Forecasting Cement Prices

8/13/2019 Forecasting Cement Prices

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Team 4

Amit Tyagi

Harmanjit Singh

Mohini Jain

Nandini ChandrasekharRahul Chakraborty


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Objective

Data

Background

Quick Look

Issues

Preparation

Analysis

Naive

Exponential Moving Average

Time Series Forecasting

OBJECTIVE

DATA

RESULTS SUMMARY & INSIGHTS

ANALYSIS


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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 productioncompanies to plan capacity expansion

Benefits

The forecasts will serve as a valuable tool to help the construction industrymake important logistic decisions such as:

Timing of purchase Inventory Management

Strategically allocate budget inpurchasing cement

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

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

INDUSTRY

PURPOSE

STAKEHOLDERS

BENEFITS


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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
http://www.indiastat.com/http://www.indiastat.com/


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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 MovingAverage and straight line method

Oil prices converted to Indian Rupees using historic conversion ratecorresponding to each data point sourced from www.oanda.com
http://www.oanda.com/http://www.oanda.com/


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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


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200.00

210.00

220.00

230.00

240.00

250.00

260.00

270.00

280.00

290.00

Cement Prices Nave 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 seasonalityNave 12-month ahead forecastForecast values and Error Measurements attached below

ValidationPeriod


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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 Nave forecasting.

Both Exponential and Navemethod systematically under

predicts the cement demand

200

210

220

230

240

250

260

270280

290

CementPrices

Time

Time Plot of Actual Vs Forecast (Training Data)

Actual Forecast

ValidationPeriod


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

200210220230240250260

Jan.2009

Mar.2009

May.2009

July.2009

Sept.2009

Nov.2009

Jan.2010

Mar.2010

May.2010

July.2010

Sept.2010

Nov.2010

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

TestData

ValidationData


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



Method has similar errors in validation

and test sets


TestData

ValidationData

200

210

220

230

240

250

260


220

240

260280

300

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

2011


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



Method systematically under predicts in

validation/Test Set


TestData

ValidationData

200

220

240

260


220

230

240

250

260

270

280

290

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


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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


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April and May have at least 10%

higher cement prices than December

Cement Prices are affected byprevious months change in Oil pricesand not oil prices themselves

Crude prices have a positive trendoverall last two years.

Inventory should be brought inDecember for next fiscal yearrather than April/May

Track changes in crude prices onmonthly basis to deduce future

cement prices

INSIGHTS RECOMMENDATIONS

Forecasting Cement Prices

Documents

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