In recent years, digital transformation has become an increasingly common trend across the globe. It has gained significant importance due to the rapid advancement of technology and the increasing adoption of digital solutions by customers and businesses.
Digital transformation often involves a range of technology initiatives to drive business growth and innovation. Looking at the trends across the industry, some of the common initiatives driven by companies as part of their digital transformation efforts include:
- Cloud adoption
- Machine Learning and Artificial Intelligence
- Internet of Things (IOT)
- Robotic Process Automation
- Big Data Analytics
While these are just a few examples of the key initiatives that companies are driving as part of their digital transformation efforts, an up and coming technology that has gained a lot of attention in recent years has been “Process Mining”.
What is process mining?
Process mining is a powerful tool that combines the power of technologies such as data science, big data analytics, machine learning and process engineering. It is a data-driven approach that provides businesses and large-scale enterprises with the capability to map their process (an X-Ray for the business), analyse it and identify opportunities for improvement.
The first step in process mining typically involves collecting digital footprints and data across various ERP, CRM, or BPM systems. This data is transformed into event logs using unique identifiers and timestamps to create a digital process map which can then be analysed using process mining.
Process mining can be broadly classified into three main areas: process discovery, process conformance, and process intelligence:
- Process discovery involves the creation of process models from the event logs. This helps businesses use data from the different systems involved to visualise their processes “as executed” and identify bottlenecks, inefficiencies, and variations in these processes.
- Process conformance builds on the capabilities of process discovery and enables organisations to compare the actual or “as executed” processes against the expected or “as designed” processes based on the process models. These variations can be caused by different factors, such as user preferences or system limitations. With process conformance, deviations from the expected processes and their corresponding root causes can be identified.
- Process intelligence enables companies to incorporate proactive capabilities into their existing processes through process automation and data science. One such example is the use of process mining in combination with data science and machine learning models to help businesses to proactively identify and avoid potential delays and bottlenecks in their processes based on historical trend.
Real-life use cases
Process mining has benefited companies, helping them unleash the power of digital transformation to drive significant improvements across their businesses. For example, at Dell Technologies, a Machine Learning (ML) based digital service solution called Predictive Case Intelligence (PCI) was built leveraging the power of process mining to reduce resolution cycle time.
An innovative aspect of the solution was that it leveraged process mining to analyse patterns, trends and interconnections between process steps or digital footprints captured during the customer journey and used as inputs in the ML model. Through PCI, Dell achieved a 10% reduction in cycle time and improved customer satisfaction by 130 basis points. Similarly, ABB has leveraged process mining to monitor their Order to Cash, Purchase to Pay and complaint to resolution processes.
With operations in over 100 countries and spanning several ERP systems, process mining has helped ABB monitor millions of transactions across the globe and derive unique insights that have helped improve their existing processes. A few other notable examples involve the use of process mining by Comcast to save $85 million, 800% improvement in touchless orders for L’Oréal, and reduced British Telecom’s customer service cycle times by 60%.
Conclusion
Process mining is proving to be a simple yet powerful tool for analysing and improving business processes. By using process mining techniques, organisations can gain valuable insights into their processes, identify inefficiencies and bottlenecks, and make data-driven decisions to improve their operations.
Further, the availability of large amounts of data, advancements in RPA, data science and machine learning has resulted in the rapid evolution of process mining, making it an increasingly important tool for optimising processes, reducing costs, and improving customer experiences. It is no doubt that organisations that invest in process mining today will be well-positioned to stay ahead of the curve and thrive in the years to come.