Six Sigma Control Phase April 2017 5th Submission

Control Phase 16/04/17: Phase Learning's and Tools Utilised.

Control Phase:

Phase Learnings:

The Control Phase of the project is the requires monitoring, standardizing and completion of FMEA's post Improve Phase.

The key learning for this phase s the importance of standardizing the improvements and ensuring the effectivity of these improvements.

All programs with cross drilled holes have been updated and the machining parameters have will be recorded in a Process Characterization Report which will form part of the design Dossier for the parts.

It is also important to reflect on the project learnings too by form of a Lessons Learned to allow for the pitfalls and benefits to be highlighted for subsequent projects.

Within the time frame assigned for this project a simple production tracker has been introduced to assess the micro-drill process performance on all applicable batches built on the Swiss Turn Machines.

Tools Utilized:

FMEA:

The FMEA for the Process was revisited to assess the impact of the studies and the changes made during the Improve Phase. This to ensured that the required actions were completed and the impact was assesed. The final Column set in the FMEA quantifies the changes made and the new RPN for the failure mode. All identified modes have been addressed and the RPN for each mode has been reduced.

Fig1: Completed FMEA

Process Monitoring:

The current production builds are being monitored and tracked for Micro Drill Failures. 

To date after 380 parts no defects have been recorded for Micro Drill Defects.

Ideally the Control Phase of the project should allow more time to verify the results of the project. 

However the trend to date over build completed is positive with 100% yield across all batches.

Fig 2: Process Yield Metrics Pre and Post Process Improvements

Lessons Learned:

The Final tool adopted for the projects was a Lessons Learned. This was completed as part of a team review of teh project and the results of the review were documented and will be recorded within the final report. 

The Key learnings highlighted for the project were the Time lines associated with the project. At times the completion of the trials was impeded by production tasks.

The otehr key learning for the team was the benefit of the drill length setup piece. This methodology will be implemented across all the other tools on the machine to aid setup and to reduce First part yield at setup.

Six Sigma Improve Phase April 2017 4th Submission

Improve Phase 02/04/17: Phase Learning's and Tools Utilised.

Improve Phase:

Phase Learnings:

The key learning of the Improve phase was that this is the stage of the project where the testing of the determinations and assumptions of the previous phases are proven out. This phase required the project team to implement and assess the process per the potential failure modes and areas to be tested based on the FMEA and C&E prioritization matrix.
The main resultants of the previous phases led the team to implement a tool life study and to implement systems to aid the setup method on the machine and to error proof the process.

Tools Utilised:
The DOE template was the only statistical tool used within the Improve phase.
It was utilised to aid all testing that concerned the determination of process impacts on the main process focus which was Tool Life.
Outside of tasks that relied on DOE inputs documented reports where used to compile and record completion of tasks..

Documenting actions completed during this phase is key for the completion of the FMEA post change assesment.

Tasks Completed:

In order for accurate bench-marking of the action list items the Tool Life study was the first Trial undertaken by the team.

This Trial involved the utilization of a DOE to determine the best machining parameter to achieve the best tool life for the Micro Drill Process.
Trials were carried out for the 0.3, 0.4 and 0.5mm micro drill processes.
For the purpose of the Trial a tool life of 150 was deemed to signify infinite tool life and the tests were stopped when the tool life reached 150 parts.

This DOE focused on three process inputs (Spindle Speed, Peck Depth & Feed Rate) with Tool Life being defined as the Experiment Response

A number of tests were then created with a combination of the Process Inputs, these tests vary one input at a time. This allows for only assessment of the process change based on one factor change at a time.

The 8 tests were then completed and the Tool Life for each trial was collected.

From these tests we were able to determine the factors with the highest interactions within the process.

We can see from the bar graphs below that Factor B+C have the highest impact on Tool Life.
We can clearly see the interactions of each factor to tool life and also the affect of a combination of the factors.

The settings for Row 1 have been standardized as they produced the best results for tool life. These settings were then utilised as the benchmark settings for to assessment of all other trials carried out on the parts per the FMEA + C&E task list.


