ASARCO LLC is a mining, smelting, and refining company based in Tucson, Arizona that mines and processes primarily copper. The company, a subsidiary of Grupo México, is currently in Chapter 11 bankruptcy. ASARCO planned to emerge from bankruptcy in 2008, and opposes calls for it to totally liquidate its mining and industrial assets.[1]
Its three largest open pit mines are the Mission, Silver Bell and the Ray mines in Arizona. Its mines produce 350 to 400 million pounds of copper a year. ASARCO conducts solvent extraction/electrowinning at the Ray and Silver Bell mines in Pima County, Arizona and Pinal County, Arizona and a smelter in Hayden, Arizona. Before its smelting plant in El Paso, Texas was suspended in 1999 it was producing 1 billion pounds of anodes each year. Refining at the mines as well as at a copper refinery in Amarillo, Texas produce 375 million pounds of refined copper each year.
ASARCO's hourly workers are primarily represented by the United Steelworkers.
ASARCO has 20 superfund sites across the United States, and it is subject to considerable litigation over pollution.
As of September 2009, ASARCO was the focus of a bidding war begun in May 2008 between its own parent company Grupo México and India-based Sterlite Industries. On August 31, 2009, U.S. Bankruptcy Judge Richard Schmidt recommended that U.S. District Judge Andrew Hanen accept Grupo México's $2.5 billion bid for ASARCO as it prepares to come out of bankruptcy. However, on September 11, Sterlite increased its bid from $2.14 billion to $2.57 billion and requested that the court evaluate its new offer before issuing a final decision.

Several Likert scale questions were positioned between introductory travel related questions and respondent demographics. The scale ranged from 1, which denoted strong preference toward transport mode A, to 9, which indicated a strong preference for mode B. Each respondent was shown three transportation pairs. Four combinations of tradeoff pairs were then randomly distributed across the airport and rotated by day parts (morning, midday and evening hours).

At an appropriate time during the interview, the respondent was shown a characteristics grid (Figure 3 at bottom) displaying information about each transportation mode. As can be seen, an attempt was made to provide the respondent with an unbiased set of features for each mode.

The study's result is based on 400 completed interviews, 100 per pair combination set. Quotas were set for both inbound and outbound respondent type, and the final dataset was weighted to overcome disparities. The data were also adjusted for the seasonal variation between vacation and business traffic.

Beyond basic descriptive statistics, the study used the Student's t test to identify significant differences about the Likert scale centroid. The conjoint methodology identified a favorable fare structure and its elasticity. Factorial analysis was used to discern if clustering existed among variables such as: reason for being in Orlando, method of transportation to/from the hotel, attitude toward automatic baggage transfer, likelihood of taking an advanced technology transport, attitude toward the maglev train as an attraction, and select demographics. Regression analysis also was helpful in understanding the relationship among variables.

The airport intercept research developed the ridership preference/likelihood grid (Fig. 4). The grid resulted from a series of crosstabulations using the set of trade-off questions and an "educated" ridership opinion question asked at the end of the survey.

FIGURE 4 Preference/Likelihood Grid

$21 Fare $16 Fare $12 Fare
Combination
Strong & Very Likely 12% 17% 20%
Moderate & Very Likely 9% 8% 9%
Neutral & Very Likely 1% 3% 1%

Based on the data illustrated in the grid, 12 percent of respondents indicated that they had a strong preference for maglev compared to the alternative mode presented at a $21 fare, and they were very likely to take the maglev train, regardless of fare. Moreover, 20 percent had a strong preference at a $12 fare and were very likely to use the train. Well over half (59 percent) had a neutral to strong preference for maglev, and were at least somewhat likely to use it from the airport to International Drive.

The study's data then was used as input for two models that provided ridership estimates. The first, a trend & cycle model, was based on analysis of linear and cyclical times series at various lag times. This model used historic airport statistics to project inbound traffic (domestic and international) through the year 2000. The second model was for actual ridership. The ridership model combined elements of the preference/ likelihood grid and the trend & cycle projections to estimate market size then ridership at various fare levels for several points in time.

Strong advantage
The study's results coincided very closely to the qualitative research. Both were conclusive: The maglev concept has merit and usability. Based on this research, a maglev train has strong competitive advantage against its main competition, the rental car. When taking into account the upper fare limit (from the fare elasticity findings), its position remains especially strong if there is seamless transfer of baggage from the airport to the visitor's hotel.

Information from this research formed an integral part of ridership projections, financial structuring and strategic transportation planning.

Postscript: During 1994 (about six months after completion of this research phase) Maglev Transit changed technology from that based on Germany's Transrapid to Japan's HSST system.
 
