IoT and Dynamic Case Management

"Dynamic case management supports end-to-end connectivity from the edge devices — on the shop floor, in the field, or with the consumer — to the rest of the enterprise."

Welcome to Part 6 of Adaptive Digital Factory (ADF) series. As you may recall, Part 1 covered the Industrial Internet and Industrie 4.0; Part 2 dealt with IoT and Supply Chain Transformation; Part 3 looked at the omni-device customer as well as the impact of these connected devices on manufacturers; and Part 4 focused the OODA loop for manufacturing: Insight to Action. Part 5 explored the digital transformation of Product Lifecycle Management. This blog looks at Dynamic Case Management (DCM) and the Internet of Things (IoT) within the adaptive factory.

Shop floors involve complex processes, each of which may encompass multiple milestones, tasks, and business units in the workflow required to handle and produce a manufactured good. Manufacturing aftermarkets (e.g., maintenance and support) also depend upon and involve complex processes that reach beyond the confines of the factory and present challenges for coordinating and managing these interconnected yet distinct resources.

The optimal methodology for facing the challenges (and opportunities) of the digital factory is dynamic case management (DCM Chapter 9 in iBPM The Next Wave), due in large part to DCM’s sophisticated end-to-end support of connected devices. DCM capabilities address each of the complex components inherent in IoT cases, including:

  • Case Stages and Objectives: Create, manage, and report on the processes and Cases of Everything. The model-driven design of cases is achieved by identifying the milestones (stages) of each case. Pega’s manufacturing solutions easily map value streams spanning customers, manufacturers, suppliers, and field progress through stages in DCM.
  • Things as Participants in DCM: Case stages and processes are acted upon by numerous participants, including manufacturing, back-office staff, field service providers, customers, and Things. DCM allows multiple entities (humans or Things) to view, review, work, and subscribe to a case and its processes.
  • Monitor and Improve: DCM portals and real-time reports allow business activity monitoring (BAM) of the manufacturing case stages. Users track the progress of work throughout each stage of the case. For example, in additional to the out-of-the-box real-time reporting capabilities and Pega DCM portals, Pega's business intelligence tools allow data to be exported to a data warehouse, providing complete DCM visibility throughout the enterprise.
  • Case Collaboration — Pulse: Collaboration takes the form of instant messages, files, and URLs that can be shared with other users within a work group. Posts are designated as public or private.
  • Case Data (Properties): Cases and processes may employ different data models from a variety of sources, including user input, external systems, the result of business rules, and calculations. This data may be propagated throughout all of the processes in the case, which enhances case and process functionality.
  • Dynamic and Ad-Hoc Tasks: Tasks are assigned to humans, Things, or automated agents (bots, robots, etc.) based on identity, role, skills, or a combination. Workers and managers can dynamically introduce ad-hoc tasks and even discover the need for new processes in the context of an individual case. Dynamic and ad-hoc task capability supports inevitable variations in work automation: involving smarter work processing, and social collaboration to achieve business objectives.

Dynamic case management supports end-to-end connectivity from the edge devices — on the shop floor, in the field, or with the consumer — to the rest of the enterprise.

The dynamic cases are executed in the adaptive steady-state execution of the adaptive digital factory. These cases orchestrate the entire lifecycle from design engineering through production and shipment to customers —connecting the consumer to the manufacturer, services, and support.

Events in any phase of the production or servicing may trigger dynamic cases that will orchestrate the decisions, sub-cases, and business processes to respond or handle the event. If machine learning is used in the predictions or any type of adaptive predictions, it is conceivable that the actions, cases, business processes actually may be enhanced on the fly. This process is adaptive, in terms of both orchestrated execution as well as continuous improvements in the models or assets themselves (i.e., changes in processes, rules, decisions, etc.).

In Part 7 we shall delve deeper into IoT References Architectures (IoT RA) and especially the importance of collaboration, Business Processes and Dynamic Cases in IoT implementations. Part 8 will go even deeper into Business Value of IoT implementations – building upon the RA discussion. For a sneak peek, check out the entire Adaptive Digital Factory eBook.