Shashikant Adlakha

Ansoff Matrix is a popular strategic framework  tool for business growth. It was described by Igor Ansoff , a   business manager in 1957.
The different growth strategies of this model are:
o  Market Penetration
o  Market development
o  Product development
o  Diversification
 
Market penetration: This refers to increasing market share  within existing segment, by increasing the sale of products or services to the customers.
The probable ways are:
o  Price decrease(economies of scale)
o  Increased promotion and distribution
o  Acquisition of  competitors
o  Mild degree of product or service refinement
 
Market Development:  This refers to  expansion and catching a share of new market in different geographical area, different country etc., using existing product or service with minimal refinement.
 
The probable ways are:
 
o  Different Niche/customer segment  of market.
o  Business to Business transactions, rather than only business to customer transactions.
o  Alliance with a local or regional leading player.
 
 
Product development: This refers to  marked refinement and development of new product or service to gain significant growth in existing market.
 
The probable ways are:- 
 
o  Significant emphasis and investment in research and development of new products and services
o  Joint development of new products with other companies
o  Acquiring quality  products from  other firms and selling it under own brand.
 
Diversification: This refers to  getting increased market shares introducing new product or offerings, in addition to the existing offerings.
 
This can again be  classified into:
 
Related diversification:  Venturing into related kind of businesses or product, so there is synergy between existing and new products.
 
Unrelated diversification:  Venturing into unrelated businesses or products, with different kinds of market segments.
 
In Ansoff Matrix, as move from one to another quadrant, the risk increases. So out of all the strategies, Market penetration option is having the least risk, and diversification entails both product and market development, so two quadrant move is required, so having the maximum risk and unrelated diversification is more riskier than related one, due to lack of synergy.
 
Ansoff matrix, with reference to Apple Inc:
 
Market Penetration: Sale of different products through multiple platforms like Apple Stores, Apple.com, in alliance with various telecommunication companies, like Vodafone. Promotion through various media outlets and own website.
 
Market development: Continuously emerging as a global brand, by manufacturing bases at USA, China  and opening exclusive Apple stores and tie up with regional branded stores across different countries.
 
Product development: Product innovation and differentiation has been the major strategy of Apple. Various range of products have evolved with time- I phone, I pad, I watch, I pod, Apple TV, Mac os, ios, I cloud platforms. The versions of different products are also getting updated regularly. The different Apps used in these products are also major  source of revenue for Apple.
 
Diversification: Apple started from a simple computer  and diversified into a number of related and unrelated products, like Apple watch, Apple Pay, Apple Credit Card,  along with new market development for products
 
 
 

 
Image reference: www.tutor2u.net
 
 

SERVQUAL model is a widely used   multidimensional questionnaire, for  inspecting and measuring the service quality of businesses  by recording and comparing  the expectations and perception of customers/respondants. The questionnaire typically  consists of 22 questions of 5 dimensions.  Additional questions may be added in the relevant dimensions,  e.g-  demographics of respondent, brand loyalty with prior use, behavioral patterns such as likelihood of customer for further referral to others.
Face to face interview is required and large number of  potential customers are to be surveyed for statistical reliability.
 
The dimensions are:
 
Reliability- 5  questions: The ability to execute the promised service accurately and in a time bound manner.
Assurance: - 4 questions:  The ability of employees to instill confidence and trust in customer’s mind.
Tangibles: 4 questions: The physical dimension/appearance of any of the service deliverables or process, like  premises, equipment, people etc
 Empathy:  5 questions:  Care and attention to customer perspectives. 
 Responsiveness: 4 questions: Response to customer issues and provision of prompt service.
This model can be used in all  most all  industries and geographical areas,e.g- 
-    In educational context to asses the quality of education
-       In hospital and other healthcare areas.
-       IT industry
-       Art Gallery
 
 
Example of SERVQUAL model in a hospital scenario
 
Dimension
Expectation
Perception
Reliablity:
Hospital promises successful treatment of the disease in shortest time
The patient is treated well by doctors and patient is completely disease free in stipulated time
Assurance
Patient is well informed and assured of best treatment.
Patient is well assured by doctors and other staff of hospital.
Tangibles
Hospital will offer well maintained patient wards and all amenities for comfort.
Hospital OPDS and patient wards are hygienic, well maintained and all comfort means are provided.
Empathy
Hospital provides round the clock emergency services including specialists on call.
Emergency services, including doctor consultation, diagnostic testing and pharmacy are provided
Responsiveness
Patient issues and feedback will be immediately actioned
Ward in charge, Quality department and PRO monitors patient comfort and immediately takes action on patient’s grievances. 
 
