Tag: RPA

Bizagi Catalyst 2020, Day 1

This week, I’m attending the virtual Bizagi Catalyst event, and I’ll be giving a short keynote and interactive discussion tomorrow. Today, the event kicked off with an address by CEO Gustavo Gomez on the impact of technology innovation, and the need for rapid response. This is a message that really resonates right now, as companies need to innovate and modernize, or they won’t make it through this current crisis. Supply chains are upside-down, workforces are disrupted, and this means that businesses need to change quickly to adapt. Gomez’ message was to examine your customer-facing processes in order to make them more responsive: eliminate unnecessary steps; postpone steps that don’t require customer interaction; and automate tasks. These three process design principles will improve your customer experience by reducing the time that they spend waiting while they are trying to complete a transaction, and will also improve the efficiency and accuracy of your processes.

He had the same message as I’ve had for several months: don’t stand still, but use this disruption to innovate. The success of companies is now based on their ability to change, not on their success at repetition: I’m paraphrasing a quote that he gave, and I can’t recall the original source although it’s likely Bill Drayton, who said “change begets change as much as repetition reinforces repetition”.

I unfortunately missed quite a bit of the following session, by Mata Veleta of insurance provider SCOR due to a glitchy broadcast platform. I did see the part of her presentation on how Bizagi supports them on their transformation journey, with a digitalization of a claims assessment application that was live in six weeks from design to go-live during the pandemic — very impressive. They are embracing the motto “think big, start small, move fast”, and making the agile approach a mindset across the business in addition to an application development principle. They’re building another new application for medical underwriting, and have many others under consideration now that they see how quickly they can roll things out.

The broadcast platform then fell over completely, and I missed the product roadmap session; I’m not sure if Bizagi should be happy that they had so many attendees that they broke the platform, or furious with the platform vendor for offering something that they couldn’t deliver. The “all-singing, all-dancing” platforms look nice when you see the demo, but they may not be scalable enough.

I went back later in the day and watched the roadmap session replay, with Ed Gower, VP Solutions Consulting, and Andrea Dominguez, Product Manager. Their roadmap has a few guiding themes: intelligent automation orchestration primarily through improved connectors to other automation components including RPA; governance to provide visibility into this orchestration; and a refreshed user experience on all devices. Successful low-code is really about what you can integrate with, so the focus on connectors isn’t a big surprise. They have a new connector with ABBYY for capture, which provides best-of-breed classification and extraction from documents. They also have a Microsoft Cognitive Services Connector for adding natural language processing to Bizagi applications, including features such as sentiment analysis. There are some new features coming up in the Bizagi Modeler (in December), including value stream visualizations.

The session by Tom Spolar and Tyler Rudkin of HSA Webster Bank was very good: a case study on how they use Bizagi for their low-code development requirements. They stated that they use another product for the heavy-duty integration applications, which means that Bizagi is used as true no/low-code as well as their collaborative BPMN modeling environment. They shared a lot of best practices, including what they do and don’t do with Bizagi: some types of projects are just considered a poor fit for the platform, which is a refreshing attitude when most organizations get locked into a Maslow’s hammer cognitive bias. They’ve had measurable results: several live deployments, the creation of reusable BPMN capabilities, and reduced case duration.

The final session of the day was a sneak peek at upcoming Bizagi capabilities with Kevin Guerrero, Technical Marketing Manager, and Francisco Rodriguez, Connectors Product Manager. Two of the four items that they covered were RPA-related, including integration with both UiPath and Automation Anywhere. As I saw at the CamundaCon conference last week, BPM vendors are realizing that integration with the mainstream RPA platforms is important for task automation/assistance, even if the RPA bots may eventually be replaced with APIs. Bizagi will be able to trigger UiPath attended bots on the user’s desktop, and start bots from the Bizagi Work Portal to exchange data. We saw a demo of how this is created in Bizagi Studio, including graphical mapping of input/output parameters with the bot, then what it looks like in the user runtime environment. They also discussed their upcoming integration with the cloud-based Automation Anywhere Enterprise A2019, calling cloud-based bots from Bizagi.

