๐๐ฎ๐ง๐๐๐ฒ'๐ฌ ๐๐๐๐๐๐๐๐ ๐๐ ๐๐จ๐ฌ๐ญ : ๐๐ก๐ ๐ ๐ฅ๐จ๐๐ญ๐ข๐ง๐ ๐๐จ๐๐ฉ ๐๐๐ฌ๐ฌ๐จ๐ง: ๐๐ก๐ฒ ๐๐๐ง๐ฎ๐๐๐๐ญ๐ฎ๐ซ๐ข๐ง๐ ๐๐ข๐ฌ๐ญ๐จ๐ซ๐ฒ ๐๐๐ญ๐ญ๐๐ซ๐ฌ
๐๐ฎ๐ง๐๐๐ฒ'๐ฌ ๐๐๐๐๐๐๐๐ ๐๐ ๐๐จ๐ฌ๐ญ
๐๐ก๐ ๐ ๐ฅ๐จ๐๐ญ๐ข๐ง๐ ๐๐จ๐๐ฉ ๐๐๐ฌ๐ฌ๐จ๐ง: ๐๐ก๐ฒ ๐๐๐ง๐ฎ๐๐๐๐ญ๐ฎ๐ซ๐ข๐ง๐ ๐๐ข๐ฌ๐ญ๐จ๐ซ๐ฒ ๐๐๐ญ๐ญ๐๐ซ๐ฌ
In 1878, the Procter and Gamble Company perfected a new bar soap formula called “White Soap”. It featured a pure white color, because their new process removed the impurities common in soaps of the time. The product was successful as an incremental improvement over earlier products.
One day in 1879, customers came to the company raving about a batch of the product that excited them. These bars floated in bathwater, unlike all other bar soaps, making them easier to find and use. But the company was embarrassed, not knowing why this one lot was unique.
They eventually discovered this batch had been left in the mixer for too long. A froth of tiny air bubbles had become entrained, remaining there during solidification. The resulting bars had a lower bulk density, allowing them to float. The process error turned into an iconic product because the company was able to tie the desirable characteristic to the specific process conditions.
There is no substitute for good record keeping in the process dependent environment to understand the source of a problem or a benefit at an end user.
Can your organization determine the production run of an especially good or bad batch of product? Which line or machine produced that lot? How was the equipment configured at the time? Who was the operator? What were the process conditions during that shift? Were these conditions distinct from other batches that had a different performance?
Which lot of raw materials was used? What were the QC measurements? How was it stored and transported afterwards? Is there more material from that same batch in the distribution channel?
For more see: https://lnkd.in/ebX5CNUy
source : Tim Oberle

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