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Project Quality Management: The Key Indicator of Project Success

The PMBOK® defines quality as the degree to which a set of inherent characteristics fulfills requirements.  A project is said to be meeting its quality expectations when all the project requirements agreed at the inception of the project are met, and thus the resulting product or service is usable for the relevant stakeholders.  Quality is Subjective  Quality for one individual will not be adequate for another. For example, the youth will consider a mobile phone as being of high quality based on its look and feel, brand name and its specification such as camera quality, memory capacity, screen resolution, ability to connect with other devices etc. and based on the support to run applications on the phone. However, for someone in the age group of 60 and above, the ability to take a phone call or send an SMS and whether this can be done without much hassle alone may define the quality of the mobile phone.  To understand the quality requirements tailored to projects, it is necessary to have an exhaustive Quality Management training such as the Certified Manager of Quality Training. Quality has many faces The definition of quality depends on the context and the business domain. For a service-oriented organization such as a bank, a restaurant, an airline etc. quality of service is identified through the level of customer satisfaction. For a product such as a mobile phone, a vehicle, a computer etc. quality means conformance to product specifications and its fitness for use.  In the context of healthcare sector, mission-critical military activities etc. quality is measured through precision and accuracy. Precision refers to the granularity of measurement and how fine the outcome can be measured. Accuracy simply put is the correctness of the output or being close to the desired value.   Here are the four things #CIOs need to know about quality https://t.co/gRfqoTEUB7 #Qualitymanagement pic.twitter.com/KHMz5iC6RN — CIOReview (@cioreview) January 7, 2018 Quality is everybody’s responsibility  Quality in project management is two-fold. Project Quality and Product Quality. Project quality is to ensure that the project is executed in line with the triple constraints of time, cost, and scope. If the project is within the defined tolerance levels of these three dimensions, then we can say that the project is of high quality. Projects are carried out to develop a solution; i.e. product, service or a result. If this solution meets its specification or meets the needs of the stakeholders then it is said that the solution is of high quality.   Meeting the quality expectations is not merely the responsibility of the project manager, but of everyone involved in the project. Achieving quality involves cost where it is the responsibility of everyone involved in the project to manage the same. Increased efforts and costs can increase the quality of output but a ceiling on this investment has to be fixed. Yes, it is the responsibility of the project manager to manage this ceiling value and to ensure optimal levels of quality within the project but he or she can only do this with the support of his team members. The optimal level of quality can be achieved when the incremental cost of achieving quality is equal to the incremental revenue from such quality improvements.    What digital transformation means for the future of #qualitymanagement: https://t.co/0gyCaHsKtb — Sparta Systems (@SpartaSystems) January 17, 2018 Cost of Quality In order to achieve this, the project will have to incur some cost and this is known as Cost of Quality. This includes all costs incurred over the life of the product by investment in preventing non-conformance to requirements, appraising the product or service for conformance to requirements and failing to meet requirements or in other words rework. Thus, the cost of quality is of two main types- Cost of Conformance – This is the money spent during the project to avoid failures through prevention or appraisal. Prevention costs are costs incurred to prevent errors by the way of training, documentation, maintenance of equipment, quality control etc. Appraisal costs are incurred to assess the quality by the way of testing (quality assurance), inspections, etc. Cost of Non-Conformance – This involves the money spent during and after the project because of failures. Two main types of such costs are cost of internal failure and cost of external failures. Internal failure costs involve rework, scrap costs that are incurred before the solution is released. External failure costs are more critical where these are incurred to rectify damages of products failing once released to the customer. Such costs include liabilities, warranty, loss of business, damage to image etc.   Quality Management is an important aspect of Project Management. PMI® thus has given a central position to the same when defining the knowledge areas in the PMBOK™. It is thus important for project managers and team members alike to understand the importance of quality and to better manage projects to achieve all expectations pertaining to quality.  
