Dinesh Selvarajan

I support View B — Treat AI as just another tool. Let us start with the only number that actually matters in this debate: 4.3 vs 4.2. That is the quality gap between AI output and human output in blind tests. That is not a difference worth disclosing. That is rounding error. The label does not change this score. It does not protect the customer. It does not improve the outcome. The only thing the label provably changes is the acceptance rate — dropping it from 82% to 68%. In other words, the disclosure actively hurts the customer by pushing them to reject a provably good outcome and wait longer for a human to reproduce the exact same result. The label hurts everyone and helps no one. Bex's healthcare example is a special case — AI disclosure in clinical settings is driven by HIPAA, FDA guidance, and state law, not voluntary trust-building. That is a compliance obligation, not a transparency strategy. Applying that logic universally — to recommendations, written replies, assessments, and draft documents — conflates mandatory regulatory compliance in a highly sensitive domain with unnecessary friction in every other context. Nobody discloses every tool they use to produce an output. A lawyer does not list the legal research software behind their brief. A financial analyst does not footnote the Excel model behind their report. A designer does not credit the software behind every asset. What the customer is owed is that the output is good and that the organisation stands behind it — and honest answers when asked directly. That is the real obligation. A tool inventory is not. Spotify Discover Weekly makes this concrete. When Spotify launched Discover Weekly in July 2015 — a fully AI-generated, personalised playlist delivered to every user every Monday — they did not label it "made by AI." They let the quality speak. By the end of its first year, over 40 million listeners had used the feature and nearly 5 billion tracks had been streamed, as reported by Fast Company in March 2016. Growth was driven entirely by word-of-mouth — no AI disclosure, no label, no friction. Nobody felt deceived. Trust was built entirely on the quality of the outcome — which is exactly what the 4.3 vs 4.2 score in this scenario already guarantees. Spotify did not hide anything — they answered honestly when asked, stood behind the product, and let quality do the talking. That is View B in practice, at scale, with documented results.The scenario itself reveals the real risk calculation: if customers find out later, trust drops sharply. But the answer to that risk is not a label — it is an honest answer when asked, a clear policy available if sought, and output quality so consistently good that discovery becomes a pleasant surprise rather than a betrayal. The $2M spent annually on unnecessary redos under View A is not the cost of transparency. It is the cost of a label that triggers an irrational reaction to a provably good outcome. Transparency means standing behind your work and answering honestly when asked. It does not mean front-loading a label that measurably leaves the customer worse off.

I support View A — Full Coverage. Let the AI decide everything. The numbers tell a story that View B's headline hides: selective coverage costs $6.5M per year for a human review team that is still 93% accurate on complex cases — meaning they get 7 out of every 100 hard decisions wrong anyway. You are not buying certainty for $6.5M. You are buying a 2.5 percentage point accuracy improvement on 30% of cases. That is $1,260 per wrong decision avoided — an expensive band-aid on a problem better solved at the root. This is not an argument for selective coverage. It is an argument for deploying full coverage now, while redirecting human effort from permanent decision-making to active model improvement — so that the AI earns its 97.5% accuracy across all cases, not just the easy ones. View Comparison — Selective Coverage vs Full Coverage + HITL Criterion View B — Selective Coverage Our Approach — View A Full Coverage + HITL Human role Permanent decision-maker on complex cases Temporary trainer to improve the AI End state Two-tier system forever Full AI coverage, increasingly autonomous Cost $6.5M permanent annual line Reduces over time as AI matures Who bears the wait 30% of customers — permanently Nobody — transitional phase only What humans do Review and decide Retrain and improve input quality Goal Patch AI weakness Eliminate AI weakness Bex is right on efficiency, but misses the more important point: that $6.5M is better spent fixing the AI than patching it with human review. Here is what selective coverage gets backwards — it treats human escalation as the destination. It should be the training ground. Every low-confidence case a human reviews is a labeled data point. Every correction is a model improvement signal. The right architecture is full AI coverage today, with humans redirected to two higher-value activities: model retraining on edge cases, and input quality improvement. Better indexed, cleaner inputs reaching the AI means fewer low-confidence cases next month. The 30,000 escalations shrink over time. The $6.5M cost disappears progressively rather than becoming a permanent operating line. Two FinTech examples prove this is not theory — it is a proven trajectory. In my current organisation, for few clients, we use BioCatch — a behavioral biometrics AI — for fraud detection. When deployed in 2025, the system flagged over 30 alert types requiring human review and decision. Full AI coverage at the detection layer, human review at the decision layer. As humans reviewed and decided, the model learned. Today, alert types have reduced by half — the AI now takes decisions autonomously on cases it has learned from, with humans retaining oversight only on genuinely novel patterns. The $6.5M question answers itself: the human review cost reduces as the AI matures. Upstart, the AI-driven lending platform, took full coverage further. During 2024–2025, over 91% of Upstart-powered loans are fully automated with zero human involvement — including non-standard borrowers with thin credit files, the exact complex edge cases View B would escalate to humans. The AI approves 44% more borrowers than traditional models while driving more inclusive lending, with nearly 29% of loans going to low-to-moderate income communities. The accuracy did not collapse under full coverage — it improved continuously because humans were redeployed to train the model, not review its decisions one by one. The appeals path handles consequential errors adequately. What no appeals path handles is a $6.5M annual cost that becomes structurally permanent, or a 30% customer segment stuck in the slow lane indefinitely — simply for being atypical. Full coverage is not the end state — it is the starting point. Direct the savings toward making the AI worthy of that trust.

