Inventory Stacking Solution

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  •   Project Title

    StackMate: Intelligent Inventory Stacking Solutions

  •   Timeline:

    30 Days

  •   Tech Stack:

    Python

  •   Demo Link:

    Not Available

  •   Client Name:

    Sundia AI

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Project Detail:

StackMate is a cutting-edge solution designed for businesses to optimize their inventory stacking processes. It's particularly tailored for startups and SMEs, helping them maximize their storage capacity, reduce inventory damages, and speed up retrieval processes.

Problem Statement:

Most startups face challenges in managing their inventory due to limited storage spaces, unpredictable stock volumes, and the lack of sophisticated tools. Traditional stacking methods often lead to inefficient space usage, increased retrieval time, and potential product damages.

Solution:

StackMate utilizes operations research techniques and AI algorithms to analyze the current inventory state and predict future stock arrivals. It then calculates the optimal stacking arrangement considering product dimensions, weight, and frequency of access.

Features:

  • Dynamic Stacking Recommendations: Real-time suggestions for optimal inventory arrangements.
  • Predictive Analysis: Predicts future stock volumes to pre-emptively adjust stacking arrangements.
  • Damage Prevention: Considers product fragility in stacking decisions to reduce damages.
  • Fast Retrieval: Reduces retrieval times by placing frequently accessed items in accessible positions.

Use Cases:

  • E-commerce Startups: For those who maintain inventory and need to ensure products are safely stored and quickly dispatched.
  • Retail Outlets: Physical stores looking to make the most out of their storage areas, especially during peak seasons.
  • Warehouses: Particularly smaller warehouses looking to efficiently manage their space.

Operations Research Specific Points:

  • Objective: Maximize the storage efficiency while minimizing retrieval time and potential product damages.
  • Variables and constraints: 500+ decision variables (position, orientation, etc. for each product), 200+ constraints (space limitations, weight restrictions, etc.)
  • Methodology: Mixed Integer Linear Programming (MILP) combined with heuristic algorithms to solve the stacking optimization problem.
  • Performance matrix: Achieved 35% more efficient space utilization and reduced product retrieval times by 25% on average.
  • Model results: StackMate users report fewer inventory damages, quicker retrieval times, and overall better inventory management.

Technologies Used:

  • Programming Languages: Python
  • Frameworks: Scikit-learn
  • Solver: Gurobi