Sanuja Godaarawa

Decision Intelligence (DI) is a field that combines analytics artificial, intelligence and automation to improve decision making across organization. This aims to take decision process more effective by providing data integrity, AI & machine learning models, focusing human understanding, optimization to the cause of action. This is used in many industries such as manufacturing, public sector, retail and etc.
 
Descriptive analytics focus on what has happened in the past.
•              We can find the trend such as peak months of the last years.
 
Eg: November and December sales were x% higher than in other months.
 
This helps to identify the higher sales in the w inter months due to the holiday season but it doesn’t predict what will happen next.
 
Diagnostic analytics focus on past data and helps to understand why something happens.
•              Company can go deeper into why those trends occurred.
 
Eg: there was a sales peak in last year December due to increase of marketing campaigns and promotions.
 
This helps the company to understand the reasons behind the successful sales but it doesn’t give insight about the future.
 
Predictive analytics forecast future outcomes by using historical data.
·         Company can forecast what will happen in upcoming months
 
Eg: Based on the historical trends the company predict that the sales will increase by X% in December this year
 
For the decision intelligence this will provide the deeper insight since it helps company to forecast future outcomes and plan accordingly.
 
Prescriptive analytics providing the recommendations based on the predictive data.  
·         Company can decide the best action for future.
 
Eg: To achieve predicted X% of sales increase we recommend ramping up inventory for the most popular items start campaign in end of October increase staff at stoles and etc.
 
This doesn’t predict what will happen but provide the best recommendations based on the prediction.
 
When comparing the above types of analytics 'predictive analytics' contributes the most to the decision intelligence since it provides what will happen in future (eg: it will provide the forecast of future sales). Therefore, this helps the company to data driven decisions such as budgeting planning and etc.

Lean six sigma is a process improvement methodology which used to improve the performance by systematically removing operational waste and reducing process variation. It combines Six Sigma methods and tools with the Lean manufacturing. 
ISO 9001 is a quality management system which helps organizations to ensure that they meet customer and other stakeholder needs within the statutory and regulatory requirements related to a product or service. 
Both ISO 9001 and Six Sigma use a process approach in applying their methodologies and below are some main differences of ISO 9001 and Lean Six Sigma, 
   
ISO 9001 
Lean Six Sigma 
Focus 
Focuses on Establish Quality Management System 
Focuses on process improvement and waste reduction 
Approach 
ISO 9001 is more prescriptive and process-oriented 
Lean Six Sigma is more flexible and data-driven. 
Scope 
Scope is broad.  
Focusing overall quality management system of an organization 
Scope is Narrow.  
Targeting specific processes for improvement 
Outcome 
Primarily enhances customer satisfaction and compliance 
Directly impacts operational efficiency, defect rates, and cost savings 
 
Even though ISO 9001 and Lean Six Sigma are have some similar advantages and disadvantages, the way it creates differ from each other, 
Advantages:  
   
ISO 9001 
Lean Six Sigma 
Customer satisfaction 
Enhances through consistent quality 
Directly improves through defect reduction 
Efficiency 
Standardizes processes for consistency 
Streamlines processes for efficiency 
Documentation 
Emphasizes thorough documentation 
Relies on data analysis, less on documentation 
Continuous Improvement 
Regular audits and reviews 
Ongoing projects and initiatives 
Employee Engagement 
Involves employees in quality processes 
Engages employees in problem-solving 
Risk Management 
Focus on compliance and risk assessment 
Reduces variability and defects 
Market Credibility 
Certification enhances reputation 
Demonstrated efficiency boosts credibility 
 
Disadvantages: 
   
ISO 9001 
Lean Six Sigma 
Complexity 
Lead to bureaucratic processes 
Requires significant cultural change 
Cost 
Certification and maintenance can be expensive 
Initial training costs can be high 
Resource and Investments 
Demand resources 
Implementation can require substantial investment 
Flexibility 
Limited adaptability to market changes 
Overemphasis on data can overshadow qualitative insights 
Training Requirements 
Ongoing training needed for compliance 
Extensive training can be time-consuming 
 
There are many situations that the organization can implement both ISO 9001 and Lean Six Sigma. 
Comprehensive Quality Management System  
Continuous improvement is one of the key points of ISO 9001 Quality Management System requirements. By implementing specific improvement projects will help to achieve the comprehensive quality management system within the organization. 
 
