Thanks for visiting this blog. Please share information about this blog among your friends interested in ISO 9001:2015 QMS Awareness.
- Keshav Ram Singhal
Blog on 'Quality Concepts and ISO 9001: 2008 Awareness' at http://iso9001-2008awareness.blogspot.in

Academic comments are invited. Please join this site. Reproduction of articles from this blog is encouraged, provided prior information is provided. Please give credit to the blog and the writer, and also send a copy of the published material to the editor of the blog.

Various information, quotes, data, figures used in this blog are the result of collection from various sources, such as newspapers, books, magazines, websites, authors, speakers, information from google search, ChatGPT (a large language model trained by OpenAI) etc. Unfortunately, sources are not always noted. The editor of this blog thanks all such sources.

Encouragement Support - Please become a member of NCQM - National Centre for Quality Management

People from following (more than 90) countries/economies have visited this blog: Albania, Algeria, Argentina, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Belgium, Bosnia and Herzegovina, Brazil, Bulgaria, Burundi, Cambodia, Canada, Chile, China, Colombia, Croatia, Denmark, Ecuador, Egypt, Estonia, Ethiopia, European Union, Finland, France, Georgia, Germany, Gibraltar, Greece, Hong Kong, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Kenya, Luxembourg, Lebenon, Macedonia, Malawi, Malaysia, Malta, Mauritius, Mexico, Moldova, Monaco, Morocco, Myanmar, Namibia, Nepal, Netherlands, Nigeria, Oman, Pakistan, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Saudi Arabia, Serbia, Seychelles, Singapore, Slovakia, Slovenia, South Africa, South Korea, Spain, Sri Lanka, Sudan, Sweden, Taiwan, Tanzania, Thailand, Trinidad and Tobago, Tunisia, Turkey, Uganda, Ukraine, United Kingdom, United Arab Emirates, United States, Venezuela, Vietnam, Yemen, Zimbabwe.

Saturday, February 10, 2024

Processes are like rivers

Processes are like rivers


Yes, processes are like rivers. Rivers flow through landscape. Processes flow through an organization, carrying various elements toward their destination, transforming inputs into outputs. They may encounter obstacles, change course, and evolve over time. Just as a river shapes the landscape it flows through; processes shape the functioning of an organization. Processes have sources, destinations, and can encounter obstacles or merge with other processes, like one river merges with another river.


Courtesy - Image Created with the help of NightCafe AI tool.

For example, in an organization after design finalization, the product design process merges with the manufacturing process of the organization. Outputs of the product design process, design specifications, are translated into instructions for production, ensuring the final product meets design specifications. Processes, like rivers, can vary in speed, size, and direction.


Courtesy - Image Created with the help of NightCafe AI tool.

The phrase "Processes are like rivers" is a commonly used analogy to describe the dynamic and flowing nature of processes within organizations. It's a metaphorical statement often used in business and management contexts to illustrate how processes move, adapt, and shape the functioning of an organization.


Kind regards,

Keshav Ram Singhal

Wednesday, January 31, 2024

Various Applications of Artificial Intelligence in the Corporate Sector and Specific AI Technologies Useful for Businesses and Organizations

Various Applications of Artificial Intelligence in the Corporate Sector


Artificial Intelligent (AI) has a wide range of applications in the corporate sector transforming the operations and decision-making processes of organizations. AI business applications include automating repetitive tasks, enhancing efficiency, and coming up with valuable insights from data analysis. It can also take charge of tasks in various fields such as customer service, marketing, finance, and operations. Manufacturing organizations use AI to analyse sensor data and predict breakdowns and accidents. Artificial intelligence systems aid production facilities in determining the likelihood of future failures in operational machinery, allowing for preventative maintenance and repairs to be scheduled in advance. Some of the AI applications used in the corporate sector are:


1.     Data Analysis and Insights – AI tools, like Tableau and Power BI, are used for advanced data analysis, helping organizations derive meaningful insights from large datasets for better decision making.


2.     Customer Relationship Management (CRM) – AI tools enhances CRM systems of organizations by providing personalized customer experiences, predicting customer needs and automating customer interactions.


3.     Chatbot and Virtual Assistants – AI-powered chatbots and virtual assistants (generally we see on websites) streamline customer support, handling routine queries and providing instant response.


