In a previous blog post What does Lean Six Sigma mean to your digital business?, my colleague Michael Skinner [Principal Pre-Sales Consultant and working on Analytics at Axway] explained the concept of Lean Six Sigma in the context of digital transformation.
In the below article he explains how combining real-time analytics and operational intelligence and Lean Six Sigma initiatives allows an organization to facilitate continuous improvement for new and existing processes, and enables identification of issues and business problems that would otherwise be difficult if not impossible to address.
Lean Six Sigma is a tried and tested methodology for resolving process problems. However, in modern organisations with increasingly complex and interconnected products, large transactional systems handling multiple processes including process management (BPM), robotic (RPA) and artificial intelligence (AI) implementations running automated processing, the amount of data needing to be analyzed has expanded exponentially and the data itself can be scattered across disparate systems and difficult to obtain in a readily usable format.
Furthermore, data analysis in Six Sigma typically is historical in nature: data is gathered and analyzed after processes have run. Even in the Control phase, when active process management is ongoing, data analysis occurs after the fact rather than as the process runs.
Thus, the challenge for Six Sigma black belts is not only defining the problem and locating suitable sources of data for measurement and analysis, but also obtaining, cleaning and manipulating the data so that it can be analyzed, all within an accelerating operational activity landscape. New technology is needed to support rapid problem solving.
Real-time analytics and Operational Intelligence tools (such as Axway Decision Insight) allows analysis of operational data both historically and in near real time, supporting Lean Six Sigma initiatives by:
- early problem identification and definition by highlighting and alerting about process issues as they are developing, allowing preventative action to be taken before there is an impact on customers
- increasing confidence in project selection as the focus is on problems identified by facts and data rather than opinion or guesswork
- using full data sets rather than samples that are open to bias, further ensuring real rather than perceived problems are being addressed
- shortening project lifecycles, and thus increasing ROI of those projects.
Real-time analytics and Operational Intelligence tools such as Decision Insight, by connecting to multiple data sources, using an iterative Agile approach to analytics implementation, and having a no code, options-driven interface for developing analytics and dashboards bespoke to each category of user, simplifies and speeds adoption of Lean Six Sigma initiatives across the organization, whilst giving additional capabilities for Lean Six Sigma belts of all colors to use.
Combining real time operational intelligence with Lean Six Sigma tools and initiatives allows an organization to leverage both capabilities to a greater extent, facilitates continuous improvement of business processes, customer experience, and revenue growth for new digital services.