Hardik Joshi

Compliance Risks in Pharma Formulation R&D Even though well-meaning messages in emails, reports, or submissions to regulators, these could break compliance rules in drug formulation research. An AI helper could stop: Regulatory Mistakes: Risk: Making inappropriate statements about how well a drug works?, how safe it is?, or how it functions? (breaking FDA/EMA rules). Example: Statement: “This mix cures Disease X”. As per correct sense, a statement should be “The drug shows promising lab results against Disease X.” Leaks of Secret Information: Risk: Sharing private details about how a product is made (like exact ingredient amounts, new delivery methods) with people who shouldn't know. Example: Statement: “We use 12% Polymer Y to control release”. As per correct sense, a statement should be “a polymer-based system to control release.” Patient Info & Privacy (HIPAA/GDPR): Risk: Talking about details of people in clinical trials (even hidden info can be risky if not removed). Example: Statement: “Patient 45, a 60-year-old man, did well”. As per correct sense, a statement should be “Some people in Group B got better.” Wrong Paperwork: Risk: Not following ICH/GLP rules (like not reporting all stability data, missing records of batches). Example: Statement: “Early data hints at stability”. As per correct sense, a statement should be “Mid-term stability data (40°C/75% RH, 3 months) shows no big breakdown.”. Contract & Partner Dangers: Risk: Promising too much to partners (like “We'll hand in the NDA by Q2” before checking) How AI Can Give Gentle, Unobtrusive Input: To keep scientific work flowing, AI should act as a “quiet compliance helper”: Smart Highlighting in Drafts: In-text Alerts: Underline risky words with color coding: Red: “Rule warning: 'cure' hints at unapproved health claim.” Yellow: “IP heads-up: Only list excipient if your readers need to know.” Mouse-over Tips: “shows lab-based antiviral action instead of 'kills Virus Y.” Compliance-Friendly Word Completion: When you type words like “safe” or “effective” AI offers pre-approved wording: "Safe": “Showed good tolerance in early tests” "Proprietary": “A new fat-based delivery method” (keeps formula secret). Quick Fixes for Common Mistakes: A “Fix It” button (in email or lab software) rewrites flagged parts with little effort. Quiet Alerts for High-Risk Text: If AI spots big issues (like missing side effects in a draft report), it tells the quality team, without stopping the writer. On-the-Spot Learning: A “Why This Matters” link in warnings takes users to company rules or drug guidelines (like “Q6A Specs: Why we don't say 'pure' in descriptions”).

AI-Driven Cross-functional Team Pattern Recognition to Gain Company Insights The Problem Bosses get scattered reports on issues, problems, and findings from HR, Finance, Operations, and Tech Support. Each team keeps track of its own numbers, but big inefficiencies or repeat issues might span several departments yet stay hidden in separate reports. AI Fix: A Prompt + Flow-Based Pattern Finder An improvised AI infrastructure could take in organized data from all departments, use NLP and grouping methods to find hidden links. How It Works (1) Input: · Numeric data: HR: People leaving, days off, slow promotions Finance: Surprise expense, slow bill approvals Operations: Delayed timeline, not enough stock Tech: Same problems persist, failed updates · Non-numeric data: How workers feel (checking Slack/MS Teams talk) Support tickets from users ("VPN crashes all the time!") Incident reports ("Finance put a hold on funds mid-project") · Background information: Time stamps (to spot recurring patterns) Groups/jobs involved (to trace who's responsible for what) (2) AI Processing: Joining the Pieces · Phase 1: Signal Collection: Standardizes information (e.g., "slow system" in HR exit interviews → connects to Tech's "tickets"). · Phase 2: Trend Identification Time-Based Grouping: "Each quarter, Finance holds up → Ops hurry → Tech make mistakes." Cause-Effect Links: "People quit more often 3 weeks after big system failures." Unusual Event Spotting: "Why is this month's Ops delay 4 times higher when other metrics stay the same?" · Phase 3: Idea Creation AI proposes root causes (e.g., "65% of HR complaints talk about 'approval tied to Finance's new process"). (3) Results: Insights Bosses Can Use · Live "Company Health" Screen Pressure Maps: Show friction between departments (e.g., "When Finance turns red, HR becomes yellow 2 weeks later"). Future Warnings: "Tech's backlog will reach dangerous levels in 10 days unless Ops stop new requests." Response Plans: "For 'Money freeze → People leaving' patterns, tell everyone about roadmap changes ." · Weekly "Patterns to Watch" Brief: Top 3 Recurring Themes: "VPN slowness → Loss of productivity → HR complaints" "Quarter-end Finance crunch → Ops shortcuts → Tech debt" One Strategic Fix: "Line up Tech/Finance roadmaps to stop Q4 from happening again." Example Scenario AI notices that whenever Tech rolls out a big update without talking to Ops/Finance: Ops fails to meet deadlines (because of surprise work), Finance goes over budget (rush approvals), HR gets feedback from leaving staff about "messy priorities." Advice: Make sure teams check how updates affect others before launching.* What Sets It Apart More Than Just Numbers: It digs deeper than charts to explain why patterns show up. It Tells a Story: It breaks down insights in plain language, not just data. It Keeps Getting Smarter: It fine-tunes its advice as it sees results. This changes scattered info into a smart early-warning setup—where AI doesn't just flag issues, it helps stop them before they start.