(These interactions will be further utilized in the future to potentially optimize Tool Life vs Cycle Time.)

Fig 1: DOE Template

Following on from the Tool life study as a bench markse was in task Completion to this effect a number of trials were carried out and the results of these were documented.
the FMEA was updated where required to address highlighted failure modes.

Some of these tasks have been highlighted below to detail how the task was completed.

FMEA Task 1: DOE
Tool Damage Study: A simple test was carried out where the integrity of a tip was assessed before installation in the machine, the tool was then impacted against titanium bar within the machine replicating the most extreme impact that could occur on the tip at installation.
A simple two factor DOE was then completed using the bench mark settings previously determined.

FMEA Task 2: Process Step
Drill Setup Piece: This tasks was completed by developing a small simple piece on the machine to verify the correct tool length. This allows for us to verify the tool is cutting to the correct depth after setup before completion of a full production piece. The machining of this tool has been added to the standard operation procedure for the machine

FMEA Task 3: Process Improvement
Implement Tool Management System:
A Tool management system has been purchased to support the production cell, the tool management system ensures that only the correct approved tools are available to the operator.
Coupled with this a Setup Sheets for each tip has also been introduced, this simple sheets details the tools required to run the part and where the tool is to be placed within the machine.

These Setup Sheets fall under the Standardise phase of 5S.

Fig 2: Setup Sheet Template

The Production area is being developed with a 5S mindset from the outset. To this effect the the 5S principles have been applied in the area and at the Machine.
5S focuses around
1: Sort: A tidy clutter free operational space has been designed for the Swiss Turn Machine
2: Straighten: The area has been laid out to promote a streamlined flow and work areas are tidy and organised with all required tools available to the cell personnel in a tidy and organised manner.
3: Shine: A Daily PM Schedule is in place in the area to ensure no clutter is built up and that the area is kept as designed
4: Standardise: The Swiss Turn area utilises written operational procedures for all aspects of the machine, For this projec the setup piece and the tool setup have been captured under this heading.
5: Sustain: The Sustain section of the 5S will be implemented when the product is launched and the area is transferred to production.

FMEA Task 5: DOE
High Pressure Coolant vs Flood Coolant: As FMEA Task 1 A simple test was carried out where the tool life was assessed with High Pressure Coolant Running and with Just Flood Coolant Running.
A simple two factor DOE was then completed using the bench mark settings previously determined

Six Sigma Analyse Phase March 2017 3rd Submission

Analyse Phase 13/03/17: Phase Learning's and Tools Utilised.

Analyse Phase:

Phase Learnings:

The key Focus of the Analyse phase is to fully review the defined problem and to research into the root causes of the part defect. This is essentially started by completing a Brainstorming session.
This step in the process is the most crucial step and it is paramount to ensure that you have the right people present in the Brainstorming sessions and the subsequent Cause and Effect Rating phase.

To this effect we included the Process Engineer, Development Engineer, Operator, Engineering Manager and Tool Supplier within the Brainstorming process.

Without this inclusive Brainstorming session the Analyse phase would not have been as robust as required to identify all the potential Defect Causes. This for me is the number 1 key learning of the Phase

To document this brainstorming session we utilised the Ishikawa Diagram.
The other tools that are then used in the phase contribute in different ways to the phase outputs but essentially at the end of the phase we have a list of prioritised tasks based on the Cause and Effect Prioritisation Matrix and the FMEA sheet,

Once the brain storming sessions were compiled in the Ishikawa Diagram each potential root cause was rated and this rating was utilised to identify the main areas to focus on. We achieved this by using a Cause and Effect (C&E) Prioritisation Matrix and a FMEA for the process.

The C&E Matrix defines the priority of Inputs to Outputs and helps define which inputs to focus on.
The FMEA contained the specific causes that were linked to the defect based off the Brainstorming.
Once this was completed we had a clear tasks to focus on for the Improve Phase.

The other key phase learning was to ensure that when a cause was identified in the brainstorming session that it was fully understood. This was attained by adopting a 5 Why's approach to each cause. (Examples of this approach can be seen in the Method Example below)
This approach has resulted in a clearly define list of Causes and has helped to highlight the areas for improvement within the process.


