RahulGarg

Kamishibai board is a visual control center in the workplace which is used for performing audits especially in a manufacturing process. A number of cards are placed on a board and ensures that the safety and cleanliness of workplace is maintained and that quality checks are being performed to ensure adherence to laid down processes.  The Name Kamishbai has its origin to Japaneese traditional storyetelling practice where Children are taught to build up a mini-theatre and show the pictures as they tell the story as we see the storytelling supported by a storyboard in cards in Kamishibai concept / board.

 
Kamishibai Cards look like the Kanban board in the manufacturing processes and Toyota is widely using this concept to do their daily / weekly / monthly audits of the processes. Toyota used this term 1st un 2006 at a seminar where emphasis was to how better track the day to day activities and guide people if something deviates from the planned output. Usage of Kamishibai boards found in tacking many processes / activities in manufacturing plant like production, maintenance, sourcing and procurement etc.
 
Why Kamishibai is used ?
Objective of using Kamishibai is not a fault finding process. The proper use of a Kamishibai is to train the mind and eyes of the involved person’s to see problems (deviations from the standard output), identify improvements even though these are small, and teach others also to see and solve these kind of problems.
 

 
In Kamishibai framework, diligently completing the audits is also as critical to success of it as the result of the audit itself. The purpose is not to find the mistakes, however the problems should certainly be made visible. The purpose is to get into the habit of checking the important activities each day.
The Kamishibai card is printed on both sides where one side is green and other side is red. To help the people who may be color blind, a symbol should be added to identify go (O) and no-go (X) conditions.
How Kamishibai helps the Manufacturing ?
Using the Kamishibai board, one can easily track and improve the daily / weekly / monthly conformance to the laid down standards. Like a supervisor for maintenance can use this method to track the critical maintenance activities checkpoints in daily / weekly / monthly basis and bring the problems to surface using this method.  These boards are located very near to actual workplace. Kamishibai board is used during the Gemba walk by the leaders and this add more effectiveness and structure to the Gemba walks which may be aimless otherwise. Kamishibai systems specify the 5W1H (who, what, where, when, why and how) for confirming process standards.
How Kamishibai can be helpful in Service Sector ?
I can think of various standard processes which can be tracked, visualized and improved using the Kamishibai board :
1.       Audits Planning and Tracking Process – Daily / Weekly / Monthly tracking of critical deliverables.
2.       Administration - Daily / Weekly / Monthly tracking of transport, maintenance etc. E.g. Transport team may use it to day to day critical activities as below :
 

 

 
1.       HR team may use Kamishibai dashboard in Recruitment function to track the candidate fitment for required profile.
2.       SCM / Procurement team may use it for tracking the materials receipt / responsibility and their conformance to standards.
3.       Internal IT teams may use this method to track incoming / outgoing / WIP faulty systems on day to day or shift to shift basis.
4.       Software development may use the same to track day to day activities related to daily submissions to client / increment / work quantum they have to complete and same way critical deliverables for the month / week / quarter.
 
Some of Famous Companies using Kamishibai :
 

So if we see since Kamishibai is an effective tracking and improving tool, it can be used across sectors / industries for tracking day to day activities and to surface out the problems and improve the same by taking the counter actions on the same. The usage of this tool is independent of nature of sector (Manufacturing / Sector) as we saw.

FIFO and LIFO are methods used in the cost of goods sold calculations in financial accounting. FIFO stands for First in First out and it assumes that the oldest products / materials / items in a company’s inventory have been sold first and goes by those production costs. However contrary to it, The LIFO stands for Last In First Out and this method assumes that the most recent products in a company’s inventory have been sold first and uses costs of recent products instead. These methods are used to manage assumptions of costs related to inventory, stock repurchases (if purchased at different prices), and various other accounting purposes.
Below diagram clearly explains the difference between the two methods :

If we want to understand LIFO and FIFO concept in Day to Day activities, Imagine a Railway Ticket counter for FIFO : (Person who gets in queue 1st is the 1st to get out normally  )

However imagine a Ticket Checker following the concept of LIFO i.e. The Person who got into the train last shall be checked for ticket 1st  (As probability of not having ticket is high).

 
Lets understand the difference between the two methods and benefits of each one of these with Financial Aspects now !
FIFO (First In First Out) :
In FIFO method, oldest inventory items are recorded as sold first (but this does not necessarily mean that the exact oldest physical object was tracked and sold). Here, the cost associated with the inventory that was purchased first is the cost expensed first.A company might use the LIFO method for accounting purposes, even if it uses FIFO (First In First Out) method for inventory management purposes (i.e., for the actual storage, shelving, and sale of its merchandise). E.g. If a company that sells many perishable goods, such as a supermarket chain, is likely to follow the FIFO method when managing inventory so that goods with earlier expiry dates are sold first and goods with later expiry dates thereafter. However, this does not mean that same company can not use LIFO method for accounting for its merchandise management.
 
While using the FIFO method, the cost of inventory reported on the balance sheet represents the cost of the inventory which was purchased most recently. FIFO most closely represents the flow of inventory, as businesses are likely to sell the oldest inventory first.
 
Lets see this with example as below where say company XYZ has following inventory in had of 600 units those were purchased at different point of time with cost as mentioned :
 
 
Number of Units
Cost (INR)
100 units (1st)
100 INR
200 units (2nd)
150 INR
300 units (3rd)
200 INR
 
Now say company sells 550 units and then the company would expense the cost of 1st 100 units at 100 INR (Lot 1) and next 200 units at 150 INR (Lot 2) and remaining 250 units at 200 INR (Lot 3) using the FIFO method. So here the total cost of Sales will be (100*100) + (200*150) + (250*200) = 10000 + 30000 + 50000 = 90000 INR.
 
And cost of remaining inventory i.e. 50 units (600 total – 550 sold) will be calculated as per cost / unit for the latest lot i.e. 50*200 = 10000 INR and hence the balance sheet will show this amount as Inventory Value. As per FIFO method, company will have low cost of goods sold and high inventory value and therefore profits here will be shown as high (Profit = Sales – Cost of Goods Sold) due to lower cost of Goods sold and hence company will be liable to pay higher taxes.
 
