Inspiration
Our inspiration stemmed from the critical need to optimize supply chain logistics for large-scale manufacturers like Kimberly-Clark. Transportation costs and coverage inefficiencies are persistent challenges in industries reliant on distributed production and distribution networks. We aimed to solve this by leveraging data-driven combinatorial optimization to ensure full geographic coverage at minimal cost turning logistics from a cost center into a strategic asset.
What it does
This tool identifies the top 3 mill locations that collectively serve all target counties while minimizing transportation costs (choosing between trucking and rail for each route). By analyzing thousands of potential mill combinations, it guarantees cost-effective, comprehensive coverage, empowering companies to streamline operations and reduce expenses.
How we built it
Parsed CSV data to map counties, mills, and transportation costs. Used Python’s itertools to generate all possible 3-mill combinations. For each combination, verified coverage of all counties and summed the minimum cost (trucking or rail) per route. Sorted valid combinations by total cost to highlight the most efficient solutions.
Challenges we ran into
Ensuring every county was serviced by at least one mill in each combination required meticulous checks.
Accomplishments that we're proud of
Built a framework adaptable to diverse industries, from manufacturing to retail
What we learned
How to prioritize cost vs. coverage in dynamic supply chain environments
What's next for Strategic Expansion the Kimberly Clark Statement
Implementing real time data for better results Incorporating carbon footprint calculations to align with Kimberly-Clark’s sustainability goals.
Log in or sign up for Devpost to join the conversation.