Bathtub Curve in Product Life Cycle

Bathtub Curve in Product Life Cycle

What is Bathtub Curve?

The Bathtub Curve is a diagram model made to speak to the disappointing pace of a gathering of items over some undefined time frame. This information enables manufacturers to anticipate when failures occur and ideally distinguish main drivers and forestall them. The Bathtub Curve is represented by three sections – Infant Mortality, Normal Life, and End of Life Wear-out.


Infant Mortality – This is the part of the curve where failures happen at the very beginning of a product’s life cycle. This part accounts for things such as DOA or dead on arrival products, manufacturing errors, material flaws, and so forth. When this data is drawn from customer reporting, a high amount of infant mortality can also be attributed to misapplication or improper installation. When it comes to repairs, we often see the most products in this part of the curve being caused by customers using the product incorrectly but the chances of it being a manufacturing defect cannot be ignored. Requesting failure analysis reporting from the repair center with regards to DOA items can be important when reporting defects and placing warranty claims.

Normal Life – The second piece of the bend are failures that happen inside the ordinary working time frame or lifespan of the gadget. This is in some cases alluded to as the “constant failure rate” as failures in this piece of the bend are generally consistent and predictable. Failures inside this opportunity for the most part arrive when the anxieties the gadget is liable to have surpassed the quality of its most fragile segment. The vast majority of the failures we see during this piece of the bend have happened in view of sudden ecological pressure or burden issues. In some random application when a gadget surpasses its capacities it can experience the ill effects of an ordinary life disappointment. Producers and machine developers need this piece of the bend to be as low as could reasonably be expected.

End of Life Wear-Out – The last piece of the bend is the finish of life for the item. This is the place you will see the bend rise steeply as the device components just reach the point where they will fail due to simple age or wear and tear. Failures of this sort are to some degree unsurprising and truth be told, frequently expressed inside the datasheets or documentation of said items – for instance, a device button will have a viable measure of presses before failure, etc. Makers will regularly uncover this life expectancy model as a piece of their item documentation. This is the place fixes can help the most, as fixing a thing and supplanting the most vulnerable segments with new, will viably “reset” the bend of the gadget. This can significantly expand the lifecycle of the item.

How it helps in decision making?

On the off chance that we settle on a choice dependent on the current behavior of a framework, when its age and behavior change after some time, that specific choice may never again be right. Thus, on the off chance that we need to settle on a choice about the support tasks of a framework, it ought to think about all ages of a framework, i.e., if a framework is in its infant mortality period it implies there are heaps of failures and breakdowns. At that point, by utilizing any MCDM (Multiple Criteria Decision Analysis) techniques we pick the best support strategy. Be that as it may, when the framework arrives at its valuable life, failures decrease and reliability increments normally, at that point the expense of insourcing may become lower than the expense of redistributing. Involving bathtub curve ages has another great advantage: we can plan a program for what and when to outsource.

Bathtub Curve in Hardware and software Production: –

Equipment reliability improves after some time and remains genuinely consistent until the finish of life when segments start to wear out however software reliability improves piecewise over the long haul yet faces intermittent downturns as a result of the bugs and expanding unpredictability related to updates. It very well may be stated, “Software doesn’t wear out”. Yet, it crumbles which we will examine in Software failure rates.

Minimizing the manufacturer’s cost:

The following shows the item’s unwavering quality on the x-axis and the maker’s expense on the y-axis. If the producer increases the reliability of his product, he will increase the cost of the design and/or production of the product. Be that as it may, production and design cost does not imply a low overall product cost. The overall product cost ought not to be determined as just the expense of the item when it leaves the delivery dock, yet as the all-out expense of the item through its lifetime. This incorporates includes warranty and replacement costs for flawed items, costs acquired by loss of clients because of blemished items, loss of subsequent sales, and so forth.

By increasing product reliability, one may increase the initial product costs but decrease the support costs. An optimum minimal total product cost can be determined and implemented by calculating the optimum reliability for such a product. The figure depicts such a scenario. The total product cost is the sum of the production and design costs as well as the other post-shipment costs. It can be seen that at an optimum reliability level, the total product cost is at a minimum. The “optimum reliability level” is the one that coincides with the minimum total cost over the entire lifetime of the product.

After repairing a device, one of the frequent concerns customers have is about the reliability of the product itself. Especially once failure analysis reporting is completed and the technician has stated that the unit failed due to normal wear and tear or end-of-life issues. Are other units of the same type within the plant doomed to fail any second? Was the product defective? What exactly were the conditions that caused the device to enter the end of life? This is where it is important to understand The Bathtub Curve and how companies use it to predict product reliability.

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