How do you support quality management with data and analytics?

Quality goals are specific, measurable objectives that organizations set to ensure product or service excellence. Metrics are quantifiable measurements used to track progress toward these goals. Examples include defect rates, customer satisfaction scores, on-time delivery, and process efficiency improvements. Regularly analyzing these metrics helps drive continuous improvement efforts and ensures alignment with quality objectives.

Effective quality management relies on data and analytics. Data is collected from various sources, ensuring its cleanliness and reliability. Descriptive analytics provides historical insights, while predictive analytics forecasts defect proactively. Statistical Process Control (SPC) and quality dashboards maintain consistent quality Supplier quality management benefits from data assessment, ensuring materials meet standards. Predictive maintenance prevents equipment failures. Customer feedback, analyzed through sentiment analysis, informs improvements. A culture of continual enhancement thrives on data.Benchmarking against industry standards ensure sustained quality excellence, reducing costs and meeting customer expectations.

1. Expectation on Quality for products and its services are defined by the customer and statutory & regulatory requirements. So, this would be very important to improve customer experience and 1st metric would be Net Promoter Score (NPS). 2. 2nd metric would "Cost of Poor Quality (COPQ)" which directly impacts the business in terms of product margin/profit. Lower is COPQ, better is margin/profit. 3. 3rd metric would be "Customer Claim Closure Days" to know how fast we provide services to customer. 4. 4th metric would be "Quality Management System (QMS) Effectiveness" to check the sustenance of the QMS and compliance requirement. 5. The 5th metric to measure and improve would be the "No. of Customer Complaints and Complaint PPM".

Quantitative Analysis is a theory which is a part of Quality Management for a long time now. Data Analysis is not a new term for making a improvement move in Quality Management. Data Analytics moves us close to take actions in near real time, rather the general wait period that organizations were undergoing earlier with Data Analysis and Improvement. Level 1 all analysis theory / logics that are already in place to be fed in to Data Analytics. Level 2 start looking for slicing and dicing of data and different analysis that are meaningful and productive in nature. From here improve your system in near real time situation.

Top management buy-in is a crucial aspect of defining your quality goals and metrics (or KPIs). Customer satisfaction is an obvious KPI, but how you quantify this important metric is crucial and goes hand in hand with upper management involvement. This is also a crucial requirement for satisfying the Management Review requirement for both ISO 9001 and AS9100.

Collecting the right date is important. You may realize you don't have the necessary data and will need to start collecting it. As you collect your data always be aware of bias, by bias I mean people act very differently when they they know they are being monitored.

Collecting the right data and ensuring reliable storage/retention as well as retrieval is extremely important for driving continuous improvement. Isn't that the main goal of capturing the data in the first place? It's also important to focus on what matters most to the business rather than just collecting the data for the sake of it. Many organizations collect tons of data but then don't have proper storage or retrieval abilities which defeats the purpose of collecting data. It also doesn't help the business drive the expected improvements.

One thing I've found helpful is Organizing simple tools for data collection . The data should be made secure, easily accessible and verifiable to enable one make annalysis on trends and also monitor quality processes.

Use spreadsheets (or other tools) to generate graphs to get an insight in the data. Define which data to collect and visualise. Also important is to share this data with the company staff.

Within the SOP of each process define the applicable forms that shall form the quality records ( your future data source). Design the forms in a way that all necessary information shall be written in. A very important asset is if the company has a good ERP system. Try to integrate the forms into the ERP system and validate the functionality. The electronic records and signatures have many other benefits but in the context of this article, they provide an effortless way to form and store homogeneous raw data.

Having spent considerable effort and resources in capturing the relevant data the next critical step is to set up a regular analysis discipline. The focus should be on establishing actionable insights rather than getting into analysis paralysis. Data needs to be analyzed in real time to get these actionable insights and drive improvements, rather than waiting for weeks or months. Then it becomes more of a post-mortem Vs using a fast PDCA/pDSA cycle. I have seen this problem in a lot of organizations where lots of data is captured but there is no regular review and a total lack of corrective/preventive actions. With the EQMS's being so easy to deploy and very affordable I strongly recommend exploring a suitable solution for your business.

There are many data visualization tools available to use like for example : Microsoft Power BI Tableau Data Studio Python Data Visualization Libraries,..etc.