Prompt-Flow Systems for AI-Driven Excipient Compatibility Screening What Makes This Unique: In pharmaceutical formulation R&D, choosing appropriate excipients (inactive components that aid in the delivery of pharmaceutically active substances) is often crucial and very tedious. Making the wrong selection may lead to problems with stability, efficacy, or complete failure of clinical trials. This step has historically depended on thorough manual literature reviews, trial-and-error experimentation, and expert deep intuition. This task can be performed by an AI system using prompts and flows to automate and optimize. Exemplar interactions: · Structured prompts support predicting interactions. · LLM knowledge retrieval support enabled predictive formulation hypothesis generation. · Guided lab workflows enable dynamic decision logic algorithms for guiding workflows. Description: How It Works - A prompt-flow orchestration 1. Knowledge Retrieval & Hypothesis Generation (Prompt Layer) Input Prompt: "Available [Drug Molecule: X] with specific properties like low pH sensitivity and poor solubility, identify constituents compatible with respect to FDA’s formulated excipients records, avoiding known incompatibilities and encounter discriminative stability enhancement order. Rank by Stability Enhancement Potential.” AI Action: AI sends queries to Internal Databases and regulatory agencies sources. Literature (RAG – Retrieval-Augmented Generation) and cross-reference outputs list as “Mannitol (high compatibility), PVP (medium), exclude other PEG and stearic acid).” 2. Dynamic Experiment Design (Flow Logic) Decision Node: If the AI proposes a novel excipient, · QSAR models will trigger stability simulation. For example, the use of an uncommon polymer would fall under this category. · Revises approval precedence (EMA/FDA approval history). · If confidence is high, sends to lab automation systems for bench testing. 3. Revised Iterative Optimization Loop Feedback request: "Would suggest alternatives or modifiers for prior formulation with [Excipient Y] cited [Issue: precipitation at pH 5]." Every new experiment and data point modifies the next recommendation the system gives. Why This is High-Impact: · Excursion screening significantly reduces formulation development time. · Prevention of significant errors is achieved through flagging incompatibilities before lab trials. · Inexperienced researchers become recipients of AI-system recommendations by achieving cost democracy. · Compliance checks were built on which ensure the confidence of regulations

Scenario: Predicting a Bioequivalence Study for Generic Drug Products In formulation development, a successful bioequivalence (BE) study is critical for a generic drug product. A successful BE study means that the rate and extent of absorption (pharmacokinetics, PK) are statistically equivalent to the reference (brand/innovator) product. Running a human bioequivalence (BE) study can be quite expensive and time-consuming, but there's also a significant risk of failure. This can lead to project delays and affect the overall business case. Key Challenges: 1. Pharmacokinetic variability: The success of a BE study depends on various factors, including the design of the formulation, dissolution data, the drug's in vivo effects, and patient-related factors such as pre-existing conditions and geographical differences. Passing criteria: Regulatory agencies like FDA/Health Canada/EMA require strict statistical passing criteria equivalence (90% CI within 80-125% for AUC & Cmax). An AI that predicts bioequivalence studies could help to customise drug products and reduce the failure probability. A Conventional AI combined with a fine-tuned pharma domain-specific LLM is the best option 1. Conventional AI (First step) Prediction of Bioequivalence parameters requires structured data like formulation properties, dissolution profiles, drug substance data, and/or preclinical PK data. Conventional AI approach includes physiological pharmacokinetic (PBPK) modelling, In Vitro-In Vivo correlation, and statistical equivalence creator. PBPK modelling is the basis for bioequivalence prediction. While conventional AI can deliver precise and consistent results, it also offers regulatory clarity. 2. Fine-Tuned LLM (Second step) A specialized pharma domain LLM can assist in generating reports that meet regulatory standards, extracting relevant study information from existing literature, and outlining a strategy for hypothetical bioequivalence testing. This LLM includes fine-tuning on regulatory guidelines, PK information, and previous pass/fail BE study reports. Why Not Other Approaches? Only Conventional AI: Good for pharmacokinetic data prediction, but lacks explainability and is limited where part of the data is missing. Training from Scratch: Bioequivalence prediction does not need to be developed from scratch, as existing PK knowledge can be fine-tuned. Pure Prompt-Based LLM: Bioequivalence prediction requires precise modelling, so pure prompt-based LLM is not efficient. This approach balances scientific knowledge with AI flexibility (LLM augmentation), making it both innovative, accurate and successful.

