Vastupal Vashisth

Central tendency is usually expressed by a measure of location such as mean, median and mode. For a normal distribution or when continous data is there we prefer to take mean but mean is very sensitive towards outliers. It fluctuates drastically with the extreme values. For example group of five people have salary of one lack each having a average of one lack but if someone is having a salary of 10 lack coming there then we take average of all 6 people, mean will change drastically it's position from one lack to 2.5 lack which is not possible because they are having salary of one lack only so mean is sensitive to extreme values. So in this type of situation where data is skewed or not distributed properly we consider to take a median for measuring as it is robust towards outliers and extreme values. Where distribution might be skewed there also median is used to measure central tendency or where small number of subjects. Outliers affects the distribution because there are extreme values but median is no more sensitive to outliers. In distribution of average salary of engineers working in a company drastically increase the mean if consider salary of CEO and upper management. Mode is used for measuring central tendency for discrete data. Median a d mean have only one value and mode can have more than one in the data while measurement. Measure of dispersion is also means of spread. Or in other words variations is the amount of dispersion or spread in a set of data. Three frequently used measures of variation are the range get, the variance and the standard deviation. The range is equal to the largest value minus the smallest value. It does give full information so we use inter quartile range which is the difference between third and first quartile. Quartiles divide the sample into four equal parts. The range does not Co sides how the values distribute around the mean. Two commonly use d measure of variation that take into account how all the values in the data are distributed around the mean are variance and standard deviation. These measure how the values fluctuated around the mean. If we consider a expanse of five reading of a instrument while measurements 0,0,0,0,25 and another five reading in second case 5,5,5,5,5. In both cases mean is same but if we see the variation and spread then it is more in first case so it's not always to consider or to look after only central tendency by seeing g mean only we need to see dispersion pr variation in data also. The range, variance or standard deviation will always be greater than or equal to zero. It depends upon the spread. The more the spread out, the larger will be the range, variance, and standard deviation. The more concentrated the data are the smaller will be the range, the variaance and standard deviation will all be zero. In a class while we will be more focusing on the range of gardes of student what is minimum grade they got and what is maximum grade for doing extra effort to call all the student on same platform. We don't consider average grades of all student because in this case if we consider average of class then we are living behind all student who gets low grades. That is not good for result of school or to increase performance of students. Another example of consumption of Wasing oil in manufacturing industries whole production of parts on machines is very less in summer as compare to winter and rainy days to prevent from rust. So if we consider average of oil per year that will be wrong me this because it changes due to more consumption in winter and rainy days. So better to go with median and look after range of consumption of oil. Sales figure of cars more during festival in comparison to normal days of a year. Ice-cream or cold rinks sale is more in summer as compare to winter. Same case with air-conditioning manufacturer sakes of which is high in summer as compare to winter so it depends on data type, that what we are consider to metre it's variation and central tendency.

While continous data is preferred over discrete but in some circumstances we prefer discrete although continous data is there for the same. For example age is continous data but sometime we consider it as a discrete for simple calculations we consider only number factor in age. Like 10,20..etc. Another example in regression analysis we use continous data with discrete as weight of jujube boxes is correlated with no of jujubes in the box here weight is continous but number of boxes is discrete data. We use chl square analysis to find out is there any statistical significant difference in amount of color in the box. We can convert continous data into discrete but not vice versa. For example we want to know how much water is there in our office for the day we can simply say it is 50 litres but instead we can also say 50 bottles if one bottle is of one litre. So its easy to count number of bottles rather than to Wight of every bottle there to find out quantity of water.

Tribal knowledge is the collective knowledge of the organisation contained within the context and boundaries of the various department, Business units, teams, projects. Tribal knowledge is rich and useful we need to know how we can capture, unlock an d transform it as a useful knowledge in reality rather than within the groups. We make groups, teams, department as per our expertise level and together we collaborate to develop initiatives, oversee programs and complete our projects. For unlocking and catering g we need to gather information and knowledge, we should have sharing nature of knowledge. So the knowledge developed within these tribes typically transmitted by those with the deepest domain expertise through conversation, demonstrations and learned over a period of time called experience. Some time it may be rightfully information that is very classified information not to share outside company or enterprise. Organisations have gatherer too as we used to same in ancient times for hunting and information gathering. Those gatherer for information knowledge can be analytical, individual with responsibility who either do proactively understand the role of connecting and various departments or units or in other words various tribes. A shared desire of an individual is another key to achieve a state of operational alignment. A vision of an orderly system that encompasses effective efficient management of information and cross organizational behaviour that forms a comprehensive method to gather the knowledge and information then a process to examine best practices clarify informational needs develop new modes of activity. First we need to know the deep understanding of organisation nature in which direction it operates then we can realise important insights into how much gathering of Information is conducted and how best to go about evolving it. For this we need to endorse executive leadership to share and to gather information from someone who belongs to another department or tribe. Then we need an influencing plan to implement there and to make understand the people surrounding by you. For launching a new car model in a car Manufacturing company there are different departments, project teams, units who are taking care of development, according to their expertise level and level of understanding. They also have a common meeting share their findings, problems they are facing to each other so that they can make a new car without any hassle. There are some designer who designed the car but In reality how it will go, what are the problems that shop floor people facing they also need to know for further improvements in future. So some information documented but some have without documents which needs to share while implementation and sharing in teams for better results towards a common goal.

