Yarra_Nethaji

Recency Bias – by the name itself, we can able to understand that, we forecast/predict the future scenarios based on the recent performance/situations etc. It is the human tendency to focus more on the recent events rather than long time. Some of the examples mentioned below Performance appraisal is considered the most impacted by the recency bias. In general, before appraisal, if we have done some good job which is considered as good performance. Even if, that there is a poor performance during the entire year.  I am thinking to go on a road trip. some of my friend mentioned that the highway/road no.123 is very dangerous due to accident happened 6months back. But this road use to consider as one of safest road due to zero accidents in last 5 years. So due to recency Bias, I tend to choose that road is unsafe.  I am going to buy brand A phone. But my friends told me that the phone is very good, due to its recent performance & battery backup is good. Based on this feedback I brought the phone, but it is not working well. As the past performance of this type of phone is very bad. I could have saved money if I brought Brand B phone, which has good performance in the past.  In a manufacturing line, one of the parts is failing from last 6 months. But in the past, there is no records of failure. Due to recency bias, I tend to decide that this equipment is a chronic problem for this manufacturing line. In service sector, I am going on vacation to other countries, I would like to stay in hotel XYZ, but the experience in last month visit was very bad, because during the visit, there was some construction work going on. But in the past the experience is always good.  If I stay in Hotel ABC, my expenses will be 2times. With out recency bias, If I would have stayed in the hotel XYZ, I could have saved money. In the chemical making line, one of the equipment failed recently, then the root cause was base vibration bolts are loose. If the equipment failed, operators are tending to tightened the bolts, instead of investigating the problem in details If we don’t consider the recent bias in project decision making, it may give short term benefits rather than a long-term benefits. For example, in the improve phase we identified that the one of the equipment speeds should increase to get the better quality. But recently the equipment got failed to high vibration due to some other reason. But while implementation, team will not approve this decision of increasing the speed of equipment. In this situation, if we could have the data like equipment history and root cause of last failure, we might have increased the speed without hesitation. Which will also improve the quality.   The reason for recency bias Memory and Attention – We tend to recollect more recent information & prioritize the recent over the old Availability Heuristic – People often rely on the recent available information, rather than the old Impacts of emotion – Recent events evokes stronger emotions, which impact decision making Short-term memory bias – Events or information that occurred recently are most likely to be at priority for the individuals, which influence the decision making or the judgement Cognitive load – The busy work environments and lot of information (cognitive load) challenging to consider the person’s performance over a period of time. This will impact the decision making Recency’s perceived relevance – We always assume that the recent events are the more indicative of the performance (most recent information is most relevant) which also impact the decision making. Some of the ways to avoid the recency bias: Data – Get enough data before making decision. In the above example of equipment failure, we need to see the equipment history, PM schedules, vibration analysis report, OEM recommendation of failure, process conditions, then only we can take decision of overhauling of equipment, change with new equipment Look at the Big Picture: Instead of focusing on what’s happening now, try to consider what has happened in the past. If we’re thinking about building new manufacturing plant, we need to look at how the returns gave over years of similar business. Standardized procedures: These procedures are can reduce the subjectivity & feelings of recent bias. In the same example, we can say that before buying a new equipment. Engineers need to bring all the relevant information, and do the proper analysis of the data and estimate the life of equipment. Even some time, we can refer to benchmark life of the equipment within the business. Training:  Raising the awareness within the team to avoid the recent factor. Which helps focus on the historical events/data/situations rather than the recent ones.  Get Different Views: By talking to people who might see things differently or consider other time frames. The SMEs/consultants comes up with more experience of various industries & expertise. They can validate properly about the recency bias of events/situation and also may guide for right decision. Slow Down: Try to avoid making quick decisions based on the recent events. By taking more time can help us to think things through more thoroughly Use a performance review system : Try to use the online system to track the performance. So that the data can be summarized for decision making

Software testing:
