The Advantages of Artificial Intelligence in Operational Decision Making

This research paper explores the advantages of artificial intelligence (AI) in operational decision making, focusing on the analysis of production processes, supply chains, and resources. The research highlights several advantages of AI in operational decision making. It empowers organizations to make data-driven decisions, reducing reliance on human intuition and biases. AI technologies can process vast amounts of data in real-time, enabling timely decision-making and facilitating agile operations. Moreover, AI can learn from historical data and continuously improve decision-making processes, leading to enhanced performance over time. The research method employed in this study is utilizing literature review as the data collection method. The literature review involved searching for relevant theories and examining findings from previous researchers, which served as the foundation for developing the analysis to discuss the research outcomes. This research underscores the significant advantages of AI in operational decision making, specifically in the areas of production processes, supply chains, and resource management. By leveraging AI technologies, organizations can achieve improved efficiency, reduced costs, and better overall performance. The findings of this study contribute to a better understanding of the transformative potential of AI and encourage its adoption in various operational domains.


INTRODUCTION
The advent of the digital era has brought about a surge in growing abundance, diversity, and speed at which data is generated, along with the increasing capacity to process it.Inevitably, this has opened up opportunities for the emergence and advancement of innovative technological solutions, prominently including the progression of AI techniques (Brynjolfsson & McAfee, 2017).AI pertains to the capability of a system to accurately interpret vast amounts of data, acquire knowledge from this data, and apply these insights to achieve specific objectives and tasks by adapting flexibly (Kaplan and Haenlein, 2019).
Artificial intelligence (AI) has had a significant impact on operational decision making across various domains.Li et al. (2020) discusses the impact of AI on accounting business, accounting theory, and the ability of accounting personnel, highlighting how AI technologies have improved efficiency and accuracy in accounting processes.As a result, this facilitates accelerated, adaptable, and streamlined procedures, yielding superior-quality products with enhanced levels of personalization.This, in turn, boosts manufacturing productivity and enables industrial expansion.(Schlick, 2014).
Several studies emphasize the benefits of AI in enhancing human decision-making by pooling knowledge through artificial swarm intelligence (Metcalf et al., 2019), recognizing business patterns and analyzing data intelligently (Min, 2009), and facilitating better decision-making from the entrepreneurial perspective (Omari et al., 2021).Business intelligence, which incorporates AI technologies, has been found to positively impact firm performance and decision-making processes (Ozigbo, 2021).AI and data science have transformed the way businesses operate and changed the landscape of strategic decision making and operations (Lakhan, 2022).Marketing decision-making can also be enhanced by AI solutions throughout different stages of the marketing process (Ljepava, 2022).Overall, the studies reviewed highlight the potential of AI to improve decision making in various aspects of business.As AI advances, it is imperative to establish supplementary guidelines and organizational frameworks to retain control over it while capitalizing on its capabilities and adaptability.It remains crucial for humans to retain ultimate authority in the implementation of AIdriven decisions, ensuring ethical conduct and consistently evaluating the consequences and ethical considerations stemming from AI decision-making (Lehner O.M. et al. 2022).
The objective of this paper is to contribute to the current body of literature concerning the influence of AI on managerial decision making, particularly in the context of production.The result of these influences hold substantial significance for decision makers in determining the level of support required to foster digital transformation and promote the adoption of AI.This paper is organized as follows.First, we examine the role of AI in analyzing production processes.Next, the research delves into the significance of AI in supply chain management.Supply chains are intricate networks that involve multiple participants, and AI can offer valuable insights across various dimensions within this complex system.Furthermore, the study explores the role of AI in resource management.Efficient utilization of resources is crucial for businesses to achieve cost savings and sustainability.

METHODS
We performed a literature review, the articles search was conducted on Emerald, ScienceDirect, ResearchGate, and Google Scholar databases.The search process involved using specific keywords such as "Artificial Intelligence," "Decision Making," "Production Process," "Supply Chain," and "Resources," followed by the selection of articles that were relevant to the research topic.
A literature review can be broadly defined as a systematic approach to gathering and combining previous research in a cohesive manner (Baumeister & Leary, 1997).When performed with efficiency, a literature review acts as a strong research methodology that establishes a firm foundation for the progression of knowledge and the development of theories (Webster & Watson, 2002).By integrating perspectives and insights from multiple empirical studies, a literature review possesses the capacity to address research inquiries with a level of strength that exceeds that of individual studies.