Concept of rational subgrouping plays a very important role in pursuit of process improvement. Excellence practitioner will loose some important aspects if he ignore this concept. The method used to select samples for a control chart must be logical or rational. In the case of x bar and r chart, it is desirable that x bar chart detects a process shift, while the r chart should capture only common cause variation. That means there should be a high probability of variation between successive samples while the variation within the sample is kept low. There will be no exact idea of central tendency and dispersion of process over time. It means excellence practitioner will not be able first d out the answer of follow questions: 1. Has a special cause of variation caused the central tendency of this process to change over the time period observed or not? 2. Has a special cause of variation caused the process distribution to become more or less consistent? Rational subgrouping is the base of control charts. Rational subgroups are composed of items which were produced under essentially the same conditions. The subgroup provides a snapshot of the process at that moment in time. A fundamental aspect of the subgroup is thus to estimate the common cause variation within the process, since the within subgroup variation is used to define the width of the control limits. Therefore it is critical that the causes of within subgroup variation be representative of the causes of variation between subgroup. Is absence of rational subgrouping practices will not able to find out short term within subgroup variation which leads to non prediction of longer term between subgroup variation, causing g the statistically uncontrolled. When the longer term variations is not predicted by shorter term within subgroup variation, then a special cause has been identified but in this case of absence of rational subgrouping central tendency is not defined exactly so special cause will not be the correct to resolve the problem.

Sponsor act as a surrogate process owner until an owner us named. Following are the desired qualities of a sponsor: 1. Sponsor should be able to provide linkage to higher levels of the organisation and to some extent able to to provide visibility across the organisation as well. 2. Sponsor should be able to remove roadblocks, anticipate potential problems. 3. Sponsor should be proactive with regard to assigned project. 4. Sponsor should actively participates in projects. 5. Sponsor should be able to review of progress of project. 6. Sponsor should be able to identify and overcome barriers and issues. 7. Sponsor should be able provide adequate resources for the completion of project. 8. Sponsor should be able to evaluate and should accepts deliverable. 9. Sponsor should be high level individual who understand six Sigma and are committed to its success. 10. Sponsor should be able to give guidance to leaders in the art of 'visioning', without a vision, there can be no strategy. 11. Sponsor should be master of communication. 12. Sponsor should demonstrate strict adherence to ethical principles. 13. Sponsor should be able to make trust, should not violate moral code that allow people to wok and live together. 14. Sponsor should have honesty, integrity, and other moral virtues.

Base line performance after improvement is not comparable with performance before improvement. It is possible sometimes when we are estimating savings. For example a project that streamlined a production control system was aimed at improving morale by reducing unpaid overtime worked by exempt employees. However no measure of employees more was obtained ahead of time. Nor was the unpaid overtime documented anywhere. Consequently, project was not able to sustainable it's claims of improvement. Process baselines are a critical part of any process improvement effort as they provide the reference point for assertion of benefits attained. In the absence of proper baseline estimate there can no credible evidence of sustainable improvements. Another example of average time. For example an improvement team uses lean techniques to reduce the time to process an order. A random sample of 25orders before the change had an average time to process of 3.5 hours. A random sample of 25 orders after the change has an average time to process of 2 hours. The team asserts they have decreased the order processing time by more than 40%. This is not credible because an improvement can not be asserted without showing that the new process is significantly different from a statistical point of view. There is no evidence that the estimate of 3.5 hours for the first 25 samples is a valid estimate of the process, since we have not shown Ruth at the process is stable or not.

In some processes, zero rework is impractical, it will affect the business Excellence. It depends on the cost of rework. If it is more than the cost of part then there is no use of doing rework. If I talk about manufacturing industry, parts which are used in inner side of the car are if deep draw and have wrinkle in most of the cases. According to standard wrinkle not allowed but after seeing and verifying location, how is it going to effect the quality of final car, although rework is needed to remove wrinkle but we can do straight pass so it will save our cost because there will not be any need to do scrap those part. We can not ignore wrinkle in inner parts due to part profile and it will come in all parts but we can't do rework for whole or scrap so based on quality point of view we take decisions to save cost.

Descriptive and Predictive areas of Business Analytics are captured by the Lean Six Sigma community reasonably well and Prescriptive is till seem largely unexplored. As we work on data gathering for results which is focusing on descriptive area of business analytics and hypothesis testing covers predictive area of business analytics.

