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The Importance of Creating Artificial Intelligence Supported Future Scenarios in Decision Making Processes

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Artificial intelligence technologies are rapidly developing and having a major impact on the business world. Decision-making processes play an important role for the success of an organization. However, in today’s business world with its complexity and uncertainty, it becomes difficult to manage decision-making processes. At this point, creating future scenarios supported by artificial intelligence and working on different scenarios helps businesses to be more prepared for uncertainty.

Artificial intelligence-supported scenarios can be utilized across various sectors and fields of work. AI enables businesses to analyze past data, predict trends, and consequently work on future scenarios to make more informed decisions. The significance of future scenarios lies in identifying risks and opportunities in advance, adapting to future changes, and being proactive in competition. By evaluating potential developments, shaping your business strategy, you can gain a competitive advantage and make more reliable decisions.

Qualitative methods were employed in the research. Interviews were conducted with managers from 6 different professional groups (software, biomedical, public, construction, university, e-commerce). Data was collected and analyzed using semi-structured interview forms consisting of 4 questions. When the findings were evaluated, no concerns or negative expressions regarding the use of artificial intelligence were expressed. Except for public institutions, everyone has AI in their planning. Each sector believes it is important. No negative concerns were expressed. The prominent concepts in the findings are: Speed, big data, gaining competitive advantage, personalized customer experience, risk analysis, cost advantage, technology adaptation, optimization, accurate and fast situation detection, efficiency, etc.

It is thought that the research will create significant awareness for businesses in the turbulent period of the 21st century, where uncertainties are greater than ever. Despite all the positive aspects, AI-supported decision-making processes also carry some risks. The most prominent risks include the applicability of AI-supported scenarios, security concerns, the existence of ill-trained AI models, ethical issues and data privacy.

Review of The Financial Health of Bank During Increasing Foreclosed Assets and The Application of Analytic Hierarchy Process (AHP) in Selecting Strategies to Reduce Them

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PT Bank Laju International Tbk (the Bank) has struggled with its increased foreclosed assets, particularly those originating from mortgage loans, which could affect its financial health. The research assessed Bank’s financial health using Risk Based Bank Rating (RBBR) framework by analysing key financial ratios and investigating the root causes of rising foreclosed assets through Fishbone and Pareto analysis. Key factors include customer income instability, slow progress in selling foreclosed assets, and lack of insurance protection during economic downturns. To select effective strategies for managing foreclosed assets, the research utilized Analytic Hierarchy Process (AHP), a Multi-Criteria Decision-Making method, to evaluate multiple alternatives based on various criteria, which were derived from Focus Group Discussion (FGD) with Bank’s officers, who directly involved in mortgage loans and foreclosed assets activities, also reference with Bank’s SWOT analysis. The research evaluated three alternatives strategy:

  1. Protection Insurance for Customer’s Mortgage Loans.
  2. Actively Partnering with property agents to accelerate sale of foreclosed assets as-is.
  3. Collaborating with property agents to renovate foreclosed assets.

The result showed that despite the increase in foreclosed assets, the Bank remains financially sound, with key financial ratios within acceptable regulatory limits. AHP analysis revealed that alternative 1 stands in first place to be prioritized, followed by alternative 2, however between both alternatives indicates a very small difference in prioritization weight, which means that both strategies are important to be executed and prioritized. Proposal to implement both alternatives in parallel would be a very effective strategy, where alternative 1 conduct as Preventive Strategy in preventing customer’s mortgage loans defaults, while alternative 2 as Corrective Strategy in lowering foreclosed assets volume. Alternative 3 acts as a complement strategy, where renovating foreclosed assets could facilitate quicker sales, however it’ll take longer time for renovation itself.

Selecting Outdoor Wireless Solution for Bara Prima Borneo Using Analytic Hierarchy Process

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The advancement of wireless communication technology is critical for improving the efficiency, safety, and productivity of mining operations. This study focuses on evaluating and recommending an optimal outdoor wireless network solution for Bara Prime Borneo (BPB) mining operations in East Kalimantan, Indonesia, using the Analytic Hierarchy Process (AHP) for decision-making. The current Wi-Fi infrastructure faces significant challenges, including limited coverage, interference, and scalability issues, which hinder its effectiveness in the demanding mining environment.

To address these challenges, the study employs a comprehensive approach to identify stakeholder expectations and value perceptions, explore alternative wireless network designs such as Private LTE and Kinetic Mesh, and systematically select the most suitable solution using AHP. Through discussions with subject matter experts and secondary data collection, the study outlines the strengths, weaknesses, opportunities, and threats (SWOT) associated with the existing Wi-Fi network and potential alternatives.