LIFO :  (Last In First Out)
In this method, most recently produced items are recorded as sold first. From 1970s, some U.S. companies shifted towards the use of LIFO, which reduces their income taxes at the times of inflation, but International Financial Reporting Standards (IFRS) banned LIFO method and hence more companies returned to FIFO.
 
LIFO method is used only in United States and it is governed by the Generally Accepted Accounting Principles (GAAP). Section 472 of the Internal Revenue Code throws the light on how to use the LIFO method.
In the example above, company XYZ using LIFO would expense the cost associated with the first 300 units at 200 INR, next 200 units at 150 INR, and the remaining 50 units at 100 INR. Under LIFO, the total cost of sales would be = (300*200) + (200*150) + (100*50) = 95000 and the ending inventory would be calculated as follows :
Remaining 50 units = 50*100 = 5000 INR, so here the balance sheet will show 5000 INR as Inventory value in contrary to 10000 INR using FIFO and profit here will be shown as low due to higher cost of Goods Sold and hence the company would be paying the less taxes here than using the FIFO method.
 
The difference in value of inventory calculated using the LIFO and FIFO methods is called as LIFO reserve which is 10000 INR – 5000 INR = 5000 INR in this example and This reserve is the amount by which company’s taxable income has been deferred by using the LIFO method.As a rule in the United States, publicly traded entities which use LIFO for taxation purposes must also use LIFO for financial reporting purposes as well and such companies should report LIFO reserve to its shareholders.
 
Examples of the Companies that use LIFO method : (US Only)
General Electric, Walmart, DOW, Caterpillar and ExxonMobil etc.

Examples of companies that uses FIFO method :

Sectors Prefer to use LIFO Methods : Where there is high difference in prices of goods / materials purchased) i.e.
Petroleum, Pharmaceuticals , Retail etc.

Sectors use FIFO Method : Where the Shelf Life is quite less and not much variation in Prices on daily basis. E.g. Dairy Products, Fruits and Vegetable Vendors, Courier Services etc.

 
So in nutshell, Companies use LIFO to take tax benefits and to trade off the inflation effects on profits in highly price volatile industries. However, this is used in US only as its banned everwhere else. FIFO is worldwide accepted and simple and clear method of accounting and largely companies use this only.
 

The term supermarket in lean six sigma refers to a predetermined market or space to be used for storage / inventory. The supermarket carries the necessary "supplies" for a work area that is close to it. When a customer (internal / external) needs an item they can take the same from the supermarket. The supermarket then replenishes their supplies based on the downstream demand.  Supermarkets prevent overstocking and help to lower the inventory levels. 
 
Supermarkets normally are located near the supplying process to help that process see customer usage and requirements. Each item in a supermarket has a designated location from where a material handler take out the products in amounts needed by the downstream process. As an item is removed, a signal to make more or to replenish (such as a kanban card or an empty bin) is taken by the material handler to the supplying process.
Toyota implemented first supermarket in year 1953 in machine shop of main plant. Toyota executive Taiichi Ohno got the idea of the supermarket from photos of American supermarkets showing goods arrayed on shelves by specific location for withdrawal by customers.
 
A supermarket is nothing but a series of parallel FIFO (First in First Out) lanes sorted by product. This term in manufacturing originated from the normal retail supermarket. The key of a supermarket (both retail and manufacturing) is that taking out any part or product gives a signal to replenish this part (via Kanban). Therefore, a supermarket also aims to keep all the required parts in stock, while at the same time avoiding overproduction.
 
Diagram of a Process with Supermarket (ꓱ) :

Diagram of a Process with Supermarket at 3 different locations i.e. close to Supplier, close to Customer and between the processes :
 

How Supermarkets Look like in real?
 

 
When to use Supermarkets?
i)  Use Supermarkets when there is difference in lot sizes at different processes
ii)  Use Supermarket if Material Flow Splits Up into Different Directions
iii)  A supermarket is also strongly recommended if two processes have very different cycle times.
iv)  Use Supermarket when there are different shift patterns
v)   Use Supermarket when next process creates different variants of the product
vi)  Use Supermarket if there is merging of the material flows
vii) Use Supermarket when there is large distance between the processes
viii) Use Supermarkets in case of very high demand on flexibility and Reaction time
ix)  Use Supermarket whenever there is change in responsibility
 
Characteristics of a Good Supermarket:
i)   Supermarkets must aim at reducing the overproduction and inventory in the process where it’s not possible to eliminate the inventory completely  or deployment of the one piece or continuous flow
ii)  Supermarket must work on a pull system i.e. as and when signal is received to replenish the supply at process or by customer via a Kanban, it should be able to supply that demand and at the same time supplier also shall be able to replenish the goods consumed in supermarket.
iii)  A good supermarket must be able to supply the right quantity of goods at the right time and there shall be sufficient stock of material / goods to meet the customer’s demand
iv)  Supermarkets should be close to close to Supplier / Supply Source so that immediate process / processes can take the material or supplies needed for doing the operations at that process OR Supermarkets can also be close to Customer as well so that customer may take up the finished goods as required from the supermarket.
v)  A good supermarket must aim to develop a continuous kind of flow and shall not allow any disruption in the operations / production by maintaining the adequate quantity of products / materials / parts for the downstream process
vi)  A good supermarket shall be sensitive to customer demand / process demand and shall be able to replenish the stock as per stakeholders / process requirement by usage of technology say RFID at Amazon / Kanban in manufacturing
vii)  A good supermarket shall have place for everything and everything at its place i.e. it must follow the principles of 5S, Lean, JIT etc.
viii)  Visual Controls / ANDON / Signage / Labels can be used in a supermarket to make it more structured / easy to refer

The term supermarket in lean six sigma refers to a predetermined market or space to be used for storage / inventory. The supermarket carries the necessary "supplies" for a work area that is close to it. When a customer (internal / external) needs an item they can take the same from the supermarket. The supermarket then replenishes their supplies based on the downstream demand.  Supermarkets prevent overstocking and help to lower the inventory levels. 
 