The Rise of Artificial Intelligence in Operation Decision Making
The COVID-19 pandemic has spurred the adoption and acceptance of AI in various domains.Studies have shown that has played a crucial role in open innovation and the adoption of Industry 4.0 by small and medium-sized enterprises (SMEs) during the pandemic (Madhavan et al., 2022).The accessibility of AI solutions has greatly expanded, making them readily available and affordable to entrepreneurs.This shift indicates that AI is no longer exclusive to large corporations, as it is now accessible to a wider range of businesses (Iansiti and Lakhani, 2020).The widespread accessibility of AI has the capacity to bring about a substantial influence on the advancement of entrepreneurship, creating fresh avenues for growth and fostering innovation.
The impact of AI on the business world has been transformative, revolutionizing, and unlocking new opportunities for innovation and growth has also been widely discussed in the literature.AI plays a vital role in successfully implementing Industry 4.0 and optimizing production processes (Javaid, M., et al. 2021).It enables efficient analysis and integration of large databases, facilitating real-time decision-making in production processes (Doguc, O. 2023).The role of AI in business models has also been explored.Start-ups and large companies alike have incorporated AI into their business models, contributing to improved efficiency and innovation (Pfau, W., Rimpp, P. 2020).AI enables companies to optimize their production processes, enhance decision-making, and gain a competitive edge in the market.AI has been applied in quality management systems to harmonize rational and creative decision making (Paliukas & Savanevičienė, 2018).The role of AI in decision making extends to educational leadership, where it aids both individual and collective organizational decisions (Wang, 2021).Decision theory provides a framework for understanding how individuals or organizations make decisions in the face of uncertainty.It can be applied to analyze how artificial intelligence algorithms and models can improve decision-making processes by incorporating probabilistic reasoning, optimization techniques, and risk analysis.
AI has had a significant impact on operational decision making across various domains.Li et al. (2020) discusses the impact of AI on accounting business, accounting theory, and the ability of accounting personnel, highlighting how AI technologies have improved efficiency and accuracy in accounting processes.As a result, this facilitates accelerated, adaptable, and streamlined procedures, yielding superior-quality products with enhanced levels of personalization.This, in turn, boosts manufacturing productivity and enables industrial expansion.(Schlick, 2014).

Analysing the Benefits of Artificial Intelligence in Production Processes
AI-based systems have the ability to analyze vast amounts of data, identify patterns, and optimize production workflows in the business world (Tariq et al., 2021;Wamba-Taguimdje et al., 2020;Min, 2009).With advancements in computing abilities, data-based AI, deep learning, cloud computing, and data management, AI has become a driving force for achieving operational excellence (Tariq et al., 2021).AI systems can improve automation, information flow, and transformation effects, leading to enhanced firm performance (Wamba-Taguimdje et al., 2020).
In supply chain management, AI can recognize business patterns, learn phenomena, seek information, and intelligently analyze data, resulting in improved decision-making processes and productivity (Min, 2009;Thatikonda, 2020).AI-powered machines can analyze large amounts of data and identify patterns that surpass human capabilities, leading to increased efficiency (Raju, 2023).Enabling automation in the gathering, transfer, and analysis of data grants managers enhanced access to a wealth of information, thereby promoting efficient and timely decision making.Moreover, incorporating automation and leveraging the availability of big data within a userfriendly decision support system facilitates effective adaptive management practices (Ditria et al., 2022).Overall, AI-based systems have proven to be beneficial in analyzing data, identifying patterns, and optimizing workflows for managers.
AI adoption in business has been shown to lead to increased productivity, minimized downtime, and improved quality control.The enhancement of machine computing capabilities serves as a significant driving force in achieving operational excellence through the utilization of AI (Tariq et al., 2021).Additionally, advancements in deep learning and cloud computing contribute to the integration of AI in operations, which can further enhance productivity and minimize downtime (Tariq et al., 2021).Organizations that adopt cognitive manufacturing practices, which leverage AI, benefit from enhanced knowledge management, improved product reliability, and increased productivity (Sira, 2022).AI's unique characteristics, like enhancing human labor and autonomous learning necessitate organizations to adopt novel approaches and models to fully harness the potential of AI (Plastino & Purdy, 2018).For marketing purposes, understanding consumer adoption of AIgenerated information is crucial for improving trust in AI recommendations (Kim & Giroux, 2021).AI adoption is also prevalent in e-commerce businesses, more than 90% of productive organizations are considering the adoption of enterprise-level AI (ThiDang & MinhNguyen, 2022).Overall, the adoption of AI in business has shown promise in increasing productivity, minimizing downtime, and improving quality control, but further research is needed to explore its social value and identify practical implications (Leszkiewicz et al., 2022). The

