The growing demand of businesses has led organizations to struggle hard to leave their mark in today’s increasing competition, and processes have become more complicated with time. The complication can also result from new events occurring, and the businesses might be unaware of them. Hence, the business process discovery tools and techniques are essential for understanding the as-is pictures and spreading awareness about the process deviations.
What is Process Discovery?
Using different tools, techniques, and methods to discover how organizations’ processes are executed is known as process discovery. With the help of process discovery, businesses can fully understand the processes, discover the process steps that they’re not aware of, and understand process deviations. visit here
How To Discover Processes?
Companies and organizations need to benefit from the raw event logs and drag applicable data to showcase their actual processes. These can be connected to various events to develop process models through cause-and-effect, including deviations. Below are some steps followed by process discovery:
- Extract data: The performance metrics and event logs are gathered from the brand’s enterprise and the departmental business process discovery software systems.
- Process and map events: It gather data that is being analyzed and mapped for every case from the event logs. The process deviations become prominent here, and the variations mostly happen due to the manual changes or the errors in the Process.
- Combine events to make ‘as-is’ Process: The collected process maps are being shared and visualized to analyze who, when, what, and where of each process variation has the related subprocesses.
Essential Prerequisites for Process Discovery
Data availability is the essential prerequisite for process discovery. The data required for process discovery includes identifying the data point, name of the activities or the events in a process, time stamps for starting and ending times of the activities, & any number of other Process attributes depending on the availability of data. Data transformation must be carried out correctly if the data is available but not accurate or not in the correct form.
Objectives of Process Discovery
The objectives of the discovery process include:
- To Reduce Sales Cycle Time. Process discovery aims to acknowledge the underlying issues and resolve them ASAP by reducing sales cycle time. It also helps analyze the automated sales cycles to ensure that resources make well-informed decisions and are of utmost use to boost each Process. And enhancing the crucial metric adds to a company’s revenue, mainly for B2B companies.
- To Foster Faster Delivery. The primary objective of Process Discovery is always to enhance customer satisfaction and avoid late deliveries. By considering automated processes with high service ticket volumes, the variety of process delivery tools classifies inefficiencies in the workflow to avoid delivery delays, wastage of time and enhanced faster delivery.
- To Increase Transparency. Process discovery tools and techniques aspire to help companies increase the transparency of their full-service desk, resulting in optimized customer support inexpensively.
- To Analyze Accurately. One of the other key objectives is to consistently get accurate results and statistics of the team’s activities in real-time.
- To Reduce Cost. Product Discovery’s advanced tools help internalize productivity measurement tasks and reduce outsourcing expenditure and time for better results.
- To Accelerate & Simplify the Process: It aims to reduce manual labor load and curtail the time needed for performance analysis.
Methods of Process Discovery
The methods & techniques of process discovery allow businesses to learn the underlying construction of the existing processes with the already available process information like data logs, employee insights, and other documentation. It also helps decompose the business processes to detect friction and discrepancies. Various process discovery methods and algorithms have been developed over the years, such as:
- α-algorithm – This process discovery algorithm deals with concurrence by arising relations between the activities that occurred in an event log. The α-algorithm also offers the basis for many other process discovery methods.
- Heuristic mining– This is reused as a representation like causal nets. Heuristic mining also uses the frequencies of events and sequences while crafting a process model. The basic idea is that occasional paths shouldn’t be combined into the model to avoid uncertainty.
- Genetic process mining – The α-algorithm and methods for heuristic and fuzzy mining offer process models in a direct and a deterministic manner. Genetic algorithms are search techniques that imitate biological systems’ natural Process of evolution. These approaches are not deterministic and depend on randomization to find a new substitute.
- Region-Based mining – Concerning Petri nets, most researchers have been searching at the so-called synthesis issue, i.e., constructing a system model from a description of its behavior. State-based regions can be used to develop a Petri net from a transition system. The language-based region techniques use algebraic constraints modeled from the event log to find the places that allow the behaviors observed in the event log.
- Inductive miner- Various inductive process discovery methods exist for processing trees, ensuring soundness from construction. Hence, the inductive mining framework is highly extendible and permits many variants of the basic approach. It is considered one of the foremost process discovery approaches due to its flexibility, formal guarantees, and scalability.
Conclusion
Enterprises or companies worldwide are undergoing a drastic digital transformation. They are adopting automation to enrich business processes and drive better results. AI-based process discovery tools help them choose effective practices to automate and maximize its value. They can record piles of human interactions and provide real-time inputs. Indeed, process discovery is a vital aspect of success in process discovery RPA. The process discovery methods and tools handle many manual measurement issues regarding efficiency and the team. Additionally, the platform provides state-of-the-art analytics tools to examine team performance and identify which Process to restructure for better productivity.