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Sandip Mittra

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  • Name
    Sandip Kumar Mittra
  • Company
    EY
  • Designation
    Assistant Director

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  1. Quality is key to success. Most of us try to measure the quality of the final product which goes to the customer and improve them. However, we fail to realize that quality is important in each step and everything a company does. It can be accounts, procurement, delivery, sales and all other processes. There are many theories, methods and tool provided by several scholars on how to improve quality. Out of all the tools and theories, we are going to understand 14 principles laid down by Dr. Deming and by Philip Crosby. Deming’s 14 Points for Total Quality Management (TQM) Deming in his book titled “Out of Crisis” has explained about his philosophy in details. This was mainly focused to manufacturing industries but can be used in any business. Crosby's 14 Steps To Improvement Philip Crosby on other hand provided 14-step quality improvement program which is more focused on how the employee understands what quality means & partner the quality movement. Below are the 14 steps or Points for Quality Improvement Sr No Crosby's 14 Steps Deming’s 14 Points 1 Management Commitment Create a Constant Purpose Toward Improvement 2 Quality Improvement Team Adopt the New Philosophy 3 Quality Measurement Stop Depending on Inspections 4 Cost of Quality Evaluation Use a Single Supplier for Any One Item 5 Quality Awareness Improve Constantly and Forever 6 Corrective Action Use Training on the Job 7 Plan for Zero Defects Program Implement Leadership 8 Supervisor Training Eliminate Fear 9 Zero Defects Day Break Down Barriers Between Departments 10 Goal Setting Get Rid of Unclear Slogans 11 Error Cause Removal Eliminate Management by Objectives 12 Recognition Remove Barriers to Pride of Workmanship 13 Quality Council Implement Education and Self-Improvement 14 Do It Over Again Make "Transformation" Everyone's Job Though both Deming and Crosby focused on improving quality, there are some similarities and differences in their thoughts and approach. Similarities: · Management commitment is important · Continuous improvement · Customer focused · Supervisor or manager responsibilities · Quality improves profit · Radical change in value Differences: · Roadmap o Deming: - No start and end point o Crosby: - Have start and end point · Workers o Deming: - Variance is not largely affected by workers o Crosby: - Workers need to be motivated to produce zero defects · Management o Deming: - Major threat to managers o Crosby: - Not many changes in the thoughts of a manager and worker · Implementation o Deming: - Difficult to implement as it requires huge structural change o Crosby: - Comparatively easy to implement Conclusion: In today’s world almost all the organization is taking up the best characteristics of these steps and framing their own structure which suits their business requirement. However, if Deming’s steps are implemented then the organization can have the best impact.
  2. During the control phase of the project, we sometime want to check how the small shift is going to impact the process target. We need to use Cumulative Sum (CUSUM) in these scenarios. It is the sum of deviation of individual sample or subgroup average from the target. These cumulative deviations over time are plotted in the CUSUM control chart and this clearly help us to identify when the process if out of control. As we are aware, the X-bar helps is also a control chart which monitors the average or the mean of the process and how it changes over time. Below are some of the distinct differences between CUSUM Chart and X-Bar Chart: CUSUM Chart X-Bar Chart Monitors the quality of a stable process when the expected shift is small Monitors the movement of average of the process over the time Used when there is high cost of false alarm Used when there is low cost of false alarms Used when there is high cost of repairing a process Used when there is low cost of repairing a process Faster detection of drift in the process with same sample size Takes almost double the time to find the drift with same sample size Subgroup size is generally 1 Subgroup size is generally 5
  3. We generally know that the companies compete each other. However, the dynamics are changing. Today we are seeing companies who are offering a better platform is flourishing. This is an economic platform which enables any small business also to connect with their customer and provide products and services. If we see there are multiple platforms around us. Some of the examples are Amazon, Airbnb, Zomato, Play Store, Flipkart, etc. Platform Economy can be categorized in 3 buckets: 1. Transaction Platforms – this is digital marketplace or digital match makers like Facebook, Amazon, etc 2. Innovation Platforms – this allows technology frameworks to reach to the customers like Microsoft, Oracle, etc 3. Integration Platform – this is combination of Transaction and Innovation platforms like Google Play store, Apple Appstore, etc The success of these platforms lies in 3 major ways. 1. How fast we can plug into the platform and transact 2. How great the platform attracts participants (both consumer and producers) 3. How well the platform co-creates the values We are seeing now bigger giants in platform economy but number of competitors are increasing. This is increasing competition as well. Also, there are some inherent challenges of platform economy. Some of them are listed below: · Outperform · New selling tactics · Maintaining customer loyalty · Maintain product quality · Capturing customer identity and its validation · Improving customer experience · Reducing waiting time · Data security · Adhering to Govt rules and regulations Therefore, if we investigate all the above challenges, we can see that we need to focus majorly on Cost, Quality and Time. These are the 3 major areas where Lean Six Sigma plays an important role. Therefore, using lean six sigma we can improve the performance of any platforms to increase their market share.