 
Service quality can be stated as:
 
Service Quality= Perception of service- Expectation of service
 
Service quality is rendered as low, when perception of service is lower compared to expectation and on contrary, service quality is rendered  as high, when perception exceed the expectations
 

Corona Virus infection has severely impacted  human life with a heavy bearing on socioeconomic life and bringing the world to the brink of a deep recession. At this critical time, government and most of the business organizations have already taken proactive and reactive measures, so that their critical operations continue unabated, while simultaneously protecting the health and safety of their employees and customers.
In terms of employee’s perspective, there are various changes and challenges ahead and adaptive responses that are needed-
- There is wide speculation worldwide regarding  job losses, salary cut, bonus omission etc. These looming uncertainties are seriously impacting the employee’s performance in this uncertain time.
-  While majority of the employees are being given task of  work from home option, most are not aptly trained for that. IT departments of the companies might be training employees, but not are all  tech savvy  and it will take lots of effort and time, before they get fully adapted.
-The biggest responsibility of employees is to maintain the critical tasks of the organizations, remain safe during this crisis by maintaining social distance and not going to offices, unless and until it is deemed to be dire urgent by the employers. Even if the employees need to go to offices occasionally, they must take all safety precautions, so that  they do not get infected, nor do they spread infection to others.
-   Each organization has different corporate culture and  human resource polices, so its important to stay tuned to corporate announcements, align with human resource department.
-   All the critical tasks, including attending crucial meetings by various online platforms, submission of projects, meeting the important deadlines are utmost necessary
-  Marketing and sales personals  must tune in  to complete digital marketing, so that marketing operation can continue unabated.
-  Last but not the least, in spite of best efforts by employers to conserve jobs and continue the operations, there may be hard realities and bad financial impacts are very likely, so employees should start looking for other likely sources of income, in case their organization is badly hit.
 
 
 

Mistake proofing (Poka Yoke) is based on detecting and solving the problems, as close to the source as possible, rather than identifying and solving when the product  or service has already been through the customers. So the products are designed in ways such  that auto detection happens at different stages and without manual or auto correction the production or the process gets halted, thereby ensuring that there are no flaws or defects.   
 
Mistake proofing is based on these six principles, based on order of priority in addressing issues
Elimination:  Eliminating the  errors by redesigning of  part of the product or process.
Replacement: Use of automation or robotics in place of manual process to prevent errors
Prevention: Using features, that prevent  wrong processes, e.g- connectors to avoid misconnecting wire or cables in electrical assembly.
Facilitation: Specific methods such as visual controls, that includes use of color coding, labelling on parts to facilitate execution of right process.  
Detection:  Using sensors or detection alarms, whenever wrong parts have been assembled or wrong processe has been executed.
Mitigation:  Reducing the impact of  errors, when they are discovered e.g- Fuses to prevent overloading in short circuits.
 
 
Mistake-proofing opportunities and their potential control actions  can be prioritized by failure modes and effects analysis (FMEA) of process and design. Mistake-proofing techniques need to be  developed for every  step in  both manufacturing and  service industry.
 
Visual control  methods can be used as a part of mistake proofing in facilitation methods. But the basic difference lies in the fact that visual control mechanisms do not  make the process completely mistake free, rather by providing visual inputs of critical processes and statistics, there  is   marked increase in  efficiency and improvement  of the process and chances of mistakes is diminished.  Where as mistake proof methods by definition should make the product or service process mistake free.
 
