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Person-Years of Labor Saved Through Process Mining


Large organizations often face difficulty managing internal processes and master data as a result of the scale at which data needs to be maintained. Without strict controls in place, it is easy for data to become stale - often as a result of data entry errors or changes that happen within an organization outside of systems. This bad data can cause real issues in labor productivity, cash flow, supply chain planning, and so many other areas of a business.

Person-Years of Labor Saved Through Process Mining





Our client had difficulty managing data within contracts they maintained across their vendor base. Given the large size of the organization and the complex and vast array of vendors with which they maintained relationships, bad data and rule violations were a constant thorn in the side of Procurement Operations. Often an error in data entry or contract creation could go unnoticed for months until goods needed to be shipped from a vendor. The inability to identify these errors caused large amounts of rework and inefficiency in the process.


To identify issues, we developed a custom data model within Celonis - a process mining execution management system. After ingesting contract data and developing the appropriate data model, rules were created in conjunction with the procurement teams to proactively identify errors in contract data. Once identified, actions were conducted leveraging Celonis' native automation capabilities so the Procurement Operations team could correct errors before process issues surfaced.


More than 15 customized rules were built to address various categories of data errors. As a result of the rule creation and automation development, the procurement operations team was able to correct over 90,000 system errors in less than 12 months. These errors reduced the amount of unnecessary rework by an estimated 7,500 hours. Unnecessary contracts were reduced by around 30% resulting in better system efficiency and easier contract maintenance.

Key Takeaways

  1. Visibility is essential: Before this implementation, the operational teams knew that issues existed but had no systematic method to correct these issues. Part of the problem was the ability to see the data at scale. With increased visibility comes increased action and awareness across the organization.

  2. Automate where possible: Automating can be crucial to realizing value. Using Celonis and process mining, the collection and transformation of data can easily be automated in real time, allowing for the ability to provide insights readily. Furthermore, native automation capabilities allowed the operational teams to quickly identify and action issues by providing them with a prioritized list of tasks regularly.

  3. Work with the business to understand their problems and coordinate a solution: This project was a success only by listening to the problems faced by the business and working together to develop a solution. Leveraging internal expertise - mainly when building customized data models - is crucial. Furthermore, enabling internal staff with the technology implemented makes adoption and value realization much quicker and easier.

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