Sandip Mittra

Quality is key to success. Most of us try to measure the quality of the final product which goes to the customer and improve them. However, we fail to realize that quality is important in each step and everything a company does. It can be accounts, procurement, delivery, sales and all other processes. There are many theories, methods and tool provided by several scholars on how to improve quality. Out of all the tools and theories, we are going to understand 14 principles laid down by Dr. Deming and by Philip Crosby.
 
Deming’s 14 Points for Total Quality Management (TQM)
Deming in his book titled “Out of Crisis” has explained about his philosophy in details. This was mainly focused to manufacturing industries but can be used in any business.
 
Crosby's 14 Steps To Improvement
Philip Crosby on other hand provided 14-step quality improvement program which is more focused on how the employee understands what quality means & partner the quality movement.
 
Below are the 14 steps or Points for Quality Improvement
Sr No
Crosby's 14 Steps
Deming’s 14 Points
1
Management Commitment
Create a Constant Purpose Toward Improvement
2
Quality Improvement Team
Adopt the New Philosophy
3
Quality Measurement
Stop Depending on Inspections
4
Cost of Quality Evaluation
Use a Single Supplier for Any One Item
5
Quality Awareness
Improve Constantly and Forever
6
Corrective Action
Use Training on the Job
7
Plan for Zero Defects Program
Implement Leadership
8
Supervisor Training
Eliminate Fear
9
Zero Defects Day
Break Down Barriers Between Departments
10
Goal Setting
Get Rid of Unclear Slogans
11
Error Cause Removal
Eliminate Management by Objectives
12
Recognition
Remove Barriers to Pride of Workmanship
13
Quality Council
Implement Education and Self-Improvement
14
Do It Over Again
Make "Transformation" Everyone's Job
 
Though both Deming and Crosby focused on improving quality, there are some similarities and differences in their thoughts and approach.
 
Similarities:
· Management commitment is important
· Continuous improvement
· Customer focused
· Supervisor or manager responsibilities
· Quality improves profit
· Radical change in value
 
Differences:
· Roadmap
o   Deming: - No start and end point
o   Crosby: - Have start and end point
· Workers
o   Deming: - Variance is not largely affected by workers
o   Crosby: - Workers need to be motivated to produce zero defects
· Management
o   Deming: - Major threat to managers
o   Crosby: - Not many changes in the thoughts of a manager and worker
· Implementation
o   Deming: - Difficult to implement as it requires huge structural change
o   Crosby: - Comparatively easy to implement
 
Conclusion:
In today’s world almost all the organization is taking up the best characteristics of these steps and framing their own structure which suits their business requirement. However, if Deming’s steps are implemented then the organization can have the best impact.

Chi-Square Test
The test which helps us to measure the differences between the observed and expected value according to an assumed hypothesis is called Chi-Square Test. It was developed by Karl Pearson in 1900 and is the most important test amongst the several test of significance. It is a non-parametric test which is not based on any assumption or distribution of any variable
 
There are 3 applications of Chi-Square Tests
1.       Goodness of fit
2.       Test for independence
3.       Test of homogeneity
 
Goodness of Fit
The Chi-square goodness of fit test is used to compare a randomly collected sample containing a single categorical variable to a larger population. For example – If we want to test if 70% of ladies take medical, then we must use Goodness of Fit.
This is measured using the below formula.
 

Where:
O = Observed Value
E = Expected value
If X2 (Calculated) > X2 (Tabulated) with (n-1) degree of freedom, then null hypothesis is rejected otherwise accepted.
 
Let’s try to understand this with an example.
 
In a medical college of 1000 students, there are 650 female students. Does it follow the theory that 70% of female students take medical?
 
  Observed
Expected
Male
350
300
Female
650
700
Total
1000
1000
 
The hypotheses for a Chi-square test of independence are as follows:
 
Null Hypothesis (HO): The collected data is consistent with the population distribution.
Alternative Hypothesis (HA): The collected data is not consistent with the population distribution.
 
  Observed
Expected
(O-E)
(O-E)^2
(O-E)^2/E
Male
350
300
50
2500
8.333333
Female
650
700
-50
2500
3.571429
Total
1000
1000
 
 
11.90476
 
X2 (Calculated) = 11.9
X2 (Tabulated) with (n-1) degree of freedom = 3.8
 
Decision: -
If X2 (Calculated) > X2 (Tabulated) with (n-1) degree of freedom, then null hypothesis is rejected otherwise accepted.
 
11.9 > 3.8 and therefore the null hypothesis is rejected
 
Test for independence
 
This test helps us to identify if there is an association between two categorical variables within the same population.
 
