Risk Management and Quality Control

Study Notes

Consists of three parts: risk assessment and analysis, risk evaluation, and risk treatment

Quality assurance (QA) is a proactive process that focuses on preventing quality failures throughout a product's development
Quality control (QC) is a narrower, more inspection-focused process that focuses on detecting defects in the product after development
QA involves activities that improve quality at all stages of a product's development, including production, testing, packaging, and delivery
QA teams focus on processes and procedures like training, documentation, monitoring, and audits to ensure quality requirements are met

Uses data analytics to understand how much of each type of product can be produced in each cycle
Calculates the extent to which production can be speeded up without harming quantity or quality
Sets optimal production targets for a given period

Manages the flow of materials, information, and finances from suppliers to customers
Ensures products meet specific quality standards
Uses predictive analytics to understand customer interest in a product and adjust supply chain decisions

Uses technology to digitize processes and collect data in factories and throughout the manufacturing supply chain
Combines human creativity, digitally connected machines, and AI-powered systems and analytics
Defined by NIST as "fully-integrated, collaborative manufacturing systems that respond in real time to meet changing demands and conditions"

Involves collecting, organizing, analyzing, and interpreting data to gain insights for informed business decisions
Uses statistical and quantitative analysis techniques to extract meaningful insights from data
Can be applied to various areas of business, such as sales, marketing, finance, operations, and customer service
Techniques used include data mining, predictive analytics, data visualization, and statistical analysis