4.     Predictive analysis - AI applications using AI algorithms predict future trends and outcomes based on historic data that help organizations in strategic planning and decision making.


5.     Supply Chain Optimization – AI optimizes supply chain processes by predicting demand, managing inventory, and improving logistics.


6.     Fraud Detection and Security – AI detects unusual patterns and anomalies in financial transactions, enhancing fraud detection and cyber security. Mastercard’s AI-based fraud detection system analyses transaction patterns to identify potentially fraudulent activities.


7.     Human Resources Management (HRM) – AI automates HR processes, including recruitment, employee onboarding, performance evaluation etc.


Courtesy - Image Created with the help of AI tool.

Specific AI Technologies Useful for Businesses and Organizations


Several AI technologies are invaluable for businesses and organizations, empowering them in various ways. Here are some of the key technologies:


1.     Machine Learning (ML) – ML algorithms enable systems to learn from data and make predictions or decisions.


2.     Natural Language Processing (NLP) – NLP helps computers understand, interpret, and generate human language.


3.     Computer Vision – Computer vision enables machines to interpret and make decisions based on visual data.


4.     Speech Recognition – AI understands and transcribes spoken language, facilitating voice-controlled systems.


5.     Reinforcement Learning (RL) – Reinforcement learning involves training models through trial and error to make sequences of decisions.


6.     Robotic Process Automation (RPA) – Robotic process automation automates rule-based tasks by mimicking human interactions with digital systems. An applicable example is automation of routine data entry tasks in finance or data processing.


7.     AI-powered Cybersecurity – AI enhances cybersecurity by identifying and responding to security threats in real-time. Many organizations use AI to detect and respond to cyber threats using machine learning algorithms.


Above applications and technologies showcase the diverse ways in which artificial intelligence can be leveraged in the corporate world bringing efficiency, innovation, and strategic advantages to organizations.


Best wishes,

Keshav Ram Singhal


Courtesy – AI ChatGPT, Bard Google, Google, Microsoft Bing, AI Image Creator NightCafe

Tuesday, January 30, 2024

Brief introduction of Artificial Intelligence and its Historical Journey

Brief introduction of Artificial Intelligence

AI = Artificial intelligence 

Artificial intelligence (AI) refers to the simulation or approximation of human intelligence in machines. AI is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. It is the theory and development of computer systems capable of performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.AI has a profound impact on organizations across various sectors, enhancing decision-making processes, automating routine tasks, and facilitating efficiency gains. 


Courtesy - Image Created with the help of AI tool.

Machine learning, a subset of AI, empowers systems to learn and improve from experience without explicit programming. Neural networks, integral to artificial intelligence, are computational models inspired by the structure and functioning of the human brain. Designed to perform tasks involving pattern recognition, decision-making, and learning from data, neural networks play a fundamental role in the field of machine learning. They utilize hidden layers to capture complex patterns, contributing to their adaptability and success in a wide range of applications.  


Historical journey of AI


Artificial intelligent (AI) has a rich history that dates back to the mid-20th century. Some key milestones in the journey of AI development include:


-        In 1943, Warren McCulloch and Walter Pitts proposed a model of artificial neurons, marking the earliest recognized work in AI.


-        Alan Turing, an English mathematician, pioneered machine learning in 1950, introducing the Turing test as measure of a machine’s ability to exhibit intelligent behaviour comparable to human intelligence. 


-        In 1955, Allen Newell and Herbert A. Simon created the first AI programme, the Logic Theorist, proving many mathematical theorems.


-        The term “Artificial Intelligence” (AI) was officially coined by American computer scientist John McCarthy in 1956 during the Dartmouth Conference.


-        In 1966, Joseph Weizenbaum developed the first chatbot, Eliza.


-        The first intelligent humanoid robot, Wabot-1, was built in Japan in 1972.


-        In 1997, IBM’s Deep Blue became the first computer to defeat a world chess champion, Gary Kasparov.


-        AI entered homes in 2002 with the introduction of Roomba, a robotic vacuum cleaner.


-        By 2006, AI started making its mark in the business world, with many companies integrating AI into their operations.


-        IBM’s Watson demonstrated natural language understanding in 2011.


-        Google launched the Android app “Google now” in 2012, providing predictive information to users.


-        In 2014, Chatbot Eugene Goostman won a Tring Test competition.