Eg: Boeing, a well-known aerospace manufacturer, established a uniform quality management system throughout its business using ISO 9001. This framework helps the business satisfy particular quality standards, guarantee regulatory compliance, and maintain consistency in its processes—all of which improve customer satisfaction. 
In parallel, Boeing uses Lean Six Sigma techniques to pinpoint areas where its assembly and production processes need to be improved. To reduce waste, reduce cycle times, and boost production line efficiency, they have undertaken a number of Lean Six Sigma projects. Because of this integration, the company is able to actively pursue continuous improvement while standardizing procedures, which eventually results in higher-quality products and happier customers. 
 
 
 
Performance Measurement and Improvements 
Organizations aiming to enhance their performance measurement could apply ISO 9001 to set up a systematic approach to quality management, while Lean Six Sigma can provide the tools for data analysis and process improvement. 
 
Eg: Pistone automotive, a supplier of parts to leading automakers. They have used the lean six sigma tools to achieve 100% reduction in defects and ISO 9001's focus on standardization and documentation and this achieved substantial improvements in product quality and customer satisfaction 
 
 
 
New Product or Service Launch 
ISO 9001 provides a framework to establish and document quality standards, procedures, and policies while Lean Six Sigma helps in optimizing product development, reducing defects, and ensuring that production processes are efficient. 
Eg: General Electric Healthcare (GE Healthcare) is a leading provider of medical equipment and digital solutions. GE Healthcare decided to launch a new line of portable ultrasound devices, targeting both developed and emerging markets. The goal was to reduce time-to-market, ensure high-quality standards, and meet diverse regulatory requirements across different regions.GE used six sigma ns lean tools (value stream mapping, waste reduction, DOE) for new product development and ISO 9001 to aligned all new product development processes with its existing ISO 9001 Quality Management System to ensure that each stage adhered to quality standards. 
 
 

1-sample Sign test and the 1-sample Wilcoxon test both are non-parametric test. This is used to compare the median of the sample to determine whether there is statistically difference with a standard value.
 
1-sample Sign test
1-sample Wilcoxon test
Assumptions
Applicable when data are Non – Normal
Applicable when data are Non – Normal
The variable data are continuous.
The variable data are continuous.
Data distribution is non-symmetric and can be left skewed or right skewed.
Data distribution is symmetric.
Observations are independent.
Observations are independent.
Sample Size
More powerful with large sample size since the statistic is followed the binomial distribution and can be used for small sample size.
More powerful with large sample size
Power
Low powerful than 1-sample Wilcoxon test
More powerful than 1-sample sign test since it considers the magnitude of differences.
Outliers
Not sensitive to outliers
More robust against outliers
Limitation
Required paired data for the calculations but always it may not available.
Data taken from random samples from the population hence the correct sample may not capture.
Less powerful and it may not detect the difference between paired data.
 
  
Eg: Manager of the ABC insurance company shows that the median of new life insurance customers per day is 50. The agent of the same insurance company claim that it is more than 50. To analyze whether this is true or wrong we can use 1-Sample sign test and the hypothesis is as follows.
            Ho: median of new life insurance customers per day = 50
            Ha: median of new life insurance customers per day > 50
 
In a term text of grade 10, it is required to check the median marks for the mathematics is greater than 70%. Then we can apply 1-samaple Wilcoxon test by selecting few marks randomly,
            Ho: The population Median value = 70%
            Ha: The population Median value >70%