Parameter Using conventional AI models and methods Fine-tuning an existing Large Language Model (LLM) to specialize it for a domain-specific task Training a new AI model from scratch using raw data and a custom architecture Designing solutions with flow and prompt engineering without retraining the underlying LLM Key consideration Rule based Specialized task First to market with unique architecture For general use Key differences - Require data in specialized formats - Uses predefined rules and machine learning - Uses Chat GPT, Deepseek, etc - Require specialized data for fine-tuning - Require a large amount of backup data - Requires huge time and resources - Uses ChatGPT, Deepseek or other LLMs for prompt generation - Uses a no-code solution available on the web Advantage - Easy to prepare - High accuracy for prediction - Can perform similar and small tasks - Low cost - Handle all types and formats of data - High performance - Faster development than development from scratch - No dependency on existing LLMs - Customization as per need - High data security - High scalability/flexibility - Faster results - Easy to prepare - No dependency on existing LLMs - Very low cost - High scalability/flexibility Limitations - Limited scalability/flexibility - Low confidence for unorganised data - Risk of data leakage - Still requires some historical database - Require coding expertise - High resources required - Time-consuming - High Risk of failure - High cost - Highly sensitive - Limited capability Use cases - Disease identification based on symptom decision tree - Regression-based demand extrapolation - Selection of raw material as per the decision tree - Legal contract review - Troubleshooting bots - Domain-specific report generation - Genome sequence identification - Self-driving car system - AI for music, image and story generation - Personalized email advertisement - Minutes of the meeting generation - Report fine-tuning

AI Agent for Pharma R&D Literature Search Key capabilities: 1. Raw Contextual Grasp: a. Analyzes scientific literature such as papers and clinical trials at a near-human level and derives hypotheses, methodologies, and results even when relying on incomplete or self-contradictory information. b. Point out implications without a clear or logical basis towards hypothesis suggesting (“X off-target effect by Compound X in The Cell paper is consistent with unpublished toxicity data”). 2. Hypothesis Generation: a. While integrating (cross-referencing) unrelated fields, propose new theory directions. Ex: Alzheimer’s mechanism in Nature could apply to your Parkinson’s work, here’s a synthesis pathway. b. Anticipate no innovation “white space” opportunities. For example, Cyst inhibition has never been investigated for this rare cancer subtype. 3. Real-Time Collaborative Curation: a. Act as a co-scientist alongside researchers as a thought partner who participates to dynamically update during meetings (new Preprint just dropped that challenges your target—do you want to review?). b. Create visual summaries, including but not limited to competing drug mechanisms such as interactive graphs. 4. Regulatory and Competitive Intelligence: a. Show awareness for worldwide rule setting, such as FDA’s new guidance on digital endpoints has an impact within phase III design, and how it relates to the supervision of competitor pipelines, like rival Y, has dropped this target because they showed signals of unsafe markers. 5. Oversight Self-Validating Citations: a. Citations would need evaluation based on rationale and scores given to documents where they achieved self-revalidation of the set hypothesis, indicating that they exceeded verification of their arguments. AI interaction with humans: 1. For Researchers: a. Voice/chat interface ("List me all active patents related to molecule A that have a controlled release drug profile”). b. "Pop-up" notifications for important updates ("Reference product is delisted from the US market due to potential adverse effect"). 2. For Executives: a. Generates a clear report stating risk and benefits involved with the diagram ("Here’s why launching product A aligns with the current product portfolio in the market."). Potential Risk to Guard Against: Overlooking important information: · The AI can be overdependent on "highly reviewed articles from reputed papers" or institutional biases (e.g., ignoring new research or recent findings due to low or no popularity). Risk mitigation plan: · Add special prompts that force AI to search the entire information irrespective of popularity, review, or rating. · Mandatory requirement of human signature or confirmation, especially in high-risk recommendations. (e.g., clinical trial design changes)