Moving on from RPA, they showed their connector with Microsoft Cognitive Services Form Recognizer, allowing for extraction of text and data from scanned forms if you’re using an Azure and Cognitive Services environment. There are a number of pre-defined standard forms, but you can also train Form Recognizer if you have customized versions of these forms, or even new forms altogether. They finished up with their new SAP Cloud Connector, which works with S/4HANA. We saw a demo of this, with the SAP connection being setup directly in Bizagi Studio. This is similar to their existing SAP connector, but with a direct connection to SAP Cloud.

I’ll be back for some of the sessions tomorrow, but since I have a keynote and interactive Q&A, I may not be blogging much.

Disclosure: I am being compensated for my keynote presentation, but not for anything that I blog here. These are my own opinions, as always.

CamundaCon 2020.2 Day 2 opening keynotes: BPM patterns and RPA integration

I’m back at CamundaCon 2020.2 for day 2, which kicked off with a keynote by Camunda co-founder and developer advocate Bernd Rücker. He’s a big fan of BPM and graphical models (obviously), but not of low-code: his view is that the best way to build robust process-based applications is with a stateful workflow engine, a graphical process modeler, and code. In my opinion, he’s not wrong for complex core applications, although I believe there are a lot of use cases for low code, too. He covered a number of different implementation architectures and patterns with their strengths and weaknesses, especially different types of event-driven architectures and how they are best combined with workflow systems. You can see the same concepts covered in some of his previous presentations, although every time I hear him give a presentation, there are some interesting new ideas. He’s currently writing a book call Practical Process Automation, which appears to be gathering many of these ideas together.

CTO Daniel Meyer was up next with details of the upcoming 7.14 release, particularly the RPA integration that they are releasing. He positions Camunda as having the ability to orchestrate any type of process, which may include endpoints (i.e., non-Camunda components for task execution) ranging from human work to microservices to RPA bots. Daniel and I have had a number of conversations about the future of different technologies, and although we have some disagreements, we are in agreement that RPA is an interim technology: it’s a stop-gap for integrating systems that don’t have APIs. RPA tends to be brittle, as pointed out by Camunda customer Goldman Sachs at the CamundaCon Live earlier this year, with a tendency to fail when anything in the environment changes, and no proper state maintained when failures occur. Daniel included a quote from a Forrester report that claims that 45% of organizations using RPA deal with breakage on at least a weekly basis.

As legacy systems are replaced, or APIs created for them, RPA bots will gradually be replaced as the IT infrastructure is modernized. In the meantime, however, we need to deal with RPA bots and limit the technical debt of converting the bots in the future when APIs are available. Camunda’s solution is to orchestrate the bots as external tasks; my advice would also be to refactor the bots to push as much process and decision logic as possible into the Camunda engine, leaving only the integration/screen scraping capabilities in the bots, which would further reduce the future effort required to replace them with APIs. This would require that RPA low-code developers learn some of the Camunda process and decision modeling, but this is done in the graphical modelers and would be a reasonable fit with their skills.

The new release includes task templates for adding RPA bots to processes in the modeler, plus an RPA bridge service that connects to the UiPath orchestrator, which in turn manages UiPath bots. Camunda will (I assume) extend their bridge to integrate with other RPA vendors’ orchestrators in the future, such as Automation Anywhere and Blue Prism. What’s interesting, however, is that the current architecture of this is that the RPA task in a process is an external task — a task that relies on an external agent to poll for work, rather than invoking a service call directly — then the Camunda RPA bridge invokes the RPA vendor’s orchestrator, then the RPA bots poll their own orchestrator. If you are using a different RPA platform, especially one that doesn’t have an orchestrator, you could configure the bots to poll Camunda directly at the external task endpoint. In short, although the 7.14 release will add some capabilities that make this easier (for enterprise customers only), especially if you’re using UiPath, you should be able to do this already with any RPA product and external tasks in Camunda.

Daniel laid out a modernization journey for companies with an existing army of bots: first, add in monitoring of the bot activities using Camunda Optimize, which now has the capability to monitor external events, in order to gain insights into the existing bot activities across the organization. Then, orchestrate the bots using the Camunda workflow engine (both BPMN and DMN models) using the tools and techniques described above. Lastly, as API replacements become available for bots, switch them out, which could require some refactoring of the Camunda models. There will likely be some bots left hanging around for legacy systems that are never going to have APIs, but that number should dwindle over the years.