Common uses of visual controls, where they are preferred over complete mistake proofing methods are: 
-Commonly  Used with 5S methods to create overall standardization and a part of continuous improvement process.
- Kanban boards for inventory controls and Heijunka boxes for production scheduling.
-Large scale communication boards for easy display of quality indicators, so that any possible deviation is  met with serious contemplation and prompt corrective actions.
-Easy display of shop floor  schedules, performance measurements, communication and feedbacks mechanisms involving supervisor and workers. Daily work flow in a car service station is a good example of this.
-Visual display of prescription pills, so that it is difficult to  miss any dosage and mistakenly take wrong pills.
-For the safety and warning signs, e.g- stop signs, handicap parking signs, no smoking signs, color coding of bins for collecting different categories of biological wastes etc
-Control  and maintenance of tools and equipments, displaying their maintenance status and the  service dates, calibration dates etc.
-Quality management in various organizations by display of quality charts, cause and effect diagrams etc.

General Electronics(GE) adapted the “Work out” theory of Jack Welch, who was CEO and Chairman of GE. Work out is a  kind of team based problem solving and employee empowerment process. Work-Out  proved to be revolutionary in GE success  and the result of this process was the Change Acceleration Process, commonly referred to within GE simply as “CAP.” 
Change Acceleration Process (CAP) is the process of significantly improving the current state of any process/product by  accelerating the transition steps. 

ARMI (Approver, Resource, Member, Interested Party) is an integral tool of CAP.  . ARMI stands for:
A - Approval of team decisions R - Resource of the team, pertaining to the skills and expertise M - Member of team I - Interested Party, who need to keep himself and  others informed during the process ARMI  clarifies the role of each individual in process and resolves any ambiguity in roles and responsibilities. 
 
Other  commonly used  CAP tools are: 
      
-       Critical Success Factor analysis
-       Stakeholder Analysis
 
The effectiveness of the change in CAP is measured by:
 
Effectiveness=quality x Acceptability
 
Quality  and Acceptability denotes the quality and acceptance of technical strategy  respectively. So it implies that apart from technical excellence, the process must be friendly and accepted by the people/employee. 
 
THE CHANGE ACCELERATION PROCESS (CAP) MODEL
1. Leading Change
 Authentic and committed leadership throughout the process is of prime importance.
2. Creating a Shared Need
The need for change  must be well felt throughout the organization and must be  able to overcome the inertia of change.
3. Shaping a Vision:
Clear vision of the organization after the change  initiative, must be well articulated and understood. 
4. Mobilizing Commitment
 Influencing  strategy to mobilize commitment to drive the change.
5. Making change last
The change must last, the gains  and knowledge of pilots, must be integrated with existing processes to achieve a sustainable change.
6. Monitoring process
Measuring the progress of change initiative, benchmarking them  and  developing accountability for any lack of progress.
7. Changing Systems and Structures
The underlying systems and processes, Standard operating procedures(SOPS) must be altered  to accommodate the changes, so that improvement becomes tenable. 
The key advantages of CAP are::
-       Speed to action
-       Simplicity of tools
-        Self confidence to bring innovative and sustainable solution
-        Faster and cost effective
 
Six Sigma Versus Workout:
-Six Sigma projects are based on systemic methodology of reducing defects and making improvement in process through problem statement, statistical analysis, testing hypothesis, implementing and controlling the improvement process. The major limitations of six sigma projects are – time consuming, significant funding and resources may be needed in complex projects, difficulty in  meeting the expectations of non belts (non six sigma  professionals). Work out on the other end is much faster, improvement can be demonstrated at earliest. The managers and employees can quickly implement their ideas in work out  and  improvements are visible very early for internal and external customers. A structured format is made in work out, assigning specific responsibilities to each of the employee in bringing out accelerated change. So the level of engagement of employees is better in work out compared to six sigma process. 
Work out process, if integrated with six sigma process, can significantly add in efficacy of six sigma implementation in companies by  accelerating implementation of solutions of six sigma, improving employee satisfaction and significant savings in financial costs of six sigma process.
 

Bootstrapping is  a kind of sampling technique, which involves random sampling along with replacement of the sample and was  documented by Bradley efron. it is a simple, yet powerful tool for drawing statistical inference without banking on much of the assumptions. Entire sampling distribution can be done just from one sample data and the best thing is that no formula is needed for any statistical inference. it is also applied in other statistical derivations such as confidence interval, regression model and machine learning.
Bootstrapping evaluates the property of a predictor (such as variance), by assessing these properties, when sampling again and again from the distribution. When the observations are coming from independent population, a number of resamples can be constituted  with replacement,  of the observed data set.
 