-        IBM’s Project Debater, capable of debating on complex topics, showcased AI capabilities in 2018.


-        Google’s AI programme Duplex, a virtual assistant, demonstrated human-like interactions in 2018.


AI has evolved significantly, with concepts like deep learning, big data, and data science gaining prominence. Many companies are actively working with AI, creating remarkable devices. The future of AI looks promising, with the potential for highly intelligent machines replacing various human tasks.


Best wishes,

Keshav Ram Singhal

Courtesy – AI ChatGPT, Bard Google, Google, Microsoft Bing, AI Image Creator NightCafe

Sunday, January 21, 2024

Potential of AI within the Quality Management

Potential of AI within the Quality Management


Artificial intelligence (AI) refers to the simulation or approximation of human intelligence in machines. AI has a profound impact on organizations across various sectors. The goals of artificial intelligence include computer-enhanced learning, reasoning, and perception. AI is being used today across different industries and areas of human life. It enhances decision-making processes, automates routine tasks, and facilitates efficiency gains. AI can be of two types: (1) Generative AI, and (2) Predictive AI.


Courtesy - Image Created with the help of AI tool. 

Let us understand both the terms. Generative AI refers to systems and processes that can create new content, data or outputs that mimic human-like patterns. This is possible because AI systems and processes are trained on large datasets and learn to generate outputs that look real. A notable example of generative AI is OpenAI’s ChatGPT models. These generate human-like realistic text, images, or music based on the input it gets. On the other hand, predictive AI involves using algorithms and models to make predictions or forecasts based on the historic data provided. Predictive AI systems and processes analyze patterns and trends to forecast future outcomes. It can be used to check the quality of produced product. In predictive maintenance, AI algorithms can analyze equipment performance data and can predict when the equipment may likely to fail. To have an example of predictive AI, consider a scenario in supply chain management of an organization. Predictive AI algorithms can analyze historical data on factors like demand patterns, supplier performance, and logistic efficiency. By identifying trends and correlations, the system can forecast future demand, optimize inventory levels and anticipate potential disruptions. This allows organizations to proactively adjust their supply chain strategies, minimizing delays and reducing costs.


AI in Quality Management


Both generative AI and predictive AI have potentials within the quality management. Generative AI can be used to create synthetic or simulated datasets for testing and validating quality control processes. For example, generative AI may generate realistic product defect images to train computer vision models for quality inspection. Predictive AI can enhance quality control by predicting or forecasting potential defects before they occur. For example, predictive AI can analyze production data to forecast issues in manufacturing process and allow adjustments to prevent defects.


Challenges for Organizations using AI


There are several challenges for organizations using AI.


Data Quality – Providing high-quality data is a big challenge as reliable AI models require high-quality data. Ensuring the accuracy and relevance of data is a significance challenge.


High Implementation Cost – Initial setup costs, including acquiring the AI technology and training personnel, can be substantial.


Ethical Consideration – Bias in Algorithms – There are chances of bias in algorithms that means presence of systematic and unfair favouritism towards or against particular groups or individuals in the outcomes produced by the algorithm. If historic data used in the AI model contains inherent bias, the algorithm may learn and perpetuate those biases. Addressing bias is a crucial ethical consideration in AI development. There may be several essential steps, such as, careful data curation, transparency in algorithms, ongoing monitoring that can mitigate bias.


Ethical Consideration – Job Displacement – Automation and AI technologies replace certain human tasks that leads to job losses in organizations. It is due to the fact that as AI and automation technologies advance, they can take over routine, repetitive, manual tasks leading to increased efficiency but potentially reducing manpower demand in those specific tasks thus creating job displacement. Managing job displacement involves consideration of many factors, such as, workforce reskilling, retraining, policies that support the transition of human workforce to new roles in organizations.


AI developers and organizations using AI need to be aware of above issues and work towards creating AI systems and processes as fair, transparent, and considerate of their broader societal implications.


Opportunities for Organizations using AI


There are several opportunities for organizations using AI.


Efficiency – AI is useful in optimizing production processes, reducing waste, and enhancing overall operational efficiency. Thus, organizations can improve their efficiency by using AI tools.


Predictive maintenance – With the help of AI, organizations can implement predictive maintenance that can anticipate equipment failure and organizations can take proactive maintenance steps thus leading to cost savings.


