Global lean six sigma methodology and shopfloor connectivity
Within the industry, we often see numerous local projects and measures being initiated to address downtime challenges across different plants. The problem is these efforts often lack the scalability required for successful global implementation. This can be overcome using a structured Lean Six Sigma DMAIC (define, measure, analyse, improve and control) methodology.
Typical Lean Six Sigma DMAIC approach
A detailed process
The process starts with clearly defining the problem at a global level, incorporating insights from multiple plants through interviews and surveys, before narrowing down to a specific local scope and use case. Shopfloor connectivity is essential for comprehensive data collection and precise definition of measurement systems to establish accurate metrics. During the analysis phase, classical statistical models, including capability analysis, hypothesis testing, regression and design of experiments (DOE) models, can be employed to identify significant impact parameters. These models derive insightful and data-driven conclusions about the process, enabling the anticipation of the right improvement areas.
Digital solutions play a vital role, including the use of predictive machine learning models and warning signals integrated into digital shopfloor tools. Operators can also engage in autonomous maintenance by autonomously inspecting machine conditions and performing preventive cleaning or adjusting process parameters. These actions should be well integrated into a robust control plan enabling long-term benefits and scalability across the manufacturer’s global footprint.
Deviating process parameters that indicate clogging and blockage phenomena are flagged to the operator on process control dashboards.
Agile solution tailored to end users
To successfully launch these digital initiatives, there must be robust collaboration between plant experts (e.g. manufacturing managers, process engineers and operators) and the global digital team (e.g. data scientists, software developers, IT/OT engineers and business analysts). By working in SCRUM teams, agile project management practices facilitate incremental development and foster close collaboration with the plant.
Visualisation of the scrum framework showcasing essential activities and artifacts for scaling agile across an organisation
Seamless integration into daily workflows
The roll-out of digital tools must be carefully planned and executed to ensure smooth adoption and maximise its effectiveness. Before official deployment, weekly check-ins with plant operators and engineers should be conducted to fine-tune the applications based on feedback and operational insights. This iterative process tailors the solution to the specific needs of the end users and helps make sure it’s seamlessly integrated into their daily workflows on the shop floor. Taking a hands-on approach not only empowers operators with the necessary skills to use the tool effectively but also fosters a sense of ownership and confidence in the application.
Once the tool has been launched, its deployment must be carefully monitored to make sure it’s being used and adopted as planned. This means tracking how end users interact with the tool, making sure that data is accurately interpreted and providing additional support when needed.
Scaling potential
From our use case, it’s evident that a structured approach with clear process understanding, data capabilities and cross-functional teams are the critical ingredients for the successful reduction of unplanned downtimes – in the use case by 25%.
Industrial companies benefit from having a solid methodology, especially when performance issues have been recurring and rapid conclusions are drawn based on preliminary data extracts and/or shopfloor emotions. Adequate time needs to be spent on understanding key processes, both from scientific, business and human interaction points of view. The latter is important as operational teams face a tremendous increase in data and the impact of human decisions on certain parameters is sizeable.
Data availability, in terms of determining and implementing additional data captures, as well as connecting existing data sources, is key for success. Too often, data sources are dealt with separately without identifying their value and no effort is put into identifying missing data and links.
As with all complex challenges, cross-functional staffing is critical to tackle diverse capabilities and stakeholders. Central data capabilities can be leveraged to process data sets into various models, whereas local representation is useful for model validation and tool acceptance.
The combination of a solid methodology (i.e. DMAIC or others) and predictive data models has great potential for the further scalability of the clogging solution seen in our use case across manufacturing plants. More importantly, this combination is perfect to address many more issues affecting industrial performance and profitability. As well as pipe clogging, the methodology could be used for deviating environmental emissions and quality out-of-specs, energy consumption variability due to heat exchanger fouling, pump breakdowns, valve malfunctioning and/or clogged filters.
Contact us
PwC Manufacturing Services has vast supply chain and operations expertise and is a market leader in Consulting. Our Belgian team boasts more than 30 Lean Six Sigma Black Belts. We call on our extensive digital manufacturing capabilities to further enhance our offerings, making sure we – and our clients - stay at the forefront of innovation. We bring all of this together with our deep industry expertise in sectors such as Chemicals, Utilities and Process Industries, advising key clients on a variety of topics and providing tailored solutions that meet their unique challenges.
Read the use case this Insight is based on here
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