Asangi

While best practices are well-documented and accessible, only a few companies achieve world-class excellence by implementing them effectively. Here’s why:
 
1.      Lack of continuous culture .
Many companies focus only one time implementations rather than treating best practices as ongoing journey. This is evident in the six sigma Competency model, where the consciously competent level is considered as a dangerous phase. If organization do not sustain their six sigma journey through continuous improvement, regular training and consistent applications, they will never reach the core competency level. While many companies recognix=ze the need for six sigmas and its benefits they often fail to set in to their culture. This Lack of sustained effort is a major reason why only a few companies achieve long tern success.
 

     2.Resistance to Change
     Employees and leadership often resist new methods due to fear, comfort with the status quo, or lack of         understanding. below are some examples 
Employees are comfortable with their current way of working. Any new process or system creates uncertainty, making them feel anxious about their role and future. Most of employees has common mind set "We've always done it this way" People prefer stability and may not see the need for change Employees may not understand the benefit of the new implementations. If leadership fails to explain the reason behind the change employees might think it as a extra work for them If previous change initiatives they might not believe the new changes Employees may feel they don’t have the time, tools, or training to adopt the new methods.

3.poor execution  Every industry has distinct processes and products, making it essential to customize implementations according to each specific aspect. Simply copying and pasting the same implementation process from one industry to another can lead to failure. Therefore, customization is crucial.
It is important to utilize standard world-class best practices, but these should be adapted to fit your unique business processes. By doing so, organizations can maximize the effectiveness of their implementations and achieve better results     4.Lack of leadership Commitment
Leaders play a key role in sustaining the implementation of best practices. Frequent changes in higher management, accompanied by varying directions, can hinder long-term success. That’s why training for upper management is crucial before any implementation begins. When leaders believe in and support the initiatives, the chances of success significantly increase. It is essential for them to collaborate and contribute actively to maintain these practices over the long term. 5.Inadequate Training 
Employees need proper training to implement them effectively. 6.Failure to Measure & Sustain Improvements
Many Companies do not track the implantations impact with their KPIs If you do not track the impact of the changes that have been implemented, those changes will not sustain over the long term.
7.Qucik Wins
 companies often seek for quick wins and expect immediate outcome from best practices. World class companies do not simply use best practices to achieve success. they set it that top their culture. employees are well educated about the processes and regular trainings ensures that these practices become habitual. As a result, these companies have succeeded in sustaining their excellence

Q 723. What is Non-parametric Analysis? In which type of industries is it mostly used? Highlight its advantages using some examples. 
 
 
Non parametric analysis is a type of statistical method that doesn’t rely on strict assumptions about data. This Data more flexible and work well for :
·        Ordinal data
·        Nominal data
·        Small sample sizes
·        Skewed data or outliers
 
Industries Where Non-Parametric Analysis is Used and examples
Industry
Reason of using non-parametric Analysis
Example
Test mainly used
Health care and pharmaceutical
 
Can experience lot of non-normal data, small sample sizes, and ordinal variables.
-Analyzing patient recovery times under different treatment.
-The effectiveness of two drugs.
-Patients satisfaction analysis.
- Comparing adverse drug reaction among patients using 3 medications
Kruskal-Wallis H Test
 
 
Mann-Whitney U Test).
 
Wilcoxon Signed-Rank Test
 
Chi square test
Retail and consumer behavior
 can handle diverse data types, such as customer preferences, purchasing habits, and survey responses.
- Customer preference by by age group
- Sales performance comparison
 
- Discount strategy evaluation
 
-Loyalty program impact
 
-seasonal sales trend
 
- correlation between spend
 
   
 
Chi-Square Test
 
 
Kruskal-Wallis Test
 
 
Mann-Whitney U Test
 
 
Wilcoxon Signed-Rank Test
 
Friedman Test
 
Spearman’s Rank Correlation
 
 
 
 
Insurance or investment companies
 
 
deal with often non-normal, skewed, or ordinal. Non-parametric methods are ideal for analyzing claims, customer behaviors, risk assessments, and portfolio performance in these sectors. 
 
 
 
 
 
- Comparing claim processing times
- Customer satisfaction pre/post-policy change
 
- Investment preferences by age group or employee’s categories
 
-Portfolio performance by advisors
- Correlation between premium and claims
 
 
 
 
 
Kruskal-Wallis Test
 
Wilcoxon Signed-Rank Test
 
 
Chi square test
 
 
 
Mann-Whitney U Test
 
Spearman’s Rank Correlation
Hotel industry
The hotel industry often deals with complex non normally distributed data, ex: customer reviews /satisfaction
 
-Analyzing guest satisfaction across multiple branches
-Comparing room service ratings across branches
- Measuring the impact of a new loyalty program
- Analyzing guest preferences for room types
- Evaluating staff performance pre/post training
Kruskal-Wallis Test
 
 
 
 
 
Wilcoxon Signed-Rank
 
 
Chi-square Test
 
 
 
 
Sign Test
Education
In Education always use in ordinal data
-Evaluating teaching methods
-comparing student performance across school
-Comparing academic stress level
 
Kruskal-Wallis Test
Mann-Whitney U Test
   
Spearman’s Rank Correlation
 
 
Non-parametric analysis is a flexible and reliable method for analyzing data, especially when traditional techniques don’t work well. It’s ideal for uneven data, small samples, or ranked information. This approach is used in many industries to uncover valuable insights, even from irregular datasets.