Team 4 Amit Tyagi Harmanjit Singh Mohini Jain Nandini Chandrasekhar Rahul Chakraborty

Objective Data

Background

Quick Look

Issues

Preparation

Analysis

Naive

Exponential Moving Average

Time Series Forecasting

OBJECTIVE

DATA

RESULTS SUMMARY & INSIGHTS

ANALYSIS

Industry Very cost intensive Major raw materials used for construction include sand, cement, steel etc. Prices of raw materials are driven by the price of crude oil, indicating

fluctuation in price of the raw materials. Purpose

To predict cement prices on monthly basis in context of India.

Stakeholders

Builders/dealers in the construction industry or cement production companies to plan capacity expansion

Benefits

The forecasts will serve as a valuable tool to help the construction industry

make important logistic decisions such as: Timing of purchase Inventory Management Strategically allocate budget in purchasing cement

In effect, the outcome would be better resource planning and sourcing leading to increased savings

The outcome would be better resource planning and sourcing leading to increased savings

INDUSTRY

PURPOSE

STAKEHOLDERS

BENEFITS

Sourc e: Monthly Crude oil and cement prices from www.indiastat.com Time Period: Data available for the period Jan 2009 – May 2011 Forecast Peri June 2011 – Aug 2011 Based on monthly prices of cement and oil for the years 2009 and 2010

Average Price of Cement in Major Consumption (Centers Per Bag of 50 kg (In Rs.241)

Monthly Average Price of Indian Basket of Crude Oil (in $/barrel converted to Rs3471)

Crude oil to be explored as a predictor for cement prices

SOURCE

TIME PERIOD

FORECAST

Gaps/Issues in data Cement prices were missing for the data period Jan 2011 to Mar 2011

Oil Prices were available in US dollar

Correlation between Cement Prices and Oil Prices was low at 0.33

Moving Average Missing data imputation in cement prices using the method of Moving

Average and straight line method

Oil prices converted to Indian Rupees using historic conversion rate corresponding to each data point sourced from www.oanda.com

Cement • Level: Averages around 237.5 • Trend: No clear Trend present in the series • Seasonality: No significant pattern • Stationary series • Peak around the month of April every year

Oil • Level: for oil prices is around 3500 •Trend: Clear increasing trend • Seasonality : No significant pattern • Non Stationary Series • 2010 values follow a very smooth pattern

200.00

210.00

220.00

230.00

240.00

250.00

260.00

270.00

280.00

290.00

Cement Prices Naïve Forecast

Parameter Value

Average Error -0.30

MAE 12.68

RMSE 16.04

MAPE -0.43%

Time Forecasted

Cement Prices

June. 2011 240.00

July. 2011 233.00

Aug. 2011 225.00

Cement data displays monthly seasonality Naïve 12-month ahead forecast Forecast values and Error Measurements attached below

Validation Period

Cement data does not depict trend but has 12 month seasonality Holt-Winter No Trend method of Exponential Smoothing Data partitioned into 24 months Training and 5 months Validation Forecasts on validation set:

MAPE -0.51%

MAE 19.24

RMSE 10.78

Time Actual Forecast Error LCI UCI

Jan. 2011 237.67 240.10 -2.43 219.09 261.11

Feb. 2011 238.56 237.66 0.90 216.65 258.67

Mar. 2011 238.69 233.27 5.42 212.26 254.27

Apr. 2011 280.00 231.33 48.67 210.33 252.34

May. 2011 276.00 237.24 38.76 216.23 258.24

Time Forecast LCI UCI

Jun. 2011 240.10 219.09 261.11

July. 2011 237.66 216.65 258.67

Aug. 2011 233.27 212.26 254.27

•The RMSE error is lower for the method than Naïve forecasting. •Both Exponential and Naïve method systematically under predicts the cement demand

200210220230240250260270280290

Cem

ent P

rices

Time

Time Plot of Actual Vs Forecast (Training Data)

Actual Forecast

Validation Period

Independent Variables : time & Dummy variables for Q1, Q2 and Q3 ( Q4 as Base Quarter)

200210220230240250260

Jan

. 20

09

Mar

. 20

09

May

. 20

09

July

. 20

09

Sep

t. 2

00

9

No

v. 2

00

9

Jan

. 20

10

Mar

. 20

10

May

. 20

10

July

. 20

10

Sep

t. 2

010

No

v. 2

010

Predicted Value Actual Value

210

230

250

270

290

Jan. 2011 Feb. 2011 Mar. 2011 Apr. 2011 May. 2011

•The RMSE error is lower for the method than previous methods. • However the method systematically under predicts in validation set • Adjusted R 2 for the model is .3345

Test

Dat

a V

alid

atio

n D

ata

Independent Variables : time & Dummy variables for each Month( Dec. as Base Month)

•The RMSE error is lower for the method than previous methods. •Method has similar errors in validation and test sets •Adjusted R 2 for the model is .5364

Test Data

Validation Data

200

210

220

230

240

250

260

Predicted Value Actual Value

220

240

260

280

300

Jan. 2011 Feb. 2011 Mar. 2011 Apr. 2011 May.2011

Independent Variables : Change in Crude oil & Dummy variables for each Month( Dec. as Base Month)

•The RMSE error is lower for the method than previous methods. •Method systematically under predicts in validation/Test Set •Adjusted R 2 for the model is .5964

Test Data

Validation Data

200

220

240

260

Predicted Value Actual Value

220

230

240

250

260

270

280

290

Jan. 2011 Feb. 2011 Mar. 2011 Apr. 2011 May. 2011

Based on Model 3 the forecast for June 2011 is:

Point Estimate: 244.67

95% confidence interval is (230.50,258.9)

-1

-0.5

0

0.5

1

1 2 3 4 5 6

Number of Lags

Autocorrelation of Residual / Data Set #1

• April and May have at least 10% higher cement prices than December • Cement Prices are affected by previous months change in Oil prices and not oil prices themselves • Crude prices have a positive trend overall last two years.

•Inventory should be brought in December for next fiscal year rather than April/May • Track changes in crude prices on monthly basis to deduce future cement prices

INSIGHTS RECOMMENDATIONS