Espirales. Revista multidisciplinaria de investigación científica, Vol. 8, No. 51
October - December 2024 e-ISSN 2550-6862. pp 1-18
DOI https://doi.org/10.31876/er.v8i50.871
Decision-making strategies for organizational processes
Estrategias de toma de decisiones para los procesos organizacionales
Erick Marcelo Loor Santana*
Leonardo Álvaro Banguera Arroyo*
Rina Jacqueline Vera Nicola*
Betsy Mabel Olvera Moran*
Received: July 13, 2024
Approved: September 22, 2024
Abstract
This research aims to explore decision-making strategies in
organizational processes and their relevance in today's business
environment, using a comprehensive methodology based on a review
of the scientific literature. Different approaches and methodologies
used for decision making are presented, such as data-driven decision
making, multi-criteria decision making, collaborative decision making
and decision making based on artificial intelligence and machine
learning. In addition, recent applications of these strategies in various
organizational contexts, such as manufacturing, logistics and supply
chain, project management, and product innovation and
development, are discussed.
Keywords:
Decision Making, Organizational Processes, Business
Environment
* Msc. University of Guayaquil. Faculty of
Industrial Engineering. Email:
erick.loors@ug.edu.ec
https://orcid.org/0009-0008-9413-7409
* Msc. University of Guayaquil. Faculty of
Industrial Engineering. PhD in Engineering
Sciences, mention in Industrial Engineering -
leonardo.bangueraa@ug.edu.ec
https://orcid.org/0000-0002-0261-2372
* Msc. University of Guayaquil. Faculty of
Industrial Engineering. Master's Degree in
Health Service Management,
rina.veran@ug.edu.ec
https://orcid.org/0000-0002-6625-0905
* Msc. University of Guayaquil. Faculty of
Industrial Engineering. Master in Integrated
Management System,
betsy.olveram@ug.edu.ec
https://orcid.org/0000-0003-4644-8209
Loor, E., Banguera, L., Vera, R.,
Olvera, B. (2024) Decision-making
strategies for organizational
processes. Espirales Revista
Multidisciplinaria de investigación
científica, 8 (51), 1-20
Decision-making strategies for organizational processes
Espirales. Revista multidisciplinaria de investigación científica, Vol. 8, No. 51
October - December 2024 e-ISSN 2550-6862. pp 1-18
2
Introduction
Decision making is an essential function in any organization, as it directly affects
operational efficiency and strategic success. In today's business environment, effective
decision making is a critical factor for the success and survival of organizations. The
ability to make informed and appropriate decisions in different environments and
complex situations is essential for organizational processes. Therefore, the analysis of
decision making strategies has acquired great importance and interest among
researchers and practitioners working in the field of business management(Brito-Carrillo
et al., 2020).
The objective of this research article is to examine decision-making strategies in
organizational processes and to analyze their importance in today's business
environment. To achieve this objective, different approaches and methodologies used
in decision making will be explored, as well as their application in various organizational
contexts.
One of the most prominent decision-making strategies is data-driven decision making.
In an increasingly digitized environment, entities have a wealth of information and data
at their disposal. The ability to gather, examine, and employ this data efficiently can
establish a solid foundation for strategic and operational decision making(Quinto et al.,
2021).. Best practices and techniques used in data-driven decision making will be
examined, as well as the benefits and challenges associated with their implementation.
In addition, other relevant decision-making strategies are discussed, such as multi-
criteria decision-making, which involves the consideration of multiple factors and criteria
in the decision-making process. Collaborative decision making, which promotes the
participation of multiple factors and the generation of consensus in organizational
decision making, is also analyzed.
Resumen
Esta investigación tiene como objetivo explorar las estrategias de toma
de decisiones en los procesos organizacionales y su relevancia en el
entorno empresarial actual, para esto se empleó una metodología
integral basada en la revisión de la literatura científica. Se presentan
diferentes enfoques y metodologías utilizados para la toma de
decisiones, como la toma de decisiones basada en datos, la toma de
decisiones multicriterio, la toma de decisiones colaborativa y la toma
de decisiones basada en inteligencia artificial y aprendizaje automático.
