Lean Vs Six Sigma

 

• When it comes to process improvement Lean and Six Sigma are both useful and complement each other.
• Lean focuses on the elimination of waste (non-value-add)
• Six Sigma tries to get to the root cause of waste to avoid problems from reoccurring 
• A common source of waste is variation 
• Six Sigma focuses on reducing variation

What comes first?  Philosophy or Tools?

• Lean and Six Sigma philosophies are essential to justify and understand the tools required to implement them.
• But these philosophies are difficult to understand without a deep understanding of the tools. 

Manage the Improvement Project 

• A project charter is useful to define the project, participants, objectives, etc.
• Project scheduling keeps track of who does what and when
• An Obeya Room is often used to focus all project improvement actions and keep everyone up to date.

Understand the Current Process 

• Before we start improving we need a deep understanding of the current process so we can Go to Gemba and build a Value Stream Map to describe what is going on
• We will need additional Gemba visits to collect significant process data
• Time is a key metric in any process so Timestamp recording is key to understand process dynamics
• The analysis of the VSM can help us understand where in the process are defects produced and where detected and so focus on the source
• Test and control process steps require special attention because failing items need repair and this may become the bottleneck for the total value stream if quality drops
• Combine automatic machine and operator times 

Understand Variation

• Variation is like a virus infecting all processes. Its understanding is key to interpret process behavior
• Simulation can help us understand some of its effects
• Lean identifies Non-Value-Add such as waiting times. Reducing or eliminating these requires digging into the root cause. Sometimes the root cause is variation and we need Six Sigma to analyse and improve it.
• Value Stream Map Simulation can help to understand the failure mechanisms leading to WIP accumulation or other problems

Is the Process Stable? 

• When it comes to analyse the process data we have collected we want to know if this data really represents this process: Is it different in the night shift? Is it different on Mondays?
• Normally a Control Plan defines the control points along the process: some automatic, some manual, etc. 
• Statistical Process Control enables the control of a critical process parameter by the operator.
• Different control charts are used to ensure process stability
• Detecting seasonal process trends is possible if timestamps have been collected
• Process stability is a requirement for any further analysis

Analyse your Process Data 

• There is no real process improvement until we get to know the root cause of a problem and resolve it. If we limit ourselves to acting on the symptoms the problem may reoccur.
• Process Data Analysis will allow us to draw statistically valid conclusions from our data
• Correlation and Regression can help us find relationships between process factors and key process outputs in order to improve these
• Process Capability measures to what extent is the process able to meet the customer requirements not just now but also in the future.
• Process auto-correlation is an consequence of inertia in some processes   

Improve your Process 

• Once we get to the root cause of problems and find the critical process factors we can often find ways to optimize the process with the Design of Experiments and Response Surface methodologies.
• Theory of Constraints is a useful methodology to optimize the process focusing on the overall constraints such as capacity and set-up times. 
• Job scheduling is required when key equipment is shared by different products and set-up times are involved
• Large lead times are sometimes caused by an accumulation of WIP. One way to avoid this from happening is Kanban logistics 
• Data entry alternatives