Yarra_Nethaji

The Biggest difference between the successful business and other is successful business attention to what customer is saying. Customer voice is a way to understand the gap between exception and reality. There are some analysis which states that, acquiring a new customer is 5 times costlier than the retaining the existing customer. Here are the some of the ways to collect the VOCs Customer Interviews, Online Customer Surveys, Live Chat, Social Media, Website Behavior, Recorded Call Data, Online Customer Reviews, In-Person Surveys, Net Promoter Score, Focus Groups, Emails, Dedicated Feedback Form Here are some more Ideas which may improve the positive response rate of getting VOCs. Customer Journey based design - Many times the Businesses design a very high-tech solutions to get the VOCs. But some of the consumers are not able able to work with new digital tools. In this case, Business needs to look at their way of getting VOCs and try to give freedom to consumer and give option to them (Physical paper, Digital Forms etc.). Customized VOC form to be implemented. Targeted and Hyper-Personalized – VOCs can be collected as and when completing the service. For example, if a hotel is providing Gym, Swimming pool experience, then VOC can be collected to those audience, who utilized and to be collected after completion of service. Which will give more insights towards the used services rather than the generalized. Image Analytics(Emotional AI) – As the lot technologically tools are available in the market, we can record the video and AI Tools can analyses the provide a consolidated feedback based on the Video & Speech. While recording the video recording can be placed at (In case of hotels reception, Dining, Lift any other suitable areas). The hotel team can have the interaction, which should include the VOC questions inside it. By leveraging the technology tools, we can get the better insights without consumer intervention. Rewards or Offers – Many consumers are very attentive to the offers provided by the business. For example, if we connect the VOCs to small vouchers or the offers for some period of time, user can start filling the VOCs of any form. Every VOC completion, consumer will get coupons, where it be diverted to lottery with valuable rewards Creative VOCs – As an individual it is difficult to get time to either fill the form or reply the mails of VOCs. But the same individual can spend more time on social media. Hence VOCs to be created such a way that consumer cannot feel that he is filling a form. VOCs to be designed as a game, so that while playing itself consumer can fill the VOC. For example, if our game user is more doing more activities on water actives, which means he might recently experience with the swimming pool or the pool side area is good. If he is selecting certain type of food, which he may like in our restaurant recently. AR, VR VOCs – Instead of a filling a form, we can present the hotel, room, in the 3D style, ask him to selected the like or dislike areas by making it green or red. There by we can collect some insights from the consumer (VOC). Even we can ask him to put some objects which he thinks that could improve the customer experience. From here we can also consolidate many customers and can decide the new buying items. Intermittent VOCs – Instead of sending the VOCs after completing the service, where the return VOCs % is very less. We should plan to send the VOCs intermittently to get part of the insights. VOC request time: Many times, after the service we send the VOCs irrespective of day & time. For example, I am travelling to Bombay and stayed hotel for business reason. Immediately after coming from Bombay, I got so many things to do. If I get the VOC in between, I will not reply and even I delete. If the same VOC mail or call came on weekends, there might be high chance of replying the call or message. Digital Omni Channels – In the modern age, businesses are reaching to upto last mile in getting the requirements such as POS transactions, calling directly to consumers. But the biggest challenging here is to connecting all the data points related to the one service to derive a meaningful insight. If we look it in silos, it will not be fruitful. Every one in this world is busy and have limited valuable time. Some time the business who looks for VOCs, they don’t even give the VOCs to their supplier or Vendors. VOCs collection has changed greatly with lot of evaluations. With the new era of AI & Digital, it has already taken upto next level. Instead of Business focusing on the volume of VOC, we should focus on the quality. There are some software which can help the companies to use the AI VOCs - Question Pro, Monkey learn, Medallia, Response Tek etc.

