In a previous role, I was tasked with analyzing a dataset of over 50,000 transactions to identify bottlenecks in our logistics process. By using SQL queries and data visualization tools, I discovered that a significant portion of delays was due to misaligned schedules between suppliers and transportation providers. This insight led to a reconfiguration of our scheduling system, reducing lead times by 15% and improving overall efficiency.

Ensuring data accuracy is crucial. I typically start by validating the data sources to confirm their reliability. Then, I use a combination of automated scripts and manual checks to clean and standardize the data. For instance, I might use Python to remove duplicates and outliers, followed by a manual review to ensure no critical data points are missed. Regular audits and cross-referencing with other datasets also help maintain accuracy.

I have extensive experience with various forecasting models, including ARIMA, exponential smoothing, and machine learning algorithms like LSTM. In one project, I used an LSTM model to predict demand for a seasonal product line. By incorporating historical sales data, market trends, and external factors like weather, the model improved forecast accuracy by 20%, leading to better inventory management and reduced stockouts.

Collaboration is key in supply chain management. I typically start by understanding the goals and challenges of each team, such as procurement, logistics, and sales. Then, I use data to create actionable insights that address their specific needs. For example, I might provide a dashboard to the procurement team that highlights supplier performance metrics, enabling them to make more informed decisions. Regular meetings and clear communication ensure alignment and continuous improvement.

I am proficient in a range of tools and technologies, including SQL for data extraction and manipulation, Python for advanced analytics, and Tableau for data visualization. Additionally, I have experience with ERP systems like SAP and Oracle, which are essential for integrating and analyzing supply chain data. These tools allow me to efficiently gather, process, and present data insights that drive decision-making in supply chain operations.