Chapter 10 Tools used Contents : Importance of using appropriate tools in Six Sigma implementation Process optimisation tools Statistical analysis tools
Importance of using appropriate tools in Six Sigma implementation Motorola discovered that that one of the keys to the success of the Six Sigma initiative had been the application of sophisticated tools that bring more power to the learning & improvement efforts Most Six Sigma tools are implemented in software programs which run directly on PCs Practitioner tools fall into two primary types Process optimisation tools & statistical analysis tools
Process optimisation tools Process optimisation tools enable designing, simulating & optimising work processes These include tools for creating process & workflow diagrams, building cause-and-effect matrices, constructing fishbone diagrams, assessing process capabilities & more The goal of these tools is to help seeing how work is performed & identifying where the source of problem is
Statistical analysis tools Statistical analysis tools enable analysing data collected either from the real world performance of a product or process or as the output of a simulation or experiment These include tools for analysing variance, conducting regressions, performing design of experiments & building control charts, plots, tables & graphs The goal of these tools is to help turning data into knowledge such that one can make informed decisions
Process optimisation tools The SIPOC CT (Critical To) tree Modeling Simulation C&E (Cause & Effect) matrix Fishbone diagram FMEA (Failure Mode Effects Analysis) Capability & Complexity analysis Plans
Process optimisation tools The SIPOC SIPOC stands for Suppliers, Inputs, Processes, Outputs & Customer SIPOC is one of the most fundamental building blocks in the Six Sigma process With SIPOC, one has the basis for defining & characterising the process, the context for measurement & the basis for analysis, identifying areas of improvement & homing in on targets of control contd
Process optimisation tools Suppliers suppliers are systems, people, organisations or other sources of materials, information or other resources that are consumed or transformed in the process Inputs inputs are materials, information & other resources provided by the suppliers Processes processes are the set of actions & activities that transform the inputs into outputs contd.
Process optimisation tools Outputs outputs are the products or services produced by the process & used by the customer Customer customers are persons, groups of people, companies, systems & downstream processes that receive the output of the processes
Process optimisation tools The CT tree CT stands for critical to whatever matters. Depending on what is being analysed & optimised, this could mean anything from the satisfaction of the customer, to the quality & reliability of the product, to the cycle time of manufacture or the cost of the delivered product or service Most CT trees begin with the output of the SIPOC, customer satisfaction at the top & the others being subordinate contd
Process optimisation tools Following CTXs are the most commonly used : Satisfaction (CTS) : what contributes to customer success Quality (CTQ) : what contributes to process or product quality Cost (CTC) : what contributes to cost or final price Delivery (CTD) : what contributes to the cycle time to deliver
Process optimisation tools Modeling a process In process modeling, a Six Sigma process is defined very precisely down to the last detail of activity, resource, decision, dependency & value Each mode, function & activity is backed by numerical descriptions & quantifiable attributes Process modeling begins with the building of process maps. A process map looks like a flow chart which exhibits the activities & events in a process contd.