Tools Used:
Ishikawa Diagram

The Ishikawa Diagram is often referred to as the cause and effect or fish-bone diagram. This Tool focuses on the potential causes of the defect on the parts and splits them into 6 Main Categories: Man, Method, Machine, Materials, Measure, Mother Nature.

Categorization is crucial to ensure correct root cause is tackled.

Man: This category covers causes related to the person interaction with the process: eg operator error at setup where setup procedure was fully defined but not followed correctly.

Method: This category relates to the way/system in place to allow the process to run. eg. Setup instructions are not fully defined leading to the operator setting up the process incorrectly. This is due to incorrect 'Method' and not 'Man' ( A 5 Why approach was used here to distill this cause to Method vs Man as when reviewed the operator set the process as instructed but the setup process was not correctly defined)

Machine: This category covers the equipment used within the process. eg. Machine Axis accuracy is not correct for process tolerances required.

Materials: This category covers the material inputs to the process, eg incoming raw material out of specification to defined specification.

Measure: This category covers the potential errors due to incorrect measures: eg Vernier Calipers used to measure a feature where a micrometer is defined as the required measurement method for the feature.

Mother Nature: This category covers all other events that could effect the process.
eg. Sunlight levels shining into a process area at different times of the day or through-out the year can for example effect vision system lighting. These type of causes are usually attributed to issues that occur sporadically or cyclically within a process. Typically these type of causes are a last port of call for investigation but it is important to be mindful of the effect of mother nature on processes however remote that may seem.

Once the Brainstorming was completed for our project the Causes were assessed and entered into the template to aid visualization of the causes.

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Ishikawa Diagram: Cause and Effects

C&E Prioritisation Matrix:
From the causes defined in the Ishikawa Diagram the Causes were distilled further into Concise Inputs and Outputs of the process.

Each point on the process is rated with regards to the effect of that point on importance to the customer. This allows for ratings that affect the quality of the final goods to be higher rated than say point that merely provide financial benefits.

The intention of the matrix is to rank the inputs vs the outputs in order to show the inputs that have the greatest impact on the outputs.
In larger projects this matrix would be utilised to reduce the amount of inputs to be assessed in a DOE or trials based on the weighted score.

Outputs:
Hole refers to both the presence of a hole and the non presence of a hole
Burr refers to presence of a burr or no presence of a burr.
Tool life refers to the tool life of the drill.

Each output is Rated for importance to the end user. For the example below the presence of a hole is the most important factor so rated with a 9, the tool life is rated as a 3 as it will only result in a cost of manufacture increase but essentially will not affect the end product to the customer.

The input variables cover causes highlighted in the Fishbone Diagram

The intention of the matrix is to rank the inputs vs the outputs in order to show the inputs that have the greatest impact on the outputs.
In larger projects this matrix would be utilised to reduce the amount of inputs to be assessed in a DOE or trials based on the weighted score.

C&E Matrix: Weighting

Failure Mode Effect Analysis:
A failure mode effect analysis has been completed on the process to further aid the assessment the impact of failure modes on the Process based off the RPN Number for the process.

The excerpt from the FMEA below shows the headings that are assessed within an FMEA.
These are then assessed based on Severity, Occurance and Detection.

Col. 1 Input: eg Micro Drilling
Col. 2 Potential Failure mode: eg Drill Damage During Installation
Col. 3 Effect of the Failure mode: eg Incomplete or Complete Hole with Burr
Col, 4 Severity of this Effect. Severity of 8 is assigned both of these.
Col. 5 Potential cause. eg Drill tip damaged by hitting off the bar within the machine during setup of the tool length.
Col. 6 Potential of the occurance: 3
Col. 7 Current process controls: eg 100% Visual Inspection
Col. 8 Detection Rate: 1
Col. 9 RPN is the Risk Priority Number for the Failure mode.
Col. 10 Actions required: Eg trial on drill to see if this step could cause drill failure
Col. 11 Owner of Action.
Col. 12 Actions Taken
Col 13-16 RPN after actions completed.