Además, se analizan las aplicaciones recientes de estas estrategias en
diversos contextos organizacionales, como la industria manufacturera,
la logística y cadena de suministro, la gestión de proyectos y la
innovación y desarrollo de productos.
Palabras clave:
Toma de decisiones, Procesos organizacionales,
Entorno empresarial
Erick Marcelo Loor Santana, Leonardo Álvaro Banguera Arroyo, Rina Jacqueline Vera Nicola,
Betsy Mabel Olvera Moran
Espirales. Revista multidisciplinaria de investigación científica, Vol. 8, No. 51
October - December 2024 e-ISSN 2550-6862. pp 1-18
3
The incorporation of artificial intelligence and machine learning into decision-making
processes is another topic of interest in this study. These emerging technologies offer
new opportunities and challenges in improving the accuracy and efficiency of decision
making. Recent applications of artificial intelligence and machine learning in
organizational decision making will be explored, as well as the ethical and liability issues
associated with their implementation.
Ultimately, it is hoped that this study will provide an overview of the different decision-
making strategies in organizational processes and their importance in improving
business efficiency and competitiveness. It is also hoped that the findings and
conclusions presented in this article may be useful to both researchers and practitioners
seeking to optimize their decision-making processes and achieve successful results in a
constantly changing and challenging business environment.
Materials and methods
This research article employed a comprehensive methodology based on a review of the
scientific literature (Quispe et al., 2021).(Quispe et al., 2021).. Exhaustive searches were
carried out in academic databases and specialized journals such as Google Scholar,
Scopus, Science Direct, and repositories of the University of Guayaquil, which allowed
the identification of relevant scientific publications related to decision-making strategies
in organizational processes. The selection of publications was limited to those less than
4 years old in order to guarantee the timeliness of the data and approaches addressed;
likewise, a smaller number of articles outside the established period were considered
given the importance of the information provided in this research.
Once the bibliographic references had been compiled, the information found was
analyzed and synthesized. Patterns, trends and common approaches in organizational
decision making were identified. Based on this review, the article was structured
following a logical sequence that covered different aspects of decision making
strategies(Quispe et al., 2021).
The analysis of recent applications in different organizational contexts was supported
by case studies and concrete examples from the reviewed scientific literature. The aim
was to provide a representative overview of how decision-making strategies are applied
in different business areas, addressing emerging trends and the challenges that
organizations must face today.
Results
Data-driven decision making has become a fundamental practice in today's business
environment. This approach involves collecting, analyzing and using quantitative and
qualitative information to support decision making. By using statistical techniques, data
mining and data visualization, organizations can extract valuable insights and hidden
Decision-making strategies for organizational processes
Espirales. Revista multidisciplinaria de investigación científica, Vol. 8, No. 51
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patterns in large data sets. By basing decisions on objective evidence, companies can
reduce uncertainty and minimize subjective biases that could negatively affect the
outcome of decisions (Calle Garcia et al., 2024; McKinsey & Company, 2023; Sarker,
2021).
This strategy also relies on the use of relevant and reliable data to better understand
problems, evaluate options and forecast outcomes.
Today, organizations have access to a large amount of internally and externally
generated data. This data can come from sources such as databases, transactional
systems, social networks, surveys, among others.(Palma Ortigosa, 2019). For Ikegwu et
al., (2022)., data-driven decision making involves the use of data analysis tools and
techniques, such as data mining, statistical analysis and predictive modeling, to convert
these data into actionable and valuable information.
By applying data-driven decision making, organizations can realize several
benefits(Palma Ortigosa, 2019). Some of them are:
• Accurate information: By basing decisions on data, the influence of intuition or
personal biases is reduced, leading to greater accuracy and objectivity in the
decision-making process.
• Improved efficiency: The availability of relevant and up-to-date information speeds
up the decision-making process by providing reliable data and analysis to support
the choice of the most appropriate option.
• Identification of opportunities and risks: Data analysis enables the identification of
patterns, trends and relationships that can help identify business opportunities or
anticipate potential risks and challenges.
• Resource optimization: By having accurate information on the performance of
operations and processes, informed decisions can be made on resource allocation,
cost optimization and improved operational efficiency.
