Business Problem Description: To help Valero Energy Corporation (a fortune 500 company involved in manufacturing and marketing of fuel and petrochemical products/power) to effectively plan for production planning, strategic procurement, pricing contracts and financial hedging and investment strategies for 2016.
Forecasting Objective: To design a forecasting model for accurately predicting consumption of different refinery products in the US for 2016. The intent is to use consumption patterns to plan & optimize operations, foresee cash flows and manage procurement of inventory.
Brief description of data: The data contains 22 years of historical monthly data on different types of US refinery products including price information
- Source: US Energy Information Administration (Forms EIA-782C, “Monthly Report of Prime supplier sales of Petroleum Products for local consumption”
- Key Characteristics: Month – Year Level ; Available Period: Jan 1983 to Dec 2015
- Series to be analyzed (Observation period 2001 to 2015)
o Total, Regular and Premium Gasoline
o Aviation Gasoline, Jet fuel and Fuel oil
- Components observed: Level, Seasonality and Trend (With Noise)
High level description of the final forecasting method and performance on meaningful metrics (compared to benchmark)
Short Term Horizon: The final model is based on running the ARIMA model over Multiple Logistic Regression (MLR) by removing auto-correlation providing us forecasts for the 1st two months in 2016 with high accuracy.
Long Term Horizon: The final model is based on running the Holt’s winter which provides us with forecasts of 12 months of fuel consumption with a relatively low accuracy or MAPE
Conclusions and Recommendations
- The short term model should be re-run every month for prediction of next month’s consumption.
- The long term model should be re-run every 12 months for prediction of next 12 months' consumption.
- External factors such as environmental, political, and economical risks are not accounted in the model
- Price and profitability should be considered in conjunction with the consumption pattern and prediction to plan production accordingly