Every problem brings an opportunity with it. The current times of COVID-19 pandemic are no exception to this. However, as far as organizations are concerned, it is a different challenge. If they act reckless, they can get washed away under the likely economic recession. To the contrary, if they act too strict, they might end up cutting more costs than required(not good for the economy, since that creates a serious demand problem). Hence, they need to strike the golden mean between the two extremes. This is where process management comes to rescue.
What is process efficiency?
The journey of process management starts with analysing and fixing inefficient processes. However, firstly, let us define ‘Process Efficiency’
Processes are designed to achieve a given objective. However, though effective in achieving results, they might not always be the most efficient. In simple words, they use up more resources as opposed to the output produced. These inefficiencies are under a scanner in a crisis and thus, process mining becomes the magnifying lens in this case.
Process Mining and Process Analytics
Process mining is the application of data mining in the field of process management. It helps us identify trends, patterns and details in event logs generated by processes, thus helping performance analysis of the same. These logs help us visualize the performance of the computer-mediated work in terms of time taken for a task, deviation from the mean and who performed it.
Furthermore, there are specialized software offering that helps you implement process mining. According to the Harvard Business Review, process mining software can help organizations easily capture information from enterprise transaction systems and provides detailed — and data-driven — information about how key processes are performing.
Along with process mining, process analytics comes into play. Process Analytics includes creating key performance indicators (KPI’s) to monitor the state of processes. Additionally, AI algorithms can unearth important trends and deviations in the processes using techniques like anomaly detection.
Also read: An Introduction to Azure IoT with Machine Learning
Nonetheless, process mining and process analytics can be applied effectively if the systems are fully/partially digitized. These techniques may give valuable insights into the inefficiencies caused by manual processes (partially digitized systems), thus opening avenues for automation. This brings us to the next big technology trend i.e. Robotic Process Automation (RPA).
RPA leverages a variety of IT infrastructure along with AI to automate manual tasks. A classic example of this is customer email query processing, where the software can identify and respond to the most common types of queries using Machine Learning. Another example could be the processing of claims in insurance firms, which usually takes a lot of manual efforts.
The human element in process management
However, coming back to process efficiency, it is measured by the amount of output produced by a process against the resources spent on it, including human resources. We need to remember that not every organization has reached the digital maturity, where all the processes can be fully automated. There will be a significant amount of human intervention involved. Thus, along with process mining and analytics, assessing the performance of the people involved becomes critical. This brings us to an important element of process management i.e. ‘Task mining’.
Task mining is the technology that helps us collect and monitor user data involved between the process steps. For instance, the time spent by recruiters on LinkedIn vs the number of profiles shortlisted could give a picture of the employee efficiency. This helps the supervisors to compare employee performances. Eventually, it helps both the employers and the employees in assessing the true effectiveness of the process.