However, data-driven decision making also presents significant challenges and
considerations(McKinsey & Company, 2023).. Some of these are:
• Data quality: Accurate, complete and reliable data is critical. Lack of data quality can
lead to erroneous or suboptimal decisions.
• Proper interpretation: Data analysis requires specialized skills and knowledge to
correctly interpret the results and make sound decisions.
• Privacy and security: The use of data implies the need to protect the privacy of
individuals and ensure the security of information. It is essential to comply with
current data protection regulations and policies.
However, data-driven decision making is a powerful strategy for organizations, as it
allows them to leverage available information to make more informed and accurate
decisions. By using data analytics tools, organizations can gain competitive advantages,
improve operational efficiency, and adapt to an ever-changing business
environment(Sanchez-De-Roux, 2022).. However, associated challenges and
Erick Marcelo Loor Santana, Leonardo Álvaro Banguera Arroyo, Rina Jacqueline Vera Nicola,
Betsy Mabel Olvera Moran
Espirales. Revista multidisciplinaria de investigación científica, Vol. 8, No. 51
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considerations, such as data quality and information privacy, need to be taken into
account.
Multi-criteria decision making is especially relevant when decisions involve multiple
objectives and criteria that must be considered simultaneously. This approach allows
different alternatives to be evaluated and compared based on their performance in
different dimensions (Alvarez et al., 2021; Haseli et al., 2020).. By applying multi-
attribute utility theory, multi-criteria linear programming and fuzzy set based methods,
organizations can assign weights and perform comparative analysis of the different
options available. Multicriteria decision making facilitates the selection of the most
appropriate option that optimizes the balance between the different criteria and
objectives established(López-Cadavid et al., 2020).
Making choices based on multiple criteria is a tactic used to solve situations in which it
is necessary to consider several factors or elements before making a
determination.(Rodriguez Pupo, 2021). Unlike traditional approaches that focus on a
single criterion, multi-criteria decision making recognizes that complex decisions often
involve the evaluation of several aspects simultaneously.
In this strategy, the different criteria relevant to the decision are identified and assigned
a relative weight or importance according to their impact on the desired results.(Díaz
Sánchez & Serrano Gil, 2020).. These criteria can be qualitative or quantitative, and may
include factors such as costs, benefits, risks, environmental impact, customer
satisfaction, among others.
Once the criteria have been identified, the available alternatives are evaluated in
relation to each of them. To do so, specific methods and techniques are used, such as
value analysis, hierarchical analysis, multi-attribute utility analysis, among others. These
tools help to structure and quantify relevant information, facilitating the comparison and
evaluation of the different alternatives(Araya-Pizarro et al., 2019).
One of the most common approaches in multi-criteria decision making is value analysis,
which seeks to identify the option that maximizes the value obtained considering the
different criteria. In this approach, a score or value is assigned to each alternative based
on its performance on each criterion, and an overall comparison is made to determine
the most favorable option.
It is important to note that multi-criteria decision making involves an iterative and
participatory process, as it requires the collaboration and consensus of multiple
stakeholders involved in decision making. This is because different stakeholders may
have diverse perspectives and preferences about the criteria and alternatives(Díaz
Sánchez & Serrano Gil, 2020).
Multi-criteria decision making is applied in a wide range of organizational contexts and
industry sectors. For example, in project management, it is used to evaluate and select
suppliers, technologies or implementation approaches. In strategic planning, it helps to
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determine the most appropriate priority investments or growth strategies(Rodriguez
Pupo, 2021). In supply chain management, it is used to select suppliers based on quality,
cost and reliability criteria.
Ultimately, multi-criteria decision making is a strategy that addresses the complexity
and uncertainty inherent in organizational decisions by considering multiple criteria. Its
proper application can help improve the quality of decisions, promote transparency and
participation, and optimize results in a variety of business contexts.
Collaborative decision making refers to the involvement of multiple stakeholders in the
decision making process. This approach recognizes the importance of involving people
with different perspectives, knowledge and skills to generate more robust solutions
supported by consensus. Facilitation techniques, such as brainstorming, focus groups
and teamwork meetings, are used to encourage active participation and idea
generation. In addition, decision support tools, such as online collaboration software
and group decision making platforms, are employed to facilitate communication and
information sharing among participants(Melendez & El Salous, 2021).