Statistically significant difference means the difference may not be big enough to be importance to the business. The bigger the sample size used in an analysis, the smaller the deviation from the null hypothesis that may be detected. We use this concept in hypothesis testing. The equivalent to one minus the probability of a Type 2 error (1- beta) is called power. A higher power is associated with a higher probability of finding a statistically significant difference. Lack of power usually occurs with smaller sample sizes. The beta risk or consumer risk is the probability of failing to reject the null hypothesis when there is significant difference.Also the power of the sampling plan is defined as 1 - beta , hence the smaller the beta, larger the power.The product is passed on as meeting the acceptable quality level when in fact the product is bad. Typically the beta is 0.10% means there is 90% probability that we are rejecting the null when is is false which is correct decision.

A capable process can be stable process but a stable process may or may not be a capable process. Every process has some inherent variations called common cause variation, we can not ignore that . Special cause variation is other that common cause which is more that +- 3 sigma. A process is said to capable if it comes under process curve between LSL & USL. A process is said to be stable if it is +-3 sigma around its mean value, which means we are getting value within 6 sigma. whether a process is capable or not, measured through process capability indices. Cp is Specification limit divided by process width but it does not tell us where the process is laying, where its mean or it is shifted. we need Cpk because if mean shifts still Cp value will remain same. If Cp is not equal to Cpk it means that process has shifted . Process mean tells us which side process has been shifted LSL or USL. Shifting the mean is very easy rather than to reduce variance. Special cause can be manageable. A process stability may be supposed to be prerequisite for all type of processes.Process stability refers to the consistency of the process with respect to mean, variance or other important process characteristics. A process is stable because it has consistent value around its mean. The process distribution remains same over a period of time. we can find out whether process is stable or not by Xbar - R Chart, X bar- Chart, Moving range chart.Process capability is an assessment of the ability to meet specification limits. If the process is unstable, we can not predict its capability. For example if process mean has been shifted and if process is stable then only we can predict from Cp or Cpk that where it is going towards LSL or USl but if it is not stable we can not predict Process capability. For example time to reach office in morning is 8:45 am to 9 am. A person is coming to office, his timing recorded over a period of time and it was found that he comes in between 8:55 am to 9:10 am. in one way he is stable while coming to office within that time span of 15min and consistently coming on that but it is out of specified time. So this process of coming to office is stable but not capable because it is shifted towards USL and he will be most of the. So by finding process capability we can find out where the process is shifted and work on that but for that process should be stable.

We use correlation in root cause analysis because correlation analysis measures the degree of linear relationship between two variables. It is used in scatter diagram which provides a graphical representation of the relationship of two continuous variables. Correlation does not guarantee causation. Correlation by itself does not imply a cause & effect relationship. From scatter diagram we can judge strength of relationship by width or tightness of scatter, and determine direction of relationship eg. positive or negative.Correlation values of -1 or +1 imply an exact linear relationship. However , the real value of correlation is in quantifying less than perfect relationships. The simplest tool used in correlation and regression analysis is often called scatter plot or scatter diagram which is plot of one variable versus another. one variable is called independent ad usually shown on horizontal axis and another one is called dependent variable and it is shown on vertical axis.scatter diagrams are used to evaluate cause and effect relationship. The assumption is that the independent variable is causing a change in the dependent variable. scatter plots are used to answer question like" does the length of training have anything to do with the amount of scrap an operator makes. A correlation problem considers the joint variation of two variables, neither of which is restricted by experimenter. Proving cause and effect requires sound scientific understanding of the situation at hand because statics can not by themselves establish cause and effect.

Overemphasis on VOC can be detrimental to business, it depends on how organization approaches to VOC and how effectively they convert it to VOP. Organizations that recognize the importance of customers and focus on their wants and needs are commonly known as customer focused or customer driven. In contrast to natural process limits, specification limits are customer determined or derived from customer requirements and are used to define acceptable levels of process performance. Said to be the 'Voice of customer", specification limits may be one sided or two sided. The difference between them is known as tolerance. In soem cases, customers provide specifications for products or services explicitly. This is often the situation when customer is the defense department or any of the military branches.In others, customers express requirements in value terms, the components that influence the buy decision, such as price, product quality, innovation, service quality, company image, & reputation. In still other cases, customers may spotlight only their needs or wants, thus leaving it upto the company to translate them into internal specifications. tools such as quality function deployment, critical to quality analysis & so on often help with the last two situations. for example a customer wants that if he stuck in road traffic jam, then car should have features like that it should be able to fly in air and cross the road traffic jam but a car manufacturing company can not give this feature in the same price of car and due to some other regulatory requirements. So it will be detrimental to business to work on this idea to provide a cheap car which can fly and run on road. When the voice of customer is fully understood and connected to products and services, the products and services can, in turn, be connected to the process that produce them. Unfortunately many organizations collect the VOC and can not relate it back to the underlying processes. This constitute a major disconnect and presents a serious situation for the organization to overcome. Notice that customer requirements are integral to the development of the strategic plan, improvement takes place at the individual process level, and we must address the basic question as to whether or not these processes are capable of meeting customer and business requirements. Voice of the customer data are essential to the long term success of an organization. Although collecting such data is critical, it is not sufficient. It must be analysed, disseminated, and acted on in a systematic manner. This will only occur when processes have been enacted to ensure that it will happen in a consistent manner.

