While new Artificial Intelligence (AI) technologies gain traction in the workplace, there seems to be more buzz around these newer advances, including Robotic Process Automation (RPA), than more established process improvement techniques such as Lean Six Sigma. This praxis research uses Lean Six Sigma as a framework for effectively deploying these emerging technologies, a challenge for 86% of companies (Ernst & Young, 2021). This research is applied to one of the legal industry’s most resource intensive processes – eDiscovery in the environment of a Big 4 accounting firm that provides services to corporations and legal professionals alike.
Electronic discovery (also known as e-discovery, ediscovery, eDiscovery, or e-Discovery) is the process of identifying, collecting, producing, and presenting electronically stored information (ESI) in response to a request for production in a law suit or investigation. ESI can include any type of electronically stored file and commonly includes emails, documents, databases, media files, social media, and web sites. The lifecycle of eDiscovery has been defined by the Electronic Discovery Reference Model (EDRM) as having the following phases: Information Governance, Identification, Preservation. Collection, Processing, Review, Analysis, Production, and Presentation. To move through the phases of the EDRM historically requires a significant investment in time, technology, and human resources.
This project had its origins as an automation effort driven by the technical advances in RPA solutions. However, RPA became a tool for to enable the program – not the solution itself. The DMAIC framework (Define, Measure, Analyze, Improve, Control) of Lean Six Sigma laid the foundation for a more wholistic analysis of the EDRM including the identification of processes that required revision prior to their automation. The Define phase identified the resource intensive strain moving through the EDRM causes corporations, vendors, and litigators. Through the measure phase, an opportunity to provide better results faster, and therefore cheaper was quickly identified. Through the analysis, several unnecessary handoffs, extraneous processes, and general bottlenecks in the process were refined. Through the Improve phase, automation played a significant part in realizing the efficiencies identified in the analyze phase. Finally, the controls phase not only put these improved processes into place but also quantified the value of ensuring these procedures were thoroughly deployed.
This research is organized using the DMAIC framework to articulate the process for completing the research, the gains and efficiencies made throughout the analysis, and to measure the impact and success of the overall program enhancements.
The impact of this project is measurable not only in the reduction of defects as defined by Lean Six Sigma, but also a significant improvement in time required to complete these processes. Even more satisfying, these efficiencies have a measurable, financial impact that has currently been realized north of $5 million USD in one year alone. This impact led to the solution becoming a finalist for an industry award where it was presented to over 3,000 industry professionals. Furthermore, the reduction and automation of manual, tedious tasks have also led to more enriching work for resources.
Operations Research and Engineering Management
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McIntosh, Emily, "The Intersection of Robotic Process Automation and Lean Six Sigma Applied to Unstructured Data" (2022). Operations Research and Engineering Management Theses and Dissertations. 14.