Data Science and Analytics Tools - Inventory Optimization with Linear Programming

 Notes:

• What problem does it solve?
  Helps businesses determine the optimal inventory levels across multiple products to minimize cost while meeting demand.

• How can businesses or users benefit from customizing the code?
  Businesses can adjust constraints, costs, and demand forecasts according to their specific product inventory.

• How can businesses or users adopt the solution further, if needed?
  It can be used to automate inventory decisions in supply chain management.

Actual Python Code:

from scipy.optimize import linprog

# Define costs (assumed for 3 products)

costs = [2, 3, 4]  # unit cost for each product

# Define inequality constraints (e.g., minimum stock for each product)

lhs = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]

rhs = [50, 30, 40]  # minimum stock levels

# Solve the linear programming problem

result = linprog(c=costs, A_ub=lhs, b_ub=rhs, method='highs')

print(f'Optimal inventory levels: {result.x}')