ASARCO LLC is a mining, smelting, and refining company based in Tucson, Arizona that mines and processes primarily copper. The company, a subsidiary of Grupo México, is currently in Chapter 11 bankruptcy. ASARCO planned to emerge from bankruptcy in 2008, and opposes calls for it to totally liquidate its mining and industrial assets.[1]
Its three largest open pit mines are the Mission, Silver Bell and the Ray mines in Arizona. Its mines produce 350 to 400 million pounds of copper a year. ASARCO conducts solvent extraction/electrowinning at the Ray and Silver Bell mines in Pima County, Arizona and Pinal County, Arizona and a smelter in Hayden, Arizona. Before its smelting plant in El Paso, Texas was suspended in 1999 it was producing 1 billion pounds of anodes each year. Refining at the mines as well as at a copper refinery in Amarillo, Texas produce 375 million pounds of refined copper each year.
ASARCO's hourly workers are primarily represented by the United Steelworkers.
ASARCO has 20 superfund sites across the United States, and it is subject to considerable litigation over pollution.
As of September 2009, ASARCO was the focus of a bidding war begun in May 2008 between its own parent company Grupo México and India-based Sterlite Industries. On August 31, 2009, U.S. Bankruptcy Judge Richard Schmidt recommended that U.S. District Judge Andrew Hanen accept Grupo México's $2.5 billion bid for ASARCO as it prepares to come out of bankruptcy. However, on September 11, Sterlite increased its bid from $2.14 billion to $2.57 billion and requested that the court evaluate its new offer before issuing a final decision.

Several Likert scale questions were positioned between introductory travel related questions and respondent demographics. The scale ranged from 1, which denoted strong preference toward transport mode A, to 9, which indicated a strong preference for mode B. Each respondent was shown three transportation pairs. Four combinations of tradeoff pairs were then randomly distributed across the airport and rotated by day parts (morning, midday and evening hours).

At an appropriate time during the interview, the respondent was shown a characteristics grid (Figure 3 at bottom) displaying information about each transportation mode. As can be seen, an attempt was made to provide the respondent with an unbiased set of features for each mode.

The study's result is based on 400 completed interviews, 100 per pair combination set. Quotas were set for both inbound and outbound respondent type, and the final dataset was weighted to overcome disparities. The data were also adjusted for the seasonal variation between vacation and business traffic.

Beyond basic descriptive statistics, the study used the Student's t test to identify significant differences about the Likert scale centroid. The conjoint methodology identified a favorable fare structure and its elasticity. Factorial analysis was used to discern if clustering existed among variables such as: reason for being in Orlando, method of transportation to/from the hotel, attitude toward automatic baggage transfer, likelihood of taking an advanced technology transport, attitude toward the maglev train as an attraction, and select demographics. Regression analysis also was helpful in understanding the relationship among variables.

The airport intercept research developed the ridership preference/likelihood grid (Fig. 4). The grid resulted from a series of crosstabulations using the set of trade-off questions and an "educated" ridership opinion question asked at the end of the survey.

FIGURE 4 Preference/Likelihood Grid

$21 Fare $16 Fare $12 Fare
Combination
Strong & Very Likely 12% 17% 20%
Moderate & Very Likely 9% 8% 9%
Neutral & Very Likely 1% 3% 1%

Based on the data illustrated in the grid, 12 percent of respondents indicated that they had a strong preference for maglev compared to the alternative mode presented at a $21 fare, and they were very likely to take the maglev train, regardless of fare. Moreover, 20 percent had a strong preference at a $12 fare and were very likely to use the train. Well over half (59 percent) had a neutral to strong preference for maglev, and were at least somewhat likely to use it from the airport to International Drive.

The study's data then was used as input for two models that provided ridership estimates. The first, a trend & cycle model, was based on analysis of linear and cyclical times series at various lag times. This model used historic airport statistics to project inbound traffic (domestic and international) through the year 2000. The second model was for actual ridership. The ridership model combined elements of the preference/ likelihood grid and the trend & cycle projections to estimate market size then ridership at various fare levels for several points in time.

Strong advantage
The study's results coincided very closely to the qualitative research. Both were conclusive: The maglev concept has merit and usability. Based on this research, a maglev train has strong competitive advantage against its main competition, the rental car. When taking into account the upper fare limit (from the fare elasticity findings), its position remains especially strong if there is seamless transfer of baggage from the airport to the visitor's hotel.

Information from this research formed an integral part of ridership projections, financial structuring and strategic transportation planning.

Postscript: During 1994 (about six months after completion of this research phase) Maglev Transit changed technology from that based on Germany's Transrapid to Japan's HSST system.

Well netra, thanks for sharing the information on ASARCO and i am sure it would be useful for many students for their research work. BTW, i also uploaded a document where people can find more useful information on ASARCO.
 

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