Algorithmic Bias: Algorithmic bias is related to the errors generated by the software system, due to the limited training data or the data given for training is not representing the population. Example : One of the company in a excellence journey, they thought that the need of building the new digital systems with the available data. Then they started collecting the one/two months data and created a model which predicting the out put of the process. During the training& testing of the model, it is giving better accuracy with limited data. But when is tested in production environment it started failing or giving erroneous readings, because in last two months, the machine had undergone for maintenance, but over a period of time the equipment behavior changed due to wear and tear. Hence, we need to train the model with entire life cycle of the equipment, which represent the population. Example2: In quality department, the supervisor doing a study of tensile of the product using input parameters. The company produces 200+ different product, But the supervisor only considered the 50 products, for which the complete data is available. When a new grade comes(other than 50 products), then the tensile prediction is going wrong. Hence he need to collect the entire range of products to predict the tensile and there by he can control the input dosages. Example 3: Once of the company started a project of efficiency improvement of Motor A. The ML developer got the data from the Scada system for the model. In production environment it got failed. After a careful study by the SCADA engineer they found that, when ever motor RPM is beyond a limits or the temperature is very high, the sensor not able to capture the efficiency data. Hence entire out of range data is missed in the training model. This is also considered as Algorithmic Bias Example 4: One of the company started building a facial recognition system. The developer has a good experience and built multiple systems. Developed used the HR system employee data to develop the model model, but most of the time, the facial recognition system showing error, because the data used by developer is not upto date. All the face pictures are very old almost like 10 yrs back photos. Hence it is not recognizing. This can also be considered as Algorithmic Some of the companies does not have the proper data capturing mechanisms or data lake kind of structure. But as the competitors started implementing the AI/ML models in their company, in burry every one wants to jump into this world. But the fact remains "Bad data will give Bad predictions". Some times we train the model with hourly frequency, but when we run the model in prediction, we look for minute base prediction, It will also give biased prediction. In summary here are the sources of Algorithmic Bias Historically bias data Implicit Human Biases Feedback Loops Lack of Diverse representation Here are the some of the Best practices one can look into to avoid algorithmic Bias Diverse and representative data: Before addressing the problem through data driven model, we need to ensure that the data is collected for training is representing the entire population ( Which can cover all Products, various speeds, various input materials, various timing, various operating conditions) Retraining the models regularly: Many manufacturing industries keep improving by changing their design of the equipment's. Hence every change, model need to retrained with fresh data set to reduce the errors in predictions Transparency: We need to have a clear documentation about how decisions are made by the developed software system. Inclusive development teams: If we are in business with various verticals, need to have a diverse team, who can to check and balance biases that may otherwise go unnoticed. Including the human interactions in the decision making - If the software system fail to give the solution, it should allow humans to correct the decision. - COBOTs can be good example

The Intermediaries are adding value/reduce value based on the situation and type of the business. Primarily distributors, suppliers, vendors referred as the middle man in the business Let us assume that ABC company producing a packaging product (B2B or B2C) in large quantities (~1000 tons). But the customers are very scattered and the required quantities are in range of 100-2000 Kgs. Assume that, if ABC company stared distributing the products in market by themselves, it will become hectic job i.e., many transactions will happen, more people needed to sell, receivables & recovery will be difficult problem, order book will be unpredictable. In summary the cost will increase drastically. Hence company’s look for Middle man handles this tedious task. So, the company sell the bulk quantities (5-10 tons) to middle man. From him it will get distributed to smaller players. It is very difficult to get the orders from these small players in the market. The middle man can gather all these order from market and place the bulk orders to this ABC company, which is viable to the company to produce as well. The middle sometimes helps the ABC company to get the new customers and develop new business (win-win) situation for the middleman and ABC company. On the other hand, Middle man play a key role in the value chain. Company sometimes may not realize the full value of the products, ad the major share will be kept by the middle man, because they are helping the business. ABC Company will also not know, what the end customers are doing with the packaging products. If the end customer is using for illegal product, ABC will be in trouble legally, as they are the supplier of the products to end-customer. The same ABC company is procuring some material from XYZ company to produce the packaging material. In this case, the XYZ is large scale company, they cannot sell smaller quantities material to ABC. Hence middle man plays a role of selling smaller quantities to ABC company. ABC company will pay little more price than buying directly from XYZ company. ABC can easily switch between the middle mans rather than the companies (Supplier, customer) In both side of supply chain, middle play a major role in terms of streamlining of supplies, cash flow. But with this company will pay some money to middle to handle these disturbances. Many companies keep middle man to safe guard them financially (ensure smooth cash flow). If ABC company is large capacity, even the supplier & customer for this company are operating on large scale then they could avoid middle man to save the money spent on middle man. Some of the times, middle man doesn’t reveal the customer, which will become Gray area for company ABC. Maybe he is charging premium from them, but paying less to the company (Value will lose to the middle man). Middle will also help companies in connecting larger pool of customer, different supplier. Companies can take better decision with such kind of information If the company have clear visibility of where the customers are located, what purpose they are using this paper and what is the value of the end product, they the company can take better position in deciding the price of the product by giving a nominal margin to the distribution. Similarly on other hand, company should have the clear visibility of the procurement process of the vendors. By removing the middle man, both company and the customers can perceive better value. Sometimes they bring lot of value on to the value. Depending on the scale of business, type of the end products, company need to decided if adding middle, whether they gain or loose.