Scenario: A patient has just completed his critical surgery and chemotherapy sessions. He has 10 unopened vials, but they are from the open carton. These vials are highly costly, and his overall treatment cost is also high. But he does not need these medicines anymore and asks the manufacturing company to take them back so they can be given to another financially weak patient. For this, he contacted to the company's patient helpdesk chatbot. Patient request: These are expensive medicines, and he feels that throwing them away is wrong, as well as he wants some refunds from the manufacturing company. Company Law: Sold medicines cannot be returned or reused, as there is no data on handling at the patient's premises. Also, there is no policy for a refund. Risk: If the company makes a refund or supplies returned medicine to another patient, then the company may face legal issues if reused meds cause harm. Conflict: Accept patient request: If the company accepts the patient request and supplies medicine to another patient, then there is a risk of safety violation and which may also lead to legal issues. Chatbot follows policy: In this case, the patient will not receive money, lose trust, and waste these life-saving medicines. Approach to guide the AI’s decision: AI’s Immediate Response: "Hello, Mr. ABC, thank you for connecting. We understand your issue, but due to patient safety, we can’t reuse these medicines. We appreciate your effort in the use of medicines for needy people. Let me suggest that you to connect with a donation program at external NGOs." Workaround: AI does not approve this return request, but guides him to connect with NGOs that help to supply sealed vials to needy people. Additionally, as a goodwill gesture, the chatbot can offer him a discount voucher. Human Escalation: If the patient still insists, then AI transfers this communication to an employee who can explain why the medicine can not be returned. Summary: AI should help patients, but not at the cost of safety, even if the rules seem to be unfair for the patient.

Scenario: AI-Driven Pharma R&D Product Development Use Case Overview In R&D, multiple activities are required to design a new drug product (e.g., a tablet, capsules, injectable, or others), which requires expertise in various domains. Here, below example is provided where three AI agents collaborate: Drug-Excipient Compatibility Agent – Analyzes drug-excipient compatibility concerning interaction, stability, and physical behaviors. Drug Product Formulator Agent – This agent suggests the most suitable formulation composition based on the best pharmacokinetic profiles. This module also suggests suitable alternatives, with the best alternatives that can be scaled. Regulatory Compliance Agent – This module ensures that the suggested formulation meets all regulatory guidelines. Product Development Process Flow Drug-Excipient Compatibility Agent screens the best excipients (e.g., binder, filler) and highlights incompatible combinations (e.g., a drug that deteriorates in UV light). Drug Product Formulator Agent provides suitable formulations for which the release profiles match with reference products. Regulatory Compliance Agent evaluates formulations as per regulatory guidance and immediately highlights any discrepancies before conclusion. Challenges in Coordination Conflicting Priorities – Drug-Excipient Compatibility Agent may suggest an excipient that is uncommon or not listed as per the regulatory guideline. In case of Drug Product Formulator Agent suggest a complex formulation that has the highest chances to match, but Regulatory Compliance Agent flags that this seems to be difficult to approve. Data Ambiguity – Regulatory Compliance Agent may not be updated with real-time updates from regulatory agencies. Regulatory Gaps – A formulation developed by Drug Product Formulator Agent could fail due to a lack of toxicity data for one of the novel excipients. Designing their interactions 1. Interactive Feedback Loops Agents share continuous awareness proposals (e.g., formulator agent suggests a formulation for a delayed release coated tablet and Regulatory Compliance Agent checks if the proposed formulation has any impact as per regulatory guidelines. If Regulatory Compliance Agent rejects a proposal, Drug Product Formulator Agent generates alternatives (e.g., matrix tablets instead of coated tablets). 2. Conflict resolution via priority scoring A rank-based decision system prioritizes ranking criteria (e.g., Regulatory compliance > compatibility material > release profile). Example: If Regulatory Compliance Agent and Drug-Excipient Compatibility Agent disagree on a selected excipient, the system defaults to the option with the highest compatibility + compliance score. 3. Rationale for decision Each agent must record rationale (e.g., "Drug-Excipient Compatibility Agent rejected Lactose monohydrate due to Maillard reaction").

