Sachinn

Audit of AI infused process is important to ensure compliance and accountability, since it verify that AI systems function or operate as per the regulatory requirement, and uphold transparency and public trust. Followings are the key points that should considered during the audit: Ø Goal & Scope: It is important that goal and scope should be clearly defined. AI audit is very important to ensure effective examination of the system’s performance, compliance, and standards of ethics. Ø In-depth data and algorithm evaluations: It is important to confirm the accuracy and integrity of data and to rigorously evaluate algorithms in order to ensure the accurate functionality and uphold principles of fairness. Ø Implementation of continuous improvement: Action to be taken on audit findings and implement regular monitoring are critical step to maintain compliance and consistently improve the perfoirmance and reliability of AI System. Followings are the main area’s for an AI Audit: Ø Define the audit scope by determining which AI system will be evaluated and identifying the specific component or process to be examined. Establishment of clear objectives, evaluation criteria, and success metrics to ensure alignment with organizational goals and regulatory requirements. Ø Collection of relevant data related with the AI system i.e. input datasets, documentation related to algorithm, and output results. Make sure that the data is cleaned and validated to facilitate accurate and effective analysis. Ø In order to check whether the AI model function correctly and same are free from errors algorithms review play crucial role. Ø It is important to verify the AI systems in order to ensure that model comply with relevant regulations and standards, such as GDPR and CCPA. Below outlined checkpoints should considered while auditing AI component: Category Checklist Item Governance & Accountability Is there a designated owner for the AI system? Have the role and responsibilities been clearly defined and communicated ? Data Quality & Management Has the source of the training data been properly documented ? Has the data undergone through evaluation to ensure that it is free from biasness? Is the data thoroughly tracked from its original source or point of origin? Are data privacy and consent requirements are met with the policy? Has the model been validated using the data set that are both diverse and representative of use case study Explainability & Transparency Can non-technical stakeholders can understand the decision of AI model’s ? Are prompts and flows (for LLMs) are accurate? Security & Robustness Have appropriate controls been implemented to protect AI System from adversarial attacks? Compliance & Ethics Is there a formalized process in place to conduct for ethical review of AI use cases? Are audit logs and documentation retained for compliance? Business Alignment Are the goals of AI initiatives aligned with the objectives of business Are Key Performance Indicators (KPIs) clearly defined and actively monitored to assess the performance of AI system? In order to maintain transparency, fairness, and alignment with business goals, following approaches should considered: 1) Transparency: · Maintain audit trails for data, models, and decisions. · Use model documentation tools (e.g., Model Card). · Provide user-facing explanations for AI decisions. 2) Fairness: · Ensure that AI systems consistently uphold principles of fairness by avoiding discriminatory outcomes. · Conduct bias periodic audits regularly. · Engage diverse group of stakeholders throughout the model design and testing phase to ensure inclusivity, fairness, and broader perspective in decision-making · Implement feedback loops to catch and correct unfair outcomes. 3) Alignment with Business: · Define clear KPIs for AI performance that align with business goals. · Ensure CFT collaboration · Use AI governance frameworks

Answer: The Swiss cheese model is the powerful tools used to identify the risk and improving the reliability of the process. This model is useful for complex systems like engineering, aviation, healthcare and even in pharmaceutical sector. Swiss Cheese Model helps to identify the risks through following approaches: Ø Detecting weak points: This approach emphasizes the identification of areas where the system is most susceptible to failure or attack or can say that the area’s where multiple small issues could combine into a major failure. Ø Highlights hidden Conditions: This approach helps distinguish between human errors and underlying systemic issues, which further helping teams to identify and address the root causes Ø Support proactive risk management: This approach analyze, where the hole might align. Through this approach organizations can proactively strengthen weak areas to prevent failures before they occur. Swiss Cheese Model helps to strengthen the reliability of that process through following approaches: Ø This model helps for implementation of backup systems and verification of the checks to enhance the reliability and prevent potential failures. Ø This model also helps through process improvement approach. It helps identify opportunities to stringent