Lean Six Sigma Project Presentation Template

1. Lean Six Sigma Project Presentation Template [PROJECT TITLE] [Project Period from mmm-yy to mmm-yy] [Company Name Division / Deptt Team]

2. Introduction • About the company • Products, Location • Wh

3. DEFINE

4. DEFINE • Project Charter should contain • Project Title: • It should include clearly the name of the process to be improved • Business Case • What is the “Pain” issue? (Problem symptoms) • How long it has been there? (Historical trends) • Current financial and non-financial impacts of the issue • Future impacts if improvement is not made now • How is the issue related to strategy?

5. DEFINE • Project Charter should contain (contd..) • Goals and objectives • SMART Objective for improvement • Stage targets (if long-term stage by stage improvement is required) • Expected Benefits from the project • Involve Finance in quantifying the expected benefits (quantify reduction in Cost of Poor Quality) • For stage-by-stage improvement, what are short, medium, long term objectives • Team members/Leader, Project Sponsor, • Project Timeframe (initial) • Project plan in Gantt chart /network diagram form

6. DEFINE • “SIPOC” or “COPIS” Diagram • Correctly identify SIPOC/COPIS (supplier, input, process, output, customer) • Make a high level block diagram of the process relating to the issue • “VOC” • Understand “Voice of Customer” based on available data (customer SLA, specification, feedback) or • Generate a VOC data collection plan including MSA. • CTQs and related Big Ys • Identify the “Critical to Quality” parameters of deliverables to customer . Define Big Y(s) of the process • Classify “Y”s as per Kano Model – Hygiene, Higher the better, Delight factors • Make CTQ Tree to cascade Big Ys into smaller deliverables (Y1, Y2…) • Prepare CTQ Specification Table to clearly define all Ys. • Defect Definition – Define what is not acceptable

7. DEFINE E. Stratification & Prioritization of Ys • Collect historical / current data regarding the Ys • Stratify data for smaller Ys • Analyse historical / collected data e.g. by applying Regression Analysis to identify significant smaller Ys (e.g. Y = f(Y1, Y2….,Yn), F. Project Scope / Boundary • Make Pareto Chart and identify major Ys to focus upon • Define Project Scope/Boundary using SIPOC • Define the boundary of the project in terms of which Ys will be included in the project and what will be done with the remaining Ys. (e.g. plan another project, consider it in the next “6σ projects wave”) • Set Baseline • Define baseline performance for all Ys in the project scope based on historical / collected data • Work out process capability (Sigma Level) for selected Ys based on historical /collected data. • Summarize the “as is” process performance with comments

8. DEFINE • Set Targets • Obtain benchmark data for selected Y based on VOC / SLA and/or competitive / best-in-class performance benchmarks • Define SMART targets for selected Ys and target process capabilities (Sigma Levels) for them • Test the targets statistically to verify if they are significantly different from the current performance level (Hypothesis test) • Review and re-define “defects” and their project targets • Review and re-define the project timeframe and milestones with due dates • Approval of the Charter • Document the Project Charter • Champion and Sponsor review and approve the Charter • Charter is registered with Program Management Office for follow up.

9. DEFINE – Key Points • Define phase is all about Ys, don’t bring in Xs (causal factors) at this stage • Don’t propose solutions in “Define” phase. • The purpose of data analysis is only to clearly define the project scope and set SMART targets for Ys based on VOC (and other considerations).

10. MEASURE

11. MEASURE • Data Collection Plan • It is preferable to collect current data from the process however in some cases historical data may be utilized (if the reliability of data is well established) • Use Prioritisation matrix or FMEA (if existing) to identify critical data to be collected. • Prepare Data Collection Plan specifying: • Parameter to be measured • Whether discrete or continuous • Which location • Measurement method (procedure/code/standard) • Sample Size and Sampling method : How was determined? • How will the data be summarized • Confirmation of calibration of measuring devices and calibration plan if necessary.

12. MEASURE • Measurement System Analysis (MSA) • Verify calibration status and accuracy of measuring devices used in the process. Calibration error should be zero. • Demonstrate MSA (if applicable): • Gauge R&R for continuous variables measurement • Statistical Test for attribute Ys

13. MEASURE • Actual Measurement • Show sample of raw data in tabulated form • Summarise data • Give comments on the process performance based on data

14. MEASURE – Assess Process Capability • Process Capability Assessment • Carry out Process Capability Analysis and determine • Cp, Cpk & • Pp, Ppk • Current defect ppm and process Sigma Levels of the process • Estimate Process Sigma Level Note: Assessment of Process Capability and Measurement of Process Sigma level may be carried out in define phase, in case reliable historical data is available.

15. ANALYZE

16. ANALYZE – Process • Detailed Process Map in appropriate ways • Flowchart or • Spaghetti Diagram • Function diagram of the process system (TRIZ) • Any other • Identify (for lean) • Value Adding activities, • Non-value Adding but necessary activities • Wasteful activities • Identify constraints / bottlenecks in the flow • Make Value Stream Maps • As is VSM • “Should be” or target VSM

17. ANALYZE - Data • Use PFMEA or Cause and Effect diagram to identify all “Potential” causes • Prepare a Cause Validation Table, and apply the appropriate validation methods: • Classify each potential cause into “Strong”, “Weak” and “Insignificant/Irrelevant” Categories • List the “possible” causes separately for further validation. • Prepare validation plan for the possible causes.

18. ANALYZE - Data • Validate “possible” cause by using appropriate tests/tools as needed: • Apply deep analysis to further drill down validated causes to reach “root causes” • Conclude Analysis by clearly identifying validated possible causes and their Root causes. • If DOE / Regression is carried out, predict optimum settings and validate them.

19. IMPROVE

20. IMPROVE- • Based on the identified and validate root causes, generate possible solutions. • Use creative / innovative thinking to explore solutions • Try to incorporate mistake proofing measures. • Prioritize solutions using appropriate methods/criteria

21. IMPROVE E. Plan for implementation • Trial runs / pilot implementation • Trial / pilot implementation plan • Risk analysis of the pilot / trial plan • Plan for MSA and data collection • Method to evidence improvement • Finalize full scale implementation plan, with phases as appropriate • Carry out thorough risk analysis of the agreed solution for full scale implementation • Define stage gates including handing over the process to the process owner.

22. CONTROL

23. CONTROL • Document / amend Procedures, standards, work instructions incorporating the improvements in the process • Define control plan for Xs • Install appropriate control charts (SPC) for Ys and critical Xs. • Document “Out of Control Guidelines” for the operators

24. CONTROL • Evaluate Process Sigma Level and compare with pre-improvement Sigma level • Update Process FMEA and recalculate RPNs, which should go down. • Carry out cost-benefit analysis, include non-financial benefits • Identify opportunities for further improvement • In the same process and • Horizontal deployment opportunities (other similar processes) • Give details of Reward and Recognition within the company. • Share knowledge by publishing / presenting the case study

25. Lessons Learnt • Identify what lessons were learnt regarding problem solving method, project management, teamwork etc