bpmNEXT 2018: All about bots with Cognitive Technology, PMG.net, Flowable

We’re into the afternoon of day 2 of bpmNEXT 2018, with another demo section.

RPA Enablement: Focus on Long-Term Value and Continuous Process Improvement, Cognitive Technology

Massimiliano Delsante of Cognitive Technology presented their myInvenio product for analyzing processes to determine where gaps exist and create models for closing those gaps through RPA task automation. The demo started with loading historical process data for process mining, which created a process model from the data together with activity resources, counts and other metrics; then comparing the model for conformance with a reference model to determine the frequency and performance of conformant and non-conformant cases. The process discovery model can be transformed to a BPMN model, and simulated performance. With a baseline data set of all manual activities, the system identified the cost of each activity, helping to identify which activities would result in the greatest savings if automated, and fed the data for actual resources used into the simulation scenario; adjusting the resources required by specifying the number of RPA robots that could be deployed at specific tasks allows for a what-if simulation for the process performance with an RPA implementation. An analytics dashboard provides visualization of the original process discovery and the simulated changes, with performance trends over time. Predictive analytics can be applied to running processes to, for example, predict which cases will not meet their deadlines, and some root cause analysis for the problems. Doing this analysis requires that you have information about the cost of the RPA robots as well as being able to identify which tasks could be automated with RPA. Good integration of process discovery, simulation, analysis and ongoing monitoring.

Integration is Still Cool, and Core in your BPM Strategy, PMG.net

Ben Alexander from PMG.net focused on integration within BPM as a key element for driving innovation by increasing the speed of application development: integrating services for RPA, ML, AI, IoT, blockchain, chatbots and whatever other hot new technologies can be brought together in a low-code environment such as PMG. His demo showed a vendor onboarding application, adding a function/subprocess for assessing probability of vendor approval using machine learning by calling AzureML, user task assignment using Slack integration or SMS/phone support through a Twilio connector, and RPA bot invocation using a generic REST API. Nice demo of how to put all of these third-party services together using a BPM platform as the main application development and orchestration engine.

Making Process Personal, Flowable

Paul Holmes-Higgin and Micha Keiner from Flowable presented on their Engage product for customer engagement via chat, using chatbots to augment rather than replace human chat, and modeling the chatbot behavior using standard modeling tools. In particular, they have found that a conversation can be modeled as a case with dynamic injection of processes, with the ability to bring intelligence into conversations, and the added benefit of the chat being completely audited. The demo was around the use case of a high-wealth banking client talking to their relationship manager using chat, with simultaneous views of both the client and relationship manager UI in the Flowable Engage chat interface. The client mentioned that she moved to a new home, and the RM initiated the change address process by starting a new case right in the chat by invoking a context-sensitive digital assistant. This provided advice to the RM about address change regulatory rules, and provided a form in situ to collect the address data. The case is now progressed through a combination of chat message to collaborate between human players, forms filled directly in the chat window, and confirmation by the client via chat by presenting them with information to be updated. Potential issues, such as compliance regulations due to a country move, are raised to the RM, and related processes execute behind the scenes that include a compliance officer via a more standard task inbox interface. Once the compliance process completes, the RM is informed via the chat interface. Behind the scenes, there’s a standard address change BPMN diagram, where the chat interface is integrated through service activities. They also showed replacing the human compliance decision with a decision table that was created (and manually edited if necessary) based on a decision tree generated by machine learning on 200,000 historical address change cases; rerunning the scenario skipped the compliance officer step and approved the change instantaneously. Other chat automated tasks that the RM can invoke include setting reminders, retrieving customer information and more using natural language processing, as well as other types of more structured cases and processes. Great demo, and an excellent look at the future of chat interfaces in process and case management.

Architecture & Process: Robert Shapiro

I met Robert Shapiro years ago, when I worked for FileNet and he was part of the impressive brain trust at Meta Software, but now he’s with Global 360 and here to talk to us about BPM and workforce management, which focuses on using analytics, simulation tools and optimization techniques together with a workforce scheduler.

He started with a quick overview of simulation in a BPMS environment, where a discrete event simulation is run based on scenarios that include the following:

  • A set of processes to be simulated
  • Incoming work (arrivals), both actual (from a BPMS or other operational system) and forecast
  • Resources, roles and shifts, including human, equipment and technology resources
  • Activity details, including the duration of each activity (likely a distribution) and the probability of each decision path.

The output of the simulation will show the staff requirements by role and time period, staff and equipment utilization, cycle times and SLAs, unprocessed work and bottlenecks, work arrival profile, and an activity summary.

He then went on to discuss workforce management schedulers, which is used to assign detailed schedules to staff within an organization based on the work load and the resource characteristics (usually from an HR management system). Note that I’m not talking about assigning work within a BPMS here; this is more general scheduling technology for creating a schedule for each resource while trying to precisely match the work load. Factors such as holidays, vacation, union rules and other factors that determine who may do what are all taken into account.

One of the key inputs into a workforce scheduler, however, is exactly what’s output from a process simulator: workload demand on a time basis. By working with these technologies together, it’s possible to come up an optimal workforce size and schedule as follows:

  • Gather analytics from a BPMS on work arrival patterns, resource utilization, work in progress and activity loads in order to extract workload demand (staff requirements by role and time period) for input to the scheduler.
  • Using the actual workload demand data and other data on individual staff characteristics, generate a best-fit schedule in the scheduler that matches workload and staff, minimizing under and overstaffing.
  • Feed the best-fit resource schedule back into the process simulator, and create a scenario based on this schedule and the actual analytics from the BPMS. The simulation can create an updated version of the workload demand and the effect of the new workforce assignment.
  • The workload demand generated by the simulator is fed back into the scheduler, which generates a new best-fit resource schedule.
  • Rinse and repeat (or rather, simulate and schedule) until no further optimization is possible.

This approach is most suited to well-structured business processes with repeatable patterns in work item arrivals, and a large total resource pool — Shapiro has seen 10-20% reduction in staff costs when these techniques are applied. A bit of scary old-style BPR fears here about cutting jobs, but that’s the reality in many industries.