Unlocking Business Efficiency with Process Mining
This article aims to demystify Process Mining and its significant benefits for businesses, particularly optimizing internal operations.
In the realm of business, inefficiency spells missed profit opportunities. Complex procedures can lead to time loss and customer dissatisfaction, while the burden of manual, routine tasks wears down employees and increases the likelihood of errors. The advent of Process Mining technology offers a solution, pinpointing problem areas within business processes and suggesting potential improvements.
What is Process Mining?
At its core, Process Mining reconstructs models of real-world business processes based on data from information systems. This technology illuminates bottlenecks and enables the transformation and optimization of team workflows. It is especially valuable in dealing with large-scale and intricate processes that span multiple departments and company branches.
The Essence of Process Mining
Process Mining relies heavily on data analysis. Smooth project execution requires a phased approach and consideration of specific nuances. A fundamental prerequisite for Process Mining systems is the digitalization of the process under analysis. Processes documented on paper or through informal memos are not suitable candidates. Process stages should be recorded electronically, preferably within interconnected software solutions, allowing the system to collect and analyze log data, identify discrepancies, and suggest areas for improvement.
Methodology Nuances
Perfect conditions for implementing Process Mining are rare, but that shouldn't deter efforts. It's feasible to begin with available resources, analyzing portions of the process or overlooking stages yet to be digitalized. Gaps in the process are not detrimental; they simply manifest as extended durations between stages, which can be adjusted for in subsequent analyses.
Process Mining's flexibility allows examination of both small-scale activities and comprehensive, end-to-end processes. Typically, a company recognizes specific areas needing close inspection. It's advisable to initially focus on the segment deemed most critical for optimization. This involves defining the process boundaries, determining the process KPIs, outlining the steps of the process model (based on available data), and selecting metrics for analysis.
Data Sources and Extraction
Data sources for Process Mining can vary widely, including corporate systems (e.g., SAP, Salesforce), databases (e.g., PostgreSQL, MySQL), file storage solutions, event logs generated from emails or Excel files, among others. Data need not be centralized; it can reside across different databases and corporate systems.
Data retrieval can be accomplished through several means:
Utilizing connectors, both pre-existing and custom-developed for the project.
Direct database connections.
Generating exports in Excel or CSV format for manual or automated upload, via FTP or API.
Data Management
In the digital age, where data reigns supreme, having a robust amount of data can significantly simplify setting metrics, validating numerous hypotheses, and ultimately achieving a more pronounced improvement from even the smallest tweak in a process. Particularly beneficial is the analysis of various process attributes and event chains.
A data snapshot covering 1.5 years is generally sufficient; however, there can be instances of data overload, leading to unnecessary expenditures on licenses and server capabilities, which are advisable to avoid. For mass processes that are repetitive in nature, data from a shorter span, such as a month or even a week, can be just as insightful. Analytics and insights gained from this timeframe can be extrapolated to the entire process, facilitating continuous improvement.
The first step should culminate in a project document that outlines the process boundaries, project KPIs, steps, and metrics of the process under study, information sources, and data processing algorithms.
For Process Mining to be effective, data must include:
A process instance identifier: a unique number linking all subsequent steps, which could be a purchase order, contract, application, or customer service ticket.
The name of the step, which can be represented by a status, department, or a combination thereof, essentially any action occurring within the process.
A timestamp is almost always necessary. Without it, the sequence of stages is based solely on transitions between them, requiring event logs to be sorted in the correct order. The absence of timestamps is typical in processes that record the occurrence of an event but not its timing.
While analytics can be built on these three indicators, it's advisable to load additional attributes or characteristics, such as performers, branches, regions, different types and features, or even amounts. These help create graphs in conjunction with the process and uncover more hypotheses for improvement. However, it's important not to overload Process Mining systems with the company's entire database or too many additional attributes. Determining the priority process for analytics and the sufficiency of data is crucial at the project formation stage.
Deploying a Process Mining platform can be done in-house or on a cloud provider's infrastructure. In-house deployment can be time-consuming in larger organizations due to the lengthy process of allocating computing resources requiring multiple approvals. However, in certain industries like finance, any data processing must be conducted on internal servers. Cloud deployment offers a quicker setup, making it the preferred choice for more agile companies.
By the end of the second stage, the Process Mining platform is ready for action, and the examination of the selected process can commence.
Reconstructing the Process Model
The Process Mining system gathers logs from the event journal, reconstructing the actual model as a sequence of actions. Engaging employees interested in process improvement (the business clients of the project) at this stage is crucial. The system's analysis results need to be verified.
Accurately capturing the process model doesn't always happen on the first try. If the analysis results are doubtful, this stage involves data model correction, including refining the interpretation of events in information systems, modifying the composition of steps and process metrics. Issues might lie in the data extraction and transformation procedure, which would also require adjustments.
For instance, some process steps might occur in parallel. It's essential to pre-sort them in the correct order by assigning a weight to each step to avoid variability in event chains. This highlights the importance of precisely defining the step composition and sequence at the outset.
The outcome of this stage is the restoration of the real process model: what was previously scattered across disparate systems is now visually represented in a single, coherent map. This integration offers a clearer understanding and a solid foundation for continuous process improvement, making it a critical endeavor for IT startups focused on streamlining operations for competitive advantage.
Diving Deep into Process Analysis
Once the process model has been reconstructed, it becomes the focal point of thorough examination by analysts. For companies that possess a benchmark model, the results are juxtaposed against this standard.
At this stage, the reasons behind any KPI deterioration are identified. Analysts pinpoint deviations from standard procedures, delays in operation execution, or instances where processes had to revert to previous steps. These insights form the basis for potential optimization efforts.
Hypotheses for process improvement are formulated here, alongside calculations of optimization potential—estimating the time and resources that could be saved by eliminating identified barriers. The skillset of analysts and methodologists plays a crucial role in interpreting the model, distinguishing between normative process transitions and those ripe for enhancement, and envisioning viable solutions to issues.
For instance, consider a scenario where a bank claims a loan application takes 15 minutes to process, yet in reality, customers wait up to 45 minutes, with some not completing the process at all. Identifying the root cause is imperative, whether it be system malfunctions, service performance issues, internal human errors, or customer-related problems. An example solution could be automating data entry with a dropdown menu to minimize errors, thereby streamlining the application process.
The primary focus at this juncture is to address the client's initial concerns and work towards resolving them, aiming to uncover and propose remedies for problem areas and calculate the impact of potential improvements.
The Implementation Phase
In the final stage, contractors present a comprehensive demonstration of the project, showcasing the benefits of the recommended enhancements. Subsequently, each company decides whether to conclude the project or integrate process analytics into their daily operations.
Setting up integrations, either in the project's intermediary stages or upon its conclusion, grants the capability to manage indicators daily and monitor key KPIs effectively. This facilitates informed decision-making based on data insights. Reporting access can be extended beyond top management and analysts to all company employees, fostering improved interdepartmental cooperation.
To expedite the adoption of new processes, companies can establish an internal center of competence and analytics specializing in Process Mining.
Initially, the team learns to navigate the system, select crucial metrics, perform integrations, and generate reports. Eventually, they independently explore data and formulate hypotheses using process analytics systems and platforms. Training can commence alongside the project, ensuring that by its conclusion, specialists are well-equipped with the necessary skills.
Implementing and configuring a Process Mining platform is straightforward, thanks to its built-in integration and customization capabilities. With vendor support and a technical assistance service, following the outlined steps, this journey can be completed in five phases, transforming process optimization from a daunting task into a manageable and rewarding endeavor for IT startups.
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