Introduction

Project selection is the process of evaluating and selecting projects that correspond with an organisation’s goals which in return, improves productivity of the organisation. In the era of globalisation, the shortage of project ideas can be concluded to being non-existent. Instead, the main problem faced by many organisations is the over-generation of too many ideas. Screening of these project ideas has never been tougher than before due to the long queue of submissions waiting to be reviewed by the upper echelon of the project management team. Given the vast scope and complexity of projects, as well as resource constraints such as time and budget, finding the right combination of projects that will produce the best results is proven to be a hurdle for the project management sector. Furthermore, it is proven that selecting and pursuing a project must be done in a careful manner to avoid project failures which affect international business.

Due to the obstacle, project portfolio management (PPM), has been introduced as an integration procedure of project execution with a high-level business strategy. PPM is carried out by incorporating critical aspects such as selection and prioritisation. It has been reported that 37 percent of project failures occur due to a lack of clearly defined objectives and discipline when implementing strategies (EcoSys 2018). The practise of rating or evaluating projects based on a set of criteria to determine their execution sequence is known as prioritisation. Due to the interconnection between the two processes, the terms “prioritisation” and “selection” are frequently misunderstood.

When conducting project selection and prioritisation, a strong strategy has to be devised to ensure the project produces various benefits. These benefits include, but are not limited to, bigger and better return of investments (ROI), shorter time to market products, successful project deliveries and better environmental and safety performance.

The energy sector is a conglomerate of companies that produce and distribute energy. It also includes companies that explore, produce, refine, market, store and transport oil and gas, coal and other consumable fuels, according to the Global Business Classification Standard, (CFI 2021). The energy sector is extremely vulnerable to the business cycle as it is cyclical in nature. Because of the cyclical nature of the energy industry, its earnings are likewise volatile (Bertelsen and Sedlacek 2014).

The energy industry is nearing the end of its expansion cycle. The fact that the industry is dominated by a few large, well-established corporations such as Exxon-Mobil, Petronas, Royal Dutch Shell and BP shows that it is extremely difficult to establish a new company to rival the big-game players. However, there are new opportunities rising for the energy sector due to the emergence of new trends such as making activities such as drilling and exporting oil more expensive for corporations involved, resulting in lower long-term profitability. Secondly, the most significant new trend is the understanding of the Paris Agreement, where harmful consequences of carbon footprint are driven by the release of greenhouse gases (GHG), on our ecosystem, which is leading to a move away from fossil fuels and petroleum.

If a company were to consider about branching out into the energy and chemical industries, a general understanding on the trends provides the company insights on looking into a variety of projects throughout the world. By doing so, the company would want to invest in projects that generate the highest value. The catch to this is that if the management feels confident in its capacity to borrow money at a competitive rate, any project may be adopted and pursued by the company. Nonetheless, if the company wants to look at project investment from an opportunity standpoint, the aspects to be focused on are listed below:

  1. 1)

    Analyse the prospects and screen out infeasible ones in a logical fashion

  2. 2)

    Conduct a coarse screening on the remaining prospects and choose an end product which provides maximum value

Decision-making is a process where choices were made by identifying a decision after gathering all the necessary and relevant information, followed by detailed assessment of different alternatives. In order to assure the good quality of the decision, understanding on the requirements of a good decision is important. This can be illustrated as the decision quality (DQ) framework, which can be dissected into 6 different elements as shown in Fig. 1. This framework is applied during the decision-making of the prospects.

Fig. 1
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Decision quality framework 