Use data analysis techniques to extract insights and patterns, employing various analytics types. Additionally, employ data visualization tools to present quality metrics and trends effectively, using charts, graphs, dashboards, or reports for clarity.

The data should feed into the management review and should be the base for next year's objectives. By visualising data it becomes more understandable. Picture says more than words.

Adding normalisation factors helps when comparing year by year performance. A good example is the power consumption per year. As a standalone figure, it gives little to no information at all. But when normalized for example by the volume of production it can present clearly the efficiency of power saving initiatives.

Few more quality management method or tools like 8D or A3 to support quality improvements projects and track down implementation actions with clear responsibility and accountability. Action implementation to be done with consideration of management of change and impacts of such changes in your process.

Monitoring of quality actions is closely related to "Analyze and visualize your quality data". Collecting the data, no matter how simple or complex it is, without having good visual management is difficult to pin point effectiveness of implemented corrective actions. Clear trend line throughout the days can guide you to justified judgment of positively confirmed action. On the other hand, without the clear data visualization you can be misdirected and guided to falsely confirm their effectiveness.

It should be monitored via meetings either operational or management. This way you can keep track on progress and correct if needed.

Visually model dashboards and use analytics to monitor cost of poor quality through specific KPIs from various processes and departments. Integrate visual elements from lean and six Sigma such as Correlation analysis, Pareto charts with data models to find high priority elements where there is high requirement of quality focus. Use

Organizations delivering products to customers are generally focused on product development, manufacturing, distribution and customer satisfaction. Often, quality system performance is not a primary focus and seen as necessary support element. Benchmarking quality system performance is a time consuming activity diverting resources from core business activities and, while value added, rarely is allowed the time and resources needed to be effective. An assessment of quality system performance based on an objective, data driven methodology against validated industry best practices is the most efficient means of establishing a performance baseline and developing strategic remediation plans.

Benchmarks from outside of the organization are preferred, but are they available for the needed performance characteristic ? If so, what's the credibility of such benchmark? One must understand context and demographic data used to derive benchmarks to ensure apple to apple comparison. If one finds this as too unrealistic, you may be right! Then start establishing your very own benchmark, and drive continual improvements based on that. If that's the whole point, then benchmark - internal or external does't matter.

Clear communication and transparent data collection is the key, people and process monitoring creates friction in achieve organizational goals. On boarding stakeholders will reduce friction in continual improvement.

It is a critical aspect of effective quality management for several reasons: -Adaptation to Changing Needs: Markets, technologies, and customer preferences evolve. Continuous improvement ensures that an organization can adapt to these changes effectively and remain competitive. -Quality Enhancement: Continuous improvement seeks to eliminate defects, reduce errors, and enhance overall quality. This leads to higher customer satisfaction and loyalty. continuous improvement is a cornerstone of quality management, driving organizations to consistently refine their processes, products, and services to meet ever-changing demands and deliver the highest possible quality to their customers.

One of the key aspect for the improvement of the quality management is to define the KPIs for each process. These indicators, if they are properly defined, provide objective indications of the performance. They shall then be communicated on a regular basis (KPI = Keep People Informed). They shall serve as a basis for the definition of clear objectives (KPI = Keep People Involved).

En Qualité, l'analyse des données est la meilleure fa?on de maintenir son système dans une démarche d'amélioration continue et d'aller au-delà du contr?le de conformité.

Few points: 1) The Organisation shall ensure the Documentation structure from Level 1 to Level 4 is well established. 2) This documentation structure shall have the Linkages of all the targets. Targets can be aligned like: Vision and mission --> Organisational goals --> Processes KPIs --> KRAs of Individuals This is how all the Top to down the level of targets shall be linked. Then once the effective linkages is set then start monitoring these targets or objectives through different levels by using KPI Matrix or else if you are using any QMS software solutions then you can configure these well defined targets then you can get the results through trends or dashboards on quality management systems.

To support quality management by data & analytics: ■The data to be collected should be specific and must be quantified. ■Evaluate cycle time effectively and efficiently. To balance the load put on the equipments, stress level of the manpower, and productivity. ■Use maintenance record to determine lifetime of the equipment. By comparing with productivity level, we can set scheduled preventive maintenance on every level, effectively. ■Set realistic target using point-2 vs existing resources(manpower, machine, material, etc), setting unrealistic target without careful manpower management is a big NO. ■Using data as worker evaluation to set reward system. By increasing motivation, can indirectly maintain quality management.