Published on: **Mar 4, 2016**

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Technology Business

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- 1. AHP as a tool to determine risks to be accounted in the Bidding Price By Dr. Poonam Ahluwalia Technical Specialist MWH India Private Limited Abstract: Water and Sanitation is a key thrust area in developing nations like India. With inflow of funds from International bodies such as JICA and World Bank, significant consultancy assignments have been floated in the past few years. Such projects are under scrutiny by not only pollution control boards, and the concerned ministries, but also by the media. Several projects in this category have been criticized in the projects for not achieving/ only partially achieving their goals. As a result for the upcoming projects, the terms of reference are being made more and more stringent for the consultants. This has increased the quantum of risks consultancy firms are knowingly taking up. However, all these risks generally are not accounted for in the quoted cost to competitively quote while bidding for the job. The current paper discusses how Analytical Hierarchy Process can be used as tool to organizes tangible and intangible factors in a systematic way, and provides a structured yet relatively simple solution to prioritize the the risks to competitively bid for projects. Introduction The Analytical Hierarchy Process (AHP) is a decision-aiding method developed by Saaty [1,2]. It aims at quantifying relative priorities for a given set of alternatives on a ratio scale, based on the judgment of the decision-maker, and stresses the importance of the intuitive judgments of a decision-maker as well as the consistency of the comparison of alternatives in the decision-making process [1]. The strength of this approach is that it organizes tangible and intangible factors in a systematic way, and provides a structured yet relatively simple solution to the decision-making problems [3]. In addition, by breaking a problem down in a logical fashion from the large, descending in gradual steps, to the smaller and smaller, one is able to connect, through simple paired comparison judgments, the small to the large. The quantum of risks consultancy firms are knowingly taking up in water and wastewater sector is increasing owing to ambiguous TORs and stringent penalty clauses. Increasing competition is forcing the bid manager to account for only selected risks to maintain their competitive edge. The current paper discusses how Analytical Hierarchy Process can be used as tool to organizes tangible and intangible factors in a systematic way, and provides a structured yet relatively simple solution to the determine which risks are required to be priced for in the bidding price and which could be excluded.
- 2. Schuyler [5] has stated decision making skill amongst one of most significant skills of project management , and notices that few of us have had formal training in decision making. Belton [4] compared AHP and a simple multi-attribute value (MAV), as two of the multiple criteria approaches. The study stated that both approaches have been widely used in practice which can be considered as a measure of success. The greatest weakness of the MAV approach was mentioned as its failure to incorporate systematic checks on the consistency of judgments. Also for large evaluations, the number of judgments required by the AHP can be somewhat of a burden. However for fewer evaluations, it is established as a robust tool for decision making. The Analytical Hierarchy Process [1, 2] The process of decision-making involves identification of the objectives, the system components, and the relations among them. The main theme of the Analytical Hierarchy process is: “Decomposition by hierarchies and synthesis by finding relations through informed judgment”. Hierarchy is an abstraction of the structure of a system to study the functional interactions of its components and their impacts on the entire system. This abstraction of the structure can take several related forms, all of which essentially descend from an apex (an overall objective), down to sub-objectives, down further to forces which affect these sub-objectives, down to the objects who influence these forces. The hierarchical presentation of the system can be used to describe how changes in priority at upper levels affect the priority of elements in lower levels. But we need to know the potency with which the various elements in one level influence the elements on the next higher level, so that one may compute the relative strengths of the impacts of the elements of the lowest level on the overall objectives. Given the elements of one level, say the 4th , of a hierarchy and one element, e, of the next higher level, compare the elements of level 4 pairwise in their strength of influence on e. Insert the agreed upon numbers, reflecting the comparison, in a matrix and find the eigenvector with the largest eigenvalue. The eigenvector provides the priority ordering, and the eigenvalue is a measure of the consistency for the judgement. The agreed upon numbers art the following, given elements A and B If, A and B are equally important, insert 1 A is weakly more important than B, insert 3 A is strongly more important than B, insert 5 A is very strongly more important than B , insert 7 A is absolutely more important than B, insert 9 In the position (A, B) where the row of A meets the column of B.
