KARTHIK MARIMUTHU

Upper Specification Limit = 24, Lower Specification Limit = 18, The average = 21.5 and Standard Deviation (within) = 0.75 The Cp & Cpk Values for the above data's are as follows: - Cp = 1.33 & Cpk = 1.11 - The Cpk Value is less than the desired level, i.e: In actual Condition Cpk>/=1.33. - We generally want the Cpk Value at least 1.33 or higher than that to meet the Customer requirements Inputs on the Kind of action: - In the above mentioned data's, the average is mentioned as 21.5. But as per the actual calculation based on the USL & LSL, the average must be as 21. - So it is clear that Given average[21.5] has been moved above the target mean [21] & so we have to bring it towards the Mean value - We must first identify the Process variations that are the causes for the increased Average condition - when the remove the Variations in the Process, the process will be in control & also the mean value can be obtained close to the target mean. - Once the Process is centered - Mean average value is obtained, the Cp & Cpk value will be improved & Standard Deviation will be reduced

Long Term & Short Term Process Capability: The Capability indices [ Cp, CpK ] & the Performance indices[Pp & PpK] can be considered as the Long Term & Short term on the Basis of Method used to calculate Sigma, an estimate of Process Standard Deviation. - Cp & CpK represent the Long term Process Capability, while Pp & Ppk represents the Short term Process Capability. - The AIAG suggests us that we can use PpK for a Production run of less than 30 days & CpK for everything there after can be used - Long Term Variability = Short Term Variability + 1.5 Sigma Shift. This Interpretation is based on the underlying assumption of Six Sigma, which indicated that the Process will drift or Shift +/- 1,5 Sigma in the Long term. When this shift is taken into account, 6 Sigma process performance equates to 3.4 ppm, otherwise 4.5 Sigma Process performance equates to 3.4 ppm. The Main reason why the Long term Performance is calculated & treated differently is that the Long term Process capability gives out the exact output of the current running Process. It also include the Common Causes that occur during the Process & gives us the result. During the Short term process, we cannot completely analyse the entire Process & Process Capability calculated during that period, may give us a hint only about the process. So Long term Process capability is preferred for complete understanding of the Process performance & calculate the Process capability

Ranking for the Five Metrics: 1. C-SAT (Customer Satisfaction Index) - Satisfaction attained by the use of product/service. 2. NPS (Net Promoter Score) - Loyalty and referral check. 3. CES (Customer Effort Score) - Customer effort assessment in getting work done/ issues resolved 4. CAC (Customer Acquisition Cost) - CAC is all the costs spent on acquiring more customers (marketing expenses) divided by the number of customers acquired in the period the money was spent. 5. Churn (Customer Churn Rate) - Customer loss assessment.

Well Said Kavitha, ZD is a Possible by Continuous improvement in the Process, upgrading ourselves to latest technologies, motivating the Workmen, etc...

Zero defects is achievable: My view is Zero defect is achievable one. Zero defects is a concept that mentions that Defects are not acceptable and Do things Right at the very First Time. Involvement of the Top Level management to the Bottom level workers, Rigid Error Proof Systems, Tight Inspection of the Products & continuous improvement are the possible methods to achieve Zero Defects. Zero defects concept also involves the Cost of the error Proofing system & their maintenance.. If the Top management is mainly focused on Zero Defects & the Cost is secondary then it will be a Great idea. Similarly in a Company there are some Zero defect Lines & it is being achieved in those lines....