Project Quality Management: The Key Indicator of Project Success
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Project Quality Management: The Key Indicator of Project Success

The PMBOK® defines quality as the degree to which a set of inherent characteristics fulfills requirements.  A project is said to be meeting its quality expectations when all the project requirements agreed at the inception of the project are met, and thus the resulting product or service is usable for the relevant stakeholders.  Quality is Subjective  Quality for one individual will not be adequate for another. For example, the youth will consider a mobile phone as being of high quality based on its look and feel, brand name and its specification such as camera quality, memory capacity, screen resolution, ability to connect with other devices etc. and based on the support to run applications on the phone. However, for someone in the age group of 60 and above, the ability to take a phone call or send an SMS and whether this can be done without much hassle alone may define the quality of the mobile phone.  To understand the quality requirements tailored to projects, it is necessary to have an exhaustive Quality Management training such as the Certified Manager of Quality Training. Quality has many faces The definition of quality depends on the context and the business domain. For a service-oriented organization such as a bank, a restaurant, an airline etc. quality of service is identified through the level of customer satisfaction. For a product such as a mobile phone, a vehicle, a computer etc. quality means conformance to product specifications and its fitness for use.  In the context of healthcare sector, mission-critical military activities etc. quality is measured through precision and accuracy. Precision refers to the granularity of measurement and how fine the outcome can be measured. Accuracy simply put is the correctness of the output or being close to the desired value.   Here are the four things #CIOs need to know about quality https://t.co/gRfqoTEUB7 #Qualitymanagement pic.twitter.com/KHMz5iC6RN — CIOReview (@cioreview) January 7, 2018 Quality is everybody’s responsibility  Quality in project management is two-fold. Project Quality and Product Quality. Project quality is to ensure that the project is executed in line with the triple constraints of time, cost, and scope. If the project is within the defined tolerance levels of these three dimensions, then we can say that the project is of high quality. Projects are carried out to develop a solution; i.e. product, service or a result. If this solution meets its specification or meets the needs of the stakeholders then it is said that the solution is of high quality.   Meeting the quality expectations is not merely the responsibility of the project manager, but of everyone involved in the project. Achieving quality involves cost where it is the responsibility of everyone involved in the project to manage the same. Increased efforts and costs can increase the quality of output but a ceiling on this investment has to be fixed. Yes, it is the responsibility of the project manager to manage this ceiling value and to ensure optimal levels of quality within the project but he or she can only do this with the support of his team members. The optimal level of quality can be achieved when the incremental cost of achieving quality is equal to the incremental revenue from such quality improvements.    What digital transformation means for the future of #qualitymanagement: https://t.co/0gyCaHsKtb — Sparta Systems (@SpartaSystems) January 17, 2018 Cost of Quality In order to achieve this, the project will have to incur some cost and this is known as Cost of Quality. This includes all costs incurred over the life of the product by investment in preventing non-conformance to requirements, appraising the product or service for conformance to requirements and failing to meet requirements or in other words rework. Thus, the cost of quality is of two main types- Cost of Conformance – This is the money spent during the project to avoid failures through prevention or appraisal. Prevention costs are costs incurred to prevent errors by the way of training, documentation, maintenance of equipment, quality control etc. Appraisal costs are incurred to assess the quality by the way of testing (quality assurance), inspections, etc. Cost of Non-Conformance – This involves the money spent during and after the project because of failures. Two main types of such costs are cost of internal failure and cost of external failures. Internal failure costs involve rework, scrap costs that are incurred before the solution is released. External failure costs are more critical where these are incurred to rectify damages of products failing once released to the customer. Such costs include liabilities, warranty, loss of business, damage to image etc.   Quality Management is an important aspect of Project Management. PMI® thus has given a central position to the same when defining the knowledge areas in the PMBOK™. It is thus important for project managers and team members alike to understand the importance of quality and to better manage projects to achieve all expectations pertaining to quality.  
