11

ISSUE

THREE

YEAR

2011

VOLUME

FOUR

Lean Six Sigma

Management and leadership training and consulting

Cost benefit analysis   performance  metric

Science of Resource Informed Decisions

LSS

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Six Sigma is a business management strategy originally developed by Motorola, USA in 1986.  As of 2010, it is widely used in many sectors of industry, although its use is not without controversy.

 

Six Sigma seeks to improve the quality of process outputs by identifying and removing the causes of defects (errors) and minimizing variability in manufacturing and business processes. It uses a set of quality management methods, including statistical methods, and creates a special infrastructure of people within the organization ("Black Belts", "Green Belts", etc.) who are experts in these methods.[3] Each Six Sigma project carried out within an organization follows a defined sequence of steps and has quantified financial targets (cost reduction or profit increase).

 

The term Six Sigma originated from terminology associated with manufacturing, specifically terms associated with statistical modeling of manufacturing processes. The maturity of a manufacturing process can be described by a sigma rating indicating its yield, or the percentage of defect-free products it creates. A six sigma process is one in which 99.99966% of the products manufactured are statistically expected to be free of defects (3.4 defects per million). Motorola set a goal of "six sigma" for all of its manufacturing operations, and this goal became a byword for the management and engineering practices used to achieve it.

 

 

 

1.   Introduction to Lean Six Sigma

2.   Leadership

3.   Strategic Planning

4.   Creating a Customer Focus

5.   Process Improvement Teams

6.   Costs of Quality

7.   Process Performance Measures

8.  Project Management 

9.   Problem-Solving Using Define, Measure, Analyze, Improve, Control

10.  Value Added Process Mapping

11.  Just-in-Time and Kanban

12.  Five S

13.  Kaizen and Error Proofing

14.  Work Optimization

15.  Productive Maintenance

16.  Supply Chain Management

17.  Statistics

18.  Variables Control Chart

19.  Process Capability

20.  Reliability

21.  Design of Experiments

22.  Failure Modes and Effects Analysis

23.  Quality Function Deployment

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