Mona Bhandari

I feel that hiring for anyone for any managerial post is completely organization based. It totally depends upon the candidates caliber and the efficiency to convince the interviewer onto the project carrying or carried out by him.

Suppose the tolerance limits on the dimension are 5.000±0.012, i.e. 4.988 to 5.012. Data collected from the process during second shift indicates that the process mean is 5.000 and its standard deviation sigma=0.004. ±3 sigma fits inside the the tolerance because ±3 sigma= ±3x0.004= ±0.012. Capability Cp = Cpk = 1. The process mean doesn't remain constant. The process mean may shift 1.5 sigma to the right or 1.5 sigma to the left. If we assume a 1.5 sigma shift to the right, the yield is the area under the normal curve to the right of -1.5 sigma or about 0.9332. Suppose if the process variation is reduced so that sigma= 0.002. Now ±6sigma exist between the tolerance limits and the process can be called 6sigma process. To calculate the yield for the six sigma process, we allow the mean to shift ±1.5sigma. Suppose the mean shifts 1.5 sigma to the right so the yield is the area under normal curve to the right of -4.5 sigma which turns out to be 0.9999966. Defect level =1- 0.9999966= 0.0000034 or 3.4 ppm. The mean may not shift exactly 1.5 sigma on each side and no process is truly normal to the sixth decimal.

VOICE OF CUSTOMER: These are the needs, wants, expectations and what they prefer in both spoken and unspoken words. The voice of customer can be internal or external. VOC can be captured on reactive and surveys, interviews, research, evaluations, feedback and meetings. VOC metrics Customer satisfaction Net promoter score Performance Evaluations Help desk support calls. VOICE OF BUSINESS: VOB are the needs, wants, expectations and preferences, both spoken and unspoken of the people who constitute the business like the shareholders, officers or other involved in corporate governance. It can be obtained from financial market analysis, competition analysis. VOB metrics: ROI( Return -On- Investment), Percentage income from returning customers Shareholder Equity. CONFLICT BETWEEN VOB & VOC The company does business to make profit and accomplish greater goals but not at the cost of the customer. The customers prefer best products at cheaper rates. In order to synchronize between the VOC and VOB mapping of processes and correcting the system that deliver value to our customer.

Tribal Knowledge is an unwritten information that is not commonly known by others in the organization. METHODS OF UNLOCKING TRIBAL KNOWLEDGE: Shared desire to achieve state of operational alignment An effective and efficient management of information Cross functional organizational behaviors that form cohesive and comprehensive methodology Clarify information needs and develop models for information and activity flow. Identify integrated set of collaboration tools. To gain a deep understanding of the organization's nature domain. METHODS OF CAPTURING TRIBAL KNOWLEDGE: The purpose of capturing tribal knowledge is to raise awareness of the problem, symptoms, it's causes and outline of a solution. To outline a problem a problem statement is generated and 1. IDENTIFICATION OF POTENTIAL EMPLOYEES 2. IDENTIFY THE AVAILABLE KNOWLEDGE 3. DOCUMENT THE REQUIRED KNOWLEDGE 4. CONFRONT THE KNOWLEDGE GAP a. Minimize the knowledge gap between the old employee and the new employee of the organization. b. Benefits the new employees by updating the knowledge and make them more efficient. METHODS OF HARNESSING TRIBAL KNOWLEDGE: Harnessing tribal knowledge: 1. COLLECTION: GROUND WORK Incorporating concepts in context to issue from knowledge support centers. 2. SORTING: HOW TO DO: Segregate critical data from massive volume of information data. 3. ORGANIZATION: WE To put forward logical frameworks in place. 4. CODIFYING: FOR Mastered the collection Segmentation and storage of critical data.

In case the excellence operator doesn't utilize the concept of rational sub grouping than It would incorporate the variations from the different streams. Identification of corrective actions once an out of control condition cannot be done. their would be inconsistency of data from processes.

Sponsor's are high level or senior business leaders. QUALITIES OF SPONSOR Proactive motivator who helps in growth of the organization and his own self. Help in defining the team's objectives and articulating the problem statement. Validating business case in the project charter. Act as liaison between the team and senior management. Accelerate decisions at critical times of the project.

