Note: the following specifically refers to Quality, but could just as well focus on Environmental, Safety, Information security, Laboratories etc…Known unknowns in quality refer to the aspects or factors that are recognized as potential sources of uncertainty or variability in the quality process, but their precise impact or nature is not fully understood or quantified. These known unknowns are acknowledged and recognized as areas where further investigation, analysis, or research is required to gain a deeper understanding. Here are a few examples of known unknowns in quality processes:
1. Measurement Accuracy: While various measurement techniques and instruments are used in quality, there may be uncertainties associated with the accuracy and reliability of these measurements. Factors such as instrument calibration, environmental conditions, and measurement techniques can introduce unknown variations that require further study. Two typical methods to study and estimate this are Gage R&R / Measurement Systems Analysis (MSA) and Measurement Uncertainty Analysis
2. Process Variation: In manufacturing or production processes, there are often known sources of variability, but the extent to which they impact the quality outcome may be uncertain. For example, the influence of machine settings, material properties, or environmental factors on product quality may not be fully understood, necessitating further investigation. A good method for this is Failure Modes Effects analysis (FMEA)
3. Human Factors: The role of human operators or inspectors in quality introduces a level of subjectivity and potential error. The impact of human factors, such as operator skill, fatigue, or training, on quality outcomes may not be precisely known, and efforts to quantify and minimize these uncertainties are ongoing. Research indicates that visual inspections are very low on reliability. FMEA is also a good analysis technique for this activity.
4. Raw Material Variability: The quality of raw materials used in manufacturing can vary due to factors such as sourcing, storage conditions, or natural variations. Understanding the extent of this variability and its impact on final product quality is a known unknown that requires continuous monitoring and analysis. This can also be analyzed in a FMEA.
5. Long-Term Reliability: Determining the long-term reliability and durability of products can be challenging, especially for items with extended lifecycles. Factors like wear and tear, aging, and environmental conditions may affect product quality over time, but accurately predicting these effects remains a known unknown. Again here, a FMEA can analyse and estimate these potential failures along with techniques such as Mean Time to Failure and Mean Time to Repair
6. Supplier Performance: Quality control relies on the consistent performance of suppliers and their ability to deliver materials, components, or services that meet the required specifications. However, uncertainties regarding supplier capabilities, reliability, or potential variations in their processes introduce known unknowns that may impact the overall quality control process.
Addressing known unknowns in quality control involves continuous improvement efforts, research, data analysis, and collaboration between different stakeholders. By identifying these areas of uncertainty and investing resources into investigating and minimizing them, organizations can enhance the effectiveness of their quality control processes and improve overall product quality.
History has taught us much about the success and failure of Risk Identification and Mitigation. Some highlights include:
- Diverse / Cross Functional team input to the Risk Identification process is essential to most accurately identify potential issues
- With good records of this activity, future, real world results can be fed back into the process incorporating the actual failures and failure rates, to make the results more accurate
- With the above feedback, future risk analysis for somewhat similar activities become exponentially more accurate in these predictions.
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