I would suggest value stream map for a sequential series process mapping in an organization with increased level of detailing, because a value stream is"all the actions(both value added and non value added currently required to bring a product through the main flows essential to every product: 1. Production flow from raw material into the arms of the customer 2. Design flow from concept to launch So Value stream mapping is simply an illustration of the sequential activities that take place within value stream. at each step of the map the practitioner evaluates whether value is being created and whether one or more of the waste exist, so the purpose of the value stream map is to identify those activities that add value and those that create waste, with the latter being targets for elimination.

Continuous data is measured and attribute data is counted. Continuous data contains more information than attribute data. It is beneficial to have continuous data rather than to have attribute data while doing some analysis like in control charts, . Continuous data can be measured, verified, and manipulated. it is known as numerical data. for example height, weight, temperature, volume, humidity etc. Attribute data is categorical and count data. They have only a finite number of points that can be represented by non negative integers. Continuous data results fro measurement on a continuous scale such as length, weight. these scales are called continuous because between any two values are an infinite number of other values. There is still confusion in some specific dataset whether it should be considered as continuous or attribute. For example 1. while measuring time we think that it is hour, minutes, month, year, so it will be attribute data but in actually it is continuous data because it can be break into minutes, seconds, pico seconds like that. but we can convert it into attribute data. 2. Another confusing dataset when we are talking about money, when we withdraw money from atm it comes like 100,200,500,2000 but if we see our bank balance in our records online then we see the figures like 200.35 rupees, so it is continuous data because it has several infinite values so it is continuous data. 3. Another confusing dataset is percentage data or we can say that derived data, how we will consider it continuous or attribute. it depends on source data what it is actually according to that % data is decided. for example number of students taking this class divided by total number of graduate students is attribute data because a student can not be 1.5,2.5 4. Another confusion about measuring in this example, imagine a young child is sick, as apparent first thing we do is to touch his forehead to feel if it is warm or not then it is attribute data but if we measure it by thermometer then it is continuous data.

Correction is the action to eliminate a detected non-conformity. It is a reactive approach. This is immediate action to make customer/managment happy. it is to fix the non conformity at same time or to reduce its impact. For example on assembly line some wrong variant parts has been delivered then to replace the wrong variant parts is the correction so that assembly line dont stop. Corrective action is the action to eliminate the cause of a non- conformity and to prevent recurrence. Corrective action can be horizontal deployment. This is the thing that you do to ensure the non conformity never happens again. It also requires some strategic thinking. Therefore, it is crucial to always identify causes of nonconformities before defining and implementing a corrective action. Corrective action for the above problem is to find out root causes of delivering wrong variant parts on line and to eliminate it like training to manpower, some identification tag, proper plan. So many ways to eliminate the root cause. Eliminate root cause by considering future threat of the occurred problem and impact of it. Preventive action is the action to eliminate the cause of a potential non - conformity or other potential undesirable situation. It is proactive in nature. It has no relation with he actual non. conformity. It means nothing bad actually happened. These actions are improvements in the system to ensure detrimental system gaps are addressed before a nonconformity occurs. Some tools can be used for this like checksheet, FMEA, FTA. As a proactive approach, i should find out initially before set up the process the potential failure on assembly line while delivery to parts, so FMEA can be done before and later on we can have checksheet for delivery to maintain a record and to ensure only right parts deliver on line. There are situations where preventive and corrective actions are undesirable and correction is the only preferred method. This happens in low risk/impact problems. A problem which is having impact on customer /management negligible, can be solved just by correcting it. No need to go further.

It will be continue in its original form or modified form because it is a structured, prepared form for collecting and analysing data. This is a generic tool that can be adapted for a wide variety of purposes. It is also called a defect concentration diagram. On the other hand when the information is collected is quantitative in nature the checksheet can also be called as tally sheet.the document is typically a blank form that is designed for the quick, easy & efficient recording of the desired information. It is used to observe an operation, process variability, identify potential problem & confirm the effects of problem. Checksheets are special types of forms for data collection. They make it easier to collect data, tends to make data collection effort more accurate & they automatically produce some sort of data summarization which is often very effective for quick analysis. The form of the checksheet is individualized for each situation. Another reason for continuation of checksheet is the simple design of the checksheet. This is of two reasons. Firstly it was meant to be a tool for data recording which itself is a quite simple. Secondly the checksheet was meant to be used by the people on the shop floor on actually it would not be very intelligent to expect them to be able to deal with complexity. Hence there is an inherent need for designing the checksheet the way it is.