In every industry Quality testing is the most important phase, once the product cleared from this phase it will be sent for customer. In most of the companies, quality function is considered as the customer face in the business. In software development, testing is also one of the crucial phases, before releasing the developed product to users.  In manufacturing, products are checking using some sampling method, which represent the population. In any software product the life cycle is very high, hence it has been rigorously tested against the user requirement, the quality of code and the external access (penetration test) etc. Here the testing done for each requirement in multiple ways. Here sampling cannot be considered as the client might have use the products in various ways in their day-to day business. White box, Black box and grey box three kind of testing techniques used in software testing.             All these types of tests are important to verify the functionality & integrity of the software. Black box testing: This test is performed by the software testers. The code internal structure is unknown to the tester. The focus more about the user requirement, functionalities are fulfilled or not. This test can be carried out easily(quick) and it can be performed by testers with no knowledge of the code. This is sometimes performed by the independent teams (out sourced). It is also called as behavioral testing. In this test we only focus on input and Output. This test doesn’t consider about the how the it is processing. The tests performed are broadly categories into function & non-functional. The details are as below. Function testing – Smoke testing, sanity testing, Integration testing, System testing, Regression testing, user acceptance testing Non-functional testing – Usability test, load test, performance test, compatibility test, stress testing, scalability testing. The black box testing techniques are mentioned below Graph based testing - All objects(node) have the expected relationship to one another. Equivalence partitioning – In this step, divides the input domain into classes of data Boundary value analysis -Not completely clear, a more no. of errors will occur at the boundaries Comparison testing – To minimize the redundant errors occurs in hardware or software Orthogonal array test – which the input domain is relatively limited White Box testing: This test performed by the developers with detailed knowledge of codebase itself. The code internal structure is known to the tester. It is about to comparing how the system actually functioning against the how the systems should function. It is very comprehensive testing (more time & high cost). If this is done during development, the bugs can be avoided during deployment. (Preventive). As it is very technical test, there may be chance of missing simple bugs (low level). This tests sometimes referred as clear test, transparent testing. Testing process include the below steps: Identify the which features to be tested Plot all possible paths in the flowgraph Identify all possible paths Create test cases Execute the test cases Repeat the cycle as necessary. The White box testing techniques are mentioned below Decision coverage – This is the most important once in white box, because it provides data on the true of false results of Boolean expressions in the source code. Condition coverage – It covers the expressions Multiple condition coverage – This verifies the different combinations of conditions and evaluate the decision that the code make for each combination Finite state machine coverage Control flow testing – Seeks to establish the execution of the program by using a simple control structure. Statement coverage – The testers should cover as much as possible of the source code Branch coverage – This part basically talks about how wide the coverage of particular elements of the cod Path coverage – Assess paths within a software application Grey Box testing: It is a combination of black and white box testing, where the internal structure of the code is partially known. The focus more about the how the system is working and whether this meets the end-user requirement. The Grey box testing techniques are mentioned below Matrix testing – States the port of the project Regression testing – Re running of test cases, if any new changes made in code Pattern testing – Re running of test cases, if any new changes made in code Orthogonal array testing – subset of all possible combination Based on the above detailed understanding, if any DMAIC project is associated with digital/tech solution, then as a business excellence professional, we should focus on black box, grey box testing to ensure that the given solution is giving the correct output also fulfilling the project outcome. But we need to take help of company IT team to go through the white box testing. By Black box, grey box testing, we will ensure that the solution is delivering the value to the business. But the underlaying model design, language used in the code, structure of the code should be flexible enough for the upgradation of IT systems as well as aligned with the company IT guidelines & futuristic scenarios. To ensure that the solution is sustainable for long term, we should do the white box testing. For example, we are working a project which predicts the sheet break in the paper machine, which is resulting into low productivity & low OEE. After doing the historical analysis & found the root cause at multiple places, which is very difficult to monitor & control by the shop floor operators. Then the team come up with the tech solution, which identify the variation in the critical causes and suggest operator about the adjustment of the parameters. In this case, as BE professional, we will ensure that the tech solution is properly identifying the variation of critical causes and timely giving the alerts to operators (Black box). But this prediction tool is designed and developed by the third-party vendor. Hence it is very difficult to know, what language they write the code, what are logic they built inside the code. Then we should involve the IT/SF professional to understand the technical details (such as conditions, logics, version control, scalability, performance) which refers as white box. As BE professional, we can play role, while building the models and at the end we should also focus on the in what condition, the crucial KPIs are identifying (Grey Box). In Summary, if any DMAIC project associated with tech solution, BE professional will do the Black box, grey box testing to ensure the projects out come are matching with the business expectation. Also take help of IT/SF professional to carry out the white box testing, to ensure that the developed solution is sustainable, capable enough.

