Home How Process Mining Helps Pass the Eight-step Pathway to Operational Excellence

How Process Mining Helps Pass the Eight-step Pathway to Operational Excellence

by sol-admin
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Process mining enhances traditional approaches to operational excellence by automating the critical step of process discovery. By definition, process mining is a family of data-driven techniques to analyze business processes using event data extracted from information systems. It allows business users to identify bottlenecks, deviations, and sources of waste or rework in their operations. With the discovered models as a foundation, the pathway to improvement becomes far more straightforward, more accurate, mitigates risks, and is significantly faster. Here’s how business leaders can lay the groundwork for automating process discovery and continuous improvement through an eight-step pathway to operational excellence.

Step 1:
Stop Doing Non-Value-Added Work

The discovered process model makes it easier to identify bottlenecks, rework, and areas where processes are stalled and makes it easier to quantify the benefits. By analyzing the difference between best practices and existing (as-is) models in a delta analysis, business decision-makers can explore opportunities to eliminate waste, reduce risk and improve operational efficiency and customer experience.

Step 2: Consolidation

Process mining’s variant analysis capabilities let users compare different paths or workflows of the same process. With this perspective, business, operations, and risk professionals can focus on the differences and their impact. This highlights opportunities for consolidation and the benefits and risks that may be involved.

Step 3: Standardization

Comparing process variants via process mining to understand pathways, gain knowledge of activity execution times and wait times, spot bottlenecks, exceptions, resource usage, and other processes quickly highlight where the opportunities to standardize lie.

Step 4:
Simplification

The visibility, transparency, and availability of process models discovered by process mining with an accurate representation of the flow and real process metrics allow analysts to identify the waste quickly. Transaction types that slow processes, duplicate controls, non-value-adding steps, broken or slow handoffs, rework loops, and exceptions can readily be identified. Removing this waste reduces cost and improves quality, delivering a better outcome to customers faster.

Step 5: Business Process Reengineering

Understand hidden tasks and identify poor handovers, rework, underutilized or overutilized resources, to make data-informed process improvement decisions. Identify highly repetitive, manual, and error-prone routines for automation. Process mining can discover the root causes of common delays in supply chains or workflows that impact delivery times and quality, thus leading to revenue loss.

Step 6: Tactical Automation

Identifying the precise steps in the process where automation can make the biggest impact requires a thorough knowledge of task time, resource utilization, and resource costs. Process modeling using process mining can help prioritize tactical automation efforts. It also ensures that time is not wasted on automating parts of the process that may not deliver a return on investment. Process mining enhances the traditional approach: automation opportunities, at the task or process level, can be identified via process mining, and the impact of their automation can be assessed quantitatively, based on complex data.

Step 7: Outsourcing/Offshoring/Right shoring

Running an automated process discovery exercise before transition ensures both sending and receiving teams are on the same page. There is a complete understanding of all exceptions and the resourcing calculations that sit behind them.

Step 8: Transformation

Process mining brings much-needed transparency to how an organization works and therefore allows transformation leaders to understand where to focus, what to prioritize, and how to transform key processes for sustained operational excellence. It’s imperative to understand “As-Is” process flows, understand where bottlenecks occur and which activities consume the highest amount of effort, and test and predict “To-Be” process models and changes before after the transformation. It is crucial to establish a clear vision of the changes required, their possible outcomes, and the customers’ reaction to them.


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