Process optimisation tools The categories of process element definitions include : Operation cycle time of the process including average time, standard deviation & a distribution curve Resources used in the process, including human, capital & natural resources Value added by the process step in the units of measure that mean the most to the organisation Costs of the resources consumed, which include the costs of personnel, facilities, direct material & can even include indirect costs
Process optimisation tools Simulating a process Simulation tools are advanced computer based programs that ingest all parameters of the model & runs thousands of trials in the computer Through simulation, one generates real life outcomes without having made a single physical change to the process & obtains vast amounts of detailed results More advanced simulation tools animates the process map, tracing where the process will run smooth & where the bottlenecks would be
Process optimisation tools Cause-and-Effect (C&E) matrix In Six Sigma, all outputs are the result of some inputs & the transformations that acted upon them With the C&E matrix, one can identify, explore & graphically display all the possible causes related to a problem or condition & search for the root cause The system level software tools that implement C&E, automatically database this information & carry it forward to FMEA, control plans & data collection plans
Process optimisation tools Fishbone diagram Fishbone diagram is a brainstorming tool used to explore & display sources of variation or influence on a process With the fishbone diagram, one can quickly create the inputs to C&E matrix, identifying the key sources that contribute most significantly to the problem or process being addressed The fishbone diagram also serves as an affinity mechanism for relating & categorising inputs
Process optimisation tools FMEA Failure Mode Effects Analysis The FMEA is key to reducing or eliminating the risk of failures FMEA provides a structured approach to identifying the potential ways a product or process can fail &detecting the failure Using FMEA, one can further prioratise the actions to be taken to reduce failure risk & can evaluate the design & control plans for their robustness to failure Applications of FMEA include Design FMEA, Process FMEA, Product FMEA & Software FMEA
Process optimisation tools One can build FMEA from process map, C&E matrix or the fishbone diagram. Following attributes need to be added to the information for FMEA 1. Severity of impact 2. Probability of occurrence 3. Likelihood of detection One of the primary indicators is the Risk Priority Number (RPN) RPN = severity rating x probability rating x detection rating
Process optimisation tools Poka-Yoke (Mistake proofing) Poka-yoke is an action taken to remove or significantly lower the opportunity for an error or to make the error so obvious that allowing to reach the customer is almost impossible Poka-yoke starts with an understanding of the causeand-effect relationship of a defect. This is followed by the implementation of a remedy that eliminates the occurrence of the mistakes that lead to that defect
Process optimisation tools Capability-Complexity matrix In Six Sigma, it is necessary to define, measure & control both the complexity of products & services as well as the capability of processes The more advanced CCA tools permit what-if analysis where one can set outcome matrix & determine what changes in process capability or product complexity are required to achieve them
Process optimisation tools Funnel reports Six Sigma is all about finding those critical few influences out of trivial many that affect the outcome. Funnel reports funnels the Xs to find the critical Xs or vital causes Sources for Funnel report come from CT trees, C&E matrices & FMEAs. In the funnel process, each candidate is subjected to a set of analytical & statistical considerations, which serve as tests to qualify if the cause is vital
Process optimisation tools Plans A Six Sigma practitioner produces & manages a set of plans that affect the DMAIC elements of the breakthrough strategy The data collection plan ensures the measurements The control plan ensures management of the critical Xs The audit plan addresses the ongoing monitoring of the vital causes
Process optimisation tools Data collection plan Data collection plan provides a concise & focused set of directives & actions required to collect all necessary data associated with a process. It addresses not only the content, but the reliability, availability & presentation or formatting of the data Data characteristics need to be attended to while collecting it Data sources, Data timing, Data stability, Data format & Data transfer
Process optimisation tools Control plan The control plan directs focus on the vital causes & ensures all participants understand the activities, items & specification limits required for the process to be under control The control plan is a proactive effort to assure long term performance & also a call to action if a triggering event occurs, indicating the process performance is deteriorating
Process optimisation tools Audit plan The audit plan acts as the measurement tool for the control plan When the control plan is in place, the audit plan is the means for regular measuring & monitoring of the outcomes
Statistical Analysis Tools Plots & Charts Most commonly used plots & charts are : Histogram : a bar chart that plots the spread of data according to frequency of occurrence Dot plot : a type of histogram where data is displayed in a single point format; used to assess or compare distributions Pareto chart : a bar chart in which the bars are ordered from highest to lowest, indicating critical contributors contd
Statistical Analysis Tools Scatter plot : shows the relationship between two variables, conveying the nature of correlation Matrix plot : shows the relationship between many pairs of variables at the same time 3D scatter plot : a three-dimensional scatter plot useful for evaluating the relationships between three different variables at the same time Interval plot : a plot of data values with added confidence intervals or error bars; useful for showing both the central tendency & the variability contd.
