TTMG 5103 Module Techniques and Tools for problem diagnosis and improvement prior to commercialization Shiva Biradar TIM Program, Carleton University

Agenda Process Behaviour charts Monitor process performance to keep the new solution in control Cause and effect diagram Investigate the root causes of performance problems Cause and Effect Matrix Identify the key input-output relationships in need of attention Control Plan Ensure that your new solution becomes commercialized as planned

Process Behaviour charts Process behaviour charts are used to monitor performance of a process, product, service or solution at the out (Y) and input (X) levels to ensure whether the process is executed as planned Charts can be used to monitor the performance of new innovation as it goes into production or commercialization after its design

Process Behaviour charts Charts help create visibility that is necessary to ensure new innovation has successfully made the transition from the drawing board into the real world Basic charts are explored in this presentation For sophisticated performance evaluation, one may need process expert or statistician, then suggested to use process behaviour charts software for calculations and drawing charts

Process Behaviour Charts General steps for constructing charts are based on type of data involved: Attribute data Data that you cannot count Variable data Data on a scale

Process behavior charts Steps to construct attribute data charts 1. Gather and plot data 2. Calculate control limits 3. Interpret the chart according to an established rules

Process behavior charts – Attribute data charts 1. Gather and plot data 1. Determine the frequency of data collection 2. Record the defect counts 3. Plot the defect data on a time series chart

Process behavior charts – Attribute data charts 2. Calculate control limits 1. Calculate the process average and this to the chart 2. Calculate the upper and lower limits (UCL and LCL)  Common cause variation - When process is in control, the control limits show the ordinary amount of variation  Special cause variation - When the measurement falls outside of the limits, the variation is extraordinary

Process behavior charts – Attribute data charts 3. Interpret the chart according to established rules Rule 1 violation – if there is a large shift in the process that should be investigated immediately (larger than UCL)

Process behavior charts – Attribute data charts 3. Interpret the chart according to established rules Rule 2 violation – when the process operates above or below its performance for an extended period of time, specially for nine or more cycles, it should be investigated to improve permanent process improvement and defect reduction

Process behavior charts – Attribute data charts 3. Interpret the chart according to established rules Rule 3 violation – when the process drifts in one direction or the other for a duration of at least six measurement cycles, it must be investigated to find the cause and fix the process

Process behavior charts - Variable data charts Many process have characteristics that are measured in variable scale than counted on discrete scale More information is available in variable data than in count data Variable charts yield more information than their attribute counter parts

Process behavior charts – Variable data charts Variable charts Xbar/R Chart or average and range chart Variable data charts construction 1. Gather and plot data 2. Calculate control limits 3. Interpret the chart according to an established rules

Process behavior charts – Variable data charts 1. Gather and plot data 1. Determine the frequency of data collection and size of the sub group Subgroup is defined as a few measurements gathered from the dame logical grouping Data from the same machine on same shift in short period of time

Process behavior charts – Variable data charts 1. Gather and plot data 2. Record the raw variable data 3. Compute the average and range of each subgroup 4. Plot the subgroup averages and ranges

Process behavior charts – Variable data charts 2. Calculate control limits 1. Calculate the process average and the average range, and add them to charts 2. Calculate the upper and lower limits (UCL and LCL) for the average chart and range chart, and add control limits to charts

Process behavior charts – Variable data charts 3. Interpret the chart according to established rules Rule 1 violation – if there is a large shift in the process that should be investigated immediately (larger than UCL)

Process behavior charts – Variable data charts 3. Interpret the chart according to established rules Rule 2 violation – when the process operates above or below its performance for an extended period of time, specially for nine or more cycles, it should be investigated to improve permanent process improvement and defect reduction

Process behavior charts – Variable data charts 3. Interpret the chart according to established rules Rule 3 violation – when the process drifts in one direction or the other for a duration of at least six measurement cycles, it must be investigated to find the cause and fix the process

Process behavior charts – Variable data charts 3. Interpret the chart according to established rules Rule 4 violation – occurs when two of any three data points reside more than two standard deviation from the process mean. This indicates that process has unnecessarily shifted higher or lower and the out of the control state should be addressed

Process behavior charts – Variable data charts 3. Interpret the chart according to established rules Rule 5 violation occurs when the process has shifted higher or lower to a smaller degree than a rule of four pattern. The fourth point of any five points that resides more than one standard deviation beyond the mean indicates that the process has shifted

Cause and Effect diagram When out of control conditions are identified, to figure out what happened and hot to figure it out from happening again, following techniques can be used: Cause and effect diagram Design of experiments Conjoint analysis Measurement system analysis

Cause and Effect Diagram Enables to brainstorm and categorize the variables that might be causing poor performances in new innovation process Use C&E diagram before going to production to reduce defects Make sure the team is aware of the system and are open to getting to root cause of any defect

Cause and Effect Diagram Using C&E diagram, one can systematically identify all the potential causes that may be contributing to low customer satisfaction

Cause and Effect Diagram Steps to construct C&E Diagram State of the effect Choose cause categories Identify inputs Ask why Discover root causes

Cause and Effect Matrix A C&E matrix helps to determine which critical process inputs have the most impact on process outputs A C&E matrix allows to qualitatively determine the importance of cause-and-effect relationships between process inputs and outputs Beneficial especially when enough quantitative data is not available to understand relationship between inputs and outputs, or figure out which factor has critical influence

Cause and Effect Matrix Steps to construct C&E matrix Identify and rank process outputs Identify process steps and inputs Rank process inputs Calculate cumulative effect

Control Plan Critical to ensuring the innovation will be produced and delivered according to design regardless of location, personal, environment or other variables Helps to mitigate risk when moving from a controlled environment( such as research lab) into an operational environment (like the factory floor)

Control Plan Enables any organizations to replicate the customer experience by clearly documenting how to keep the process in control what to do if it goes out of control who is responsible for putting it back in control Results in reproducible process that delights customers and maximizes profits

Control Plan Steps to prepare control plan 1. Identify process Step 2. Identify inputs 3. Identify outputs 4. Identify Specification limit 5. Identify process capability 6. Identify measurement system 7. Identify current control method 8. Identify who – roles 9. Identify when and where 10. Identify reaction plan 11. Identify transition plan

Conclusion Charts can be used to monitor the performance of new innovation as it goes into production or commercialization after its design A C&E diagram enables to brainstorm and categorize the variables that might be causing poor performances in new innovation process

Conclusion A C&E matrix allows to qualitatively determine the importance of cause-and-effect relationships between process inputs and outputs Control plan ensures the innovation will be produced and delivered according to design regardless of location, personal, environment or other variables

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