Amol Ingole

Capturing the VOC with a good response rate is like trying to catch butterflies with a net: you need the right strategy and a bit of finesse. Below are some of the methods to improve the response rate: 1. Keep the review / feedback very short and crisp, probably only pictorial view: Applicability: Where one required to gather the feedback on the mobile app service UAE government applications – RTA App ( Road and Transport Authority, DARB, UAE Pass, UAEICP & ADDC (Abu Dhabi Distribution Company) Medical hospital Applications like Cleveland Clinic Abu Dhabi, Burjeel Hospital These applications show 03-05 Emojis towards end of transactions and by clicking one, your feedback is completed. 2. Personalized Invitations: People love feeling special. Use their names and reference their past interactions to make your survey invite feel like a personal letter rather than a mass mail-out 3. Timing when the feedback is requested is very essential: Catch them when they’re most engaged. Post-purchase or post-interaction moments are prime times for feedback. E.g.: Post motor repair, customer visits Garage / Agency to collect the car and good service with some very good car cleaning post repair, customers are obelized to provide survey feedback 4. Incentives: Offer something irresistible. Discounts, gift cards, or entry into a prize draw can Motivate even the most survey-averse customers 5. Multiple Channels: Meet customers where they are. Use emails, SMS, social media, and even good old-fashioned phone calls to reach out 6. Follow-Up: Send reminders. Sometimes people just need a gentle nudge (or two) 7. Clear Purpose: Let them know why their feedback matters. Transparency about how you’ll use their input can encourage participation 8. Thank Them: Show appreciation. A simple thank-you message can go a long way in making them feel valued 9. Confidentiality: Assure them their responses are confidential. People are more likely to be honest if they feel safe Remember, capturing VOC is like having a good conversation – it’s all about being engaging, respectful, and appreciative.

Algorithmic bias refers to systematic errors in computer system that create unfair outcomes and decisions produced are biased and not fair. Some causes & examples of Algorithm bias – Biased training data, Human Bias, Inherent bias in model Examples – 1. Some company uses algorithms for job screening and mostly one kind of Gender shortlisted as the algorithm favour one Gender than other 2. It is found in some policing algorithms that some minority communities are disproportionately targeted leading to over-policing in those areas 3. Medical algorithms sometimes perform worse on minority populations because they were trained primarily on data from majority segment of patients, leading to disparities in healthcare outcomes How to avoid algorithmic bias – 1. Consider Diverse and Representative Data - Ensure the training data is diverse and representative of the population to mitigate biases. E.g. When designing a healthcare algorithm, include data from a wide range of demographics to ensure it works well for everyone 2. Transparency and Accountability - Make the decision-making process of algorithms transparent and establish accountability mechanisms. E.g. Implementing explainable AI systems where users can understand how decisions are made and challenge them if necessary 3. Regular Updates, tracking & Monitoring: Algorithms should be regularly updated and monitored to ensure they adapt to new data and continue to perform fairly E.g. An organization might set up a committee to periodically review the performance of its algorithms and update them based on the latest, most representative data 4. Bias Audits and Testing - Regularly test algorithms for biases and address any issues that arise. E.g. Companies like Facebook and Google perform bias audits on their algorithms to identify and rectify discriminatory patterns Organizational Examples: 1. IBM has developed a toolkit called AI Fairness 360, which helps developers detect and mitigate bias in machine learning models. 2. Microsoft has established an AI Ethics and Effects in Engineering and Research (Aether) Committee to guide responsible AI development. 3. Accenture uses fairness and ethics reviews as part of its AI development process to ensure that its algorithms are free from bias.