I support View A — Deploy the AI. The case rests on one fundamental principle: not all costs are equal. A $3.8M annual scrap bill is painful, visible, and fixable. A field safety incident, a recall, and a fractured OEM relationship are none of those things. Look at what the data actually tells us. Human inspection catches defects at 94% with a 1.5% false reject rate. AI vision catches defects at 99.3% with a 5.5% false reject rate. In monthly terms: human inspection lets 240 defective units escape to the customer every month. AI brings that down to 28. That is an 88% reduction in consumer risk — not a marginal improvement, a structural one. Yes, false rejects jump from 2,940 to 10,780 units per month — translating to a certain $3.8M annual scrap cost. That number is real and it stings. But it is a process problem. You can attack it through model threshold tuning, a secondary re-inspection loop for borderline rejects, or tightening the incoming defect rate at source. The cost is predictable, budgetable, and improvable month over month. An escaped defect on a safety-relevant part follows a completely different cost curve — one that no balance sheet can fully absorb. The GM Ignition Switch case makes this concrete. In the early 2000s, GM engineers identified a defect in the ignition switch fitted to the Chevrolet Cobalt and several other models. The switch could inadvertently slip from the "run" position to "accessory," cutting engine power mid-drive and — critically — disabling the airbag system. Quality inspection and safety classification processes failed to escalate this as a safety-critical risk. The estimated cost to correct the defect at that point: under $1 per vehicle. GM did not act. By 2014, the consequences were irreversible — 2.6 million vehicles recalled, 124 deaths linked to the defect, $900M paid in DOJ settlements, and congressional hearings that put the CEO in front of the nation. A defect that cost less than a dollar per unit to fix at source ultimately cost the company nearly a billion dollars and its reputation as a safety-first manufacturer. The parallel to this question is exact: the cost of deploying AI and absorbing higher false rejects is $3.8M per year — the manageable side of the equation. Tune the model threshold, implement a secondary review loop to recover borderline rejects, improve the incoming defect rate at source. These are solvable engineering problems with predictable costs. What is not solvable is a safety incident that has already reached a vehicle, or an OEM relationship that has already been broken by a stop-ship order. On a safety-critical part, you manage costs. You do not gamble with consequences.

