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  1. Yesterday
  2. Supriya Rao Dasari

    Jan - March 2024

    This album contains Benchmark Six Sigma training photographs from January to March 2024.
  3. Sumukha has given the winning answer to this question. Short and crisp. Well done! Answer from Anish is also a must read.
  4. Q 663. Latest business model that most organizations, even start ups have adopted is the Freemium Business Model. What is it? How do we integrate Lean Six Sigma principles in this model? Quote examples of businesses that have successfully adopted the Freemium model. Note for website visitors - This platform hosts two weekly questions, one on Tuesday and the other on Friday. All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/. The question will be open until Tuesday or Friday at 5 PM Indian Standard Time, depending on the launch day. Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be approved. If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term. Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error prone as our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/ We also use an AI content detector at https://crossplag.com/ai-content-detector/. Only answers with less than 15-20% AI-generated content will be approved.
  5. To access the full capability of the model it is essential to understand the explanatory performance as well as predictive performance. In explanatory understanding of regression model we generally see difference between observed values and predicted values is considered. If the difference between the values is small, the model is a good fit. R-square is an indicator that shows goodness of fit. It indicates the percentage of variation explained by the model. If R-squared is 0%, it means none of the variation is explained by the model and 100% means all variation is explained. Hence, one may assume that high value of R-sq is good and low value is bad. However, that’s not always the case. If R-square values are low, it may be good to check if the predictors are statistically significant. If yes, you can still draw some conclusions from the model. Also, in some fields like psychology generally lower R-sq values are also acceptable based on the nature of the study. If the R-square value is high, it may not always be a good fit. In some cases, R-sq may be biased. Example- it could be because of using linear model to explain non-linear data. In some cases, R-square value may be high due to overfitting. Generally, if we add more variables to the model, the value of R-square will increase even if the variable is not significant. To solve this, we need to modify R-squared in a way that it is not affected number of variables. R-squared adjusted is that modified version which only increases if added variable improves the model. To assess the predictive performance, we need to systematically remove each observation from the data set, estimating the regression equation, and determining how well the model predicts the removed observation. Predictive R-squared indicates how well a regression model predicts responses for new observations.
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  7. In advanced regression techniques, we use R-sq (Pred) to assess the predictive performance of a model, this needs to be assessed separately even though we have R-sq and R-sq (Adj) calculated as part of the model which focuses on measuring the goodness of fit of any new factors to the model but don't assess the predictability of any new factor to the model. In order to make the model more predictable higher R-sq (Pred) is required against the R-sq and R-sq (Adj) and also fitment of any new factor or data to the model can be tested. This also helps in avoiding the multicollinearity in the model. Eg. Consider examples of predicting the prices of flats based on different factors like area of the flats, locality, bedrooms and amenities. You create a model based on historical data where R-sq and R-sq (Adj) values are calculated as 0.82 and 0.81 respectively, which indicates there are 81-82% variability in historical data. R-sq (Pred) is 0.75 predicting 75% of variability in new data. The predicted value will be lower as the data is new as compared to historical data aligned for other measures. These predicted values are more focused on future sales and decision making.
  8. R squared = Measures the proportion of variance in the dependent variable(y) that is explained by the independent variables (x). It ranges from o to 1 and higher R-Squared value indicates that model is a good fit. The regression model is created based on training dataset. R Squared prediction = used to assess the predictive performance of a regression model. This is usually done using the test datasets (unseen data) to know how well the model could predict in the real world. Example: R- squared is a good indicator of how well the model fits based on the training data whereas R-square prediction will actually show how well the model fits the unseen data in the real world. This prediction is usually done with test data. One good example is the loan default prediction model created by a bank where they want to predict if the customer will default on loan based on various parameters(X factors such as age, gender nationality, loan amount, occupation, purpose of loan etc). The regression model is created based on the historical data using training data set. R-squared value = 0.93. This indicates that the model fits well.The regression model was then used on training dataset to predict how well the model fit for unseen data. R-squared prediction value =0.87 which also indicates that the model is a good fit for unseen data.
  9. It was a pleasure reading all the answers. Jayanth Sura has written the best answer to this question. Answers from Anish and Sachin are a must read. Well done!
