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The underlying bug or process failure, not just the stolen amount headline.
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Bài đọc này hiện có bằng tiếng Anh. Giao diện sử dụng ngôn ngữ bạn đã chọn.
Đọc bản gốc tiếng Anh →In science and reliability engineering, root-cause analysis (RCA) is a method of problem solving used for identifying the root causes of faults or problems. It is widely used in IT operations, manufacturing, telecommunications, industrial process control, accident analysis (e.g., in aviation, rail transport, or nuclear plants), medical diagnosis, the healthcare industry (e.g., for epidemiology).
Root-cause analysis is a form of inductive inference (first create a theory, or root, based on empirical evidence, or causes) and deductive inference (test the theory, i.e., the underlying causal mechanisms, with empirical data).
RCA generally serves as input to a remediation process whereby corrective actions are taken to prevent the problem from recurring. The name of this process varies between application domains. According to ISO/IEC 31010, RCA may include these techniques: five whys, failure mode and effects analysis (FMEA), fault tree analysis, Ishikawa diagrams, and Pareto analysis.
There are essentially two ways of repairing faults and solving problems in science and engineering.
Reactive management consists of reacting quickly after the problem occurs, by treating the symptoms. This type of management is implemented by reactive systems, self-adaptive systems, self-organized systems, and complex adaptive systems. The goal here is to react quickly and alleviate the effects of the problem as soon as possible.
Proactive management, conversely, consists of preventing problems from occurring. Many techniques can be used for this purpose, ranging from good practices in design to analyzing in detail problems that have already occurred and taking actions to make sure they never recur. Speed is not as important here as the accuracy and precision of the diagnosis. The focus is on addressing the real cause of the problem rather than its effects.
Root-cause analysis is often used in proactive management to identify the root cause of a problem, that is, the factor that was the leading cause. It is customary to refer to the "root cause" in singular form, but one or several factors may constitute the root cause(s) of the problem under study.
A factor is considered the "root cause" of a problem if removing it prevents the problem from recurring. Conversely, a "causal factor" is a contributing action that affects an incident/event's outcome but is not the root cause. Although removing a causal factor can benefit an outcome, it does not prevent its recurrence with certainty.
Imagine an investigation into a machine that stopped because it was overloaded and the fuse blew. Investigation shows that the machine was overloaded because it had a bearing that was not being sufficiently lubricated. The investigation proceeds further and finds that the automatic lubrication mechanism had a pump that was not pumping sufficiently, hence the lack of lubrication. Investigation of the pump shows that it has a worn shaft.
Investigation of why the shaft was worn discovers that there is not an adequate mechanism to prevent metal scrap getting into the pump; this enabled scrap to get into the pump and damage it.
The apparent root cause of the problem is that metal scrap can contaminate the lubrication system. Fixing this problem ought to prevent the whole sequence of events from recurring. The real root cause could be a design issue if there is no filter to prevent the metal scrap getting into the system. Or if it has a filter that was blocked due to a lack of routine inspection, then the real root cause is a maintenance issue.
Compare this with an investigation that does not find the root cause: replacing the fuse, the bearing, or the lubrication pump will probably allow the machine to go back into operation for a while. However, there is a risk that the problem will simply recur until the root cause is dealt with.
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