Bootstrapping  is based on the principle that  representation of a population from sample data(sample→ population) can be further modelled by resampling the  sample data and draw the inference about sample data (resampled→ sample). The error in a sample statistic against the original  population value is unknown, as we are unaware of entire population. As we are aware of the sample taken in,  the quality of representativeness of  resampled data (resampled → sample) to  original sample data can be evaluated by bootstrapping.
 
I.   Confidence Intervals: 
   There are  different tests available to  build confidence intervals:
·       T-Test
·       Two sample t-test
·       Z-test
·       chi-square Test
 
Bootstrapping approach can be substituted in  place of any of these. First, we calculate the mean of the original sample, that is presumed  to be representative of the entire population. By bootstrapping thousands of samples from original sample, means of all the samples  can be obtained . We can plot the sampling mean distribution curve and compute 95% confidence interval of means and evaluate if our original mean is falling in 95% interval. 
II. Hypothesis Testing with bootstrapped data:
After defining null and alternate hypothesis clearly, we can verify  according to 95% confidence interval of means of bootstrapped samples and conclude, if we are rejecting the null hypothesis and go with alternate hypothesis or fail to reject the null  hypothesis. We can also compute P values and also reject or  go with null hypothesis.
 
III. Power calculation:
 
Power and sample size calculations are dependent  mostly on the variance and standard deviation of the statistic of interest. When a small pilot sample is available, bootstrapping can  be done to derive large number of samples and calculation of variance.
 
IV. Assessing the distribution of the statistical data of interest:  To evaluate a theoretical  distribution of a data, when it is unknown and analyse the different parameters arising from this data. Bootstrapping is distribution independent and provides indirect assessment of distribution of the data.
 

Stratified sampling is a  popular statistical method to allocate the population into strata/subgroups.  The stratum should essentially be a representative of a subpopulation of the entire population. Each member of the stratum should be mutually exclusive, should not be included in more than one stratum. After allocating the population into different strata,  simple random or systemic sampling is applied. The purpose of doing stratified sampling is to reduce the sampling error. The weighed mean obtained is much less variable than the arithmetic mean of simple random sample.
Stratified sampling strategies :
1.     Proportionate allocation:  The sampling fraction in each of the stratum  is proportional to that of  proportion of stratum in total population. This kind of sampling is commonly used. Suppose there are three  groups/strata- A,B,C with size of 50, 70, 80 respectively-Total 200 and we have only resources  to study a total  of  60 individuals, so we will have samples- with group A-(50/200)*60- 15 samples, group B- (70/200)*60-21 samples and group C- (80/200)*60-24 samples
2.     Optimum allocation/disproportionate allocation: The sampling  fraction is not proportional to the fraction/size of the stratum in the entire population. Rather  it is proportional to the standard deviation of the distribution of the variable in the stratum. So, largest sample are taken from the stratum with greatest standard deviation or  the variability to obtain the the least possible total sampling variance. The best example is - economical surveys, which fails to form homogenous strata. So optimum allocation is preferred.
 
     Advantages of stratified  sampling:
-    Stratification leads to  more precision and smaller error in estimation, if measurements in strata have lower standard deviation.
-  By stratified sampling, we get estimates of population parameter of different groups.
  Disadvantages of stratified sampling:
-       Analyzing entire  population and allocating  into subgroups may be quite exhaustive and may  not be feasible.
-       Overlapping commonly occurs in few of the characteristics. It may be difficult to place a sample strictly into a subgroup.
 
