Role of Advanced Analytics in Process Intelligence

In the latest SAPinsider process automation state of the market research, majority of SAPinsiders indicated that intelligent automation and process intelligence are key capabilities they are planning to build within next two years. Advanced analytics methodologies will play a key role in building intelligent automation solutions. In this article, we will explore few high level examples of how ML algorithms can help transform business processes through intelligent automation.

Examples of Advanced Analytics Transforming Processes

Let us explore how advanced analytics categories can be applied in process transformation: Descriptive analytics: Descriptive analytics methodologies have been used for process management for decades in the form of Six Sigma methodologies. These are still extremely useful in process intelligence domain and can help in aspects like process control, measurement and monitoring. Statistical methods are the primary type of methodologies leveraged in this category. Prescriptive analytics: Prescriptive analytics methodologies like optimization and simulation can help design and optimize processes, thereby reducing process cycle time, creating efficient process components and reduce operating cost. Optimization algorithms like linear programming, integer and non-linear programming are primarily leveraged, along with simulation methods like discreet event, agent based and system dynamics. Prescriptive analytics: This is the most advanced category and also the one that can help create differentiating capabilities. AI based algorithms can be leveraged in this type of analytics. These algorithms can help predict process failures, before the failure happens (unlike descriptive analytics). They can also help predict what the end result may be, based on how the process is behaving at a certain time. All these aspects can be leveraged to automate certain decisions as well, in order to keep the process optimal. Primary category of prescriptive analytics algorithms used in the context of process intelligence are classification, regression and clustering. Deep learning can also be used, specifically in context where computer vision needs to play a role (Example: detecting quality issues in finished products).

What Does This Mean for SAPinsiders?

As you embark on leveraging advanced analytics in your process intelligence capabilities, there are certain aspects you need to keep in perspective: Explore more advanced automation technologies. RPA augmented with AI technologies stretch the realm of the possible in process automation. AI-augmented process automation, popularly known as intelligent automation, creates the opportunity to reimagine your business processes. Intelligent automation can help you interweave your people, processes, and technology like never before. Cognitive automation that comes embedded within many RPA platforms helps with basic decision making in a human-machine collaboration context. Build process monitoring and measurement capabilities. Process measurement and benchmarking are critical not only for measuring the impact of process automation but also for continuous improvement of processes. Develop a robust set of measures instead of using what was already being used or is being used by your industry peers. When possible, leverage more advanced process control methods like multivariate statistical process control (MSPC) for controlling your processes and ML-based predictive algorithms for predicting anomalies or failures within your process. All this can only be built if you have seamless access to the data being generated by your processes. Build specialized reskilling training for your employees. Eliminating headcount should not be the central focus of a process automation initiative. While process automation does generate cost efficiencies, people working in these processes have accumulated expert knowledge that can be harnessed in many fruitful ways. This expert knowledge can then be inculcated into automated processes to create true human-machine symbiosis in the digital age. While involving those who will remain working within the processes early in the initiative is critical, equally important is the need to train them on how to best leverage these tools. Automation pilots play a critical role in evaluating any training needs for optimal usage