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Best Practice of Quality Engineering in Product Life Cycle.

Experts in quality engineering must be able to understand what contribute to consistently good quality product throughout product life cycle and able to understand and address the source of variations. Quality engineering professional must be well versed in all aspects of product creation from product designing, process development to mass production of the product till product delivery to end consumer who will use the product.  It is sad to say that there are not many true quality engineering and/or management expert available in Asia, the hub of manufacturing.  

Attached is the table which I have established to give an overview/ guideline on what best practice should be at each of the product life cycle.

Product Life cycle
Best practice
Quality tools/
models
Define Concept
Use Kano analysis to understand what the delighters to consumers are; quality function deployment matrix to understand voice of customer and translate their wants and needs into a plan to develop a product which customer would want to buy.  Should have consideration if   there current supply chain ecosystem is able to support such product concept.
Kano analysis
Quality function deployment
Product development
Apply proper project management methodology to develop product till it is ready to be marketed to consumer.  Quality models use would be product development phase gate process, Advance product quality planning (APQP) and design manufacturability.  Identify suppliers and properly qualify supplier through structural approach to ensure suppliers are able to supply parts which full fill the intended design requirement and specification.
Conduct Design Failure mode effect and analysis (DFMEA) per APQP requirement
Plan and establish proper inspection and testing sequence to ensure there is check and balance of the product quality at appropriate manufacturing process steps. The measurement data collected must reflect product quality according to consumer requirement.
Set quality goal in manufacturing process - yield rate which measure product compliance rate according to test and inspection and field failure rate. 
Plan and setup infrastructure to collect useful data.
Project management plan, Phase gate,
APQP
Design for six sigma
Design for manufacturability (DFM)
DFMEA
Prototype phase
Conduct process FMEA in APQP to establish process which is error proof.    Design process and parameter setting which is sustainable and document the process in standard operating procedure
Train operator according to standard operating procedure.  Review material quality and supplier process.
Determine if the inspection and test able to measure the product quality characteristic, product  capability or test product according to go no go (pass fail)  preset specification. 
During proto build product quality data must be collected, analyzed and revised. Understand what cause a product to comply to spec (good quality product) and what contribute to defective product.
Have corrective action generated thru structure problem solving (5C, 7 steps or 8D) to address defective rate which does not meet goal. 

Yield collection
Pareto analysis
Error proofing
Product capability analysis
Design of experiment (DOE)

7 steps problem solving/5 C closed loop corrective action/ 8 D corrective action methodology/
Pilot run
Use design of experiment to optimize process setting. Where applicable, implement correct action plan generated from previous phase in pilot run and validate the effectiveness through data.
Collect process performance data and/or yield.  The data collected should be statistically analyzed to check if there is improvement in quality metrics performance and predict the trend in future mass production. 
Enhance own process and supplier process where applicable.  Evaluate if all process input such as man, machine, method,  material, measure and environment are ready for mass production
Develop and implement action plan to address gaps in meeting quality goal.
Create  appropriate process control on process input could impact product quality
Check supplier material and work with supplier using the same approach.

Design of experiment (DOE)
Statistical analysis
Process capability study
Yield collection
Pareto analysis

Mass production
With all activities well performed and documented from all above phase, by now we should have a robust manufacturing process through supply chain from raw materials to finished goods. There is a need to setup appropriate process control to monitor all critical to quality process parameter using statistical process control tools. The monitoring tools should be able to identify type of problem such as special or common cause.   There should be action plan to resolve special cause/excursion problem immediately to ensure we continue to deliver consistently good quality product to our customer. 
Monitor product performance at consumer with field data collection. 
 Continuous improvement plan is use to reduce product variation from common cause.
Process capability study
Yield collection
Pareto analysis
Statistical process control (SPC)
Statistical analysis
Six sigma methodology
Kaizen project
ANOVA comparative methods





From the above table you will notice that there are actually a lot of work needed to done since the start product development phase,  most of multinational company who are market leaders in electronic product such as Dell, Motorola, IBM,  Western Digital,  Panasonic, Sony etc. had well  documented procedure to follow in reviewing input and output of the phase. 

Over the years of working with various type of suppliers,  I found that the extend  best practice compliance level generally  increase with more established type of  company per below.



You would notice that if there is a lot of effort taken with useful data analysis and systematic approach in the product development phase to proactively address problem, we would have eliminated a lot of opportunity for defective product.  If there is not much work done up front to address all the potential problems then the organization would end up with a lot of “headache” later on in mass production.  There will be reactive firefighting everywhere to resolve problems.   Worst still sometimes those problems will need time to resolve and customer delivery schedule could be missed.  In reality a lot company tends to operate in firefighting mode rather than putting more resources during development to prevent fire from happening.  The reasons ascribe to have a “firefighting” culture in company could be

·        Top management does not have the right mindset drive for proactive culture

·        Organization does NOT hire the correct management team with preventive mind set

·        There is lack of talent with such ability to be able to predict potential problem and have action to prevent

·        Organization encourages firefighting culture as someone will be the firefighting “hero” and get rewarded thus looks good.  By right the “hero” should be punished as they could be the creator of the problem.
Say NO to firefighting

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