The Project

A EU-based manufacturer of electronic devices and components faced operational inefficiencies and profitability losses due to missed delivery timeframes. Seeking a data-driven approach to optimize supply chain performance, they engaged our partner’s expertise to develop an AI/ML-powered solution capable of analyzing vast datasets and making accurate delivery predictions.

Advanced Machine Learning Integration Our team contributed to the development of a sophisticated machine learning platform designed to assess key factors influencing procurement efficiency. They engineered a logical layer that clusters data into relevant cohorts and trains tailored models for each group. Additionally, an explainability layer was integrated, enabling end users to validate model behavior and enhance transparency in estimations.

Streamlined Workflow & Predictive Insights The platform operates by allowing managers to input critical vendor details, including material requirements, delivery timelines, and warehouse stock levels. Leveraging advanced ML algorithms, the system analyzes this data and predicts procurement dates based on historical interactions, vendor reliability, and external risks.

Enhanced Decision-Making & Operational Efficiency With the implementation of machine learning and data science algorithms, the client now benefits from continuous monitoring of potential procurement disruptions and improved shipment planning. This solution eliminates information silos and empowers decision-makers with actionable insights, driving operational excellence and maximizing revenue across digital sales channels.

Technologies

Tensorflow, Keras, PyTorch, Scikit-learn, MLFlow, Pandas, Matplotlib, Plotly, Numpy, TypeScript, React, Node.js, Nest, MobX

Team

DDT, 1 developer, 1 ML engineer.