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MSF DECISION ANALYTICS PROJECT

Doctors Without Borders - Médecins Sans Frontières (MSF)

Doctors Without Borders or Médecins Sans Frontières (MSF), is a secular humanitarian-aid non-governmental organization best known for its projects in war-torn regions and developing countries facing endemic disease. The organization currently provides health care and medical training to populations in 78 countries and is reconsidering the following 52 countries:

1. Armenia
2. Bangladesh
3. Belarus
4. Bolivia
5. Bulgaria
6. Cambodia
7. Cameroon
8. China
9. Colombia
10. Democratic Republic of Congo
11. Egypt
12. Ethiopia
13. France
14. Georgia
15. Greece
16. Guatemala
17. Haiti
18. Honduras
19. India
20. Iran
21. Iraq
22. Italy
23. Japan
24. Jordan
25. Kenya
26. Kyrgyzstan
27. Lebanon
28. Libya
29. Mexico
30. Morocco
31. Mozambique
32. Myanmar
33. Nepal
34. Nigeria
35. Pakistan
36. Paraguay
37. Philippines
38. South Africa
39. Sri Lanka
40. Sudan
41. Swaziland
42. Syria
43. Tajikistan
44. Thailand
45. Tunisia
46. Turkey
47. Uganda
48. Ukraine
49. Uzbekistan
50. Yemen
51. Zambia
52. Zimbabwe

Aside from injuries and death associated with stray bullets, mines and epidemic disease, MSF volunteers are sometimes attacked or kidnapped for political reasons. In some countries afflicted by civil war, humanitarian aid organizations are viewed as helping the enemy, if an aid mission has been set up exclusively for victims on one side of the conflict, and attacked for that reason. However, the War on Terrorism has generated attitudes among some groups in US-occupied countries that non-governmental aid organizations such as MSF are allied with or even work for the Coalition forces. Since the United States has labeled its operations "humanitarian actions" independent aid organizations have been forced to defend their positions, or even evacuate their teams.

You are required to consider the following (Raw MSF Data)(Euclid MSF Data) with 900 factors and 52 countries in four continents for this project. Organize your 280 factors into opportunities (strengths) and threats (weaknesses) reasonably or by using Internet resources for questionable factors.  Also, clean up the data and watch for duplicates. You are then required to do all necessary calculations in excel for the Euclid model.  Your Euclid model and the CSV file used in the Euclid program must produce similar results.  While the Euclid numbers are precise, you may experience up to ± 0.02 rounding differences in the average opportunity and the average threat scores between Excel and Euclid due to Excel rounding. This may also result in a small variation among the final rankings of the countries in Excel and Euclid. The Excel file must include a complete scatter chart for the Euclid model.  This includes all data labels, ideal point, nadir point, coordinates, average partition lines, and the four quadrants. The spreadsheet must also include the Euclidean distances and rankings with reference to the ideal point. All formulas must be preserved in the spreadsheet.  Finally, your spreadsheet must include a radar chart and a world heatmap of the Euclidean distances for the 52 countries considered in your study.  Please refer to Get started with 3D Maps (maps.pdf) for this requirement.

Additional Resources:

MSF ACTIVITY AND FINANCIAL REPORTS

Useful Links:

NationMaster - World Statistics
World Factbook - Central Intelligence Agency
Library of Congress - Country Studies
World Bank - World Development Indicators
Transparency International
Organization for Economic Co-operation and Development
Organization for Economic Co-operation and Development: Better Life Index

Decision Models:

Multi-Criteria Decision Making (MCDM) (Problem) MCDM (Solutions)

André and Angelique: Buying Their Dream Home (Problem) Problem CSV File

André and Angelique: Buying Their Dream Home (Solution) Solution

McDonald’s Corporation Location Planning (Problem) Problem

Labeling Points on a Euclid Scatter Chart

Fixing the #VALUE! Error Messages in your Euclid Spreadsheet

Get started with Excel 3D Maps

README

Useful Papers:

• Tavana, M. (2008) 'Fahrenheit 59: An Environmental Decision Support System for Benchmarking Global Warming at Johnson Space Center ,' Benchmarking: An International Journal, Vol. 15, No. 3, pp. 307-325.
• Tavana, M., Bourgeois, B.S. and Sodenkamp, M. (2009) 'Fuzzy Multiple Criteria Base Realignment and Closure (BRAC) Benchmarking System at the Department of Defense ,' Benchmarking: An International Journal, Vol. 16, No. 2, pp. 192-221.
• Tavana, M. and O’Connor, A. (2010) 'An Integrated Strategic Benchmarking Model for Assessing International Alliances with Application to NATO Membership Enlargement ,' Benchmarking: An International Journal, Vol. 17, No. 6, pp. 791-806.
• Tavana, M., Sodenkamp, M. and Suhl, L. (2010) 'A Soft Multi-Criteria Decision Analysis Model with Application to the European Union Enlargement ,' Annals of Operations Research, Vol. 181, No. 1, pp. 393-421.
• Tavana, M., Di Caprio, D., Santos-Arteaga, F.J. and O'Connor, A. (2015) 'A Novel Entropy-Based Decision Support Framework for Uncertainty Resolution in the Initial Subjective Evaluations of Experts: The NATO Enlargement Problem ,' Decision Support Systems, Vol. 74, pp. 135-149.

Final Report Outline:

• Cover Page
• Executive Summary
• Introduction and Problem description
• Proposed method (include step-by-step description)
• Results (include Euclidean distance and overall rankings, scatter chart with four quadrants, radar chart, world heath map, etc.)
• Recommendations and conclusions
• References
• Appendices

Sample Consulting Reports:

McKinsey-2017

McKinsey-2018

McKinsey-2020

Final Report Submission:

Submit your WORD, EXCEL, and CSV files via the following E-mail: tavanadropbox@gmail.com