Productivity, defined: six sigma

Developed in 1986 by telecommunications giant, Motorola, Six Sigma’s goal is to improve the quality of process output by identifying and removing the causes of defects. It also minimizes variability in manufacturing.

It is used by some of the world’s biggest manufacturers, including Caterpillar, General Electric and PepsiCo.

Using a set of quality management and statistical methods, Six Sigma creates an infrastructure of people within an organization who become experts in specific processes. Single processes follow a series of defined steps and always have financial targets, where it’s to cut costs or increase profits.

Six Sigma originated from manufacturing, specifically terms dealing with statistical modeling of manufacturing processes that suggests the maturity of a manufacturing process can be described by a sigma rating, indicating the percentage of defect-free products the process produces.

A Six Sigma process is one that produces 99.99966 per cent of its product defect-free. (3.4 defects per million).

Six Sigma doctrine asserts that:

• Achieving stable and predictable process results are of vital importance to business success.
• Manufacturing and business processes have characteristics that can be measured, analyzed, improved and controlled.
• Achieving sustained quality improvement requires commitment from the entire organization, particularly from top-level management.

Six Sigma projects follow two project methodologies inspired by the Plan-Do-Check-Act cycle, composed of five phases, bearing the acronyms DMAIC and DMADV.

DMAIC is used for projects aimed at improving an existing business process.

• Define the problem, customer needs and project goals.
• Measure key aspects of current processes, collecting relevant data.
• Analyze data, investigating and verifying cause-and-effect relationships to determine what those relationships are, attempting to ensure all factors are being considered, seeking out the root cause.
• Improve or optimize current processes based on data analysis to create new processes.
• Control the future state process to ensure any deviations from target are corrected before they result in defects.

DMADV is used for projects aimed at creating new product or process designs.

• Define design goals consistent with customer demands and the enterprise strategy.
• Measure and identify CTQs (critical to quality), product capabilities, production process capability and risks.
• Analyze to develop alternatives, creating a high-level design, evaluating design capability to choose the best design.
• Design details, optimize the design and plan for design verification.
• Verify the design, setting up pilot runs, implementing the production process.

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