✔ Developed a BQA workbook to calculate recommended pallet counts, validated results through an API endpoint, and constructed a pallet compliance dashboard for stakeholders, potentially generating annual savings of $2 million.✔ Architected a "Control Tower" in python to capture vehicle rule changes to improve the accuracy of Truck Type Model.✔ Led the data cleansing of 25M orders and developing inventory-aware sourcing logic to improve OTC performance. ✔ Spearheaded the… Show more ✔ Developed a BQA workbook to calculate recommended pallet counts, validated results through an API endpoint, and constructed a pallet compliance dashboard for stakeholders, potentially generating annual savings of $2 million.✔ Architected a "Control Tower" in python to capture vehicle rule changes to improve the accuracy of Truck Type Model.✔ Led the data cleansing of 25M orders and developing inventory-aware sourcing logic to improve OTC performance. ✔ Spearheaded the implementation of 120+ vehicle rule changes, maintaining Truck Type model effectiveness.✔ Collaborated with Online Delivery Analytics team to devise an optimization model to pinpoint the optimal carrier for each vendor, maximizing discounts from the primary carriers and potentially yielding over $30 million in savings.✔ Optimized the truck type compliance dashboard, reducing load time from 226 to 2.56 seconds (100-fold improvement). ✔ Consistently delivered on multiple ad-hoc requests, including effectiveness analysis of new sprinter vans, rural area box additions, and extraction of pallet data for MDS. Show less

✔ Developed KPIs for expedited shipments to quantify performance and optimized the Power BI dashboard load time by 40% leveraging power query ETL in order to provide real-time information to stakeholders.✔ Implemented Power Automate and AI to automate cloud flow for invoice data collection, analyzing $1.8M and identified amount of $200K in savings with carriers.✔ Engineered 10+ Power Automate flows, 5+ Power Apps and 5+ Power BI dashboard resulting in a productivity boost for the team… Show more ✔ Developed KPIs for expedited shipments to quantify performance and optimized the Power BI dashboard load time by 40% leveraging power query ETL in order to provide real-time information to stakeholders.✔ Implemented Power Automate and AI to automate cloud flow for invoice data collection, analyzing $1.8M and identified amount of $200K in savings with carriers.✔ Engineered 10+ Power Automate flows, 5+ Power Apps and 5+ Power BI dashboard resulting in a productivity boost for the team and saving over 300 hours in the current year.✔ Implemented time series forecasting and machine learning approaches to predict weekly inbound shipments, which improved the performance of existing models by 8% over the past three months.✔ Created Deviation Log, a Power App to streamline identification and escalation of team deviations, driving the adoption of Kaizen strategies and cultivating a culture of continuous improvement.✔ Analyzed carrier bids from RFQ (Request For Quota) round with Ocean Drayage Buyer, leading to a 12% rate decrease year-over-year and a reduction of 750 tonnes in CO2 emissions.✔ Developed a 300+ line reusable Python script for data cleaning and analysis in an LTL sourcing project, resulting in a time savings of 4 weeks.✔ Led data analysis for contingency plan involving at-risk carrier and collaborated with cross-functional teams to facilitate transition of $12M annual business to backup carriers.✔ Created swim Lane chart for visualization of the expedite shipment✔ Assist Closely our LTL, FTL, Cross-Dock, High and Heavy and Ocean & Drayage Buyer on RFQ for minibid Show less

✔ Developed and implemented data analysis methods to predict and identify root causes of extreme heating in the heating tunnel, resulting in a 10% reduction in heating process issues. Analyzed time series data from various sensors, using statistical methods and machine learning techniques such as unsupervised clustering on Python. ✔ Modeled and developed Track association problem algorithm in marine surveillance Using the Hybrid deep neural network method to predict the vessels to their… Show more ✔ Developed and implemented data analysis methods to predict and identify root causes of extreme heating in the heating tunnel, resulting in a 10% reduction in heating process issues. Analyzed time series data from various sensors, using statistical methods and machine learning techniques such as unsupervised clustering on Python. ✔ Modeled and developed Track association problem algorithm in marine surveillance Using the Hybrid deep neural network method to predict the vessels to their actual tracks with 85 percent accuracy. ✔ Assisted the professor with feature selection and running the PCA and clustering algorithms on the patient data to conduct his research on a disease by cleaning the data, running the algorithms, and interpreting the results. Show less

✔ Mentoring Six teams on their projects of aggregate production planning including creating product structures followed by BOM Sheet, S & OP, and Enterprise Resource Planning ✔ Helped the students developed the supplier evaluation metrics and set the objective for choosing the supplier.✔ Teaching the process capability and Total Quality Management of a class of 45 students.✔ Assisting Student learning forecasting methods and techniques

✔ Led a Kaizen team of 5 members supervising 200 workers, where implementing the kaizen project were able to reduce the cycle time upto 10% for certain product line ✔ Identified the bottleneck of the processes and Leveraged the Business process re-engineering to improve the processing time by eliminating some of the non-value added task and automating some manual processes. ✔ Improved the change over processing time by 10 percent by setting the machine outside of the production line.… Show more ✔ Led a Kaizen team of 5 members supervising 200 workers, where implementing the kaizen project were able to reduce the cycle time upto 10% for certain product line ✔ Identified the bottleneck of the processes and Leveraged the Business process re-engineering to improve the processing time by eliminating some of the non-value added task and automating some manual processes. ✔ Improved the change over processing time by 10 percent by setting the machine outside of the production line. ✔ Performed data analysis to assist with forecasting and production planning for the first two quarter of the year where MAPE (Mean Absolute percentage error) was reduced from 13.5% to 9.5% for the first quarter✔ Measured the OEE(overall equipment effectiveness) to figure out the overall maintenance performance and initiated the Kanban flag system that saved approximately 800 productive hours per month✔ Leveraged data to build PowerBI Dashboard measuring the skill metrics of the 200 workers using time study, reported the findings to the management, and suggested the strategy for line balancing✔ Assisted the Industrial Engineering team in capacity planning for ready-made garment products Show less