Collaborative decision making is also a strategy that seeks the active participation and
consensus of many stakeholders in the decision making process. Unlike a traditional
approach, where decisions are made by a single person or a small group of leaders,
collaborative decision making seeks to leverage the diversity of knowledge,
perspectives and skills of team members.
In this approach, the generation of ideas, the exchange of information and open
discussion among participants are encouraged. Each member has the opportunity to
express his or her point of view, raise options, and argue in favor of certain
decisions(Brito-Carrillo et al., 2020).. The goal is to reach a consensus or a decision that
is accepted and supported by all involved.
There are several advantages and benefits associated with collaborative decision
making. First, by involving multiple people, a wider range of perspectives and expertise
can be considered. This can lead to a better understanding of the situation, a more
complete assessment of options, and higher quality decision making.
In addition, collaborative decision making promotes greater commitment and
ownership among participants. By feeling heard and having the opportunity to
contribute, team members are more committed to the implementation of the decision
and feel responsible for its success(Melendez & El Salous, 2021).
Another important benefit is relationship building and improved communication among
team members. Collaborative decision making fosters openness, respect, and mutual
trust, which can strengthen the bonds between team members and improve
collaboration in the future(Brito-Carrillo et al., 2020).
However, it is important to keep in mind that collaborative decision making can also
present challenges. The need to reach consensus may take longer and require a greater
Erick Marcelo Loor Santana, Leonardo Álvaro Banguera Arroyo, Rina Jacqueline Vera Nicola,
Betsy Mabel Olvera Moran
Espirales. Revista multidisciplinaria de investigación científica, Vol. 8, No. 51
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investment of resources. In addition, there is a risk of protracted conflict or
disagreement, which can hinder efficient decision making.
To successfully implement collaborative decision making, it is important to establish an
enabling environment that fosters active participation, respect and effective listening. It
is helpful to have facilitators or team leaders trained in facilitation and conflict resolution
techniques, who can help guide the process and maintain focus on common
objectives(Brito-Carrillo et al., 2020).
Thus, collaborative decision making is a strategy that seeks to leverage the diversity of
knowledge and perspectives of team members to make sounder, more supported
decisions. Through active participation and consensus, benefits such as better
understanding of the situation, increased commitment and improved decision quality
can be achieved. However, it is important to manage challenges and establish an
environment conducive to the success of this collaborative approach.
Decision making based on artificial intelligence (AI) and machine learning (ML) has seen
significant advances in recent years. These techniques involve the use of algorithms and
models to make decisions or provide recommendations based on patterns identified in
historical data sets. For example, classification and prediction algorithms can analyze
large volumes of information to identify patterns and trends to help make informed
decisions(Quinto et al., 2021).. In addition, machine learning enables systems to make
decisions autonomously through real-time data analysis and processing. These
approaches are especially useful in dynamic and complex environments, where speed
and accuracy of decision making are crucial.
Decision-making based on artificial intelligence (AI) and machine learning is an
increasingly relevant strategy in organizational processes. AI refers to the development
of computer systems and programs that can perform tasks that would normally require
human intelligence, such as reasoning, perception, and learning(Tames et al., 2020).
Machine learning, on the other hand, is a branch of AI that focuses on developing
algorithms and models that allow machines to learn and improve their performance
through experience and data.
AI and machine learning-based decision making is based on the analysis of large
volumes of data, using algorithms and models to identify patterns, trends, and
relationships(Quinto et al., 2021).. These algorithms can perform predictive and
prescriptive analytics, meaning that they can predict future outcomes and recommend
optimal actions or decisions based on the data and established goals.
One of the key advantages of AI and machine learning-based decision making is its
ability to process large amounts of data in real time. This enables more agile decision-
making based on up-to-date information, which can improve an organization's
responsiveness and adaptability to dynamic business environments.
Decision-making strategies for organizational processes
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AI and machine learning can also help identify hidden or complex patterns in data,
which can generate valuable insights and knowledge for strategic decision making.