Leveraging Artificial Intelligence for Optimal Supply Chain Management
Similarly, in the field of supply chain management, the adoption of AI decision-making technology has the potential to drive the evolution of conventional supply chains into intelligent supply chains.This transformation enables intelligent management practices and fosters the advancement and enhancement of the entire supply chain ecosystem (Lei, Y. et al. 2023).
Several studies highlight the various applications of AI in different aspects of the supply chain, such as planning, prediction, purchasing, procurement, transportation, distribution, quality control, warehouse management, and inventory tracking (Hlyal, 2022;Patel, 2022).AI, coupled with automated digital recording systems, can facilitate analysis of large datasets for better conservation and management decisions.AI has the ability to leverage other innovative technologies such as digitization of patient records, and genetic data to enhance healthcare innovation (Arora, 2020).Integrating AI into digital healthcare systems can support clinical decision-making, manage chronic disease burden, and contain rising healthcare costs (Shinners et al., 2019).
AI enables the analysis of unprecedented amounts of digital data, opening up new avenues for theory generation and addressing associated challenges (Pagani & Champion, 2021).Furthermore, AI is being adopted across various industries, including in supply chains that leverage blockchain technology for traceability and intelligence (Karadgi et al., 2021) The use of AI in supply chain management is not limited to traditional supply chain domains; it extends to sectors like financial management, marketing management, and performance management (Naved, 2022).Moreover, AI and Big Data Analytics have the potential to significantly enhance supply chain resilience and resource management (Zamani et al., 2022).AI technology can also coordinate the earnings and environmental performance of supply chain enterprises through interactions with all participants (Jiang, 2023).Despite the popularity of AI in supply chains, there is a need for conceptual frameworks to categorize and review the subtopics of AI that can contribute to different subfields of supply chain management (Brintrup, 2020).The review of selected studies in this field offers valuable insights for operations managers and policymakers (Dhamija & Bag, 2020).Overall, these studies demonstrate the potential of leveraging AI to optimize supply chain management in various industries and domains.For example, in the pharmaceutical industry, the adoption of AI in supply chain management has been found to improve patient outcomes and drive the success of pharmaceutical companies (Guo, 2023).
AI possesses the capability to examine data, forecast demand, optimize logistics and transportation routes, and detect inefficiencies within the supply chain.(Mohsen, 2023).However, the adoption of AI in organizations is influenced by social and technical factors, and human insight is still required to effectively utilize AIsupported methods (Smit, D. et al, 2023).Additionally, AI-guided predictive analytics, supplemented by data analytics, can provide critical input for decision-making processes (Shaap, 2020).
Furthermore, AI can play a crucial role in optimizing supplier selection in agrifood supply chains, aiming to increase sustainability and resilience.By utilizing AI techniques, such as data analysis and pattern recognition, the selection process can be streamlined, and criteria for assessing suppliers can be analyzed and refined (Alcívar, A. Z., et al. 2020).
In the context of post-COVID-19 supply chains, AI play a crucial role in identifying, evaluating, and mitigating risks, while also offering valuable managerial insights.(Kanti, 2022).By interpreting and evaluating multidimensional data in dynamic situations, such as supply chain disruptions, AI can help evaluate alternative solutions (Gupta et al., 2022).Moreover, AI technology has proven to be a valuable asset in diverse sectors such as the food industry, where it aids in improving the quality and efficiency of restaurants, café's, online food delivery chains, hotels, and food outlets through the use of data science and fitting algorithms for sales prediction Kumar, I. 2021).Overall, AI can make a substantial contribution to minimizing the likelihood of supply chain disruptions and bolstering the resilience of the supply chain.

Enhancing Resource Management through Artificial Intelligence
AI is capable of analyzing data related to resource consumption, identifying inefficiencies, and proposing optimization strategies.This ability is supported by various research studies.For example, Liu & Ma (2013) conducted an analysis of the energy consumption structure of Shandong Province, finding a positive relationship between economic growth and energy consumption.Letaief et al. (2022) highlighted the resource-intensive nature of state-of-the-art AI systems, which can lead to latency, energy consumption, network congestion, and privacy concerns.According to Hu (2023), the focus was placed on the technical components of current machine learning algorithms that play a role in optimizing resource management and decreasing energy consumption.AI can also contribute to resource allocation and communication improvement, as demonstrated by (Victor, 2023).
Furthermore, AI can leverage data analysis to identify valuable waste streams and materials for recovery and recycling.Zakariyya et al. (2019) presented techniques to reduce the computational cost of running AI algorithms, while Nam et al. ( 2023) tackled the complex resource planning problem associated with AI workloads.These studies collectively highlight the role of AI as a powerful tool for analyzing data, managing resources, and optimizing processes (Porras & Daugherty, 2022;Sasikumar et al., 2022).The future of AI systems research is expected to focus on reducing resource consumption, managing transient resource availability, and optimizing resource usage in specialized hardware (Krishnan et al., 2019).However, resource planning for AI workloads remains a complex optimization problem that requires significant time and effort (Nam et al., 2023).Finally, once reliable data is available, AI and machine learning techniques can determine patterns and behavioral models of urban systems (Minerva & Crespi, 2021).

CONCLUSION
This study has explored the benefits of incorporating artificial intelligence (AI) into decision-making processes in operations, focusing specifically on its impact on production processes, supply chains, and resource management.Through an analysis of relevant literature, several key findings have emerged.
First, the integration of AI technologies in operational decision making offers numerous benefits.Automating data collection, transfer, and processing enables managers to access a wealth of information, leading to more timely and effective decision making.The use of AI-driven decision support systems with user-friendly interfaces promotes adaptive management practices and facilitates data-driven decision making.AI-driven systems have the capability to process immense volumes of data, identify patterns, and optimize production workflows.