  10. Q 662. In advanced regression techniques, we use R-sq (Pred) to assess the predictive performance of a model. Why do we need to assess it separately? Aren't R-sq or R-sq (adj) sufficient indicators of a good model? Support your answer with suitable examples. Note for website visitors - This platform hosts two weekly questions, one on Tuesday and the other on Friday. All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/. The question will be open until Tuesday or Friday at 5 PM Indian Standard Time, depending on the launch day. Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be approved. If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term. Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error prone as our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/ We also use an AI content detector at https://crossplag.com/ai-content-detector/. Only answers with less than 15-20% AI-generated content will be approved.
  11. In the improve phase, process benchmarking can help in identifying best practices by comparing the current process to the benchmark organization. This way the team can easily identify solutions and implement in their organization. This makes the improvement much faster as it reduces the time for experimentation and team can find lot of quick wins to improve their process. In some cases, team can also save cost of designing new solutions or prototype building as they can use the exiting design from the benchmark organization. In case of solutions needing investments, it is easier to get the data from benchmark organization to build business case/justification. Thus, process benchmarking can make the entire Improve phase more efficient and cost effective and team can target realistic solutions.
  12. During the course of a DMAIC project, we establish current process performance, or baseline, at the Define stage. This tells us where the process is at the start of the project or what the current baseline metric is. With the baseline established, we establish a project goal. The goal is typically one that is stated by the customer or by internal stakeholders. The DMAIC project aims to meet the defined project goal. At the Improve phase, we identify potential solutions that will help us achieve our project goal. Process Benchmarking helps us get more out of our project by meeting industry standards or best process performance. Simply put, benchmarking is our final goal and is determined by understanding best practices at the market or even within the organisation. Let's take an example of a cycle time reduction project in an insurance process: Insurer ABC takes on an average 60 days to resolve a claim. This has been deemed high since it leads to customer complaints, customer churn, loss of revenue, etc. A GB project was taken to reduce the overall claim process cycle time from 60 days to 45 days since it was observed that customers usually start following up post 45 days of claim submission. The project started and solutions were identified at the Improve phase that would help achieve the goal of 45 days. However, when a study was conducted, it was observed that the average time taken by top Insurers in the same geography was around 30 days. These Insurers had the highest customer retention and minimal complaints. Therefore, while the project goal of 45 days could have been met, in order to achieve best-in-class solutions at an industry level, the Project Lead added more solutions that would help him meet the goal of 30 days.
  13. Process benchmarking is a tool that involves a comparison between the internal processes/practices of a company against the practices/processes followed by the best-in-class leaders or competitors leading to continuous improvements. It involves different steps of 1. Identifying a process 2. Selecting the best in class/benchmarking processes to whom you want to compare to 3. Collection of data 4. Analysis and comparison 5. Identifying areas of improvement and monitoring the process and its performances DMAIC DMAIC is a structured approach that stands for Define, Measure, Analyze, Improve, and Control. Proper implementation of this approach results in reducing cost, enhancing quality, and improving customer satisfaction across different industries. Benefits of process benchmarking in DMAIC projects 1. Identification of a best-in-class process: This is a major part that helps DMAIC projects identify the best-in-class projects across the industry. This step helps them to understand what measures the industry leaders are implementing to be the top in class. 2. Setting a benchmark to compare your project: Once you have identified the project, it is crucial to set a benchmark that enables you to drive your actions/planning towards achieving the set objectives/benchmarks. 3. Data collection: This step involves the collection of data that is to be compared and analyzed. The data can be qualitative and quantitative. 4. Comparison and analysis: Involves comparison between your process with the set standard. It helps you to identify the difference between where you are and where you want to be “Gaps”. Once gaps are identified, it helps you to brainstorm and identify what effective measures can be implemented to reach the set standard. 5. Control/Measure: This step focuses on monitoring the performance/results of the process and ensuring that improvements are made as and when required. Example: Certainly! Here's a reframed version: Company Y Ltd., a paper manufacturing company, produces 10 short tons of paper napkins with a total production cost of $500/short ton. Upon benchmarking their processes against the industry leader - Company X Ltd, they found out that Company X Ltd. is producing the same production volume at a total cost of $300/short ton. On further data collection market analysis and comparison, Company Y Ltd. identified a substitute for their current raw material, priced at half the cost. Implementing the new raw material, helped them achieve a 47% reduction in production costs, resulting in Cost savings.
  14. 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.