While Pull based flow is considered better than Push based flow in many ways in general, it is not always that a pull system can be implemented. Before proceeding to answer this question let us discuss about what is push or pull system? Both push and pull systems known as Kanban system and used for supply chain management, inventory control etc. A pull system is opposite of the traditional push system. A pull system is the one in which, customer order process withdraws the neeeded items from a supermarket and the supplying process produces product to replenish what was withdrawn. From the definition of pull system , it can be seen that many manufacturing enterprises traditionally operate in a push system. this refers to the fact that raw materials and subassemblies are pushed through the production process with the anticipation that they will be needed by customer. Now the main question is how large should be push or pull system or kanbans be? if too small then purchase orders are placed out of stock most of the time and when too large, WIP inventory builds up & incurs carrying costs that may include a tax burden as well. A system can be designated area on floor , space on self, a rack, a bin or any place that is convenient to draw from and resupply. For example on assembly lines small bins are provided with cards. Another example is of CBU Yard, where it is full of final Cars which are ready to dispatch, after dispatch CBU Yard again filled by fresh new cars which are coming after final inspection from assembly line. This is also a pull system whenever CBU Yard is empty, new cars are being produced to fill the gap. However pull system is not practically better than push system in certain situations. Let us see how and why is this possible with an example. This is particularly true for seasonal products. An air conditioner manufacturer may sell 95% of its products in a three month span. if it were to operate on a strict pull system it would be necessary to greatly expand manufacturing capacity- capacity that would not be needed the other nine months of the year. instead the company will likely spend most of the year building inventory that it hopes to sell during the rush months. Same case is of icecream manufacturing, soft drinks manufacturing company. Their sale ratio is much higher in summer than in winter. if we talk about car manufacturing companies, here also both system work together during planning and assembly it depends on the situation and forecasting. Company may sell 20 to 30 % more of its cars on various occasion like Dipawali, Holi, New Year. if it were to operate fully on pull system it would be necessary to greatly expand the manufacturing capacity, that would not be neeeded the other time of the year. Instead the company builds inventory in remaining time to cope up the situation that hopes to sell during rush times.

Cost of poor quality (COPQ): The costs associated with providing poor quality products or services. There are four categories: internal failure costs (costs associated with defects found before the customer receives the product or service), external failure costs (costs associated with defects found after the customer receives the product or service), appraisal costs (costs incurred to determine the degree of conformance to quality requirements) and prevention costs (costs incurred to keep failure and appraisal costs to a minimum). Cost of quality is a methodology that allows an organization to determine the extent to which its resources are used for activities that prevent poor quality, that appraise the quality of the organization’s products or services, and that result from internal and external failures. Having such information allows an organization to determine the potential savings to be gained by implementing process improvements. Quality-related activities that incur costs may be divided into prevention costs, appraisal costs, and internal and external failure costs. 1. Prevention cost: Prevention costs are incurred to prevent or avoid quality problems. These costs are associated with the design, implementation, and maintenance of the quality management system. They are planned and incurred before actual operation, and they could include: •Product or service requirements—establishment of specifications for incoming materials, processes, finished products, and services Quality planning -creation of plans for quality, reliability, operations, production, and inspection Quality assurance - creation and maintenance of the quality system •Training—development, preparation, and maintenance of program 2. Appraisal cost Appraisal costs are associated with measuring and monitoring activities related to quality. These costs are associated with the suppliers’ and customers’ evaluation of purchased materials, processes, products, and services to ensure that they conform to specifications. They could include: •Verification—checking of incoming material, process setup, and products against agreed specifications Quality audits—confirmation that the quality system is functioning correctly •Supplier rating—assessment and approval of suppliers of products and services 3 Internal failure cost Internal failure costs are incurred to remedy defects discovered before the product or service is delivered to the customer. These costs occur when the results of work fail to reach design quality standards and are detected before they are transferred to the customer. They could include: •Waste—performance of unnecessary work or holding of stock as a result of errors, poor organization, or communication •Scrap—defective product or material that cannot be repaired, used, or sold •Rework or rectification—correction of defective material or errors •Failure analysis—activity required to establish the causes of internal product or service failure 4. External failure cost External failure costs are incurred to remedy defects discovered by customers. These costs occur when products or services that fail to reach design quality standards are not detected until after transfer to the customer. They could include: •Repairs and servicing—of both returned products and those in the field •Warranty claims—failed products that are replaced or services that are re-performed under a guarantee •Complaints—all work and costs associated with handling and servicing customers’ complaints •Returns—handling and investigation of rejected or recalled products, including transport cost. The costs of doing a quality job, conducting quality improvements, and achieving goals must be carefully managed so that the long-term effect of quality on the organization is a desirable one. These costs must be a true measure of the quality effort, and they are best determined from an analysis of the costs of quality. Such an analysis provides a method of assessing the effectiveness of the management of quality and a means of determining problem areas, opportunities, savings, and action priorities.