Kano Model has three requirements: Basic Needs: Allow an organization go get into the market. Performance Needs: Allows the organization to sustain in the market. Excitement Needs: Allows the organization to excel, power of excellence. BASIC NEEDS: Expected features or characteristics of a product or service. They are unspoken words which are expected by the customer/patient. If not fulfilled causes dissatisfaction to customer/patient. For Example: Cleanliness of the room. Bed preparation. Effective Communication Patient uniform. PERFORMANCE NEEDS: Are spoken needs that increase or decrease the customer/patient performance. For example: Admission and Discharge time. Waiting time in OPD Breakdown of equipments Average length of stay Healthcare Associated Infection Internet Access EXCITEMENT NEEDS: An unspoken and Unexpected feature that impresses customer/patients and earn an extra credit to the organization. For Example: Hospital integration like App for hospital wide services. Play area for the kids Online medicine indenting and dispatching Excitement Needs – Unexpected features or characteristics that impress customers and earn the company “extra credit.” These needs also are typically “unspoken.” Think of the Doubletree Hotels. Those who stay there are delighted by a freshly baked, chocolate chip cookie delivered to their room during turn-down service. Expected features or characteristics of a product or service

Statistically significant means that we are very sure that the statistic is reliable. It is a statistical term that gives the surety of a difference or relationship exists. For example: Suppose we give 1000 employee an IQ test, and we are ask if there is a significant difference between male and female scores. The mean score for males is 98 and that of female is 100. We use independent groups t- test and find that the difference is significant or the 0.001 level. The difference between 98 and 100 on an IQ test is a very small difference.., which is not important After finding a significant relationship, it is important to evaluate its strength which could be weak or strong; large or small. This depends upon the sample size. ONE TAILED OR TWO TAILED SIGNIFICANT TESTS: One tailed or two tailed significance depends on hypothesis. ONE TAILED SIGNIFICANT TEST: When the hypothesis states the direction of the difference it is said to be one tailed significant probability test. Example: females will score significant higher than males in test. Blue collar workers will not buy significantly more product than white collar. In the above mentioned examples the null hypothesis predicts the direction of the difference. TWO TAILED SIGNIFICANT TEST: A two tailed test would be used to test these null hypotheses. For example: There is no significant difference in test scores between females and males. There is no significant difference between blue collar and white collar workers. THE ONE TAILED PROBABILITY IS EXACTLY THE HALF THE VALUE OF THE TWO TAILED PROBABILITY.

In healthcare sector; our ultimate goal is to fix the problem, reduce risk, and keep our patients, patients' families and safe safe. Their is a specific correlation between the Incident and the related Causal Factors, the Root Causes for which corrective actions are recommended. Once we have the root cause, we can work on the corrective actions to fix the root cause problem. A cause that produces an effect, or that which give s rise to any action, phenomenon or condition for example: if a change in X produces a change in Y than the X is said to be the cause of Y. Every cause itself is the result of some prior cause or causes. Two variables may be found to be causally associated depending on the study. If two variables are found to be either associated or correlated, that doesn't mean that cause- and -effect relationship exists between the two variables. In conclusion, if we choose the corrective action first followed by a cause that justifies, in that their is a specific relationship between them.

The Voice of Customer is the process for capturing customer- related information. This process is proactive and continuously innovative to capture stated, unstated, and anticipated customer requirements, needs and desires. A mistake occurred on a small scale is acceptable by the customer. There are situations which aren't acceptable and are detrimental to the business. They are: Long waiting time. Long response time. Poor attention. Lack of orientation about the company. Unprofessional and impersonal interactions. For Example: Services generated in the hospital. Waiting for room allotment. Lack of timely response from nursing staff.

The confusion about continuous data and attribute data is the percentage data. In true sense, percentage data is discrete because the underlying data that the percentages are calculated from is discrete. For example: the percentage of defects is calculated by dividing the number of defects(discrete count data). In practice, percentage data are often treated as continuous because the percentage can take on any value along the continuum from 0 to 100%. Adding to it, dividing a percentage point into two or more parts. Discrete data are easy to collect and interpret. Continuous data senses variation. For example: Speeding of car at highway with a speed limit of 70 miles per hour. If we collect continuous data we have more information. If we use discrete data, we only know whether someone was speeding over the speed limit of 70 mph or not speeding i.e at or under 70 mph. For Example: knowing that travelling 70 mph gives a different understanding of their speed than knowing that they were travelling 90 miles per hour, even though both would be classified as speeding using is discrete data. It is advisable to collect continuous data in practice and convert it into discrete as per the threshold value. In the above mentioned example, we would collect: Continuous Data- how fast was the automobile travelling in miles per hour. Later determine whether the result is speeding or not speeding by comparing the actual speed to the threshold of 70 miles per hour