the process or introducing automation in order to enhance efficiency and minimize the risk of error. Ø Through monitoring and feedback, this model promotes continuous monitoring to detect and patch new holes as they appears. We have recently completed out project to shift .manual documentation into eLogbooks and eBatch Record System. Followings are the slice of the cheese: Ø Standard Operating procedure: Implementation of a new procedure ensures consistency and robustness, and same will helping teams to execute task reliably and efficiently. Ø Employee Training Program: This approach helps employees stay informed and up-to-date with newly implemented procedures, which ensures smoother adoption of procedure and consistent execution. Ø Validation: This approach ensure consistency in process and also ensure that the new process will remain in a state of control of validation in future. Ø Apart from aforementioned approaches, Automations check, QA review, employee feedback loops and regular audit for compliance are the remaining slice of the cheese. Following were the holes: Ø Outdated or manual procedure: Manual entry in the system is a time consuming process that often require number of manual check as a result increase the chances of error. Ø Inadequate training: Employees are trained across all shifts; however, manual procedures sometimes lead to errors. By implementing a new system-based procedure, online training becomes mandatory before an employee can access the workplace. The system restricts access until the training is successfully completed, ensuring readiness and reducing the likelihood of mistakes. Ø Lack of real-time monitoring in manual procedures often leads to backdated entries, which can result in non-compliance issues and even 483 observations during audits. The implementation of the new system prevents users from proceeding without completing prior tasks, ensuring compliance and audit readiness. Ø Apart from aforementioned approaches, ignorance of feedback and audit findings, over reliance of the manual procedure, communication gap between team members are the remaining holes of the cheese. Business excellence framework ensure continuous improvement, stakeholder alignment and focus and process optimization: Ø Business excellence framework ensure proactively risk identification Ø This approach (Business excellence framework) fosters a culture of quality and safety. Additionally, also helping to prioritize that which layers require additional attention and reinforcement. Ø BE framework also align risk mitigation with strategic goals. Ø It also help, regularly reviewing and strengthening each layer of defense and minimize vulnerabilities and enhances overall system resilience. Ø Monitor performance to ensure defense are effective and risk are minimize. Ø Use feedback and incident analysis to patch holes and add new layers.

Answer: For a sustain AI model, it is important to update and re-train the AI model time to time. Followings are the symptoms of fragile or outdated AI Solution: 1) The use of system in unintended way, where logic, prompts and flows are no longer aligned with the customer or user expectation: 2) Error in Expandability: If the output or language of model become harder to interpret as a result customer can lose trust. 3) Integration failure: Breakdown in “Application Programming interface” data pipeline error in real time responses generally break the trust of user. 4) Outdated/old/incorrect information and lack of updates to domain-specific data or FAQs can also be an example of outdated AI Solution. 5) Frequent involvement/intervention of human, bypass of logics and flow are also an example of fragile or outdated AI Solution. Below are the strategy needs to be considered for sustainability of AI deployments: 1) Eco-friendly cloud computing: carbon-neutral and carbon-negative computing solutions are two most important keys. These are environment responsible services which are significantly reducing the carbon footprint generated from AI and other compute intensive operations. 2) Collaborative intelligence: This leaning enable model training across decentralized data source, thus eliminate the bridge between the transfer large data set to centralized servers. This approach is useful to conserves energy through reducing data movement and also strengthens data privacy by keeping sensitive information local. 3) Ethical and Social Sustainability in AI: Promoting clarity and trust in AI systems is essential for ensuring ethical and socially sustainable technology. This approach ensure that how AI models takes decision and help to identify and mitigate biases. 4) Continuous improvement monitoring and evaluation: Implementation of dashboard can be helpful; to track performance metrics. 5) Regular re-training and updates: This approach is helpful for model to refresh new information's periodically. Like update prompts and logics etc. 6) Robust Documentation and Governance: It is important to maintain clarity in documentation of logics , assumptions and limitations. 7) Periodic Maintenance of Knowledge base: Regular audits of contents and sources helps AI models for long term sustainability.