Common and special causes are the two distinct origins of variation in a process. "common causes", also called natural patterns, are the usual, historical, quantifiable variation in a system, while "special causes" are unusual, not previously observed, non-quantifiable variation. An example explains the two types of causes of variation: Common cause: It normally takes me 25-35 minutes to commute to a neighboring town. Note that it does not take me exactly 31.5 minutes each time because there is attribution of common cause variation. The value in the range could be affected by factors such as the number of red lights I hit, traffic volume or weather conditions, such as rain or sun. These are a normal part of the drive. Expected common cause variation may be predicted by a control chart, often with limits of the mean +/-3 standard deviations. Common cause variation is present in every process. Special cause: One day, I arrive at the town in two hours, which is statistically peculiar. There is a special cause associated with this incident that is outside the normal system: On that day, a blizzard contributed to the delay. To solve a problem with a special cause, the team should be looking for what changed or is different, whereas solving problems attributed to common cause will require reducing the variance, increasing the spec range or shifting the process mean. All of these relate to not what is different, but rather what is the same (intrinsic) in the process.

SIPOC is an acronym for Suppliers-Inputs-Process-Outputs-Customers. It is a high level process map that describes the boundaries of the process, major tasks and activities, key process input, output, variables, suppliers and customers. when we refer to customers, we usually talk abut both internal and external customers. it can be used to identify the key stakeholders and describes the process visually to team members and other stakeholders. A stakeholder is anyone who is either impacted by the project or could impact the outcome of the project.this helps to establish the scope of the process, identify significant issues and frame the more details.it is used to document the atual process and helps locate values and non. value added steps.

The statement Human error may happen, the defect will also happen but will be detected and corrected automatically seems to be correct because Poka - Yoke helps us to detect and preventing errors while doing some work whether it is manufacturing industry or service industry. Either the operator is alerted when a mistake is about to be made, or the poka-yoke device actually prevents the mistake from being made. The former implementation would be called a warning poka-yoke, while the latter would be referred to as a control Poka- Yoke. For example in manufacturing industry a digital counter attached on spot welding machine which indicates to an operator to have an exactly required number of spots to parts for welding. Another example also A fixture is redesigned or modified so that a part can fit into it in a particular orientation, so it will prevent to produce wrong part while welding. if we see mobile phones, sim slot is called as Poka- Yoke in which sim goes only in one direction, every time we used to do mistake while putting sim inside mobile but it does allow us because of the design of sim slot. If we see automatic transmission cars, cars will not start unless we don't press the brake pedal, on the other hand, some have a switch that requires being a car in the park of neutral before the car to start. Let me discuss one case of my company, two different cars using two different Door Panel of different number of holes into it but of same size & orientation but the problem is that both door panel can be fixed in the fixture at the time of spot welding and operator was not able to identify that which model door panel is going to process every time. There was a chance of door panel mixing of both models at back end due to same size & same orientation. So Some Poka- Yoke done in the fixture to differentiate in both model door panel by identifying some holes which are not common in both panels. A PU Pad Pressing Pin connected with a sensor which is connected with alarm. PU Pad used so that it will not generate dent to the panel.Whenever door panel of another car fix into the fixture it presses the pin and gives alarm because this pin is in the same direction where hole location is there in the door panel of one car but not in another car door panel. So by doing this it prevents wrong door panel to be processed and gives warning also to the operator that there is the wrong panel in the fixture.

First let us know about what is exactly false alarm & missed alert? False alarm is nothing but suggests that the object is actually good but alarm is showing as bad object. on the other hand a missed alert suggests that object is actually not good but passed. I would prefer false alarm over missed alert for pursuing business excellence. A false alarm occasionally may be okay or we may never want it. it depends on situation to situation. However, too many false alarms can lead to the assumption that something is wrong leading to an unwanted change in a well behaving process. A missed alert may never be acceptable or may be sometimes okay, it also depends on situations. Let us understand it with some examples below: 1. A false alarm might cause inhouse rejection increase but customer is not bothered about it on the other hand missed alert will pass the NG object to customer and based on the severe nature of the NG , a customer complaint may arise. 2. Alarms are set based on specific criteria. Based on some trend observed in past false alarms it can further be calibrated to improve the predictability. The end objective is that we do not end up in a situation that can lead to unwarranted consequences. On the other side A missed alert will lead to a negative outcome in most cases. If the alerts (control system) do consider adequate buffer it will help us to recover but not in all scenarios. Although practical solution will depend on various factors like cost of deploying the mechanism, maintenance, criticality of process being monitored, etc. 3. A false alarm will not cause any loss to company rather than creating some confusions on shop floor, but if a alert is missed, it may affect the economy and heavy loss can be seen(if alert of coolant is missed in CNC and is not turned off, the overfilling of coolant may cause loss for company). Thus, according to me we should prefer a false alarm and not a missed alert. 4. An occasional false alarm is fine but too many of these can lead to loss of business or even closure of a company. As an extreme example, let us consider an automotive company that decides to do 100 percent inspections on all parts leading to high cost of product which finally leads to loss of market. We need to also consider that a false alarm can lead to process adjustments when none is required in an existing good process. 5. A false alarm is highlighted to everyone. However, excessive alarm is something that one need to keep an eye on it. A missed alert is something like a loss is coming for e.g. financial, mechanical, human or anything, but negligence or a lazy attitude did not trigger the awareness about the same. 6. Though false alarms can increase the incidence of rechecks, it can definitely avoid reworks. If we see healthcare industry missed alerts can be fatal. 7. False alarm is nothing but defect in defect identifying method, and missed alert can be handled by error report developed in the application so missed alert should not be that big issue. An outlook giving me a false alarm about a meeting that is cancelled. I can review and correct it. Too many false once means there is a series that has been cancelled and I am receiving the alert unwantedly. On the other hand an alert that should remind me of a meeting getting missed cannot be taken..A team huddle getting missed because of it missed.