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Developed by Thomas L. Saaty during the 1970s, the analytic hierarchy process (AHP) is a basic decision-making methodology (Saaty 1987). It is designed to handle both the rational and intuitive parts of deciding on the best option from a range of alternatives based on a set of criteria. In AHP, the decision maker makes simple pairwise comparison judgments, which are then used to create overall priorities for ranking the possibilities. The AHP allows for judgement inconsistency while also giving a mechanism to increase consistency. The AHP was created in such a way that the human mind may employ hierarchical decomposition of complex systems as a fundamental strategy for dealing with diversity. From the broad, at the top of the hierarchy, to the precise, at the bottom, the elements influencing the decision are organised in progressive degrees. The purpose of the structure is to evaluate the importance of things at one level with respect to some or all of the elements at the level above. After the structuring is complete, the AHP is fairly simple to use as a decision-making tool for various scenarios. Therefore, AHP applies vividly in the case of selecting the most economically feasible prospect, where a lot of considerations to be made during the selection process. It uses a multi-level hierarchical structure of goals, criteria, sub-criteria and alternatives as shown in Fig. 2. By conducting pairwise comparisons, the weights of importance of the decision criteria will be determined, along with the relative performance measures of alternatives in terms of each individual decision criterion (Tan et al. 2014). Along with its variants such as fuzzy AHP, these methods can also be used in making decisions on non-quantitative targets (Tan et al. 2016). In the case where the comparisons are not consistent, mechanisms for improving the consistency were introduced (Triantaphyllou and Mann 1995). AHP is widely applied in multi-criteria decision-making, resource allocation and planning as well as in resolving conflict (Saaty 1987). Some of the notable applications of these tools include the design of extraction processes (Ten et al. 2021), simultaneous consideration of process and molecular design (Ooi et al. 2018) and microalgae harvesting process (Tan et al. 2016). In an important contribution, a methodology to use AHP for project evaluation and selection has been illustrated with a proof-of-concept example (Palcic & Lalic 2009). In this work, an MS Excel-based tool has been developed for performing the analysis. In another contribution, AHP has been used as a tool for project selection by targeting sustainable development (Jurik et al. 2022). This work has highlighted the subjective nature of decision-making during project selection and proposed a more accurate approach based on AHP. Another approach has combined AHP with a linear programming model to select the among a set of construction projects (Parvaneh & El-Sayegh, 2016). This approach offers a unique combination of qualitative and quantitative approaches for project selection.

Fig. 2
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Hierarchical structure of AHP

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While multi-criteria decision-making tools have been extensively employed for project selection, they often lack integration with the key value drivers, especially when dealing with projects of significantly different scopes. In this work, we have developed an approach for systematically assessing the potential of various investment options in the energy sector for International Conglomerates. This methodology not only facilitates the comparison of diverse investment options but also allows for evaluations based on the specific priorities of the investing parties.

Methodology

In the early phase of concept selection, it is crucial to decide the guidelines or basis to be applied when assessing the different prospects. In this section, two main techniques, namely the decision quality and analytic hierarchy process (AHP), will be analysed for better understanding before applying during the concept selection. The decision-making process had a multi-lobed structure. It consisted of a series of workshops and brainstorming sessions whose intention was to rely on expert knowledge to identify the following items:

  1. 1.

    Decision parameters,

  2. 2.

    Risks and issues associated with individual industrial projects,

  3. 3.

    Success criteria specific to individual industrial projects which were selected based on the risks and issues identified in the previous step,

  4. 4.

    Strategy table based on decision parameters.

The DQ framework provides an easily accessible, yet general framework to conduct a decision-making process which is agnostic to the industry type. The workshops and brainstorming sessions are intended to fit the DQ framework to the specific industrial projects which form the portfolio. The AHP meanwhile acts as a tool within the DQ framework for the decision-making process.

The selection of the experts needed for the process is not covered in the paper. In general, subject matter expert (SME) level knowledge is recommended for individuals pertaining to specific components. For example, to create a “low CAPEX” strategy table for the industrial project, someone with a background in project development and/or facilities engineering and/or cost engineering is recommended. Variations might also be required for specific industries; for example, someone with expertise in ethanol manufacturing would be able to provide input for ethanol manufacturing project, wherein someone with expertise in oil and gas industry might be selected for oil and gas project. Where needed, a poll can be conducted to capture differing opinions.

The overview of the entire process selection is shown in Fig. 3, and each of these will be carried out in the following sections.

Fig. 3
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Overview of project selection

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As illustrated in Fig. 3, objectives, key value drivers, success criteria and issues were identified for each project. The objective of a project reflects what we want to achieve by the end of the project, which can be translated into deliverables, assets or other forms. Besides, key value drivers are the factors that can increase the value or worth of the project. Success criteria are the variables that measure and determine whether the outcome of a project is successful (Lamprou and Vagiona 2018). On the other hand, issues are problems that might be encountered during the execution of the project.

The next step is the concept identification and screening. In this step, each prospect needs to be screened thoroughly by conducting literature research. For each of the projects, the objectives, key value drivers, success criteria and issues were identified. Since the objective is to identify the project with the highest return on investment, the project that can achieve maximum profit with the lowest risk needs to be identified. Besides, the operation of the plant must be also safe, environmentally friendly and sustainable. Project on schedule is also crucial to ensure smooth operation without delays since delays will incur more cost and time. In the next stage, the key value drivers of the potential projects need to be identified. Some of the typical value drivers are shown in Table 1. Low capital and operating cost will help in maximising the net profit generated. With high ease of business, proven technology and high marketability of products, these will reduce the risk and ensure that our products are marketable. In addition, to govern the safety, both environmental and social, adherence to ESG and HSE is highly encouraged. Good control of the project schedule and making sure the progress is on the right track are crucial steps in project management to make sure there is no delay in the schedule of the project.