Daniel also teased some of the “smart low-code” capabilities that are on the Camunda roadmap, which will covered in more detail later by Rick Weinberg, since support for RPA low-code developers is going to push them further into this territory. They’re probably never going to be a low-code platform, but are becoming more inclusive for low-code developers to perform certain tasks within a Camunda implementation, while professional developers are still there for most of the application development.

This is getting a bit long, so I’m going to publish this and start a new post for later sessions. On a technical note, the conference platform is a bit wonky on a mobile browser (Chrome on iPad); although it’s “powered by Zoom”, it appears in the browser as an embedded Vimeo window that sometimes just doesn’t load. Also, the screen resolution appears to be much lower than at the previous CamundaCon, with the embedded video settings maxing out at 720p: if you compare some of my screen shots from the two different conferences, the earlier ones are higher resolution, making them much more readable for smaller text and demos. In both cases, I was mostly watching and screen capping on iPad.

DecisionCAMP 2019: DMN TCK, BPO with AI and rules, and business logic hidden in spreadsheets

Close Is Not Close Enough. Keith Swenson, Fujitsu

A few months ago at bpmNEXT, I saw Keith Swenson give an update on the DMN Technology Compatibility Kit, and we’re seeing a bit of a repeat of that presentation here at DecisionCAMP. The TCK defines a set of test cases (as DMN decision models, input data and expected results) that assure conformance to the specification, plus a sample runner application that will pass the models and data to the vendor’s engine and evaluate the results.

DMN TCK. From Keith Swenson’s presentation.

There are about 120 test models and 1600 test cases, supporting only DMN 1.2; these tests come from examining the specification as well as cases from practice. It’s easy for a vendor to get involved in the the TCK, both in terms of running it against their engine and in terms of participating through submitting new test models and cases. You can see the vendors that have submitted their results; although many more vendors claim that they “have DMN”, their actual level of compatibility may be suspect.

The TCK committee is getting ready for DMN 1.3, and considering tests for modeling tools in addition to the current tests for the engine. He also floated the idea of a standardized API for DMN as a service, so that the calling application doesn’t need to know which engine it’s calling — possibly something that’s not going to be a big hit with vendors.

Business innovation of BPO realized by Task Center and AI and Rule Engine. Yoshihito Nakayama, NTT DATA INTRAMART

Yoshihito Nakayama presented on the current challenges of BPO with respect to improving productivity, and how they are resolving this using AI and a rules engine to aggregate and assign human tasks from multiple systems to different team members. This removes the requirement to manually review and assign work, and also provides a dashboard for visualizing work in progress and future forecasts.

Intramart’s Task Center for aggregating and assigning work. From Yoshihito Nakayama’s presentation.

AI is used to predict and optimize task classification and assignment, based on time required to complete the task and the individual worker skill level and productivity. It is also used to predict workload for task types and individual workers.

Their visualization dashboard shows drilldowns on current and past productivity, plus future forecasts. The simulation models for forecasting can be fine-tuned to optimize for cost, performance and other factors. It brings together work monitoring from all systems, including RPA processes. They’re also using process mining on a variety of systems to create a digital twin of the organization for better tracking and predictions, as well as other tools such as voice and image identification to recognize what tasks are being done that are not being recorded in any system logs.

They have a variety of case studies across industries, looking at automating non-routine work using case management, BPM, RPA, AI and rules.

Spaghetti Spreadsheets Untangled – Benefits of decision modeling when uncovering complex business logic hidden in spreadsheets. Charlotte Bouvy, M.C. Bouvy Consultancy

Charlotte Bouvy presented on her work done with SVB, the Netherlands social insurance administrator, on implementing business rules management. They are using DMN-based wizards for supporting 1,500 case workers, and the specific case was around the operational control and audit departments and the “lawfulness” of how the assessment work is done. Excel spreadsheets were used to do this, which had obvious problems in terms of being error prone and lacking domain-specific business logic. They implemented their SARA system to replace the spreadsheets with Oracle OPA, which allowed them to more accurately represent knowledge, as well as separate the data from the decision model while creating an executable model.

Decision model to determine lawfulness. From Charlotte Bouvy’s presentation.