Takt time may be thought of as a measurable “beat time,” “rate time” or “heartbeat.” In Lean, takt time is the rate at which a finished product needs to be completed in order to meet customer demand. If a company has a takt time of five minutes, that means every five minutes a complete product, assembly or machine is produced off the line because on average a customer is buying a finished product every five minutes. Described mathematically, takt time is: Available time for production / required units of production Takt time is driven by both decisions strategic & tactical but it depends on situations when and where? Before going to details about this we need to know what is the purpose of takt time and what really is it? The Purpose of Takt Time: Here is one statement :Running to takt time is wholly unnecessary. Many factories operate just fine without even knowing what it is. What those factories lose, however, is a fine-grained sense of how things are going minute by minute. Truthfully, if they have another way to immediately see disruptions, act to clear them, followed by solving the underlying problem then they are as “lean” as anyone. So here is the second one: You don’t NEED takt time to “be lean.” What you need is some way to determine the minimum resource necessary to get the job done (JIT), and a way to continuously compare what is actually happening vs. what should be happening, and then a process to immediately act on any difference. This is what makes “lean” happen. Takt time is just a tool for doing this. It is, however, a very effective tool. It is so effective, in fact, that it is largely considered a necessary fundamental. Honestly, in day to day conversation, that is how I look at it. I made the above statements to get you to think outside the mantras for a minute. What is takt time, really? Takt time is an expression of your customer demand normalized and leveled over the time you choose to produce. It is not, and never has been, a pure customer demand signal. Customers do not order the same quantity every day. They do not stop ordering during your breaks, or when your shift is over. What takt time does, however, is make customer demand appear level across your working day. This has several benefits. It makes capacity calculations really easy through a complex flow. You can easily determine what each and every process must be capable of. You can determine the necessary speeds of machines and other capital equipment. You determine minimum batch sizes when there are changeovers involved. You can look at any process and quickly determine the optimum number of people required to make it work. You can see opportunities where a little bit of kaizen will make a big difference in productivity. More importantly, though, takt time gives your team members a way to know exactly what “success” looks like for each and every unit of production. This gives your team members the ability to let you know immediately if something is threatening required output. Put another way, it gives your entire team the ability to see quickly spot problems and respond to them before little issues accumulate into working on Saturday. The key point here is that to get the benefit, you have to have a takt time that actually paces production. It has to be real, tangible, and practically applied on the shop floor. Otherwise it is just an abstract, theoretical number. Further, in a complex flow, there may be local takt times – for example, a process that feeds more than one main line is going to be running to the aggregated demand, and so its takt will be faster than either of them. Likewise, a feeder line that builds up a part or option that is not used on every unit is going to be running slower. And finally if disruptions do cause shortfalls to the required output, you have to make it up sometime. If you are constrained from running overtime (and many operations are for various reasons), then your only alternative is to build a slight over speed into your takt time calculation. If everything goes well, you will finish early. Stop and use the time for organized improvement of either process or developing people. Continuing to produce is overproduction, and just means you run out of work sooner if you have a good day tomorrow. If there are issues, the use the buffer time for its intended purpose. If there are more issues than buffer time, there is an operational decision to make. Have a policy in place for this. The simplest is “hope for a better day tomorrow” and use tomorrow’s buffer time to close the gap. If this isn’t enough, then a management decision about overtime or some other remedy is required. What about just allowing production to fall short? Well, if this is OK, then you were running faster than customer demand already. So pull that “extra” out of your schedule, stop overproducing (which injects its own disruptions into things), and deal with what just actually have to accomplish. Stop inflating the numbers because they hide the problems, the problems accumulate, and you end up having to inflate even more. If we talk about decisions then there is some difference between strategic & tactical decisions: General characteristics of strategic decisions Strategic decisions usually: Have a long-term impact on the business Have an impact on the whole organization On its future direction On the scope of its activities On the boundaries of the firm Define the basis on which the firm competes or co-operates: Based on which competencies and/or advantages In which markets By providing which value for the customer Align the organization’s activities with its environment, its resources, and capabilities. Hence, strategic decisions have a major impact on the organization’s future development. They determine long-term success or failure.From this, we can conclude some additional characteristics. These are not a prerequisite for a decision to be strategic, but they are a feature of most of them: Are taken at top-management level Have a significant impact on resource allocation Require irreversible commitments and hence risk some sunk cost investments Involve major change Are taken under uncertainty / under incomplete information The frequency of strategic decisions: Many organizations have a defined strategic planning process with regular strategic planning meetings. In most cases, this is an annual cycle; sometimes with an even lower frequency. This might lead to the conclusion that strategic decisions are taken on a more or less regular basis. I don’t want to play down the value of a regular strategic planning process. However, such a process does not necessarily lead to regular decisions that are truly strategic. The real value of such annual meetings is to review the current strategy and to make adjustments as necessary. True strategic decisions occur infrequently. They are taken when top managements identifies new opportunities or threats. Such changes of conditions don’t have a fixed time schedule. It would be unwise to postpone such a decision to the next strategic planning workshop scheduled in five months. The opportunity may be gone by then. Otherwise, it would be similarily unwise to re-cast a successful strategy, just because the annual top-management-meeting is supposed to lead to some decisions. Decisions are taken at all organizational levels. They have a varying impact on the business. In general, a distinction is made between strategic, tactical. Particular consideration should be given to the separation between strategic and tactical level. Depending on the terminology and the emphasis employed in the formulation, tactical decisions sometimes come masked as strategic ones. Strategic decisions Support the organizations' vision, mission, and values Have significant resource allocation impact Set precedents for decisions further down in the organization occur infrequently, may be irreversible have a potentially material effect on the organization’s competitiveness, Are made by top managers Tactical decisions Involve formulation and implementing policies for the organization Are less all-encompassing than strategic ones Are usually made by mid-level managers Often materially affect particular business functions such as marketing of production, or a business unit Have fewer resource implications