I support View A — Make the AI fully transparent. Here is a question worth asking before this debate even begins: Before AI evaluated performance, did we hide the evaluation logic from employees? No. KPIs, scorecards, and performance rubrics were shared openly. Employees knew what they were measured on. AI changes the engine, it does not change the employee's right to understand how they are being judged. Bex is right on this one, and for a stronger reason than the Google example suggests. The concern that transparency leads to gaming, agents avoiding difficult cases, teams neglecting unmeasured work is a real risk. But it is not a transparency problem. It is an evaluation design problem. If your AI can be gamed by avoiding difficult cases, your AI is not measuring the right things. The answer is to build a smarter evaluation model, not to hide a flawed one. A real example: Teleperformance, one of the world's largest customer service organizations, rolled out AI-based agent evaluation across its global operations and chose to make evaluation dimensions visible to agents (customer satisfaction, resolution quality, escalation rate, and sentiment trends). Rather than gaming the system, agents used the transparency to self-correct in real time. Gaming was self-limiting because the metrics were outcome-based and you cannot fake a satisfied customer consistently over weeks of data. The dilemmas the question raises, agents cherry-picking easy cases, managers inflating scores. These emerge when evaluation logic is weak or gameable. The solution is to design the AI to detect those patterns and make them visible too. An agent who consistently avoids complex cases will show a skewed case-mix distribution. Flag it. That is what AI does best. Hiding evaluation logic does not eliminate gaming... It just makes employees distrust the system they cannot see...

I support View B --> Do not experiment on live operations. The real solution is not choosing between experimentation and customer safety. It is putting a human in the loop between the AI's recommendation and the live decision. A simple interface: Accept and proceed (with a quick note on what worked) or Reject and propose a manual alternative (which feeds back into retraining the model). Two clicks. Zero customer impact. Continuous improvement. For the large online retailer in question, instead of running a live 20% experiment, deploy the AI in shadow mode alongside the current process. Let it recommend the new picking and routing approach for every order, but have a warehouse supervisor validate before it goes live. Every accept and every rejection becomes labeled data that improves the next model version. No customer waits longer than they should. I know this works because I built it. In my Auto Dispute Handler project, we designed exactly this architecture. AI handles the dispute, human reviews and approves. It ran faster than the old manual process from day one. Over time, as the model was retrained on accumulated approvals and rejections, it became more accurate on straightforward cases — and those were progressively auto-approved, while complex disputes continued to be routed to human reviewers. We saved customer experience, improved model performance in production, and never once used a customer as a guinea pig. Bex's Amazon example is impressive — but Amazon spent years and billions building infrastructure to fail safely at scale. Most organizations do not have that runway. The principle stands: let AI do the thinking, let humans hold the pen — until the model earns the right to hold it itself.

Artificial intelligence or AI refers to the ability of the machines (especially computers) to perform human like intelligent tasks. It may be language understanding, learning, problem solving, reasoning, and may also be a physical action (robotic machines). It works by processing large amount of data to make predictions and take decisions. Capability wise, AI can be classified as follows: 1. Narrow AI 2. General AI 3. Super AI Let us see one by one about each level, along with some use cases, its limitations and challenges in advancing from one level to the another: Narrow AI: This type of AI also called as Artificial Narrow Intelligence is designed to perform a specific task or set of related tasks. It has limited constraints as it doesn’t have generalised intelligence like humans. Meaning, unlike humans who can adapt and learn to handle wide range of situations and solve various types of problems, these AI systems are typically trained for one domain or function. Some theoretical use cases of Narrow AI are: Voice assistants: Siri, Alexa and Google Assistant use NLP (Natural Language Processing) to understand the request / prompt and perform simple tasks like answering questions, setting reminders, making calls, etc., Image recognition: Used in facial recognition software like Siri, android face unlock, etc., Autonomous Vehicles: AI algorithms are used to detect objects, follow traffic rules and make driving decisions within specific environments More importantly the AI assistant tools like ChatGPT, GEMINAI and Copilot are also examples of Narrow AI General AI: This type of AI also called as Artificial General Intelligence has the capability to understand, learn and apply artificial intelligence across a wide variety of tasks similar to human cognitive abilities. It could also transfer knowledge from one domain to the another and think, reason and plan autonomously Some theoretical use cases of General AI are: Scientific Research: It will be able to conduct research across multiple scientific disciplines, generating hypothesis and conduct experiments without human intervention Healthcare and Diagnostics: It could also simultaneously diagnose multiple diseases by analysing symptoms, patient history and medical research Robotics: An AGI powered robot could adapt to any new environment and can perform simple to complex tasks starting from doing house hold chores to assisting in dangerous environments like nuclear plant related works. This technology can also be used in space exploration where it is risky and complicated for humans to perform such tasks Super AI: This type of AI also called as Artificial Super Intelligence is an advanced stage of AI where it surpasses human intelligence in every field. Super AI would not only be able to perform tasks but will also have an understanding of emotions, reasoning and social contexts far superior to human brains Some theoretical use cases of Super AI are: Defence and Security: Super AI can be used in defence & security purposes like Global peacekeeping, cybersecurity, etc., It could predict and mitigate conflicts before they happen, using advanced simulations and geopolitical analysis and also it could autonomously defend against cyber-attacks and can enhance the security of organizations and nations. Solving Global Problems: Super AI could tackle complex global issues like climate change, poverty and disease by offering solutions beyond human capability Human Augmentation: Super AI could also be used in enhancing human abilities by improving areas such as health, memory and creativity through brain-machine interfaces Challenges and Limitations: The capabilities of Narrow AI are already in active use today across industries like Customer service, entertainment, healthcare, finance, etc., The General AI is a long term goal and is still theoretical. Whereas, Super AI is still theoretical and considered as an aspirational but distant possibility. Limitations of Narrow AI to advance into General AI is that it lacks the ability to generalise across different tasks and building such system requires significant progress in learning algorithms, memory and cognitive understanding Similarly, challenges of advancing from General AI to Super AI includes incorporation of Human cognition complexity, requires enormous details on computational resources, vast datasets and training environments and also more of human ethical concerns such as decision accountability, impact on human employment, etc., Advancements in AI research are continually moving us towards AGI but it will likely take many more years or even decades to achieve.