In simple words Status Quo Bias is resistance to make any change for any thing unfamiliar. How it prevents organizations to make impactful decisions: Some examples: 1. Kodak's Reluctance to Digital Photography - Kodak was a pioneer in film photography but failed to transition to digital despite having early technology. Their commitment to the status quo of film photography led to their decline 2. Nokia's weak / slow Response to Smartphones: Nokia dominated the mobile phone market but was slow to adopt smartphone technology, allowing competitors like Apple and Samsung to take over 3. Blockbuster's Failure to Compete with Netflix: Blockbuster was slow to adopt a digital streaming model, sticking to its brick-and-mortar rental stores. This allowed Netflix to capture the market How to prevent Status Quo Bias: 1. Support the innovation culture – Google encourages a culture of innovation by allowing employees to spend good amount of their time on projects they are passionate about, leading to products like Gmail and Google Maps 2. Incremental Change and Pilot Programs – Amazon regularly tests new ideas through pilot programs and small-scale rollouts (e.g., Amazon Go stores) 3. Customer-Centric Approach – P&G uses a customer-driven innovation model to develop new products based on deep consumer insights 4. Adopting new Technology - GE embraced digital transformation by investing in the Industrial Internet of Things (IIoT) to optimize operations

The framing effect is where decisions are made based on how the options are presented, rather than on the options themselves Methods to avoid framing effect: A. Present the information equally B. Compare with reference C. Provide balanced reporting D. Present facts E. Present with clear data Some industry and real-life examples: 1. In one of the grocery markets, I was buying Smoked Salmon fish. On the 200 gm packet costing 30 AED, it is mentioned that 10g Carb whereas on the other packet weighing 100 grams costing 14 AED, it was mentioned 4% carb over 100 gm. Both packets are from the same company but the information is presented differently. If someone has to make a choice basis Carb and cost, the person needs to compare both packets on the same scale. 2. In the hospital before getting into surgery, the Doctor mentions a 97% success rate whereas he should also mention a 3% failure rate. Considering health is extremely critical to human beings, one might decide on alternative medicine rather than surgery when he/she hears 03% failure. 3. Investment consultants present options like a fund has a 15% return whereas presenting like this - a fund has had a 15% return while the market average is 8%. This allows investors to make more informed decisions by comparing standardized data 4. Keep the content in natural language – Instead of marketing buy 01 and get 50% off on the second and many more confusing and complex campaigns to be avoided and a clear picture of what the total AED discount is if the purchase value is this much should be practiced. This reduces the framing effect by straightforward comparison. 5. When reporting on a study, include both the benefits and the risks. For instance, "A new drug reduces the risk of heart attack by 30% but may increase the risk of stroke by 5%”. This reduces the framing effect by helping readers understand the full context 6. When presenting a policy, provide both the benefits and the costs. E.g. The new tax reform will save middle-class families an average of $1,000 per year but will also reduce government revenue by $50 billion annually." This helps voters understand the trade-offs involved without being swayed by emotional appeals. 7. Basic example in corporate board rooms, presenters modify the graph scale to show performance visually appealing even though the performance is low. The presenter should provide the real picture to ensure reducing/eliminating the framing effect, appropriate decisions can be made.

How does Bandwagon thinking affect logical decision-making in an organization? Resistance to changes: When everyone is focused on what is currently popular, there is less incentive to explore new ideas or approaches that could be more effective but are not yet widely adopted. Example: Everyone in today’s world is now focused on AI tools/technology but there can be traditional automation which is simple and more suitable to the requirement. Groupthink: Team members feel pressured to align with the majority opinion, suppressing their own insights and critical thoughts. This leads to a lack of diverse perspectives and a higher risk of overlooking potential issues Overlooking Data: Decisions might be made based on what appears to be popular rather than on solid data and evidence. This can lead to choices that are not supported by the actual needs or objectives of the organization Herd Behaviour: If one department or team adopts a new technology or strategy that seems successful, other departments might follow suit without proper analysis, assuming the trend must be beneficial because others are doing it How can an organization avoid falling prey to the bandwagon effect? Anonymous Feedback: Use anonymous surveys or suggestion boxes to gather honest opinions and ideas from employees without the influence of peer pressure. Pilot Programs: Test new initiatives on a smaller scale before full implementation. This allows for real-world feedback and adjustments based on actual performance rather than hype Data-Driven Decisions: Base decisions on data and evidence rather than trends or popular opinion. Implementing robust analytical tools and processes can help in making informed choices Devil’s Advocate: Assign a devil’s advocate in decision-making processes. This person’s role is to question and challenge the consensus, ensuring that all sides of an issue are considered. Training and Education: Provide training on cognitive biases, including the bandwagon effect. Educate employees about the pitfalls of herd behavior and the importance of independent thinking