Categories of legitimate reservations:
The Fishbone diagram (Cause-effect) was developed by Kaoru Ishikawa. This Diagram will explain the relation ship between X’s versus single Y. Categories used are Man, Machine, Method, Material, Measurements, Mother nature. During the project as a team along with SMEs we sit together find out the relationship between X’s and Y. In general these logical relationship based on experience, assumptions. Hence there is no proper way to validate(logical) connections. However, there is a powerful tool in theory of constraints i.e. Categories of Legitimate Reservations. These rules developed to verify and validate the cause -and -effect relationship Goldratt developed these logical rules names as Categories of Legitimate Reservations. The main purpose of CLR to check the logic of our ideas with the others logic. These categories provide a methodology to pinpointing errors in our & other person thinking. In other words, CLRs are type of doubt (self or other) might have. The doubts or concerns may pertain to logic or the reason discussed.  The below listed are the type of CLRs Clarity (Level-1) Entity of existence (Level-2) Causality Existence (Level-2) Cause insufficiency (Level-3) Additional cause (Level-3) Cause-effect reversal (Level-3) Predicted effect existence (Level-3) Tautology (Level-3) Clarity – It gives understanding the between the cause & effect. This is the first reservation, which is considered. While preparing and presenting, we seek clarity from each member participated in the session about the cause-effect relationship. This is very important; we should not make the cause-effect diagram just for sake of presenting to senior management or the project completion. The quality of the cause will impact the subsequent steps in analysis. The clarity reservation gives chance to each member to understand the what effect we are discussing, what causes we are putting on the diagram. if someone who are not know about the diagram, can they understand by seeing it. If not, they can reach out to the presenter for clarification. Entity Existence: If the relationship is not clarified by first level, we need to use this rule for validation. In this step, we can verify whether the given relationship is existed in the real process. Because during team exercise, people will confuse and provide the relationship which are assumptions, but not the realities. For example, Car milage(Y) has a relationship with driver age(X). This kind of relationship can be dropped off after looking into this rule. Causality Existence:  In this level 2 rule, we will validate the given cause is really responsible for the effect. Some time, people tend to give the causes which are closely associated/correlated with the effect. For example, we are doing the fishbone for low bulk (Bulk = calliper/gsm) of a paper. The two causes mentioned is grammage of the paper and calliper of the paper. But these two are associated. Because if the grammage increases, calliper will increase. To take one of the causes, we need to have much stronger evidence Cause insufficiency: In this levl-3 rule, we will validate that given cause is responsible alone to the effect and it is combined with another cause to create the particular effect. Reference to the previous examples of paper bulk, the bulk is low is due to the paper is pressed between rolls. But loading the rolls is insufficient. The load is more than the required is combined to creating the low bulk. Which mean higher press load will give lower bulk Additional Cause:  In this level-3 rules, we will find/validate that is there any other independent causes are the to give the same effect. Reference to previous example of bulk, along with higher press load, higher moisture will also impact the lower bulk. Another example is employee morale goes down when we cut the staff, but another independent cause is removing/reducing the benefits Cause-Effect reversal:  In this level-3 rules, we will find/validate that is the arrow drawn between cause & effect is correct, which means some time the cause written is actually effect. For example, I spent less family time(cause) for long working hours(effect). But in it should be reverse. i.e. I spent less time with family(effect) due to long working hours(Cause) Predicted effect existence: In this level-3 rule, we will find/validate that is the given cause will lead to any other effect. If not, then we can go for the additional cause. For example, in health report we got high cholesterol (effect). My diet is having more oily foods (Cause). But this also might have another effect is (fatty liver). If the additional predicted effects are also observed, it will give confidence about the causality identified initially. But if the predicted effects are not observed, then we can look for additional causes. Tautology: This is also known as circular logic. In this level-3, we often check if the effect is the only and inadequate evidence provided for the given cause. People sometimes don’t examine their beliefs, when we verify for a cause, they often give the cause with different words. Some of the examples are Machine produce highest quantity, because it is only machine available with higher capacity Japanese trains come on time, because they are punctual Motor fail very frequently, because it repaired many times The additional features given in customer which is free that cost nothing at all Each category (Rules) is used as a checkpoint to test the validity and logical relationship while preparing & discussing the cause-effect diagram. These rules provide a systematic way to scrutinize and validate the logical structure of a problem and its solution, enhancing decision-making.