These approaches can also reduce the influence of human biases or prejudices in
decision making by relying on objective analysis and unbiased algorithms(Gómez
Monsalve & Jurado Calderón, 2020).
In the manufacturing industry, decision-making strategies are applied in areas such as
production optimization, inventory management and supplier selection. For example,
by analyzing historical production data and using machine learning techniques,
companies can predict future demand and adjust their production levels
accordingly(Encalada et al., 2019).. Likewise, data-driven decision making can help
manage inventories efficiently, minimizing costs associated with storage and
obsolescence. In terms of supplier selection, multicriteria criteria can be used to
evaluate and select those suppliers that best fit the requirements of quality, cost and
reliability(Londoño-Patiño, 2020).
In the manufacturing industry, decision making plays a key role in optimizing processes,
managing the supply chain and improving operational efficiency. In recent years, there
have been significant advances in the application of various decision-making strategies
in this sector, taking advantage of emerging technologies and innovative
methodologies(Encalada et al., 2019).. Some of the recent applications of decision
making in the manufacturing industry are detailed below:
• Production optimization: By using data-driven decision-making techniques,
production planning and scheduling can be optimized. Optimization algorithms
maximize resource efficiency, minimize lead times and reduce production costs,
taking into account variables such as demand, production capacity, available
resources and operational constraints.
• Predictive maintenance: The implementation of predictive maintenance systems
based on data analysis and machine learning algorithms allows informed decisions
to be made about the maintenance of equipment and machinery. Through
continuous monitoring of sensors and early detection of possible failures or wear, it
is possible to schedule maintenance in advance, avoiding unplanned shutdowns and
minimizing downtime.
• Supply chain management: Decision-making in supply chain management is crucial
to ensure a smooth and efficient operation. The use of data analysis tools and
optimization models enables real-time decisions to be made on demand planning,
inventory management, logistics routing and supplier selection, among other
aspects, with the aim of minimizing costs and maximizing customer satisfaction.
• Quality improvement: Decision-making based on data analysis and advanced
statistical techniques can help improve the quality of manufactured products. By
analyzing data from quality control, inspections and customer feedback, patterns and
trends can be identified and proactive decisions can be made to correct quality
problems, reduce defects and improve customer satisfaction.
• Innovation management: Strategic decision making in innovation management and
product development is essential in the manufacturing industry. Applying
Erick Marcelo Loor Santana, Leonardo Álvaro Banguera Arroyo, Rina Jacqueline Vera Nicola,
Betsy Mabel Olvera Moran
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approaches such as risk analysis, market opportunity assessment and product
lifecycle management helps to make informed decisions about new product
introductions, investment in research and development, and adaptation to changing
market demands.
In summary, decision making in the manufacturing industry has experienced significant
advances thanks to the application of data-driven strategies, advanced analytics and
emerging technologies. These recent applications allow optimizing production,
improving quality, managing the supply chain efficiently and fostering innovation,
leading to greater competitiveness and business success in this sector(Rojo Gutiérrez et
al., 2019)..
In the field of logistics and supply chain, decision-making strategies are critical for route
planning, warehouse management and supply chain coordination. By using
optimization tools and routing algorithms, organizations can determine the most
efficient routes to deliver products to customers, taking into account time, capacity, and
cost constraints(Sánchez Suárez et al., 2021).. In addition, warehouse management
benefits from data-driven decision making to optimize product placement, workflow
design, and resource allocation. Supply chain coordination involves collaborative
decision making, where different actors in the chain work together to coordinate the
demand, production and distribution of products efficiently.
In recent years, decision making in logistics and supply chain has experienced significant
advances due to technological advances and new market trends. These recent
applications have allowed for improved efficiency, optimization and visibility in supply
chain management, generating positive impacts on customer satisfaction and
profitability of organizations(Gómez Montoya et al., 2020)..
One of the key applications of decision making in logistics and supply chain is supply
chain planning and scheduling. Decision making in this area involves designing efficient
distribution networks, determining the optimal location of warehouses and distribution
centers, as well as the optimal allocation of resources such as transportation and storage
capacity. These strategic decisions are based on analysis of historical data, demand
forecasts, operational constraints, and costs, and can be optimized using linear
programming algorithms, combinatorial optimization, or other advanced
techniques(Gómez Montoya et al., 2020).