- 3. An element is equally more important when compared with itself, so where the row of A and column of A meet in position (A,A) insert 1. Thus the main diagonal of a matrix must consist of 1’s. Insert the appropriate reciprocal 1,1/3,……..,1/9 where the column of A meets the row of B, i.e., position (B,A) for the reverse comparison of B with A. The numbers 2,4,6,8 and their reciprocals are used to facilitate compromise between slightly differing judgements. The next step consists of the computation of a vector of priorities from the given matrix. In mathematical terms the principal eigenvector is computed, and when normalised becomes the vector of priorities. To avoid large-scale computation, estimates of that vector can be obtained in the following four ways: 1) The crudest: Sum the elements in each row and normalise by dividing each sum by the total of all sums, thus the results now add up to unity, The first entry of the resulting vector is the priority of the first activity; the second of the second activity and so on. 2) Better: Take the sun of the elements in each column and form the reciprocals of these sums. To normalize so that these numbers add to unity, divide each reciprocal by the sum of reciprocals. 3) Good: Divide the elements of each column by the sum of that column (i.e., normalize the column) and then add the elements in each resulting row and divide this sum by the number of elements in the row. This is a process of averaging over normalized columns. 4) Good: Multiply the n elements in each row and take the nth root. Normalize the resulting numbers. Judgment consistency can be checked by taking the consistency ratio (CR) of CI with the appropriate value in Table 2. The CR is acceptable, if it does not exceed 0.10. If it is more, the judgment matrix is inconsistent. To obtain a consistent matrix, judgments should be reviewed and improved. Table 1: Pair-wise comparison scale for AHP preferences Numerical Rating Verbal judgments of preference 9 Extremely Important 8 Very Strongly Important to Extremely Important 7 Very Strongly Important 6 Strongly to Very Strongly Important 5 Strongly Important 4 Moderately to Strongly Important 3 Moderately Important 2 Equally to moderately Important 1 Equally Important Table 2: Average random consistency (RI)
- 4. Size of matrix Random consistency 1 0 2 0 3 0.58 4 0.9 5 1.12 6 1.24 7 1.32 8 1.41 9 1.45 10 1.49 Example: A bid manager at the outset has to consider the following before any bid submission: Check for conflicts of interest Discuss with Internal approving authorities to arrive at “Go / No Go decision” If a “Go Ahead” is decided, Identify strategy for winning bid, which includes Optimum resource selection- to ensure competitive cost and maximize technical score Identify hidden cost implications (Risks) Determine acceptability of risks and based on the same proceed ahead with bid submission The Risk Management Process consists of three stages: – Risk Identification – Risk Analysis- Assessment of Impact – Risk Treatment- Providing for mitigation The following are some of the key risks the consultancy firms in the water/ wastewater sector are accepting to ensure they have key projects on their portfolio, to help them qualify for similar projects in future: Risk of non payment due to shortage of fund allocation for proposed project. One way to reduce that risk is to ensure the owner has the financial wherewithal to pay before the project is even started. If the information is not available this risk needs to be suitably built in [Risk A] Availability of human resources right through the project cycle [Risk B]. Time frame mentioned in the ToR is inadequate. Penalty clause for non timely delivery [Risk C]
- 5. Vague criteria for acceptance of deliverables- Likelihood of revision in deliverables/ delay in acceptance of submitted deliverables [Risk D] Conflicting priorities/ suggestions of various stakeholders having a say in acceptance of deliverables [Risk E]. Client is not technically sound - Likelihood of revision in deliverables [Risk F]. Availability of required data and its authenticity- Additional Investigations over and above those stated in the ToR may be required [Risk G]. The ideal scenario is that each risk is accounted for in the bid price. However, owing to more and more stringent terms of reference and highly competitive bidding, currently the trend is to either decide on a quote and see which risks are being covered or prioritize the risks and see covering which set of risks impacts the bidding price in what quantum. Generally risks are quantified as product of probability and consequences (impact). Further whether mitigation can be effectively planned or not is another important aspect to consider. Product of these three factors can be an effective indication to deliberate further and make guided decision to arrive at risk priorities. For example the following numbers were arrived at using Delphi technique for the various risks identified above. Likelihood Consequence Mitigation LCM score Risk A 0.5 0.8 0.9 0.36 Risk B 0.8 0.5/0.8 0.3 0.12 Risk C 0.5 0.5 0.3 0.075 Risk D 0.8 0.5 0.7 0.28 Risk E 1 0.5 0.3 0.15 Risk F 0.8 0.5 0.5 0.20 Risk G 0.8 0.8 0.7 0.28 The following correlation between subjective judgment and numerical representation was provided to various experts: Likelihood
- 6. Highly unlikely 0.1 Unlikely to happen 0.3 Could happen 0.5 Will probably happen 0.8 Will happen 1 Consequence No effect 0.1 Minimal effect 0.3 Moderate effect 0.5 Significant effect 0.8 Disastrous 1 Mitigation Excellent 0.1 Effective 0.3 Moderate 0.5 Low effectiveness 0.8 Ineffective 1 The following pairwise matric comparison was arrived at for various risks: Risk A Risk B Risk C Risk D Risk E Risk F Risk G Risk A 1 3 9 1.29 2.25 1.8 1.28 Risk B 0.333 1 3 0.43 0.75 0.6 0.428 Risk C 0.111 0.333 1 0.142 0.25 0.2 0.14 Risk D 0.777 2.33 7 1 1.75 1.4 1.25 Risk E 0.444 1.333 4 0.571 1 0.8 0.571 Risk F 0.555 1.666 5 0.714 1.25 1 0.714 Risk G 0.777 2.333 7 0.8 1.75 1.4 1
- 7. Priority Vector Risk A 0.2498 Risk B 0.0833 Risk C 0.0277 Risk D 0.2009 Risk E 0.1110 Risk F 0.1388 Risk G 0.1885 Consistency Index (CI)= 3.6367 x 10-6 Random Consistency ratio (RI)= 1.32 Consistency ratio (CR) = CI/ RI= 2.75508 x 10-6 As the value of CR is less than 0.1, the judgments are acceptable. Results: The following priorities were arrived which show consistency between values obtained by LCM score and AHP. Priority by AHP Priority by LCM score Priority Risk Priority Risk 1 Risk A 1 Risk A 2 Risk D 2 Risk D; Risk G 3 Risk G 3 Risk F 4 Risk F 4 Risk E 5 Risk E 5 Risk B 6 Risk B 6 Risk C 7 Risk C References: [1] Saaty TL. The analytic hierarchy process. New York: McGrawHill, 1980. [2] Saaty TL, Kearns KP . Analytical planning: the organization of systems. The analytic hierarchy process series 1991; vol. 4RWS Publications Pittsburgh, USA.
- 8. [3] Skibniewski MJ, Chao L. Evaluation of advanced construction technology with AHP method. Journal of Construction Engineering and Management, ASCE 1992;118(3):577-93. [5] Schuyler JR. Decision analysis in projects. Upper Darby, PA, USA: Project Management Institute, 1996. [4] Belton V. A comparison of the analytic hierarchy process and a simple multi-attribute value function. European Journal of Operational Research 1986; 26:7-21.