Abhishek Shukla

Hiring a Lean Six Sigma leader? Here are 5 great #interview questions with answers for you to consider. 😊 1. Explain the #DMAIC Process 🙋 “DMAIC stands for Define, Measure, Analyze, Improve, and Control. It’s a structured approach to problem-solving. I use it to break down complex issues: first defining the problem, then measuring and analyzing data to understand root causes, implementing solutions in the Improve phase, and ensuring sustained results through Control measures. It’s highly effective for long-term impact.” 2. Share a Project Where You Eliminated #Waste 🙋 “In a recent project, I identified redundant steps in our order fulfillment process, leading to a 20% reduction in cycle time. I used VSM to analyze the workflow, then implemented Kanban to streamline inventory control. The improvement boosted efficiency and saved costs without compromising quality.” 3. Describe Your Approach to #RootCauseAnalysis 🙋 “I start with a Fishbone Diagram to categorize potential root causes, then drill down using the 5 Whys technique. By focusing on data and direct observations, I ensure that solutions address the real issues, not just symptoms.” 4. How Do You Gain #Team Buy-In? 🙋 “I engage teams from the beginning, explaining the project’s value and inviting feedback. By respecting their expertise and incorporating their insights, I build trust. I also celebrate small wins to keep motivation high.” 5. How Do You Align Lean Six Sigma with Organizational #Strategy? 🙋 “I align my Lean Six Sigma initiatives with key business goals by consulting leadership on priorities, such as cost savings or customer satisfaction. This approach ensures projects contribute directly to strategic objectives.” In my next post, I'll share what are the core #skills to look out for when hiring a Lean Six Sigma leader. Stay Tuned! 😉 🎯 #leansixsigma #interview #jobseeker #interviewquestions #job #hiring #recruitor #HRBP #Talent #Talentmanagement

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🛑 𝐒𝐭𝐨𝐩 𝐩𝐚𝐲𝐢𝐧𝐠 𝐟𝐨𝐫 𝐨𝐧𝐥𝐢𝐧𝐞 𝐜𝐨𝐮𝐫𝐬𝐞𝐬 𝐞𝐯𝐞𝐫 𝐚𝐠𝐚𝐢𝐧. 𝐆𝐨𝐨𝐠𝐥𝐞 𝐡𝐚𝐬 𝐛𝐞𝐞𝐧 𝐤𝐧𝐨𝐰𝐧 𝐭𝐨 𝐨𝐟𝐟𝐞𝐫 𝐚 𝐟𝐫𝐞𝐞 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐜𝐨𝐮𝐫𝐬𝐞 ✅ Big companies don't value your degree. They value your SKILLS! Become a Google Certified Data Analytics within a few months. No Paymnet required ✅ 📌 7000+ Course Free Access : https://lnkd.in/gNmikFve <LimiTed> < FREE RESOURCES - https://lnkd.in/gjw8zYbb 1. Google Data Analytics: In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required. 🪢https://lnkd.in/gATGu4Wj 2. Learn Python Basics for Data Analysis Learn how to use and write functions, practice with data analysis, and work on your first algorithm! 🪢https://lnkd.in/gK5dh9fx 3. Data Analysis with R Programming In this course, you’ll learn about the programming language known as R. You’ll find out how to use RStudio, the environment that allows you to work with R. 🪢https://lnkd.in/d39sgKcQ 4. Foundations: Data, Data, Everywhere This course will help you gain an understanding of the practices and processes used by data analysts in their day-to-day job. 🪢https://lnkd.in/gjckCqWC 5. Ask Questions to Make Data-Driven Decisions This course will help you learn how to ask effective questions to make data-driven decisions while connecting with stakeholders’ needs. 🪢https://lnkd.in/gxQTyv3b 6. Process Data from Dirty to Clean This course will help you learn how to check and clean your data using spreadsheets and SQL as well as how to verify and report your data cleaning results. 🪢https://lnkd.in/g4y63Ftu 7. Share Data Through the Art of Visualization In this course, you’ll learn how to visualize and present your data findings as you complete the data analysis process. 🪢https://lnkd.in/g-Q59G7x 8. Analyze Data to Answer Questions In this course, you’ll learn how to organize and format your data using spreadsheets and SQL to help you look at and think about your data in different ways. 🪢https://lnkd.in/gdJmFkDR 9. Get Started with Python 🪢https://lnkd.in/gWQYmuV4 10. Go Beyond the Numbers: Translate Data into Insights 🪢https://lnkd.in/g3EhCruq 11. The Power of Statistics 🪢https://lnkd.in/gV_CZ8gX 12. Regression Analysis: Simplify Complex Data Relationships 🪢https://lnkd.in/geeV3NzJ 13. The Nuts and Bolts of Machine Learning 🪢https://lnkd.in/gUdxdwVX 14. Google Advanced Data Analytics Capstone 🪢https://lnkd.in/gnnMVBWd Google , Meta Resource - https://lnkd.in/g4Qdf_6R [Happy Learning📚] [Follow for more such content] #DataAnalytics #DataScience #dataanalysis #python #datascience #ds #linkedinforcreators