FMEA Example

The Severity, Occurrence and Detection rates are defined within standard tables that rate them based on the impact.
Severity assesses the impact of the Failure mode from 1 - 10 with 1 having no effect and 10 resulting in a failure mode that could happen without warning or endanger an operator. For our project example below a rating of 8 is assigned as it will disrupt the production line and part are scrapped if the defect is present.
Occurance is assessed vs failure rate or process Cpk. This rating can be difficult to define as it typically will be based on opinion vs facts. Where historical facts are available these should be utilised. A rating of 1 reflects an occurance of 1 in 1,500,000 and a rankingof 10 reflects a rate of 1 in 2., For this example a rate of 3 is specified as it is believe that the failure mode would be an isolated mode of failure but may happen if not addressed.
Detection is based on the ability of the process to detect the failure mode. 1 refers to a process where detection is almost certain. A rating of 10 reflect where detection is almost impossible, For this example a detection rate of 1 is selected as we have 100% inspection for this defect.

Six Sigma Project Measure Phase Feb 2017 2nd Submission

Measure Phase Feb 19/02/17: Project Outline, Phase Learning's and Tools Utilised.

Project Outline:
As can be read in the Define Phase Blog the goal of this project is to increase the yield of the Swiss Turn Lathe process that will be introduced to the Manufacturing site in 2018.

As a result of the completion of the Define Phase and subsequent project review by the project sponsor a realignment of the Project Charter was conducted to further define the project scope to ensure that the project charter satisfied the SMART requirements.

S  : Specific

M : Measurable

A  : Achievable

R  : Relevant

T  : Time Bound

This Acronym is a vital tool to help to ensure that the right projects are defined and actioned within a manufacturing site. 

Specific:

The initial project charter specified to increase production yield. This target was not specific enough to have a properly defined project target. The project goal has been now fully defined as the reduction of Micro Drill Defects in the Swiss Turn Process by 80%, from 11% to 2% by 16th April 2017.

Measurable:

This project goal is now fully specific. The defect Rate is measurable throughout all development runs.

Achievable:

Based on process knowledge the result is believed to be achievable within this project.

Relevant:

The relevance of the project is driven by the Manufacturing Transfer agreement which will not allow for the transfer of a project from development to production based on the current yield projections.

Time Bound:

The project is also accurately Time bound as the project deadline for the product transfer to production aligns with the timeline within this project.

Based on the realigned project scope we are now fully compliant on the SMART deliverable required within the Manufacturing Site for Green Belt Projects.

Measure Phase:

Phase Learnings:

The key learning that I have taken away from this stage of the project is to fully ensure that before moving on in the process it is vital to ensure that you have an assessment system in place that will fully capture the data required and will ensure that a robust Analyse and Improve Phase can be implemented. 

One other learning is that it is never too early to start the measurement phase. For this project there will be only 3 data sets available as we move into the Analyse Phase. Ideally the more data that you have the more assured you can be regarding the correct direction of the project. However I can also see a potential for having too much data and becoming bogged down in the data before the project starts.

The Define phase data that was collected greatly overlapped with the Measure phase and formed the basis of the direction that the Measure Phase was conducted in.

To that end whilst it is not ideal to have so few data sets it is clear from these data set that a process problem is present. 

From the Pareto Analysis the Micro Drill Defects account for over 94% of the defects seen through out the production run. 

These defects were further assessed and the Micro Drill Defects were further defined to 3 distinct categories:

Incomplete Micro Drill with Burr

Complete Micro Drill with Burr

Incomplete Micro Drill without Burr

All of the above defects result in a product that is deemed to be scrap.

To this effect the data we are utilising will be Attribute data. 

Dealing with attribute data will bring challenges to the project in the Analyse phase.

For the Measure phase a data collection plan was implemented and a better defined process monitoring has been introduced across all batch runs with Micro Drills. This data is collected in a Pass Fail Criteria, a Defect Compilation and also in a run chart format.

This Measure data was utilised to complete a P Chart for Attribute Data. 

The key phase learning that I have taken from this project is the importance for implementation of robust data collection for as early as possible when working on a process. 