DEFINITIONS OF CORRECTION; CORRECTIVE ACTIONS; PREVENTIVE ACTIONS CORRECTIONS: Action to eliminate the detected non conformity. It's taken immediately. It addresses to short term need. For Example: In case of fire. It would be ludicrous to go for RCA. Once the initial correction is implemented,there is time to do RCA and implement corrective action and follow with verification of the effectiveness of the RCA. CORRECTIVE ACTION: Action to eliminate the cause of a detected non conformity to prevent the recurrence of the non conformity. It addresses to long term solution. For Example: Administration of chemotherapeutic drug on hold as the transcription of the dosage was not correct. PREVENTIVE ACTION: is avoiding the initial occurence of the non conformity by proactively implementing improvements. It may result i.e from trending of a process data, of analytical data, of audit findings, trending of root causes for non conformists or complaints. For Example: Double check prior to transcribing the chemotherapeutic drug. Preventive action is taken to prevent occurrence whereas corrective action is taken to prevent recurrence. There are instances whenin correction is required over corrective action and preventive action. We need to make sure to qualify the boundaries for issues requiring informal, formal, immediate or long term actions. For Example: Maintenance department responsible for water testing and documenting of RO plant. Hospital staff admitting in the hospital due to consumption of water resulting in ill health. An immediate replacement of water prior to any further testing is done. A correction of issue is important.

A Check sheet is an easy custom designed for quick, easy and efficient recording of desired information which can either be quantitative and qualitative data. Check sheets are being replaced by modern Business Process Management Software which enables more complex data to be recorded automatically. The process is now neither dependent on human intelligence nor on check sheets. The data recorded is arranged in whichever manner required which are ready to use even in graphical format enabling convenience to the users.

Sigma Level and the Cost of Quality Sigma Level DPMO Cost of Quality as Percentage of Sales 2 298,000 More than 40% 3 67,000 25-40% 4 6,000 15-25% 5 233 5-15% 6 3.4 Less than 1% Assuming that the average performance of a company is 3 sigma, 25 percent to 40 percent of its annual revenue gets chewed up by the cost of quality. Thus, if this company can improve its quality by 1 sigma level, its net income will increase hugely.

Common Causes of Variance Referred to as ‘Natural Problems', ‘Noise' and ‘Random Cause' was a term coined by Harry Alpert in 1947. Common causes of variance are the usual quantifiable and historical variations in a system that are natural. Though a problem, they are an inherent part of a process. This kind of variance will eventually creep in, and there is nothing you can do about it. Specific actions cannot be taken to prevent this failure from occurring. It is ongoing, consistent, and predictable. Characteristics of common causes of Variance are: Variation predictable probabilistic Phenomena that are active within the system Variation within a historical experience base which is not regular Lack of significance in individual high and low values. This variation usually lies within three standard deviations from the mean where 99.73% of values are expected to be found. On a control chart, they are indicated by a few random points that are within the control limit. These kinds of variations will require management action since there can be no immediate process to rectify it. You will have to make a fundamental change to reduce the number of common causes of variation. If there’s only common causes of variation on your chart, your process is said to be ‘statistically stable'.When this term is applied to your chart, the chart itself becomes fairly stable. Your project will have no major changes, and you will be able to continue process execution hassle free. Examples of Common Causes of Variance Take, for example, an employee who takes a little longer than usual to complete a certain task. He is given two days to do a task and instead he takes two and a half days; this is considered a common cause of variation. His completion time would not have deviated a lot from the mean, since you would have had to consider the fact that he could submit it a little late. Here’s another example: you estimate 20 minutes to get ready and ten minutes to get to work. Instead, you take five minutes extra getting ready because you had to pack lunch and 15 additional minutes to get to work because of traffic. These would be Common Causes of Variance. Other examples that relate to projects are inappropriate procedures, as in the lack of clearly defined standard procedures, poor working conditions, measurement errors, normal wear and tear, computer response times, etc. Special Causes of Variance Special Cause of Variance, on the other hand, refers to unexpected glitches that affect a process. The term Special Cause of Variance was coined by W Edwards Deming and is also known as an ‘Assignable Cause'. These are variations that were not observed previously and are unusual, non-quantifiable variations. These causes are sporadic, and they are a result of a specific change that is brought about in a process resulting in a chaotic problem. They usually relate to some defect in the system or method. However, this failure can be corrected by making changes in a certain method, component or process. Characteristics of Special Causes of Variation are: New and unanticipated or previously neglected episode within the system This kind of variation is usually unpredictable and even problematic. The variation has never happened before and is thus outside the historical experience base. On a control chart, the points lie beyond the preferred control limit or even as random points within the control limit. Once identified on a chart, this type of problem needs to be found and addressed immediately so as to prevent recurrence of it in the project. It is not usually part of your normal process and occurs out of the blue. Examples of Special Causes of Variance An example to better explain Special causes: you are driving to work, and you estimate arrival in 10 minutes every day, but, on a particular day you reach 20 minutes later, since you encountered an accident zone and were held up. Examples relating to project management are if the operator falls asleep during the execution of your project, or a machine malfunctions, a computer crashes, there is a power cut, etc. One way to evaluate a project's health is to track the difference between the original project plan and what is actually happening. Use of control charts helps to differentiate between the Common Causes and the Special Causes of Variation making the process of making changes and amends easier.

