Inventory Optimization: Case Study

Download the dataset below to solve this Data Science case study on Inventory Optimization.

In the world of supply chain management, accurately predicting future demand and optimizing inventory levels are critical for efficient operations and customer satisfaction.

The provided dataset contains historical data related to a specific product (Product_ID: P1) over a period of time. The data includes information on the date, product demand, and current inventory levels. This dataset serves as the foundation for analyzing demand forecasting and inventory optimization techniques.

Your task is to:

  1. Utilize time series analysis to forecast the future demand for the given product (Product_ID: P1).
  2. Generate accurate predictions for the next several days.
  3. Calculate and implement safety stock levels to account for demand variability and uncertainties.
  4. Determine the reorder point, which indicates when to replenish inventory based on forecasted demand and safety stock.
  5. Apply the Economic Order Quantity (EOQ) concept to identify the optimal order quantity that minimizes total inventory costs.
  6. Define the reorder cycle, representing the time interval between placing orders.

References to Solve this Data Science Case Study