  4. Chi-Square Test The test which helps us to measure the differences between the observed and expected value according to an assumed hypothesis is called Chi-Square Test. It was developed by Karl Pearson in 1900 and is the most important test amongst the several test of significance. It is a non-parametric test which is not based on any assumption or distribution of any variable There are 3 applications of Chi-Square Tests 1. Goodness of fit 2. Test for independence 3. Test of homogeneity Goodness of Fit The Chi-square goodness of fit test is used to compare a randomly collected sample containing a single categorical variable to a larger population. For example – If we want to test if 70% of ladies take medical, then we must use Goodness of Fit. This is measured using the below formula. Where: O = Observed Value E = Expected value If X2 (Calculated) > X2 (Tabulated) with (n-1) degree of freedom, then null hypothesis is rejected otherwise accepted. Let’s try to understand this with an example. In a medical college of 1000 students, there are 650 female students. Does it follow the theory that 70% of female students take medical? Observed Expected Male 350 300 Female 650 700 Total 1000 1000 The hypotheses for a Chi-square test of independence are as follows: Null Hypothesis (HO): The collected data is consistent with the population distribution. Alternative Hypothesis (HA): The collected data is not consistent with the population distribution. Observed Expected (O-E) (O-E)^2 (O-E)^2/E Male 350 300 50 2500 8.333333 Female 650 700 -50 2500 3.571429 Total 1000 1000 11.90476 X2 (Calculated) = 11.9 X2 (Tabulated) with (n-1) degree of freedom = 3.8 Decision: - If X2 (Calculated) > X2 (Tabulated) with (n-1) degree of freedom, then null hypothesis is rejected otherwise accepted. 11.9 > 3.8 and therefore the null hypothesis is rejected Test for independence This test helps us to identify if there is an association between two categorical variables within the same population. Let us understand this with an example: Table with Observed Value Qualification / City or Village Middle school High School Bachelors Masters PHD Total City 18 36 21 9 21 105 Village 12 36 45 36 6 135 Total 30 72 66 45 27 240 The hypotheses for a Chi-square test of independence are as follows: Null Hypothesis (HO): There is no association between the qualification and the places the students come from. Alternative Hypothesis (HA): There is association between the qualification and the places the students come from Table with Expected Value Qualification / City or Village Middle school High School Bachelors Masters PHD City (105x30)/240 = 13 (105x72)/240 = 32 29 20 12 Village 17 41 37 25 15 Observed Value (O) Expected Value (E) (O-E) (O-E)^2 (O-E)^2/E 18 13 5 23.8 1.8 36 32 5 20.3 0.6 21 29 -8 62.0 2.1 9 20 -11 114.2 5.8 21 12 9 84.4 7.1 12 17 -5 23.8 1.4 36 41 -5 20.3 0.5 45 37 8 62.0 1.7 36 25 11 114.2 4.5 6 15 -9 84.4 5.6 31.2 Degree of freedom = (Column -1) x (Row-1) = (5-1) x (2-1) =4 x 1 = 4 X2 (Calculated) =31.2 X2 (Tabulated) with 4 degree of freedom = 9.48 Decision: - If X2 (Calculated) > X2 (Tabulated) then null hypothesis is rejected otherwise accepted. 31.2 > 9.48 and therefore the null hypothesis is rejected Test of homogeneity We perform this test to confirm if the event is following uniformity or not. The basic difference from Test for independence is for two categorical variables within the same population and Test of Homogeneity is for single categorical variable within different population. Let us see if the TV watching pattern of males and females are same. The hypotheses for a Chi-square test of homogeneity are as follows: Null Hypothesis (HO): The distribution of watching pattern of TV for Males and females is same. Alternative Hypothesis (HA): The distribution of watching pattern of TV for Males and females is not same. Table with Observed Value Qualification / City or Village Movies Sports Serials Other Total Male 72 84 49 45 250 Female 91 86 88 35 300 Total 163 170 137 80 550 Table with Expected Value Qualification / City or Village Movies Sports Serials Other Male 74 77 62 36 Female 89 93 75 44 Observed Value (O) Expected Value (E) (O-E) (O-E)^2 (O-E)^2/E 72 74 -2 4.4 0.1 84 77 7 45.3 0.6 49 62 -13 176.2 2.8 45 36 9 74.6 2.1 91 89 2 4.4 0.0 86 93 -7 45.3 0.5 88 75 13 176.2 2.4 35 44 -9 74.6 1.7 10.1 Degree of freedom = (Column -1) = 4-1=3 X2 (Calculated) =10.1 X2 (Tabulated) with 3 degree of freedom = 7.8 Decision: - If X2 (Calculated) > X2 (Tabulated) then null hypothesis is rejected otherwise accepted. 10.1 > 7.8 and therefore the null hypothesis is rejected. The watching pattern of TV is not same between males and females.