Rolled Throughput Yield: Throughput Yield: It is a measurement taken taken at each step of the process, based on the No. of defects, the No. of Unit processed & the no. of defects opportunities per unit. (Total No.of Units processed - No.of Actual defects) / Total no.of Unit Processed Rolled Throughput Yield: It is a Process by metric used to express the Probability that the given Unit will make it through the Whole System without producing a Defect part. It is calculated by multiplying out all of the individual through put yields. RTY Provides us a chance to look into the Cumulative effects of the Ongoing Process. It measures the Yield for each of Several process steps & provides us the Probability that a unit will come through the process defect free. RTY helps us to understanding the Hidden factors by giving us a Visibility into the yield of each process step. This helps us to identify the Poorest performing Process steps - REWORKS & gives us idea to where to look to find out the most impact process improvement opportunities. Calculation of RTY: In a Process there are multiple processes comes together to deliver the Final product.These Processes may be placed in a row or operate parallel & deliver output to a single process. To calculate the overall Throughput yield of the Process, the Rolled Throughput Yield is being used. RTY uses two different formulas for the Serial Process & for the Parallel Process: - For a serial Process, RTY is calculated by multiplying the Through Put Yield of all the individual Processes - For a Parallel Process, RTY is calculated by selecting the Minimum of all the individual processes operating parallel The Calculation of RTY for the following yields are 0.994, 0.987, 0.951 & 0.990 RTY = 0.994 * 0.987 * 0.951* 0.990 = 0.924 If we simply count all the defects & divide by total units we would get a mean DPU for our Process. This is different but not necessarily less value than RTY. RTY helps us to find the Probability that a unit comes through Process defect free. Can a process with a 100% RTY can be Considered as inefficient: The process which can be produce a part involves all the sub processes to make it a complete process. In such condition there are possibilities for error to occur. A 100% RTY process will be efficient one if it satisfies all the necessary condition to be a defect free process. once a Process has achieved 100% RTY, then automatically it is considered an efficient process.

Hawthorne Effect: The Hawthorne effect is named after a series of experiments that changed the way we think about work & productivity. While earlier studies had already focused on individuals & how their performance could be improved, the Hawthorne effects placed the Individual in the Social Context for the First time. - The experiments were conducted at Western Electrical works in USA, between 1924 ~ 1932 - It was initially designed by western Electrical Industrial Engineers - Four parts of Hawthorne Studies are a. Part I - Illumination Experiments B. Part II - Relay assembly Test room study C. Part III - Mass interviewing Program D. Part IV - Bank wiring observation Room experiment The hawthorne Effect is a form of reactivity & says the temporary change to behavior or Performance in response to the change in the environmental condition, with the response being typically an improvement. The Hawthorne studies have a dramatic effect on Management in organisation & HOW PEOPLE REACT TO DIFFERENT SITUATIONS. It is a Short term improvement caused by observing worker Performance. Significant of the Hawthorne Effect: The findings are expected to bring advantages to the following 1. Employee -Motivation -Understanding on Hawthorne effect Positive impact, -Reduce Turnover 2.Employer - Provide better Working environments - Better understanding about the Employees - Interpersonal Skill 3.Firms - Global competitive advance - Increase Profit - Continuous improvement Limitations: - Limited to Three Firms only - Poor Quality of Data - Respondent Bias - Constraints - Time & Money How to manage it during the Base lining of the Process: During the Initial time of the Project, trials will be taken & the Process cycle time will be calculated. So during the Trials we can come to a conclusion about the Time taken for a particular process and also the output of the Process by an Operator. So keeping a record of this would help us to retain the data taken for a process. That is we can easily identify if a Process produces while we are observing him or he is following the regular cycle time to complete the Product. Meanwhile as we have the Trial data, we can also identify if the Persons are slowing down a process & showing as if they are tring to improve it during the Observation time. Conclusions of Hawthorne Studies: - The social & psychological factors are responsible for workers, productivity & job satisfaction - Informal relation among them influence the Workers behavior & performance more than the Formal relation in a Organisation -Employee will perform better if they are allowed to participate in decision making - They will work more efficiently when they believe that the management is interested in their welfare. - When employees are treated with respect & dignity, their performance will improve - Financial incentives alone cant increase the performance. Social & psychological needs must also be satisified - Good communication between Superior & subordinates can improve the Productivity