First of all, let us understand Bandwagon effect in short. The Bandwagon means, it is a wagon carrying the band (music instruments) for campaigns, circuses. It has started in 1848 during the American presidential elections. If the people are adopt certain behaviors, styles, or attitudes simply because other people are doing it or following it, the effect is called Bandwagon effect. The more people doing/following of particular thing, more likely other people will jump on the bandwagon. Example1: The studying elder kids, now a days staying more time on social media, just because their friends are always online. Sometime, they don’t want to stay on social media, but the feeling of if I don’t do, I might have missed some thing made them to follow their friend’s path. Example 2: Mr. A is supports team A from his childhood. Recently team B formed with international players, and it is performing very good compared to team A. The fan base to team B increasing drastically, Mr. A friends started supporting team B. Due to Bandwagon effect, Mr. A also started supporting team B. Even though he doesn’t want to support the team B, but most of the people are there with them. Mr. A got a fear of supporting failure team and fear of not part of winning team. These two leads to change of his thinking Example 3: Now a days many companies are adopting AI. There is company A, which is manufacturing company. All their competitors are started lean, six sigma long back and now they stared adopting the AI. But company neither had any excellence program nor they have the data, but because people adopting, he started engaging consultants to implement the AI. The peer pressure and fear of missing value, if they don’t adapt AI. This Bandwagon effect, makes company A to adopt new digital tools, but the base need to created before that. Example 4: One of the companies started doing the rain water harvesting (rain fall is less), even though the ground water level is very high (with in 50ft, can get water) & river is also passing by (less water cost). The company manager attended program in Chennai, where the situation is different. Because many companies are started harvesting rain water part their EHS program, this manager decided to built the rain water harvesting infrastructure across plant. But the situation for the company is different (less rain fall, high groundwater level, river is passing nearby). End of the day company invested, but the returns are Zero. Example 5: One of the companies supplying the good across India. The logistic company associated with this company from 50 years. The drivers of the logistic company cannot drive more than 8hours. The company delivery time is slightly higher compare to the other industries. The profit margin to this business is very thing. So, the company decided to change the logistic company. Their operating model, vehicle, will not stop once it leaves factory. Truck will continue to move 24*7, the drivers will join after every 8 hours. But the cost of transportation is double than earlier. Because many people adopting this new concept with digital tools, company started moving to new logistic company ended with paying higher cost, less margin and leading to loss making business. We need to analyse the situation, market before taking such decisions. This is one more example of bandwagon effect. Some other general examples such as Diet plan, Weight loss, Elections, Fashion, music, social network Factors effecting the Bandwagon effect Heuristics: Our brains tend to find the shortcuts, rule of thumbs to fasten the decision-making process. These shortcuts, Rules thumbs always influenced by what we see everyone else doing Need to be included (We want to fit in): In general, we don’t like feeling excluded from the communities, social events to avoid odd one out. By making similar decision of other, we can see way to gain access in particular social group A desire to be right (We wanted to be on winning side) : We want to be the part of the winning team. Part of the reason people conform is that they look to other people in their social groups of information about what is right or acceptable. How to avoid it Be cautious of simple solution: Most of times, the shortcuts, rule of thumbs offers a very simple solution to the complex problems. The solution looks like applicable & works for every situation & the companies. We need to think multiple times before taking the simple solutions. Seek Diverse Information: While making the decision, don’t go by shortcuts or don’t listen to the single source. Get more and more information historical of same company or from the other companies. Compare the scenarios and decide on the based-on data, experience. Look for evidence: Many times, we get message on social media about the health issues. Then suddenly we started avoid following such things. Before we go for it, we need to looks for stronger evidence such as government statement, press release, health ministry update. When we practice such decision-making based evidence (information) which will support our thinking (consulting doctor). Make decisions more slowly: Once we received sufficient information, we need give a break from outside inputs while making a decision. Don't let someone pressure us into an immediate choice In examples 3,4,5 the companies could have done a proper study of their situation in the market, the resource availability, budget availability rather than just looking into what majority people are doing outside. In example 3, then can start the excellence program to get some benefit meanwhile they can implement the data collection digital tools. Once the margin has improved to certain level, they could look at AI for further enhancement. In example 4, company should have looks for the natural resource availability near to them. Cost of operation, and the returns from the new project of rain water harvesting. The company would not go for this decision, if they engaged experts from the FICCI, CII. In example 5, company should have discussed with the existing logistic company, and try to help them to improve their process and reduce the delivery time without increasing the cost. The company might have studied cost benefit analysis before shifting to new logistic company.