Answer: In DMAIC Project {i.e. Define (D), Measure (M), Analyze (A), Improve (I) and Control (C)} change management plays a crucial role to ensure that improvement are implemented, adhere and sustained by organization. It plays a crucial role at last two phases i.e. improvement and control phase. In order to effectively drive the project from inception to conclusion “Master Black Belt” Leader plays essential role which is act like a mentor and provide strategic direction, facilitate the continuous improvement project at each stage with best industrial practices. The Guidance of MBB is act like a instrumental which ensures that six sigma project are complete on time. MBB plays a crucial role in change management across all the five stages of DMAIC Project and the successful closer of Lean six sigma project, effective implementation of change management is very important. 8 steps of “Kotter’s Guideline are important for effective change management: It is important to integrate change management at each step of DMAIC to ensure the alignment with business goal. Following summary provides a comprehensive overview of change management: Define Phase: Ø During this phase, first defined the urgency of proposed change and their long term benefits. Ø Identify the key department and stakeholder, clearly communicate the vision and importance and explain that how their involvement is crucial for this project. Ø Define the chronology of events of each phase in project charter and get it signed off from each CFT’s. Measure and analyze Phase: Ø In this phase communicate the urgency of the project clearly and consistently and Emphasize critical focus areas and maintain a strong prioritization to ensure alignment and momentum throughout the initiative. Improvement and implement: Ø This is one of the most important phase where change initiated through change control QMS system, performance reviews and training. Ø Assessment of the issues, concerns and react. Ø Encourage the people and remove obstacles. Ø Empower people to lead change and take meaningful action. Ø Identify the quick win and celebrate the success to build momentum and reinforce positive change. Control: Ø Ensure the long term sustainability of change by fixing the new process and values into the organizations culture. MBB play an important role in order to sustaining the results, below are the key techniques that can keep the things on track Ø Implementation of Control Plan Ø Clarity defined Metric Ø Standard operating procedure Ø Process Control Evaluation Ø Training and regular communications Ø Effectiveness check Ø Periodic inspection and review Let’s discuss with real time example, where through DMAIC methodology, improvement happened. As a part of continuous process improvement, DMAIC tool was utilized to address the issue of nitrosamine impurity (a carcinogenic impurity), in one of our products. This product was initially manufactured using a wet granulation process. During the investigation phase at R&D, it was discovered that this impurity was generated during the drying process at high inlet temperatures. After so many trials, we were unable to control the impurity. Ultimately, we decided to change the manufacturing process from wet granulation to roll compaction (dry granulation) to mitigate the formation of this carcinogenic impurity. This change was significant according to SUPAC guidelines, and we were aware that we would need to halt the commercialization of this product temporarily. However, after receiving approval from the regulatory agency, we achieved double benefits. Ø Control of Nitrosamine Impurity: The change in process effectively controlled the carcinogenic impurity. Ø Reduced Process Time: The process time was significantly reduced from approximately 48 hours per batch to 29 hours. Ø Increased Production: The number of batches delivered per month increased from approximately 12-13 to 20-21. Ø Enhanced Revenue: The cost per batch is approximately 93 lakh INR, and the monthly delivery cost increased from 12.09 crore INR to 19.53 crore INR. In our efforts to improve the product, we successfully reduced process time, eliminated rejections, and enhanced output.