Coefficient of Variation: The Coefficient of Variation is a measure of Relative variability. It is the Ratio of the Standard Deviation to the Mean. It is a useful method for comparing the Degree of Variation from One Data Series to another, even if the means are drastically different from one another. It has the following characteristics: -Measure of relative variation - Shows Variation relative to mean - Used to compare 2 or more groups How to calculate Coefficient of Variation: The main purpose of finding the Coefficient is used to study of Quality assurance by measuring the spread of the Population data of a Probability or frequency distribution or by determining the content or Quality of the sample data of substances Calculation of Coefficient of variation: - Calulate the Mean of the Data Set - Calculate the Sample SD of the Data Set - Finding the Ratio of the Sample SD to mean brings the Coefficient of Variation [CV] of the Data set Formulas to calculate coefficient of variation: Examples for Coefficient of Variation: Calculate the relative variability (coefficient of variance) for the samples 60.25, 62.38, 65.32, 61.41, and 63.23 of a populationSolution:Step by step calculation:Step 1: calculate meanMean = (60.25 + 62.38 + 65.32 + 61.41 + 63.23)/5= 312.59/5= 62.51Step 2: calculate standard deviation= √( (1/(5 - 1)) * (60.25 - 62.51799)2 + (62.38 - 62.51799)2 + (65.32 - 62.51799)2 + (61.41 - 62.51799)2 + (63.23 - 62.51799)2)= √( (1/4) * (-2.267992 + -0.137989992 + 2.802012 + -1.107992 + 0.712012))= √( (1/4) * (5.14377 + 0.01904 + 7.85126 + 1.22764 + 0.50695))= √ 3.68716σ = 1.92Step 3: calculate coefficient of varianceCV = (Standard Deviation (σ) / Mean (μ))= 1.92 / 62.51= 0.03071 2. A company has two sections with 40 and 65 employees respectively. Their average weekly wages are $450 and $350. The standard deviation are 7 and 9. (i) Which section has a larger wage bill?. (ii) Which section has larger variability in wages? Solution: (i) Wage bill for section A = 40 x 450 = 18000 Wage bill for section B = 65 x 350 = 22750 Section B is larger in wage bill. (ii) Coefficient of variance for Section A = 7/450 x 100 =1.56 % Coefficient of variance for Section B = 9/350 x 100 = 2.57% Section B is more consistent so there is greater variability in the wages of section A.

FMEA: FMEA is a Procedure that helps us to identify & understand the Potential Failure modes, their Causes & the effects of the Failure in the Process or the end User. It helps to analyse the Risks involved with the Failure Modes, Effects & Causes & Categorize the issues for the Corrective Action. The Main goal of the FMEA is to align the Risks that are available within the resources. This helps to identify the Root causes of the Risks involved, allows to detect the occurrence of the Risks & to get a better solution to overcome the Risks.The Primary objective of the FMEA is to improve the Process & reduce the failures that would occur in the Process Types of FMEA: - Design FMEA - System FMEA - Process FMEA [Which is being commonly used] Risk, Risk Assessment & Risk analysis: - Risk can be said as the likelihood of Occurrence of an undesirable event. - Risk Assessment is the determination of quantitative / qualitative value of Risk related to a Concrete situation - Risk analysis included 1. Identification of Risks 2. Estimation of the likelihood of Occurrence 3. Estimation of the Causes & impact 4. Risk evaluation & development of risk Limitations of FMEA in Risk Analysis: RPN - Risk Priority Number plays an important role in the FMEA, to determine the Risk factors involved. RPN = Severity X Occurrence X Detection There are certain limitations in the FMEA during the Risk analysis. They are as follows - It needs an effective team to carryout the Risk analysis in an efficient manner. Small disputes between the Team members may lead to improper FMEA - FMEA can describe the Risks involved in the Process to a certain extent only. Unknown hidden failures cannot be addressed completely. - Sometimes it doesn't directly capture the Failure mode & doesn't cover the Multiple failures involved in it - And another limitation is not covering the entire Failure modes involved. For example what happens if a Product fails at the End user will not be clearly explained in the FMEA So FMEA when it is done correctly & effectively it will be an useful tool to Prevent Problems, reduce the Costs & develop the Process

ZERO DEFECTS: Zero defects is a way of thinking that joins the notion that defects are not acceptable and Do things Right the very first time. The idea with the Zero defects, that we can increase both by eliminating the Cost of Failure & increasing the Revenues through Increased Customer Satisfaction. Zero Defects is Not about being Perfect, it is about changing our Idea & Mind. It does by demanding - Recognizing the High Cost of Quality Issues - Continuously think of the Areas where Rejections may occur - Work Proactively to identify the Rejections in Our Process, which allow the defects to occur Advantages & Disadvantages of Zero Defects: Advantages: - Cost reduction caused by the decrease in waste - Cost reduction due to the fact that time is now being spend on producing products that are Produced according to the Customer Specification - Producing & Delivering the Finished Product that conforms to fulfill the Customer requirements, that increase the Customer satisfaction Disadvantages: - The Process can be over engineered by an Organisation in its efforts to create a Zero Defect - Impossible to Produce to Zero Defects parts continuously all the time required ZERO DEFECT SLOGAN, CAMPAIGN, TOP MANAGEMENT + WORK FORCE INVOLVEMENT: In Many of the Manufacturing Companies we may easily See the Slogans of "ACHIEVE ZERO DEFECTS" & many slogans supporting to it. The Zero Defects Slogan in the Company indicates us the Company has started the ZERO Defects Campaign & undergoing the Process to achieve it. The ZERO DEFECTS campaign will be initially started by the Customer of the Company with the name of the KICK OFF Meeting. During that the Concepts of the Zero Defects Campaign will be explained to all the Staffs & Operators of that Company. After the Kick Off Meeting the Top Management involves the Staff & Operators to organize a Team to follow the Road Map of the Zero Defects Program. During the Discussion various Points will be discussed such as Which Model Line to be selected, Who are the Team members involved, what is our Target to be reached, etc. And finally the Zero defects campaign will be started in the Company. So Zero Defects can be achieved by Top Management involvement, Zero Defects Slogans Display & Work Force involvement