Common Uses of Stratified Sampling:-
 
1.     Trial of the Pyx: It involves selecting, analyzing and certifying that  minted coins conform to the required standards, in United Kingdom. This procedure has been conducted from twelfth century till date, usually once per calendar year. Coins to be tested are selected  from the regular production of the Royal Mint.  Selection of the coins are done randomly and in a fixed proportion in different groups - example-  for every 5,000 bimetallic coins issued, one is selected , whereas for silver ones, one out of every 150 is chosen. The criteria for assessment  includes:- diameter, chemical composition and weight for each class of coin.
2.     Stratified random sampling can be used,  to assess the student’s grade point averages(GPA) across the nation, taking into the account major and minor subjects opted by the students.
3.   People that  work overtime in profession, taking into account the different types of jobs, males and female subgroups etc. 
4.   Life expectancy  across the world , taking into account regional characteristics, demographic population data including age, sex, ethnicity , lifestyle etc.
5.     Political Surveys.  In political surveys, diversity of population has to be taken into account. Various minority groups of different races, religion are chosen and  number of samples from each group is taken, based on the proportionality to total population.
6.  Stratified sampling is  used as a method of variance reduction in computational statistics, when Monte Carlo methods are used  in estimating population statistics from a known population. 
7.     Water use estimation across the population in a city or town.
8.  Resident travel information in urban cities, for planning of urban transportation
 

The Pascal's triangle is an imaginary triangle in mathematics, that was discovered way back and used by mathematicians of different  countries like India , Iran, China, Germany, Italy  and finally highlighted by French mathematician Blaise Pascal, who is encredited with this phenomenon. It is applicable for binomial distributions and contains binomial coefficients, arranged in triangular array. It finds probability of events  and combination of events.  The sum of  numbers in rows in Pascal triangle is given by 2n. Any probability evaluation, with two equally, independent and no predetermined order can be resolved , using Pascal’s triangle. 
The initial  row of Pascal's triangle is  conventionally designated as  the 0 th row, n=0 at the top. The value of  0 th row is assigned as a  non zero  value and usually assigned as 1.  The entries in each row are  numbered from left  and both extreme ends of a row are assigned values of 1.  Each  value entered in next row is the sum of value  in the above  and  to the left with above and to the right.
 
Example : 

A group of 10 people  needs to be picked  to create a committee of 4 people. We need to figure out the number of possible different committees of size 4 , that can be created from 10 people. While solving this issue, combination of people is important, not the mentioned order of the people. There will  10C4 possible committees. By scrutinizing, 10th row of Pascal's Triangle and selecting  over to the 5th term (As first term is 10C0),  it  will give us the number of possible different committees. 
 
So we can conclude that there will be  210 possible committees of 4 people each, from a group of of 10 people.
 
 

 
 
 
 
Applications of Pascal’s Triangle:
 
-       Algebra and probability
-       Graphic designers
-       Finance
-       Architect
-       Mapping
 
 

Zipf’s  law as proposed by American linguist George Kingsley Zipf, states that frequency of any word in a language relates inversely with the rank of that word in frequency table. Frequency is the number of time, the word is appearing in the sample or a text .
-The most commonly evident word in a corpus suppose has a  frequency f
-The second most appearing word , will have a  frequency around f/2
-The third most appearing word would have frequency nearly  f/3
-The fourth most appearing word would have frequency around f/4
Zipf’s  law can be applied in many other data and rankings such as:
- Poulation ranks of cities in various countries
 -Corporation size
 -Income rankings of richest persons
 -Ranks or number of people watching the same TV channel
  -Temprature trends over recent years
  -Facebook likes of favourite teams
 - Number of citations to papers 
 - Number of hits on web sites
- Copies of books sold in the US
- Telephone calls received
- Magnitude of earthquakes 
- Diameter of moon  craters
-  Intensity of solar flares
-  Intensity of wars
- Net worth of Americans 
 -Frequency of family names
The level of fit between the  data and Zipf’s distribution, can be tested by Kolmogorov-Smirnov test and then it  be compared with the fits to alternative distributions, like  lognormal, exponential distribution.
Zipf’s Law is more or less in compliance   with one of the most widely acclaimed economical and statistical  principles ‘Pareto Principal’. The Zipf distribution is also labelled as  the discrete Pareto distribution,  as it includes primarily the discrete data and deals with   frequency and rankings.
The  Pareto principle states that 20% of the invested input  accounts for 80% of output. 20 % of work-related input yields 80% of the results. Similarly Zipf’s law accounts for the fact that few of the words, only first 20% of words, accounts for 80% frequency of entire corpus.
The probability mass function(pmf) of the Zipf distribution is
 F(x)= C/xs  , C-  Calculated Constant, x=1, 2, 3-------------------n
S- Value of exponent, characterizing the distribution
 