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If you are in the supply chain domain, and eagerly looking for a role as a supply chain analyst. You need to be good in following analysis with the help of data. Try to incorporate those in your resume, Hiring managers look for it in the resume. 𝐃𝐞𝐦𝐚𝐧𝐝 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠: Analyzing historical sales data to forecast future demand for products or services. This involves using statistical methods and predictive modeling techniques. 𝐈𝐧𝐯𝐞𝐧𝐭𝐨𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Analyzing inventory levels, stockouts, and replenishment patterns to optimize inventory levels and minimize carrying costs while ensuring product availability. 𝐒𝐮𝐩𝐩𝐥𝐢𝐞𝐫 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Evaluating supplier performance based on metrics such as on-time delivery, quality, cost, and responsiveness. This helps in identifying top-performing suppliers and areas for improvement. 𝐓𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭𝐚𝐭𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Analyzing transportation routes, modes, and costs to optimize transportation networks for efficiency and cost savings. This may involve route optimization, carrier selection, and freight cost analysis. 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Analyzing warehouse operations to optimize layout, storage capacity, picking processes, and labor utilization. This helps in reducing order fulfillment times and improving overall warehouse efficiency. 𝐑𝐢𝐬𝐤 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: Identifying potential risks in the supply chain such as disruptions in supply, geopolitical issues, or natural disasters. Analyzing these risks and developing strategies to mitigate them. 𝐂𝐨𝐬𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Analyzing costs associated with various aspects of the supply chain, including procurement, transportation, warehousing, and inventory carrying costs. This helps in identifying cost-saving opportunities and optimizing overall supply chain expenses. 𝐑𝐨𝐨𝐭 𝐂𝐚𝐮𝐬𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Investigating the root causes of supply chain issues such as stockouts, delays, or quality problems. This involves analyzing data to identify underlying factors and implementing corrective actions. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠: Monitoring key performance indicators (KPIs) such as fill rates, order cycle times, inventory turnover, and on-time delivery performance. Analyzing trends in these metrics helps in assessing overall supply chain performance and identifying areas for improvement. 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Creating visualizations such as charts, graphs, and dashboards to communicate key insights and findings to stakeholders effectively. Any thoughts on it? Add if I am missing anything.

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Why do I post data analytics tutorials and advice for breaking into the field if I barely have 2 years of experience myself? For the same reasons why people seek guidance from peer mentors and peer tutors. Sometimes, I need to hear something explained by multiple different people before I understand. In college, I remember most of my “aha” moments happening while a peer mentor explained something to me, even though the exact same concept was so confusing to me coming from my professor with decades of experience in the field. A peer remembers learning the same concepts, and knows the type of explanation that will resonate with another peer. Additionally, the tutorials I create are based on objective facts. If I teach something that’s blatantly wrong, I have no doubt my TikTok followers would not hesitate to let me know (as they should). Correct is correct, no matter who’s mouth it comes out of. People seeking advice should gather data from multiple different sources. I’ve found that entry-level advice is complementary to experienced advice—not contradictory. I encourage less experienced creators, like myself, to only speak to what we know. And I encourage more experienced creators to be open to the idea that anyone can provide value. How many years of experience do you think is enough to share your thoughts and advice? #dataanalyst