  15. Process Benchmarking: Process benchmarking is a systematic comparison and analysis of once business processes against those of high performing organizations with the goal to of identifying best practices and implementing improvements to enhance your own process efficiency, quality, and performance. Process benchmarking plays a crucial role in the Improve phase of a DMAIC project by providing valuable insights and inspiration for improvement strategies. Here's how: Identifying Best Practices: Benchmarking exposes your team to how leading companies or similar organizations within your industry tackle similar processes. This allows you to identify best practices in areas like workflow design, automation, error reduction, and resource allocation. For instance, imagine you're working on improving the efficiency of your order fulfillment process. Benchmarking a company known for its fast and accurate order fulfillment might reveal their use of real-time inventory tracking or automated packaging systems. Setting Realistic Goals: By comparing your current performance metrics with industry benchmarks, you gain a clearer understanding of the potential for improvement. This helps set realistic and achievable goals for your improvement plan. Let's say your current order fulfillment cycle time is 5 days, while the industry benchmark is 3 days. Benchmarking highlights the potential for a 2-day improvement, guiding your team to focus on strategies that can achieve this target. Sparking Innovation: Benchmarking can spark creative thinking within your team. Studying successful approaches used by others can inspire them to develop innovative solutions for your specific process challenges. In the order fulfillment example, learning about a company's use of AI-powered order picking systems might inspire your team to explore implementing similar technology or develop alternative automated solutions. Prioritizing Improvement Ideas: By understanding which aspects of the benchmarked process contribute most to their superior performance, you can prioritize your own improvement efforts. Benchmarking might reveal that the industry leader's focus on clear communication between departments significantly reduces order fulfillment errors. This can guide your team to prioritize improvements in communication channels within your own process. In conclusion, process benchmarking during the Improve phase of DMAIC equips your team with valuable knowledge about successful approaches, helps set achievable goals, inspires creative solutions, and prioritizes improvement efforts for maximum impact.
  16. Process benchmarking is like peeking over the neighbor's fence to see how green their grass is so you can make yours just as lush. In the Improve phase of a DMAIC project, it's all about leveling up your own processes by learning from the best. Because sometimes you need Inspiration right? Take, for instance, a local bakery aiming to boost efficiency in their production line. They might look at a competitor who's churning out pastries like nobody's business and figure out what techniques or equipment they're using to streamline things. Maybe they discover that their rival's secret lies in a special oven that cuts baking time in half. Imagine if are aware of this knowledge, our bakery can invest in similar equipment and enhance their own process. Likewise, think about a small-town car repair shop striving to reduce TAT for customers. They could look at how larger, more established garages handle their workflow. Perhaps they find out that these big shops have digital systems for tracking repairs, which helps them prioritize tasks and keep things moving smoothly. Inspired by this, our local repair shop might implement a similar system, allowing them to serve more customers without sacrificing quality. It's all about borrowing wisdom from others to make your own business shine brighter. [ No comparisons please ]
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  18. Process benchmarking involves evaluating your organization's processes in comparison to those of leading organizations known for their excellence in one or more aspects of their operations. It offers crucial insights to gauge how your organization stacks up against similar entities, even if they operate in different sectors or cater to different customer bases. Additionally, benchmarking aids in pinpointing areas, systems, or processes that warrant enhancements, whether through gradual, continuous improvements or transformative, large-scale business process re-engineering initiatives. Sequence of activities in process Benchmarking : Step 1: Identify the process to Benchmark Select the process Fully understand the process and identify non-value adding areas or scope of improvement Find defects, opportunities, and sigma level Step 2: Select organization to Benchmark Find out industries/ functions which perform the process Identify the leaders or best performer (different industry or same industry) Step 3: Research the process to be benchmarked Step 4: Develop a questionnaire based on your requirements Step 5: Exchange Ideas Conduct visit, if necessary Step 6: discuss the comparison internally with the team Step 7: Devise an action plan and implement best practices Example: UAE based Bank identified the need to enhance its digital account opening process to provide customers with a seamless and efficient customer experience while onboarding. The bank aimed to achieve the following: Reduce the lead time to open an account Enhance customer satisfaction by simplifying the account opening process and provide clear guidance. Optimize resource utilization by streamlining internal procedures and reducing manual interventions Recognizing that other Banks who are considered as Digital leaders might have more advanced digital account opening processes, our Bank decided to leverage benchmarking exercise to expedite improvements without reinventing the wheel. A cross functional team was established, and we began by mapping out the current digital account opening process to establish a baseline, paying particular attention to the steps involved from application submission to account activation. The team conducted surveys and interviews with both existing and potential customers (user testing) to gather insights into expectations regarding the digital account opening process, as well as any pain points, they encountered. Following the survey, team experimented with the digital applications of leading Banks (within UAE) and reached out to Digital Enhancement team to understand the technologies and methodologies utilized, engaging in a dialogue to gain insights into the process. Based on the findings, the team complied a benchmarking report outlining the best practices observed at leading Banks and proposed recommendations tailored to our requirements. Finally, our Bank implemented several of the recommended changes, such as simplifying the online application form, integrating automated verification process and OCR technology. As a result, we witnessed a significant improvement in account opening process by reducing the lead time and providing a seamless experience to customers.