Ambiguity Aversion:
Ambiguity aversion influence the decision of doing something. If I don’t know much about the various options, then I will choose the safest option. Which means, stay away from the situations, where outcomes are unknown In personal saving, I divert the major % of money into FD rather than in stock market. Because i know the returns on the FD (safe option). But by doing this I will miss the higher returns in stock market. If my boss taking some decision, I know that the decision could go wrong but I don’t know how my boss will react to me if I say anything, hence I kept silence during the decision making rather than saying it out. Ambiguity is associated with the feeling of discomfort that we experiment when we cannot predict the other side/outcome. There is an experiment that explains the above concept: The experiment famously known as “Ellsberg Paradox” (Economist – Daniel Ellsberg) to under this concept. In this experiment there are 90 balls filled in the Jar. One ball will be drawn at randomly. Out of 90 balls, 30 balls are red and remaining 60 balls are (black+yellow) balls. The probability of picking red ball is 1/3. Option1) If we pick the red ball we can earn INR 100, else zero. Option 2) If we pick the black ball, we can earn INR 100, else zero In option 1, the chance of winning 100INR is ~33%. But in option2 we don’t know how many black balls are available in the jar. But there could be a chance of 59 black balls in the jar, then chance of winning INR 100 is ~66%. Most of the cases we choose the option1, because we know the chance of winning. Option 3) If we pick either red or yellow we can earn INR 100, else zero Option 4) If we pick either yellow or black we can earn INR 100, else zero In option 3 picking red ball is 33%, but the picking yellow ball we don’t know. But the probability between 33% to 100&. In option 4, the probability of picking yellow or black is 66%, hence the chance of winning is 66%. We tend to choose option 4 rather than option 3 when we don’t know the outcome. The Ellsberg Paradox, in its elegant simplicity, shows our tendency to choose know probabilities even when the unknown could offer a better outcome. This bias (known probability) called Ambiguity aversion. Example1 :While justifying a decision Infront of C Level, generally ambiguity aversion will be increased. While defending one’s decision it will move towards accountability, hence the ambiguity will further increase. When we defend the decision, with huge uncertainty scenario, we have to justify with unknown outcomes. Example2: Sometimes we lack in expertise in particular subject (Coding), ambiguity aversion will be very high. But we are expert, due to the knowledge and experience, we can justify uncertain, unknown outcomes. Example3: Assume that student appeared for online exam. while answering MCQs, he is thinking to select option C, but he doesn’t know whether it is correct or not. Then he checks with chat GPT and selected option A. As per the answer key, the correct answer is Option C. Due to Ambiguity Aversion, he selected the known rather than unknown option. If had a studied well and good knowledge to explain the answer he could have got correct mark for this option. Due to Ambiguity aversion, one might have missed the larger opportunities. Example4: Assume that company launching a new sustainable packing product anti-microbial paper during covid time in the market. Company has studied well about the target customers, location, segments. If the company launches to only medical segment, it is well and good, they will get expected revenue and margin. But companies should explore the other markets such as note books, copier paper, food packaging segment to get the higher margin. Because company never know, whether they succeed or fail in newer markets (Can get higher margin). They tend to choose known markets, which will give marginal profits (Due to competition) Example 5: In Many restaurants, we tend to order the known dishes, because there is no sufficient information not available in the menu card about the how the dish or drink going to be. By providing addition information such visuals, taste information which reduce the uncertainty and increase the customer spend Some of the approaches that can help mitigate its impact.
Framing effects: The best way to mitigate the impact of ambiguity aversion is to reframe the alternative choices. Understanding the hidden uncertainties and explore the potential of ambiguity decision Optimistic : highly optimistic people are less ambiguity averse than pessimistic people. because they’re better at considering the benefits of unknown situation

LOTO
Lockout/Tagout (LOTO) protocols are a critical safety precaution to avoid incident/accident. LOTO decreases the likelihood of mistakes and injuries by locking, labelling, and disconnecting machinery or equipment from power sources. In general, in manufacturing industry, before doing the maintenance of any equipment this procedure. This will ensure that equipment is properly shut off and cannot be restarted. In LOTO, we do shut down of electrical circuits, valves and etc.  Lockout & Tagout are the two steps. Tagout system alerts users that the equipment should not be operated, the lockout system prevents users from running the equipment, physically.  A tagout device is the first line of defense, while the Lockout is second. Here is the LOTO sequence Notify all the employees Know the machine power source Turnoff the machine Deactivate Energy Lockout & Tagout Release stored energy Remove the device from power source Here are the some of the use cases where LOTO system can be implemented in service sector If a hotel is going to renovation/modification for a month, then it should stop accepting the booking (Lockout), show limited rooms availability online website (Tagout and then can start the work. If not, guests will reach hotel and get into trouble, leading to dissatisfaction If you are doing money transaction through a bank website, then Bank will hold money (>10000) for a day (Lockout) and will also notify the user (Tagout). After a day the bank will release the money to avoid any online scam and sending the wrong recipient Blocking of accessing the website(lockout), if the wrong password entered multiple times and it will also send message or mail (Tagout)to user about the unauthorized