Overall equipment effectiveness (OEE) is one of the most effective and commonly utilized tool in lean  methodology and lean manufacturing. Importance of OEE lies in :
 
1.Identifying the source of inefficiency
2.Quantifying the degree of inefficiency
3. Labelling the quality of goods produced/service rendered
 
OEE combines three factors namely -1) availability 2) performance 3) quality
 
Ideal OEE score is 100%, meaning production of cent percent defect less products/process, at maximum speed possible,  and  with no interruptions/breakdown. The output emerging from Ideal OEE is called as planned output
 
Availability: availability accounts for planned and unplanned breakdowns or interruptions. The unplanned fractions may be due to equipment failure, material shortage, manpower crunches, change over time etc.
 
Availability can be calculated in different ways:-
 
Availability=  Runtime/Planned production time
 
 Where  Runtime =Planned production time- breakdowns
 
 Gross output= Planned output- loss of output due to availability losses
 
Availability Rate(a)= Gross output /planned output
 
Performance: It takes into account the performance loss because of process operating at submaximal or slow speed and also accounts for small stops in between leading to loss of efficiency and sell optimal production.
 
The probable causes of poor performance can be:
 
-       Low quality input material
-       Old equipments with wear and tear
-       Small duration stops, may be due to some minor jams or process congestion(Long duration stops are generally classified under availability domain)
 
Net Output= Gross Output-loss of output due to speed loss
 
Performance rate(p) = Net output/Gross output
 
Quality: Quality accounts for any defects/scraps due to some flaw in any of the process.
 
Valuable output= Net Output-loss of output due to scraps/quality issue
 
Quality Rate(q)= Valuable Output/Net Output
 
                                  OEE=a*p*q
 
Six Big Losses: The three types of major losses have been further subdivided to yield 6 types of big losses, that an organization is prone to suffer because of equipment/process related issues.
 
       -Availability loss:
 
1)    Unplanned stops
2)    Planned stops
 
-       Performance loss
 
1)    Slow Cycles
2)    Small stops
 
-       Quality loss
  
1)    Production Rejects/scraps
2)    Startup Rejects/scraps
 
Ways of eliminating/mitigating the six big losses:
 
-       Availability losses:
  
1.    Equipment failure:
 
-       Regular preventive maintenance of the instrument,  with linkage to computerised maintenance system(CMMS)
-       Tracking the downtime
-       Investigating the reasons for each shutdown/downtime
 
2.     Setup and adjustments:
 
-       Batch production  instead of  equipment delivering throughout the day, to minimize the retooling adjustments throughout day.
-       Single minute exchange of die(SMED): switchover/change from one process to another process in production in  a single minute or single digit time frame, less than 10 minutes.
 
-       Performance losses:
      
1.     Idling/Minor stops:
 
-       Prompt  signaling and initiation of action
-       Patterns of performance loss, needs to be analyzed
-       Process standardisation
 
2.    Speed reduction:
 
-       Adequate maintenance of equipment to prevent wear and tear and maintain the speed and efficiency
-     Continuous improvement by regularly looking for the ways to remove waste, inefficiency and  improve operation 
 
-       Quality losses:
 
1.    Production rejects/process  defects:
 
-       Direct inspection of the equipments and materials, providing regular maintenance.
-     If defects become overwhelming and  routinely detected, then change of the equipment remains the best  economically viable option in long run.
 
2.    Reduced yields/start up rejects/scraps:
-    Usually predictable ones, sometimes an inherent part of the process and commonly attributed to setups, changeovers and initial warm up of  equipments.
        Can be  prevented by:
-          Reduced initial production- Production of small  batches at startups, rather than large batches
-       Reduced Variation: maintaining uniform equipment settings, standard and uniform material quality in all batches and stringent quality control.
     