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General Concepts And Goals Of Hypothesis Testing

Hypothesis means a supposition or an assumption or a claim. Hypothesis testing forms a critical part while conducting Statistical test and is a part of “DMAIC” methodology in Six Sigma, Analyse phase. Let us look at simple examples where we make assumptions : Increase in price will increase the sales revenue of an organization Pizza delivery outlet has sample of delivery times for the last one month and would like to compare with the competitors target time of 30 minutes In all the above cases we make an assumption and with the help of Statistical tests, we arrive at a conclusion whether the assumption is true or false. So let us see how we can develop the hypothesis and the different criteria used to arrive at conclusion. There are 2 types of Hypothesis: Null and alternative. Null Hypothesis : It means No difference, No effect, Zero impact. Null hypothesis is an assumption that there is no difference in two or more populations with reference to their means or variances. It is denoted by Ho. Thus in the above examples our Ho is : Increase in price does not affect the sales revenue of the organization Target delivery time of the outlet # 30 minutes Alternative hypothesis is exactly the opposite of Null hypothesis. The alternative hypothesis is the hypothesis which the belt is trying to prove. It is denoted by Ha or H1. In the above cases the alternative hypothesis is : Increase in price affects the sales revenue of the organization Target pizza delivery time = 30 minutes Now we need to test the supposition or prove whether the Null hypothesis is true or the alternative hypothesis. Following are the steps which will help us arrive at a decision. Step I : Set up a hypothesis Step II : Set up a Suitable Significance level Step III : Setting a test criterion Step IV : Doing computations Step V : Decision making We have seen how to set up a Null and alternative hypothesis. Step II: Having set up the hypothesis, next step is to test validity of Null against Alternative Hypothesis i.e. Ho vs H1 at a certain level of significance. The confidence with which an experimenter rejects or accepts Ho depends upon the significance level. Here we need to look at Type 1 and Type II errors. Type I error : Rejecting the Null hypothesis when it is true.  is the level of significance and is the probability of rejecting the Null hypothesis when it is true and is usually at 5%. When the hypothesis is accepted at 5% level, we are running the risk in the long run of making a wrong decision 5% of the time. Type II error : Accepting the null hypothesis when it is false. is the probability of accepting the null hypothesis when it is false. (Type I and Type II error is an extensive topic which can be considered for a separate session). Step III : Setting a test criterion After we have decided the Hypothesis and the significance level, we need to understand which is the statistical test to be conducted. One of the most widely used test is the Student t-test. Other tests are F, Chi square etc. Each test makes assumption about your data and the sample drawn from the population. Step IV : Doing computations Depending upon the selection of the test we go ahead and do the computations. There are very good statistical software available and the most commonly used is “Minitab”. We can also perform these tests with the help of Excel. Excel add-in has good features. The statistical significance of the test result is given by the p value. If the p value is less than alpha (significance level) we reject the Null hypothesis. Step V : Decision making The rejection of Ho means the differences are statistically significant and the acceptance means they are due to chance. Depending upon the outcome we then take decision of our sample and the population. Many frameworks exist for implementing the Six Sigma methodology. Six Sigma Consultants all over the world have developed proprietary methodologies for implementing Six Sigma quality, based on the similar change management philosophies and applications of tools.
