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This paper's top-level goal is to provide an overview of research conducted in the many academic domains concerned with forecasting. By providing a summary encompassing these domains, this survey connects them, establishing a common ground for future discussions. To this end, we survey literature on human judgement and quantitative forecasting as w...
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... In other words, this method can be used as a basis for consideration in making decisions for forecasting cases with previous data. However, if a lot of previous data is available and meets the criteria, then forecasting with quantitative methods is more effective than qualitative ones [32]. Forecasting using quantitative methods has several requirements. ...
... No method is inherently superior, and its effectiveness depends on the context. Both approaches can synergize and complement each other effectively [30], utilizing a combined GIS, AHP, and VBD approach. VBD is utilized in this research as a decision-making procedure according to the flowchart model, as indicated in Figure 1. ...
Site selection is one of the main activities in technical system planning to achieve the best design and location of the power plant. Improper site selection methods tend to increase the construction cost, create difficulties in securing primary energy sources, and cause inefficient electricity distribution. The earlier civil site selection process using the scoring method adopted by several utility companies still had some disadvantages that required improvement. This study aimed to propose and test a civil site selection method based on the economic Multi-Criteria Decision-Making (MCDM) that combines Geographical Information System (GIS), Analytical Hierarchy Process (AHP) and Value Based Decision (VBD) simultaneously and based on the collaborative assessment of several engineers. The study investigated Kupang GEPP 40 MW with five alternative locations using the Expert Choice 11 tool to determine the weight of the criteria, alternative locations rating, and the weight of the cost estimate based on GIS data. The analysis revealed that only alternative 1 and alternative 5 are considered feasible. Alternative 5, Panaf, emerges as the most favorable site for Kupang GEPP with a value of 7.087. Further research has been suggested to include more detailed data for site selection.
... however, they were not utilised for specific reasons in favour of our human-centred Foresight approach. For instance, Quantitative Forecasting Models (Zellner et al. 2021), while they are valuable for quantitative predictions, they often lack the human-centred perspective, crucial in understanding the nuanced needs and behaviours of home care recipients. also, ethnographic Research (Whitehead 2005), which is an excellent methodology for understanding current user experiences but was deemed not be as effective for forecasting future technological impacts. ...
The rapid expansion of home health care has raised many unresolved issues and will have far-reaching consequences that can only be overcome with a holistic approach to help build and use collective intelligence in a structured, systemic way to anticipate developments. In this frame, the set of issues covered by the human factors research field will significantly impact the safety, quality, and effectiveness of home health care. However, only with a gaze of strategic foresight will we be capable of exploring, anticipating, and shaping the future. A group of researchers from the Italian Society of Ergonomics and Human Factors (SIE) has developed a road map to help all the stakeholders involved in this process.
... One key benefit is the reduction of subjectivity inherent in human interpretation. By directly accessing neural signals, the analysis becomes more objective and less reliant on the individual examiner's judgment, minimizing the potential for bias [10]. ...
Handwriting Detection System that integrates Brain-Computer Interface (BrainNet) technology with advanced Artificial Intelligence (AI) algorithms. The system leverages the power of neural networks and deep learning to accurately identify and authenticate individuals based on their handwriting patterns. The BrainNet interface allows for direct communication between the human brain and the computer system, enabling a more natural and seamless interaction for handwriting input. This innovative approach not only enhances user experience but also opens new avenues for biometric authentication by utilizing the unique neural signatures associated with handwriting. Our AI algorithm employs deep learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to analyze and recognize intricate patterns within the handwriting data obtained through BrainNet. The model is trained on diverse datasets to ensure robust performance across various handwriting styles and individuals. The proposed system include real-time handwriting recognition, adaptability to individual writing variations, and a high level of accuracy in user authentication. The integration of BrainNet technology ensures a more intuitive and user-friendly interaction, making the system accessible to a wide range of users.the effectiveness of the Handwriting Detection System, showcasing its potential for secure authentication and document verification applications. The combination of BrainNet and AI algorithms establishes a synergistic relationship, pushing the boundaries of what is achievable in the realm of handwriting recognition and biometric authentication. The evolving landscape of human-computer interaction, offering a novel perspective on the integration of brain-machine interfaces with artificial intelligence for enhanced handwriting-related applications. The proposed system holds promise for applications in security, finance, forensics, and other domains where reliable user authentication and document verification are paramount.
... All forms of human behaviour consist in the implementation of various decisions, taking into account uncertainty. (Zellner et al., 2021) Due to the nature of uncertainty, every decision is then weighed down by the risk aspect. Being able to correctly use vague data opens up the possibility of applications in key spheres, e.g. ...
... Research pertaining to the elicitation of subjective probabilities and values indicates that debiasing is possible and that certain elicitation approaches give normatively superior outcomes than others (Zellner et al., 2021). The eliciting values, including those on eliciting subjective probability, have relied on non-computerized solution such as the short-term diversity training whereby providing the users on the exacerbated by overt conflict within the organization. ...