Another relevant application is inventory management. Decision making in this field
involves determining optimal inventory levels, reorder points, replenishment policies,
and demand and supply management. With data analytics tools and forecasting
models, organizations can make more accurate and timely inventory decisions, avoiding
both shortages and overstocks. In addition, the use of technologies such as the Internet
of Things (IoT) and warehouse automation has enabled real-time inventory management
and greater efficiency in storage and picking processes.
Decision-making strategies for organizational processes
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Transportation optimization is another important application area in logistics decision
making. By using routing and route planning algorithms, organizations can determine
the most efficient routes and optimal transportation modes to minimize costs, reduce
delivery times and improve customer satisfaction. In addition, real-time track and trace
solutions enable accurate monitoring of transportation operations, facilitating decision
making in cases of deviations or unforeseen problems.
Supply chain management also benefits from decision making based on data analytics
and advanced technologies. End-to-end visibility into the supply chain is crucial for
identifying bottlenecks, assessing supplier performance and optimizing workflows.
Through real-time data analytics, organizations can make informed, proactive decisions
to improve coordination, collaboration and efficiency throughout the supply chain.
In addition, emerging technologies such as blockchain have been used to improve
transparency and traceability in the supply chain, enabling more reliable decision
making in terms of authenticity, quality and origin of products (Fontalvo-Herrera et al.,
2019).
So, recent applications of decision making in logistics and supply chain have
revolutionized the way organizations manage and optimize their operations. By
harnessing the power of data analytics, optimization techniques, and advanced
technologies, organizations can improve efficiency, visibility, and strategic decision
making in all aspects of the supply chain, leading to competitive advantage and
improved customer satisfaction(Gomez Montoya et al., 2020).
Conclusions
Decision-making strategies are central to organizational processes and their application
in a variety of business contexts can improve efficiency and competitiveness. Data-
driven approaches, multi-criteria decision making, collaboration and artificial
intelligence are powerful tools for making more informed and evidence-backed
decisions. However, emerging challenges, such as uncertainty, ethics and the need for
specific skills, require constant adaptation and evolution in the practice of decision
making.
Thus, decision making in organizational processes is a critical element for the success
and efficiency of an organization. Throughout this research, we have explored various
dimensions and aspects related to decision making in different contexts, including
competitive strategies, applications in innovation and product development, as well as
emerging challenges and the development of necessary skills and competencies.
In terms of decision-making strategies, we have highlighted the importance of data-
driven approaches, such as artificial intelligence and machine learning-based decision
making, which harness the power of data analytics to obtain valuable information and
make more informed decisions. We have also mentioned the relevance of multi-criteria,
Erick Marcelo Loor Santana, Leonardo Álvaro Banguera Arroyo, Rina Jacqueline Vera Nicola,
Betsy Mabel Olvera Moran
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collaborative and ethics-based decision making, which considers diverse factors and
perspectives to make more balanced and responsible decisions.
In terms of product innovation and development, we have seen how decision making
plays a key role in identifying opportunities, selecting concepts, managing risks, and
launching products to market(Diaz et al., 2018). The application of market analysis
techniques, concept testing and customer feedback helps us to make better decisions
and maximize the potential for success in this area.
In addition, we have addressed emerging trends in decision making in organizational
processes, highlighting the importance of speed, agility and the use of data and
analytics. The ability to adapt quickly to changes in the environment and make
evidence-based decisions has become essential to maintain competitiveness and
relevance in the market. The need to develop skills in strategic thinking, ethical decision
making and collaboration has also been mentioned, as these aspects are crucial to face
current and future challenges.
To conclude, effective decision making in organizational processes requires the
application of appropriate strategies, the development of relevant skills and
competencies, and the ability to adapt to emerging trends and overcome challenges in
an ever-changing business environment. Data-driven decision making, innovation and
strategic thinking, along with ethics and collaboration, are key elements in driving
organizational growth and competitiveness in today's business world.
..........................................................................................................
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