Yield management is a pricing strategy that is used for maximizing the sales and revenue of a company by adjusting the prices of their goods and services based on multiple factors. These factors includes, demand, capacity, time-sensitive / perishable inventory, market segmentation, etc., Let us see one by one about each of these factors and it’s conditions for successful implementation: Demand: Yield management is ideal when there is a fluctuating Customer demand for a product or service. Demands for Hotel rooms, flights and transportation services fluctuates / changes based on the season, time of the day and market conditions. Differential pricing works well there Capacity: Yield management works well if a business has fixed capacity that cannot be easily expanded based on the requirement. Business like airlines, theatres and hotel rooms will always have limited capacity and it is ideal to optimise sales for each available slot Time sensitive inventory: Yield management gives good results when applied on time sensitive products. Time sensitive products like, airline seats and hotel rooms lose value after the booking date. Yield management ensures the sales of unsold time sensitive products before they lose the value and maximises revenue Market Segmentation: Yield management plays an important role in segmented market. Some customers must be willing to pay more for flexibility and some customers will look for low costs. Such businesses who have the leverage to segmentize their customers will do well in Yield management Yield management works well in, Airline industry: The airline industry is one of the classic examples of yield management. The price is adjusted based on multiple factors like when you make the booking, seat availability and the demand. This helps them optimise their revenue by offering different prices to different customers based on their booking patterns and preferences Hotels: Hotels use variable pricing based on occupancy rates, seasonal trends and local events. The room rates are adjusted to maximise occupancy and also the revenue for the unsold rooms Event Management: Yield management provide excellent results in Concert events, theatres, sport venues and other entertainment related businesses. The price is adjusted based on Popularity, time of event and also the seat location Public Transport & car rentals: Public transport companies use variable pricing based on travel time, class, bookings made in advance, etc., and car rental companies adjust prices based on location, demand, season, etc., Conclusion: Yield management is effective in industries with time sensitive inventories, fluctuating demands and the capacity is fixed. Which means, it is not applicable for all kind of industries. Industries that doesn’t meet these criteria and industries that are extremely time sensitive may have challenge in implementing this strategy successfully.