Customization – AI enables producing product based on customer preferences, needs and expectations.


AI can be applied to check the quality of produced products by utilizing computer vision and machine learning techniques. For example, in a manufacturing setting, AI-powered visual inspection systems can analyze images of products in real time. The system learns from a dataset of acceptable and defective products, identifying patterns associated with defects. This allows it to flag potential issues during production, ensuring that only high-quality products move forward in the manufacturing process. In essence, AI acts as a real-time quality control tool.


We can conclude, both generative AI and predictive AI have significant potential in quality management, offering ways to enhance efficiency, reduce defects, carry out predictive maintenance, and optimize organization’s processes. However, organizations must suitably address challenges related to data quality, costs and ethical considerations of bias in algorithms and job displacement to take the optimum benefits from the opportunities AI presents to the organizations.


Best wishes,

Keshav Ram Singhal 

Friday, January 12, 2024

Person Versus System

Person Versus System

A bad system will beat a good person every time. Always look at improving the systems and processes.
W. Edwards Deming was a renowned statistician, professor, author, lecturer, and consultant. He made the statement once said, "A bad system will beat a good person every time." His intention was to highlight the importance of focusing on the overall system and processes within an organization rather than solely blaming individuals for problems or failures.

Courtesy - Image Created with the help of AI tool. 

Deming was a key figure in the development of Total Quality Management (TQM). He emphasized the idea that the majority of issues within an organization are a result of systemic problems, not individual incompetence. His point was that even if you have talented and well-intentioned individuals, if they are working within a flawed or inefficient system, their efforts will likely be thwarted, and the system will prevail.

In other words, individuals can be doing their best, but if they are constrained by a poorly designed or inefficient system, their efforts won't lead to optimal results. They will certainly fail in their efforts because of the bad system. Deming advocated for a holistic approach to management that involves continuous improvement of processes and systems, fostering a culture of quality, and empowering employees to contribute to improvements. This philosophy aims to create an environment where individuals can excel and contribute meaningfully to the organization's success.

In summary, Deming's statement underscores the need for organizations to focus on improving their systems and processes to achieve better outcomes, rather than placing sole blame on individuals when things go wrong.

Keshav Ram Singhal

Friday, December 22, 2023

ISO 9000 Series Standards

ISO 9000 Series Standards


Standards published under the responsibility of ISO/TC 176/SC 1 (ISO Technical Committee on Concepts and terminology)

One standard published

(1) ISO 9000:2015 - Quality management systems - Fundamentals and vocablary


Standards published under the responsibility of ISO/TC 176/SC 2 (ISO Technical Committee on Quality systems)

Six standards published

(1) ISO 9001:2015 - Quality management systems - Requirements

(2) ISO/TS 9002:2016 - Quality management systems - Guidelines for the application of ISO 9001:2015

(3) ISO 9004:2018 - Quality management - Quality for an organization - Guidance to achieve sustained success 

(4) ISO 10005:2018 - Quality management - Guidelines for quality plans

(5) ISO 10006:2017 - Quality management - Guidelines for quality management in projects

(6) ISO 10007:2017 - Quality management - Guidelines for configuration management 


Standards published under the responsibility of ISO/TC 176/SC 3 (ISO Technical Committee on Supporting technology)

Fourteen standards published

(1) ISO/TS 10020:2022 - Quality management systems — Organizational change management — Processes 

(2) ISO 10019:2005 - Guidelines for the selection of quality management system consultants and use of their services

(3) ISO 10018:2020 - Quality management — Guidance for people engagement

(4) ISO 10017:2021 - Quality management — Guidance on statistical techniques for ISO 9001:2015

(5) ISO 10015:2019 - Quality management — Guidelines for competence management and people development

(6) ISO 10014:2021 - Quality management systems — Managing an organization for quality results — Guidance for realizing financial and economic benefits 

(7) ISO 10013:2021 - Quality management systems — Guidance for documented information

(8) ISO 10012:2003 - Measurement management systems — Requirements for measurement processes and measuring equipment

(9) ISO 10010:2022 - Quality management — Guidance to understand, evaluate and improve organizational quality culture 

(10) ISO 10008:2022 - Quality management — Customer satisfaction — Guidance for business-to-consumer electronic commerce transactions

(11) ISO 10004:2018 - Quality management — Customer satisfaction — Guidelines for monitoring and measuring 

(12) ISO 10003:2018 - Quality management — Customer satisfaction — Guidelines for dispute resolution external to organizations

 (13) ISO 10002:2018 - Quality management — Customer satisfaction — Guidelines for complaints handling in organizations 

(14) ISO 10001:2018 - Quality management — Customer satisfaction — Guidelines for codes of conduct for organizations 

For obtaining the relevant standards of your needs, you should contact the International Organization for Standardization (ISO) or the relevant national standards body in your country.

