2
DEFINITION OF MULTICRITERIA
DECISION ANALYSIS (MCDA)
MCDA helps to evaluate and compare multiple alternatives
based on multiple criteria or attributes (Campos, 2020).
It provides a systematic approach to decision-making pro-
cesses by considering the preferences of a company, or an
organization, to quantify the quantitative or qualitative
value associated with each alternative being considered.
In MCDA, decision problems are approached by
breaking them down into a set of attributes or criteria that
are relevant to the decision being analyzed. These attri-
butes should represent the different factors that need to
be considered when evaluating the alternatives. Examples
of attributes could include economic (such as cost, time,
revenue, profit), environmental (such as carbon emissions,
discharge, noise), social (such as safety, health, sustainabil-
ity), and, strategic (such as risk, supply), and Innovation
enabling attributes (such as (simplification, automation,
digital transformation, artificial intelligence,)
MCDA recognizes that any given organization has
different preferences and priorities for these attributes. To
capture these preferences, the organization assigns weights
to each attribute, indicating their relative importance or
priority. The weights reflect the company’s qualitative or
quantitative value and can be determined through various
methods, such as direct rating, pairwise comparison, or
analytic hierarchy process (AHP) briefly described in the
weight assessment section below.
To ensure comparability across different attributes, the
scores are often normalized by transforming them onto
a common scale. Normalization eliminates biases aris-
ing from differences in measurement scales and allows for
meaningful comparisons between alternatives.
After normalization, the weights assigned to each attri-
bute are applied to the corresponding normalized scores.
This results in weighted scores for each attribute, reflecting
the relative importance of each attribute to the company.
By summing up the weighted scores across all attributes,
the overall value of each alternative is calculated.
Based on the calculated value for each project, the
alternatives can be ranked from highest to lowest value. The
alternative with the highest added value is considered the
best choice according to the organization’s priorities.
MCDA offers a flexible framework that accommo-
dates different decision contexts and allows for sensitivity
analysis. Sensitivity analysis involves exploring the effects of
changes in attribute weights or scores on the rankings of the
alternatives. This analysis helps organizations understand
the robustness of their decisions and identify critical factors
that significantly influence the outcomes. Dyer, J. (2005).
MULTICRITERIA DECISION ANALYSIS
APPLIED IN MINING INNOVATION
Mining is a complex industry that needs to constantly
innovate to bring new solutions to enhance operational
efficiency, sustainability, and safety. In the quest for innova-
tion, selecting the most suitable projects or ideas for imple-
mentation is critical. (Nieto, 2019)
Just recently innovation-based strategies have been
considered by the mining industry such as recent imple-
mentation of the 5S innovation model (Nieto, 2024) by
GoldFields, Penoles Industries, FLSmidth, Respec, and
others. The key driver in all these cases have been the
inclusion of innovation in their business plan in order to
increase productivity, growth, and value, by also consider-
ing improving safety and environmental factors. (Xenaki,
2023)
MCDA in this case serves as a valuable tool offering
a systematic and objective approach to evaluate and pri-
oritize potential innovation projects based on multiple cri-
teria, highlighting its ability to enhance decision-making
processes, foster strategic thinking, and drive sustainable
development.
Innovation thus plays a pivotal role in mining to
unlock new opportunities to improve productivity and
secure sustainable development. However, the selection of
R&D innovation projects is often subjective and filled with
uncertainties. MCDA provides a robust framework to eval-
uate and compare various innovation options objectively.
MCDA if applied within a strategic innovation plan-
ning process, could enable mining companies to make
more informed and rational decisions by considering mul-
tiple attributes or criteria simultaneously. By defining and
quantifying these criteria, such as safety, productivity, cost,
environmental impact, and social acceptability, MCDA
allows for a comprehensive evaluation of innovation alter-
natives. This approach helps companies to gain a deeper
understanding of the potential benefits of each R&D
option. Campos, V. (2022).
One of the key advantages of MCDA is the ability to
quantify and assign weights to different criteria. By utiliz-
ing data-driven approaches, mining managers can assign
relative importance to each attribute based on their spe-
cific goals and preferences. This process helps establish a
clear hierarchy of criteria, ensuring that decision-making is
objective and transparent.
Considering that the mining industry operates in a
complex and uncertain environment. MCDA provides
a mechanism to incorporate uncertainties and risks into
the decision-making process. Through sensitivity analysis
and scenario modeling, managers and decision makers can
DEFINITION OF MULTICRITERIA
DECISION ANALYSIS (MCDA)
MCDA helps to evaluate and compare multiple alternatives
based on multiple criteria or attributes (Campos, 2020).