When planning work within. to study a process or system, it is important to first identify the boundaries to work within. The two primary tools used to identify process boundaries are the basic process model and the supplier- input- process-output-customer(SIPOC). The SIPOC can be enhanced by also capturing the requirements of the process and customer. It can also help in identifying these organizations and functional; areas as process suppliers and customers. Identify the outputs of the process, including data, services, products, information, records, etc. The internal and external customers- those that receive the identified outputs. This is done to stimulate thinking. For example: new outputs are often identified when discussing inputs or customers. External suppliers to a process inputs including materials, purchased parts , contracted services, electrical power, so on. Internal suppliers to process are department or processes inside the enterprise that provide process inputs.. Similarly, a process's external customers are those outside the enterprise who receive outputs while internal customers are those inside the enterprise who receive process outputs while internal outputs are those inside the enterprise who receive process outputs. Suppliers of either side are responsible for the meeting the requirements of the customers. Customers of either type are responsible for communicating their requirements to the suppliers.

Jidoka is a methodology to provide machines and operators with the ability to detect when an abnormal condition has occurred and immediately stop the work. Jidoka is Autonomation, meaning automation with human intelligence. It frees the operator from watching the machine leading to large productivity gains. Jidoka allows the operation to have a build in quality at each step of process. Autonomation is automation with a human touch. Autonomation therefore is not full scale automation, it automates the tasks that operators would find boring, repetitive or unsafe but retains human beings to look after the process, often loading the machines and monitoring for abnormalities highlighted by the machines. Autonomation is part of Jidoka, jidoka being a simple set of rules that were inspired by Toyoda’s first loom. Discover an abnormality STOP Fix the immediate problem Investigate and correct root cause. Example: A simple coil feeder that provides a continuous supply of steel sheet to an automated press stamping out components, without any form of autonomation sensor an operator would have to watch this to ensure that the tension was correct and that the steel has not run out. Simple sensors will alert the operator if any problems occur and stop the press to prevent defects being produced or even damage to the press. This frees the operator to conduct other work and improves productivity and improves quality. The use of autonomation can automate mundane tasks while keeping oversight, reducing errors and the cost of shipping returns. EXAMPLES: Inline monitors for CO2 in a ventilator tubing connection detecting rising CO2 level in the patient's exhalation and the alert caregivers in the need to change ventilator settings. Incresingly automation has been major factor in effort of improving patient care safety.

PACEMAKER PROCESS Any process along a value stream that sets the pace for the entire stream. (The pacemaker process should not be confused with a bottleneck process, which necessarily constrains downstream processes due to a lack of capacity.) The pacemaker process usually is near the customer end of the value stream, often the final assembly cell. However, if products flow from an upstream process to the end of the stream in a FIFO sequence, the pacemaker may be at this upstream process.