What is Recency Bias? How does it impact decision making in projects? What are some methods to avoid its impact and instead make well informed decisions? Recency Bias – It is a tendency to focus on recent experiences and the latest information in hand to make decisions rather than thoroughly studying historical information. Examples: 1. If the project has recently passed 02 milestones whereas the project had long-term historical issues due to project nature, resources, and stakeholders; the project manager will report everything is in order and the project is doing very well and does not highlight any risk or take action on risk mitigation. This will lead to momentary happiness but it will be an issue soon. 2. After encountering some recent issues with the software testing vendor on SOW commitment, the Project Manager might suddenly want to change the vendor but there will be a big loss on the learning curve of the already allocated team and if the vendor is engaged for a long time then such issues can be sorted out. So PM should refer to all historical details rather than making sudden decisions basis recent events Mitigating Recency Bias in Project Decision-Making: 1. Document the lessons learned and regularly refer to them while making decisions 2. Conduct structured project reviews from time to time to get insights 3. Use comprehensive data analytics spanning the entire period of the project rather than just recent data 4. Implement a systematic approach to risk assessment that considers both recent and historical data 5. Encourage input from team members who may have different perspectives and who can provide a broader view beyond recent events

Differences between White Box / Black Box / Gray Box software testing: White Box testing Black Box testing Gray Box testing Testers have sufficient knowledge of algorithms, codes, and logic Testers do not know the codes/logic and internal structures/algorithms. This is mostly business user acceptance testing where business units may not have any IT background and try to check all desired functionalities for an external user perspective Testers have partial knowledge of codes/logic Thorough testing can be possible due to the availability of information Limited testing can be possible and support from AI tools can help to do more effective testing This is in between the white box and black box testing but more effective output is possible using AI-enabled tools Detailed code-level testing by internal IT organization user Functionality-based testing with no code knowledge – mostly for business users The middle ground – combining partial code insights with functional testing In DMAIC, the suitability of white box, Gray box, or black box testing depends on the specific phase and nature of the tech-enabled solution. Please find some examples Real-life example: Consider that you are renovating an old house to sell DMAIC: is like renovating the house: First, you figure out what needs fixing (Define), measure how bad it is (Measure), diagnose the root problems (Analyze), make the improvements (Improve), and then maintain the new and improved house (Control) Box Testing: White Box: You're the handyman with the blueprints, poking into the walls and checking the wiring. Gray Box: You're the inspector with partial blueprints, checking both the visible structure and peeking into the attic. Black Box: You're the potential buyer, looking at the house from the outside, trying to see if the doorbell works and if the lights turn on without caring about what's behind the walls. Software Development Industry: DMAIC Project Objective: Improve the reliability and performance of a financial software application. Define: The problem is identified as frequent software crashes and slow performance during peak usage times. Measure: Collect data on crash frequency, response times, and user complaints and perform MSA Analyze: Examine the software development process, code quality, and user interaction patterns Testing Approaches: White Box Testing: Developers conduct thorough code reviews and unit testing. They look at the software’s internal structure to find inefficiencies, bugs, and areas for optimization. Gray Box Testing: Quality assurance teams with some knowledge of the codebase perform integration tests and simulate real-world user scenarios. They check how different modules interact and ensure overall system stability. Black Box Testing: End-users test the application’s functionality without knowing its internal workings. They focus on the user interface, performance, and overall user experience. Improve: Refactor code, optimize algorithms, and enhance the software architecture based on the testing insights. Control: Implement continuous testing using white, gray, and black box methods to ensure sustained performance and reliability. Healthcare Industry: DMAIC Project Objective: Enhance the accuracy and efficiency of a medical diagnostic system in a hospital. Define: The problem is identified as inconsistencies and errors in diagnostic results from a medical imaging system. Measure: Gather data on diagnostic accuracy, error rates, and turnaround times and perform MSA Analyze: Investigate the diagnostic process, from image capture to diagnosis. Testing Approaches: White Box Testing: Medical technicians and IT professionals dissect the diagnostic system's software and hardware components, ensuring each part works correctly and efficiently. They analyze the algorithms used for image processing and diagnosis. Gray Box Testing: Healthcare IT specialists with partial knowledge of the system evaluate the interaction between the software, hardware, and medical staff. They perform tests to ensure data integrity and accuracy of the diagnostic results. Black Box Testing: Doctors and nurses use the diagnostic system without understanding its internal workings. They focus on the output, ensuring that the diagnostic reports are accurate and user-friendly. Improve: Based on findings, upgrade the diagnostic software, train staff, and refine data processing protocols. Control: Regularly test the system using all three approaches to ensure ongoing accuracy and efficiency.