Standard Work or Work instructions are Process management techniques to ensure Consistency, Quality and Efficiency. Typically if a person just performs the tasks or activities based on experience or the known process flow it will lead to Increased process variability, reduced quality and decreased efficiency. However if the process is well documented by putting together the best practice and accurate procedures, it mitigates the above said risks. This is what a Standard Wok and Work Instructions does. Standard work is a written work procedure which needs to be followed by processors in their day to day work cycle. It is a most efficient and reliable way to perform a task or process. The elements of a standard work are as follows: 1. Takt Time: Defines the Required production rate vs Demand rate 2. Work Sequence: Process flow defined in an most efficient manner 3. Standard Inventory or WIP: The WIP status to keep a tab on flow and to maintain smooth operations The benefits of Standard work includes Consistency, Quality and Efficiency which will indeed lead to Continuous Improvement Work Instructions in other hand provides a detailed step by step guide on how to perform a task or procedure. When compared to Standard work, Work instructions offer a much more granular detail on how to perform a work. Work Instructions include details about: 1. Tools 2. Materials 3. List of quality checks that needs to be done 4. Safety requirements, etc., It will also has Visuals, images or diagrams which would be easy for those who review the same. Work instructions ensures High quality, provides clear guidance in new or unskilled users and so is very useful for training purposes. Conclusion: In short, Standard Work ensures overall process efficiency, while Work Instructions ensure accuracy and compliance with each task's specific requirements.

ISO 9001 is one of the Global standards for Quality Management, It focuses on organizations Quality standards and also helps meet Customer and regulatory requirements. Few advantages of using ISO 9001 are, it focuses on standardization by ensuring structured well documented approach to Quality management and also earns Customers trust by committing to regulatory requirements. One main disadvantage would be, it is a document heavy process which requires more paperwork and slows agility. In the same time it is costly and time consuming, More importantly, it is excellent for standardization but less focuses on Problem solving On the other hand, Lean Six Sigma is into Problem solving. It is used across industries to address issues, minimize variations, and ultimately enhance customer satisfaction. One advantage of Lean six sigma is that it provides access to use various tools and techniques to drive Continuous improvement and problem solving. Lean methodology helps focuses on reducing waste and to standardize the process where Six sigma leads to reduced variation and enhances significant cost savings. One major drawback would be the cost and time involved in training the people. Also, it may not provide broad organisational framework for QMS but is more useful in problem solving So, it is crucial for an organization to use Lean Six Sigma in its strategic decision making process and also to achieve the goals in the long run However, organisations can use both ISO 9001 and Lean Six Sigma where the organisation will get the benefit of ISO 9001 in terms and structural foundation and Quality management and Lean six sigma will help the organization drive ongoing improvements within that structure. Some examples are: A classic example is that, manufacturing industries use ISO 9001 to manage compliance with industry standards and they also use Lean six sigma for continuous improvement initiatives like reducing process variations, enhancing product quality and minimising production costs In healthcare, hospitals can make use of ISO 9001 to ensure standard patient care process where they can also make use of Lean Six sigma to reduce wait times and eliminate operational inefficiencies

Confirmatory bias means when individuals favour information influenced by their existing beliefs and fail to think about alternative viewpoints. One can also influenced by the decisions and viewpoints of others in a discussion forum or a group. This bias affects effective problem solving, as it limits the project manager’s ability to explore all possible solutions. So, it is important for a Project manager to identify and address Confirmatory bias to take informed and balanced decisions. One major area where, project managers face Conformity Bias is mostly during Brainstorming sessions. Ideation techniques like, classic brainstorming, Round robin will push individuals align their opinions with those of the group because of multiple reasons like Influence towards senior people in the group or influenced by people who are vocal in the group. To mitigate this a Project manager should: 1. Ensure open communication where team members should feel comfortable in sharing their opinion without being influenced by the group 2. Ensure the team members contribute unique ideas reiterating its importance and benefits 3. Ensure each team members contribute equally to the ideation process based on their area of specialization Project managers can also use ideation techniques like Six Thinking Hats, Delphi technique, and Nominal Group Technique which encourages structured thinking, diverse viewpoints, and unbiased decision-making, leading to more effective project outcomes.

