Forecasting Analytics

Topic: Forecasting Fuel Consumption At Nation Level – US

Group Project Report (Proposed By):

Team B8 ; Section: FCAS-B

Name PG ID DHRUV GOEL 6171-0938

NAIREET GHOSH 6171-0078

DIWAKAR SANDUJA 6171-0727

PRABHJOT KAUR 6171-0710

AMEY RAJPUT 6171-0570

SRI RAM 6171-0592

Executive Summary

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

month’s 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

Technical Summary

Data Preparation / Issues As quality of data is critical for forecasting results, it is important to

refine and prepare data to make it usable for analysis. The following aspects of collected data

were checked:

Data sources – multiple or single/future sustainability/frequency/ Extreme Values

We have used a single data source. Hence there are no expected issues pertaining to

inter-mixing of different data sources. As this data is periodically shared by U.S.

government, sustainability of data shall not be a problem. Monthly frequency is the

optimum granularity for this analysis as the monthly aggregation averages out

fluctuations. Also a monthly forecast is most relevant for action-ability and agility.

Missing values and Choice of time span

We collected monthly data from Jan-1983 to Dec-2015. We encountered missing values

in the 1980s and 1990s. Therefore we limited our time span to 2001 to 2015 which does

not have the issue of missing values.

Forecasting Methods with details

As the preliminary investigation by chart-plotting revealed the presence of Level, Trend and Seasonality, therefore we have considered forecasting methods that consider both trend and seasonality. The following predictive models were performed and their performance evaluated to arrive at the final model

Forecasting Methods and Performance Comparison to benchmarks – Aviation Fuel (Charts for

other fuel types added in Appendix):

Long Term (Holts’ Winter Model): To obtain forecasts of next 12 months (MAPE: 11.7%)

Short Term: To predict accurate results for in the short term, we ran MLR followed by ARIMA

(3) which led to decrease in MAPE and more accurate results for latest 2 months.

Forecast

Residuals show no trend or

seasonality left over after Holts’

Winter Model

Forecasted only 2 months of data

in 2016 with improved MAPE of

11.3

Residuals show no trend or

seasonality left over after Holts’

Winter Model

Appendix ( Results for the Other Series)

Jet Fuel (Long Term: Holt Winters’ Model)

Jet Fuel (Short Term: MLR + ARIMA (3)):

Residuals show no trend or seasonality left over after

Holts’ Winter Model

MAPE : 2.3

Residuals show no trend or seasonality left over after

ARIMA(3)

MAPE : 2.3

Forecast generated for only 2 months using ARIMA (3)

Fuel Oil (Long Term: Holt Winters’ Model)

Fuel Oil (Short Term: MLR + ARIMA (3)):

Forecast generated for only 2 months using ARIMA (3)

Residuals show no trend or seasonality left ARIMA(3)

MAPE : 19.3

Residuals show no trend or seasonality left after Holts

Winter

MAPE : 21.9

Total Gasoline (Long Term: Holt Winter’s Model):

Total Gasoline (Short Term: MLR + ARIMA (3)):

Residuals show no trend or seasonality left over after

Holts’ Winter Model

MAPE : 3.3

Forecast generated for only 2 months using ARIMA (3)

Residuals show no trend or seasonality left ARIMA(3)

MAPE : 1.8

Regular Gasoline (Long Term- Holt Winter’s Model)

Regular Gasoline (Short Term):

Residuals show no trend or seasonality left over after

Holts’ Winter Model

MAPE : 2.6

Forecast generated for only 2 months using ARIMA (3)

Residuals show no trend or seasonality left ARIMA(3)

MAPE : 1.6

Premium Gasoline (Long Term – Holt Winter’s Model)

Premium Gasoline (Short Term- MLR + ARIMA (3) Model)

Residuals show no trend left over after Holts’ Winter Model

MAPE : 11.1

Forecast generated for only 2 months using ARIMA (3)

Residuals show no trend or seasonality left ARIMA(3)

MAPE : 2.2

ACF Plots