How Should Indicators be Found for Scenario Monitoring?

Author : Zheng He

Advisor : Dr. Shardul Phadnis

Sponsor: BASF

MIT SCM ResearchFest May 22-23, 2013

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• Make contingent decision • Get benefits from scenarios

Which scenario was coming to pass?

Agenda

• Introduction

• Case overview

• Data

• Methods

• Conclusion

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Scenarios and driving forces

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Political stability

Stability of financial systems

Environmental regulation

Investment in transportation infrastructure

Availability of qualified

employees

Transfer and application of

global knowledge Awareness

towards sustainability of the society

Energy costs

Free trade agreement

Mobility of people

The Collaborative World

The Low Cost World

The Demanding World

The Lean World

The relationship between scenarios and driving forces

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DrivingForcesCollaborative

WorldLeanWorld

Demanding

World

LowCost

World

High Low High Low High Low High Low

Politicalstability

Freetradeagreement

Stabilityoffinancialsystems

Availabilityofqualifiedemployees

Investmentintransportationinfrastructure

Transferandapplicationofglobalknowledge

Awarenesstowardssustainabilityofthesociety

Energycosts

Environmentalregulation

Mobilityofpeople

Data

• PESTEL

• Political, economics, social, legal, environment, technology

• World Bank

• World Development Indicators

• 2005-2010

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Overall Research Approach

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Step 1: Developing a Theoretical Framework

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Energy Cost

Consumption Production

Step 2: Selecting Variables

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Selection criteria: relevancy, non-repetition, coverage and data-availability

Step 3: Imputation

• Assume nothing changed during the period.

• Use the data of the closest previous year from the same country to substitute the missing data

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2004 2003

0.1855565 0.1824113

0.1181926 0.1201639

0.1225446 0.1219192

0.1488835 0.1488835

0.0953952 0.0985601

Step 4: Normalization

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Normalize to the base year.

Step 5: Multivariate analysis

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Loading Matrix of Factor Analysis

Back to Step 1: Developing a Theoretical Framework

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Energy Cost

Efficiency

Electric power transmission and

distribution losses

GDP per unit of energy use

Supply&Consumption

Electricity production from renewable

sources Energy production Energy use

Step 6: Weighting and Aggregation

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0.22

0.21

0.2

0.22

0.15

Electricity production from renewablesources (kWh)

Energy production (kt of oil equivalent)

Energy use (kt of oil equivalent)

Electric power transmission and distributionlosses (% of output)

GDP per unit of energy use (PPP $ per kg ofoil equivalent)

• Weights are calculated based on the results of factor analysis.

Step 6: Weighting and Aggregation

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• Indicators are aggregated linearly • The boundary is used to judge the level of driving force.

Step 7: Scenario Monitoring

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DrivingForcesCollaborative

WorldLeanWorld

Demanding

World

LowCost

World

High Low High Low High Low High Low

Politicalstability x

x

x

x

TCF x

x

x

x

Stabilityoffinancialsystems x

x

x

x

Availabilityofqualifiedemployees x

x

x

x

Investmentintransportationinfrastructure x

x

x

x

Transferandapplicationofglobalknowledge

x

x

x

x

Awarenesstowardssustainabilityofthesociety x

x

x

x

Energycosts

Environmentalregulation x

x

x

x

Mobilityofpeople x

x

x

x

Result

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• In the year 2010, the world seems to be more like a collaborative world. It looks like that the scenario is evolving to a “Collaborative World” or a mix of “Collaborative World” and “Demanding World”.

Conclusion

• Advantage of the approach • 1. The data are easy to get. • 2. The most part of the process can be automated. • 3. The result is simple and easy to understand.

• Limitation of the approach • 1. The selection of individual indicator is subjective. • 2. The whole model need to be rebuilt if some data are

missing.

• Future work • 1. Expert's opinion. • 2. Sensitivity analysis.

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