Courtesy Source - ISO

Best wishes,

Keshav Ram Singhal 

Please have your comments / reaction.


To train your employees with ISO 9001:2015 QMS Awareness, please supply them “TRAINING HANDBOOK ON ISO 9001:2015QMS AWARENESS” (ASIN: B093YFFY7Z), which is available worldwide at Amazon. Please search this Training Handbook in Amazon Website of your country. 


 You may also supply your employees following eBooks - (i) A Concise Guide on Creating and Updating Documented Information (eBook) https://store.pothi.com/book/ebook-keshav-ram-singhal-concise-guide-creating-and-updating-documented-information/ (ii) Applying Risk-based Thinking in an Organization Implementing ISO 9001:2015 QMS (eBook) https://store.pothi.com/book/ebook-keshav-ram-singhal-applying-risk-based-thinking-organization-implementing-iso-9001-2015-q/ 

For more information on Kindle Book - Implementing An Effective Quality Management System, please Click Here.



Wednesday, December 20, 2023

Latest News - ISO/IEC 42001:2023 - An Standard on Artificial Intelligence Published

 Latest News - ISO/IEC 42001:2023 - An Standard on Artificial Intelligence Published


ISO/IEC 42001:2023 is an international standard that specifies requirements for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System (AIMS) within organizations. It is designed for entities providing or utilizing AI-based products or services, ensuring responsible development and use of AI systems.

Number of pages - 51

Price - CHF 187

Title - ISO/IEC 42001:2023, Information technology - Artificial intelligence - Management system

Technical committee responsible - Joint Technical Committee ISO/IEC JTC 1, Information technology, Subcommittee SC 42, Artificial intelligence.

Publication date : 2023-12

ISO/IEC 42001:2023 is the world’s first AI management system standard providing valuable guidance for the rapidly changing field of technology. It addresses the unique challenges AI poses, such as ethical considerations, transparency, and continuous learning. For organizations, it sets out a structured way to manage risks and opportunities associated with AI, balancing innovation with governance. 

Main benefits of ISO/IEC 42001:2023 -

- Responsible AI: ensures ethical and responsible use of artificial intelligence.

- Reputation management: enhances trust in AI applications.

- AI governance: supports compliance with legal and regulatory standards.

- Practical guidance: manages AI-specific risks effectively.

- Identifying opportunities: Encourages innovation within a structured framework.

Organizations of any size, involved in developing, providing, or using AI-based products or services, can use this standard. This standard is designed to be applicable across various AI applications and contexts.

An AI management system, as specified in ISO/IEC 42001:2023, is a set of interrelated or interacting elements of an organization intended to establish policies and objectives, as well as processes to achieve those objectives, in relation to the responsible development, provision or use of AI systems. ISO/IEC 42001:2023 specifies the requirements and provides guidance for establishing, implementing, maintaining and continually improving an AI management system within the context of an organization.

The ISO/IEC 42001:2023 standard offers organizations the comprehensive guidance they need to use AI responsibly and effectively, even as the technology is rapidly evolving. Designed to oversee the various aspects of artificial intelligence, it provides an integrated approach to managing AI projects, from risk assessment to effective treatment of these risks.

Other ISO Standards for AI - 

ISO has a number of standards that help mitigate the risks and maximize the rewards of AI, including ISO/IEC 22989, which establishes terminology for AI and describes concepts in the field of AI; ISO/IEC 23053, which establishes an AI and machine learning (ML) framework for describing a generic AI system using ML technology; and ISO/IEC 23894, which provides guidance on AI-related risk management for organizations.

As a management system standard (MSS), ISO/IEC 42001 can be considered as an overarching document for the sound governance of an organization in relation to AI. It provides a practical way of supporting decisions resulting from the implementation of an AI management system.