Table 1 Typical key value drivers 
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For all the prospects, there are success criteria and issues that are affecting the feasibility of each project. Therefore, before choosing any of the prospects, it is important to focus on the difficulty to decide whether mitigations can be taken to reduce the risk of issues from occurring. The typical success criteria are.

  1. 1.

    High income or revenue obtained

  2. 2.

    Continuous exploration and development

  3. 3.

    Improvement of technology

  4. 4.

    Reduction of greenhouse gas (GHG) emissions

  5. 5.

    Safety and environmental improvements

The first element for a good decision is setting up the appropriate frame by clearly defining the purpose of decision-making, the scope and the perspective of the decision maker(s) (Spetzler et al. 2016). A decision problem may be framed broadly or narrowly. However, for a decision with broad frame, it will consume longer time as well as have significant impact on more parties involved. For example, the stakeholders and other relevant parties will be involved when a company decides to launch a new product. With the involvement of several parties, it is crucial that the frame set is agreed by everyone.

The second element is creative alternatives, where consideration was made on different possible solutions. If there are no alternatives, no decision is required to make. Therefore, it is worth the time and effort to create and brainstorm better ideas or alternatives since DQ needs good alternatives. When various alternatives are generated, relevant information should be collected from reliable sources to make comparison between them. Relevant information includes the important details that we know, should know and would like to know about the outcomes of the decision. The information should be gathered from reliable information to avoid biases. In addition, information collected should be associated with uncertainties, which can be either expressed as possibilities or probabilities of certain events from occurring (Spetzler et al. 2016).

Furthermore, values, which describe what we are aiming for, should be clear for every party so that a quality decision can be reached easily. However, when making a decision, it is usual to have multiple targets, such as greater shareholder value, environmental sustainability and a positive brand impact. Thus, trade-offs should be done to decide how much of one value the decision maker is willing to give up so that it is possible to get more of another. The last two elements in the DQ framework are sound reasoning and commitment to action. Sound reasoning will incorporate all the information collected and analyse in order to get the alternative which is able to deliver the most of what we want. In the case where uncertainty is crucial, tools such as tornado diagrams and decision trees can be used in this process. Lastly, commitment to action refers to the action to be taken once the decision is made so that real values can be created. Without effective action, all the time and effort in decision-making will be wasted (Spetzler et al. 2016).

To select the project from a list of promising options, analytic hierarchy process (AHP) has been applied. In this step, the decision problems are divided into three major components, namely goal, alternatives and criteria. The definition of a goal of the problem is the comprehensive objective which drives the decision problem. Next, the alternatives are referring to various choices that are being weighed in the decision. Lastly, the criteria are the factors being utilised for the evaluation of the alternatives towards the goal of the decision problem. If more differentiation is required, sub-criteria can be specified (Kluhto 2013). However, it is to be noted that not every criterion requires sub-criteria, nor do those with sub-criteria require the same number of sub-criteria.

The process of developing an AHP can be described into 5 steps:

  1. 1.

    The main problem is determined.

  2. 2.

    A decision hierarchy is structured from the top with the goal of the decision.

  3. 3.

    Next, the criteria and alternatives are identified and structured.

  4. 4.

    The pairwise comparison matrices are developed. For every criterion, a set of sub-criteria were identified and compared between one another.

  5. 5.

    The weightage obtained for the criteria is utilised for weighing the sub-criteria. This is repeated for each criterion and the weighing process is continued until the final score of the alternative is obtained.

In summary, the mathematical representation of AHP is as follows, a decision maker has n objectives and m alternatives. During the first stage of AHP, a weight wi is generated for the ith objective. Next, a score sik of the kth alternative is given on the ith objective. Lastly, the final score of the kth alternative is then evaluated using the Eq. 1 below (Nguyen 2014): 

$$\mathrm{Final Score}=\sum\nolimits_{i=1}^{n}{w}_{i}{s}_{ik}$$
(1)

For the pairwise comparisons, a fundamental scale which is used in the AHP evaluation is important to indicate how many times more dominant one element is to another element with respect to the criterion on which they are compared. Table 2 depicts the fundamental scale which is utilised in the AHP for prospect selection in the latter part (Saaty 2008).

