5 steps to process intelligence for operational excellence

Process intelligence uses advanced analytics to assess business processes and workflows to identify bottlenecks, inefficiencies and areas for improvement. It roadmaps how processes are executed by collecting, analyzing and interpreting data from multiple applications across various functions. The aim is to pinpoint where and how advancements can be made to expand operational excellence.

“Achieving operational excellence through process intelligence projects presents a formidable challenge, with every step playing a pivotal role in the project’s success,” Nikki Webb, global channel manager at Custodian360, tells PEX Network. Methodologies and technology are both integral to successful efforts eliminating waste, enhancing efficiency, automating tasks and improving accuracy, she adds.

However, both must be complemented by a culture that values continuous improvement and employee engagement in these initiatives. Regular review and adaptation of processes, aligned with evolving business goals, are essential in this ever-changing business landscape, Webb says. “It is the meticulous execution of each phase that collectively determines the fate of a process intelligence project. Every step is a building block, essential in its own right, and overlooking any can jeopardize the entire structure’s integrity.”

Here are five steps to process intelligence for operational excellence in an organization.

1.     Build a foundation

First, businesses must create a foundation upon which to construct digital transformation and business process automation. “This initial stage is about more than just laying the groundwork, it is about building something that the entire project will be built on,” Webb says. This phase is characterized by the articulation of clear, achievable goals and the strategic alignment of the project with the broader ambitions of the organization. “This foundational stage also acts as a compass, offering direction and facilitating informed decision-making throughout the project’s lifespan. It’s a time for judicious allocation of resources, selection of appropriate tools and concentration on areas where the greatest organizational impact can be realized,” Webb explains.

Large volumes of legacy, decentralized and complex systems are significant hurdles to overcome in this stage, potentially requiring organizations to engage with specialized services and tools to effectively map out and analyze all processes. Once cross-functional processes are mapped and the prioritization of digital transformation projects is in place, further analysis can then identify and re-engineer bottlenecks.

2.     Gather the right data

Process intelligence is essentially a data exercise, and gathering the right data is critical for success. The identification and management of data points is one the biggest challenges of process intelligence, requiring resources to find data and manipulate it for successful ingestion.

Democratizing data to enhance decision-making, consolidating data into a data lake for users to explore and promoting data literacy to help people understand what they can accomplish via process intelligence efforts can be beneficial strategies to implement.

3.     Engage the right stakeholders

Collecting the right data is key, but so is engaging with the right stakeholders from the beginning of any process intelligence project. A lack of proper buy-in can significantly hinder efforts at various stages of the process. Engagement with stakeholders is not merely beneficial, but imperative, Webb says. “Their input and grasp of the shared vision foster endorsement and backing, propelling the project past the formidable barrier of resistance to change.”

Opt for a “bottom-up” approach that collaborates with frontline employees and subject matter experts to understand pain points and challenges. Also, regularly ask users for feedback to ensure the continued accuracy and relevance of the insights you share. Projects that have an alignment of business goals and a clear understanding of how improvement is defined are more likely to be successful.

4.     Look to automation

The emergence and growth of innovative AI and machine learning are impacting all business matters, and process intelligence is no exception. Organizations are starting to use this technology within process intelligence to not only understand process but generate predictive insights. Autonomous technology can predict how changes might impact the customer experience and cash flows, help turn insight into action and provide contextual guidance on next steps. The opportunities in this area are significant, and such insight can prove invaluable to process intelligence endeavors. As AI and machine learning mature (cue generative AI) its potential to aid process intelligence will only increase, improving the speed and accuracy of projects.

5.     Don’t forget cost-benefit analysis

Finally, while the potential of technology such as intelligent automation and process mining for process intelligence is undeniable, cost-benefit analysis is key to ensuring that the right investments are made for specific needs. For example, automation works best on high volume, repeatable processes, but doesn’t deliver the same ROI for processes that are low volume or unstructured. Simple process reengineering, training, custom development or redesigning a process with an AI solution can be considered instead of or as well as more sophisticated technology and tools.

Learn more about optimizing complex, end-to-end business process with process intelligence by downloading PEX Network’s Process Intelligence for Operational Excellence: 2024 Industry Report