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Bidding process for engineering consultants (Softcopy is also available)

By: Contributor(s): Material type: TextTextPublication details: 2020-21Description: vi,55pDDC classification:
  • B TH-0892 INA
Contents:
Table of Contents ABSTRACT 4 Chapter 1: Introduction: 8 1.1 Background: 8 1.2 Need for the study: 8 1.3 Scope of the study: 8 1.4 Objectives of the study: 9 1.5 Research methodology adopted: 9 1.6 Expected outcome from the study: 11 1.7 Assumptions and limitation for the study: 11 1.7.1 Assumptions for the study: 11 1.7.2 Limitations of the study: 11 1.8 Tentative timeline adopted for the study: 11 Chapter 2: Literature review: 12 2.1 Scope and objective of literature review: 12 2.2 Overall project life cycle and role of consultants: 12 2.3 Overall process followed by consultants before considering a project/client: 13 2.4 Summary for probable parameters for making a Bid/No-bid decision for consultants: . 14 2.5 Overall bidding process followed from clients end: 15 2.6 Bid evaluation criteria: 15 2.6.1 Quality based selection (QBS): 15 2.6.2 Quality Cost Based Selection (QCBS): 16 2.6.3 Cost Based Selection (CBS): 16 2.6.4 Single source selection: 16 2.7 Research gap: 16 2.9 Research questions: 16 3. Data collection: 17 3.1 Overview and methodology: 17 3.2 Basis and objectives of the questionnaire survey: 17 3.4 Overview and objectives of semi-structured interviews: 18 4. Data analysis: 19 4.1 Overview: 19 4.2 Age of organisations of respondents: 20 4.3 Areas of work for the respondants 4.4 Type of process adopted for go/non-go decision: 21 4.5 RII analysis and ranking of parameters: 22 4.5.1 Overall RII ranking of parameters: 22 4.5.2 RII ranking for small scale consultants: 23 4.5.3 RII ranking for medium scale consultants: 24 4.5.4 RII ranking for large scale consultant 25 4.6 Similarity variation analysis for top 15 parameters identified from RII analysis for all the three categories of consultants mentioned: 26 4.7 Comparison of RII ranking: 27 4.8 Accuracy of bidding process adopted: 28 4.9 Analysis for other parameters: 29 4.10 Percentage distribution for interview respondents: 30 4.11 ANOVA test: 31 5.Analysis of Survey Results: 33 5.1 Top 10 influencing parameters identified by Consultants: 33 5.1.1 Past payment record of client: 33 5.1.2 Cash-flow on the project: 33 5.1.3 Fair conditions of contract: 33 5.1.4 Clarity in the ToR: 33 5.1.5 History of the client with the company: 34 5.1.6 Current financial capacity of the client: 34 5.1.7 Overall staff expertise: 34 5.1.8 Revenue generated from previous similar projects: 34 5.1.9 Reputation of client: 34 6. Conclusions 35 6.1 Major inferences: 35 6.1 Major inferences for small and medium consultants: 35 6.2 Major inferences for large consultants: 35 6.3 Major challenges faced in the current bidding process followed: 35 7. Future scope: 36 Bibliography 36 Appendix A (summary for interviews) 37 Appendix B (Questionnaire survey form) 44 Appendix C (Summary for questionnaire responses) 51 1. Classification of consultants: 51 2. Likert scale responses: 52 3. Summary for true-false answer: 53
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Item type Current library Collection Call number Status Date due Barcode Item holds
Thesis CEPT Library Faculty of Technology B TH-0892 INA Not For Loan 023635
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Table of Contents
ABSTRACT 4
Chapter 1: Introduction: 8
1.1 Background: 8
1.2 Need for the study: 8
1.3 Scope of the study: 8
1.4 Objectives of the study: 9
1.5 Research methodology adopted: 9
1.6 Expected outcome from the study: 11
1.7 Assumptions and limitation for the study: 11
1.7.1 Assumptions for the study: 11
1.7.2 Limitations of the study: 11
1.8 Tentative timeline adopted for the study: 11
Chapter 2: Literature review: 12
2.1 Scope and objective of literature review: 12
2.2 Overall project life cycle and role of consultants: 12
2.3 Overall process followed by consultants before considering a project/client: 13
2.4 Summary for probable parameters for making a Bid/No-bid decision for consultants: . 14
2.5 Overall bidding process followed from clients end: 15
2.6 Bid evaluation criteria: 15
2.6.1 Quality based selection (QBS): 15
2.6.2 Quality Cost Based Selection (QCBS): 16
2.6.3 Cost Based Selection (CBS): 16
2.6.4 Single source selection: 16
2.7 Research gap: 16
2.9 Research questions: 16
3. Data collection: 17
3.1 Overview and methodology: 17
3.2 Basis and objectives of the questionnaire survey: 17
3.4 Overview and objectives of semi-structured interviews: 18
4. Data analysis: 19
4.1 Overview: 19
4.2 Age of organisations of respondents: 20
4.3 Areas of work for the respondants
4.4 Type of process adopted for go/non-go decision: 21
4.5 RII analysis and ranking of parameters: 22
4.5.1 Overall RII ranking of parameters: 22
4.5.2 RII ranking for small scale consultants: 23
4.5.3 RII ranking for medium scale consultants: 24
4.5.4 RII ranking for large scale consultant 25
4.6 Similarity variation analysis for top 15 parameters identified from RII analysis for all the three categories of consultants mentioned: 26
4.7 Comparison of RII ranking: 27
4.8 Accuracy of bidding process adopted: 28
4.9 Analysis for other parameters: 29
4.10 Percentage distribution for interview respondents: 30
4.11 ANOVA test: 31
5.Analysis of Survey Results: 33
5.1 Top 10 influencing parameters identified by Consultants: 33
5.1.1 Past payment record of client: 33
5.1.2 Cash-flow on the project: 33
5.1.3 Fair conditions of contract: 33
5.1.4 Clarity in the ToR: 33
5.1.5 History of the client with the company: 34
5.1.6 Current financial capacity of the client: 34
5.1.7 Overall staff expertise: 34
5.1.8 Revenue generated from previous similar projects: 34
5.1.9 Reputation of client: 34
6. Conclusions 35
6.1 Major inferences: 35
6.1 Major inferences for small and medium consultants: 35
6.2 Major inferences for large consultants: 35
6.3 Major challenges faced in the current bidding process followed: 35
7. Future scope: 36
Bibliography 36
Appendix A (summary for interviews) 37
Appendix B (Questionnaire survey form) 44
Appendix C (Summary for questionnaire responses) 51
1. Classification of consultants: 51
2. Likert scale responses: 52
3. Summary for true-false answer: 53

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