Scenario: Error in AI-driven R&D Formulation Development Scenario: A Pharma R&D organization uses an AI system to generate formulations for a new Anti-cancer medication. The AI suggests a formulation with an upper limit of excipient, provided by the USFDA, which claims that it improves efficacy and stability. The formulation clears automatic system checks but is later found to cause mild adverse reactions (e.g., vomiting and acidity) in a few volunteers during trials. The investigation confirms that the AI was trained mostly on data from healthy young populations, but it was missing sensitivity in paediatric and geriatric populations with different metabolisms. This is a critical matter due to the involvement of patient safety, regulatory risk, and reputation damage. Assigning Responsibility (1) AI Engineer – Prime responsible - Due to not including diverse population data e.g., paediatric and geriatric patients. Also, not setting stringent standards for the safety of excipients. (2) R&D Scientists – Scientists have conducted trials without validating facts regarding excipients. (3) Complinace Teams – The Team has skipped review due to confidence on AI system. Design Safeguards for Transparency & Traceability Training Data Data should include all patient demographics (age, gender, comorbidities) and include treatment-specific data for edge cases (e.g., patients with gastric disorders). Confidence score for Formulation Decisions The AI should output not just a recommended formulation, but also include confidence scores (e.g., "90% as highly recommended, but only 70% recommended for elderly patients"). Human interference for Critical Decisions Suggested formulation is near safety thresholds (e.g., excipient limits) and must require the scientist's sign-off. Real-time Feedback Integration Model also updates data simultaneously, including all adverse events. This approach helps AI-driven formulation development without compromising safety, keeping accountability on the humans who design, develop, validate, and use the system.

Case study: In a generic Pharma company, an AI Formulation Co-scientist is introduced to improve product development efficiency via first time right bioequivalence study, longer stability, faster product development, and cost efficiency. Formulation and regulatory affairs teams are in a mode of resistance to put trust in their suggestions having fear that errors of AI could lead to failed bioequivalence studies, a higher number of queries from regulatory agencies and/or costly reformulations. If both teams do not believe in an AI-driven solution then it may delay the filing or launch timeline. Example of Situations where an AI agent would need to earn the trust Scenario 1: Formulation scientists do not trust formulation compositions suggested by AI without proper justification. Expected Response from AI: · AI justifies why it has recommended specific excipients in a specific ratio. (Eg. 5% croscarmellose sodium has shown improved dissolution in 10 approved products from the USFDA). · AI provides a supporting document that a similar formulation is previously been approved in at least one product. Scenario 2: The Regulatory team does not trust on filing strategy suggested by AI. Expected Response from AI: · AI suggests a recommendation that includes a guideline provided regulatory agency. (eg. This strategy matches with guidance provided in the dissolution guideline USP <1092>. These kinds of expected responses are based on evidence or scientific justification provided by AI helps to build trust over time.

Many organizations face business exigencies where multiple critical goals must be achieved within overlapping or conflicting timelines. Current or routine way struggles with Resource allocation, Timeline, and Priorities between departments. An AI-driven approach can help to optimize the above issues efficiently. 1. Scheduling & Prioritization A company has its data on resource availability, team capacity, and historical performance. After analyzing these data, predictive analysis to be done along with scheduling algorithms to adjust timelines. 2. Resource/workforce optimization Based on AI-driven evaluation of employee skills and workload, tasks are assigned. This approach helps to predict and mitigate potential roadblocks. 3. Predictive Risk identification & Solution strategy A specific AI-driven forecast helps to identify potential delays and recommends advanced actions. The example also includes a Monte Carlo simulation in which alternative timelines can also be evaluated. 4. AI-driven team negotiation With the help of AI, a fruitful negotiation to be done between teams or between departments. This considers the current workload, business requirements, and stretch capabilities. 5. Real-Time Project Tracking & Proactive Planning Similar to a project management team, AI continuously reviews milestone achievements and adjusts the timeline according to business needs. AI also considers the actual time required for each task as per historical knowledge. Conclusion AI-driven solutions help to refine schedules, optimize resource/workforce, and mediate between teams or departments to complete goals as per stipulated timeframes. In this way, a positive and synergistic output can be achieved without delaying timelines so that all goals can be completed in the defined time frame.