What is Recency Bias? Recency Bias is a cognitive bias that is about taking decisions / arriving at conclusion based on the most recent events / data points rather than considering the historical events / data. This often leads to a misrepresentation of the true nature or overall picture of the situation For Example: In employee performance evaluations, a manager focusing on an employee's recent accomplishments or mistakes rather than considering his / her overall performance throughout the entire evaluation period. This can lead to skewed assessments that do not accurately reflect long-term performance How does it impact decision making in projects? Data Analysis: While evaluating Process performance data, a recency bias might cause a team to focus too much on recent data trends, by overlooking long-term patterns that are more indicative of a process's true performance Root Cause Analysis: While performing RCA, the Green belt / Black belt might prioritize recent incidents or issues over older ones, even if the older issues are more significant or prevalent Performance Evaluation: In performance evaluation of a change Project, there might be a tendency to judge success or failure based on the most recent results, without considering the full range of data from before and after the change Decision Making: In resource allocation the manager might be influenced more by recent successes or failures rather than a comprehensive analysis of all relevant historical data How to Mitigate Recency Bias: Use statistical methods and tools which will enable you consider the full range of data Ø Ex 1: Pareto Charts: Pareto charts highlight the most significant factors in a dataset based on the principle that roughly 80% of problems are caused by 20% of the causes. This tool helps teams focus on the most impactful issues rather than recent occurrences Ø Ex 2: Control Charts – Control charts help visualize data over time and identify true trends and patterns. They also provide a comprehensive view of the process stability over a long period Ø Ex 3: Run charts display data points in a time sequence, allowing teams to detect trends, shifts, or cycles. They help identify whether recent changes are part of a longer-term trend or just short-term fluctuations In addition to this, ensure that data analysis includes a thorough review of historical data, not just the most recent information. In DMAIC, ensure that you consider data from all relevant time periods. Also, train the Project resources and team members on cognitive biases, including recency bias, to improve awareness and encourage more objective decision-making

Capability Maturity Model Integration or CMMI is a proven framework that helps organizations to improve their Processes. It focuses on continuously improving Process performance. There are 5 maturity levels in CMMI. They are: Level 1 – Initial Level 2 – Managed Level 3 – Defined Level 4 – Quantitively managed Level 5 – Optimising As the Level increases, the Productivity & Quality increases and the Risks decreases. Level 1 - At this level, the processes are usually ADHOC. The processes in this level Unpredictable, Poorly controlled, reactive. There is no formal process management in place Level 2 – At level 2, processes are managed by basic project management techniques. Here, the processes are Planned, documented, performed, monitored and controlled at a Project level. These processes are often Reactive Level 3 – At level 3, processes are focused on Proactive process management and the projects are usually executed based on Standard process which is managed at an organizational level Level 4 – At level 4, processes are focused on Quantitative management which means, Processes are controlled using Statistical and other quantitative techniques Level 5 – This is highest level of maturity which is characterized by Continuous improvement. The focus of Level 5 is on Process Improvement Organizations can adopt CMMI along with LSS to: Enhance Product Service & Quality Improve Process Efficiency Improve Process Effectiveness Improve Customer Satisfaction Improve Productivity

What is Management By Objectives (MBO)? How does Lean Six Sigma principles integrate with the MBO framework? Illustrate by providing examples. Management By Objectives (MBO): Management By Objectives (MBO) is a management approach which is primarily to align Goals and objectives in an organization. It is a strategic approach to enhance the performance of an organization. In this set up the Managers as well as the employees work together to set up and Goals and collectively monitor the same till closure. These goals are derived from the Organizational goals which is then translated into Personal goals for the employees of the Company. It was 1st termed by Peter Drucker in the year 1954 in his book “The Practice of Management”. However, the concept was derived from multiple other management practices to create the complete model. Lean Six Sigma Principles in MBO: MBO framework adopts most of the Lean Six Sigma principles like Setting SMART objectives, Data Driven approach, Continuous Improvement model, Employee involvement and Performance Measurement Setting SMART Objectives: MBO sets Specific, Measurable, Achievable, Relevant, and Time-bound objectives to various levels of an organization. Lean Six sigma tools like SIPOC, Process Mapping and VSM may help identify areas within each of it’s organization’s Goal that need improvement. In case of VSM, by mapping out the current state and defining the future state can help the teams align their approach towards achieving the goals. Data Driven Approach: MBO is measured based on the Performance data of each of it’s Goals. Some examples of data driven KPIs: C-Sat Scores, Sales Numbers, NPS Scores, etc., Lean Sixgma Analytical tools like RCA and Statistical tools like Pareto provides excellent opportunity for the Employees / Teams to identify most significant factors affecting each of these metrics and can indeed be improved Continuous Improvement: Achieving most of MBO’s Goals require a Proper Project management approach. Lean Sigma’s DMAIC (Define, Measure, Analyse, Improve & Control) and DMADV (Define, Measure, Analyse, Design & Verify) provides a structured methodology to ensure sustainable results Performance Measurement: MBO requires periodic Performance measurement against objectives to track the progress. Lean Six Sigma provides tools and metrics to measure performance and track improvements. For example, the Sigma level can be used to measure process performance and identify areas for further improvement. Similarly, Control charts can be used to enforce Statistical Process Control Integrating Lean Six Sigma principles with the MBO framework enhances Goal clarity, Data-driven decision-making, Continuous improvement, Employee involvement, and Performance measurement, leading to improved organizational performance

