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Bathtub Curve is a graphical representation of the failure rate for a population of products (and not for only 1 product) over time. The curve has three parts to it

1. Infant Mortality or early failures having a decreasing failure rate

2. Useful Life or normal life with almost a constant low failure rate

3. End of Life or wear out failures having an increasing failure rate

It is a very common phenomenon used in reliability engineering

 

An application-oriented question on the topic along with responses can be seen below. The best answer was provided by Saravanan S on 13th December 2019. 

 

Applause for all the respondents - Jaishree R, Kiran Kumar, Praful Bonde, Saravanan S. 

 

Also review the answer provided by Mr Venugopal R, Benchmark Six Sigma's in-house expert.

Question

Q 217. Explain Bathtub Curve and its utility in reliability engineering. Is this concept applicable for all products?

 

Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday.

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The Bathtub Curve Analysis:

 

(not able to copy/attach the bathtub curve from my office laptop)

 

The Bathtub Curve is being used for likely failure rates of any products, be it manufacturing products or technological products.

 

This bathtub concept holds good for almost all the products which are coming new to the market, and has been widely accepted by the reliability community over the years.

 

Predominantly, it has three parts, decreased failure rates (Infant mortality failures), constant failure rates (useful lifetime), and increased failure rates (wear-out failures).

 

During the initial period of any new product, the product life cycle faces a lot of failures because of design failure of the manufactured goods, bugs in the software, wrong positioning of the product, etc.  This phase of the product life cycle is being backed up by the strong support team to make the product a success.  Hence, we experience decreased failure rates of the product life, which can be otherwise called as Infant Mortality Failure, which was high when the system came into existence and then started ramping down.

 

The second phase of the bathtub curve experiences, less failure rates due to stability achieved in the product.  This phase is also called as Useful lifetime of the product, wherein the product experiences only chance failures.  The products are designed to operate under certain external conditions with certain stress level and when it crosses these constraints, the failure occurs, which is of rare situation.  The end users of the products, would know how to use the products, and hence they do not attempt to cross that limit, and would try to extract more usability from the product.  Hence this phase of the product is aptly called as useful lifetime of the product.

 

The third phase of the bathtub curve is called as increased failure phase, wherein the products face a lot of failures due to the deterioration of the machinery, wearing out of its parts, outdated technology, slowness, etc.  Either we need to repair the products, increase the support activity or else we need to install a new product/equipment or resort to newer technologies with respect to the technological products.  This phase is also called as wear-out failure phase.

 

For any product, the initial and final phases are very short when compared with it middle phase, which is the useful lifetime of the machinery/technological product.

 

In my opinion, the bathtub curve concept is applicable for all the products that are being manufactured, technologically innovated or the written software.

 

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Benchmark Six Sigma Expert View by Venugopal R

Bathtub Curve (BTC) is well known for its depiction of the behavior of failure rate of a product during its life cycle.

 

image.png.56bcc19117ecb2c4d97946a816ae60eb.png

 

As per the principle of BTC, the failure rate or the probability of failure for a product is high during its initial life time and once it survives a certain initial time period, the failure rate reduces and remains more or less constant until it reaches a ‘wear out’ period, when the failure rate starts increasing.

 

The failures that occur during the initial phase of life are also known as ‘Infant mortality’ and the failures that occurs during the last phase are ‘ageing or wear out’ failures.

 

The causes for the failures at initial stages and the wear out stages would be different. The initial failures could be due to design flaws, manufacturing defects or shipment related. Companies resort to various measures to reduce or manage the infant mortality. It is a common practice to subject electronic products to a ‘burn-in’ test with the intent of weeding out most of the initial failures before shipping the product. Some other methods involve pre-delivery inspections and testing of the product at dock or after shipment to their locations. Manufacturers also provide ‘warranty’ that covers free replacement / repair of products that fail during the early life, subject to certain conditions.

 

The causes for increase in failure rates towards the later part of life are due to wear out of components, other environmental factors such as corrosion, degradation etc. Methods to reduce the rate of ‘wear out’ failures include preventive maintenance, operating procedures, periodic replacement of certain parts such as bearings, belts, tires etc to protect the rest of the product.