Table 2 The fundamental scale
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The core steps in applying AHP calculations are as follows (Nguyen 2014):

  1. 1.

    The pairwise comparison matrix for a decision maker with n objectives is an n x n matrix \(A=[{\alpha }_{ij}]\):

$$\begin{array}{cc}(\mathrm{a}) {\alpha }_{ij}>0 & \mathrm{for }i,j=1,\dots ,n\end{array}$$
(2)
$$\begin{array}{cc}(\mathrm{b}) {\alpha }_{ji}=\frac{1}{{\alpha }_{ij}}& \mathrm{for }i,j=1,\dots ,n\end{array}$$
(3)

A matrix \(A\) which fulfils condition (a) is defined as a positive matrix whereas condition (b) as a reciprocal matrix. Besides that, Eqs. (2) and (3) also indicate that \({\alpha }_{ii}=1\) for \(i=1,\dots ,n\).\({\alpha }_{ij}\) entry in \(A\) represents the importance of objective i compared to the objective j. Hence, the \({\alpha }_{ij}\) entry can be estimated in Eq. 5.

  1. 2.

    A consistent pairwise comparison matrix \(A\) which satisfies the conditions above is shown in Eq. 4.

$${\alpha }_{ij}=\frac{{w}_{i}}{{w}_{j}}$$
(5)

where \({w}_{i}\) is the weight of objective i and \({w}_{j}\) is the weight of objective j. The above equality is true only if the decision maker is consistent.

$$\begin{array}{cc}(\mathrm{c}) {\alpha }_{ik}={\alpha }_{ij}{\alpha }_{jk}& \mathrm{for }i,j=1,\dots ,n\end{array}$$
(4)
  1. 3. 

    The pairwise comparison matrix A of a consistent decision maker can be summarised in Eq. 6.

$$A=\left[{\alpha }_{ij}\right]=\left[\begin{array}{ccc}\frac{{w}_{1}}{{w}_{1}}& \cdots & \frac{{w}_{1}}{{w}_{n}}\\ \vdots & \ddots & \vdots \\ \frac{{w}_{n}}{{w}_{1}}& \cdots & \frac{{w}_{n}}{{w}_{n}}\end{array}\right]$$
(6)

where \({w}_{i}>0\) and \({\sum }_{i=1}^{n}{w}_{i}=1\).

  1. 4.

    The weight vector w of a decision maker is estimated as shown in Eq. 7.

$$w=\left[{w}_{i}\right]={\left[\begin{array}{ccc}{w}_{1}& \cdots & {w}_{n}\end{array}\right]}^{T}$$
(7)

where \({w}_{i}>0\) and \({\sum }_{i=1}^{n}{w}_{i}=1\).

The definitions 1 to 4 are then led to two important theorems in AHP.

Theorem 1: If a decision maker is consistent and has n objectives, let \(A\) be the corresponding pairwise comparison matrix, and w be the weight vector. Then, w is an eigenvector of \(A\) with corresponding eigenvalue \(\lambda =n\) as shown in Eqs. 8 and 9.

$$Aw=\left[\begin{array}{ccc}\frac{{w}_{1}}{{w}_{1}}& \cdots & \frac{{w}_{1}}{{w}_{n}}\\ \vdots & \ddots & \vdots \\ \frac{{w}_{n}}{{w}_{1}}& \cdots & \frac{{w}_{n}}{{w}_{n}}\end{array}\right]\left[\begin{array}{c}{w}_{1}\\ \vdots \\ {w}_{n}\end{array}\right]=\left[\begin{array}{ccc}\frac{{w}_{1}}{{w}_{1}}{w}_{1}& \cdots & \frac{{w}_{1}}{{w}_{n}}{w}_{n}\\ \vdots & \ddots & \vdots \\ \frac{{w}_{n}}{{w}_{1}}{w}_{1}& \cdots & \frac{{w}_{n}}{{w}_{n}}{w}_{n}\end{array}\right]=\left[\begin{array}{c}n{w}_{1}\\ \vdots \\ n{w}_{n}\end{array}\right]=n\left[\begin{array}{c}{w}_{1}\\ \vdots \\ {w}_{n}\end{array}\right]=nw$$
(8)
$$Aw=nw$$
(9)

Theorem 2: The normalised form of any column of the matrix \(A=\left[\frac{{w}_{i}}{{w}_{j}}\right]\) is a solution to the eigenvalue problem \(Aw=nw\), where \(w={\left[\begin{array}{ccc}{w}_{1}& \cdots & {w}_{n}\end{array}\right]}^{T}\) is the weight vector solution and n is the number of objectives.