As it stand for this projects we have now attained three data sets which will be utilised for compilation of the P Control chart.

Using the Flow Chart for Chart Selection a P Chart is deemed the suitable chart as the data set size is variable and the the data is tracking a defective part.

Attribute Chart Type Selection (Ref Lecture 16 Course Notes)

Tools Used:

P Chart:

The P chart variables were then calculated utilising the P Chart equation:

P Chart Calculation Formulas (Ref Lecture 16 Course Notes)

The data was then defined based on the following attribute data

Batch Run Mirco Drill Defect Data

Utilising the Data above the The Centre Line and UCLp were defined as 0.0923 and 0.1684 respectively.

P Chart of Run Data

MSA:

A trial MSA has been completed with the three project members. This study is vital to the project as it helps to define if a viable inspection method has been developed to detect the defect on the parts.

This tool is used in conjunction with the visual standards that have been defined for the process. 

From the Trial MSA data it is evident that an acceptable inspection system has been implemented to visually inspect these parts for defects. 

From the MSA data we can see a number of parts were incorrectly identified. The root cause of these incorrect definitions was due to operator distraction. An official MSA will be conducted prior to product launch with trained operational personnel. This is a key project goal and for the purpose of this test we are happy to accept the results as is and to progress with the defect assessment in this way. 

Type 2 Trial MSA

 

Six Sigma Project Define Phase Feb 2017 1st Submission

Define Phase Feb 07/02/17: Project Outline, Phase Learning's and Tools Utilised.

Project Outline: In 2018 a new product will be launched within my manufacturing site. This new product launch requires the development and introduction of a new Sliding Head Lathe Manufacturing Cell to the factory. The projected Yr 1 Output for this product is 44,000 Units. A Trial manufacturing run has been completed utilising the proposed Sliding Head Lathe Production process. The Yield achieved during this trial run of Qty 300 parts was 88%. For an effective process to be transferred from to Operations a targeted yield rate of 95% is required from the Sliding Head Lathe Manufacturing process. The undertaking of this project will focus on analysing the production process and implementing process improvements in order to increase the process yield. Define Phase Learning's: The key Phase challenge that I encountered was the clear and concise definition of the project without jumping to the solution phase. The key challenge for me was to compile the data as presented and to let the Define tools drive the direction of the Investigation for the project. Previous to utilising these tools I would have jumped into the solution without defining the key benefits, the scope of the improvements and subsequently the plan for measuring and implementing the solution. Define Phase Tools Utilised: Project Charter: I found that completion of the project charter within the project was instrumental to focus the benefits of the project and to quantify the savings associated with the proposed project. The project charted also helped to fully define the scope of the project and the key areas for focus. Without this definition the potential for the project to creep from the overall goal would be a major source of concern. The physical completion of the Project charter was difficult as the Business Case, Project Goal and Opportunity Statement are intrinsically linked that a clear definition of what was required within each pane was required. SIPOC: A full process SIPOC has been developed for this project. Completion of the SIPOC is a key task as it helps to clearly define the Inputs/Outputs of each process Step. Previously my experience of this process step would have been to merely produce the process flow only without the inclusion of the S,I,O,C, The addition of these steps to the flow help to clearly define key process contributors and the downstream Customers. Pareto Analysis: The scrap parts produced during the Production run have been analysed and initially quantified on a simple defect Pareto graph. This graph has highlighted the area that the key defect area to assess with regards to Yield Increase is around the Microdrill process. This pareto was further distilled to further defined the defects arising in the process to aid the Is-Is Not matrix generation. The cause of these defects will be fully determined during the subsequent phases. Pareto of Defects: High Level Pareto of Defects: Defined Failures IS-IS NOT: I found the IS-IS NOT tool extremely beneficial at the end but at the start I found the format somewhat confusing. The key issue was regarding the difference between the Is and the Is Not columns. The approach that I took on the IS-NOT was to describe what had not occurred but could occur in the future. This is somewhat difficult to complete as it potentially overlaps with the Cause and affect Diagrams that would be deployed during the Analyse phase of the project. Define Phase IS - IS NOT