Supermarkets normally are located near the supplying process to help that process see customer usage and requirements. Each item in a supermarket has a designated location from where a material handler take out the products in amounts needed by the downstream process. As an item is removed, a signal to make more or to replenish (such as a kanban card or an empty bin) is taken by the material handler to the supplying process.
Toyota implemented first supermarket in year 1953 in machine shop of main plant. Toyota executive Taiichi Ohno got the idea of the supermarket from photos of American supermarkets showing goods arrayed on shelves by specific location for withdrawal by customers.
 
A supermarket is nothing but a series of parallel FIFO (First in First Out) lanes sorted by product. This term in manufacturing originated from the normal retail supermarket. The key of a supermarket (both retail and manufacturing) is that taking out any part or product gives a signal to replenish this part (via Kanban). Therefore, a supermarket also aims to keep all the required parts in stock, while at the same time avoiding overproduction.
 
Diagram of a Process with Supermarket (ꓱ) :

Diagram of a Process with Supermarket at 3 different locations i.e. close to Supplier, close to Customer and between the processes :
 

How Supermarkets Look like in real?
 

 
When to use Supermarkets?
i)  Use Supermarkets when there is difference in lot sizes at different processes
ii)  Use Supermarket if Material Flow Splits Up into Different Directions
iii)  A supermarket is also strongly recommended if two processes have very different cycle times.
iv)  Use Supermarket when there are different shift patterns
v)   Use Supermarket when next process creates different variants of the product
vi)  Use Supermarket if there is merging of the material flows
vii) Use Supermarket when there is large distance between the processes
viii) Use Supermarkets in case of very high demand on flexibility and Reaction time
ix)  Use Supermarket whenever there is change in responsibility
 
Characteristics of a Good Supermarket:
i)   Supermarkets must aim at reducing the overproduction and inventory in the process where it’s not possible to eliminate the inventory completely  or deployment of the one piece or continuous flow
ii)  Supermarket must work on a pull system i.e. as and when signal is received to replenish the supply at process or by customer via a Kanban, it should be able to supply that demand and at the same time supplier also shall be able to replenish the goods consumed in supermarket.
iii)  A good supermarket must be able to supply the right quantity of goods at the right time and there shall be sufficient stock of material / goods to meet the customer’s demand
iv)  Supermarkets should be close to close to Supplier / Supply Source so that immediate process / processes can take the material or supplies needed for doing the operations at that process OR Supermarkets can also be close to Customer as well so that customer may take up the finished goods as required from the supermarket.
v)  A good supermarket must aim to develop a continuous kind of flow and shall not allow any disruption in the operations / production by maintaining the adequate quantity of products / materials / parts for the downstream process
vi)  A good supermarket shall be sensitive to customer demand / process demand and shall be able to replenish the stock as per stakeholders / process requirement by usage of technology say RFID at Amazon / Kanban in manufacturing
vii)  A good supermarket shall have place for everything and everything at its place i.e. it must follow the principles of 5S, Lean, JIT etc.
viii)  Visual Controls / ANDON / Signage / Labels can be used in a supermarket to make it more structured / easy to refer

A quality circle is a group of people who do the same or similar work, meet regularly to identify, analyze and solve the work-related problems. Quality circle is a people building philosophy based on the fact  that an employee doing a particular job is biggest expert of that field and thus is in a better position to identify, analyze and resolve the work related problems through their ideas. In reality, Quality Circle is a practical application of McGregor’s theory ‘Y’ which says that people enjoy and take pride in their work if they are given the right environment with a decision making power.
Generally, It consists of minimum 3 and maximum 12 members in number. Quality Circle Groups are usually small in numbers and led by a supervisor or manager and they present solutions to management and also implement the solutions themselves to improve the performance of the organization and also to motivate the employees across the organization. These groups were most popular in 1980s but today also such groups exists in the form of Kaizen Groups etc.
Typical areas of interest for Quality Circle members are improvement in Product design, Processes, Occupational Health and Safety and workspaces etc. These are the formal groups in organization who meet regularly to discuss the problems and they are by competent people and Industry experts in problem identification, analysis, basic statistics and solving the problem in a structured way.
 

 
Origin of Concept : The foundation of this concept was put in by Dr. W. Edward Deming during his working with Allied Occupation of Japan in 1950s and then Professor Kaoru Ishikawa built upon the work done by Deming and defined this term in detail in his book “What is Total Quality Control ? The Japanese Way” and later on it was circulated across the Japanese Industry by JUSE (Union of Japanese Scientists and Engineers) in 1960. Nippon Wireless and Telegraph was the 1st Japanese company to deploy the Quality Circle concept in 1962.
 
Key Elements of Quality Circles :
 

 
Mechanism of Quality Circle :

 
Do Quality Circles Exist Today as well ?
 
Yes, Concept of Quality Circles exist even today (though the wave was more prevalent in 1960s and 1980s) as it’s a philosophy that uses the wisdom of the people on Ground to solve the problems. In Manufacturing Sector, the knowledge and experience of people on ground or at Gemba like operators and workers, foreman and line managers is used to get an idea and same is considered in problem solving / arriving at the final solution as they are the best judge or witness of the problem happening at the ground day in and day out and their thoughts must be given considerable importance even if we are using other techniques like Six Sigma / Lean (Lean also Focus on importance of Gemba / Genchi Genbustu). Normally in Morning / Evening meetings with the workers, this approach is promoted and asked to look at the problems with the help of 7 QC tools and training is also given in line with that.
 
In Service Sector as well, people on Ground discuss the problems in huddles, team meetings and work together to fix the same at ground level. Though the concept is not so formal in Services Industry but initiatives (Like Idea generation / A3 / Kaizen / SPS / Lean Workshops / Solution Programme / Hackathons / War on Errors / Power of Zero etc.) are taken in the organizations and participation from people on ground is encouraged in solving the problems as small – 2 ideas sometimes cause big improvements at the organization level. In Agile way of working also, development team is given the total ownership for development of the softwares guided by the Product Owner and this development team acts as a Quality Circle Only (<10 members team) and this team acts in a self organized way, solve problems with collective wisdom and take decisions independently keeping Product Owner in loop  to improve the product quality.
 
