Shakespeare wrote that the course of true love never did run smooth. The same could be said of diving into a process improvement project without a plan.
Before jumping in head first with process mining to identify and fix a broken or inefficient process, start with a strong plan to avoid headaches for yourself and others. A written plan provides direction, while giving your stakeholders something to react to. They’ll see exactly what you want to do, and have the opportunity to make suggestions.
Include these five phases of process improvement in your plan:
We’ll define these phases below, and break them out into different elements. The poster below walks through these steps, or you can download a PDF version of the process improvement plan here.
A process improvement plan is a document that lays out the steps you’ll take as you approach improving a business process, complete with owners, timelines, and technologies.
As is true with any great plan, you have to start by identifying where you are and where you want to go. In the Define phase, begin by defining the problem, goal, stakeholders, and critical process output. As you work through this information-gathering stage, you’ll want to identify the invested parties and learn about their difficulties with the process. Find out what they’re trying to achieve and why, and where their roadblocks are.
1. Process selection.
In this step, decide what process you want to improve and why. Consider the following criteria:
2. Relevant stakeholders.
Ensure that all process participants are involved in the project. When it’s time to redesign your process, who will be involved? Develop a RACI (responsible, accountable, consulted, and informed) model for staffing your process improvement plan and identify these stakeholders:
3. Process KPIs.
Define the KPIs relevant for the analysis in your business domain to ensure you’re capturing the right data for analysis. While some KPIs are generic, like return on investment (ROI), you’ll likely want to get more specific with metrics that are relevant to the department. Here are a few common process KPIs to evaluate:
4. Process-related questions.
Specify the question or questions to be answered in the process analysis.
5. Creation of a target model.
Determine whether a target model of the process already exists.
The target model can be used as the basis of comparison once your actual process has been mapped.
In the Prepare phase, you'll need to extract and transform the data for analysis.
6. Data and data sources.
Check which process data is available and ready for use. Where is it located? How can it be used? Consider the following:
7. Data extraction and transformation.
Next, you’ll need to ensure data is in the correct format. For analysis to work, data must be prepared as event logs. Check whether the log files contain at least the following: case ID, activity name, start and end times. Including additional attributes is optional. Mining Prep, a module of Appian Process Mining, simplifies data preparation with a no-code tool for extracting and transforming.
Using data science and machine learning, process mining analyzes system log files to create clear and concise maps of your processes. This replaces subjective and costly interviews with facts—a far better way to document how work happens. The data-driven approach provides insight into what people, systems, and organizations are actually doing, as opposed to what you think they’re doing.
8. Data import and processing.
Now you’re ready for analysis.
In the Analyze phase, you can start to pinpoint problem areas in your process. Use the discovered model that process mining created for you in Step 8 to highlight optimization potential. This visual helps you quickly identify inefficiencies, like bottlenecks, rework, and other activities that stall progress.
9. Model enhancement.
Model enhancement is where you dig into how your actual process works by analyzing your discovered model. Analysis reveals details about the following factors:
10. Conformance checking.
In conformance checking, the discovered model is compared with the target model (Step 5 or Step 8) to detect deviations and check process conformity. For example, you may find that users are skipping or duplicating process activities or executing unplanned activities that pose compliance risk.
11. Root cause analysis.
Root cause analysis helps you better understand process deviations. Use it to identify problematic attributes, patterns in deviations, and indicators for effective optimization. In Appian Process Mining, you can use root cause analysis to understand the why behind process bottlenecks and inefficiencies, so you can build new workflows with confidence.
12. KPI analysis.
Evaluate your process data using easy-to-understand dashboards that provide visualizations of the relevant KPIs (Step 3). You’ll see where the process is falling behind, and learn where to focus your efforts.
In the Improve phase, you’re finally ready to develop and implement solutions. Building your new process on a unified, enterprise-grade, low-code automation platform will help you move quickly and efficiently. Optimize workflows with AI-powered intelligent document processing (IDP) to quickly process documents for teams; use business rules to continually replicate the right procedures; and implement robotic process automation (RPA) to save employees time on repetitive, high-volume tasks.
13. Addressing the issues.
Based on your findings, determine which process improvement/optimization measure best suits your needs. Examples include:
14. Solution implementation.
Work with the team and all relevant stakeholders (Step 2) to put your solutions into practice. Remember to give each invested party a chance to review, suggest changes, and buy in before you begin. Provide the “what’s in it for me” angle for each stakeholder. What about this new process will help them reach their goals faster or more effectively than before?
15. New target model.
Replace your earlier target model (Step 5 or Step 8) with the current, optimized process. This will be your new target model and serve as the basis for comparison during future optimization efforts.
In the Monitor phase, it’s time to test solutions for long-term usability. This is the point in your process improvement plan where you gather and analyze data on the new process, so you can assess how it’s performing. Keep in mind that an effective process improvement plan doesn’t stop with the first iteration. Processes require continuous monitoring for the most return on investment. Process mining makes it easy to run the same analysis on your revised process again and again, to root out any new issues that arise.
16. Measuring success.
Gauge the success of your optimization efforts by revisiting the questions asked in Step 4:
17. Evolution of the new process.
To re-evaluate your newly optimized process, follow these steps:
Process improvement is a journey that can add tremendous value to all areas of an organization. But where you start, and how well organized you are in those beginning phases can have a big impact on how successful your initiatives are over time. The key is to “think big, start small, and iterate.” This means selecting an initial project that can be delivered quickly, but has a high impact on the business; promoting early successes; and pushing the continuous improvement mindset, along with process mining best practices, out to other areas of the business.
After all, the course of true love may never run smooth, but who says your business processes have to follow the same path? With a solid plan in place, you’ll set your process improvement project—and your business—up for success.
[ Looking for more information about process mining? Check out our Process Mining Guide. ]