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An Introduction to Process Capability
Process capability is the ability of a process to meet customer requirements and expectations. Preliminary
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Process capability is the ability of a process to meet customer requirements and expectations. Preliminary
A probability plot is a powerful tool for data analysis
A priortisation matrix is a
In the exploration of measurement errors within research methodology, LeanScape delves into two critical components:
Parts per million (PPM) in Lean Six Sigma is a measure of
Wondering whether you should get a Six Sigma Green Belt certification? This credential trains you to lead quality improvement projects
In the quest for operational excellence, organisations invest heavily in digital dashboards and analytics. Yet the simplest, most powerful method
A recent Medium feature, “On Navigating Change: How ChangeOps, Strategy, and Maturity Drive Success”, argues that most transformation efforts falter
Key Takeaways The Kano Model, created by Dr. Noriaki Kano in 1984, categorises product features based on their impact on
BNY’s CEO Robin Vince has turbocharged the bank’s transformation since 2022 by tearing down silos, unifying the corporate culture, and
Organisations today face unrelenting pressure to do more with less. Yet chasing cost cuts without a coherent framework can undermine
In a world increasingly shaped by artificial intelligence, operations leaders face a pivotal question: how do we deploy Gen AI
In an era where “faster, cheaper, better” is more than a catchphrase, generative AI (gen AI) stands out as a
As the pace of change accelerates, organisational success increasingly depends on the ability of leaders to guide teams through transformation.
Process capability is the ability of a process to meet customer requirements and expectations. Preliminary process capability involves statistical measurements that assess a process’s ability to produce outputs within specified limits, focusing on newer or unregulated processes. It is a quality control measure and can be determined using three primary tools: specification limits, statistical process control (SPC) tools such as X-bar and R charts, and calculation of Cp and Cpk values. Process capability can be improved by analyzing existing processes, identifying underlying causes of variability, and responding to identified issues quickly and effectively.
A probability plot is a powerful tool for data analysis that provides a visual representation of the underlying probability distribution of a dataset. By plotting each measurement against probability paper, it is possible to identify outliers and non-normal distributions within the dataset, which can help inform process adjustments and better understand variation in the system.
A priortisation matrix is a powerful tool that can be used to quickly and effectively assign priority levels to tasks, projects, and goals. This allows users to identify which tasks should be completed first, taking into account any time or budget constraints. By establishing criteria for what is most important within a project, it is possible to make better decisions regarding how resources and efforts should be allocated for optimal outcomes. A well-crafted prioritisation matrix allows project managers, team members, stakeholders, and other participants to better visualise the order in which tasks should be handled while taking into account any constraints they may face. Ultimately, this can save time and money while ensuring that customer needs are met within the agreed-upon timeline.
In the exploration of measurement errors within research methodology, LeanScape delves into two critical components: bias and precision. The article illuminates how bias, stemming from systematic discrepancies between the measured values and true values, can significantly skew research outcomes. It further articulates that precision, or the consistency of repeated measurements under unchanged conditions, plays an equally pivotal role in ensuring the reliability of research findings. By comprehensively analysing these elements, LeanScape underscores their influence on the validity of research methodologies, advocating for meticulous attention to minimise errors and enhance the integrity of scientific inquiry.
Parts per million (PPM) in Lean Six Sigma is a measure of how many defects exist in a process or product. It is typically used to measure the defect rate of products or services during a particular period of time and can be calculated by dividing the number of defective items by the total number of units produced, multiplied by one million.
Wondering whether you should get a Six Sigma Green Belt certification? This credential trains you to lead quality improvement projects
In the quest for operational excellence, organisations invest heavily in digital dashboards and analytics. Yet the simplest, most powerful method
A recent Medium feature, “On Navigating Change: How ChangeOps, Strategy, and Maturity Drive Success”, argues that most transformation efforts falter
Key Takeaways The Kano Model, created by Dr. Noriaki Kano in 1984, categorises product features based on their impact on
BNY’s CEO Robin Vince has turbocharged the bank’s transformation since 2022 by tearing down silos, unifying the corporate culture, and
Organisations today face unrelenting pressure to do more with less. Yet chasing cost cuts without a coherent framework can undermine
In a world increasingly shaped by artificial intelligence, operations leaders face a pivotal question: how do we deploy Gen AI
In an era where “faster, cheaper, better” is more than a catchphrase, generative AI (gen AI) stands out as a
As the pace of change accelerates, organisational success increasingly depends on the ability of leaders to guide teams through transformation.
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