Answer: Control phase is one of the key area in all the five phases of process improvement. i.e. Define (D), Measure (M), Analyze (A), Improve (I) and Control (C).So it is important to sustain the process through consideration of several key approaches, so that improvement remain consistent in future. Followings are the techniques that can keep the things on track: 1) Implementation of Control Plan: This plan summarize the processes that what check points needs to be in-place to keep a system performing at its current state. For logistic firm, document the new routing procedure and implement GPS tracking system, so that real time location and progress of vehicles can be monitored. In addition, alternative route can also be in-place as a backup in case of any obstructions in new route 2) Clarity defined Metric: The importance of this to know whether a process changes are effective or is being maintained. For logistic firm, key performance indicators like on time delivery status and fuel efficiency. 3) Process Control Evaluation: Always use statistical process chart to monitor and control the process. This tools is used to track the process performance over the period of time and detect the variations which needs attention. , for logistic firm delivery time should be track to ensure that it remain inline to the improved process, any significant change can be immediately identified and addressed. 4) Standard operating procedure: It is important to document the improved process changes in the SOP to ensure the consistency and each individual follow the same procedure. This approach helps to maintain the improvement and provide guidance to new joinees about the procedure. 5) Training and regular communications: Before effective of Standard operating procedure, ensure that the person are trained with the new procedure so that he/she can understand their role for consistency of improved process. Sometime, personals are not adequately training on new route, so there are high probability that they may revert to using the old method/procedure/route, resulting win sometime slip away. 6) Effectiveness check: It is important to understand that the new procedure implemented correctly by employees or required some additional training. Since in-effective trainings or CAPA impact the sustainability of improved process, so it is always important to check the effectiveness of the training and process. 7) Periodic inspection and review: Regular inspection and review of the process ensures compliance and identify the area of improvement. For logistic firm, this procedure ensure that drivers and dispatch personals followings the new improved procedure

Answer: MBB’s plays crucial role in AI-enabled process where regular improvements are made by human teams. Communication and clarity are really important for effective collaboration between human and AI, where AI systems can clearly summarize their decision-maker processes while human can effectively communicate their vision and feedback, resulting to create a trust and helps for effective collaboration. In this process, AI model learnt from human guidance and behavioral pattern which allowing them to refine algorithms and improve their performance, while humans develop new skills and perspectives through their work with AI. This whole process will create virtuous cycle. Refer below flow chart: The main role of six sigma experts (Master black belt) is to identify the areas that needs attention and accordingly reduce the process variability through applying different approaches i.e. DMAIC (Define, Measure, Analyse, Improve, Control) and DMADV (Define, Measure, Analyse, Design, Verify), PDCA (Plan Do check act) and A3 technique – which a structured problem-solving and continuous improvement approach MBB plays an important role in context of continuous improvement journey: · Strategic alignment ensure translation of business issues into data driven opportunities and also Identification of critical pain areas and guide AI tools like Failure mode effect analysis and SIPOC (suppliers, inputs, process, outputs, customers). · Working together with data analytics engineer to define different factors and responses and data cleansing approach. ·During the process of integration and validation of AI models, various statistical tools like DoE, SPC, hypothesis testing and MLR ensure the accuracy and reliability of the model's assumptions resulting robust process. · Effective collaboration of AI model in the Six Sigma (DMAIC) provides organizations to improve accuracy, and sustainability in continuous process improvement at initial phase when critical business process is under development phase. ·AI can monitored the process continuously and provide real time data and feedback and as a result improvement are sustained, thus any deviation from the written procedure are immediately addressed. Clarity in feedback mechanism, improving the performance and reliability of AI systems. 1) Clear objective of AI model can improve the accuracy and enhance the user experience and overall performance. 2) User friendly interface can help the user to provide easy and concise feedback. 3) Guide AI tool to analyze the feedback form in such a way that it is easy to identify the common issues and areas which needs attention. MBB’s also helps to align six sigma projects with organization goals which ensuring improvement projects are directly connect with strategic goals of organization. A six-sigma expert can also prioritize urgency of the project. Additionally, also define the KPIs for continuous tracking the progress of cost saving and improvement projects. MBBs are creating a culture, where awareness should be provided to the people about the continuous improvement within the organization which leads to the adoption of best practices which finally enhance the creativity among team member.

