Applying Quality Management (QMS) Principles in Manufacturing Analytics

ISO 9001 Quality Management System requires an organization to use factual information as a basis for decision making. The clause 9.1 related to monitoring, measurement, analysis and evaluation in ISO 9001 caters to this requirement. The goal is to report to the degree to which processes meet their stated objectives.

Following principles also need to be met –

  • The parameters to be monitored and measured need to be determined
  • The methods for monitoring, measurement, analysis, and evaluation have to be determined
  • Activities required to ensure valid results of monitoring and measurement have to be defined
  • Stages and intervals of monitoring and measurement should be determined
  • Frequency and schedule of analysis of results should be defined
  • Evaluation of the performance and areas and actions for improvement need to be determined
  • Results of the performed activities need to be documented and retained

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Applying these principles to a continuous improvement cycle in a manufacturing analytics environment, as shown in Figure 1, we start with the key goals of improving availability, performance and quality. We may look at each of these items individually or simultaneously during a continuous improvement cycle. Similarly, we may look at the whole plant or individual machines.

The next step is to define the parameters that need to be monitored which will help us achieve our goals of improving availability, performance and quality. For example, equipment availability may be impacted due to frequent downtimes. An equipment could represent anything that is used in the operations of a manufacturing facility shop floor. Examples of equipment are machine tools, ovens, sensor units, bar feeders, etc. This equipment may have the ability to publish information or may need to be augmented to do so.

A manufacturing analytics solution includes machine monitoring which automates the monitoring and measuring aspect by recording all the parameters published by the equipment. This could include a variety of parameters like temperature, load, pressure, speed, feed, etc. To predict machine downtime and learn from past occurrences, one may need to monitor temperature and/or load over time. To improve performance, one may need to monitor feed, speed, etc. The advantage of the combined and automated monitoring and measurement step is that it is real-time, no manual efforts are required and human error is avoided. However, a complete solution does provide for operator inputs where machine generated information is not sufficient. These could be cases like machine stoppage due to meetings, breaks, etc.

The biggest value provided by a manufacturing analytics platform is in the analysis step. This is because the amount of analysis possible with the manual approach would have been limited by the amount of data collected manually. Additionally, even if the data collection is automated, it would be difficult to analyze it without the right infrastructure and tools. A cloud based manufacturing analytics solution streams all the data from the manufacturing equipment, transforms and prepares data for analysis and automatically computes the defined KPIs to track device, operator, part, and tool performance. Predictive and preemptive alerts can be generated based on response to events and anomalies as per defined rules. Domain-driven machine learning algorithms can also be applied to identify opportunities for operational improvement.

The next step in the continuous improvement cycle is Evaluation. This involves decision making by the human-in-the-loop using the recommendations, trends, patterns and alerts provided by the manufacturing analytics platform. The goal of the decision making step is to take advantage of the inputs provided by the platform to take the right decision at the right time to achieve the improvement goals we set out to achieve at the beginning of the cycle. This cycle then continues in forward moving spirals of continuous improvement thus iteratively and incrementally achieving gains on the measures of availability, performance and quality.

The manufacturing analytics platform helps simplify and automates the process significantly also helping adhere to the basic principles stated at the beginning of this article. Once the system is setup, all activities related to measurement, recording and documentation of results is automated. No schedule or frequency of measurement needs to be determined since the data is streamed (almost) real-time from the equipment. The data is stored in a cloud infrastructure and is accessible for further analysis and evaluation in a visual user friendly environment anytime on any device via a web browser.

Thus we see that a manufacturing analytics platform vastly simplifies the application of QMS principles thus helping monitor, measure, analyze and evaluate the relevant parameters to take the right actions and achieve our goals of higher availability, performance and quality.

Have you tried MINTVIZOR while applying QMS principles on your factory floor? Simplify the monitoring activities through real-time machine monitoring driven through a globally accepted standard like MTConnect. Remove the risk of errors and overhead of efforts typically associated with manually measurement by automating the measurement. Get benefits of automated analysis powered by machine learning based algorithms. Make the decision making process and evaluation by operators  and managers easy through configurable dashboards accessible with any device through a web browser.

Write to mintvizor.marketing@hcl.com now to know more and experience the benefits.

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