Companies where this Concept is  working : BHEL, Kirloskar Oil Engines, Mahindra & Mahindra, Bajaj Auto, HMT, Maruti, Modi Xerox. SBI, Hindustan Aluminum, Modi Rubber, TELCO, LUCAS-TVS, etc.

Cost-benefit analysis is the method used to measure the benefits of a decision minus the costs associated with taking that action or decision. In 1772, Benjamin Franklin wrote of its use. But the concept of CBA (Cost Benefit Analysis) dates to Jules Dupuit, a French engineer, who put forward the process in an article in 1848. This concept become quite popular in 1940-1950s in US and was vastly used in U.S. Flood Control Act to prove that benefits of flood-control projects largely exceed their costs. A Cost Benefit Analysis involves measurable financial values such as revenue earned or costs saved as a result of the decision to pursue a project. Also intangible benefits are considered into the cost benefit analysis like customer satisfaction, Employee Satisfaction etc.
In today’s competitive world, its very important to conduct the Cost Benefit Analysis and take the decisions as per that. It helps in identifying the projects which are generating the higher benefits (revenue / sales) than the cost incurred on them and will be beneficial for the company from the financial standpoint. Also sometime opportunity cost (Opportunity costs are alternative benefits that could have been realized when choosing another alternative over the selected one) is also included in this analysis.
 
How Cost Benefit Analysis is Conducted?
Step1 : List down the direct costs involved in the project like Raw Material, Labor, Manufacturing Expenses, Machinery Cost etc.
Step2 : List down the indirect costs like electricity, overhead costs from management, rent, utilities etc.
Step 3 : List down the intangible costs too like impact on customer satisfaction, employee satisfaction, or delivery timelines etc.
Step 4 : List down opportunity costs associated with decision as well like purchasing a new machine vs taking the same on rent.
Step 5 : List down the potential risks as well regulatory risks, competition, and environmental impacts etc. as well.
Step 6 : List down the benefits e.g. Revenue / Sales etc.
Step 7 : List down the intangible benefits as well like impact on customers, employees, or faster delivery etc.
Step 8 : List down the competitive advantage gained as result of this decision like Market Share.
Step 8 : Do the sensitivity Analysis and consider the risks and uncertainties in projections.
Step 9 : Take the decision
 
Example : Lets say Company ABC Plans to Start a New Production Plant with details as below. Lets see if they shall set up the new plant or not basis the cost and benefits listed below.
 
Costs and Benefits Involved :
Cost Heads
Cost Amount (INR)
Benefits Type
Benefits Amount (INR)
Land Cost
1000000
Revenue
4000000
Raw Materials
200000
Scrap
1000000
Machinery
1000000
Client Satisfaction
100000
Labour
1000000
 
 
Electricity, Water etc.
100000
 
 
Inventory
100000
 
 
Manufacturing Expenses
100000
 
 
Total Costs
3500000
Total Benefits
5100000
 
So If we see in example above, total costs are 3500000 INR and Total Benefits out of it are 5100000 INR and hence there are benefits of (5100000-3500000) = 1600000 INR and hence company can take the decision to setup the new plant.
 
Limitations of Cost Benefit Analysis :
i)  There are number of forecasts built into the process, and if any of the forecasts are inaccurate, the results may be called into question. E.g. Cost of Machines, Land, Expected Revenue etc.
ii)  Cost Benefit Analysis works well for short term to mid term projects with low to medium complexity but it fails for long term and complex kind of projects as there is always some elements of uncertainty in the long run e.g. costs may go up, interest rates may change and technology may become obsolete.
iii) Cost Benefit Analysis of concept does not take into account the NPV concept (Net Present Value) and IRR (It is the rate of return at which the net present value of a project becomes zero or rate at which your investment is expected to generate the money, higher the IRR, more attractive is the investment because of higher rate of return) of money while calculating the Benefits as Costs would be incurred in near future but the benefits will be realized in long run or say 2-3 down the line OR even if it is taken into account the assumptions may not be correct because of inflation, time value of money / interest rate fluctuations. One of the benefits of using net present value  (NPV) for deciding on a project is that it uses an alternative rate of return that could be earned if the project had never been done. That return is discounted from the results. Or we can say, a project needs to earn at least more than the rate of return that could be earned elsewhere or the discount rate. So to avoid the same NPV shall be taken into consideration :
NPV = F / [ (1 + r)^n ] where, PV is Present Value, F is Future payment (Cash Flow), r = Internal Rate of Return and n = the number of periods in the future is based on future cash flows.
iv) There is always subjectivity involved in Quantifying the Intangible costs and benefits and may not be totally true and accurate.
v) Sometime Cost Benefit analysis done is taken forward as the budget of the project and which may not be actually true and hence the stakeholders are pressed hard to meet these numbers like cost / sales etc.
vi) Wherever the assumptions / forecasts are involved, person doing the analysis has the tendency to assign more weights or values to factors / elements which he wants to go with basis his instincts and hence it may cause bias in the results.
vii) The supposed clarity in determining and listing costs and benefits can prove harmful as the actual outcome is dependent on several variables that you can only know with time.
 
Can organizations take decisions in favor of unfavorable Cost Benefit Analysis ?
 
Yes, organizations sometimes may take decisions in favor of the projects where Cost Benefit Analysis is not in favor of them. Some Examples are as listed below :
1. Corporates are going for its own vaccination drives for the safety and health of its employees. If we see here there are lot of costs involved in setting up the system, softwares, vaccination, facilities etc. with no direct immediate benefits for the company but companies do so for the benefits and wellness of their employees.
2. An organization taking a project to plant 10000 trees to make the environment better.  If we see here there are no direct benefits to company but they do so for their responsibility towards society or nature.
3. An Organization taking a project to install the safety devices  / equipment. If we see here, there will be cost involved to company but still they do so for the safety of their staff.
4. Twitter to appoint additional staff in India to comply with the Government Regulations - Though this may not be required or beneficial from company standpoint but they have to do so (Cost) to adhere to government rules and regulations. 
5. Organisation providing free transport, food, gifts etc. to employees to their employees to increase their employee satisfaction though these results in cost to the company.