The Categories of Legitimate Reservation (CLR) are rules designed to verify the validity, logic and soundness of cause-and-effect relationships within a problem-solving framework called the Theory of Constraints (TOC) Thinking Processes (TP). These categories act as a quality checker for logic trees and diagrams, ensuring clear and accurate cause-and-effect connections. There are eight CLRs, categorized into three levels that delve deeper into the logic structure: Level 1: Clarity and Existence Clarity Reservation: Ensures clear and unambiguous wording of entities (boxes) in the Fishbone diagram. Each entity should represent a single, well-defined concept. Entity Existence Reservation: It questions the actual existence of the entity within the scope of the problem you're analysing. Level 2: Causality Causality Existence Reservation: Challenges the cause-and-effect relationship between entities. Does one truly cause the other, or is it just a correlation? Cause Sufficiency Reservation: Asks if the identified cause is truly enough to bring about the effect. Are there other contributing factors? Additional Cause Reservation: Examines if there might be other, unidentified causes leading to the same effect. Level 3: Structure Cause-Effect Reversal Reservation: Checks if the cause and effect haven't been accidentally reversed in the diagram. Indirect Effects Reservation: Ensures the diagram captures all the necessary steps leading from cause to effect. Are there missing intermediate effects? Tautology Reservation: Identifies redundant statements in the diagram that don't add new information. Applying these categories helps ensure the fishbone diagram accurately represents cause-and-effect relationships, leading to better problem-solving and root cause analysis. They are widely used across various industries, including manufacturing, healthcare, quality management, and project management. Example with Food Manufacturing Industry: As a food manufacturer, you must solve manufacturing-related problems quickly to avoid hazards, contamination, or operational inefficiencies. Categories of Legitimate Reservation helps to identify the cause of a complicated problem or inefficiency in your facility. To uncover potential root causes food manufacturer (production managers) must ask several Why’s. Manufacturing plant machines & related technologies are essential tools to grow the production capacity ultimately resulting into growth of business. However, any problems or issues related to machines may increase potential threat for production & pre-defined processes. Problem: Machines overheat several times a week which need to be shutdown for quality & safety concerns. Instead of machines shutdown we need to find root cause of issue with help of Whys. 1) Why machines overheat around 2 pm each day? (answer: Climate surrounding manufacturing plant is too hot) 2) Why the climate surrounding manufacturing plant is too hot? (answer: Plant facility manager refuses to lower building temperature) 3) Why? (answer: some ingredients become too cold at input) 4) Why can’t those ingredients be kept warm another way? (answer: we have no portable coolers space) 5) Why? (answer: no good reason). Solution: put the ingredients in portable coolers and lower the building temperature to prevent the machines from overheating. Similarly, expand the analysis by asking more Why’s if the answers fail to resolve root cause of problem. Factors affecting in Food Manufacturing: 1. Machinery 2. Methods 3. People 4. Equipment Consider the scenario below in which 5% of a cake manufacturer’s batches of double chocolate chip cake are burnt. Asking the “Whys” will likely NOT uncover the problem this complex, so many manufacturers use the fishbone diagram and ask ‘why?’ in that context.