 
 
 
      
 
 
  

Quantile regression methodology is a  method  of regression that incorporates relationships between variables, beyond the mean of data and  is quite useful in evaluating non linear relationship between variables. Quantile regression  is a valuable  alternative to linear- ordinary least squares (OLS) regression and  other related methods, which are based on the concept that there is some kind of association between independent and dependent variables.
 
Quantile regression (QR) was developed as an alternative to mean based regression and largely used in various fields such as financial and risk management, healthcare, tourism etc.. Quantile regression can be widely used because of suitability in nonnominal , longitudinal and data with heterogenous conditional distributions. QR  can tackle outliers much more efficiently compared to  mean-based regression.
 Applications of Quantile regression:
1)    Financial Risk Management:
There are usually a number of variables , that determine  a farm’s equity growth. Mean based regression is not the ideal way to study the interaction of  multiple dependent and independent variables. The  equity analysis of a farm employs quantile regression method to investigative the heterogenicity of different components of equity. Farms adopt multiple strategies for business growth. Many of the strategies are complementary to each other. The important strategies are:
-       Asset Management strategy
-       Financial management strategies
-       Minimising the borrowing cost or interest paid, through refinancing
-       Cost reduction strategies
-By  prudent use of quantile regression, important insight is obtained on ways to use different strategies to  enhance farm net worth/equity and building of ideal portfolio  of investments.
2)  Measure racial and ethnic differences across the distribution of health care expenditures:
 Identification of racial or ethnic differences in health care expenditures is carried out using  multivariate linear regression or quantile regression.  Racial or ethnic differences in health care expenditures are computed, using  a multivariate regression equation of health care spendings, which are  conditional on a number of covariates.  In order to analyse  the difference at the upper and lower ends of the distribution, we use quantile regression models. Log of total health expenditure is used to use the nonlinear data and investigate the multiplicative effects of different predictor variables, in case of  heavy spenders on healthcare.
 
 
3. QR finds  lots of applications in health  and behavior related sciences. Some of the examples are- evaluation of effect of physical activity, dietary intake, on different quantile level of variables such as- Body mass index(BMI), waste circumference, socioeconomic status, , various health related scores and biomarker data. QR can also be used  to evaluate and improve various behavioral interventions and sustaining the behavioral change, by separately implementing measures in  two extreme ends of population distribution. Many similar applications are there, including  various determinants of weight in obese versus only  slightly overweight,  various dietary predictors of HbA1c levels among non-diabetics and  Type I or II diabetics, and those with high levels of glucose levels

Fast-tracking and crashing are  important techniques of project management to shorten/compress the project schedule. These techniques, though not commonly used ones, but have important applications in project management. 
 
-There may be various business reasons to do them,  when the project is already running late due to various unexpected conditions like  crunch of manpower and other  resources . They may be also mooted in  the event of  internal and external pressure from various stakeholders of the organization to expedite the project and finish before deadline.
 
FAST TRACKING
-Fast tracking is a technique that is usually the first line of action, when project compression is warranted. It encompasses doing of multiple activities in parallel, even though there may be some degree of  finish to start dependencies of both the activities. 
 
-One of the best examples is -starting to work on product development, when product design is not complete, but a part of product design is accomplished. Whereas earlier plan was to initiate product development at the end of complete product design.
Other relevant examples are:
-Start laying foundation of construction, even if architectural drawings/designs are not completely done.
-  Constructing a different portion of highway simultaneously with another initiated portion
- We  need to analyze the dependencies of activities, if they are really mandatory or   just  presumed.  If it is only discretionary, then we can manipulate the schedule of activities, so that overall time gets shortened. For  an example – Activity 1 and Activity 2 have essential  finish-to-start dependencies, with length of each activity being 5 days and the total duration  being ten days. Let us assume that project manager gets a deadline from project sponsor to finish the project in 8 days. If we start  the second activity by the end of 3rd day or beginning of 4 th day, we will be able to cut short  2 days and finish both the activities in 8 days 
 

 
 
The biggest drawbacks of fast tracking are:
-       Cannot be done, when there is complete interdependency or finish to start relationships of processes .
-       Leads to rework, project extension and project failure many a times.
 
-A  general rule that applies in fast tracking is that ,the second activity can be started when the first activity is at least 2/3rd or around 66% complete. This usually fits well and is commonly practiced.
 
