Six Sigma is not just one of the most well-known methods to transform whole organizations into a data driven and continuously improving company but also a philosophy that should be everyone’s day-to-day way of thinking.
At the heart of Six Sigma is the Define Measure Analyze Improve Control (DMAIC) Cycle, which is at center stage in order to achieve organizational wide continuous improvements. Even if you do not follow the six sigma method the DMAIC cycle is a valuable approach for any continuous data driven improvement project.
One of the challenges Operational Excellence executives face, however, is getting their entire organization to contribute and participate in Continuous Improvement programs. Most companies have multiple ongoing projects, but only a handful of trained team members who are proficient enough in the use of the statistical methods to validate cases before and after implementing the improvements. Allowing process and asset specialists to contribute to these projects would dramatically increase the operational improvements needed to meet the expected organizational goals, and that can be achieved utilizing time-series-based advanced analytics — with better and faster results.
DMAIC is a good start — but current tooling doesn’t deliver in the age of Big Data
At first glance, the DMAIC approach seems like a perfect fit for continuous data-driven improvement within the organization and therefore, facilitating a strong involvement of the Six Sigma philosophy in daily operations. The current tools and methods used, however, within the DMAIC cycle prove to have limitations for the Six Sigma stakeholders on plant-level as well as on central-level when dealing with this reality.
Typically, Continuous Improvement experts may serve the organization via a central Operational Excellence Center, and with few experts in this field, bottlenecks may arise. Additionally, many projects may concern production performances where asset and process expertise is required. If those subject matter experts are unfamiliar with the statistical project approaches, many projects can end up going unfinished.
Additional impacts for the organization and everyone involved include:
- Underutilization of the local process expertise (plant level)
- Missed improvement opportunities
- Long project cycles
- No smooth symbiosis between plant and central level stakeholders
- Potential financial losses for the organization
The structure of the DMAIC cycle is well-suited for data-driven analysis, but the tooling is not currently up for the challenge. What is needed is a way to provide a common way of analyzing process data between central and plant level team members that significantly lowers the threshold for starting improvement projects.
The Combo Approach
Self-Service Industrial Analytics takes a new approach in providing industrial process data analytics for stakeholders throughout the organization. The approach combines the necessary elements to visualize a process historian’s time-series data, overlay similarly matched historical patterns, and enriches it with data captured by engineers and operators. Furthermore, unlike traditional methods, performing this analysis doesn’t require the skill set of a data scientist or black belt expertise since the user is always presented with easy-to-interpret results.
Key elements of a self-service industrial analytics platform to look for include:
- A system that brings together deep knowledge of both process operations and data analytics techniques to gain value from the operational data already collected. Such a system will minimize the need for specialized data scientists or complex, engineering intensive data modeling and can turn human intelligence into machine intelligence.
- A model-free predictive process analytics (discovery, diagnostic and predictive) tool that complement and augment, rather than replace, existing historian information architectures.
- A system that supports cost-efficient virtualized deployment and is “plug and play” within the available infrastructure, however have the ability to flawlessly evolve into a full scalable set-up that fits in with corporate Big Data initiatives and global environments.
Based on these key elements, self-service industrial analytics is exactly what is currently lacking for supporting each phase of the DMAIC cycle. By empowering local subject matter experts with advanced analytics tools enables them to contribute to the operational excellence goals, whether a project is smaller in scope or has a more long-term focus.
Benefits & Conclusion
Using Self-Service Industrial Analytics to support the DMAIC cycle can result in various organizational benefits. By avoiding the need for high statistical expertise, many more people such as process, asset and field experts can start contributing to Continuous Improvement projects. It might even become fun to work on those structured improvement projects and help people get more quickly certified as green or black belts. Because of this, executives responsible for Operational Excellence can change the organization’s culture and meet their certification targets much faster.
Furthermore, using self-service analytics can result in more projects being executed per year, bringing faster results in areas such as reducing carbon footprint, quality improvement and reducing waste. The contributing subject matter experts will proactively be generating new insights with use of the extensive capabilities of Self-Service Industrial Analytics platform. Finally, the improvements achieved for one asset, production line or plant can be shared across the organization, improving operational excellence within the entire business unit or fleet.
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.
Edwin van Dijk is Vice President of Marketing at TrendMiner NV.