An influential point is a point that has a large impact on the regression analysis and an outlier is a point with a large residual. Interestingly, these are not the same thing. A point can be an outlier without being influential point too. A point can be influential even if its not an outlier. A point can be both or neither of these as well.
 
An outlier is a data point whose response Y does not follow the trend of rest of data and a data point considered as an outlier only if that point is extreme with respect to the other Y values and not the X values. A data point is influential if it influences the regression analysis in a big way, such as predicted responses, the estimated slope coefficients, or the hypothesis test results. Kindly note that Outliers and high leverage data points have the potential to be influential, but we need to investigate further to determine whether or not they are actually influential.
 
Let's take a look at some of the scenarios that should help us to clarify the distinction between these two types of extreme values i.e. Outliers and Leverage Points.
 
Lets take Dependent Variable (Y) here as Marks Obtained in Exam and Independent Variable as Hours Studied (X). 
As we know, equation of line is represented as below : 

Y = mX+C
Y - Dependent Variable i.e. Marks Obtained , m - Slope of Line or rate of change , X - Independent Variable i.e. Hours Studied and C - Intercept on Y line 
H0 - X  (Hours Studied) has no effect on Y (Marks Obtained).
H1 - X  (Hours Studied) has effect on Y (Marks Obtained).
Scenario 1 : 
As per definitions above , do you think there are any outliers, leverage points or influential observations in below :

Definitely not, as all of the data points follow the general trend as rest of the data, so there are no outliers (in the Y direction). And, none of the data points are extreme with respect to X as well, so there are no high leverage points too. To conclude, none of the data points appears to be influential with respect to the location of the best fitting line. So, more and more I am studying, higher and higher are my scores.
 
Scenario 2 : 
Do you see any outliers or any high leverage points or any influential observations in below ?

 
Definitely Yes, because the red data point does not follow the trend of rest of the data, it would be considered as an outlier. So here, though I have studied for less hours, but my scores were quite high. So such scenario must be analysed to know what exactly happened at that instance might be better concentration or exam was easy or nothing came outside the syllabus than what i had studied. However, this data point does not have an extreme X value, so it does not have the high leverage. Is the red data point influential? An easy way to determine if any data point is influential is to draw the best fitting line twice — first with the red data point included and another with the red data point excluded. The following graph illustrates the two best fitting lines :
 


Great,  it's hard to even tell the difference between the two estimated regression equations! The solid line represents the regression equation with inclusion of the red data point, while the dotted line represents the estimated regression equation with the red data point being excluded. The slopes of the two lines are also very similar i.e. 5.04 and 5.12 respectively.
Do the two samples yield different results when testing H0 : m = 0? Well, we get the following output when the red data point is included in this data set :

and the following output when red data point is excluded from the data set :

There certainly are some minor side effects of including the red data point, but not very serious.
 
As we can see here, R^2 value has decreased slightly, but the relationship between Y and X would still looking strong.
The standard error (SE), which is used in calculating our confidence interval of m, is larger when the red data point is included, therefore increasing the width of the confidence interval. As we know that the standard error depends on the mean squared error MSE, which tells us the difference between the observed and predicted responses. It is because the red data point is an outlier i.e. in the Y direction so the standard error is increasing, not because the data point is influential.
 
In each case, the p value for testing H0: m = 0 is less than 0.001;  we can conclude that there is sufficient evidence at the 0.05 level to conclude that, in the population, X is related to Y.
 
Therefore, the predicted responses, estimated slope coefficients and hypothesis test results are not impacted by inclusion of the red data point. Therefore, the data point is not deemed influential. In nutshell, the red data point is not an influential data point and does not have a high leverage too, but it is definitely an outlier.
 
Scenario 3 :
Now, lets look at the below scenario for outlier and leverage point.

Here, the red data point follows the general trend of rest of the data. Therefore, it is not deemed an outlier. However, this point has an extreme X value, so it has the high leverage. So here,  I have studied for a large number of hours which are substantially more than the other days,  but my scores were also quite high in that proportion and following the same trend. Such scenario again must be analysed as why all of sudden i have studied for so many hrs than normal and reasons may be important exam, less subject preparation, difficult exam pattern etc.. Now, lets see the red data point influential? It certainly appears to be far away from rest of the data (in the X direction), but is it sufficient to make the data point influential ?
 
The following plot depicts the two best fitting lines; one obtained when the red data point is included and another when the red data point is excluded:

Again, is difficult to separate both the regression lines. Solid line represents the estimated regression equation with red data point included, while the dotted line represents the estimated regression equation with the red data point excluded. The slopes of the two lines are also very similar i.e. 4.927 and 5.117 respectively.
 
Do the two samples yield different results when testing H0: m = 0? Well, we obtain the following output when the red data point is included in the data set :

 
and the following output when red data point is excluded from the data set:

So we see here that there are hardly any side effects from including the red data point:
The R^2 value has hardly changed, increasing slightly from 97.3% to 97.7%. In both the cases, the relationship between Y and X is looks strong.
 
The standard error is also same in each case i.e. 0.172 when the red data point is included, and 0.200 when the red data point is excluded. Therefore, the width of the confidence intervals would remain unaffected by presence of red data point. You can see that this is because the data point is not an outlier heavily impacting MSE. In each case, the p value for testing H0: m = 0 is less than 0.001. In either case, we can easily conclude that there is sufficient evidence at the 0.05 level, in the population that X is related to Y.
 
Therefore,  the predicted responses, estimated slope coefficients, and hypothesis test results are not affected by inclusion of red data point. Therefore, the data point is not appearing as an influential. To summarize, the red data point is neither influential, nor is it an outlier, however it has the high leverage.
 
Scenario 4 :
Lets look at the last scenario as below for any outliers and leverage points.

