Lean Six Sigma

Typical Situations Lean production problem in the manufacturing plant

Your current problem solving is based on trial-and-error, does not identify root causes, and consequently problems are not fixed but reoccur instead?

Do you find your current process quality initiatives being slow, bureaucratic and unable to deliver quick, tangible and sustainable results?

You want to move your organization to the next level of performance and instill quality in every aspect in the company culture?

Our Solution

Lean Six Sigma is a continuous improvement methodology that was originally designed to improve manufacturing processes, but its application was subsequently extended to business processes. Lean Six Sigma concentrates on the systematic application of statistical and quality methods to reduce variation and defects in existing processes. It ensures that decisions are made on the basis of statistically sound data, rather than assumptions and guesswork. Lean manufacturing tools are integrated to identify and remove waste in the process to optimize product lead times.

Lean Six Sigma is a structured and disciplined way of solving the critical issues from a business perspective that you haven’t been able to solve with other methodologies. It differs from other quality initiatives such as TQM and Zero Defects in that it is fact based and data driven, as well as result oriented, providing relatively fast measurable bottom-line results, linked to the business strategy and related to customer requirements. Lean Six Sigma has the following main objectives:

Lean Six Sigma: operator inspecting water bottling production line

Our Method

Based on our hands-on experience in Lean and Six Sigma deployments, Innovensys has developed a Lean Six Sigma training curriculum (DMAIC) that can be customized to improve manufacturing, engineering, transactional, and administrative processes. It includes the following tools:

  • SIPOC
  • Process Map
  • Cause & Effect Matrix (C&E) 
  • Process FMEA
  • Basic Statistics
  • Variable & Attribute MSA
  • Process Capability (Cp, Cpk) 
  • Central Limit Theorem (CLT)
  • Confidence Intervals
  • Hypothesis Testing
  • Analysis of Variance (ANoVA)
  • Non-normal Data Transformation
  • Single & Multiple Regression

 

  • Design of Experiments (DoE)
  • Response Surface Modeling (RSM)
  • Multiple Response Optimization (MRO)
  • Quality Control Strategies
  • Statistical Process Control (SPC) 
  • Value Stream Mapping (VSM)
  • Product Families
  • Product Flow Analysis
  • Employee Activity Analysis
  • Set-Up Reduction  
  • Cell Implementation
  • 5S & Visual Workplace
  • Standard Work