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Winter Arc Day 7: SQL Challenge - Walmart Interview Question Welcome to Day 4 of the Winter Arc SQL Challenge! Today’s challenge comes from a Walmart interview question. 🛒📊 🔍 Problem Statement: Calculate each store running total Growth ratio compare to previous month return store name, sales amount, running total, growth ratio git: https://lnkd.in/gqFxvxnP Share your approach and solutions in the comments! Keep the learning momentum going as we conquer SQL one question at a time. #WinterArc #SQLChallenge #WalmartInterview #DataAnalytics #SQL

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When it comes to crafting a resume for a data analyst or a BI role, quite often I have observed individuals put details like “utilized tableau dashboards to deploy metrics tracking leading to x% conversions”. These are all very quantitative measures. While most of those are absolutely important and must-haves to show your accomplishments and value-add, the qualitatives many times get ignored. Why are the qualitatives important aspects to be shown in a resume? The qualitatives such as communication, team player attitudes and trust building are critical factors that determine the person’s ability to be able to work with others and ensure alignment between technical and non-technical stakeholders. In my experience, when I look back at my university days, I spent most of my time doing data analysis on datasets to build outcomes and score the good grades. In the real industry world, while the data analysis matters, other factors such as stakeholder engagement and alignment were often more critical. How does one mention the qualitative factors in a resume? Within one’s work experience, points such as “collaborated with stakeholders to conduct interviews and gathering of requirements” or “ensured alignment between technical and business teams establishing a sense of trust across the organization” can really go a long way in landing those BI or data analyst interviews. Feel free to reach out for discussion regarding your experiences on the same. Always happy to chat. #dataanalytics #businessintelligence #data #resume

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🚀 𝐊𝐞𝐲 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐟𝐫𝐨𝐦 𝐁𝐥𝐢𝐧𝐤𝐢𝐭 𝐒𝐚𝐥𝐞𝐬 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 🚀 Leveraging Power BI, I've uncovered some compelling insights into Blinkit's sales performance across various dimensions like fat content, location, outlet size, and item types. Here's a breakdown of the findings: 🍽️ 1. 𝐅𝐚𝐭 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Low-fat products dominate with total sales of $776K, significantly outperforming regular-fat products at $425K. The highest sales for low-fat products come from Tier 3 locations, contributing a massive $307K (40% of overall low-fat sales). This shows a strong preference for healthier options in less urbanized regions. While low-fat content leads in volume, regular-fat products have higher average sales per product, indicating greater value per item sold. 🌍 2. 𝐋𝐨𝐜𝐚𝐭𝐢𝐨𝐧-𝐰𝐢𝐬𝐞 𝐒𝐚𝐥𝐞𝐬: Tier 3 locations lead with $472.13K in sales, followed by Tier 2 at $393.15K, and Tier 1 at $336.40K. This indicates promising market potential in Tier 2 and Tier 3 regions, which could be focal points for expansion. 🏗️ 3. 𝐘𝐞𝐚𝐫-𝐰𝐢𝐬𝐞 𝐎𝐮𝐭𝐥𝐞𝐭 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: Outlets established in 2011 show lower sales compared to other years, while those set up in 2018 have the highest sales. This trend suggests that newer outlets, especially those post-2018, are major growth drivers, likely due to better site selection and operational efficiency. 🛒 4. 𝐓𝐨𝐩-𝐒𝐞𝐥𝐥𝐢𝐧𝐠 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐞𝐬: The top 3 sales categories are: Fruits & Vegetables Snacks Foods Household Products While rankings vary across Tier 1, Tier 2, and Tier 3 locations, these three categories consistently dominate across the board. 🏬 5. 𝐎𝐮𝐭𝐥𝐞𝐭 𝐒𝐢𝐳𝐞 𝐚𝐧𝐝 𝐒𝐚𝐥𝐞𝐬: Larger outlets in Tier 3 locations drive significant sales of low-fat products, totaling $98K, compared to $25K in Tier 2. This suggests that larger outlets in less urban regions are key to driving low-fat product sales. Large outlet sizes are notably absent in Tier 1 locations, hinting at a different retail dynamic, where medium and small outlets prevail. In Tier 3, smaller outlets contribute very little to overall sales, while medium outlets maintain a steady presence across all tiers. 💡 𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧: The data highlights the importance of regional strategies, product preferences, and outlet size in shaping Blinkit’s sales. Tier 3 locations are emerging as key markets, particularly for low-fat products, indicating untapped potential in these regions. 🌍 Git Link: https://lnkd.in/dMv3bQT8 #DataAnalyst #DataScience #PowerBI #Tableau #MySQL #StatisticalAnalysis #Excel #Analytics #BusinessAnalyst