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.

Benchmarking is a method that companies use to compare the performance of their process or products with the standard/Best in Industry Every industry has certain criteria or standards that an organization needs to meet to survive the competitive business environment and meet employees and customers’ expectations. In order to do that, organizations need to know where they stand in the competition and what in their operations is lacking compared to other organizations, especially those that are considered leaders in the industry. Therefore, benchmarking – as the term implies – helps in implementing that. There are many way companies are using the benchmarking. The following are the some of the examples. Compare performance to an industry-standard Increase product performance Improve the product quality Increase market share Reduce manufacturing cost Develop a measurement system In a large companies having manufacturing setups at multiple locations, Internal benchmarking conducts to help the lower performance plants to improve their performance. One of the most important thing is, we need to use the similar KPI for comparison. For example, chemical consumption is directly proportionate to production rate, hence we need to consider the specific consumption (chemical usage/ Ton) TYPES OF BENCHMARKING Internal benchmarking - Internal benchmarking involves identifying and analyzing best practices established within the same organization. External benchmarking - External benchmarking is quite similar to internal benchmarking; however, external benchmarking focuses more on identifying and analyzing best practices established by different organizations within or outside of the industry. Performance benchmarking - Performance benchmarking involves measuring the quantitative data of employee performance and product characteristics/production (i.e., employee surveys, key performance indicator, cost, reliability, durability, etc.). For example -OEE for manufacturing Process benchmarking - Process benchmarking concentrates on the daily operations/processes conducted within an organization. Competitive benchmarking- Competitive benchmarking is implemented to compare with an organization’s direct competitor. The House of Quality matrix and Gantt charts are often used to plot the benchmarking evaluation.

Control charts are being used to study the variation in the process. Statistical control limits establish process capability. Statistical control limits are another way to separate common-cause and special-cause variation. It separates the special causes from the common causes. Control charts can give early identification of special causes so that there can be timely resolution by the users, before poor-quality products are being made. To control a process using control charts, the monitoring should be of key process input variables, and the process flow is stopped for resolution when this variable goes out of control Assume the scenario that most of the data points are out CL due to high frequency. Then it is next to impossible to control the process performance. In general a control chart frequency of any KPIs, is completely depends on the feedback or response time (Change adjustment) from the measurement and the measurement frequency. For Example, the control chart is tracking the porosity of the paper, for which the online measurement is giving data in every 5min data & lab measurement in every 30mins. Online gauge trend is follows the lab gauge. If any issue of porosity, the user adjust some input parameters to bring back into control, this process takes 10 mins. Hence based on the the above data, we consider 5mins is the frequency of control chart. The lab gauge value used to calibrate or validate the online gauge. So here i am concluding that frequency is always depends on the response of time, represent the source of variation & measurement frequency. It is believe that, if we cant measure the change of any KPI, then we cannot control. Frequency become more crucial when the process is having more time lag. If the users making the control chart for the out put parameters, it always recommended to keep the specification limits along with control limits. Some times if the process data showing out of control limits, but within specification limits, then user no need to worry, there is some to adjust back the process to keep the all the good produced are with in customer specification.