  19. Anish Mohandas' is the only answer that we were able to publish and I am glad that it was the best answer! Well done P.S. there were a couple of more good answers but could not be published as they failed in the AI generated content test. While you can do your research online or using AI tools, however, we want the answers to be originally written.
  20. Q 661. How does process benchmarking help in Improve phase of a DMAIC project? Provide examples to support your answer. Note for website visitors - This platform hosts two weekly questions, one on Tuesday and the other on Friday. All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/. The question will be open until Tuesday or Friday at 5 PM Indian Standard Time, depending on the launch day. Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be approved. If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term. Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error prone as our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/ We also use an AI content detector at https://crossplag.com/ai-content-detector/. Only answers with less than 15-20% AI-generated content will be approved.
  21. Machine learning researchers and data scientists often use in-sample and out-of-sample testing to refer to training and test sets respectively. In-sample data refers to the set of data which is used for training or fitting a model. When analysts try to build statistical or machine learning models they usually make use of historical data which enables the model to be taught about how predictions or classifications can be made. During this process, inputs are given, along with their corresponding outputs to enable it to learn the underlying patterns and relationships that exist in the dataset. This is essentially what is meant by in-sample data; it is the dataset upon which the model learns from. On the other hand, Out of sample data refers to unseen data by a model when undergoing training phase. After a model has been trained, measuring its performance on new unseen information is important for assessing its ability of being generalized. For this reason, out-of-sample testing is employed. By trying out a model using real time cases that have never encountered before, analysts can make an estimation as regards making predictions or classification of unseen instances inside such models as well. This stage helps confirm its applicability within practical contexts (Data modelling and learning steps are illustrated and attached) In-sample (Training data) Out-of-sample (Testing data) Advantages: It facilitates model evaluation based on the same data used for training, It gives us insight on how well the model fits the training data. Computationally well efficient Disadvantage: It is prone to overfitting Advantages: Provides a more accurate estimation of model’s performance in unseen data. Validates the model effectiveness in real world scenario Disadvantage: It requires a separate dataset for testing. It can be computationally intensive if multiple iterations or cross validations are performed Example of in-sample and out-of- sample data in real world scenario Assumption:80% in-sample and 20% out-of-sample The examples demonstrate how in-sample and out-of-sample testing are applied across all domains from Finance, Healthcare, cybersecurity etc. Credit decisioning model Training machine learning models on historical data to predict stocks Developing a spam email classifier Fraud detection algorithm Evaluating the performance of medical diagnosis model
  22. Q 660. Compare In-Sample and Out-of-Sample testing, highlighting their advantages and disadvantages. Provide examples where their use is preferred. Note for website visitors - This platform hosts two weekly questions, one on Tuesday and the other on Friday. All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/. The question will be open until Tuesday or Friday at 5 PM Indian Standard Time, depending on the launch day. Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be approved. If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term. Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error prone as our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/ We also use an AI content detector at https://crossplag.com/ai-content-detector/. Only answers with less than 15-20% AI-generated content will be approved.
  23. In principe, reverse innovation start by focusing on needs and/ or exigence in developping products for low income markets and later fitted to others. nowaday companies put a lot of efforts on deleting unnecessary future in develelopped country product to reduce cost and make them accessible to low income markets. for exemple: GE developed an ultrq portable cardiograph for American Market low cost around 80% less than similar one originally build for Indian and China doctors.