Hypothesis testing is used in ANALYZE PHASE for testing potential causes. The probability that the data samples actually came from the same underlying population can be calculated. If this p-value is less than 0.05 than we conclude that the two samples likely came from different underlying populations. Example: p-value= 0.02; means only 2% chance that data samples came from the same underlying population. It assists us in problem-solving: Evaluating a proposed process improvement to see if its effect is statistically significant, or if the same improvement could have occurred by random chance. Evaluating several process factors (x's) in a designed experiment to understand which factors are significant to a given output and which are not. To understand the data samples follows which given probability- normal, exponential, uniform, etc Example: An individual working to improve his vehicle's fuel economy might run a hypothesis test comparing fuel economy at driving speeds of 60 mph and 70 mph on a highway. The result might show that driving at the lower speed has a statistically significant effect on the CTQ, win the case is miles-per-gallon fuel economy. The actual improvement in fuel economy might only be 0.5 miles per gallon, which be deemed not worth the extra time it will take to get work each day.

The human error does happen but it can be corrected before creating defects. The effectiveness is judged after observing the performance. The rating of "Occurrence" and/or "Detection" have increased thereby increasing the RPN for the process. Poka Yoke techniques of mistake proofing are applied to these processes in order to lower the ratings of Occurrence and / or detection. Analysis of Customer complaints also reveals activities which are in need of mistake proofing, in order to achieve 'Zero Defect' level of working. Undertake application of Poka Yoke techniques to these processes. The selected mistake proofing technique should qualify the following criteria: Inexpensive. Based upon common sense, preferably of the operator or the 1st line employee. It MUST eliminate Occurrence / Detection of the problem at the source itself. Occurrence oriented Poka Yoke should follow the procedure as below: A) First classify the source of 'Occurrence' as follows: After having classified the source, apply one of the following techniques, as appropriate, to prevent the occurrence: Required action is NOT performed or is performed incorrectly. Undesired action is exercised. Information essential for performing the action is mis-interpreted. Mistake occurs due to complexity. Use of 100% prevention devices such as Fouling Pins, Contoured locators or templates, Proximity or Photo-electric sensors, Limit or Micro switches, Warning lights or Buzzers, Pressure transducers. Design to modify to ensure that in assembly the parts shall not join if aligned wrongly.Machine will not run if operators' hands or feet are not outside or if the job & tooling are not in right position. These techniques should be integral part of the process.The devices are placed sufficiently close to where the mistakes occur, providing fast feedback to the operator, of mistakes occurring. Detection oriented Poka Yoke should use one of the following techniques for ensuring 100% detection of the mistake: It should be autonomous inspection occurring without intervention. It should be 100% inspection which occurs without intervention. It should determine 'before the fact' whether the conditions for 100% quality exist or not. It should make the error visible to the operator. Consider supply of exactly made kits of components to the assembler, so that any balance part will signal error in assembly. Consider use of electronic sensors to activate warning lights or buzzers. Use color coded parts or graphics. Make use of contact devices e.g. Fixtures, Limit switches, probes or Non-contact devices e.g. LEDs, Pressure transducers etc.

Takt time is used to synchronize pace of production with the pace of sales. A sort of just-in-time production. The goal of Takt time is to produce to demand Can only be changed if the available time or customer demand changes. Must be recalculated on a regular basis. Should drive staffing decisions and cell layout. Should NOT be adjusted to account for system problems. Pace the process to a planned cycle time to accommodate system problems. Combination of both Strategic and Tactical Strategic work is a management role. It involves setting the direction for the organization (or group), deciding what to do and what not to do, who to hire and when. If it involves committing the organization to money in some way, that’s strategic work. Here are some examples (not an exhaustive list): managing the project portfolio, deciding on a product line, deciding when to hire which kinds of people, deciding on a software process initiative. Project management is mostly tactical, the operational approach to the day-to-day decisions. The one exception is at the beginning of the project, when you decide on release criteria and a life cycle. When you decide on release criteria, you have defined the boundaries of this release, a strategic decision. When you decide on a life cycle, that’s a strategic approach to how you use the people. The rest of a project or a program is tactical. Looking for and managing risks? Tactical. Understanding how people are working together–or not? Tactical. Conducting a meeting? Tactical. Problem-solving? In the context of a project, tactical. There’s also work that requires tactical time, and is strategic management work. For example: one-on-ones, feedback, coaching, career development/discussion, working across the organization to smooth the way for a project, solve other problems, or accomplish something that managers needs to do, such as collaborating on the project portfolio. This is the day-to-day work of a manager, which makes it tactical. It’s strategic in nature, because it builds culture, retains people, builds a trusting relationship with people across the organization, and implements the mission. I can never tell if this is strategic or tactical. Strategic work is difficult. It requires thought and discussion. Tactical work is difficult in a different way. Tactical work often demands answers quickly. Strategic work, assuming you don’t postpone it and create management debt should take longer because reflection is a good thing for strategic work.