Statistical Analysis Tools Box plot : a side-by-side comparison of sample distribution, the central line is the mean, the boxes are +/- 25% & the lines are the limits CDF (Cumulative Distribution Function) plot : a stepped cumulative histogram overlaid with a best-fit normal CDF Time series plot : a plot of data spread over time used to evaluate patterns in activity across time Marginal plot : a scatter plot with an added histogram used to assess the relationship between two variables & their distributions
Statistical Analysis Tools Time series Trending : fits a general model to past data & observe the trends Forecasting : simple forecasting & smoothing methods to decompose data into its component parts & then extend the estimates into the future to predict ongoing performance Decomposition : separates seasonal or cyclical trends into groups & profile repetitive performance contd
Statistical Analysis Tools Moving average : averages the consecutive observations & observes the trend over time Exponential smoothing : smoothes the time series data & calculates the average level & in a double exponential smoothing, both the average level & trend Autocorrelation : discovers repeating patterns in time-series data Cross-correlation : computes, plots & discovers the relationship between two separate time series
Statistical Analysis Tools ANOVA ( analysis of variance ) ANOVA tools include one-way & two-way analysis (variance testing with classification by one or two variables); analysis of means (test the equality of population means) & balance ANOVA (accounting for data collected by different designs or procedures) All these tools are defined & executed in software application programs available in the market
Statistical Analysis Tools Tolerance analysis Tolerance analysis is the statistical analysis tool that helps in determining the right specifications & limits on individual parts & components to ensure that they fit together properly as a system once manufactured Tolerance analysis is applied in cases where parts or components must come together precisely for the system to function properly satisfying customer expectations
Statistical Analysis Tools Design of Experiments ( DOE ) DOEs statistically investigates the variables that influence a process & the resulting quality of products & services in experimental settings DOE also allows the practitioner to simultaneously understand the effects of changing the settings of multiple variables
Spider Diagram
Statistical Analysis Tools Process capability analysis : Process capability analysis is how one determines if the process, once in control, is also meeting specifications Capability analysis takes the voice of the process (VoP) & compares to the voice of the customer (VoC) to see if it is capable of meeting the requirements
Statistical Analysis Tools Commonly applied tools in capability analysis : Normal analysis : analyses process capability when the data is from a normal distribution Non-normal analysis : analyses process capability when the data is from a non-normal distribution Between/within analysis : analyses process capability for between subgroup & within subgroup variation Multi-variable analysis : analyses the capability of an in-control process when each of multiple continuous variables follow a normal distribution contd..
Statistical Analysis Tools Binomial analysis : analyses the process when the data is from a binomial distribution Poisson analysis : analyses the number of defects observed where the item occupies a specified time or space Capability six-pack : a set of six charts which collectively contains key process capability metrices
Statistical Analysis Tools Regression analysis Regression analysis is used to discover & characterise the relationship between a response & one or more predictors In regression analysis, models or distribution functions are fit to observed data & depending on the data, it can lead to a variety of functions The goal of regression analysis is to create an equation to explain or predict the way the process output is behaving
Statistical Analysis Tools Multivariate analysis Multivariate analysis helps to understand the structure in the mix of data. It helps assigning different observations to statistically-significant groups & visually explore the relationships among the grouped variables Principal component analysis & factor analysis are two methods for determining structure
Statistical Analysis Tools Measurement systems analysis Measurement systems analysis is the practice of determining the extent to which observed process variation is due to variation in the measurement systems Measurement system errors are classified into two broad categories : accuracy & precision. With most measurement systems, both errors are present contd..
Statistical Analysis Tools Accuracy is the difference between the observed measurement & the true value. Linearity, Bias & Stability are the three sources that contribute to the accuracy error. Gauge linearity, gauge bias & gauge stability studies help in analysing measurement systems accuracy issues
Statistical Analysis Tools Precision error is the condition of observing variation from measurement to measurement, or from part to part Repeatability & reproducibility are two components of precision error Gauge R&R studies help in determining the extent to which device & process variation contribute to overall measurement system variation
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