More information can be obtained from IEC / ISO headquarters or national standards body of your country, who is member of ISO.


Keshav Ram Singhal

Courtesy - ISO and IEC


Friday, September 8, 2023

New Kindle Book - Implementing An Effective Quality Management System

New Kindle Book - Implementing An Effective Quality Management System



359 pages
Price ₹300
For more details, Please visit Amazon site - CLICK HERE.

Request friends to support and post book-review at Amazon site.


Thursday, September 7, 2023

My Efforts got Recognition



My Efforts got Recognition


25 Best Quality Management Blogs & News Websites - Please CLICK HERE.

My blog lists at number 13.

Top 10 Quality Management Blogs in 2020 - Please CLICK HERE.

My blog lists at number 9.

This is for your information.


Keshav Ram Singhal 

Saturday, September 2, 2023

Adding Value to Internal QMS Audit


Adding Value to Internal QMS Audit



Internal audit is used as a tool to monitor and determine the health of the quality management system implemented in the organization. Internal audit is carried out to measure the effectiveness of the quality management system. The findings of internal audit can help in initiating appropriate measures.


Clause 9.2 of ISO 9001:2015 QMS standard stipulates the requirements of the internal audit. Please go through the chapter #43 on ‘Internal Audit’ in this book. If we compare the requirements of the internal audit with the earlier version, there is nothing new in the internal audit requirements of ISO 9001:2015 QMS standard. However, we find that ISO 9001:2015 QMS standard has less focus on ‘documented information’ and greater focus on achieving outcomes and results. Many clauses in ISO 9001:2015 QMS standard do not mention any requirements to maintain or retain any documented information and it is a challenge before an auditor or internal auditor how to audit compliance of standard’s requirements in such a situation.


A few of the tips are mentioned below for the benefit of the auditor or internal auditor, by which the auditor can add value to the audit:


·       Understand the intent of ISO 9001:2015 QMS standard and its concepts, such as context of the organization, risk-based thinking, addressing risks and opportunities, etc.


·       Make use of ISO 19011:2018, Guidelines for auditing management systems, as your guidance manual for auditing ISO 9001:2015 quality management system.


·       Make use of ISO 9001:2015 QMS standard as a reference standard.


·       Study the guidance papers and presentations issued by International Organization for Standardization (ISO) and International Accreditation Forum (IAF) and other publications, literature on ISO 9001:2015 QMS.


·       Get auditor’s competence by attending the relevant training. Make a habit to attend ISO 9001 related training, conference and seminar.


·       Pursue the outcomes from the previous audits, if any, both internal and external, to identify any specific issues or concerns still require improvement.


·       Understand the organization, its processes, its stakeholders, risks and opportunities, applicable legal requirements.


·       Seek adequate time for auditing.


·       Focus more on the process, process performance and its outcomes and results.


·       Understand and remember seven quality management principles (QMPs) and use of PDCA approach to evaluate the process effectiveness.


·       Look for objective evidence.


·       Use 5 W’s and 1 H – What, Why, Where, Who, When and How – appropriately to obtain objective evidence.


·       Provide adequate opportunity to the auditee to correct any nonconformities, if noticed.


·    Make efforts to identify the root causes of problems or nonconformities. Don’t see who is responsible. Rather consider why and what caused the problem or nonconformity.


·       Adopt a ‘holistic’ approach while gathering objective evidence during audit.


·     Analyze the findings and relate them to the organization’s ability to provide products and services that meet customer and applicable legal requirements.


·       Report audit findings.


·       Also emphasize positive findings as appropriate.


·   Consider solution and corrections proposed by the auditee in response to the ‘negative findings’ (nonconformances).


·       Carry out process audit by following the path the auditee takes to carry out the process.


·       Don’t make the audit difficult by adding on requirements. Do not add any additional requirements, which are not required.


Value added auditing aims to add value, the organization will find useful. Value added auditing encourages result-focused systems, with minimum bureaucracy. It helps to identify strong and weak points and focus on the ways to improve. Value added auditing provides confidence that the quality management system is the king and the organization is consistently providing conforming products and services to its customers.