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Six Sigma Methods and Formulas For Successful Quality Management

Six Sigma is basically the application of Statistical formulas and Methods to eliminate defects, variation in a product or a process. For example if you want to find the average height of male population in India, you cannot bring the entire population of more than 2 billion into one room and measure their height for a scenario like this we take samples that is we pick up sample(people) from each state and use statistical formulas to draw the inference about the average height of male population in a population which is more than 2 billion. One more example would be say a company manufactures pistons use d in motor cycles the customer demand is that the piston should not a diameter more than 9 cm and less than 5 cm anything manufactured outside this limits is said to be a variation and the six sigma consultant should confirm that the pistons are manufactured within the said limits else if there is variation in the range then the company is not operating at 6 sigma level it is operating at a very low level. A company is operating at six sigma level implies that there are only 3.4 defects per million opportunities for example an airline company operating at six sigma level means that it loses only 3.4 baggage’s per million of the passenger it handles. Below is Shown the Six Sigma Table and a graph explaining the meaning of various levels of Six Sigma. Sigma Level Defect Rate Yield Percentage 2 σ 308,770 dpmo (Defects Per Million 69.10000 %   Opportunities)   3 σ 66,811 dpmo 93.330000 % 4 σ 6,210 dpmo 99.38000 % 5 σ 233 dpmo 99.97700 % 6 σ 3.44 dpmo 99.99966 % Six Sigma is Denoted by the Greek alphabet σ which is shown in the table above and is called as Standard deviation. The father of Six Sigma is Bill Smith who coined the term Six Sigma and implemented it in Motorola in the 1980’s. Six Sigma is implemented in Five Phases which are Define, Measure, Analyze, Improve, Control and we will discuss each phases in brief and the various methods used in Six Sigma. Define The objectives within the Define Phase which is first phase in DMAIC framework of Six Sigma are:- Define the Project Charter Define scope, objectives, and schedule Define the Process (top-level) and its stake holders Select team members Obtain Authorization from Sponsor Assemble and train the team. Project charters the charter documents the why, how, who and when of a project include the following elements Problem Statement Project objective or purpose, including the business need addressed Scope Deliverables Sponsor and stakeholder groups Team members Project schedule (using GANTT or PERT as an attachment) Other resources required Work break down Structure It is a process for defining the final and intermediate products of a project and their relationship. Defining Project task is typically complex and accomplished by a series of decomposition followed by a series of aggregations it is also called top down approach and can be used in the Define phase of Six Sigma framework. Now we will get into the formulas of Six Sigma which is shown in the table below. Central tendency is defined as the tendency for the values of a random variable to cluster round its mean, mode, or median. Where mean is the average for example if you have taken 10 sample of pistons randomly from the factory and measured their diameter the average would be sum of the diameter of the 10 pistons divided by 10 where 10 the number of observations the sum in statistics is denoted by ∑. In the above table X, Xi are the measures of the diameter of the piston and µ , XBar is the average. Mode is the most frequently observed measurement in the diameter of the piston that is if 2 pistons out 10 samples collected have the diameter as 6.3 & 6.3 then this is the mode of the sample and median is the midpoint of the observations of the diameter of the piston when arranged in sorted order. From the example of the piston we find that the formulas of mean, median , mode does not correctly depict variation in the diameter of the piston manufactured by the factory but standard deviation formula helps us to find the variance in the diameter of the piston manufactured which is varying from the customer mentioned upper specification limit and lower specification limit. The most important equation of Six Sigma is Y = f(x) where Y is the effect and x are the causes so if you remove the causes you remove the effect of the defect. For example headache is the effect and the causes are stress, eye strain, fever if you remove this causes automatically the headache is removed this is implemented in Six Sigma by using the Fishbone or Ishikawa diagram invented by Dr Kaoru Ishikawa. Measure Phase: In the Measure phase we collect all the data as per the relationship to the voice ofcustomer and relevantly analyze using statistical formulas as given in the above table. Capability analyses is done in measure phase. The process capability is calculated using the formula CP = USL-LSL/6 * Standard Deviation where CP = process capability index, USL = Upper Specification Limit and LSL = Lower Specification Limit. The Process capability measures indicates the following Process is fully capable Process could fail at any time Process is not capable. When the process is spread well within the customer specification the process is considered to be fully capable that means the CP is more than 2.In this case, the process standard deviation is so small that 6 times of the standard deviation with reference to the means is within the customer specification. Example: The Specified limits for the diameter of car tires are 15.6 for the upper limit and 15 for the lower limit with a process mean of 15.3 and a standard deviation of 0.09.Find Cp and Cr what can we say about Process Capabilities ? Cp= USL-LSL/ 6 * Standard deviation = 15.6 – 15 / 6 * 0.09 = 0.6/0.54 = 1.111 Cp= 1.111 Cr = 1/ 1.111 = 0.9 Since Cp is greater than 1 and therefore Cr is less than 1; we can conclude that the process is potentially capable. Analyze Phase: In this Phase we analyze all the data collected in the measure phase and find the cause of variation. Analyze phase use various tests like parametric tests where the mean and standard deviation of the sample is known and Nonparametric Tests where the data is categorical for