Bingo, the red data point is most certainly an outlier and also has high leverage! The red data point does not follow the trend as rest of the data and it also has an extreme X value. So here though I have studied for substantially high number of hours but my scores have not increased in that proportion or rather it came down; so as a general observations it shall influence my regression analysis definitely. Therefore, in this case the red data point is certainly influential. The two best fitting lines i.e. one obtained when the red data point is included and one obtained when the red data point is excluded:

are (not surprisingly) substantially different. The solid line represents the estimated regression equation with the inclusion of red data point, while the dashed line represents the estimated regression equation with the red data point exclusion. The existence of the red data point significantly reduces the slope of the regression line i.e. dropping it from 5.117 to 3.320.
 
Do the two samples yield different results when testing H0: m = 0? Well, we obtain the following output when the red data point is present :
 

and the following output when the red data point is not present :

Here the R^2 value has decreased substantially from 97.32% to 55.19%. Therefore, if we include the red data point, we conclude that the relationship between y and x is only moderately strong, whereas if we exclude the red data point, we conclude that the relationship between Y and X is very strong.
 
The standard error also is almost 3.5 times larger when the red data point is included i.e. increasing from 0.20 to 0.686. This increase would have a substantial effect on the width of our confidence intervals too. Again, the increase is because the red data point is an outlier in the Y direction.
 
In each case, the p value for testing H0: m = 0 is less than 0.001. In both the cases, we can conclude that there is sufficient evidence present at the 0.05 level to conclude that, in the population, X is related to Y as largely the data points are in favor of it . Note, however, that the t-statistic decreased dramatically from 25.55 to 4.84 upon inclusion of the red data point. (Measure of how many points actually fall on the regression line which has decreased here with inclusion of red data point) 
 
Here, the predicted responses and estimated slope coefficients are certainly affected by the presence of the red data point. While the data point does not affect the significance of the hypothesis test, the t-statistic did change largely (The greater the magnitude of T, the greater the evidence against the null hypothesis). In this case, the red data point seems to have both high leverage and also an outlier, and finally it turns out to be influential too.
 
Summary
In the above scenarios,  through the use of simple plots, we have highlighted the distinction between outliers and high leverage data points. There were outliers in scenarios 2 and 4. There were high leverage data points in scenarios 3 and 4. However, only in scenario 4, data point that was both an outlier and a high leverage point turn out to be influential. That is, not every outlier or high leverage data point strongly influences the regression analysis. Therefore, its our duty as an analyst to determine if regression analysis is unduly influenced by one or more data points and if these are incorrect observations or values we can delete / ignore them however if these are right observations we must study them in detail as these are kind of special causes and indicating to something special that has happened at that particular instance E.g. Though I have studied for more hours but my scores were not high rather dipped (Scenario 4), which may be i did not study the relevant subject or topics or may be my concentration was not good on that particular day or may be exam questions have not come from the syllabus i have studied. So Its always good and advisable to study these points in details so that something interesting or unusual causes can surface out as a result of this study and decisions can be taken accordingly to consider the same in further analysis / study.
 

A decisional balance sheet is a tabular / grid based method for depicting the pros and cons of different options which helps to decide what to do in a particular situation. Sometimes when we are faced with an important decision, we spend a lot of time looking for a solution hoping to avoid making the wrong choice. One of the simplest ways to decide what we shall do in a particular situation is Decision Balance Sheet. It is a quite simple however a very powerful and effective decision-making method that enables the team to make more confident and balanced decisions. 
It is said that decisional balance sheet was first used by Benjamin Franklin. In 1772, Franklin described his own use of the method, often called the Ben Franklin method. It includes making a list of pros and cons about a decision, estimating the importance of each one, eliminating items from the pros and cons lists of roughly equal importance until one column is dominant. In 1959, Irving Janis and Leon Mann coined the phrase decisional balance sheet and used the concept as a way of looking at decision-making.This approach / technique is quite often used in working with ambivalence in people who are engaged in behaviors that are harmful to their health (for example, problematic substance use etc.), and in certain circumstances in motivational interviewing too.
Another corollary of this tool is PMI which stands for Plus, Minus and Interesting. An Interesting column is used when the elements of the idea are not resulting into positive or negative effect. It can be used to record what you consider interesting and the future implications of the idea. E.g. Pursuing a new hobby say painting where there are no as such plus and minus but you are taking it forward as it interests you.
 
How Decisional Balance Sheet Looks ?
 
A decision balance sheet is simply a table of two columns called as Pros and Cons. This table is used to record all the pros and cons for the given idea or the proposal. You can also use a scoring system by giving numerical weights to the different pros and cons. The result will help you to decide whether to go ahead with the decision or not to go with it. (Images shown in examples below)
 
How to Make and Use the Decision Balance Sheet ? 
 
Steps :
i) Clearly write down the situation or idea at the top of a flip chart or whiteboard.
ii) Draw a table of two columns, name one “Pros” and the other as “Cons”.
iii) Record all possible benefits in the pros column and all possible negative effects in the cons column.
iv) Give numerical weights to the pros and cons by assigning a score from one to five. Be objective while scoring.
v) Sum up the scores in each column and then subtract the total cons from the total pros.
vi) Consider the overall score to decide whether to go ahead with the decision or not.
vii) Take time to identify other factors that you may have missed. Remember to use common sense too when you suspect that the reached result is not feasible.
 
Some of the Real Life Examples and Applications of Decisional Balance Sheet :
 
DECISIONAL BALANCE SHEET – GO FOR PREVENTIVE MAINTENANCE OR NOT
 
PROS
CONS
CHANGE (Go for it)
i)               It will reduce the unknown / unscheduled                             failures (+5)
ii)              Will reduce the unplanned downtime (+4)
iii)             Will help in avoiding the accidents and improve                   workers safety (+5)
iv)             Will improve company’s brand image (+4)
 
i)               There will be cost incurred on it (+3)
ii)              Additional Efforts needs to be put in making                   the schedule (+2)
iii)             There is downtime involved of machines as                     per schedule (+2)
 
NO CHANGE (Don’t Go for it)
i)              Cost Savings due to no downtime for                                    Preventive maintenance
ii)             Increased availability of machines
iii)            Savings of Resources (Man Power) involved in                      making the schedule
iv)            No impact on the Production
i)             There may be frequent failures and                                 downtimes
ii)            Unexpected downtimes / failures may lead                     to high revenue loss or costs
iii)           Stress / Pressure to resolve the issue                               immediately
iv)           Safety concerns for workers and Brand image                 of company will hamper
 