Answer: In the modern world of “Artificial Intelligence” and automation, things are going significantly faster however, this rapid pace sometime leads to skip the critical steps, although the final outcome appears right. Let’s understand this with an example through creating a real scenario of pharmaceutical company who is going to launch their first ever ulcerative colitis medicine in the market: So there are multiple critical steps needs to be followed before launching a medicine into the market which includes but not limited to pilot scale batches, commercial scale up, exhibit, PPQ batches. In every stage, multiple trials and testing are required to evaluate the quality, safety and efficacy of the medicine. Apart from above, for consistent process and outcome multiple troubleshooting initiatives are also required. To implement, all the above scenario privacy of documents plays an important role. There is a possibility that by using advanced analytics and machine learning algorithms, significant amount of historical data like outcomes of the trials, different troubleshooting phase and stages can helps to make better decisions and improve process performance and as a result effective manufacturing of the medicine even significantly faster and end result can be technically correct. But missing of some critical information like data privacy/security, ethical consideration, regulatory compliance can leads to disclose the information to other pharmaceutical competitors which are also worked on same molecule. At this stage, MBB plays a crucial role to ensure successful implementation of AI. Following are the steps should considered for effective implementation: 1) Data privacy/security: Companies should implement strict rules with respect to data governance policies /procedures, such as data encryption, access controls and robust security protocols. 2) AI model interpretation: Some time it is difficult for human to understand the complex and opaque language of AI model resulting wrong interpretation. At this stage, MBB should use some techniques such as feature visualization. Explainable AI and Model interpretability techniques such as Local Interpretable Model-agnostic explanations, and Shapley Additive explanations, so that model can be transparent. 3) Consideration of ethics: It is important for MBB to establish a strong governance framework and ethical guidelines to ensure that their AI systems are aligned with their values and principles. Practically MBBs influence AI problem-framing through following: 1) Define clear goal: It is important to clearly define the objectives, so that goals are aligned with the business requirement. 2) CFT collaboration: It is responsibility of MBB to collaborate with CFTs, so that divers thoughts can be considered, as a result comprehensive problem can be defined. 3) Regular validation of AI Model: It is important to validate the model and process time to time to ensure that the model is aligned with the problem statement. So it is key responsibility of MBB to review the regular updates, feedback and new data. Through considering all the above aspects, MBB can significantly enhance the efficiency, effectiveness and influence AI problem-framing.

Since the launch of AI solution into business excellence world, the decisions are completely shifted manual to automate. 1) Followings are the elements should be included in a governance framework: Ø Strategic approach: · Organizations objectives and goal should be clearly defined at the time of AI integration and identify the critical area’s where AI can add values. · Build a separate team which includes but not limited to data analyst, AI operation experts and machine learning engineers. Conduct training sessions for existing employees in AI can be a cost effective approach. Ø Governance and Quality of data: Ensure that quality, integrity and security of the data can’t compromised. Ø Data privacy policy: Build a policy to evaluate, storage and collection of data inline to the regulatory guidelines so that compliance can be maintained. Ø Continuous improvement approach: Updation of the AI model inline to the regulatory guidance and based on continuous learning, so that model can adopt changing data and development requirement. 2) In order to mainlined both agility and control, it is necessary to build a team contains different stakeholders and also establish a system that can harmonized the practices. Ø Executive leadership: Management board should be involved for strategic directions and long term business goals. Ø Management review board (MRB): MRB should include different CFTs or representatives of different department for effective implementation and up gradation of AI model. MRB should include but not limited to: Operations, IT/AI/machine learning engineers, Quality assurance, Legal, compliance and business excellence. 3) In order to implement practically in the real world, Ø 1st start with the pilot program which resulting low risk and high value application Ø 2nd based on the existing learning, risk and compliance AI governance checkpoint should be placed. 4) In order to align AI with business excellence, some crucial steps needs to be taken: Ø Customer Service and Engagement: AI programs like chatbots and virtual assistants needs to be utilized, for 24 hours customer support. Ø Integration of methodology: Integration of six sigma and operation excellence methodology with AI in order to bridging the common language/ terminologies, mind-set and skill gap.