Case Study


Ram Dubey, a Chartered Accountant, held the position of Finance Manager at ABC Manufacturing's headquarters, overseeing two ISO 9001:2015 Quality Management System (QMS) certified manufacturing plants. Ram often visited these plants for financial management and financial audits. One day, the CEO of ABC Manufacturing praised Ram's work and requested him to conduct an internal audit of the plants’ QMS within 15 days, as the certification surveillance audit was scheduled for the following month. The CEO provided Ram with copies of ISO 9000:2015 and ISO 9001:2015 standards, blank nonconformance report forms, the quality manual, and an internal audit file. The CEO also explained the significance of opening and closing meetings, as well as the process for raising and resolving nonconformities. The CEO expected Ram to identify at least 10 nonconformities in each plant to demonstrate compliance with the company's quality manual. Ram learned that internal audits were to be conducted annually, as stated in the quality manual.


After dedicating two days to studying the relevant standards and the company's quality manual, Ram felt confident about conducting the audit, drawing on his financial audit experience as a chartered accountant. A week later, he visited both plants and identified more than 10 nonconformities in each.

Some of these nonconformities, along with the relevant standard clauses and actions taken to address them, were as follows:


1.              No records were maintained for determining external and internal issues - Clause 4.1. The nonconformity was closed with the auditee's explanation that the standard did not require such records.


2.              No records were kept for determining interested parties and their requirements - Clause 4.2. The nonconformity was resolved with the same explanation provided by the auditee.


3.              The quality policy was not displayed in the fabrication and purchase sections, and employees were unaware of it - Clause 5.2. The plant manager promptly supplied copies of the quality policy to all employees and displayed it in the relevant sections.


4.              No specific individuals were assigned duties and responsibilities for QMS monitoring - Clause 5.3. The plant manager claimed to perform these duties personally.


5.              No formal risk register or risk records were maintained - Clause 6.1. While a risk register wasn't required, the auditee affirmed that risks were identified, and preventive actions were taken.


6.              The painter in the painting section couldn't state the quality objectives for his department - Clause 6.2. The head of the painting section provided the quality objective and demonstrated the related register tracking product reception, painting, and storage.


7.              Weighing equipment for raw materials and weighing balances were not calibrated or checked for accuracy regularly, as legally required - Clause 7.1.5. The concerned staff was unaware of the requirement but agreed to perform the necessary calibration and checks.


8.              Employee competence records were not available at the plant - Clause 7.2. It was suggested that these records be kept at the company headquarters, which was confirmed by the HRD section.


9.              No evidence was provided to determine applicable statutory and regulatory requirements - Clause 8.2.2. The Plant Manager clarified that this was managed by the Product Research Cell at the company's headquarters.


10.           Re-evaluation of suppliers had not been conducted as required by the established procedure - Clause 8.4. The head of the purchase department assured that re-evaluation would be completed by the end of the month and shared the re-evaluation proforma sent to suppliers.


11.           Nonconforming products were found without proper marking - Clause 8.7. The plant manager committed to segregating nonconforming products with special markings.


Upon returning to headquarters, Ram submitted his audit report to the CEO, who was pleased with his work. Ram was promoted to Chief Manager, and his emoluments were increased by 20%.


Analysis of Ram Debey’s Internal Audit by the CEO


Ram Dubey saved the company from a non-compliance in the upcoming certification audit by conducting an internal audit.   


Ram Dubey conducted a thorough internal audit of the quality management system of the company’s plants. Within a short duration, he demonstrated a good understanding of ISO 9001:2015 QMS standard and the company’s quality manual. He identified more than 10 nonconformities in each plant addressing various aspects of the quality management system, such as documentation, policy implementation, risk management, competence, supplier evaluation, segregation of nonconforming products, calibration etc.


According to the CEO, Ram Dubey’s internal audit appears to be of good quality. He followed ISO 9001:2015 QMS standard requirements and the guidelines set out in the company’s quality manual. His ability to identify relevant nonconformities and document them with clause number accurately indicates his competence in conducting internal audit. This internal audit added significant value to ABC Manufacturing. By identifying nonconformities and suggesting corrective actions, he helped the company address potential issues in its quality management system. This proactive approach ensures that the company is better prepared the upcoming certification surveillance audit.  


Independent Analysis of Ram Debey’s Internal Audit


If we consider the above case study with an open eye, we will have different outcomes. With an independent analysis, it is now evident that Ram Dubey’s internal audit had more significant shortcomings. He neglected several critical areas of the quality management system and did not measure the effectiveness, adequacy, and performance of the quality management system in many key clauses of ISO 9001:2015 QMS standard.


