Identifying high-level enterprise requirements and modeling a top-level business process flow in an end-to-end enterprise-centric application context, covering the enterprise and associated OT perspectives—and doing it in the spirit of "low code" programming.

Detailing sub-processes to reach common resources and tasks as part of decomposing the top-level business process, hence linking the enterprise and IT perspectives, and ideally reaching application-independent and reusable resources and components.

Mapping the most granular sub-processes to functional components that can be represented by underlying microservices and IoT resources, the latter providing the link to OT.

Orchestrating and executing both enterprise process relevant events and IoT event and data streams in a consistent framework of reusable components.

In order to capture the full potential of IoT across different applied sectors, the means for effectively developing and integrating IoT applications—that consider the richer and wider needs of an enterprise—are still missing, but baseline work exists that can be further exploited. Our approach builds on previous work, and we propose further work that can help close the gap between the enterprise process-centric and IoT-centric perspectives that we believe will make IoT applications more enterprise friendly.

Business process modeling

Process modeling is about identifying and defining patterns of processes inside an enterprise, or even between enterprises that are repeatable and that involve various enterprise assets and resources. Our focus is on processes that involve physical assets and machines and how they are engaged with IoT. This will enable us to analyze and optimize operations and further lead to fully automated processes of asset-intensive enterprise operations, such as manufacturing, buildings, utilities, agriculture, and process industries.

Business Process Modeling and Notation (BPMN) is a specific standard developed for business process modeling. It can produce formal process specifications that are executable and that can be mapped to microservices and IoT resources. Generally, BPMN provides enterprises with the capability of understanding and communicating their internal business procedures in a graphical notation that is based on Extensible Markup Language (XML). Furthermore, it enables organizations to adjust to new internal and B2B business needs and circumstances quickly.

Applying the BPMN approach to IoT application modeling

The IoT operational and delivery model is via IoT application enablement platforms that provide horizontal tools for building applications. To date, those platforms have primarily been about infrastructure services such as connectivity, protocol adaption, device management, and secure device onboarding. The more data-centric aspects of the platform are, to a large extent, still rather simple and point-application focused. For instance, applying a specific AI-based task such as predictive analytics to understand when and how a machine might fail, or using rule engines to achieve some level of programmability. But what remains is how IoT applications can be made both richer in features and capabilities, as well as effectively integrated and enriched into the more complex overall enterprise business processes.

Let’s take a look at a simple example using machine failure, which we touched upon earlier.

Figure 2 shows a predictive maintenance scenario. In the simplest form, predictive maintenance is performed by applying predictive analytics on data from the machine in order to predict failure as in what and when in time. In a wider production cycle perspective, more data, such as from product inspections, can augment the prediction, for example, by understanding degraded product quality from wear of a cutting tool. Further, in the bigger enterprise picture, the servicing of a production machine has an impact on production plans and customer commitments. It’s dependent on the right supply chain as well as the availability of other enterprise resources, such as service technicians—all requiring the proper planning and adaptive processes from a systemic perspective.

The machine-centric bottom-up approach to the IoT application (applying predictive analytics) clearly needs to be complemented with an enterprise-centric top-down approach, and the solution will also rely on a larger set of diverse AI-based analytics and planning tools. All this requires proper interaction across the enterprise, IT and OT layers. We will later get back to the details of the IT layer, and for this discussion limit ourselves to the Enterprise and OT perspectives as depicted in Figure 2. As also indicated, different enterprise systems such as for Enterprise Resource Planning, Supply Chain Management and Product Lifecycle Management would also be part of the enterprise layer.

Figure 2: A predictive maintenance scenario.

As mentioned, our approach builds on capturing the application domain expertise in a formal top-level enterprise business process model. Let's see how this could look like by taking another example; in this case Smart Logistics.

Smart Logistics is an IoT-enabled application that goes across different enterprises, and is rich in terms of actors involved, associated data, and its process flow interactions. The actors—including suppliers, purchasers, logistics providers, transportation vehicles and so on—are both consumers and producers of data across the processes and sub-processes of the end-to-end application. Smart Logistics target more timely and predictive deliveries, more flexibility and adaptivity to unforeseen events, and with more transparency at a lower cost. What IoT will bring to logistics is a fully automated and an adaptive end-to-end process that provides optimization across all involved actors. The Smart Logistics solution implements the end-to-end tasks starting from performance-based supplier recommendation, contract negotiation, and logistics planning, to goods handling and overall process monitoring, as well as contract fulfillment.

We have built a holistic solution (conceptually shown in Figure 3) that combines the power of building blocks which are standalone and reusable microservices. The approach taken here is the same as in the previous example, in other words, starting with the enterprise top-level process flow and further detailing of sub-processes that connect to the physical entities, assets and processes, that is, what we relate to as the OT layer.

Figure 3: A typical high-level process flow for a Smart logistics solution.

As can be intuitively understood from these two high-level examples, there are processes that are similar, like monitoring and applying analytics, planning and re-planning, and so on. A next step is to drill deeper into the subprocesses and down to how the finest granular level of the subprocesses map to IoT resources, components, and microservices. Our objective is to find solutions for a conceptual framework that leads to a formalism and executable solutions that connects across the Enterprise, IT, and OT layers, along with the second objective of relying on reusable sub-processes, components, and eventually resources and microservices.

Integrating IoT applications into enterprise processes

IoT applications should not be looked at in isolation or as isolated solutions to a very specific and confined problem. They should rather be viewed as parts of, and integrated into, the overall processes of an enterprise. The Business Process Modeling approach is a way to ensure this integration is effective across different departments of an organization and that the cross-enterprise processes can and should be fully automated. As a result, it would help break internal silos as part of the digitalization journey.

In our next article, we will explain a framework for how to build, orchestrate, and execute solutions following our process-oriented approach. Stay tuned to learn how to do it. Until then, visit our IoT pages for service providers.