Quality management tools: Daily challenges and logical core for quality control engineers
In the career of quality control engineers, quality management tools are like well - known old friends. However, in the actual scenarios of daily work, how to flexibly use these tools and how they can effectively solve practical problems in work are topics worthy of in - depth discussion. Some engineers may only use one of these tools to convey specific information, or they may just make the use of these tools a mere formality, using them just for the sake of using, without truly realizing their value.
In fact, the seven quality control tools embody the logical essence of DMAIC (Define, Measure, Analyze, Improve, Control). Even if some engineers haven't taken relevant courses and are not familiar with the specific concept of DMAIC, it doesn't hinder the application of the logic of these tools. After all, the effective application of these tools essentially follows the logic of DMAIC.
Genus differentiation method: The precise starting point for problem classification
The categorization method is like a precise scalpel used for detailed classification of problems. It can be regarded as an intuitive classification method of Plato. Taking the defective problems of Logic Up as an example, we can classify them from different dimensions. For instance, from the perspective of suppliers, this material defect may come from Manufacturer A or Manufacturer B; from the levels of customers and models, we can identify which specific customers and models in the company the problems occur in. However, just doing these is far from fulfilling all the contents of the Define phase in DMAIC. We also need to further clarify the specific time when the Logic Up defects occur, that is, during which production time period they are concentrated; the exact location, which workshop and which production line; the specific quantity of defects to quantify the severity of the problem; and the current real - time status of the production line, including production efficiency, equipment operation status, etc. Only by comprehensively integrating this information can we lay a solid foundation for subsequent problem - solving.
Checklist: The Rule-based Approach to Data Collection
In the entire quality management process, the checklist undertakes the important mission of data collection, which is one of the key tasks in the Measure phase of DMAIC. However, it should be clear that the core of the checklist is not just the action of collecting data. More importantly, it is to plan in advance the rules for data collection. This includes several key elements. For example, what kind of data to collect, such as the specific parameters of defective products, the time nodes of the production process, or the relevant information of operators; how to collect data, whether to use manual recording, automatic collection, or a combination of both; who is responsible for collecting data. Different personnel may vary in professional ability and sense of responsibility, which will affect the accuracy of the data; and how much data to collect. If the amount of data is too small, it may not reflect the real situation, while too much data will increase unnecessary workload. Of course, all this is based on the premise that the Measurement System Analysis (MSA) is in good condition to ensure the reliability of the data.
Histogram: Intuitive Insights into Data Distribution
After we successfully collect the required data, we enter the Analyze phase of DMAIC. In this phase, the first thing to do is to gain an in - depth understanding of the distribution characteristics of the data. The distribution patterns of data may vary. The peaks may be left - skewed or right - skewed, and the graphs may be flat or sharp. At this time, the histogram plays a huge role. It can present the distribution of data in an intuitive graphical way. By observing the histogram, we can quickly grasp important information such as the central tendency and dispersion degree of the data, providing strong support for subsequent problem analysis.
Plato: An Effective Tool for Problem Division
Plato plays the role of a dividing tool in the problem - solving process. Just as when we want to enjoy a watermelon, we must first cut it with a knife. Here, "the knife" is Plato. We need to reasonably divide the problem according to the types of defects and determine how many parts to divide it into. Still taking the Logic Up defects as an example, there may be different types of defect situations such as scratches (60%), shrinkage (20%), and assembly defects (10%). According to the famous 20/80 principle, we should prioritize solving the problems that have a greater impact on the product, that is, the scratch problem. Since this type of problem has a relatively high proportion, solving it can greatly improve the product quality.
Fishbone Diagram: Problem Analysis in Combination with Pareto Chart
Fishbone diagrams usually need to be used in combination with Pareto charts. The key lies in how to correctly select the "fish head" when solving problems. Many people tend to make mistakes in actual operations. For example, they use Logic Up defects as the fish head. In fact, this approach doesn't really touch the core of the problem. It only uses the tool in form and fails to effectively solve the problem. In fact, according to the main problems analyzed by the Pareto chart, such as scratches (60%), it should be used as the "fish head" to draw the fishbone diagram. If shrinkage (20%) is also within the scope of improvement, a separate fishbone diagram analysis should also be carried out for the shrinkage problem. Sometimes, 95% of the causes of scratches and shrinkage may be the same. In this case, the two can be combined in one fishbone diagram for illustration. This can not only improve the analysis efficiency but also comprehensively show the root causes of the problems.
Scatter diagram: A bridge to explore the relationships between factors
When we use the fishbone diagram to identify potential causes (X1, X2, X3... ) and determine the scratching problem as the "fish head", we can then use the scatter diagram to show the relationship between scratching and these potential factors. The scatter diagram can intuitively present the correlation between variables and help us find the factors that have a significant impact on scratching, such as X1 and X2. It should be noted that there is no fixed order for using the histogram, Pareto chart, fishbone diagram, and scatter diagram. They can be nested within each other and used flexibly to analyze problems more comprehensively and in - depth.
Control Chart: A Powerful Guarantee for Process Control
After identifying the important factors X1 and X2 that affect scratching, in order to prevent the problem from recurring, these factors must be strictly controlled. The control chart plays a crucial role in this process. It can monitor the changes of X1 and X2 in real - time to ensure that the production process is always in the optimal state. This is exactly the core function of the Control phase in DMAIC. Through continuous monitoring and adjustment, the unstable factors affecting product quality are eliminated to ensure the stability and reliability of product quality.
Flexible Application: The Winning Strategy of Quality Management Tools
In summary, the seven QC tools all have their unique values and functions in the quality management process. However, to truly bring their effects into play, the key lies in selecting the appropriate tools at the appropriate time. Only by combining these tools in an orderly manner according to the DMAIC logic and applying them flexibly can various problems encountered in work be effectively solved. Otherwise, the tools will only exist in isolation, unable to be combined with actual problems, and thus the ultimate goal of improving product quality cannot be achieved.