What is workflow analysis? Workflow is a group of tasks followed in particular sequence whereas workflow analysis is defined as path to improve efficiency. It is the process through which improvement area and process hurdles are to be identified. Workflow analysis steps: Analysis steps depend on business to business. However, broadly it can be performed via asking four questions. (1) What do we do?: In this steps we identify core steps that performed from beginning to end. (2) How do we do?: In this steps processes are identified for each core steps. (3) Why do we do?: In this steps, needs to be identified and importance of each steps to be calculated. (4) What does each department do?: In this steps, significance and activities performed by each department are identified. Based on above questions and answers, inefficiencies in workflows are identified and improvement steps are to be confirmed. In DMAIC approach, (1) Define: Value stream mapping is used to map entire process. Workflow analysis can be done to optimise each step and problem can be defined efficiently. (2) Measure: Data recording to capability analysis activities can be done efficiently through workflow analysis and optimization. (3) Analyse: Validity of route cause can be done via RCA and FMEA. Workflow sequence can be optimised which includes discussion between cross functional group. (4) Improve: Documentation workflow includes what changes are made and why it is implemented as well as impact on improved process can be harmonized. (5) Control: Final documents preparation with control strategy which includes multiple signing from cross functional team. Example: (1) EDMS: Electronic Document Management System: During document preparation, review and signing, particular workflow is followed. To manage this workflow, EDMS can be used which flows document from one department to another department sequentially. (2) iLIMS: When there is OOS (out of specification) or OOT (out of trend) data generated then investigation initiated. iLIMS is used to manage all workflow from identification to CAPA implementation which links all inter department documentation. (3) Solvexia: It is one of software which can help to automate workflow of code and data management. Thereby it improves productivity.

Algorithmic bias means the accuracy of AI or machine learning tools towards one side. It is also explained as when accuracy of AI is higher for certain inputs rather than all. It was first discovered by Joy Buolamwini when she notices facial detection bias in Face++, Microsoft Face Detect and IBM Watson. Each platform shows correct result between 87 to 93% when lighter skinned person evaluated. Accuracy decreases to 65% when skin gets darker. Six Sigma mainly depends on statistical analysis and informed decision is made based on the interpretation of data. Algorithmic bias during analysis can put accuracy of result in six sigma projects under question mark. Example of Bias in Six sigma Project: 1) When data selected for algorithm creation are from specific population then it will form output towards one direction. E.g. During manufacturing process optimization project when data was collected from one operator rather than multiple operator then it will provide solution based on one operator only. 2) When measurement tools used for data collection systemically generate data that defers from true values. This bias can also lead to inaccuracies and distortions in the data that affect validity and reliability of the project. 3) Interpreting data is a critical concern while analyzing six sigma data. When analyst inadvertently change their interpretation with preconceived outcomes. Due to this, wrong conclusion can be taken which impacts root cause and solution identification. How to prevent Algorithm bias: 1) Data transparency: Data source, measurement system and sampling procedure should be well controlled and documented. 2) Diverse analysis teams: Data to be verified from cross functional teams. 3) Robust statistical methods: Rigorous statistical tools to be used to minimize the impact of bias. 4) Continuous training: Training to be provided to Six sigma project members and regularly training update also to be provided. 5) Document assumption: Identify potential source of bias and document properly. This helps to understand stakeholder for the potential risk.

Status quo bias is defined as the continuing current strategy or idea and rejecting new strategy or ideas even though it may change state of affairs. It was first identified by Mr. William Samuelson and Mr. Richard Zeckhauser in 1988 in the academic article “Status Quo Bias in Decision-Making.” They have done various experiments regarding given the choice between the status quo and a new option and they found that people were more likely to follow already existing situation. It is the situation where decision is taken emotionally rather than logically. Whenever changes occur or proposed then people may feel uncomfortable where the outcome is unknown. This tendency is to keep people themselves safe and the things continue as it is. Ultimately status quo bias negatively affects ability to make decisions. Due to this people may miss valuable opportunities. Status quo bias can also hinder the growth of a business. If top management is not willing to take risks which could benefit the business at large, the company could begin to stagnate. Examples of Status Quo Bias in the Workplace? If a company already have CDA with vendor and CDA is about to expire. Then company will renew agreement without giving opportunity to another vendor which may be better than earlier. When a company is introducing SAP system to replace ERP then employee show preference for keeping existing ERP for which they are more familiar. However, in actual scenario, SAP provides better flexibility and more options for management. How to Overcome Status Quo Bias? If you’re in a leadership position then it is up to you that how to communicate organizational change to your team. Learn to recognize status quo bias in yourself and others It is a leadership skill that understand status quo bias when it happens. Weigh the advantages and disadvantages Sometime status quo bias may be beneficial to take wrong decision. So, we need to identify pros and cons for this. Collective decision needs to be taken. Frame the default option as a loss We need to consider loss as a default option according to loss aversion principle. Follow the REDUCE framework Leader need to establish certain framework that reduces barrier for change.