Let us understand this with an example:
 
Table with Observed Value
Qualification / City or Village
Middle school
High School
Bachelors
Masters
PHD
Total
City
18
36
21
9
21
105
Village
12
36
45
36
6
135
Total
30
72
66
45
27
240
 
The hypotheses for a Chi-square test of independence are as follows:
 
Null Hypothesis (HO): There is no association between the qualification and the places the students come from.
Alternative Hypothesis (HA): There is association between the qualification and the places the students come from
 
Table with Expected Value
Qualification / City or Village
Middle school
High School
Bachelors
Masters
PHD
City
(105x30)/240 = 13
(105x72)/240 = 32
29
20
12
Village
17
41
37
25
15
 
Observed Value (O)
Expected Value (E)
(O-E)
(O-E)^2
(O-E)^2/E
18
13
5
23.8
1.8
36
32
5
20.3
0.6
21
29
-8
62.0
2.1
9
20
-11
114.2
5.8
21
12
9
84.4
7.1
12
17
-5
23.8
1.4
36
41
-5
20.3
0.5
45
37
8
62.0
1.7
36
25
11
114.2
4.5
6
15
-9
84.4
5.6
        31.2
 
Degree of freedom         = (Column -1) x (Row-1)
                                           = (5-1) x (2-1)
                                           =4 x 1 = 4
X2 (Calculated) =31.2
X2 (Tabulated) with 4 degree of freedom = 9.48
 
Decision: -
 
If X2 (Calculated) > X2 (Tabulated) then null hypothesis is rejected otherwise accepted.
 
31.2 > 9.48 and therefore the null hypothesis is rejected
 
Test of homogeneity
 
We perform this test to confirm if the event is following uniformity or not. The basic difference from Test for independence is for two categorical variables within the same population and Test of Homogeneity is for single categorical variable within different population.
 
Let us see if the TV watching pattern of males and females are same.
 
The hypotheses for a Chi-square test of homogeneity are as follows:
Null Hypothesis (HO): The distribution of watching pattern of TV for Males and females is same.
Alternative Hypothesis (HA): The distribution of watching pattern of TV for Males and females is not same.
 
Table with Observed Value
Qualification / City or Village
Movies
Sports
Serials
Other
Total
Male
72
84
49
45
250
Female
91
86
88
35
300
Total
163
170
137
80
550
 
Table with Expected Value
Qualification / City or Village
Movies
Sports
Serials
Other
Male
74
77
62
36
Female
89
93
75
44
 
Observed Value (O)
Expected Value (E)
(O-E)
(O-E)^2
(O-E)^2/E
72
74
-2
4.4
0.1
84
77
7
45.3
0.6
49
62
-13
176.2
2.8
45
36
9
74.6
2.1
91
89
2
4.4
0.0
86
93
-7
45.3
0.5
88
75
13
176.2
2.4
35
44
-9
74.6
1.7
        10.1
Degree of freedom         = (Column -1)
                                           = 4-1=3
X2 (Calculated) =10.1
X2 (Tabulated) with 3 degree of freedom = 7.8
 
Decision: -
If X2 (Calculated) > X2 (Tabulated) then null hypothesis is rejected otherwise accepted.
 
10.1 > 7.8 and therefore the null hypothesis is rejected. The watching pattern of TV is not same between males and females.
 
 
 
 

Time Series data is very common in most of the projects in Business Excellence. There are lot of analytics performed using this data. The most common is Time Series Regression. This helps us to understand and predict the behavior of the data. Most of the organization uses time series data to understand the underlining causes of trends. They also use data visualization tools to analyze the seasonal trends and to go in depth to understand the reason behind the trends.
 
We can use this technique to Forecast
·       Volume
·       Customer Satisfaction
·       Staff Turnover
·       Online hits, and so on
 
Any time series can contain some or all the following components:
Type
Description
Example
Trend
Describes movement along the term. Describes the pattern with long term increase or decrease in data
Mathematical finance, weather forecasting, earthquake prediction, and so on
Cyclical
Describes Seasonal changes or pattern due to the calendar
Sale of a commodity, Height of tides, etc.
Seasonal
Describe Periodical but not seasonal. The data exhibits rise and falls that are not of fixed period.
Temperature, Rainfall, etc
 
In most of the cases both seasonal and cyclic looks similar. Let us understand the difference.
Seasonal
Cyclic
Constant Length
Variable Length
Average length is shorter
Average length is longer
Magnitude is less
Magnitude is more
 
Since the peaks and troughs are not predictable in Cyclic, this time series is difficult to use.