Many areas were left by Ram Dubey, where he has neither raised any nonconformities nor he submitted any compliance reports meeting requirements, such as:


·       Scope of the quality management systems – 4.3

·       Leadership and commitment – 5.1

·       Design and development activities – 8.3

·       Production and service provision – 8.5

·       Release of products and services – 8.6

·       Monitoring, measurement, analysis and evaluation including customer satisfaction – 9.1

·       Internal audit – 9.2

·       Management Review – 9.3

·       Improvement – 10.1, 10.2, 10.3


From the omitted audit areas, it is clear that Ram Dubey’s internal audit lacked thoroughness and professionalism. His limited knowledge of quality management system audit and the absence of formal training in ISO 19011:2018, combined with his lack of understanding of ABC Manufacturing’s production processes, raised serious concerns about the quality of the audit.


While Ram Dubey identified nonconformities in some areas, the overall effectiveness and reliability of his internal audit are questionable because:


·       Ram Dubey omitted critical audit areas, such as the scope of the QMS, leadership and commitment, design and development activities, production and service provision, release of products and services, monitoring, measurement, analysis and evaluation, internal audit, management review and improvement. This incomplete audit scope raises concerns about the comprehensiveness of the internal audit.


·       Ram Dubey’s lack of knowledge about ISO 19011:2018 management systems auditing standard highlights a significant deficiency in his audit approach. This standard provides guidance on auditing principles and techniques and is essential for conducting effective internal audits. Ram Dubey has limited expertise and knowledge in quality management system auditing.


·       Considering the gaps in the audit and Ram Dubey’s limited expertise in quality management system auditing, it raises questions about the motives behind the audit. It appears that the audit may have been conducted primarily to please the CEO, rather than to genuinely assess the quality management system of ABC Manufacturing.


·       Ram Dubey’s lack of formal quality management system audit training is a critical shortcoming. Proper training is essential to conduct thorough and effective audits, and his background as a financial professional may not adequately prepare him management systems auditing.


·       It is clear that Ram Dubey’s internal audit was not conducted to the level of quality and professionalism expected in the field of quality management system auditing. It may have lacked objectivity, depth, and compliance with auditing standards and guidelines. The internal audit carried out by Ram Dubey was inadequate.


·       Since Ram Dubey’s lack of formal quality management system audit training, he did not develop an audit plan or criteria for selecting audit areas. A structured plan could improve the efficiency and effectiveness.


·       During the audit, Ram Dubey focused his attention on finding and reporting the nonconformities, he omitted to note those objective evidence that showed compliance with ISO 9001:2015 QMS standard requirements.


Learning from the Case Study: From the above case study, we find that Ram Dubey lacked formal quality management system audit training and also lacks of knowledge about ISO 19011:2018 management systems auditing standard. Had he been given the formal training he would have been a competent QMS internal auditor and the outcome of his internal audit would have been different. With formal training, he could have added value the quality management system internal audit.


Concluding Summary


This write-up emphasized the essential concepts and considerations for conducting a value-added internal audit within the framework of ISO 9001:2015 QMS. The key takeaways are:


·       Internal auditor should grasp the intent of ISO 9001:2015, understand its requirements including its new concepts, like context, risk-based thinking, addressing risks and opportunities.


·       ISO 19011:2018 provides valuable guidelines for management systems auditing, serving as an indispensable resource for the auditor.


·       A holistic, solution-oriented approach is crucial. The auditor should focus on processes, outcomes and root-causes while emphasizing positive findings alongside nonconformities.


·       Continual training and development are vital for an auditor to stay updated and competent in its role.


·       The case-study given in this chapter demonstrated practical application, highlighting the importance of thoroughness, competence of the auditor, and a balanced perspective in the internal audit.


In conclusion, value-added internal audit goes beyond mere compliance checking. It serves as a means to drive continual improvement, enhance organizational performance, and ensure effectiveness of the quality management system. By adhering to value-added internal audit, the internal auditor helps the organization to achieve greater confidence in its ability to consistently provide quality products and services to ita customers.


Best Regards,

Keshav Ram Singhal

(This write-up is a part of a forthcoming book on ISO 9001 being written by Keshav Ram Singhal)