Planning for the start of production also means checking the input before officially going into production, which is not what Chinese mid-sized factories often do.
However, in appropriate cases, performing an input test (FAI) can save the entire product from disaster.
So what is the entry test?
Input testing does not refer to pre-production models, but refers to performing the first part of the steps before the product is actually put into mass production.
This means considering products manufactured with the same materials/components, on the same equipment, by the same level of labor, etc. as in mass production.
For example, a company that has an approved pre-production prototype is perfect and they continue to mass-produce it based on how they manufacture it. During this process, something went wrong and all parts failed. This can happen even in the best factories. FAI is a safety net that detects those situations and prevents production from going on until the problem is fixed and another FAI process confirms all is well.
What happens during an FAI implementation process?
If a number of problems are found, production must be stopped until the cause is identified, the root cause is resolved and another FAI shows effective results.
What problems does FAI detect?
For which product is the most necessary FAI?
This makes more sense when products exit the process one by one, or in small batches, instead of a large batch after long processing (for example, microprocessor manufacturing or pharmaceutical drugs – in those schools) – in that case, there is a much greater emphasis on process control and the use of statistics).
Another important factor is the level of replication built into the process. If you create two cables, the process is numerically controlled and you switch from one type to another several times a day, after a while you may decide not to implement FAI.
Finally, if there is a plan for continuous sampling and if the problems that often occur in FAI will also be encountered in routine verification, no FAI is needed.
(To be continued)
Productivity and Quality Office