Ambiguity Aversion - Ambiguity Aversion is choosing a known option rather than an unknown with the attitude of being safe than sorry How it affect decision-making in an organization? 1. Legacy system and new system In the organization, we have system “X” (a Homegrown system with fewer of the latest security features) for policy booking which can serve simple and complex of complex policies functionality-wise whereas there is a new system “Y” purchased to improve information security and latest features and some count of policies are booked. Due to inadequate information about the new system “Y” and whether this new system will be able to service complex policies or not, the business side of management is not confident and both systems are running in parallel and have major overheads. 2. Credit card business: Company “X” offers a credit card offer that mentions 20,000 bonus miles whereas company “Y” offers an AED 500 welcome bonus. Now due to insufficient/unknown information about how much 20,000 bonus miles correspond in AED or whether we have requirements to spend such bonus points and where to spend and all. People choose an AED 500 welcome bonus, in the actual case be 20,000 bonus points may equivalent to more than 500 AED but the decision goes toward known information Approaches to mitigate its impact: 1. Knowing more about the known options available and if found that a known option might have so many risks then the optimistic person might prepare the other option 2. Building brand – Like Apple mobile – customers can make decisions based on the price and features expected and do not think whether this version is riskier or less risky 3. Social media reviews are another influencing factor for customers to decide about product options and hence adequately addressing customer needs and gathering social media reviews can mitigate aversion risk to a certain extent

History / Background: Value engineering is referred to in 1940 when General Electric Co. experienced a shortage of raw materials, parts, and skilled labor. To maintain continuity in the production process, Lawrence Miles, Harry Erlicher, Jerry Leftow, and other engineers sourced acceptable substitutes that would reduce the production costs without compromising the functionality of the products. This technique was eventually named “value analysis” and enables companies to reduce production costs, improve products, and improve performance. Combining the two concepts formed what is now called VA/VE Following steps to be followed to ensure a good quality product is developed using value engineering. 1. Planning and documentation: All stages of the product life cycle are to be planned / adequate documentation is to be prepared throughout the product life cycle and all minor activities and work breakdown structures are to be planned to ensure robust project management 2. Data / Information collection: This is a very crucial step where all relevant information/data about the product is to be collected: · Expected product specification · Material used · Material Vendors available in the market · Material durability · Functionality expected · Product design · Manufacturing Process Options · Cost associated · Expert opinion ( Involving customers, vendors, and cross-functional stakeholders during product and process designs) · Project Timeline Expectations 3. Supplier selection and management: Robust procurement process, supplier qualification, supplier selection criteria, Master service agreement, and Statement of work, service level agreements, reverse SLAs to be properly defined and documented 4. Manufacturing processes: manufacturing processes, assembly, location of manufacturing, machinery and production equipment, skilled labor, waste management, and sustainability aspects to be adequately defined and executed 5. Testing and production trials: Rigorous testing and validation are very critical – stress testing, HVPT (High volume production trial), temperature variation testing, and product changeover testing to be adequately planned and executed 6. Risk Management: Performing Risk analysis ( E.g. FMEA Failure Mode Effect Analysis / other methodology) to understand the risks associated/capturing environmental risks / capturing risks on the go ( Risks found during production trials and initial sustenance period). Planning risk elimination/mitigation plan to ensure risks remain within acceptable levels 7. Continuous improvement framework: During the complete product life cycle, structured analysis for improvement to be conducted and all opportunities of product optimization to be adequately captured and implemented 8. Government Authorities regulations and Safety measures: Government Authorities norms to be properly evaluated and additionally mandatory/optional best-in-class safety measures to be implemented ( e.g. OHSAS 18001) 9. Life cycle management: Product life cycle from the time the product idea has come to mind till the time the product dies from the market. All stages to be adequately studied and precautionary measures are to be taken to ensure optimum profitability and enhanced customer experience References Various articles and write-ups on the web on VE Miles, L. D. (1972). Techniques of Value Analysis and Engineering. McGraw-Hill. Kelly, F. P., & Maleyeff, J. (2004). Value Planning: The New Approach to Building Value Every Day. Productivity Press. Hansen, R. C., & Mowen, M. M. (2000). Cost Management: Accounting and Control. South-Western College Pub.