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Get exclusive tips and resources to help you manage your TPM career and get promoted quickly! Comment "Career" and we will send it to you along with exclusive offers #tpm #technicalprogrammanager #programmanagement #jobpromotion #nextlevel #free #toolkit less

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Lean Manufacturing You Must Know | Evolving Industrial Concept In today’s fast-paced, ever-evolving industrial landscape, companies are constantly searching for ways to enhance efficiency, reduce waste, and stay competitive. One of the most effective methodologies to achieve this is Lean Manufacturing, a concept that has revolutionized production processes across the globe. Central to Lean Manufacturing is the principle of Continuous Improvement, often referred to by its Japanese term, Kaizen. This blog will take you on an in-depth journey through Lean Manufacturing and Kaizen, helping you understand how these practices can transform your business. Read More : - https://lnkd.in/ddeeJMHF #leansimcorp #beawarechannel #blogs #blogger #education #learning #ENGINEERING #mechanical #industrial

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My Practice in Minitab Anova Attribute Agreement Analysis- Procedure #anova #gageR&R #Sixsigma #blackbelt #practice #benchmarking 1. Set aside 15 to 30 test samples of the item you’re measuring. Make sure these samples represent the full range of variation being encountered and make sure approximately equal amounts of each possible attribute category 2. Create a “master” standard that designates each of the test samples into its true attribute category. 3. Select two or three typical inspectors and have them review the sample items just as they normally would in the measurement system, but in random order. Record their attribute assessment for each item. 4. Place the test samples in a new random order, and have the inspectors repeat their attribute assessments. (Don’t reveal the new order to the inspectors!). Record the repeated measurements. 5. For each inspector, go through the test sample items and calculate the percentage of items where their first and second measurements agree. This percentage is the repeatability of that inspector. 6. Going through each of the sample items of the study, calculate the percentage of times where all of the inspectors’ attribute assessments agree for the first and second measurements for each sample. This percentage is the reproducibility of the measurement system. 7. You can also calculate the percent of the time all the inspectors’ attribute assessments agree with each other and with the “master” standard created in Step 2. This percentage, which is referred to as the Accuracy (effectiveness) of the measurement system

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Very useful tools for every engineer.

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🌟 Knowledge Series 🌟 🔍 Understanding Waste in the Toyota Production System (TPS) In the Toyota Production System (TPS), waste is any activity that consumes resources but doesn’t add value from the customer's perspective. TPS emphasizes identifying and eliminating waste to improve efficiency, reduce costs, and enhance quality. 🚀 The 7 Wastes (Muda): 1️⃣ Overproduction: Making more than needed or too early. 2️⃣ Waiting: Idle time for people, materials, or machines. 3️⃣ Transportation: Unnecessary movement of materials. 4️⃣ Over processing: Doing more work than required. 5️⃣ Inventory: Excess stock without immediate demand. 6️⃣ Motion: Inefficient movement of workers or tools. 7️⃣ Defects: Errors leading to rework or waste. 💡 TPS uses principles like Just-In-Time (JIT), automation, and Kaizen (continuous improvement) to minimize waste and maximize value. 📘 Knowledge is power—share your thoughts or examples of waste reduction below! Symbiosis Centre for Distance Learning#KnowledgeSeries # #ToyotaProductionSystem #Efficiency #ContinuousImprovement #LeanThinking #WasteReduction