CRASHING
Crashing is a technique, which entails use of additional resources, e.g.- overtime, manpower, additional material and equipment. The motto is to finish the activities or reach the project  deadline at earliest, well ahead of the anticipated or projected deadline.
 
-Crashing works very well  in certain scenarios like construction industry- more workers finish the task earlier compared to a smaller number of workers.  The best example of crashing was seen in Year 2000-Y2K- projects, when many of the companies accelerated the project to meet the deadline of completion of projects by the end of 1999.
 
-The biggest issue with crashing is that, it increases financial burden. So the cost vs time tradeoffs have to be carefully decided, when deciding for crashing.
 
- Also crashing is usually never the first choice, it is usually carried out, when fast tracking does not yield the desired result.  
 
-Crashing also can lead to waste of resources,  especially with more of manpower leading to more confusion and errors. For example in complex neurosurgeries, which goes on for many hours and If we try to add more surgeons in team to shorten surgery time, it may rather lead to more complications due to difference in opinion, difference in skills of surgeons and lack of coordination.
 
-Both Fast track and crashing needs to be implemented only in critical path  activities. If we employ them on non-critical path activities, there will be no shortening of duration.

Mean (Average)
Mean  is the best measure of central tendency in normally distributed data without significant outliers. As  large number of  distributions are symmetrical, mean  represents the  true estimate of distribution. Example: Mean height, weight etc.
 
Mode
Mode is  the most repeated value in a set of data. Like,  people become more inclined to things, that are undertaken by majority of the people. 
Median
The median is the  mid value that divides  a set of values into top and bottom 50%.  
 The income distribution in a country  is asymmetrical, with 20% of population, accounting for  major proportion of wealth in the country and remaining 80% of the people  have lower income, in the way that wealth of top 20% is equal to bottom 80%.
In this case, mean income will give a false and biased picture, due to distribution peaks in two different regions. Median will  be the best representer of  the  income of the people in the country.
 
In India, many educational institutes place their advertisements to attract students by stating the ''placement packages'' of their passing students either due to on campus selection attract students using “placement packages”.  In this example, average placement package, which is commonly quoted, is a wrong way of assessing the students. Rather, median serves as the best measure, as the salary range is quite wide, for example  for those selected for  India location -20 students(salary up to 3 million INR)  and  those  for USA location-5 students (Salary in range of 8 to 10 million INR after conversion  of USD to INR)
 
Median also finds use in measurement of commonly measured  health indices such as  blood pressure. If we measure the blood pressure of 5000 persons in a community health survey and tabulate the  systolic and diastolic pressures separately, mean will give an  erroneous  impression,  as 10-15%. of the patients may have very  high systolic and diastolic blood pressures, much above the normal reference range( say, Systolic> 200mm of Hg and Diastolic> 140mm of Hg , which is not represented by majority) . Here, median will be the best measure for blood pressure levels of the community people and can be used to initiate  health intervention for the community.
 
 
 

Historically this phenomenon was  proposed in  In 1985 to describe the 'stage changes'  of cancer patients by changing the criteria of stage-specific prognosis, even though there was no change in  outcome of individual patients. Will Roger phenomenon finds much application in medical field. In oncology,  sensitive tools allowed detection of cancer metastases much earlier,  leading to categorization of more number of patients into more severe metastatic disease stage . Such a  radical change in staging of the patient 's stage resulted in an improved  prognosis  of patients and higher 5 year survival  in both the less and the more severe disease stages.
 
In  terms of business and commercial aspect also it may be used in various aspects to improve the process efficiency in terms of running average. The best examples are:
- In industry  to increase the productivity of two different shifts, morning and evening, the workers with moderate efficacy, lying in between the mean of the efficacy of two shifts may be shifted from one shift to another, thus both the batches have better yield.
- Reallocation of budget and resources from one to another potential project in a company, may result in better  Return of Investent(ROI) for both the projects, by reducing the investment of the project with stronger expected output/yield. Whereas the weaker project, gets more funding and resources and get benefited tremendously by yielding  much higher gain, though at the cost of mild to moderate increased investment.