example as Excellent, Good, bad etc. Parametric Hypothesis Test – A hypothesis is a value judgment made about a circumstance, a statement made about a population .Based on experience an engineer can for instance assume that the amount of carbon monoxide emitted by a certain engine is twice the maximum allowed legally. However his assertions can only be ascertained by conducting a test to compare the carbon monoxide generated by the engine with the legal requirements. If the data used to make the comparison are parametric data that is data that can be used to derive the mean and the standard deviation, the population from which the data are taken are normally distributed they have equal variances. A standard error based hypothesis testing using the t-test can be used to test the validity of the hypothesis made about the population. There are at least 3 steps to follow when conducting hypothesis. Null Hypothesis: The first step consists of stating the null hypothesis which is the hypothesis being tested. In the case of the engineer making a statement about the level of carbon monoxide generated by the engine , the null hypothesis is H0: the level of carbon monoxide generated by the engine is twice as great as the legally required amount. The Null hypothesis is denoted by H0 Alternate hypothesis: the alternate (or alternative) hypothesis is the opposite of null hypothesis. It is assumed valid when the null hypothesis is rejected after testing. In the case of the engineer testing the carbon monoxide the alternative hypothesis would be H1: The level of carbon monoxide generated by the engine is not twice as great as the legally required amount. Testing the hypothesis: the objective of the test is to generate a sample test statistic that can be used to reject or fail to reject the null hypothesis .The test statistic is derived from Z formula if the samples are greater than 30. Z = Xbar-µ/σ/ √n If the samples are less than 30, then the t-test is used T= X bar -µ/ s/√n where X bar and µ is the mean and s is the standard deviation. 1-Sample t Test such as an ideal off center (Mean v/s Target) this test is used to compare the mean of a process with a target value goal to determine whether they differ it is often used to determine whether a process is 1 Sample Standard Deviation This test is used to compare the standard deviation of the process with a target value such as a benchmark whether they differ often used to evaluate how consistent a process is 2 Sample T (Comparing 2 Means) Two sets of different items are measured each under a different condition there the measurements of one sample are independent of the measurements of other sample. Paired T The same set of items is measured under 2 different conditions therefore the 2 measurements of the same item are dependent or related to each other. 2-Sample Standard This test is used when comparing 2 standard deviations Standard Deviation test This Test is used when comparing more than 2 standard deviations Non Parametric hypothesis Tests are conducted when data is categorical that is when the mean and standard deviation are not known examples are Chi-Square tests, Mann-Whitney U Test, Kruskal Wallis tests & Moods Median Tests. Anova If for instance 3 sample means A, B, C are being compared using the t-test is cumbersome for this we can use analysis of variance ANOVA can be used instead of multiple t-tests. ANOVA is a Hypothesis test used when more than 2 means are being compared. If K Samples are being tested the null hypothesis will be in the form given below H0: µ1 = µ2 = ….µk And the alternate hypothesis will be H1: At least one sample mean is different from the others If the data you are analyzing is not normal you have to make it normal using box cox transformation to remove any outliers (data not in sequence with the collected data).Box Cox Transformation can be done using the statistical software Minitab. Improve Phase: In the Improve phase we focus on the optimization of the process after the causes are found in the analyze phase we use Design of experiments to remove the junk factors which don’t contribute to smooth working of the process that is in the equation Y = f(X) we select only the X’s which contribute to the optimal working of the process. Let us consider the example of an experimenter who is trying to optimize the production of organic foods. After screening to determine the factors that are significant for his experiment he narrows the main factors that affect the production of fruits to “light” and “water”. He wants to optimize the time that it takes to produce the fruits. He defines optimum as the minimum time necessary to yield comestible fruits. To conduct his experiment he runs several tests combining the two factors (water and light) at different levels. To minimize the cost of experiments he decides to use only 2 levels of the factors: high and low. In this case we will have two factors and two levels therefore the number of runs will be 2^2=4. After conducting observations he obtains the results tabulated in the table below. Factors Response     Water –High Light High 10 days     Water high – Light low 20 days     Water low – Light high 15 days     Water low – Light low 25 days     Control Phase: In the Control phase we document all the activities done in all the previous phases and using control charts we monitor and control the phase just to check that our process doesn’t go out of control. Control Charts are tools used in Minitab Software to keep a check on the variation. All the documentation are kept and archived in a safe place for future reference. Conclusion: From the paper we come to understand that selection of a Six Sigma Project is Critical because we have to know the long term gains in executing these projects and the activities done in each phase the basic building block is the define phase where the problem statement is captured and then in measure phase data is collected systematically against this problem statement which is further analyzed in Analyze phase by performing various hypothesis tests and process optimization in Improve phase by removing the junk factors that is in the equation y = f(x1, x2,x3…….) we remove the causes x1, x2 etc. by the method of Design of Experiments and factorial methods. Finally we can sustain and maintain our process to the optimum by using control charts in Control Phase.