The Central limit Theorem states that when sample size tends to infinity, the sample mean will be normally distributed. The Law of Large Number states that when sample size tends to infinity, the sample mean equals to population mean. The two statements are not contradictory. The Central Limit Theorem tell us that as the sample size tends to infinity, the of the distribution of sample means approaches the normal distribution. This is a statement about the SHAPE of the distribution. A normal distribution is bell shaped so the shape of the distribution of sample means begins to look bell shaped as the sample size increases. The Law of Large Numbers tells us where the center (maximum point) of the bell is located. Again, as the sample size approaches infinity the center of the distribution of the sample means becomes very close to the population mean. Addressing Reasons for Non-normality When data is not normally distributed, the cause for non-normality should be determined and appropriate remedial actions should be taken. There are six reasons that are frequently to blame for non-normality. Reason 1: Extreme Values Too many extreme values in a data set will result in a skewed distribution. Normality of data can be achieved by cleaning the data. This involves determining measurement errors, data-entry errors and outliers, and removing them from the data for valid reasons. It is important that outliers are identified as truly special causes before they are eliminated. Never forget: The nature of normally distributed data is that a small percentage of extreme values can be expected; not every outlier is caused by a special reason. Extreme values should only be explained and removed from the data if there are more of them than expected under normal conditions. Reason 2: Overlap of Two or More Processes Data may not be normally distributed because it actually comes from more than one process, operator or shift, or from a process that frequently shifts. If two or more data sets that would be normally distributed on their own are overlapped, data may look bimodal or multimodal – it will have two or more most-frequent values. The remedial action for these situations is to determine which X’s cause bimodal or multimodal distribution and then stratify the data. The data should be checked again for normality and afterward the stratified processes can be worked with separately. An example: The histogram in Figure 2 shows a website’s non-normally distributed load times. After stratifying the load times by weekend versus working day data (Figure 3), both groups are normally distributed. Figure 2: Website Load Time Data Figure 3: Website Load Time Data After Stratification Reason 3: Insufficient Data Discrimination Round-off errors or measurement devices with poor resolution can make truly continuous and normally distributed data look discrete and not normal. Insufficient data discrimination – and therefore an insufficient number of different values – can be overcome by using more accurate measurement systems or by collecting more data. Reason 4: Sorted Data Collected data might not be normally distributed if it represents simply a subset of the total output a process produced. This can happen if data is collected and analyzed after sorting. The data in Figure 4 resulted from a process where the target was to produce bottles with a volume of 100 ml. The lower and upper specifications were 97.5 ml and 102.5 ml. Because all bottles outside of the specifications were already removed from the process, the data is not normally distributed – even if the original data would have been. Figure 4: Sorted Bottle Volume Data Reason 5: Values Close to Zero or a Natural Limit If a process has many values close to zero or a natural limit, the data distribution will skew to the right or left. In this case, a transformation, such as the Box-Cox power transformation, may help make data normal. In this method, all data is raised, or transformed, to a certain exponent, indicated by a Lambda value. When comparing transformed data, everything under comparison must be transformed in the same way. The figures below illustrate an example of this concept. Figure 5 shows a set of cycle-time data; Figure 6 shows the same data transformed with the natural logarithm. Figure 5: Cycle Time Data Figure 6: Log Cycle Time Data Take note: None of the transformation methods provide a guarantee of a normal distribution. Always check with a probability plot to determine whether normal distribution can be assumed after transformation. Reason 6: Data Follows a Different Distribution There are many data types that follow a non-normal distribution by nature. Examples include: Weibull distribution, found with life data such as survival times of a product Log-normal distribution, found with length data such as heights Largest-extreme-value distribution, found with data such as the longest down-time each day Exponential distribution, found with growth data such as bacterial growth Poisson distribution, found with rare events such as number of accidents Binomial distribution, found with “proportion” data such as percent defectives If data follows one of these different distributions, it must be dealt with using the same tools as with data that cannot be “made” normal. No Normality Required Some statistical tools do not require normally distributed data. To help practitioners understand when and how these tools can be used, the table below shows a comparison of tools that do not require normal distribution with their normal-distribution equivalents. Comparison of Statistical Analysis Tools for Normally and Non-Normally Distributed Data Tools for Normally Distributed Data Equivalent Tools for Non-Normally Distributed Data Distribution Required T-test Mann-Whitney test; Mood’s median test; Kruskal-Wallis test Any ANOVA Mood’s median test; Kruskal-Wallis test Any Paired t-test One-sample sign test Any F-test; Bartlett’s test Levene’s test Any Individuals control chart Run Chart Any Cp/Cpk analysis Cp/Cpk analysis Weibull; log-normal; largest extreme value; Poisson; exponential; binomial