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Understanding Poka Yoke: A Simple yet Powerful Tool for Error Prevention In the world of manufacturing and process improvement, the quest for reducing defects, enhancing quality, and eliminating waste is constant. One effective approach that has gained widespread popularity is Poka Yoke—a Japanese term that means "mistake-proofing" or "error-proofing." This concept, developed by Shigeo Shingo as part of the Toyota Production System (TPS), aims to prevent human errors during production by designing systems that help operators avoid mistakes. Though rooted in manufacturing, the principles of Poka Yoke can be applied across various industries. The Origins of Poka Yoke Poka Yoke was introduced by Shigeo Shingo in the 1960s as a part of the Toyota Production System. Shingo, a Japanese engineer and management consultant, was tasked with identifying ways to reduce defects and improve quality control in the manufacturing process. The term itself translates to "Poka" (mistake) and "Yoke" (prevention), which collectively refers to any mechanism that prevents human errors from turning into defects. Before Poka Yoke, the emphasis was largely on identifying and correcting defects after they occurred. However, this reactive approach led to inefficiencies and high costs. Shingo's concept of mistake-proofing shifted the focus to proactively designing systems that would either eliminate errors or make it impossible for them to happen in the first place. Key Principles of Poka Yoke The core idea of Poka Yoke is simple: if a mistake can be made, it will be. Therefore, systems should be designed to either prevent errors from happening or ensure that they are caught immediately when they do occur. There are several principles to guide the implementation of Poka Yoke: Prevention: The goal is to design a process or tool that prevents an error from occurring in the first place. This might involve physical changes to equipment or introducing new methods that eliminate the possibility of mistakes. Detection: If a mistake is made, it should be detected immediately, and corrective action should be taken right away. This could involve visual or auditory signals that alert workers to the presence of an error. Corrective Action: In the case that an error is detected, the system should allow for immediate correction or automatic adjustment to ensure that the product or process continues correctly. Prevention: Conduct FMEA to identify potential errors during design, build Poka Yoke during the design phase, and do the same during the operation phase. Types of Poka Yoke Devices Poka Yoke devices come in many forms, each designed to address specific error types. These devices can be classified into three main categories: 1) Contact Method 2) Fixed-Value Method: 3) Motion-Step Method Benefits of Poka Yoke - Cost Savings - Improved Efficiency - Enhanced Safety - Higher Customer Satisfaction #pokayoke #errorproofing #mistakeproffing Dinesh Sharma

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Efficiency isn't just a buzzword; it's a mindset. As a Lean Six Sigma Green Belt, I’ve seen how small tweaks can lead to big transformations in manufacturing and process optimization. Here’s one example: In a previous role, I tackled inefficiencies in a manufacturing assembly line where delays were increasing production time by 15%. Through a Lean Six Sigma approach, I led a root cause analysis and discovered a bottleneck caused by unnecessary material handling between workstations. We implemented a simple yet impactful solution: reorganizing the workstation layout to streamline material flow and introducing a Kanban system for inventory replenishment. These changes reduced downtime, cut production time by 10%, and improved overall throughput. It’s amazing how addressing even minor inefficiencies can yield significant results! What strategies have worked for you in streamlining workflows? Let’s share insights! #Engineering #STEM #TechTrends #Innovation #IndustryNews #EngineeringLife #Design #Tech #FutureOfEngineering #Automation #Manufacturing #Robotics #MechanicalEngineering #ArtificialIntelligence #Coding #ProjectManagement #Jobs #LeanManufacturing #SixSigmaGreenBelt

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