It provides a systematic approach to decision-making pro-
cesses by considering the preferences of a company, or an
organization, to quantify the quantitative or qualitative
value associated with each alternative being considered.
In MCDA, decision problems are approached by
breaking them down into a set of attributes or criteria that
are relevant to the decision being analyzed. These attri-
butes should represent the different factors that need to
be considered when evaluating the alternatives. Examples
of attributes could include economic (such as cost, time,
revenue, profit), environmental (such as carbon emissions,
discharge, noise), social (such as safety, health, sustainabil-
ity), and, strategic (such as risk, supply), and Innovation
enabling attributes (such as (simplification, automation,
digital transformation, artificial intelligence,)
MCDA recognizes that any given organization has
different preferences and priorities for these attributes. To
capture these preferences, the organization assigns weights
to each attribute, indicating their relative importance or
priority. The weights reflect the company’s qualitative or
quantitative value and can be determined through various
methods, such as direct rating, pairwise comparison, or
analytic hierarchy process (AHP) briefly described in the
weight assessment section below.
To ensure comparability across different attributes, the
scores are often normalized by transforming them onto
a common scale. Normalization eliminates biases aris-
ing from differences in measurement scales and allows for
meaningful comparisons between alternatives.
After normalization, the weights assigned to each attri-
bute are applied to the corresponding normalized scores.
This results in weighted scores for each attribute, reflecting
the relative importance of each attribute to the company.
By summing up the weighted scores across all attributes,
the overall value of each alternative is calculated.
Based on the calculated value for each project, the
alternatives can be ranked from highest to lowest value. The
alternative with the highest added value is considered the
best choice according to the organization’s priorities.
MCDA offers a flexible framework that accommo-
dates different decision contexts and allows for sensitivity
analysis. Sensitivity analysis involves exploring the effects of
changes in attribute weights or scores on the rankings of the
alternatives. This analysis helps organizations understand
the robustness of their decisions and identify critical factors
that significantly influence the outcomes. Dyer, J. (2005).
MULTICRITERIA DECISION ANALYSIS
APPLIED IN MINING INNOVATION
Mining is a complex industry that needs to constantly
innovate to bring new solutions to enhance operational
efficiency, sustainability, and safety. In the quest for innova-
tion, selecting the most suitable projects or ideas for imple-
mentation is critical. (Nieto, 2019)
Just recently innovation-based strategies have been
considered by the mining industry such as recent imple-
mentation of the 5S innovation model (Nieto, 2024) by
GoldFields, Penoles Industries, FLSmidth, Respec, and
others. The key driver in all these cases have been the
inclusion of innovation in their business plan in order to
increase productivity, growth, and value, by also consider-
ing improving safety and environmental factors. (Xenaki,
2023)
MCDA in this case serves as a valuable tool offering
a systematic and objective approach to evaluate and pri-
oritize potential innovation projects based on multiple cri-
teria, highlighting its ability to enhance decision-making
processes, foster strategic thinking, and drive sustainable
development.
Innovation thus plays a pivotal role in mining to
unlock new opportunities to improve productivity and
secure sustainable development. However, the selection of
R&D innovation projects is often subjective and filled with
uncertainties. MCDA provides a robust framework to eval-
uate and compare various innovation options objectively.
MCDA if applied within a strategic innovation plan-
ning process, could enable mining companies to make
more informed and rational decisions by considering mul-
tiple attributes or criteria simultaneously. By defining and
quantifying these criteria, such as safety, productivity, cost,
environmental impact, and social acceptability, MCDA
allows for a comprehensive evaluation of innovation alter-
natives. This approach helps companies to gain a deeper
understanding of the potential benefits of each R&D
option. Campos, V. (2022).
One of the key advantages of MCDA is the ability to
quantify and assign weights to different criteria. By utiliz-
ing data-driven approaches, mining managers can assign
relative importance to each attribute based on their spe-
cific goals and preferences. This process helps establish a
clear hierarchy of criteria, ensuring that decision-making is
objective and transparent.
Considering that the mining industry operates in a
complex and uncertain environment. MCDA provides
a mechanism to incorporate uncertainties and risks into
the decision-making process. Through sensitivity analysis
and scenario modeling, managers and decision makers can