  5. Design of Experiment is a great scientific tool which is having used extensively in many areas. Medicine, engineering & biochemistry, physics & computer science contributes to 50% of usage of DOE. In Corporate world, we do not see much usage of DOE. There are several factors which make use of DOE more complicated. Some of them are: 1. Target Low hanging opportunities – We must initiate DOE at the earliest so that we can analyze multiple factors and arrive to a best design. However, since the stakeholder / client is always looking for quick result, we sometime investigate the option which are faster to implement. 2. Force fitting DOE – There are multiple automation scope available in the corporate world. We tend to initiate automation. Later we try to force fit DOE and try justifying our investment. 3. Data quality– Data is one of the vital factors. In most of the processes, we have data captured by the users and which may not be accurate. This may result in incorrect interpretation of hypothesis. 4. Sample data – Sometime the sample data we take may not represent the population and therefore, the analysis can go wrong 5. Lengthy steps – due to shorter timeline, most of the experts avoid DOE as there are multiple steps and analysis involved. 6. Personal Bias – There may be cases where we may see some personal bias of the researchers may creep in. 7. Human Error – Because of lengthy statistical steps, there is always a probability of error 8. Human response – This is one of the most common challenge we see in corporate world. The human responses cannot be measured and therefore performing analysis may be difficult. Most of the cases we tend to ignore the factor with human response which is also not a right approach. Therefore, though this is one of the best tools, we do not use in most of the projects in the corporate world.
  6. While writing codes, for any software development, there are several best practices the developer needs to follow. These best practices help the codes to be more readable so that in future anyone can understand the code and perform modification. This obviously needs some additional comments and standards which will increase the coding time. In Agile we will have a strict timeline to deliver the required minimum viable product and therefore there is time constraint. The developer tends to ignore and violates the best practices with a hope that they will be modifying it later. This results into Technical Debt. We need to understand if the technical debt is accepted and if yes to what extent. The below picture clearly explains the implication of the technical debt. Debt in financial term means the company must repay this debt with interest. The longer the time taken to repay, more the interest is added to it. Still the company borrows loan to increase the business and similarly, technical debt is also acceptable to a certain limit. Result of Technical Debt can also be understood with the below diagram. It clearly demonstrates that if we do not plan the sprints properly, technical debt is bound to happen and will result in low morale of the developer. We can also derive a mathematical formula which will help us to understand to what percentage of Technical Debt is acceptable. Technical Debt Ratio = (Cost to fix the software coding) / (Cost to develop it) x 100% This means till the cost of fixing the software coding is less than the cost of developing it, we can accommodate Technical Debt. As a product owner we must try to plan the Sprint in a way that we will not end up in Technical Debt.
