Harsh Jegadeesan of SAP set the dress code bar high by kicking off the Thursday demos in a suit jacket, although I did see Thomas Volmering and Patrick Schmidt straightening his collar before the start. He also set a high bar for the day’s demo by showing how to illuminate business operations with intelligent process intelligence. He discussed a scenario of a logistics hub (such as Amazon’s), and the specific challenges of the hub operations manager who has to deal with inbound and outbound flights, and sorting all of the shipments between them throughout the day. Better visibility into the operations across multiple systems allows problems to be detected and resolved while they are still developing by reallocating the workforce. Harsh showed a HANA-based hub operations dashboard, where the milestones for shipments demark the phases of the value chain: from arrival to ground handling to warehouse to outbound buffer to loading and takeoff. Real-time information is pulled from each of the systems involved, and KPIs show; drill downs can show the lower level aggregate or even individual instance data to determine what is causing missed KPIs – in the demo, shipments from certain other hubs are not being unloaded quickly enough. But more than just a dashboard, this allows the hub operations manager to add a task directly in the context of the problem and assign it (via an @mention) to someone else, for example, to direct more trucks to unload the shipments. The dashboard can also make recommendations, such as changing the flights for specific shipments to improve the overall flow and KPIs. He showed a flight map view of all inbound and outbound flights, where the hub operations manager can click on a specific flight and see the related data. He showed the design environment for creating the intelligent business operations process by assembling SAP and non-SAP systems using BPMN, mapping events from those systems onto the value chain phases (using BPAF where available), thereby providing visibility into those systems from the dashboard; this builds a semantic data mart inside HANA for the different scenarios to support the dashboard but also for more in-depth analytics and optimization. They’ve also created a specification for Process Façade, an interface for unifying process platforms by integrating using BPMN, BPAF and other standards, plus their own process-based systems; at some point, expect this to open up for broader vendor use. Some nice case studies from process visualization in large-scale enterprises.
Dominic Greenwood of Whitestein on intelligent process execution, starting by defining an intelligent process: it has experiences (acquired data), knowledge (actionable information, or analytical interpretation of acquired data), goals (adoptable intentions, or operationally-relevant behavioral directives), plans (ways to achieve goals through reusable action sequences, such as BPMN processes) and actions (result of executing plans). He sees intelligent process execution as an imperative because of the complexity of real-world processes; processes need to dynamically adapt, and process governance needs to actively apply constraints in this shifting environment. An intelligent process controller, or reflective agent, passes through a continuous cycle of observe, comprehend, deliberate, decide, act and learn; it can also collaborate with other intelligent process controllers. He discussed a case study in transportation logistics – a massively complex version of the travelling salesman problem – where a network of multi-modal vehicles has to be optimized for delivery of goods that are moved through multiple legs to reach their destinations. This involves knowledge of the goods and their specific requirements, vehicle sensors of various types, fleet management, hub/port systems, traffic and weather, and personnel assignments. DHL in Europe is using this to manage 60,000 orders per day, allocated between 17,500 vehicles that are constantly in motion, managed by 300 dispatchers across 24 countries with every order changing at least once while en route. The intelligent process controllers are automating many of the dispatching decisions, providing a 25-30% operational efficiency boost and a 12% reduction in transportation costs. A too-short demo that just walked through their process model to show how some of these things are assigned, but an interesting look into intelligent processes, and a nice tie-in to Harsh’s demonstration immediately preceding.
Next up was Jakob Freund of camunda on BPMN everywhere; camunda provides an open-source BPM framework intended to be used by Java developers to incorporate process automation into their applications, but he’s here today to talk about bpmn.io: an open-source toolkit in Javascript that provides a framework for developers and a BPMN web modeler, all published on GitHub. The first iteration is kicking off next week, and the web modeler will be available later this year. Unlike yesterday’s demonstrators who firmly expressed the value of no-code BPM implementations, Jakob jumped straight into code to show how to use the Javascript classes to render BPMN XML as a graphical diagram and add annotations around the display of elements. He showed how these concepts are being used in their cockpit process monitoring product; it could also be used to demonstrate or teach BPMN, making use of functions such as process animation. He demonstrated uploading a BPMN diagram (as XML) to their camunda community site; the site uses the Javascript libraries to render the diagram, and allows selecting specific elements in the diagram and adding comments, which are then seen via a numeric indicator (indicating the number of comments) attached to the elements with comments. He demonstrated some of the starting functionality of the web modeler, but there’s a lot of work to do there still; once it’s released, any developer can download the code and embed that web modeler into their own applications.
We finished the first morning session with Keith Swenson of Fujitsu on letting go of control: we’re back on the topic of agents, which Keith initially defined as autonomous, goal-directed software that does something for you, before pointing out that that describes a lot of software today. He expanded that definition to mean something more…human-like. A personal assistant that can coordinate your communications with those of other domains. These type of agents do a lot of communication amongst themselves in a rules-based dynamic fashion, simplifying and reducing the communication that the people need to do in order to achieve their goals. The key to determining what the personal assistants should be doing is to observe emergent behavior through analytics. Keith demonstrated a healthcare scenario using Cognoscenti, an open-source adaptive case management project; a patient and several different clinicians could set goals, be assigned tasks, review documents and other activities centered around the patient’s care. It also allows the definition of personal assistants to do specific rules-based actions, such as cloning cases and synchronizing documents between federated environments (since both local and cloud environments may be used by different participants in the same case), accepting tasks, and more; copying between environments is essential so that each participant can have their information within their own domain of control, but with the ability to synchronize content and tasks. The personal assistants are pretty simple at this point, but the concept is that they are helping to coordinate communications, and the communications and documents are all distributed via encrypted channels so safer than email. A lot of similarities with Dominic’s intelligent process controllers, but on a more human scale. As many thousand of these personal assistant interactions occur, patterns will begin to emerge of the process flows between the people involved, which can then be used to build more intelligence into the agents and the flows.