When managing the lifetime of material, reliability design is a sub-discipline of process engineering that emphasizes the importance of dependability. Therefore, reliability defines the ability of a system or component to work properly under specified circumstances over a certain amount of time. Reliability is closely connected to dependability, which is often described as a component or process’s capacity to perform at a certain time or time interval throughout a period. The probability of performance is the abstract notion behind Reliability concept’s abstract idea (Slivinski et al., 2019). Quality, testability, repairability, and advancement are all often referred to as reliability technology. The reliability of components greatly influences the cost-effectiveness of a system. For instance, when vehicles break less often, their resale value is greater. At the highest levels of “lifetime” systems, reliability technology has difficulty anticipating, preventing, and managing the complexity and danger of failure that might occur.
“Margin” reliability is characterized by developing highly particular strength margins (force, chemical, and electrical) and then designing to those margins. The product’s toughness must be reduced if it is lightweight, inexpensive to make, and effective. There is little room for error in the manufacturing process. The consumer has to know exactly how the product will be used and how it will benefit them. In addition to defining particular environmental variables, life expectancy forecasts must be based on specified instances. The focus on quantification and the selection of objectives indicates a limitation to good performance; nevertheless, there is no intrinsic limit. The development of better value does not have to be more expensive than the creation of lower quality. On the other hand, they assert that predicting performance using past data may be quite wrong, with only acceptable correlations between identical models, items, manufacturing processes, and maintenance under equal operating loads and circumstances of usage being discovered (Slivinski et al., 2019). Furthermore, even modest adjustments to any of these variables might have a major influence on performance. Consequently, as historical information is collected, the most erroneous and substantial things are quite highly probable to be modified and re-engineered, making the conventional statistical procedures and strategies used in, for example, the healthcare or insurance markets less effective.
The chance failure phase is primarily concerned with the occurrence of random failures in the product. The most common cause for this type of failure is that the consumers are not utilizing the device by the manufacturer’s instructions for usage (Zidi et al., 2017). As a consequence of the overworked situation, the gadget fails as a result of its overworked state. Consequently, the following are some of the most typical causes for the possible failure of a product:
 1. Overusing the product: when a product is overused, there are high chances of running into a failure. This may be a result of poor handling or lack of proper maintenance.
 2. Using the product for wrong purposes: manufacturers usually indicate what a product can do and how it should be used (Zidi, Moulahi & Alaya, 2017. However, some individuals may use it for the wrong purposes. It was not meant to, therefore, force it into a failure.