It is a psychological phenomenon in which selection of option differs based on how information is represented - how it is framed or shown. According to this phenomenon, people may be more likely to go with high success rate than if it is advertised as having low failure rate, even though the information is mathematically equivalent. Techniques to detect framing effect: There are three techniques for detecting the framing effect i.e. data techniques, analysis techniques, and presentation techniques. Data techniques: Origin of data along with quality, structure, frequency or gaps identified. Analysis techniques: Different statistical models / algorithms can be applied and predicted vs actual results were compared. Presentation techniques: Format to be finalized for discussion and same format to be used for subsequent discussion. Minimizing framing effect: There are three ways to minimize the framing effect i.e. diversify, validate, and communicate. Diversify: Multiple source data need to be verified instead of refereeing one source data. For collecting data, same format to be used. Validate: Model/ algorithm based statistical analysis to be done to validate predicted data vs actual data. Communicate: During presentation, objectives need to be communicated clearly with uncertainties and limitation.

Disintermediation is the removal of intermediate between manufacturer and customer. It is also called as B2C model. By this step, both (end user and manufacturer) are impact. This impact can be beneficial to both but its all depend on type of business company have. There are two scenarios in which company should go for disintermediation or not. (1) Go for disintermediation: Major driving force for disintermediation is internet. In recent years, internet has reached to almost all corner of world. Business which has high potential to go along with internet will have high potential to grow more with disintermediation. Below are factors which should be considered for disintermediation: Cost saving: This is a major benefit of disintermediation. In today’s world, profit margin is decreasing day by day due to competition. By removing commission, fees or markup, company can save some cost and increase profit. Increasing efficiency: Lead time of product delivery is reduced. Quick decision: Companies will have greater control on decision making and implement easily without taking concurrence from supply chain. Customer satisfaction: If company is giving commitment then there will be more confidence in customer mind and feel safer for buying product. Example: a) Apple/Dell: Both companies sells product directly to customer via avoiding traditional retail model. These companies have their own online and offline store to cut down commission and markup fees. b) Airbnb: Airbnb provides lower price rooms to customer directly through its website. c) Amazon/Flipkart: Amazon sells wide variety of product to customer though its website and provide low cost product to customer. d) Google/Facebook: These two platforms hits traditional advertising companies via providing a digital advertisement service to manufacturer which reaches to large crowd easily. (2) Not to go for disintermediation: There are certain businesses where disintermediation not required or negative impact on business. For these businesses, intermediator provide support for better reach to customer. Below are some cases where disintermediation has negative impact. Direct marketing cost: High cost product where a direct marketing is not possible or customer base is low then direct marketing cost will not suffice. Companies must need intermediator to reach specific customer base. High volume products: Sometime product cost is too low and customer base is large then it will be difficult to reach each and every customer. In this case service to customer will be largely impacted. Low shelf life product: If product having low shelf life then reaching to each and every customer will be difficult. Display of product: Business where a display is needed and customer must visualize product before purchase. Example: a) Automobile Industries: Customer will always like to see and try product before purchase. b) Food industries: Food products (eg. Frozen foods) have low shelf life and require controlled temperature to transport product to customer which will cost more to manufacturer. c) Common stationary: Generally stationary items have low margin and volume are high. It will be difficult to send each pencil/pen/rubber to each customer.

It is the phenomenon in which people generally follow what other people or group follow even if his own belief differs. It is also called as herd mentality. It was first identified in 1848 United States presidential election in which Zachary Taylor received significant support after his successful campaign. It is inherent human behavior to follow crowd. It is belief that where ever a large mass are present to support that means it is more perfect or suitable or successful can be easily selected. In majority cases, bandwagon effect overrides logical thinking. As a human behavior, we think that a decision should be taken with majority opinion instead of following own logical thinking. One of example is riots after false news. If a person is a big fan of a cricketer or film star and that film start promotes certain political party then we also follow and vote for that party even if we didn’t know its past performance. For buying a product from an e-commerce market place, we generally follow rating or positive comments written about that product. Sometime we buy expensive or out of budget item just by seeing its positive review. During trading in share market, we generally follow upward trend to buy stocks based on recommendation from broker. We do not verify from candle chart or PE ratio. Sometime it is beneficial too. At early age of working life, we starts an investment for retirement and for that we follow what our colleague are doing or strategy followed by super senior who are already retired. Bandwagon is group thinking and it is difficult to avoid. At some extent we can minimize it. There are certain steps by which we can minimize its effect. 1) Critical thinking: There should be two way thinking. Based on decision making position a person has, he has to think differently and comment. Then need to take collective decision based on group and individual opinion. 2) Look for reliable source of information: Decision taken based on group thinking should be verified against some source before accepting it. Source must be genuine. 3) Take a pause: Before finalizing a decision, take a small pause and do deep thinking that what can be positive and negative implication. Then a decision can be taken to go ahead with group opinion or not. 4) Analytical Hierarchy Process: For business decision, an AHP can be utilized for finalizing any project instead of what our competitor is working on. 5) Interview form: Instead taking decision in a group meeting, an interview form can be given to each participant and answer can be collected. We can also mention to provide support to justify answer.

