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Lean Six Sigma Design of Experiments Y = f(x)

By on July 15, 2012 in Lean Six Sigma, Uncategorized
Design of Experiments
§DOE was originally developed in 1930’s by Sir Ronald Fisher to improve agricultural methods
§Fisher used DOE to maximise the yield of agricultural crop (Y) by changing the key process inputs; fertilizers & seed type (x’s)
§ he DOE approach allowed Fisher to understand the main effects of the inputs, and the interactions between the inputs which impact the process output
§The objective is to logically organise changes to 2 or more input variables (x’s) and evaluate if any variable, or any combination of the variables, significantly affect the output (Y)
What is Design of Experiments?
§A DOE is a set of tests on the process output with at least 2 process inputs, each set at 2 or more levels
§The key principle behind the DOE technique is to create a perfectly balanced design which includes an equal combination of process settings

Lean Six Sigma Why Learn Hypothesis Testing?

By on July 6, 2012 in Lean Six Sigma, Uncategorized

To identify sources of variability using historical or current data:

  • Passive: a process is sampled or historic sample data is obtained
  • Active: a modification is made to a process and then sample data is obtained

Provides objective solutions to questions which are traditionally answered subjectively

Works for differences in means, or Variances (Standard Deviations), or proportions

Lean Six Sigma and Value Stream Mapping

By on June 27, 2012 in Lean Six Sigma, Uncategorized
§The current state VSM was created in the Review Phase and is analysed in the Investigar Phase for improvement opportunities.
§There are many lean tools & techniques which can be used but the majority of these are outside the scope of EGB training (see EBB Session 2).
§However, the majority of Lean tools & techniques are common sense improvements that become obvious after modelling the whole value stream.
§Potential improvements opportunities are represented as Kaizen Bursts (call-outs) on the VSM, at the appropriate place within the value stream.
§Kaizen is Japanese for improvement.

new KPI TEEP in Lean Six Sigma

By on June 17, 2012 in Information Technology, Uncategorized

World Class OEE is generally accepted as >85%

The world class OEE performance of 85% is comprised of:

Availability = 90%

Performance = 95%

Quality = 99.9%

Research indicates that average OEE for manufacturing plants is 60%

How does your organisation compare against the ‘best in class’ performance?

Imagine what a 40% improvement in OEE (going from 60% to 85%) could do for your organisations competiveness and profitability!

Organisations are now factoring in how often the equipment is used throughout the year (24/7) – this is called the Loading.  For example if the equipment is used for 40 hours in a week (168 hours) the loading is 40/168 = 23.8%

This can be factored into the relatively new KPI – Total Effective Equipment Performance (TEEP) metric as follows TEEP = Loading x OEE

Design of Experiments Lean Six Sigma

By on May 24, 2012 in Information Technology, Lean Six Sigma, Uncategorized

Y = f(x)

DOE was originally developed in 1930’s by Sir Ronald Fisher to improve agricultural methods Fisher used DOE to maximise the yield of agricultural crop (Y) by changing the key process inputs; fertilizers & seed type (x’s) he DOE approach allowed Fisher to understand the main effects of the inputs, and the interactions between the inputs which impact the process output

The objective is to logically organise changes to 2 or more input variables (x’s) and evaluate if any variable, or any combination of the variables, significantly affect the output (Y)

What is Design of Experiments?

A DOE is a set of tests on the process output with at least 2 process inputs, each set at 2 or more levels

The key principle behind the DOE technique is to create a perfectly balanced design which includes an equal combination of process settings

Consider the example below with 3 key process inputs, each set at the high end (1) and low end (0) of their respective specification (or process variation) limits

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Black Belt training during the summer in Würzburg

By on April 28, 2012 in Lean Six Sigma, Uncategorized

during the months of July and August there will be several green and black belt session for Lean Six Sigma; all events will be held in the Maritim Hotel in Würzburg.

Zentral und unweit des Hauptbahnhofs erwartet das stilvolle Maritim Hotel Würzburg seine Gäste. Direkt am Mainufer gelegen, bietet es einen herrlichen Ausblick auf die Festung Marienberg, die hoch über der Stadt thront. Die barocke Innenstadt mit ihren zahlreichen Sehenswürdigkeiten lässt sich bequem zu Fuß erkunden. Elegantes Ambiente, verbunden mit herzlicher Gastfreundschaft, und der direkte Anschluss an das Congress Centrum Würzburg schaffen ideale Voraussetzungen für jeden Reisezweck.

 

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Lean in Engineering

By on April 28, 2012 in Lean Six Sigma, Uncategorized
Two dimensions of Lean in Engineering
Lean in Engineering / Product Development deals primarily with information as the material to its processes
It goes beyond Lean in Manufacturing in so far as the product is not yet defined

Thus there are 2 dimensions to Lean in Engineering / Product Development:

1.Process dimension:
–mastering processes to meet the time, quality, and cost expectations of the markets
–making processes manageable and measurable
–providing as much space for knowledge and creativity to unfold as possible
2.Product dimension:
–finding the best solutions to a given problem
–changing less in detail phase

Generally, mastering the process dimension is the prerequisite for mastering the product dimension

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Lean Development in Lean Six Sigma

By on April 28, 2012 in Information Technology, Lean Six Sigma, Uncategorized

The 13 Lean Development principles are:

  1. Establish customer value
  2. Front-load the Product Development process
  3. Levelled Product Development process flow
  4. Rigorous standardisation
  5. A Chief Engineer System to integrate development
  6. Balance functional and cross-functional expertise
  7. Towering technical competence in Engineers
  8. Integrate suppliers into the PD system
  9. Build in learning and continuous improvement
  10. Build a culture to support excellence and relentless improvement
  11. Adapt technology to fit your people and processes
  12. Align the organisation through visual communication,, ensuring problems are visible
  13. Enable organisational learning
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Visual Management is key within Lean Six Sigma projects

By on April 25, 2012 in Lean Six Sigma, Uncategorized

The key to world class flexibility and high quality is the ability to understand at a glance what is going on in the workplace. Visual Management helps everyone in the workplace become involved in monitoring progress and customer service. Visual Management guarantees increases in efficiency, quality levels, productivity, and reductions in man hrs on the job. VM not only makes problems obvious, it provides a means to solve them The purpose of VM is to make everybody’s job easier VM uses all 5 senses to create a simpler, self regulating facility, resulting in increased Quality, productivity and morale.

Pursue Perfection through Standardisation;

Now that improvements have been made it is important that they become the new STANDARD and the team do not fall back into the old ways of working. It does not stifle creativity, it enhances it.

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Lean Six Sigma TIM WOOD in engineering

By on April 25, 2012 in Lean Six Sigma, Uncategorized

Waste in Engineering Examples

Transportation:  Excessive data or information handoffs

Inventory: Requirements, specifications, documents waiting to be processed, test data waiting to be validated

Motion: Searching for information, or data, attending unnecessary, ineffective meetings

Waiting:  Inter-task variation, bottlenecks, failure of supplier to meet customer need dates

Over Production: Mass document releases, Preparing excessive reports, broadcast email of information

Over Processing: Gold plated designs (Including design features not required by customer, Re-inventing what has already been designed

Defects: Faulty, incomplete or inaccurate data, data translations

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