How to Determine Process Inputs which have an Impact on Process Output

If you had been following all the articles in this website,  you would have already know that there are 6 types of process inputs, Xs  (man, machine, method, material, measure and environment) which could impact a process output/s, Y/s.   However all the 6 process inputs do not impact process output Y in the same magnitude.

There are some process inputs could have very minimal impact while other process inputs could have more influence to the process output.  In turn each process inputs could have its own numerous factors which could impact key output parameters to customers.  

Process input	Examples of factors
Man	Operator training, experience, skills, type of training program,  skill of trainer, management direction etc
Machine	Machine brand, setting of various parameters, level of automation
Method	Work instruction clarity, creator of work instruction, process step and layout, skillset of engineer
Material	Different vendor, different batch, raw material,  manufacturing variation contributed by 5M 1E
Measure	Measurement instrument, measurement method,
Environment	Humidity, temperature, pollution , seasons etc

In order to achieve consistent quality products as perceived by customer, manufacturers must be able to find which vital few factors from each process input/s has/have  major effect on the process output and then control the setting those factors.

The best methodology  to determine which process input factor/s which the most influence on process output which are key to customer would be design of experiment (DOE) .  DOE is a systematic planning and conducting a series of experimental runs in which controlled changes are made to inputs in order to observe and identify causes for changes in the outputs of a system or process. 

DOE methodology consist of the following steps using a good statistical software such as minitab or JMP :-

1. Define  - Understand the quality related problem, identify the key parameters output to customer known as responses, Y.  Use skills and experience to map to the potential input X
2. Design -  Select process input X and  set process input X to high and low setting (level), and design the experiment according to number of factors and setting levels
3. Conduct 1^st experiment  – Verify measurement system for process output measurement (refer to this article and what is measurement system verification http://www.360qualitymanagement.com/2017/09/importance-of-performing-gage.html).  Run experiment according to design and collect data on process output
4. Analyze – Develop a prediction model to estimate the effect of process input factor, X to process output Y.  Identify the potential process input factor which have significant effect on process output Y
5. Optimize throught 2^nd or more experiment  – Fine tune the model to optimize the setting of the process input X to get the best results for process out put Y through prediction modeling
6. Validate through another series of experiment -  Validate the optimum setting and measure process output Y,  Check if the actual results against prediction results.

So far I have not really met any real DOE expert in computer component manufacturing industry which  I have deal with,  and  there are many processes had never been able to optimize their output,Y, due lack of expertise to really understand and able to conduct a true DOE.  I have seen many pitfalls in design of experiment in the following areas :-

1. Trial and errors,  wild guess methods had been mistaken as DOE method
2. Unable to measure process output correctly and there are measurement error associated to measurement process
3. Do not separate controllable and uncontrollable factors
4. There are no real modelling been done to be able to conclude which parameter.
5. Did not use statistical software to do prediction modeling
6. Use the wrong prediction modeling such as process output with binomial distribution (yield rate pass or fail) should use Logistic binary regression
7. Sample size for DOE is not big enough to predict experimental errors contribution in an experiment.
8. Do not understand how to analyze interaction effect between factors.
9. Jump into conclusion after running the 1^st experiment and did not do consecutive experiments such as reduction, optimization and validate all the findings.
10. The actual model did not fit prediction model and there are no attempt to understand why the model did not fit such as the factors X selected does not have impact on response Y or  the impact of controllable factor is more than controllable factor.

Design of experiment is a very powerful tool which enable a manufacturer to understand, optimize and control the vital few process inputs, X to obtain a desirable process output, Y.  This require systematic approach to define, conduct and analyse the experiment and its results under the supervision of a DOE expert.