DECISIONAL BALANCE SHEET – CHANGE JOB AFTER CERTAIN TIME OR STAY AT SAME COMPANY
 
 
PROS
CONS
CHANGE
i)              Fast Growth in Career / Position (+5)
ii)             Higher Salary Increase (+4)
iii)            New Skills Learning (+5)
iv)            More Connects in Industry (+4)
v)             Exposure to different Cultures (+3)
i)              Instability  / New Comer's Risk in New                              Organisation (+5)
ii)             Uncertainty in New Environment (+4)
iii)            Establish your connects from scratch (+3)
iv)            Work Harder to settle and for Growth (+2)
DON’T CHANGE
i)               Job Security
ii)              Less Efforts to Survive
iii)            Comfort Level
iv)            Good Knowledge about existing company                      (People / Culture / Connects etc.)
i)              Slow Growth Rate
ii)             Less Increments in Salary
iii)            Stagnancy in Skill / Career
iv)            No knowledge about outside world /                                technology
v)              Less Personal Development due to working                     in same culture / environment
 
DECISIONAL BALANCE SHEET – DO NEW CERTIFICATIONS OR WORK AS USUAL
 
PROS
CONS
CHANGE (DO CERTIFICATIONS)
i)              New Skills Learning (+5)
ii)             Fast Career Growth (+5)
iii)            Sense of Achievement (+5)
iv)            Stay Relevant & Avoid Obsolescence Risk                     (+4)
v)              More Connects outside (+4)
i)                Extra Efforts on certifications may impact                        routine work (+2)
ii)              High Certifications Cost (+2)
iii)            Impacts personal time / space due to                             weekend efforts (+3)
 
Summary : If we look at the above examples, it is evident that scores in favor of Preventive Maintenance, Change Job after certain time period and Do Certifications are high (Basis Personal Assumptions and scoring) and hence decisions may be taken in favor of these events. 

A North Star Metric (NSM) is one measure that is most predictive of a company’s success in long term. A Metric would be truly a "North Star" metric if it can encompass these three aspects into it : Lead to revenue, Reflect customer value, and Measure progress. If any metric that ticks well on these three points, and every department contributes to improving it, the company is going to grow in long run. This metric term was coined by start up investor Sean Ellis. 
It is very important to have a North Star Metric so that everyone in a company is aligned towards a ONE common goal and also it helps in reduction of duplication of work in different department due to no clarity about the common goal.
 

Examples of North Star Metrics from some of the renowned companies:
Airbnb: Number of Nights Booked
Facebook : Daily Active Users
Quora : Number of questions a user answers
WhatsApp : Number of messages a user sends
Spotify : Time spent on Listening
Amazon : Number of Purchases per month
Uber : Rides per week
A major reason Myspace (American social networking service) failed and Facebook succeeded is correlated to NSM they focused on. Myspace focused on Registered Users (a vanity metric) as their North Star Metric. At that moment, Facebook focused on Monthly / Daily Active Users as their NSM. This difference in focus finally led Facebook to succeed whereas Myspace failed badly. Social media apps must be aware of the changes in registered user numbers over a period of time. But Myspace failed because Registered Users means that it will show only who have signed up. It doesn’t show if these users are continuing to use their platform (a sign users continuing to receive value from it). Users who don’t get value from a product will stop using it and churn over a period of time. By tracking Monthly Active Users, Facebook could monitor changes in user numbers and see which users are finding the value from using the platform.
 
North Star Metric (NSM) must not be confused with OMTM (One Metric that Matters) in Lean as NSM is the number on which your entire company focuses to achieve long-term growth during a period of several years to infinity in comparison to One Metric That Matters (OMTM) is the number on which one team focuses to achieve rapid growth for a period of 2 to 6 months.
 
How to Select your NSM ?
 
Five points to keep in mind when deciding your North Star Metric:
i) The metric shall indicate what your user experienced the core value of the product
ii) It should reflect user’s engagement and activity level.
iii) It points out to “one thing” that indicates if your business is heading in right direction
iv) Ideally, the metric should be easy to understand and communicate across teams
v) Don’t fall into the trap that you have to have a perfect North Star Metric. What you are trying is to find is a metric that makes the most sense for complete business to focus on. It might take a few iterations as well to get a final NSM
Believe the crux and intent for North Star metric is rightly explained below :
“It is important to keep in mind that NSM is not an end solution in itself. Its a key metric gives focus on growth efforts and can serve as a seed to grow in a company's ecosystem that gives you deep insight into your customer’s behavior and create levers to drive future growth. It leads to understanding your customers and integrating your team on how to better bring value.”
– Andrew Miller
To correctly define your NSM, it requires understanding your customers, including what kind of value they get from your product and how they use your product to get that value. With that information, you can make better product to help customers get the value they’re looking for which will increase customer lifetime value, decrease churn rates, and increase revenue and growth.
How North Star Metric can help generate Long term value for Customer ?
Taking Example here of Airbnb whose North Star Metric is - Number of Nights Booked. So, company would be focusing on it heavily and will be on company's radar on real time / weekly / monthly monitoring and continuous efforts will always be there to improve the performance of this metric. To improve the metric, company must be rolling out frequent customer friendly offers (discounted price), improvement in its current service and hygiene of its current properties, providing superior locations, bundled offers (Meals / Amenities / Facilities / certain nights free if you book for more than say 5 nights), loyalty / membership cards etc.. By focusing on NSM, a company would be always working hard and continuously to get the night booking done (Its NSM) and which will lead better value to customer with better and better services over a period of time at lesser / same price OR more benefits will be passed on to customer even if the company increases the price to stay ahead of competition and maintaining the growth momentum in its business. Also, company would never think of loosing a customer as cost of acquiring a new customer is always quite high (5-10X) than retaining an existing customer.
Finally to Summarize, Revenue is the price that your customer pays however NSM is the value your customer gets in return of that price. So please always keep on maximizing that value over time to get more and more growth in your customer base and which will finally lead to growth in your revenue.