 

High Infant mortality would adversely impact the reputation of a product if not controlled within acceptable limits. Further, the resulting expenses due to warranty replacements and product recalls would be a drain due to costs of poor quality (COPQ).

 

Some products like cell phones, computers turn obsolete very fast, due to rapid up-gradations of technology and features. Most users may change over to newer models, even before these products complete their ‘useful life’ period and may not reach the point of ‘wear out’ failures. However, the concept of infant mortality very much applies to them.

 

The concept of BTC applies of all ‘durable’ products that are expected to be in use for a considerable period of time. A related metric that is used for products such as consumer durable is the MOL (Month of Life) failures. As per this, the shipped products are tracked as ‘month wise’ batches based on installation dates, and the service incidents that occur during each month of their life are tracked for a certain period of time. The company will have performance targets on MOL to be achieved.

 

Interestingly, the ‘early failure’ concept applies not only to 'Manufactured products', but to ‘New Processes’ as well. We would have experienced that after rolling out a new process, high incidents of process failures are likely during the early days after implementation, based on which corrections would be carried out. For example, a newly introduced courier service had several incidents of consignments going to wrong locations due to a flaw in their bar-coding system.

 

While ageing failures may be controlled to certain extent for a manufactured product by adequate maintenance procedures, we can reduce or even prevent ageing failures on services by exercising good knowledge management.

 

The concept of 'Bath Tub Curve' may not be very much applicable to goods that are consumed very quickly, such as food items, certain FMCG goods etc. For such products, the life cycle is quite less to distinguish the 3 phases as per the BTC. Instead, the concept of ‘shelf-life’ is more applicable for such products.

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Bathtub curve is a form of hazard function and has three parts to it.

 

1. The first part is called decreasing failure or early failure

2. The second part is constant failure or random failure

3. Third one is increasing failure or wear out failures

 

This cannot be applicable to all products, wherever product failures happen more before the wear out period this bath tub curve will not be applicable 

 

Jaishree R

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Explain Bathtub Curve and its utility in reliability engineering. Is this concept applicable for all products?

My interpretation : Mostly used in New Product to the Market offerings : Eg : Air Purifiers, Hotel Baking Industry machine new model or a simple NEW to market Coffee Maker.

For simple reasons of Product Stabilization 1) The Early stage failures would be high. As they get fixed gradually one after another, the 2) Random/Natural usage based defects popup. Some of these can be solved via quick fixes and some need a product design modification or intervention. 

Eventually, as the product moves into the 3) Last 1/3rd phase of it's expected life, brands will start receiving breakdown complaints due to the parts that end up wearing off faster than anticipated. The first wave, if addressed, the product's "Usable Life Span" can be validated/improved.

The final set to wear off, usually are not aimed for reeingineering as the investment could mean a significtant markup in the production costs.

In some ways, this also applies to new service offerings : Eg : Hotels introducing new Cuisine / SPA services etc. The learning curve here helps in maturing the offerings and the last section may not be applicable as in service lines, the improvement in service is a "Continuous Improvement" journey. One way UP !!

Note : The image from wikipedia can act as a good pictorial representation.

 

Bathtub_curve.png

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Bath curve is representations of product life cycle failure rate . It's combines three kind of failure 

1. Early failure modes

2. Random failure modes

3. Failure modes due to wear and tear

 

In early product life cycle most of the failure are faced due to process and quality issues . It's more of functional failure in nature as customer irritent.

Random failure is of random nature and its combination of design and quality issue.

Every product has declared product life, so as it covers time product faces wear and leads to functional failure. This is more of design issues in nature.

 

This concept is applicable for products which cover product life cycle and do not have self healing capability. If the life cycle is terminated before wear and tear of product than this curve can not be obtained.

Screenshot_20191213-125136_Chrome.jpg

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Correct answers by Jaishree, Kiran Kumar, Praful, and Saravanan. Saravanan's answer has been selected as the winner as it explains all the components very well and also mentions the applicability correctly. Well written, Saravanan!

 

Please have a look at Benchmark expert view by Venugopal as well. 

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