In every industrial enterprise, choices are crucial since they may decide success or failure. Consequently, the decision-making process is crucial to lean production. However, further study is necessary to appreciate the relevance of Decision Support System (DSS) in the lean manufacturing business. This study exposes the classification of DSS, its range of applications, and suggestions for future research based on two key factors, namely, behavioral, and technological problems. This study makes considerable use of academic databases such as Scopus, Google Scholar, Emerald Science, and ResearchGate. In total, 50 papers have been identified. Each result, criterion, and categorization are supplied and classified. It is proved, from the literature treasure that DSS has the highest contribution on the Evaluation, 38%, following by, Mixed Application 26%, and others. Henceforward, this paper discussed the Behavior Aspects of users' confident, prejudice and discrimination issues, and customization. From the Technical issues, the discussion was on the technological capabilities, software language, parameters aggregate, user interface, market culture and social norm, giving out criticism for the future development of DSS-Lean, especially for the manufacturing industry. The authors believe that this study will lead the way for future research in the same field based on its primary findings.
... Whereas the impact of AI on business planning and predictive analytics has been investigated in literature streams (Zellner et al., 2021) considering Advanced Forecasting Methods (ARIMA), as shown in Fang et al., 2022, or Artificial Neural Networks (see Abiodun et al., 2018), there is no evidence, to the authors' best knowledge, of any link between the traditional business planning (AI-sensitive) and the dynamics of network theory forecasting, with its new nodes and edges created by AI. In other words, these literature streams explain how AI impacts the prediction of sales, etc., but not how it affects future revenues (and OPEX). ...
The objective of this study is to determine the impact of artificial intelligence (AI) on the earnings before interest, taxes, depreciation, and amortization (EBITDA) of firms as a proxy of their financial and economic margins by improving revenues and minimizing expenses. This impact is positive on the market value and scalability by improving the economic and financial sustainability of companies. The methodology is based on a business plan that considers the savings obtained by a traditional firm implementing AI. Specifically, a sensitivity analysis will demonstrate that AI savings impact key parameters, leading to economic and financial sustainability. Additionally, a mathematical interpretation, based on network theory, will be produced to provide and compare the added value of two ecosystems (without and with AI that adds up new nodes and strengthens the existing ones). The main contribution of this paper is the combination of two unrelated approaches, showing the potential of AI in scalable ecosystems. In future research, this innovative methodology could be extended to other technological applications.
... leading edge of this forefront because it combines expert opinion and algorithmic procedures, a suggestion found in Zellner et al. (2021). Before turning to a description of the study and our results, however, we first describe our major objectives, which is followed by a discussion of key concepts and terminology in regard to population forecasting. ...
... Similar observations apply to extrapolative methods, including simple ones such as those that are linear or exponential and those that are more complex, such as ARIMA (Auto-Regressive Integrated Moving Average) and other forms of time-series models; they also apply to structural models (Smith et al., 2002(Smith et al., , 2013. In regard to subjective and quantitative methods, Zellner et al. (2021) find that neither is universally superior and in regard to simple and complex methods, Green and Armstrong (2015) find that complex methods provide no more accuracy than do simple methods. ...
This case study shows how GIS and Expert Judgment can be used to develop small area population forecasts in the United States. It starts by organizing 2000 and 2010 block group population data into the 2020 block group geography and then examines 2020 indicators used to evaluate the effect of Differential Privacy on the 2020 population data. Preliminary population projections to 2050 are then generated by averaging the results of three standard small area projection methods. Using local expert judgment, GIS overlay maps and satellite imagery in a virtual environment, the 301 block groups of Greenville County, South Carolina were classified into seven categories of future population change. These categories were then applied to the preliminary projections to generate informed forecasts. Following this step, the sums of the BG results were then compared, respectively, to independently generated county population forecasts for 2030, 2040, and 2050. At this point, 25 BGs were selected for additional review, which resulted in a final set of forecasts. We find that the increase of 152,840 people in the year 2050 spread over all of the 301 census block groups in going from the preliminary projections (675,626) to the final informed forecasts (828,467) is largely generated by these same 25 BGs, which expert judgment determined were currently poised to “take off” in terms of population growth. Having this much change generated by such a small number of BGs is consistent with findings elsewhere.
... At present no argumentation-based systems aimed at legal reasoning have been deployed in practice, making law a particularly urgent and promising target area. Another emerging area of applications is judgmental forecasting [342], a decision-making approach to situations when statistical methods are not applicable. A recently proposed variant of an argumentation formalism, a forecasting argumentation framework [186] is guided by forecasting research and aims to support argumentation-based forecasting. ...
Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade.
... Therefore, marketing strategies must be formulated based on real data to be able to predict accurately based on future market development trends [5]. Quantitative forecasting eliminates most subjective factors and no longer relies heavily on the forecaster's individual ability [6]. Good sales of UMKM products are one of the benchmarks for realizing the success of these contributions. ...
Micro, small and medium enterprises (UMKM) is one of the important aspects to support the improvement of the economy in Indonesia. Zee Mart’s business is one of the UMKM shop in Pematang Siantar City with sales and purchase transaction activities for supplies. The purpose of this study is to predict the sales of Zee Mart store goods in the coming month using the adaptive response rate single exponential smoothing (ARRSES) method. ARRSES is a method with the advantage of having two parameters, alpha and beta, where alpha will change every period when the data pattern changes. The dataset obtained will be pre-processed through data selection, cleaning, and transformation. The best beta is determined based on the level of accuracy calculated using the mean absolute percentage error (MAPE). Model development using the ARRSES method will produce forecasting percentages and errors for each product using MAPE. The number of sales data is 23,092 before preprocessing and 23,021 after pre-processing, with the total quantity of goods sold being 149,764 of 1,492 products. The results obtained using sales data 23,021 show the lowest MAPE value of 9.85 at the best beta of 0.6 with the highest accuracy of 90.15% and the model is implemented into a web interface.
