Machine Learning Optimization for Logistics Client

Daniel Anderson

Data Scientist
Data Visualizer
ML Engineer

  • Objective Understanding: The primary goal was to reduce transport costs and improve service levels by determining the optimal locations for micro warehouses. This would enable the client to meet their delivery time key performance indicators (KPIs).
  • Data Analysis and Preparation: We analyzed the client's sales data and geographical distribution of points of sale to understand the existing logistics network and identify areas for improvement.
  • K-means Clustering Algorithm:
    • Utilized K-means clustering to identify natural groupings of points of sale based on geographical proximity and volume of sales.
    • Determined the optimal number of clusters that would represent potential locations for the micro warehouses, ensuring coverage across the identified regions.
  • Integration with Google API:
    • Leveraged the Google Maps API to refine the location data and validate the accessibility and practicality of the proposed micro warehouse sites.
    • Assessed the potential impact on delivery times, with the aim of achieving or surpassing the specified delivery time KPI.
  • Optimization and Strategy Development:
    • Analyzed the costs and benefits associated with each potential micro warehouse location, considering factors like land costs, construction, and operational expenses.
    • Developed a phased implementation plan to guide the establishment of the micro warehouses, prioritizing locations based on potential cost savings and service improvement.
  • Lead and Collaboration:
    • As the lead on the optimization project, I coordinated with cross-functional teams, including logistics, finance, and operations, to ensure that the strategy was aligned with overall business objectives.
    • Facilitated discussions and decision-making processes to finalize the warehouse placement strategy and implementation roadmap.

This project demonstrated the power of combining analytical techniques with geospatial data to drive strategic decisions in logistics and supply chain management,

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