It is a cognitive bias that influences judgment in favor of recent events. It is a tendency to focus on recent situation and events over past experience. This kind of situation can impact decision making. For example, employee get promotion based on recent performance or achievement instead of cumulative performance over last few years. Another example is investment in stocks based on recent spike in price instead of review of past performance and fundamentals. The same situation may also be observed in project management sometime. We took decision based on certain time-lapse instead of past information which can significantly impact project success. A project manager manages a project through out lifecycle. He has complete knowledge about project. But during situation of emergency, stakeholder and project manager takes decision based on recent example or situation instead of overall experience of project lifecycle. Recency bias can create a problem in project because decision is made based on recent events, ignoring historical background, avoiding long term planning and making emotional decision. There are certain ways how to avoid recency bias 1) Structured Evaluation Criteria: Integration of transparent evaluation criteria or point based system with defined KRA. There should be certain benchmark at each stage of project life cycle on which basis decision will be made. 2) 360 degree feedback: Feedback to be taken from all members involved in project management and collective decision to be taken. 3) Historic performance review: There should be such performance tracking tools on that basis decision to be taken instead of recent performance. 4) Delaying decision: Delaying decision make project manager to overcome bias due to cooling off period. 5) Diverse decision-making panel: Diverse expertise or experience panelist should be selected which consider past experience and make crisp decision. 6) Training and awareness program: Training should be provided to all decision-making people. Confirming the presence of recency bias and mitigating risk, project manager makes more accurate, fair and precise decision

Ambiguity aversion is an irrational tendency to prefer the known over the unknown. We always like to follow known situation even if high cost or investment involved. People always think for potential draw back first rather than benefits which leads to ambiguity aversion. E.g. If we want to invest INR 10000 and we have two option to choose. Let us take a look. Option 1: Invest in fixed deposit in back where we can earn 6.5% interest. Option 2: Invest in mutual fund where risk is there and can earn 6.5% or more annually. Although there is more chance of higher benefit in second option but people choose option 1 as safest option due to ambiguity aversion. In an organization where a strategic decision is required then ambiguity aversion plays a critical role for growth of organization. For eg., when a decision to be taken for expansion of business and business has two options. Either expand same product pipeline or introduction of totally new portfolio. A business may have an ambiguity aversion that go ahead with extension of existing pipeline products only although a firm knows that overall profit may be higher in new portfolio. There are certain way by which one can avoid or overcome ambiguity aversion. (1) Implement uncertain decision as pilot project or do small scale trials. (2) Focus on benefits rather than comfort. (3) Calculate risk factor based on BME analysis. (4) Think for back up plan along with implementation All above are few ways amongst many by which one can overcome ambiguity aversion and can take fruitful business decision.

Lock Out Tag Out is a technique to protect a person from unwanted danger. Primary purpose of LOTO is to protect an operator or maintenance person when machine is in non-working condition or in routine maintenance condition. Danger is not limited to electrical or chemical but sometime data breach also from equipment having SCADA. LOTO should be done under strict supervisor. As failing to this may lead to loss of life, high cost of insurance, reputation of a firm, loss of data, etc. LOTO is using locking system at source of energy. Key of the lock should be held by authorized person only. Then labelling should be carried out which describe kind of work is ongoing, duration of work, hazardous impact, etc. LOTO can be very helpful in-service industries also. Like, audit is critical part of any industries and data in audit activity must be protected from unwanted access from external person. One can control access to data via password control mechanism or store data in separate device which has limited control. Another example is cyber security trial in banking system. To check protection system at banking transaction or to find out bugs, cyber security person does trials to break the protection. At that time, system should not allow active transaction and shows banner that system is under maintenance.

"It was a good experience to learn about Six Sigma. I liked the training strategy and how it was conducted." Hardik Joshi, Research Scientist, Sun Pharmaceutical Industries Ltd.