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Lean Consultant: You Need to Compete With Acceptable Product Quality

In the competitive business world, ultimate satisfaction of the customer is the only key to open the door of sustainable success rate. Customers have become smarter because of availability of more options; they expect the best quality at least price. Competent businesses are always on the spree of innovating unique methods to offer better quality at lower cost. The task is really challenging. Strategic incorporation of ‘lean theory’ in all the processes under the expert guidance of Lean Management Consultant is seen as the most efficient way to delight the customers with supreme quality at reasonable cost. The Scope of Lean Methodology: Lean Methodology is a fundamental approach focused to minimize the waste and to maximize the flow.  The ‘Lean’ oriented organization collectively understands, communicates and implements the lean principles throughout all the functional and operational processes. Lean Methodology comprises of five principles also known as 5s Lean Management: * Defining value of products in the line of customers’ expectations * Value mapping through data-based analytical approach from the customers’ perspective * Ensuring the seamless consistent process to add competitive values * Ensuring prompt flexibility to address the ever-changing needs of buyers/users * Investing in continuous innovative efforts to improve the quality for the new benchmark Why and Where Do the Companies Need Lean Consulting Experts?    Lean Consultants structure a perfect roadmap for the strategic implementation of 5s Lean Management principles. The expertise in Kaizen and SCORETM Methodologies help the organizations to add new competitive values to their products. Customized consultancy and training are provided to deliver the long- term benefits by improving the production and innovation skills within the organization. The Lean trained professionals tackle the problems in a smoother way to sustain the flow of improved production and operational functions. The elevated satisfaction of customers helps the organizations to solidify the social brand image and to face the competitive marketing challenges successfully. Leading Lean Management Consultancy companies provide on the site training and consultancy through structured modules to hit the goals.