  24. Reverse innovation: Reverse innovation, also sometimes called frugal innovation or trickle-up innovation, flips the traditional development process on its head. Instead of creating products for developed markets first and then adapting them for developing economies, reverse innovation starts with the needs of developing markets and then brings those innovations, often with tweaks, to wealthier countries. Reverse innovation can be a powerful tool for companies looking to establish product-market fit in a new geographic market, particularly developing economies. Here's how: Understanding Resource Constraints: By developing solutions with limited resources in mind (common in developing economies), companies can create products that are simpler, more affordable, and more durable - qualities often valued globally. Addressing Unmet Needs: Developing markets often have unique needs not addressed by existing products. Reverse innovation encourages companies to identify these gaps and create solutions specifically tailored to the local context. Early Market Testing: Launching a product in a developing market with less stringent regulations allows for faster iteration and testing before a broader rollout. Reverse innovation encourages companies to develop with these constraints in mind, leading to: Simpler designs: Less complex products are generally cheaper to produce and maintain. Focus on core functionalities: Fancy features get stripped away, leaving a product that does the essential job well. Resourcefulness: Finding creative solutions using readily available materials and technologies. The result? Products that are not only successful in developing markets but can also be adapted and find new markets in developed economies, often appealing to customers who value affordability and practicality. Benefits for Global Organizations: Cost Reduction: Streamlined designs and focus on core functionalities can lead to cost savings that benefit all markets. Increased Innovation: The focus on resource constraints can spur fresh ideas applicable globally. New Market Access: Products developed for developing markets can open doors to entirely new customer segments in developed economies. Challenges of Reverse Innovation: Internal Resistance: Companies may struggle to adapt their existing processes and mindsets to a more frugal approach. Quality Perception: Products designed for developing markets might be perceived as inferior in more developed markets. Brand Management: Balancing the brand image across different product lines catering to varied markets. By overcoming these challenges, companies can leverage reverse innovation to achieve global product-market fit and unlock new growth opportunities. Examples of Successful Reverse Innovation: Nestlé's Chilled Maggi cubes: This single-serving, pre-cooked Maggi variant, created for India's on-the-go consumers, has become a popular option in other markets. GE Healthcare's Maci ECG machine: Designed to be used in remote areas of India with limited electricity, this portable ECG device is battery-powered and very affordable. It's become a valuable tool for medical professionals worldwide because of its simplicity and effectiveness. Ericsson's low-bandwidth mobile network solutions: Created for emerging markets with limited network infrastructure, these solutions allow for efficient mobile phone service in remote areas. They've been adopted by carriers in other parts of the world facing similar challenges. Unilever's Pureit water purifier: Designed for rural India where clean water is scarce, this affordable, in-home water purifier uses readily available chlorine tablets to disinfect water. It's now sold in multiple developing countries.
  25. The methodology of creating new products/ services in developing countries, rather than developed countries is called Reverse Innovation. This, slightly contronversial subject, was introduced by Jeff Immelt (Chairman and CEO of General Electrics), Vijay Govindarajan (Professor of International Business) and Chris Trimble. By applying concepts and innovations in developing countries, the success can then be transferred to the developed countries. Implementing Reverse Innovation is developing countries/ emerging markets, helps them in the following ways: Cost: Solutions implemented in emerging markets generally tend to be less expensive. This helps in creating a wider market for adaptability and usage. Accessibility: Since the cost is low, these markets are able to access the products/ services more easily and that helps in achieving a larger sample size of usage feedback. Prototype: Upon successful implementation in developing countries, these products can be taken to developed countries with a higher degree of confidence of success. Some examples of Reverse Innovation in the Indian market: Tata Nano car Tata Swacch water purifier Pepsico Kurkure Low cost Air Conditioners by multiple brands Vicks Honey Cough The benefits of Reverse Innovation are: Learning opportunities: By running cost efficient prototypes, companies can learn from mistakes made and best practises shared during the journey. This helps them in perfecting the product for future developments. More innovations: Multiple iterations of the product can help companies come up with more innovations that they could potentially prototype in the developing markets. Testing out the market: New products in developing countries help the manufacturers do a thorough testing of the emerging market and apply the concepts in the developed countries. Challenges in deploying Reverse Innovation: High investment cost: Since new products are being developed and even though the market is developing countries, high costs such as customer acquisition, market strategy, infrastructure and distribution. Acceptance of change: Since reverse innovation relies of product disruption, accepting these new products are usually a challenge. Classic example of the Tata Nano car. Conceptually good but the sales weren't as expected by Tata. Environmental hazards: New products generally do not focus on environmental impact, thus carbon emission is high and recycling is not a priority.
  26. Q 659. How can reverse innovation help a company achieve product market fit in a new geography? Provide some examples where it has been successfully done. What are its potential benefits and challenges for a global organization? Note for website visitors - This platform hosts two weekly questions, one on Tuesday and the other on Friday. All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/. The question will be open until Tuesday or Friday at 5 PM Indian Standard Time, depending on the launch day. Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be approved. If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term. Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error prone as our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/ We also use an AI content detector at https://crossplag.com/ai-content-detector/. Only answers with less than 15-20% AI-generated content will be approved.
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