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European Energy Megaprojects Report Results from a Multi-Case Study

by Dr. Giorgio Locatelli Dipl.-Ing. Paul Littau. European Energy Megaprojects Report Results from a Multi-Case Study. Content. 1. Introduction. 2. Research Questions & Methodology. 3. Results. 4. Conclusions. Introduction.

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European Energy Megaprojects Report Results from a Multi-Case Study

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  1. by Dr. Giorgio Locatelli Dipl.-Ing. Paul Littau European Energy MegaprojectsReport Results from a Multi-Case Study

  2. Content 1. Introduction 2. Research Questions & Methodology 3. Results 4. Conclusions

  3. Introduction • Megaprojects: extremely large-scale investment projects – typically: more than EUR 0.5 billion • In EU Megaprojects planned, or are in the execution or delivered • Many of them are power plants and capital intensive • Because of bad performance the EU financed the COST Research project aiming "to understand how megaprojects can be designed and delivered more effectively to ensure their effective commissioning within Europe" • This document present the methodology used in the COST action and the results related to the Energy group

  4. Content 1. Introduction 2. Literature & Methodology 3. Results 4. Conclusions

  5. Literature & Methodology • Megaproject have high degree of uniqueness, "white elephant" • Megaproject usually are late and over budget (Cantarelli et al., 2012) • Few of them are successful (Giezen, 2012) • Once completed usually provide benefit less than what was expected (Flyvbjerg, 2006) • Still not clear which aspects make a project successful

  6. Literature & Methodology • Cross case analysis (REF) • Protocol • Definition of a common template for all the researchers involved • For each project data collection according to the template • Competition of the template areas with enough data • General overview at project level and lesson learned • Data organisation in an excel spread sheet • Statistical analysis (Fisher exact test) • Result analysis • Investigation of the most relevant variables. • Eisenhardt (1989):“researchers should stop adding cases when theoretical saturation is reached […] incremental learning is minimal because the researchers are observing phenomena seen before […] the incremental improvement in its quality is minimal.”

  7. Content 1. Introduction 2. Literature & Methodology 3. Results 4. Conclusions

  8. Results

  9. Results: delayplanning Correlations: • more than 50% share of the client is under government control (p-value 17%)correlates with not delaying in planning phase • A possible explanation: if the main shareholder is the government itself, then its plans are very likely to meet requirements of local and regional authorities. • the client and owner are different (p-value 5%), correlate with not delaying in planning phase • (Possible explanation and CASE).

  10. Results:delayconstruction (1/3) Correlations: • the presence of one major stakeholder (p-value: 5%) • Possible Explanation: HERE Examples: • Flamanville 3: main contractor, EDF (Électricité de France) is client and project owner and caused authorities to stop construction works for a month. • Olkiluoto3 (nuclear power plant): turnkey contractor Areva was responsible for problems that led to construction delays

  11. Results: delayconstruction(2/3) Correlations: • Project is supported financially by the EU correlated to not delaying in construction phase (p-value 7%). • Possible explanation: demanding approval process required by the European Union before funding an energy project could motivate the EPC-company to increase the quality of plans and cost estimations • Examples: • Andasol project • AnholtOffshore project.

  12. Results: delayconstruction(3/3) Correlations: • Tough physical environmental conditions are correlated to not delaying in construction phase (p-value 18%). • Possible explanation: tough physical environmental conditions are (explanation + CASE)…

  13. Results: overbudget Correlations: • Project has been delayed by the authority (p-value 20%) Example: • Moorburgpower plant project: problems with the environmental requirements, led to a construction of an additional cooling tower which caused a huge cost increase • fact that the project is a nuclear power plant (p-value 17%). • Possible explanation is provided by G. Locatelli & M. Mancini (2012): Flamanvilleand Olkiluoto 3 showing that the budget overrun is mainly caused by too low original estimations

  14. Results Common sense independent variables not correlated • Some independent variables are presented which surprisingly do not correlate to common-sense-expectations regarding the performance of megaprojects. • First of a kind (FOAK) • FOAK-megaprojects tend to be over budget and delay (REF), • In sample one might expect that FOAK-megaprojects in the energy sector also might perform badly. But results are showing, that … • Thus, XY proofs that • Mono cultural • Mono cultural are expected to perform better, because of cultural barriers (Anbari et al., 2004). • Analysed sample does not show relevant correlations

  15. Results Common sense independent variable not correlated (continued) • The project is modular • Modular projects are expected to reduce project complexity (especially in megaprojects) and thus increase project performance (REF). • Analysis: no such correlation • Local residents were involved in the project • In many cases: local residents try to stop the project • Communicating with critical stakeholders and to integrating them into the project could should increase their support of the project • Not found any relevant correlation

  16. Results – Don‘tdo‘s

  17. Content 1. Introduction 2. Literature & Methodology 3. Results 4. Conclusions

  18. Conclusions • Despite the media coverage focused on projects over budget and late there is a clear evidence that it is possible to successfully deliver energy megaprojects in Europe • The statistical analysis shows which project characteristics are correlated to project performance • Internal and external stakeholders play a major role toward project success • A well designed project governance is critical success factor

  19. Further developments • To deep the analysis among the statistical correlated variable to investigate the causation. • Test 3 level (e.g. the different technologies) using the chi-squared test. • Enlarge the analysis to the infrastructure life cycle with a particular focus on the benefit delivered. • There are several specific areas (like stakeholders management and governance) deserving a specific investigations. A further step in the research should be the cross-case analysis on them.

  20. References Anbari, F. T., Khilkhanova, E. V., Romanova, M. V., & Umpleby, S. A. (2004). Cross Cultural Differences and their Implications for Managing International Projects. Journal of International Business Ethics. Flyvbjerg, B. (2006). From nobel prize to project management: getting risk right. Project management Institute, 37(3), pp. 5-15. Giezen, M. (2012). Keeping it simple? A case study into the advantages and disadvantages of reducing complexity in mega project planning, International Journal of Project Management, Volume 30, Issue 7, October 2012, Pages 781-790. Cantarelli, C.C., Flyvbjerg, B., Buhl, S.L. (2012). Geographical variation in project cost performance: the Netherlands versus worldwide, Journal of Transport Geography, Volume 24, September 2012, Pages 324-331, Eisenhardt, K. M. (1989). Building Theories from Cases. The Academy of Management Review , Vol. 14, No. 4 (Oct., 1989), pp. 532-550

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