The difference between a successful transformation and a poorly executed innovation could come down to what organizations do in order to gain a real understanding of their own processes.
Role Of Artificial Intelligence In Understanding Operations To Operational Insights
Understanding operations is not enough. You need operational insight. Understanding your business is more important than ever.
Organisations with a heavy operations component are constantly under pressure to improve efficiencies, and digital natives continue to challenge them. Leaders are left with the choice of re-inventing operations and betting on creating new advantages or maximising insights to improve current operations and betting on their abilities to compete on current advantages.
It’s not black and white but organisations are embracing technology and increasing their due diligence in order to ensure that transformation occurs in the right areas with the best possible intervention.
The difference between a successful transformation and a poorly executed innovation could be what organisations do to gain a real understanding of their own processes.
Understanding your organisation
Experts who are in touch with reality are at the heart of every organisation. By supplementing their expertise with automated and intelligent methods of extracting insights from operational data, experts can focus on solving problems instead of trying to prove why and how they are a problem.
Innovation is more than just building understanding. It’s about guiding improvements. A compass instead of a road map.
Process Intelligence is driving the step change. This technology uses system data in order to quickly build digital twins for operations and identify scenarios that lead to process variances and lost productivity. The process mining technology has been around for a few years. However, the use of AI/ML to optimise processes has allowed the correlation of events and their impact on business performance.
Consider the Finance department, where early payment to vendors can lead to a reduction in working capital. The traditional methods of understanding payments processes can be time-consuming and complex. Interviews with operators only cover a limited number of people and managers might not know all the scenarios. Models that correlate working capital with variances in invoicing processes can give organizations insight into conditions in which early payments occur and their impact on working cash. The most effective interventions can be designed using this information.
Embedding your organization
Businesses with business intelligence can use this capability to drill down on performance and pinpoint improvements. They can answer the question “What is happening?”. Then, using analytical engines, large volumes of operational information are processed to uncover inefficiencies and answer the question “why?”. This should lead to an investigation of why inefficiency exists and the implementation of new processes – “how can we fix it?”.
Process Intelligence Platforms are widely available and can fill this need. These platforms use event-based data from a standard data structure to generate visualizations and interactive analyses of processes. Vendors are primarily software companies that offer Process Intelligence and CRM/ERP providers who add Process Intelligence to their services. This makes it possible for organisations to find a solution of the right size.
Data engineering, AI/ML and seasoned operations experts are required to enable the ability of Process Intelligence, which is the ability to leverage common identifiers from multiple logs in order for a process to be reconstructed and visualized. These skills are scarce, but the ability to make more of data than your competitors is at stake. This makes it worthwhile to invest in them and integrate them into improvement initiatives.
Pulling the trigger
Process Intelligence is advantageous in three key areas:
Understanding systems and processes up front to guide the play. The speed of understanding that is gained by rapidly building a digital model of operations gives organisations an edge. Automated system crawlers are faster than workshops ordocumenting business process.
Monitoring the effectiveness of interventions and their uptake. As live system data are monitored, frequent and targeted corrections can take place while transformation takes place. A shorter feedback cycle reduces the risk of mediocre results after a sustained effort.
Incorporating insights into intelligent operational models. As air traffic controllers optimise flight paths digitally, operations leaders can use intelligence in process to monitor bottlenecks and simulate changes in operations to model their impact on performance. They can also make appropriate investments to intervene.
ERP transformations are a good example. The stakes are high and the success of these transformations is dependent on successfully navigating through massive organisational complexity in order to identify key value drivers.
The ability of an organisation to integrate Data and AI into operations in a way that is fit for purpose, and not overlook the complex nature of those operations on the ground, is a frontier of new advantage. While most organizations look for a road map, they need a compass to guide them in the right direction.