Segmentation & the ways it is related Root cause analysis: The Purpose of Segmentation is to cut down the number of Suspected process steps or sub components within a system involved in Problem creation. Successful segmentation will narrow down the investigation of fewer models & also it will narrow the original root causes to be analysed further by cutting out the Process steps or components that are not involved in creating the Problem. The effort in segmentation is to find & learn the data items that can differentiate between the segments involved [Process Steps or Functional Components] by telling if the original Problem source is there or not there. SO it will be useful in the Root cause Analysis to identify the Exact Root cause of the Problem. Wise selection of Data items to collect will cut out irrelevant Sub segments part by part, until isolating the exact segment that is identified as the MOST PROBABLE SOURCE OF THE PROBLEM. This methodology works nicely with the Condition tree, Since Segments arr Natural Building Blocks required to build the Problematic IUI. When running a Segmentation Process, we will try to : - Narrow down the No. of Suspect Process Steps into Fewer steps or Even to One Process step that caused the Problem: Example: If a visible defect is found at the Late stage of the Process, a visual Check after every process step could find the earliest Process Step in the flow to exhibit this defect. Once this is found, all the subsequent process steps are no longer suspect - Reduce the Number of suspected Components within the System or a Tool that caused the Problem: Example: When turning the Key in Our Car ignition, the starter spins, but he engine only roars. They are two main suspect segments, the Fuel & Electrical Ignition System. Each of these two segments could cause the Problem & each contains many Sub components. The Best way to confront the Root cause is by understanding which of these two Major segments is causing the Problem. Checking whether there is Spark in the Cable leading to the Spark Plugs could verify the functionality of the most of the electrical systems. If a Spark fails to appear at the end of the cable, it is necessary to continue to isolate the component [Smaller Segment], within the Electrical system causing the Failure. Suppose if there a Nice & healthy Spark, One should suspect the Problem lies in either in the Fuel System or a small no. of electrical components behind the cable. From the above the Steps & examples we can clearly understand that Segmentation is very helpful to identify the Possible root causes & Select the Most likely factors from that collection. Finally it helps us to classify the Factors & identify the EXACT Root cause among them