For inconsistent decision maker, the eigenvalue problem can be represented by Eq. 10.

$$A{w}_{0}={\lambda }_{max}{w}_{0}$$
(10)

where \({\lambda }_{max}\) is the unique largest eigenvalue for \(A\) and \({w}_{0}\) is the corresponding eigenvector.

Since the decision makers do not normally make “perfect” judgement when doing the pairwise comparison, it is possible to produce a result where transitivity property is not satisfied, eventually leading to an inconsistent outcome (Alonso and Lamata, 2006). Hence, to ensure the transitivity property is always fulfilled, it is important to check the consistency of the result. To check for the consistency of the outcomes from AHP, consistency index (CI), random index (RI) and consistency ratio (CR) should be calculated. As suggested by Triantaphyllou and Mann (1995), the result is said to be consistent only if the corresponding CR value is lower than 10%, which is also in agreement with Rass et al. (2020).

Equation 11 shows the consistency index, which can be further divided into the definition for RI and CR.

$$CI=\frac{{\lambda }_{max}-n}{n-1}$$
(11)

Random index is the average value of CI calculated from a huge set of randomly generated reciprocal matrices. With the expansion of both CR and RI, consistency ratio is introduced which will indicate the consistency of the matrix as shown in Eq. 12. It is the ratio of CI (A): RI (A), where RI(A) is the random index for matrices of size n. Random index values for matrices of order 1 to 10 are presented in Table 3 (Saaty, 1987). For values higher than 10%, the comparison and ratio matrix will be revised, and re-evaluation will be done (Kluhto 2013).

Table 3 Random consistency table
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$$CR=\frac{CI}{RI}$$
(12)

Once the relative weights are estimated, the selection of the project is done based on the total score and the individual scores which depend on the designers’ choice.

Case Study

In this work, various past potential prospects in manufacturing and oil and gas exploration and development worldwide which were considered by international conglomerates were explored.

The brief description of each prospect is presented in Table 4.

Table 4 Potential prospects
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If these projects are to be invested as an opportunity, it can be concluded as a short-term investment than a long-term investment. All the key value drivers mentioned in the methodology are relevant for all six potential projects.

For all the prospects, there are success criteria and issues that are affecting the feasibility of each project, which are listed in Tables 5 and 6. Therefore, before choosing any of the prospects, it is important to focus on the difficulty to decide whether mitigations can be taken to reduce the risk of issues from occurring.

Table 5 Success criteria for each project
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Table 6 Issues for each project
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To apply the decision quality framework to each project, it is necessary to list out the possible outcomes for different aspects, such as in terms of feedstock(s) and end-product(s). The detailed data listed out can be found in the Appendix. From the key value drivers mentioned in the “Methodology” section, 4 of the most important drivers shown were used to form the strategy tables by using the decision hierarchy data in the Appendix. These drivers are low capital cost, low operating cost, environmental, social and governance and ease of doing business which are presented in Tables 7, 8, 9 and 10.

Table 7 Strategy table for low capital cost
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Table 8 Strategy table for low operating cost
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Table 9 Strategy table for environmental, social and governance (ESG)
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Table 10 Strategy table for ease of business
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For each of the strategies, the raw data in the decision hierarchy are selected in order to achieve to the respective strategy. For instance, for project 1, the mode of transportation with the lowest CAPEX is by using existing pipelines, which is the cheapest out of the options given in the decision hierarchy tables.

By applying AHP in the prospect selection in this work, the relative importance given during the pairwise comparison is determined by referring to the quantification tools available for various criteria. For example, indexes are available to compare the ease of business and political situation in various countries, which can be used to quantify these criteria, which are otherwise difficult to assess. The pairwise comparison has been performed by a team of industrial practitioners and academic researchers. Consensus on the scoring was achieved through brainstorming sessions for each pair. The hierarchical structure of this problem is presented in Fig. 4.

Fig. 4
figure 4

Hierarchical structure of AHP with sub-criteria

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In order to determine the weightage of each strategy relative to each other, ratings were given as shown in Table 11. It can be seen that CAPEX is having the highest global weightage, followed by ease of business, OPEX and ESG. The outcome of this pairwise comparison is also reasonable since all these projects are CAPEX intensive, so it is crucial to consider CAPEX during the selection process. The highest weightage indicates that it is the most important strategy as it has the most significant effect on the final decision.