Product Portfolio Matrix is a very famous tool used by most of the companies to decide the product portfolio of their products basis the market share of products and growth rate potential. This tool is also known as the BCG (Boston Consulting Group) Matrix and was developed by Bruce Henderson (Founder and CEO of BCG) in 1968. This matrix is used by more than half of Fortune 500 companies and is still one of the central point in business school teachings on strategy.
This matrix helps businesses to develop the long term strategic planning and is a torchbearer in deciding products to keep and invest, discontinue or develop in the long run. It is divided into four quadrants (2*2) based on market growth and relative market share as depicted below (It's also known as Growth Share Matrix) :

1. Dogs: These are the products with low growth and low market share. Normally, expert advice here is to remove dogs from your portfolio as they are a drain or waste of resources. For example, Sony Digicam can be placed into 'Dog' category at present because every phone has a good camera now a days and hardly anyone buys them now. However it may not be always true and to quote an example here from automobile sector, in case company decides to discontinue some of the car models (dogs) however there is still a need of the spare parts and hence spare parts production operations can be continued with very less efforts and investments which may emerge as a new revenue stream for the company.To avoid over-investing in your question marks, ensure that all products in your portfolio have clear business goals and use the right KPIs to track product performance.
2. Question marks : These are products in high growth markets with low market share. As the name suggests, these products can be in early stage of development and we are not sure (question mark) if they will turn into stars or may also end up falling into the dog quadrant. These products often require significant investments to push these into the star quadrant. For example, while working on a new car model, the design department works on a large number of design options and out of those only a few or one only go for the final production. Then, there are marketing and advertising expenses involved to move this new product to 'Star' quadrant. However, on the other hand if design chosen is bad and is launched without doing proper pre launch study, it may end up falling into 'Dog' quadrant too.
3. Stars: These are products in high growth markets with high market share and these products are usually the market leaders. However, significant investment may be required in them to sustain the 'Star' position. E.g. Google which is a leader in many of its services e.g. Search, Marketing & Advertising etc. and is in 'Star' quadrant, however Google keeps on investing in their R&D efforts and new technologies to stay ahead of the competition and keep on launching the new products & features and enhancing the existing ones with help of new technologies like AI, Machine Learning etc. (Recommendations display while doing Search)
4. Cash Cows: These are products in low growth markets with high market share. Approach here that is often followed is - Milk the cow as much as possible without killing the cow ! These products are often well established products. E.g. In FMCG sector, we have certain soft drink brands (Pepsi, Coca Cola etc.) which have high market share in India but as people are becoming more and more health conscious and are avoiding eating junk food and taking these drinks, these companies are milking these well established products as much as possible by spending on Advertising, Packaging, Offers etc.
Though this tools is popularly used and huge benefits for deciding the right product portfolio mix, there are certain limitations / drawbacks as well as given below (one shall keep in mind while using this) : 
i) This neglects the effects of synergies between business units / streams
ii) High market share is not the only success factor for decision
iii) Market growth is not the only indicator for attractiveness of a market
iv) A high market share does not necessarily lead to profitability every-time
v) The model uses two dimensions only – market share and growth rate. This may result into emphasize on a particular product, or to divest prematurely.
vi) A business with a low market share can be profitable too so what to be done in that case
vii) The model neglects the small competitors those have rapidly growing market share
 
In my opinion, Six Sigma Projects can be taken up in any of these categories as Six Sigma methodology is an approach of problem solving and it can be used at any stage of product life-cycle (figure below) :

 
In Dogs category, six sigma principles (DMAIC / DMADV) can be utilized to identify the root causes of low market share and appropriate solutions post analysis can be deployed to address the same however as business benefits will be quite less in this category due to low growth potential; companies may not be really interested in taking up such projects. 
For Questions Marks Category, DMADV / DAMIAC approaches can be used for better design of the products and increasing their potential to become stars. Here the potential is high with high business benefits, therefore most of the organisations will be interested in launching such projects at this stage to attain the leader position for their new products. 
For Stars Category, Six Sigma projects (DMAIC / DMADV) can be initiated to retain or increase the high market share with development of new design / concept that can help the product to retain the leader's position in long run by generating the higher revenues. As Business benefits are again high here, companies would be very much interested in launching such projects. 
For Cash Cows Category, since company already has the high market share for the product; six sigma projects can be taken up to reduce the cost and also to improve the design if possible which may help the product to retain its high market share.
So if I have to order the priority of the projects, Question Marks & Stars must be top priority due to huge growth potential ahead followed by Cash Cows and Dogs (We may not even take up for 'Dogs' category due to less benefits but high costs involved) .

The noise factors are the design or process parameters those are difficult or expensive to control and affect the output of variable of key interest negatively. On the other hand control factors are controllable factors and can be controlled by the person doing the process or experiment. 
For example : 
i) Say a farmer wants to grow the wheat, then the seed quality, time of sowing the seeds etc. are control factors and can be controlled by the farmer; however on the other hand outside temprature, humidity etc. are the noise factors which are difficult or expensive to control.
ii) On the production floor, say itensity of the light impacts the productivity of the worker; then light intensity can be treated as the noise factor.
How to overcome the effects of noise factors ?
Effects of the noise factors can be reduced / eliminated by Blocking. We can divide the total population into homogenous groups called blocks. The logic behind making the blocks is the variation due to noise factors is less between the blocks and effect of the treatment is more clearly evident while we do the blocking. E.g. Say we want to calculate the productivity of the team and we think that shift timing is one of the noise factor that imapcts the team's productivity; so then we can calculate the team's productivity shift wise and address the problem effectively if it lies in a particular shift only.
Compounding noise factors is also a strategy in which you group the noise factor levels into different combinations that you anticipate will result into the extreme response values. Because estimating the effects of individual noise factors is not the primary goal, compounding is a useful method to reduce the amount of testing. For example, if you have three noise factors, each factor with the two levels, you will have eight different combinations of settings to test. Instead, you may group noise factors into two overall settings – one setting in which the noise factors levels increase the response value and the other one in which the noise factors levels decrease the response value.