Value is the regard that something is held to deserve; the importance,worth, or usefulness of something. It's the relationship between satisfying needs and expectations and the resources required to achieve them and getting what you require for what you will pay. Activities can be value added; non value added and essential non value added. The eliminated non value added activities might be reduced, or simplified by becoming lean(muda). Some non value added activities may be necessary to comply with certain standards/or regulatory requirement. Some activities perform functions that do not change the form or function of the product or service, and the customer is not willing to pay. Example- Rework. T caused by errors of the he customer expects to pay for the printing of a document, for instance, but does not want to pay for corrections caused by errors of the supplier. The elimination value added activities present opportunities and/or management through economics of scale and scope realized using shared service models. Optimizing value added activities is a consideration for improvement where applicable and relevant. Automation is also relevant for both value added and essential non value added activities

5S is a workplace organization method that can help improve efficiency and management of operations. A process is impacted by environment, as is the ability of personnel to respond to process change. Improvements in general state of work place, including access to hand tools and so on are an aid to process control. Especially critical here are the cleanliness, lightening, and general housekeeping status of any area where measurements are conducted since process control data are filtered through the measurement system. For Example: A work bench cluttered with the tools and accessories wastes the valuable time of skilled workers and causes distraction from work resulting in poor quality. Similarly an office table covered with disorganized files and papers can cause clerical errors and delays in processing. 5S is one of the first tools to apply in the path to achieving lean enterprise organizations. The sequence for 5S is: SORT: Remove unneeded items. Be it in the office or home, we tend to collect items that are rarely needed or not needed at all.. Over a period of time these items accumulate into a mess and make it less efficient to search for needed items and sometimes even cause safety issues. The first step of sorting through the items as required and cleaning up the work area. Never used items should be discarded immediately. SET-IN-ORDER: Arrange the required and rarely- required items for ease of accessibility. The items that are required more often like drawings, instructions, tools, safety goggles and so on are replaced in designated and marked locations so that they cannot be placed elsewhere. In short, a place for everything and everything in its place. The rarely- required items like machine manuals, shop floor layout plans, and so on can be kept out of the way. SHINE: This involves cleaning the work area and equipment. Many quality issues are uncovered through effective cleaning of the work area. Example: Cleaning of the inspection surface plate provides better measurement results, cleaning of the equipment work table provides for better movement, cleaning of the floor prevents accidents. For some industries, like semiconductor manufacturing, cleanliness is mandatory and is measured in particle count. STANDARDIZE: This involves developing checklists, standards, and work instructions to keep the work area clean and orderly condition. SUSTAIN: This is the most difficult sequence in 5S. Most organizations are initially successful in the first four steps but sustaining the efforts and continuing them require support from management and empowerment of employees. Management needs to realize that time is well spent and be willing to invest in the time. the time invested in 5S improves productivity and overall efficiency and reduce accidents. Management should also empower the employees by allowing them to take ownership of their work areas.

The research has been carried out regarding the link between the Lean Six Sigma and Innovation. Lean six sigma methodology is commonly viewed as fostering process/product/service innovation, Incremental Innovation or Innovation Capability. The application of lean six sigma is gaining momentum for example in drug discovery. It remains controversial because of perceptions that process improvement will suppress much needed creativity and innovation. The conditions reviewed to support both creativity and innovation and the principles and benefits of lean six sigma of drug discovery. Hence, it is desirable to create a unified environment that encourages and enables both innovation and continuous improvement. The potential traps occur in the interpretation of standardization, role of variation, and the choice of how to use liberated capacity.