Answer: Business Excellence metrics plays crucial role in terms of continuous improvement, operational efficiency and foremost strategic goals, while AI has their own advantages which includes but not limited to, analysis of huge amount of data which helps to make informative decisions and with future prediction, reduce individual error and most important build attractive business models which can build trust and enhance customer satisfaction. · Followings are the new metrics that should emerge: 1. Accuracy of decision: Teach the AI or feed the information in such a way that, the response should be correct first time and every time with zero error. 2. Data quality index: The information from AI should be complete, consistent and precise. 3. Effectiveness check of human and AI integration: This approach should be used to evaluate that how well human and AI work together. · Followings are the metrics that may become outdated or misleading in an AI-enabled setup: 1. Average handling Time of customer. 2. Customer services, personalized interactions may be outdated, as NLP and chatbots are generally customer focus by enabling effortless communications through AI agent. 3. Business excellence contributes to process improvement, lean procedure and process optimization. For example: If we start six sigma project considering all the above four factors, it takes significant resources, manpower, sometime increase cost and most important time, while enabling the AI integrated robotics process, improve productivity, reduce cost, time and manpower and most importantly will maintained the high quality standard. · Linkage to artificial intelligence integration and Business Excellence 1. This integration will provide highly accurate strategic decision, reduce human bias and enhance flexibility. 2. The effective implementation of linkage ensures leadership development, skill enhancement and AI awareness programs will drive successful integration, which will enabling smarter strategic outcome, resilience, and long-term value creation.

Answer: MBB’s plays crucial role in lean six sigma world. The key role of MBBs to identify the areas that needs attention and reduce process variability through applying two most important approaches i.e. DMAIC (Define, Measure, Analyse, Improve, Control) and DMADV (Define, Measure, Analyse, Design, Verify) – which gives a structured approach to solving problems. Merger of both AI with Six Sigma provides a transformative platform to process optimization and improvement. During the development phase of AI-powered solution for critical business process, MBBs plays crucial role as outlined below: 1) For strategic alignment which ensure translation of business issues into data driven opportunities. 2) Identification of critical pain areas and provide guidance to AI tools like Failure mode effect analysis and SIPOC (suppliers, inputs, process, outputs, customers). 3) Work together with data analytics personal to define different input factors and output responses and data cleansing approach. 4) MBBs also provide direction to AI during integration and statistical model validation where different statistical tools like hypothesis testing, design of experiment, SPC and multiple linear regression are used to validate AI model assumptions. 5) Integration of AI in the Six Sigma DMAIC which provides organizations to improve accuracy, effectiveness, and sustainability in continuous process improvement at primary phase when critical business process is under development phase. Let’s discuss with one example: Define (D): During this phase, teach AI to identify the problem areas and project goals by utilizing advanced data mining techniques, such as clustering and dimensionality reduction algorithms like Principal Component Analysis (PCA). In addition to that, also teach AI to use NPL to improve customer voice analysis which allows automatic extraction of information from customer feedback found in social media, and online reviews. Measure (M): In this phase requires precise and fast data gathering. Teach the AI in such a way that both organized and unstructured data can be gathered, like operational environmental parameters Analyse (A): During this phase teach AI to speeding up cause analysis. Machine learning techniques like support vector machines (SVMs) can filter massive volumes of data to discover the important factors causing errors or inefficiencies resulting in faster discovery of root cause. Improve (I): During this phase, teach AI w.r.t virtual testing before implementation into real operation. Control (C): During this phase, teach the AI in such a way that sustainability of improvements can be enhance. MBBs has to create a universe in such a way that systems can automatically detect deviations if any parameter goes out of limit, enabling quick corrective actions. And accordingly Statistical Process Control chart can proactively update using on real-time information, so that manual interventions can be control. · During Practical strategies for collaboration with AI teams, Its important to use simple language and try to translate operation, six sigma and business languages in such as way that AI can understand the such terminologies. · Aligned the AI with business goal, so that understanding should be clear that what is the problem we are trying to solve and how success looks. · It is important to teach the clear objective of organization like cycle time reduction and cost saving etc. · We can provide training to AI team regarding lean six sigma and operational GMP procedure in order to bridging the mind-set and skill gap. · Always use modern platform like Miro and Power BI with interfaces which are business friendly. · Conduct regular workshop for both AI and Six sigma team, where different case studies and discovery sessions can be discussed. Through considering all the above approaches, MBBs and AI team can co-create a better solution.