  7. Time Series data is very common in most of the projects in Business Excellence. There are lot of analytics performed using this data. The most common is Time Series Regression. This helps us to understand and predict the behavior of the data. Most of the organization uses time series data to understand the underlining causes of trends. They also use data visualization tools to analyze the seasonal trends and to go in depth to understand the reason behind the trends. We can use this technique to Forecast · Volume · Customer Satisfaction · Staff Turnover · Online hits, and so on Any time series can contain some or all the following components: Type Description Example Trend Describes movement along the term. Describes the pattern with long term increase or decrease in data Mathematical finance, weather forecasting, earthquake prediction, and so on Cyclical Describes Seasonal changes or pattern due to the calendar Sale of a commodity, Height of tides, etc. Seasonal Describe Periodical but not seasonal. The data exhibits rise and falls that are not of fixed period. Temperature, Rainfall, etc In most of the cases both seasonal and cyclic looks similar. Let us understand the difference. Seasonal Cyclic Constant Length Variable Length Average length is shorter Average length is longer Magnitude is less Magnitude is more Since the peaks and troughs are not predictable in Cyclic, this time series is difficult to use.
  8. Statistic says about 60% of the corporate lean six sigma projects fail. If we perform RCA on these failed projects, we will see that most of them have failed as it could not continue with the improvement proposed. There are several factors to it. However, the most common factor is the change management was not factored properly in the scope of the project. To improve the success rate of LSS project, we need to factor few of the concepts below: · Management Support · Process Owner Support · Selection of the Project · Scoping the Project · Data Driven approach · Selection of Team members · Change Management If we analyze the above points and compare the Kotter’s 8 step Model of Change, we find lot of similarity. Let us understand the Kotter’s change model and analyze how it may help in improving the probability of LSS project success. Below are the 8-step model of change proposed by John Kotter. 1. Creating an Urgency: - Management team can help us to suggest on the projects which could be picked up on priority, having huge impact in revenue, multiple escalations, Client with renewals coming days, etc. Once we have this, we are sure to get the management support. This, therefore, helps us in project selection and get the buy-in from Management. 2. Forming a Powerful Coalition: - Process owner is the one who can drive the change. He / She can be from the team or someone appointed by the management. If we have a person who can influence the team and make them adapt the change, the entire project will be just a cake walk 3. Developing a Vision and a Strategy – The process owner & Management place a vital role here. He / She can share the vision and may assign some target to the team. 4. Communicating the Vision: - If the process owner can show the benefit with the project with the team, the adaption can increase. They also select few of the team members (SPOCs) who can help in implementing the ideas. The process owner may set some of the parameters as KPI, which will help the change management to sail through 5. Removing Obstacles: - The process owner or the SPOCs who will enable the Project team to deploy the improvements. He / She will be driving the success of the improvement opportunities within the team. 6. Creating Short-Term Wins- Any sort term wins give some confidence to the professionals and therefore, any future improvement can be implemented without much of resistance. 7. Consolidating Gains – Capturing the benefit & presenting the success stories in different forums will help the team feel proud and motivated. 8. Anchoring Change in the Corporate Culture – Marketing the success stories from all around will help the team to be motivated for new projects and improvement ideas. RnR also can be a mechanism to support change. I, therefore, would like to re-iterate that the probability of LSS project success lies in better change management and Kotter’s model is one which can obviously follow to make it happen.