What is DMAIC? While the acronym gives an accurate summary of the process, it is only the beginning. The Six Sigma process improvement methodology encompasses much more than an acronym can describe. The heart of DMAIC is making continuous improvements to an existing process through objective problem solving. Process is the focal point of DMAIC. The methodology seeks to improve the quality of a product or service by concentrating not on the output but on the process that created the output. The idea is that concentrating on processes leads to more effective and permanent solutions. When to use DMAIC DMAIC is used by a project team that is attempting to improve an existing process. DMAIC provides structure because each phase of the process contains tasks and tools that will lead the team to find an eventual solution. While DMAIC may be sequential, it is not strictly linear. The process encourages project teams to backtrack to previous steps if more information is needed. The phases of DMAIC The phases or stages of DMAIC include: Define – The project begins by creating a team charter to identify team members, select the process the team will be improving and clearly define the objective of the project. The project team will then identify the CTQ's to help measure the impact the problem has on the customer. This phase is completed when the team creates a process map that includes the Process inputs & outputs. Measure – This phase includes creating and executing a data collection plan that provides reliable and significant data. The data indicates how the process is performing and helps identify the villain in the Six Sigma narrative – variance. After this point, the project team’s efforts focus on eliminating or reducing variance as much as possible. Analyze – Once process performance has been quantified, the analyze phase helps identify possible causes of the problems. A sub-process map can help identify the problems in the process and tools such as ANOVA and regression analysis can help narrow these problems to root causes. In this phase, the team is able to quantify the financial benefit of solving the problem. Improve – Once the problem’s root cause is brought to light, the improve phase focuses on finding a permanent solution to the problem. This is where the project team’s creativity comes into play in finding an answer to a longstanding process problem. The team then tests a proposed solution in a pilot program to test if the solution is effective and financially viable. Control – In this phase, the project team documents the new solution that they have created so that it can be passed on to process owners. The project team then implements the solution according to the timeline and key milestones they have developed. Once the solution has been implemented, the project team monitors it for several months and if it meets performance expectations turns it over to the process owner. 8D: The 8D problem solving process is a detailed, team oriented approach to solving critical problems in the production process. The goals of this method are to find the root cause of a problem, develop containment actions to protect customers and take corrective action to prevent similar problems in the future. The strength of the 8D process lies in its structure, discipline and methodology. 8D uses a composite methodology, utilizing best practices from various existing approaches. It is a problem solving method that drives systemic change, improving an entire process in order to avoid not only the problem at hand but also other issues that may stem from a systemic failure. STEPS IN 8D & HOW TO APPLY: D0: Prepare and Plan for the 8D Proper planning will always translate to a better start. Thus, before 8D analysis begins, it is always a good idea to ask an expert first for their impressions. After receiving feedback, the following criterion should be applied prior to forming a team: Collect information on the symptoms Use a Symptoms Checklist to ask the correct questions Identify the need for an Emergency Response Action (ERA), which protects the customer from further exposure to the undesired symptoms D1: Form a Team A Cross Functional Team (CFT) is made up of members from many disciplines. Quality-One takes this principle one step further by having two levels of CFT: · A Core Team uses data-driven approaches (Inductive or Convergent Techniques) The Core Team Structure should involve three people on the respective subjects: product, process and data · SME Team comprised of members who brainstorm, study and observe (Deductive or Divergent Techniques) Additional Subject Matter Experts are brought in at various times to assist with brainstorming, data collection and analysis Teams require proper preparation. Setting the ground rules is paramount. Implementation of disciplines like checklists, forms and techniques will ensure steady progress. 8D must always have two key members: a Leader and a Champion / Sponsor: · The Leader is the person who knows the 8D process and can lead the team through it (although not always the most knowledgeable about the problem being studied) · The Champion or Sponsor is the one person who can affect change by agreeing with the findings and can provide final approval on such changes D2: Describe the Problem The 8D method’s initial focus is to properly describe the problem utilizing the known data and placing it into specific categories for future comparisons. The “Is” data supports the facts whereas the “Is Not” data does not. As the “Is Not” data is collected, many possible reasons for failure are able to be eliminated. This approach utilizes the following tools: · 5 Why or Repeated Why (Inductive tool) · Problem Statement · Affinity Diagram (Deductive tool) · Fishbone/Ishikawa Diagram (Deductive tool) · Is / Is Not (Inductive tool) · Problem Description D3: Interim Containment Action In the interim, before the permanent corrective action has been determined, an action to protect the customer can be taken. The Interim Containment Action (ICA) is temporary and is typically removed after the Permanent Correct Action (PCA) is taken. · Verification of effectiveness of the ICA is always recommended to prevent any additional customer dissatisfaction calls D4: Root Cause Analysis (RCA) and Escape Point The root cause must be identified to take permanent action to eliminate it. The root cause definition requires that it can be turned on or off, at will. Activities in D4 include: · Comparative Analysis listing differences and changes between “Is” and “Is Not” · Development of Root Cause Theories based on remaining items · Verification of the Root Cause through data collection · Review Process Flow Diagram for location of the root cause · Determine Escape Point, which is the closest point in the process where the root cause could have been found but was not D5: Permanent Corrective Action (PCA) The PCA is directed toward the root cause and removes / changes the conditions of the product or process that was responsible for the problem. Activities in D5 include: · Establish the Acceptance Criteria which include Mandatory Requirements and Wants · Perform a Risk Assessment / Failure Mode and Effects Analysis (FMEA) on the PCA choices · Based on risk assessment, make a balanced choice for PCA · Select control-point improvement for the Escape Point · Verification of Effectiveness for both the PCA and the Escape Point are required D6: Implement and Validate the Permanent Corrective Action To successfully implement a permanent change, proper planning is essential. A project plan should encompass: communication, steps to complete, measurement of success and lessons learned. Activities in D6 include: · Develop Project Plan for Implementation · Communicate the plan to all stakeholders · Validation of improvements using measurement D7: Prevent Recurrence D7 affords the opportunity to preserve and share the knowledge, preventing problems on similar products, processes, locations or families. Updating documents and procedures / work instructions are expected at this step to improve future use. Activities in D7 include: · Review Similar Products and Processes for problem prevention · Develop / Update Procedures and Work Instructions for Systems Prevention · Capture Standard Work / Practice and reuse · Assure FMEA updates have been completed · Assure Control Plans have been updated D8: Closure and Team Celebration Teams require feedback to allow for satisfactory closure. Recognizing both team and individual efforts and allowing the team to see the previous and new state solidifies the value of the 8D process. Activities in D8 include: · Archive the 8D Documents for future reference · Document Lessons Learned on how to make problem solving better · Before and After Comparison of issue WHY 8D IS PREFERRED OVER DMAIC IN SOME SITUATIONS: The 8D problem solving process is typically required when: · Safety or Regulatory issues has been discovered · Customer complaints are received · Warranty Concerns have indicated greater-than-expected failure rates · Internal rejects, waste, scrap, poor performance or test failures are present at unacceptable levels The 8D method is sometimes preferable over DMAIC due to its focus on Interim Containment Action. Whenever there is a need an action to be taken to protect the Customer from rejections in the future. Also, if the scope for use of statistical tools is limited, 8D is easier to understand and explain to people who are new to problem solving. As i work in the Automobile field, we are widely using the 8D method for taking an immediate action for the Problems occurred & providing an appropriate solution. Though DMAIC will be an ideal methodology to solve the problems in the longer term, 8D is preferred over it for simplicity, quick-fix and easy engagement sometimes. Which is Best? 8D or DMAIC? · Both processes can generate huge improvements for an organization. · Anything is better than nothing. · Pick one approach or the other or select from one of the many other structured problem-solving approaches that focus on data collection, data analysis, and prevention of recurrence. · Stick with the same approach throughout a corporation to build a common understanding of the process and terminology throughout the organization. · If necessary, alter the process to meet your organization’s needs, but don’t cut out any of the steps in either process – they are critical to getting to the root cause of a problem.