These type of audit processes require sampling over a wide variety of case files to compare actual payments against expected amounts, with some degree of aggregation within specific laws being applied. Moving to a rules engine allowed them to model calculations and decisions, and separate data and model to avoid errors that occurred when copying and pasting data in spreadsheets. The executable model is now a single source of truth to which version control and change management can be applied. They are trying out different ways of using the SARA system: directly in Oracle Policy Modeler for building and debugging; via a web interview and an RPA robot for data input; and eventually via direct integration with the SVB’s case management system to load data.

bpmNEXT 2019 demos focused on creating smarter processes: decisions, RPA, emergent processes and machine learning with Serco, @FujitsuAmerica and @RedHat

A Well-Mixed Cocktail: Blending Decision and RPA Technologies in 1st Gen Design Patterns, with Lloyd Dugan of Serco

Lloyd showed a scenario of using decision management to determine if a step could be done by RPA or a human operator, then modeling the RPA “operator” as a role (performer) for a specific task and dynamically assigning work – this is instead of refactoring the BPMS process to include specific RPA robot service tasks. This is shown from an actual case study that uses Sapiens for decision management and Appian for case/process management, with Kapow for RPA. The focus here is on the work assignment decisioning, since the real-world scenario is managing work for thousands of heads-down users, and the redirection of work to RPA can have huge overall cost savings and efficiency improvement even for small tasks such as logging in to the multiple systems required for a user to do work. The RPA flow was created, in part, via the procedural documentation wiki that is provided to train and guide users, and if the robot can’t work a task through to completion then it is passed off to a human operator. The “demo” was actually a pre-recorded screen video, so more like a presentation with a few dynamic bits, but gave an insight into how DM and RPA can be added to an existing complex process in a BPMS to improve efficiency and intelligence. Using this method, work can gradually be carved off and performed by robots (either completely or partially) without significantly refactoring the BPMS process for specific robot tasks.

Emergent Synthetic Process, with Keith Swenson of Fujitsu

Keith’s demo is based on the premise that although business processes can appear to be simple on the surface when you look at that original clean model, the reality is considerably messier. Instead of predefining a process and forcing workers to follow that in order, he shows defining service descriptions as tasks with their required participants and predecessor tasks. From that, processes can be synthesized at any point during execution that meet the requirements of the remaining tasks; this means that any given process instance may have the tasks in a different order and still be compliant. He showed a use case of a travel authorization process from within Fujitsu, where a travel request automatically generates an initial process – all processes are a straight-through series of steps – but any changes to the parameters of the request may modify the model. This is all based on satisfying the conditions defined by the dependency graph (e.g., departmental manager requires that the manager approve before they can approve it), starting with the end point and chaining backwards through the graph to create the series of steps that have to be performed. Different divisions had different rules around their processes, specifically the Mexico group did not have departmental levels so did not have one of the levels of approval. Adding a step to a process is a matter of adding it as a prerequisite for another task; the new step will then be added to the process and the underlying dependency graph. As an instance executes, the completed tasks become fixed as history but the future tasks can change if there are changes to the tasks dependencies or participants. This methodology allows multiple stakeholders to define and change service descriptions without having a single process owner controlling the end-to-end process orchestration, and have new and in-flight processes generate the optimal path forward.

Automating Human-Centric Processes with Machine Learning, with Kris Verlaenen of Red Hat

Kris demonstrated working towards an automated process using machine learning (random forest model) in incremental small steps: first, augmenting data, then recommending the next step, and finally learning from what happened in order to potentially automate a task. The scenario was provisioning a new laptop inside an organization through their IT department, including approval, ordering and deployment to the employee. He started with the initial manual process for the first part of this – order by employee, quote provided by vendor, and approval by manager – and looked at  how ML could monitor this process over many execution instances, then start providing recommendations to the manager on whether to approve a purchase or not based on parameters such as the requester and the laptop brand. Very consistent history will result in high confidence levels of the recommendation, although more realistic history may have lower confidence levels; the manager can be presented with the confidence level and the parameters on which that was based along with the recommendation itself. In case management scenarios with dynamic task creation, the ML can also make recommendations about creating tasks at a certain stage, such as creating a new task to notify the legal department when the employee is in a certain country. Eventually, this can make recommendations about how to change the initial process/case model to encode that knowledge as new rules and activities, such as adding ad hoc tasks for the tasks that were being added manually, triggered based on new rules detected in the historical instances. Kris finished with the caveat that machine learning algorithms can be biased by the training data and may not learn the correct behavior; this is why they look at using ML to assist users before incorporating this learned behavior into the pre-defined process or case models.