Production Quality Optimization
Production Quality Optimization - Related Videos
00:05
Preparation of High-Quality Fermented Fish Product
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Cited by 4 •
2019
The goal of the protocol is to provide an industrialized fish fermentation technique based on inoculation of Saccharomyces cerevisiae.
23:21
Optimization and Utilization of Agrobacterium-mediated Transient Protein Production in Nicotiana
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Cited by 89 •
2014
Transient protein production in Nicotiana plants based on vacuum infiltration with Agrobacteria carrying launch vectors (Tobacco mosaic virus-based) is a rapid and economic approach to produce vaccine antigens and therapeutic proteins. We simplified the procedure and improved target accumulation by optimizing conditions of bacteria cultivation, selecting host species, and co-introducing RNA silencing suppressors.
06:24
Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
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Cited by 9 •
2017
This manuscript describes a generic approach for tailor-made design of microbial cultivation media. This is enabled by an iterative workflow combining Kriging-based experimental design and microbioreactor technology for sufficient cultivation throughput, which is supported by lab robotics to increase reliability and speed in liquid handling media preparation.
13:24
Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
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Cited by 2 •
2016
An integrated system for targeted next-generation sequencing of oncology specimens is described. This cross-platform system is optimized for low-quality and low-quantity tumor biopsies, accommodates low DNA inputs, includes well-characterized multi-variant controls, and features a novel variant caller that is informed by quantitative pre-analytical quality control measures.
06:01
Optimized PCR-based Detection of Mycoplasma
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Cited by 14 •
2011
The LookOut Mycoplasma PCR Detection Kit utilizes the polymerase chain reaction (PCR), which is established as the method of choice for highest sensitivity in the detection of Mycoplasma, Acholeplasma, and Ureaplasma contamination in cell cultures and other cell culture derived biologicals.
FAQs
Statistical process control provides a structured way to track process outputs over time and use those measurements to support stable operations. Within production quality optimization, the data reveal whether a process is maintaining consistent performance or showing variation that requires investigation. This shifts attention toward controlling the process before defects create waste or rework.
Root-cause analysis helps teams move beyond observing a defect or inconsistent result to identifying the source of the variation that produced it. That distinction supports corrective action directed at the process itself. In engineering production, addressing underlying causes helps prevent recurring defects, reduce rework, and improve reliability more effectively than relying only on final detection.
Process capability assessment connects observed production performance with the requirements the product must meet. It helps teams judge whether a process can consistently support defined performance, safety, and customer expectations, rather than examining isolated results alone. In engineering decisions, this evidence supports improvement priorities and helps verify that process changes preserve required quality.
Feedback-based adjustment closes the loop between measurement and action. Teams use process-output information to evaluate a change, determine whether operations remain stable, and refine the process when results do not support the intended quality level. This mechanism supports continuing control of quality, throughput, cost, and resource use instead of treating improvement as a one-time correction.
An effective workflow begins by measuring process outputs against defined requirements, then identifying important sources of variation. Teams can apply root-cause analysis, assess process capability, and make targeted adjustments based on the findings. Subsequent feedback helps evaluate the changes and maintain stable operations, creating a recurring improvement cycle rather than a single defect-response exercise.
Quality decisions should consider more than defect reduction alone. Production quality optimization uses data to evaluate how process changes affect product performance, safety, customer requirements, cost, throughput, and resource use together. This broader comparison helps engineering teams select changes that strengthen consistency and reliability without overlooking operational efficiency or the resources needed to sustain the process.
The approach is valuable wherever manufacturing teams must deliver consistent performance while controlling waste, rework, and resource use. Engineering applications include improving production efficiency, supporting product reliability, maintaining stable operations, and strengthening compliance with quality standards. Its data-driven structure also helps teams evaluate process changes and connect manufacturing decisions with defined safety and customer requirements.