Reverse Engineering: Reverse Engineering is also called as Back engineering. It is the process of learning / extracting design information by deconstructing a Product which may be a Machine, software, technology, Biological function, etc., I would like to quote two main areas in which Reverse Engineering is more beneficial. They are Product Improvement and Cost Reduction. Product Improvement: Reverse Engineering helps companies to analyze it’s competitors products for understanding their features, the types of materials used and the way the product is manufactured. By learning this, the company can understand their rival products and use the learnings to improve the Quality or Efficiency of their Products. Example: In the booming E Vehicle industry, Tesla analyses battery technology of the traditional EV makers to understand their Design, Performance and Manufacturing process through which they will identify opportunities to improve the efficiency of Tesla batteries Cost Reduction: Reverse Engineering helps businesses to analyze their own Process (Manufacturing process / Supply chains). This can be achieved by analyzing their existing Process or systems in which they can find ways to streamline operations, identify and reduce different types of wastes and cut costs. Example: A software company examines it’s own Software Development Life Cycle process which includes Requirement gathering, Design, Coding, Testing and Deployment. By Reverse Engineering their own process, they identifies that there were process inefficiencies such as Long Feedback loops, redundant documentation, etc., By fixing these inefficiencies, the Software company can save costs. The other areas where Reverse Engineering is successfully used are: Forensics & Investigations, Legacy System Mitigation, Intellectual Property protection, etc.,

A Company usually depends on two key contributors as part of their Supply Chain. A Supplier/Vendor who is a Person or another company who provides goods or services for manufacturing of the products. Similarly, A Distributor who is a Person or an organization or a business who purchases goods or services from the industry and supplies them to the Customers. Let us take a Smart Phone Company for an example. To manufacture a Smart Phone, the Smart Phone company requires different parts like Display, Camera & it components, Processors, Memory chips etc., Where the Smart Phone company only owns the process of assembling the parts to produce a Smart phone, they always rely on their suppliers for producing the parts they need. Each supplier will be owned by a different entity. Similarly, to distribute the Smart Phones the Smart phone company relies on the Distributors who procure the Smart Phones and sell it to the Customers. As you can see in the example, the Smart Phone company relies on the Suppliers and Distributors as major part of their supply chain and hence may face multiple challenges like, Dependency on Suppliers, No direct Quality control, Higher costs due to intermediates, etc., which may lead to disruption of the Company’s Operations and also the affects the integrity of their brand Vertical Integration Strategy helps a Company to overcome these challenges by taking direct ownership of these Key parts of their supply chain which are the Suppliers and the Distributors. In vertical integration, a Company can also directly manufacture the goods required for producing their product (Backward Integration) and also Sells them directly to their Customers (Forward Integration). Taking the Smart Phone company for Example, the Smart phone company will own the manufacturing of the goods required for producing a Smart Phone and the same time they directly sell the Smart phones to their Customers. This helps the company in Saving costs, having direct Quality control on End to End process, Increases market power, etc., Few other examples of Vertical integration are: Amazon owning and operating its own logistics (Amazon Logistics) – Forward integration Starbucks acquiring Coffee farms and roasting facilities – Backward integration Netflix started producing its own shows (Original content TV shows & Movies) – Forward integration IKEA owning Swedwood, a furniture company – Backward integration Similarly, there is also Horizontal integration in which an industry acquires merges it competitors (A company in the same industry) to grow their business. A simple example is: Facebook acquiring WhatsApp and Instagram…