c-Chart & u-Chart: The c-chart is used for "Poisson" processes. These are used with random arrival models, or when "counting" attributes. This type of chart, for example can monitor the number of "defects" in each of many equal samples (constant sample size). Occurrence Reporting data (number of reports per month) empirically appear to fit the "Poisson" model, and the c-chart is recommended when charting occurrence report counts. The u-chart is used when counting "defects" per sample when the sample size varies for each "inspection." A good example at DOE facilities is the number of lost or restricted workday cases per 200,000 man-hours. The number of cases is counted for fixed time intervals, such as monthly or yearly, but the sample size (number of man-hours worked during each time interval) changes. Calculate and Plot the Upper Control Limit: Add three times the standard deviation to the average. This is the Upper Control Limit (UCL). Plot the UCL on the graph. The UCL will be a horizontal line on c-charts. The UCL will be variable on u-charts. Do not plot the UCL on a p-chart if it exceeds 100%. Calculate and Plot the Lower Control Limit: Subtract three times the standard deviation from the average. This is the Lower Control Limit (LCL). Plot the LCL on the graph. The LCL will be a horizontal line on x-charts and c-charts. The LCL will be variable on p-charts and u-charts. Do not plot the LCL on a p-chart if it is below 0%. SPECIAL NOTE: If the LCL is negative (less than zero), and the data could not possibly be less than zero, e.g., a negative number of reports written, or a negative time period, then the LCL is assumed to equal zero. EXAMPLE C-CHART The C-chart The chart below is an example of a C-chart. It is counting the number of occurrence reports per month. Since there are multiple dates on Department of Energy occurrence reports (discovery date, categorization date, final report date), the date used for the basis of the graph is identified on the x-axis label. The c-chart is used when counting discrete events, as in a random arrival process. Counting events is a good example of a random arrival process, as long as each of those events are independent from each other, and overall occur at a constant rate. This is also known as a Poisson process. In this graph, an initial 24 month baseline was established at the end of 1994 for January 1993 through December 1994. This baseline remained valid until the end of 1995. Then a shift occurred, with 10 of the next 11 points below average (starting with November 1995). A new baseline was established for November 1995 through September 1996. Note the gap in the two baselines from January 1995 through October 1995. This is acceptable. One does want to avoid overlaps between adjoining baseline averages, however. The current baseline (Nov 95 - Sep 96) was based on much less than 25 points. It does appear that the new data is coming in lower than the new baseline (but so far, no statistically significant difference has been detected). If a statistically significant difference does generate, perhaps the choice of November 1995 through September 1996 was insufficient. A better alternative may turn out to be to continue to 21.8 average through March 1996, and calculate a new baseline starting in April 1996. This serves to illustrate how control charts can evolve over time, and baselines with less than 25 points may prove to need to be readjusted. EXAMPLE U-CHART The U-chart The chart below is a typical chart used by a safety department - cases per 200,000 hours. Rather than just plotting the number of accidents, this graph plots accidents per 200,000 hours. This allows the rate on the graph to be consistent even with different size work forces. In this example, the number of cases each month is determined, then divided by the number of hours worked in the month, and finally multiplied by 200,000. The control limits vary from month to month as hours change. When hours worked are low, the control limits are far from the average line. One expects a large amount of variability in the data when there is a small work force. When hours worked are high, the control limits move inward. One expects a small amount of variability. Note that the initial average was calculated for a time period starting prior to the beginning of the graph. This is typical for an existing graph which has accumulated many years of data, and a decision is made to remove some of the "old" data. In this case, it was decided to remove the data prior to fiscal year 1995. However, the baseline average was left as is, rather than recalculating it for Oct 94 to Jan 95. There are several significant trends in this graph. First was a decrease from 2.28 to 1.64 in early 1995. The five points from Feb 95 to Jun 95 were greater than one standard deviation below the previous average. However, there was then a significant spike in August 1995. August 1995 reflects special cause variation, and we will assume the cause in question was identified. When developing the proper average to use, it was decided to remove August 1995 from the average. Note the average line cuts through the center of the remaining data for the time interval. A more recent shift of seven points in a row below average occurred from Sep 96 to Mar 97. As April 1997 remained below the 1.64 previous average rate, an initial baseline was calculated for Sep 96 through Apr 97. During this interval, the Lower Control Limit was mathematically less than zero. Since you cannot have a negative case rate, the Lower Control Limit is not plotted. This new baseline contains less than 25 points, so it may not be stable. However, this eight point average does provide some basis to determine if a decrease in accidents is continuing, or if the accident rate has steadied out.