Using AHP, the study prioritizes various criteria such as coverage, reliability, cost, scalability, and security. The recommended design aims to bridge the gap between current capabilities and future needs, ensuring robust and extensive network coverage that supports various digital applications essential for modern mining.

By selecting the proposed solution using AHP, BPB can achieve a more reliable and scalable wireless network, enhancing overall operational efficiency and safety while meeting the evolving demands of its mining operations.

Strategic Decision-Making: Implementing Artificial Intelligence for Customer Experience in XYZ Electricity

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This case study outlines the challenges in resolving customer complaints at XYZ electricity provider, where the industry achieves only 89.16% against a 100% service level agreement, leading to poor customer experience (CX). The objective of this paper is not only to identify the root causes of poor CX and validate artificial intelligence (AI)’s potential role as a solution, but also to pioneer the identification of critical success factors (CSFs) and strategic areas for AI implementation, leveraging computational ratings to enhance decision-making processes. This research employs comprehensive data collection methods, including primary data from interviews and workshops involving 300 participants and secondary data from observation and literature studies. It utilizes an integrative strategy framework (ISF) to strategically synthesize internal and external analyses. Additionally, it ranks critical areas for AI implementation using the analytic hierarchy process (AHP) based on pairwise judgment and Likert scale surveys from ten experts. The most significant findings reveal that direct impact on customers, at 28.54%, is the strongest CSF, while customer service, 14,63%, is the most impactful implementation of AI in the XYZ to fix poor CX. A pilot project on customer service can improve CX, revenue, and cost savings. The authors suggests that another researcher implement and evaluate AI in various businesses and specific client categories.

Application of the Analytical Hierarchy Process (AHP) for Innovative Technological Projects Evaluation and Prioritization in an FMCG Company

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The FMCG industry is known for its dynamic and competitive nature. To thrive in this business sector, companies must possess the ability to swiftly adapt to the market demand changes, continuously enhance operational efficiency and drive innovation. Those abilities are crucial for maintaining competitiveness and ensuring long term viability of the company. Over the past few decades, technology along with its advancement has emerged as a factor that disrupts the ecosystem of various industries by reshaping the way businesses operate and interact with the customers. In Indonesia, many organizations including FMCG companies have continuously embraced and adopted emerging and innovative technologies within their business operations. Although it offers various benefits for the companies, the execution process has usually encountered various challenges which causing the implementation projects to experiencing delays, especially during the decision-making process. This was also the case in one of the largest FMCG companies in Indonesia due to varying interpretations of project importance, as well as the absence of clear prioritization criteria and an unorganized decision-making process. In order to address the issues, the organization plans to develop a decision-making framework that harmonizes diverse stakeholder perspectives related to project importance based on a number of key criteria while also analyze the benefit and impact provided by the implemented technologies through the application of the Multi Criteria Decision Making (MCDM) approach using the Analytical Hierarchy Process (AHP) framework. There are three technology innovation projects to be analyzed and assessed in this research: AGV Implementation, and Digital Warehouse Management System, and Universal QR for Traceability. In order to evaluate the projects, eighteen criterion which are divided into eighteen sub criteria that has been established through Secondary Data Collection and Focus Group Discussion (FGD) process. The data were processed by leveraging the systematic decision-making structure provided by the AHP framework. The research yields two primary outcomes: a structured decision-making framework and a project prioritization scheme intended for application at the organization. The findings of the research highlight the critical role of structured decision-making in navigating the complexities of evaluating and prioritizing innovative technological projects, while also proposing a scalable model that can be repeatedly utilized by the company in the project evaluation and prioritization contexts.

Decision Making to Choose Communication Network System for Teleremote Dozer Operation Using Analytic Hierarchy Process

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One type of heavy equipment is a mining material pushing tool called a dozer. Dozers generally work in areas near cliffs that are prone to landslides, where these areas can be classified as dangerous areas. New technology is needed to increase the safety of dozer operators from the threat of danger when operating a dozer. Teleremote dozers are one method that can reduce the risk of accidents that can happen to dozer operators. Instead of operating the dozer from inside the cabin, the operator operates the dozer via a remote control device.

There is an important aspect in operating a teleremote dozer, namely the need for a signal that will transmit data and commands from the remote control to the dozer unit operating in the field. Good and uninterrupted signal quality is the main key to good teleremote dozer operations with minimal risk.

The wireless signal network system currently used by companies will enter its obsolete period. However, the old system was tough and not easily damaged. There are strategic options for supporting the signal network for teleremote dozer operations: maintaining the old network system, replacing it with a new one, or using the old and new systems in a hybrid manner. Because there will only be one strategy chosen, a decision must be made. The SWOT AHP method is used for decision making regarding the strategy to be taken. The results of the AHP SWOT will produce a strategy, which will become a benchmark for making subsequent alternatives. Determining alternative types of network systems to support teleremote dozer operations will use the AHP method.