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Design For Six Sigma Methodology: A Toolset To Address Customers’ Need

A number of businesses face problem in performing as per planning and expectations, despite offering the quality product at a reasonable price. The primary reason triggering this problem is the failure to offer the quality product/services in the line of customers’ expectations that are ever changing. Designing the successful processes for delivering competitively priced flawless products / services is the prime need of businesses that makes the Design for Six Sigma (DFSS) Methodology popular and widely accepted. Many advantages of implementing six sigma which improves the business operation. What Is DFSS?  Design for Six Sigma is a data-driven methodology that determines the current needs and expectations of customers to streamline the product designing/manufacturing/services backup procedures accordingly. DFSS guides to develop the new processes where the in-force procedures are inadequate to address the customers’ requirements. The strategic DFSS also helps for developing the required efficiency to excel with a competitive edge in emerging markets. DFSS implementation guides the responsible professionals to foresee and correct the potential defects by improving the processes; the improved customers’ satisfaction delivers long-lasting support for the businesses to sustain and improve the growth rate despite complex new challenges. In addition, DFSS minimizes the risks in new product introduction. Application of Design for Six Sigma:    Guided DFSS implementation goes through the four steps process- assess; plan; enable and sustain. The successful application of any advanced theory for the improvement of products and services need the help of an experienced expert; DFSS implementation is also not an exception. Numbers of Business Management Consulting Firms provide comprehensive customized support for DFSS implementation. The implementing process of DFSS involves five steps: Define –        Defining the deliverables with the perspective of the project and customer Measure –    Measuring different parameters to determine the customer needs Analyze –    Analyzing the processes for improvement to meet the changed needs of customer Design –      Designing the improved process to deliver in the line of customer’ needs. Verify –       Testing the design performance to ensure customer satisfaction DMADV methodology makes the designing of new products cheaper besides guiding the concerned fellows to identify the reasons for not impressing the buyers despite offering the quality products. The Scope of Design for Six Sigma Methodology: The efficiency of Design for Six Sigma Methodology is being experienced by the industries worldwide. A number of businesses experienced a boost in the performance because of increased demand of product within few months after deployment of DFSS. DFSS oriented designing/ redesigning approach makes the diverse operations robust, flawless, cost effective and competitive, ensuring consistent delivery and 100% customers’ satisfaction. The scope of DFSS implementation also includes designing/ redesigning the plant layouts/ equipment to ensure flawless manufacturing. The training to integrate DFSS tools into the all the procedural frameworks is also the part of DFSS implementation. In total, DFSS methodology deployment for planned growth rate follows a specific approach structured with data-driven analysis of production capabilities, available resources and market feedback.
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Lean Six Sigma Training- Experience the Wonders of Lean & Six Sigma

Offering the quality product at the competitive price in the line of customers’ preference is the only way to succeed in the competitive global market. This simple looking statement involves two challenges- to know the customers’ preferences and to produce the quality product at the genuine price. The first challenge of knowing the consumers’ need can be successfully tackled with Lean concept. The second challenge of producing the best quality product can be managed successfully with Six Sigma methodology. Lean Six Sigma online training delivers the combined benefits of Lean and Six Sigma to help you sustain the growth despite changing market conditions. Lean Six Sigma online Training– You Too Need It: Lean-Six Sigma is the structured set of different methodologies that any business can apply to manufacturing, service and transactional processes. Lean – Six Sigma theory encompass all the basic principles: ‘Define Value’ to address the customer needs; ‘Stream Value Mapping’ to check the effectiveness of processes; ‘Consistent Flow’ to ensure on time delivery; Pull Production to ensure the flexibility responding to ever-changing needs; Perfection at best level. The benefits come from the reduction of waste and elimination of no- value activities. Lean – Six Sigma is a program that can be incorporated into organization’s culture to get the short-term benefits through long-term transitions. The Lean -Six Sigma training also keeps the innovation activities on the right track to deliver in the form of ‘quality product in high demand’ that puts challenges to your rivals. The benefits of six sigma certification can be proven by corporations and the individuals that has been improves the business operation. Six Sigma Online Training Is Available for Different Levels: Lean Six Sigma training is available online at different levels; the more in demand formats are lean six sigma green belt training and lean six sigma black belt training. Business Management Consulting Companies also provide Six Sigma Lean Yellow Belt training for the basic level. The black belt and green belt Lean- Six Sigma training ensure the benefits of yellow belt training also but both these training are structured for advanced level professionals. After the successful completion of Black and Greenbelt Lean -Six Sigma Training, participants experience improved confidence and perfection in: Defining the scope and execution of DMAIC projects Application 5s principles of Lean Conducting statistical analyses for effective planning Application of DMAIC for transitions Managing the team dynamics Presenting the projects Closing and Handing over the complete projects to owners The lectures and experiential exercises maximize the participants’ comprehension. During the Lean Six Sigma training period, each participant gets enough opportunities to apply the learned skills to solve the actual business issues related to strategy, planning, innovation, processes and management. Within few months after the completion of training, organizations experience the improvement in processes and management efficiencies at different levels.
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