OUTLIER An outlier is an observation that lies in an abnormal distance from other values in random sample from population. outliers are extreme value that fall a along way outside of other observations. for example, in a normal distributions outliers may be the values on the tiles of the distributions.the outlier is identified as the largest value in the data set outlier should investigate carefully often the contain the valuable information about the process under investigations are the data gathering and recording process before considering the possible elimination of this point one should try to understand why the appeared and its wheather likely similar values will continue to appear. METHOD AND APPROCHES ARE IDENTIFYING OUTLIERS we always need to be on the lookout for outliers sometimes they are caused by errors and other time it indicates the presences of previously unknown phonomenon. the following are the methods to detect outliers 1.extreme value analysis 2.propablistics and statistical models 3.linear models 4.proxmity ased models 5.information theortic models 6.high dimensional outlier detection

Process Maturity: Process maturity is an indication of how close a developing process is to being complete and capable of continual improvement through qualitative measures and feedback. Thus, for a process to be mature, it has to be complete in its usefulness, automated, reliable in information and continuously improving. The maturity of a process or activity can be defined to be at one of five levels, from Level 1 (the least mature) to level 5 (the most mature). The processes at higher levels also address the features of the lower levels. The ground level is Level 0 where no process exists for the activity. Level 0 – Person-Dependent Practices Level 1 – Documented Process Level 2 – Partial Deployment Level 3 – Full Deployment Level 4 – Measured and Automated Level 5 – Continuously Improving If a process is supposed to be improved or redesigned periodically, does an assessment for the maturity of a process carry any significance? The Highly Matured process is the Fifth stage, that is the Continuously Improving stage. Though the Process is in a Highly Mature stage also there is a chance or Scope for improvement, since it is a Continuously improvement phase. Processes at this level focus on continually improving process performance through both incremental and innovative changes and improvements. We have to monitors whether the Goals of the Process are achieved & completed, so that we can make New Goals & make continuous improvement