Table 11 Strategy pairwise comparison
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For CAPEX pairwise comparison between the projects, estimation of cost for each project was based on the existing plants in the country. In order to ensure reliable comparison, the estimated cost was divided by the production capacity provided. The capital cost estimated is illustrated in Table 12. With these values, it is used as the basis for pairwise comparison for low CAPEX as shown in Table 13.

Table 12 Estimated CAPEX for each project
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Table 13 Pairwise comparison for low capital cost (CAPEX)
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Referring to Table 13, it can be observed that project 1 has the highest weightage, followed by project 4, project 3, project 2, project 5 and project 6. The reason that project 6 scores the lowest is because of its high CAPEX since refineries usually require lots of distillation columns, while for project 5, the cost of electrolyser is very high even though there are subsidies from the government. With the high CAPEX incurred, it will be very challenging to obtain a good return. For project 1, the process is mainly focusing on the purification of oil extracted, and it only requires relatively cheap equipment such as separators.

The results of pairwise comparison for other strategies, such as OPEX, ease of business, environmental, social and governance (ESG), are shown in the Appendix. As a summary, based on Table 14, project 1 to project 4 have relatively similar final weightage after comparing them in terms of strategies. On the other hand, project 6 scores the lowest as the CAPEX incurred for this project is remarkably higher than the rest. With the final weightage, the ranking of projects was done, whereby project 4 scores the first, followed by projects 1, 3, 2, 5 and 6.

Table 14 Summary of weightages for different projects
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With the application of decision quality framework, decision hierarchy and AHP, it has enabled the selection of best concept. As a sequel to this study, project 4, LNG import and re-gasification terminal in Southwest India, is chosen as the final project for further study. This is also supported by the trend of LNG observed in India in the recent years, especially when India is promoting natural gas as a “transition fuel”, which is also one of the commitment under the 2015 Paris Agreement to reduce the greenhouse gas (GHG) emission intensity of its gross domestic product (GDP) by 33 to 35% by 2030 (Lopes 2021). Natural gas, which is a cleaner fuel than oil and coal, plays an extremely role in transitioning from fossil fuels to other energy sources (Pospíšil et al., 2019).

Besides, as of September 2021, natural gas made up 6.5% of India’s energy mix, and the share of natural gas in its energy mix is expected to be 15% by 2030. In 2020, India’s government had announced the “One Nation One Gas Grid” program to expand the country’s LNG infrastructure. As a result, more than 15,000 km of gas pipelines, which can cover 407 districts, is scheduled for completion by 2023 (Lopes 2021). With the positive rise in demand of natural gas as well as support from the government, project 4 has the best potential for further consideration. At the same time, projects 1, 2 and 3 also have similar scores to project 4. However, the relative weights of individual criteria are significantly different. Since the overall scores are comparable for these three projects, the investors can make the final decision based on the specific priorities.

As observed from the AHP outcome, every multi-criteria decision-making (MCDM) strategy can be used to break down complex problems into manageable components. With the use of MCDM in the AHP, several dimensions that are relevant for the decision-making context can be considered and evaluated one at a time. Using group decision-making procedures such as the pair-wise matrix comparison, the scores obtained from the various grading methods can be gathered and integrated into the final scoring system which produces a final score compromising of all existing data to aid the final decision of selecting a project. Although all projects are not the best in all categories, as long the project does not score poorly in any high-scoring criteria, it has a higher chance of being selected.

Conclusions

Project management demands various discerning talents and strategies in order to make sound conclusions in complex decision-making scenarios. The AHP is described in the paper as a decision-making process that permits several factors to be considered when undergoing the screening process implemented in the first gate of the stage gate process. To show the detailed use of AHP in project management, a detailed example of AHP on project selection was constructed. This was done to demonstrate that by considering key factors into several criteria in the AHP and analysing them accurately based on a wide range of data instead of approximations, a complex decision involving multi-criteria decision-making process can produce a clear-cut answer for decision makers instead of uncertain answers. By doing so, project management experts will be more inclined to incorporate the use of AHP as a powerful decision-making tool.

Based on the AHP study carried out, the major achievements that have made this study a success are that the overall decision-making ability from the preliminary AHP has been enhanced greatly to produce clear-cut answers. Secondly, the uncertainties present in the preliminary AHP which may hinder a decision-maker’s ability to select a valuable project have been mitigated. Furthermore, the AHP can be considered to be robust as the methods and techniques used to conduct pair-wise comparisons are able to produce consistent, reliable ratings which can be trusted by future decision makers if they decide to use this tool.