In the first part of this series of posts, we looked at how self-service analytics can be applied to virtually all phases of the Six Sigma DMAIC cycle, adding fuel to your continuous improvement projects by allowing subject matter experts to contribute on a much larger scale. Additionally, the approach of utilizing self-service industrial analytics within your projects impacts execution from an organizational scale: it enables a vertical integration throughout the whole organization. Therefore, Operational Excellence teams are empowered with more contributors than ever before, and with the contribution from all levels of the production site, more efficient outcomes can be realized.
In a classical Six Sigma setting, there are multiple roles involved within improvement projects. Depending on the certification level, they will be either Champion, Leader of the Six Sigma Efforts (Master Black Belt), Central Group Project Leader (Black Belt) or local plant-based project leader. When focusing on the roles that continuously conduct various projects, namely the black and the green belts, it is important to include the nature of their work when assessing their challenges.
Generally, black belts are usually operating within a central operational excellence group where green belts are responsible for improvement projects on plant level. The consequence is that central group experts have a dedicated focus towards globally-conducted improvement projects with special skills in statistical analysis. Local process experts, however, are still heavily involved in the daily business of running a plant which comes with a plethora of tasks while their expertise is more characterized by production process knowledge instead of deeper statistical skills.
Current set-up comes with its fair share of challenges posed for improvement projects on an organizational scale.
Because of this, process experts on plant level often lack the foundation to use the expert-aimed tools within the classical DMAIC cycle (watch our previous mini-webinar to learn more about this), which means that the threshold to start a time-consuming improvement project with MS Excel is very high. Depending on central Six Sigma improvement teams, results in time delays and many improvement options are left unexplored. It also leads to many “island solutions” growing within the organization.
All Hands on Deck
By empowering your subject matter experts with TrendMiner for use within the DMAIC phases, you create one platform for data-driven process improvements that involves all roles, from process engineers to operators to management.
With this approach, everybody knowingly contributes to corporate KPIs through continuous improvement projects. There is also a high-utilization of everyone’s specific skills and a common workflow with a consistent set of tools suited for everyone.
One example of the value of this solution is related to Energy Management. Reduction of carbon footprints is, on some level, on most company’s list of corporate goals. Typically, these continuous improvement initiatives are time-consuming, centrally-led data modeling projects. But now self-service analytics tools can allow subject matter experts, such as process and field engineers, to handle 80% of energy-related cases so can contribute to the corporate goals for reducing the carbon footprint.
Whether you are seeking to reduce maintenance costs, improve plant safety, or reduce your carbon footprint – the application of self-service analytics within your continuous Improvement projects will help your organization reach corporate goals faster and more efficiently by empowering one of your most valuable resources: your process and asset experts.
Learn More about the Organizational Impact of Self-Service Continuous Improvement 4.0 in our latest webinar