access. To activate the account, we need to go to bank or use other methods which will notify the bank (similar to LOTO removed after maintenance). If an employee resigns from the company, IT team will block immediately the mail & other access to avoid data leakage, unauthorized transactions (Lockout), Employee will also notify through physical/message by HR (Tag out). Employee cannot access anything during this time. (Similar to no one operate machine once LOTO given). To Access again (during notice period time) employee need to take approval from the reporting manager & HR manager. (similar to LOTO removed after maintenance) To avoid the stock out position of a critical materials, procurement team can block user to withdraw materials from the store once it reaches the ROL(Lockout) and also notify in ERP system about the non-availability (tagout). Once the re ordering done, and the material is in transit, then users can start the withdrawing  To avoid the unnecessary spending by the team, while procuring any item team leads to approve the transaction. In sort approval mechanism (2 or 3 levels) like LOTO. Without this user cannot buy or order any items In Sports, if a player is not following the rules, then he can be suspended (Lockout) for some time and notified to the respective sport board authorities (Tagout). During this time, he cannot play, even if he wants to do it. He can only come back after the suspension removed. (similar to machine can operated after LOTO removed). Some more examples in service sector where LOTO is used.  Elevator maintenance ATM service Photo copier maintenance POS system repair Coffee machine servicing Indoor appliance servicing

Freemium Business Model
Freemium Business model is a combination of Free + Premium. Customer will get basic features of the products at no cost, but the additional services is available at premium price. Several factors contribute to appeal of the freemium strategy. Because free features are marketing tool, the model helps a new company to scale up and attract customers without expending resources on costly ad campaigns. The premium subscription by the customer will drive the profit of the company. Social networks are very good drivers. Many services offer incentives for referring friends. Here are the some of the examples for the freemium business models are Spotify, Chat GPT, Canva, Youtube etc.  ChatGPT: Chat GPt is a chatbot language model. When it launched, it was free to everyone for the basic version. But for more updated version, users need to pay. Canva: Canva is a graphic designing platform that allows people to create presentation, graphics and visual. If user want to add more advanced options to their presentation, user need to pay to access those tools  Spotify: Spotify allows users to access 1000’s of songs, podcast. But the free account holders will be annoyed by the ads between the two songs and cannot download songs, cannot skip to liked songs. Spotify offer premium account for better options.  You tube: We all watch plenty of the videos for free, but the ad in between the video is very disturbing. To avoid this, user need to pay for premium account. Advantages:
Free Trial: The freemium business model is that to provides a free trial of the product or service.  Wide range of features: Users can upgrade to access more features of the product or service if they like the freemium product. Low cost: for trial purposes or for basic needs, freemium products are an affordable solution. Improved customer retention: Since freemium products give users a taste of the product before they even have to pay for it, it helps in improving user retention and building loyalty for the brand Disadvantages:
Offers only Limited features: The preliminary or trial version of the product is minimal and offers only  Hidden costs: The company might not reveal some extra charges and expenses that come along with the freemium product. Expensive upgradation cost: Sometimes, the cost of upgrading to access more features can be higher than what users expect the price to be. Challenges faced:
Balancing Paid and Free Features: Producers must find the right balance between the free features and paid features that are being offered to users. The paid feature must be reasonable as it might be a barrier in convincing users to upgrade from the free featured product. Managing User Expectations: Companies must be clear about what they are offering with the free product; if not, users can expect more from them and raise queries regarding the same. Retaining Users: When using a freemium model, it can be challenging for companies to keep users as they might only show interest in using the free model, not the paid one. Companies must focus on providing a good user experience. Six Sigma methodologies can help the companies to provide optimum solutions to the challenges
What features should be free & premium : The startups can launch their product to the certain demography and collect the data for analysis.  By studying the patterns of customer trends, startups should change their initial free offers by adding /omitting the features. How the user can understand the what is premium services of product: Six sigma methodologies can help companies, which features are should be available to customers are premium. The creative methods of communication will help in this regard. If a customer using freemium product from long time, but he never subscribed for premium service, by identifying such customer, company can give one more feature for free. Then customer will think of subscription. Customer conversion life cycle (growth & revenue forecast): Six sigma methodologies will help the business to understand the customer life cycle pattern. Early adopters are the less price-sensitive than others, so they are more likely to upgrade. But a bigger reach of customer happens, price -sensitive customer will come on board. Many doesn't value the free service.  Six sigma will help the business to test these hypotheses and provide a solution to increase the customer reach & better conversion rate. Based on the large set of data, and past behaviour of the customer, growth & revenue can be forecasted. One the biggest thing is to recognize the free plan users. They help in two ways. some of them will become subscribers and some bring new users who become subscribers.