  9. There are several data visualization tool available in the market like Tableau, Looker, IBM Cognos Analytics, Microsoft Power BI, etc. However, Tableau and Power BI is the most widely used Data Visualization tool. Some of the basic differences are: Parameter Tableau Power BI Language MDX DAX Platform Mainly used for Data Visualization Apart from data visualization, it can be also used for other data points Volume - It can handle almost any volume of data - It works better with the cloud data - Works better with only limited volume - Performance dips if we use huge data from cloud Usage Most of the data analytics expert professionals can work on Tableau Any professional with basic knowledge of tool can use this Ease of using Working on Tableau needs experience Tool is very well designed for any new users to learn it faster Speed & Performance Is faster with multiple capabilities Slower with ease of work Cost It needs both software and skilled professionals This comes in bundle with Office 365 The targeted audience for both the software is completely different. As Power BI is coming bundled with Office 365 most of the organization prefer this. However, when we talk about analytics on huge data, it fails as the tool becomes very slow. For me, due to ease of use and easy availability, Power Bi is better than tableau. As a Black Belt, we need to perform analysis extensively. In one of the recent examples, I had to club almost 83 excel files (of 83 professionals) with their few month of daily tracker. In no time, I was able to merge all these files into one sheet using Power Query (part of Power BI). Power Pivot (part of Power BI) helped in further slicing and dicing the data and get more insights. The data with the insight was then presented to the stakeholder with the Power BI.
  10. When the code used for development is restructured without impacting any of the external behaviour or any functionality is known as Code Refractoring. The code refactoring can be achieved in multiple ways. The most common is to add or remove, introduce or replace parameter with explicit method and method call and so on. There are several techniques available which can be used for achieving code refractoring. Some of the most common are Red Green Factoring, Refracting by Abstract, Simplifying methods and so on. When we want to implement any new feature in the application, code refractoring should be considered. Below are some of the common areas where we need to follow this approach. · Standardize the coding so that the defects can be fixed without impacting the other functionality · Uniformity in usage of the code so that any developer can understand in future · Patch any new features without impacting the entire functionality · Reuse of the code for any similar development In the real life scenario it is important to have code refractoring as every software will keep on evolving. However, we can avoid this in those areas where below conditions prevails. · No new feature & functionality to be added · When the development has to be done in very faster pace as Code Refractoring is time consuming In my opinion Code Refractoring is a very strong method a developer can use. There are many developer using this concept in a complete different level.
  11. In Agile User Story, if we want to express the challenges/problems/issues, we use either one or more of the 6 different qualities which is coined as INVEST. Product owner can use either of the INVEST qualities to make a good user story. INVEST is an acronym for: · Independent · Negotiable · Valuable · Estimable · Small · Testable In my opinion Small is one of the qualities which is most challenging to follow. There are several challenges we see. Some of them are below: · Fail to plan for small iteration – We are so used to waterfall model that we add more deliverables in the Sprint and fail to plan for smaller sprints · Misunderstanding the Sprint as Project and not a product – Most of the cases, it is difficult to get the by in from the stakeholder as they see it from a project angle and not as a product. · Financial challenge - We will not be able to update the finance team for overall cost for the project. However, as the Sprint progresses, the financial is looked into. This becomes a problem to get sign off from the Finance. · Overcommitting – To deliver some specific product we sometime add more items into the sprint and therefore, keeping it small is very difficult. Since we are evolving every day, I am sure we will embrace the change from Waterfall to Agile in coming days and therefore, we will overcome the challenge and will be able to break down the project into smaller product smoothly.
  12. Human Centered Design can be also called as User Centered Design. This is an is an approach that focuses on solving problems by keeping user’s preferences and characteristics. This result in creating a useful, meaningful, and relevant product experience. Most of these designs are very simple but very effective. What I have experienced is China is focusing more on this approach. Many products which are made in China are user centric. To give few examples: · TikTok – This gives a sense of pride as they can lip-sync some of the famous songs and dialogues. We on our daily life also use this many a times. · Led Lights used during Diwali – We used traditional diyas with oil and then Chinese company came with candles which are easy to use. Then they came with the similar diyas with LED which are more convenient. Not only China, India market is also focusing extensively on human centric design. With the increase in Covid cases, the manufacturers targeted with lots of option on immunity related food. Various sanitizers' were manufactured with small bottles even if it came with higher cost so that person can carry in their pockets or purse. In IT industry as well we see lots of examples. There are many software designed to edit the pictures and videos and enable us to create some attractive videos for special occasion like birthdays or anniversary, etc. In short we have to be user centric to sustain in the market.
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