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Ad hoc case management processes are like combining numerous easily recognizable tunes on top of each other to produce a cacophony of sounds, making it impossible to easily discern any apparent order or structure. In this chaotic scenario, process intelligence optimized for case management shines by analyzing the chaos, identifying the true hidden patterns, and distinguishing them from random variations.
In the world of business process optimization, separating the signal from the noise is a metaphor for the formidable task of distinguishing useful patterns from random variations in ad hoc case management processes (a.k.a. unstructured processes). This task is made even more daunting by the inherent variability and unpredictability of these processes in contrast to their more consistent, regular counterparts.
Process intelligence designed specifically to address the significant variability and related challenges of case management across its full range of capabilities—including process mining, task mining, process monitoring, process prediction, and process simulation—is the key to navigating and gaining understanding of this complexity.
Regular processes, like order-to-cash and procure-to-pay, follow a fixed order of operations, making them relatively easy to understand and optimize using more conventional business process mining tools. In these simpler process types, it's like identifying a clear, repetitive signal amidst a limited level of static noise. In other words, the important “signal” (our process pattern) comes through loud and clear, overpowering any background static.
However, ad hoc case management processes are more like combining numerous easily recognizable tunes on top of each other to produce a cacophony of sounds, making it impossible to easily discern any apparent order or structure. In this chaotic scenario, process intelligence optimized for case management shines by analyzing the chaos, identifying the true hidden patterns, and distinguishing them from random variations. This involves sifting through a mountain of data, tracing pathways across different systems, and making sense of the variations to provide a complete operational view.
For a Fortune 100 company, an order may start on a global e-commerce platform, move to Salesforce for customer management and sales processing and then to SAP for supply chain and inventory, and end with payment confirmation on a treasury management system. Each step involves different teams, data formats, naming conventions, and software tools, making the data highly variable and dynamic.
The chaotic nature of ad hoc case management processes is also the result of these process types being very dependent on the human element. These process types typically depend on knowledge workers who inject their decision logic and often personal intuition in the execution of each process instance. Process intelligence optimized for unstructured processes provides a purpose-built set of tools across the entire process intelligence continuum.
In addition to the challenges raised by process variability and dependence on multiple underlying technologies (systems of record), modern case management often involves multiple teams who are engaged for specific steps of the process. Process intelligence ensures that the journey remains clear and understandable for everyone involved and provides an overall context for the case being managed.
Just because a process is ad hoc does not mean there are not opportunities to identify improvements in how the process is performed. In fact, this is one of the most compelling drivers for these more advanced features being specialized for unstructured processes.
The ability to understand the various execution patterns for each process subtype provides analysts with actionable information on which to perform surgical improvements optimized for the unique characteristics and context that define that cohort. Extending the power of these insights to define and refine process monitoring logic closes the loop with continuous feedback on the effectiveness of each change.
One of the greatest benefits of being able to deeply understand context-based process variability is the ability to facilitate risk monitoring. By understanding the nuances of how and why any process subtype behaves, organizations can specify process compliance and conformance monitoring strategies to be automatically executed and monitored by the process intelligence platform.
By separating the signal from the noise, process intelligence helps businesses optimize operations, improve decision-making, and encourage a culture of continuous improvement in the chaotic world of ad hoc case management. Whether it’s for small businesses using platforms like Shopify and NetSuite or Fortune 100 giants using global e-commerce platforms, Salesforce, SAP, and treasury management systems, process intelligence is the foundation for informed, agile, and resilient business strategy and execution--even across highly variable process types.
In conclusion, while process intelligence is undoubtedly valuable for regular and consistent business activities, its true potential is unlocked in the chaotic and dynamic world of unstructured process scenarios arising from ad hoc case management use cases. Here, it plays a critical role in separating the signal from the noise, ultimately empowering organizations to optimize operations, improve decision-making, and encourage a culture of continuous improvement and risk mitigation.
Scott Opitz
Chief Technology Officer at ABBYY
Scott Opitz is Chief Technology Officer and responsible for the global product strategy and execution. Prior to this, he held the position of Chief Marketing Officer, driving global marketing strategies. He joined ABBYY with the acquisition of TimelinePI, for which he was co-founder, President, and CEO from its inception. In this role, he oversaw the integration of TimelinePI’s process intelligence products (now ABBYY Timeline) into ABBYY’s worldwide sales and distribution channels.
A 30-year veteran of the computer industry, Scott has founded and built companies in the application integration, business process management, and business intelligence spaces. Scott founded and served as President and Chief Executive Officer of Altosoft Corporation, a business intelligence software company that was ultimately acquired by Kofax. Following the acquisition of Altosoft, Scott served as Senior Vice President & General Manager for Analytics.
In previous positions, Scott served as Senior Vice President, Worldwide Marketing & Business Development for webMethods (now Software AG), where he was responsible for webMethods’ marketing, business development, and strategic product planning. He joined webMethods as a result of its acquisition of IntelliFrame Corporation, a provider of data integration products and the InVista integrated workflow and BPM platform that he co-founded. Scott has also held numerous executive-level positions in technology, marketing and business development roles for public and private companies.