Predictive Analysis for Personalized Machine: Leveraging Patient Data for Enhanced Healthcare

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This research explores predictive analysis for personalized machine: leveraging patient data for enhanced healthcare. By leveraging the power of information and analytics, the healthcare industry can be driven towards a more patient-centric, proactive model that enhances outcomes and improve the overall quality of care. The objectives of the study are to: determine the significance and challenges of predictive analytics in healthcare, ascertain the data analytics techniques used in healthcare to enhance patient care, find out how predictive analytics can be applied for enhanced healthcare, and determine the ethical considerations associated with healthcare predictive analytics. This study employs the case study approach and experimental design. The study analyzes case studies of real-time deployment of predictive analytics models in healthcare centers, examines how these models enhance the healthcare delivery in those centers. Experiments were also conducted to understand how predictive analytics works. The C4.5 learning algorithm was employed to predict the presence of chronic kidney disease (CKD) in patients and differentiate between those not affected by the condition. The C4.5 classifier shows reasonable strength, evident in the large number of rightly classified occurrences (396) and a low misclassification of only 4 occurrences. This is further demonstrated by a low error rate of 0.37, as shown in table 5. The prevalence of this algorithm is emphasized by the large value of KS (0.97), indicating the classifier’s ground-breaking accuracy and performance. The performance of C4.5, featured by its minimal execution time and accuracy, puts it as a decent classifier. This characteristic makes it specifically well-suited for application in the healthcare sector, particularly for tasks involving prediction and classification. The application of data analytics methods for predictive analysis holds significant benefits in the health sector, as it gives us the power to predict and address potential threats to human health, covering different age groups, from the young ones to the elderly. This proactive method enables early disease detection, helping in timely interventions and contributing to better decision-making.

 

Enhancing Community Participation in Corporate Social Responsibility Activities at PT. Prima Bara Nusantara through Improved Decision-Making Process

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Corporate Social Responsibility (CSR) is crucial for companies that have a substantial ecological footprint, such as coal mining in Indonesia. PT. PBN strives to achieve a harmonious combination of responsible behaviors and economic viability. However, attaining this equilibrium necessitates strong community engagement to alleviate negative effects and provide beneficial contributions at the local level. This study focuses on the underexplored issue of poor community participation in CSR initiatives within Indonesia’s coal mining sector. Although PT. PBN has made substantial investments in environmental and community activities, recent evaluations indicate a substantial disparity between stakeholder expectations and the level of actual participation in activities. Gaining insight into the factors contributing to this disparity is vital for the effectiveness of CSR endeavors, as the support of stakeholders and active involvement of the community are crucial for ensuring social sustainability and enduring stability. The study utilizes analyzed data from interviews conducted with both internal and external respondents. It uses problem tree analysis in order to uncover the root causes of low community participation. Focus group discussions are used to delve deeper into the objectives of Value-focused Thinking (VFT) and help determine which alternative solutions should be chosen. The integration of VFT with Analytical Hierarchy Process (AHP) aids decision-making by recognizing the criteria and sub-criteria used to evaluate solutions based on their values. The findings emphasize identifying skill gaps and providing formal acknowledgment to improve the sense of responsibility and involvement of the community in CSR initiatives. This is in line with PT. PBN’s commitment to its stakeholders and the sustainable development of the community in the long run. In the end, decision-makers give the utmost significance to the strategy of licensing and training, considering its long-term impact, effectiveness, resource availability, and ease of control.

Corporate Renewable Energy Procurement Prioritization Using Analytic Hierarchy Process (AHP) by Energy Service Company Perspective in Response to COP26

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To be able to participate in the COP26 commitment to work together to make clean and sustainable solutions, making clean power is the most affordable and reliable option for Indonesia to meet its power needs efficiently by 2030. The purpose of this research is to create a prioritization of prospective renewable energy projects from the point of view of a service company as an oil and gas company’s contractor. These include Indonesia’s prospective projects, which are geothermal or carbon capture and utilization, and decarbonization or reduction emission from existing services. This research also decides criteria that important for service company in providing renewable energy. A literature study, interviews with key decisionmakers in the company, questionnaires to company experts, and a questionnaire to practitioners in the industry were done to determine the criteria and prioritization technique. Four criteria of governance, marketing and sales, financial and project management were found to support the prioritization process using the Analytical Hierarchy Process (AHP). Among the nine sub-criteria, the sub-criteria with the highest global weights are profitability, cashflow, and service quality assurance.