The Central Limit Theorem tell us that as the sample size tends to infinity, 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. Law of Large Numbers says if we have very large sample, the mean will converge to a number. 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. Central Limit Theorem requires "less data" Comparing to Law of Large Numbers, because it require "less data", it has a relaxation in conclusion: not converge to a number, it converge to a normal distribution. In the application of central limit theorem to sampling statistics, the key assumptions are that the samples are independent and identically distributed.

The fault tree analysis (FTA) was first introduced by Bell Laboratories and is one of the most widely used methods in system reliability, maintainability and safety analysis. It is a deductive procedure used to determine the various combinations of hardware and software failures and human errors that could cause undesired events (referred to as top events) at the system level. The deductive analysis begins with a general conclusion, then attempts to determine the specific causes of the conclusion by constructing a logic diagram called a fault tree. This is also known as taking a top-down approach. The main purpose of the fault tree analysis is to help identify potential causes of system failures before the failures actually occur. It can also be used to evaluate the probability of the top event using analytical or statistical methods. These calculations involve system quantitative reliability and maintainability information, such as failure probability, failure rate and repair rate. After completing an FTA, you can focus your efforts on improving system safety and reliability. Advantages of Applying FTA FTA can be advantageous to software projects in at least three ways: Value addition: FTA has the potential to serve as a defect-prevention tool. If FTA is performed before baselining the design, it can provide valuable information on application failures and their mechanisms. This information could be utilized to improve the design by preventing the potential defects or by introducing fault-tolerating abilities. FTA is most effective for more complex functions but may not be adding much value when applied to the simple functions of a software application. FTA utilizes the potential of teamwork to bring in a variety of ideas and broaden thinking. Simplicity: FTA is very simple and can be prepared by project teams with minimum training. Its graphical presentation improves readability and makes it easy to maintain in the event of changes. Traceability: Some of the conventional test case tools provide a unique identification to individual test cases. Such traceability could be added to FTA by appropriately identifying the individual scenario. Fault tree analysis can be used to: · understand the logic leading to the top event / undesired state. · show compliance with the (input) system safety / reliability requirements. · prioritize the contributors leading to the top event - Creating the Critical Equipment/Parts/Events lists for different importance measures. · monitor and control the safety performance of the complex system (e.g., is a particular aircraft safe to fly when fuel valve x malfunctions? For how long is it allowed to fly with the valve malfunction?). · minimize and optimize resources. · assist in designing a system. The FTA can be used as a design tool that helps to create (output / lower level) requirements. · function as a diagnostic tool to identify and correct causes of the top event. It can help with the creation of diagnostic manuals / processes. Limitation: A limitation of the fault tree analysis is that the undesired event evaluated must be foreseen and all significant contributors to the failure must be anticipated. This effort may be very time-consuming and expensive. And finally, the overall success of the process depends on the skill of the analyst involved.