5
and the results are used to construct a preference matrix.
The preference matrix is then used to derive the weights or
priorities of the projects and attributes.
Analytic Hierarchy Process (AHP)
Analytic Hierarchy Process (AHP) is a structured decision-
making method that incorporates pairwise comparisons to
determine the relative weights or priorities of alternative
projects and attributes. Kazibudzki, (2013). AHP involves
breaking down the decision problem into a hierarchy of cri-
teria and sub-criteria, which are then compared in pairs to
establish their relative importance. Managers and decision-
makers assign numerical values, typically using a 1–9 or
1–4 scale, to represent the relative importance of the cri-
teria and sub-criteria. These values are used to construct a
preference matrix and calculate the priority weights of the
projects through a mathematical process. AHP allows for
the consideration of multiple criteria and facilitates a more
comprehensive decision-making process. Once the attri-
butes and weights are defined, the user evaluates each alter-
native against each attribute and assigns scores or values to
represent the performance or desirability of the alternative
on each attribute. These scores can be based on empirical
data, expert opinions, or subjective assessments.
Table 2 shows the relative importance for each specific
attribute.
Assessment Performance and Score Definition
Evaluate each R&D innovation project against the
identified attributes and assign scores or values to quan-
tify the performance of each project on each attribute. The
scores should be relative and reflect the project’s effective-
ness or desirability in relation to each attribute.
Table 3 shows the score for each specific attribute.
Table 1. List of attributes for each category
Economic ESG Strategic Innovation Enablers
NPV Environmental Strategic fit Safety innovation
IRR Shareholder
expectation
Portfolio
management
Simplification innovation
Market CO2 Emissions Regulatory risk Smart innovation
Feasibility Discharge Reputation risk Stealth innovation
Project timeline Noise Market and
business risk
Sustainable innovation
Revenue Footprint Resource
requirement/
Supply risk
Partnerships with universities/
industry
Cost Sustainable Intensity use IP, patent opportunities
Profit Tailings Scarcity Disruption
Payback Disruption Scalability
Revenue
Table 2. Relative importance for each attribute
Economic e ESG g Strategic t Innovation Enablers s
NPV e1 Environmental g1 Strategic fit t1 Safety innovation s1
IRR e2 Shareholder expectation g2 Portfolio management t2 Simplification innovation s2
Market e3 CO2
Emissions
g3 Regulatory risk t3 Smart innovation s3
Feasibility e4 Discharge g4 Reputation risk t4 Stealth innovation s4
Project
timeline
e5 Noise g5 Market and
business risk
t5 Sustainable
innovation
s5
Revenue e6 Footprint g6 Resource requirement/
Supply risk
t6 Partnerships with
universities/ industry
s6
Cost e7 Sustainable g7 Intensity use t7 IP, patent opportunities s7
Profit e8 Tailings g8 Scarcity t8 Disruption s8
Payback e9 Disruption t9 Scalability s9
Revenue e10
and the results are used to construct a preference matrix.
The preference matrix is then used to derive the weights or
priorities of the projects and attributes.
Analytic Hierarchy Process (AHP)
Analytic Hierarchy Process (AHP) is a structured decision-
making method that incorporates pairwise comparisons to
determine the relative weights or priorities of alternative
projects and attributes. Kazibudzki, (2013). AHP involves
breaking down the decision problem into a hierarchy of cri-
teria and sub-criteria, which are then compared in pairs to
establish their relative importance. Managers and decision-
makers assign numerical values, typically using a 1–9 or
1–4 scale, to represent the relative importance of the cri-
teria and sub-criteria. These values are used to construct a
preference matrix and calculate the priority weights of the
projects through a mathematical process. AHP allows for
the consideration of multiple criteria and facilitates a more
comprehensive decision-making process. Once the attri-
butes and weights are defined, the user evaluates each alter-
native against each attribute and assigns scores or values to
represent the performance or desirability of the alternative
on each attribute. These scores can be based on empirical
data, expert opinions, or subjective assessments.
Table 2 shows the relative importance for each specific
attribute.
Assessment Performance and Score Definition
Evaluate each R&D innovation project against the
identified attributes and assign scores or values to quan-
tify the performance of each project on each attribute. The
scores should be relative and reflect the project’s effective-
ness or desirability in relation to each attribute.
Table 3 shows the score for each specific attribute.
Table 1. List of attributes for each category
Economic ESG Strategic Innovation Enablers
NPV Environmental Strategic fit Safety innovation
IRR Shareholder
expectation
Portfolio
management
Simplification innovation
Market CO2 Emissions Regulatory risk Smart innovation
Feasibility Discharge Reputation risk Stealth innovation
Project timeline Noise Market and
business risk
Sustainable innovation
Revenue Footprint Resource
requirement/
Supply risk
Partnerships with universities/
industry
Cost Sustainable Intensity use IP, patent opportunities
Profit Tailings Scarcity Disruption
Payback Disruption Scalability
Revenue
Table 2. Relative importance for each attribute
Economic e ESG g Strategic t Innovation Enablers s
NPV e1 Environmental g1 Strategic fit t1 Safety innovation s1
IRR e2 Shareholder expectation g2 Portfolio management t2 Simplification innovation s2
Market e3 CO2
Emissions
g3 Regulatory risk t3 Smart innovation s3
Feasibility e4 Discharge g4 Reputation risk t4 Stealth innovation s4
Project
timeline
e5 Noise g5 Market and
business risk
t5 Sustainable
innovation
s5
Revenue e6 Footprint g6 Resource requirement/
Supply risk
t6 Partnerships with
universities/ industry
s6
Cost e7 Sustainable g7 Intensity use t7 IP, patent opportunities s7
Profit e8 Tailings g8 Scarcity t8 Disruption s8
Payback e9 Disruption t9 Scalability s9
Revenue e10