This study aims to obtain the results of employee performance appraisal through a decision support system (DSS) on situational leadership with work motivation as a connecting variable to employee performance. The use of this type of quantitative research with the number of respondents with a saturated sample technique will make this research more relevant and objective in showing the re-sults. Data analysis using smart PLS 3.0 which resulted in the finding that the decision support sys-tem (DSS) in situational leadership had not been able to contribute to improve employee perfor-mance. It can be concluded that the role of technology in semi structured decision making must be based on sufficient data to help improve employee motivation and performance. In fact, leadership is still used to make unstructured decisions without paying attention to the data stored in the perfor-mance system. Performance indicators are not well-paid attention so that the value generated from this study shows negative results.

The palm oil industry has a strategic role in economic development in Indonesia, especially in alleviating poverty and creating other businesses that can support the industry. Operational activities in the palm oil industry are closely related to environmental issues (deforestation, land-use change, and air pollution) and social conflict. The certification program is an effort for the palm oil industry to implement sustainable development. The certified palm oil industry will increase industrial profitability in the long run to increase investor interest in the future. The decision to choose palm oil industry stocks that carry out sustainable practices and generate maximum returns is an exciting issue, but how investors can choose the right stocks and the minimum risk level. This study aims to apply the decision-making model to choose the optimal stock in the palm oil industry, which involves sustainable certification and risk criteria. The method used in this study was the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) based on the Indonesia Stock Exchange (IDX) data. Determinants of stock selection decisions from previous research are considered criteria for decision making. Through the PROMETHEE method, a list of the rankings of the oil palm industry shares can be generated. The sustainable certification and risk criteria can be used as a reference for relevant stakeholders such as investors. Further studies need to be developed by adding non-financial criteria in the firm and developing the criteria to differentiate each other.

The development of small and medium enterprises (SMEs) becomes the benchmark and leading position for developing countries’ economies. The digital transformation demands strategies, desires, and awareness of Information Technology (IT)-based market players and investments. Despite the transformation of a digital business platform, many SMEs have stumbled in the middle road. Therefore, this study aimed to determine priority indicators in assessing SMEs’ readiness towards digitalization and evolving a readiness model for SMEs based on the Decision Support System (DSS) approach. Multiple stakeholders’ viewpoints, particularly regarding academicians, governments, investors, market places, and SMEs’ business actors as targeted respondents, were scrutinized quantitatively and qualitatively to verify the proposed factors. The priority weights of factors have been examined from economic and IT perspectives and derived through deploying the Fuzzy Analytical Hierarchy Process (F-AHP) method. This study reveals the rank of measures necessary to assess the readiness of the digital revolution of SMEs. Transaction preparedness in SMEs’ cultural, educational, financial, and technological infrastructure views grows into the principal components during this assessment with 0.30 of vector value, accompanied by marketing and micro-environment at 0.24, management at 0.20, macro-environment at 0.03 and business activities at 0.02, respectively. For the recommendation purposes, the rubric segmented SME fitness into three levels, low, middle, and high performance. The prototype system DSS-SMEsReadiness was then evolved in order to simplify the adoption of the DSS method in the SME performance measurement model. The software analysis demonstrates that this application would assist decision-makers to ascertain SMEs’ readiness to digitalize. The future recommendation provides SMEs and stakeholders with knowledge transfers and acclimatization for taking the appropriate option about their business strategy, management resources, skills, and assistance programs for SMEs. This model attempts to reduce SME digitalization disruptions and achieve a digital business’s growth and sustainability in a nutshell.

Systematic literature review in supply chain and decision support system, in general, have been rapidly performed during the last decade. However, the studies on the epistemological progress of decision support system related to the supply chain are still lacking. This study intended to provide comprehensive information on the trends, methodologies and the applications on different sectors and platforms used by scientists for building their decision support systems in supply chain. We used different keywords to collect the raw data based on articles published in well-known journals in the world to select the eligible studies which furthermore assembled. The data were processed by using bibliometric tool in VOSviewer and Microsoft Excel. The results of this systematic review give some key learning of the trends on the use of decision support system on smoothing the flow of supply chain and the logistic performance in the last decade and also provide a background for future research related to the fields.

Airport Collaborative Decision Making (ACDM) flight scheduling plans rely on accurate predictions of both optimal and reliable aircraft turnaround times, which is one of the most difficult things to complete. In order to account for the effects of randomness and fuzziness on the turnaround process duration, this paper deals with transforming the probability distribution of well-fitted sub-processes into a cumulative density function, which is equivalent to the fuzzy membership function (FMF), and then using goodness-of-fit to determine the fuzzy membership grade of each turnaround sub-process. The turnaround time is calculated using the fuzzy critical path method (FCPM), which is a combination of the Critical Path Method (CPM) and fuzzy set theory. In order to verify this estimate, we created the FMF using historical data and compared it to turnaround times based on FCPM. A linear regression model is used to examine the relationship between arrival delays and the FCPM-based turnaround process. We also use historical data to generate fuzzy sets of different arrival delays using Frankfurt airport data from the summer of 2017 and conclude that delays are positively correlated with the FCPM-based turnaround process.

Identification of critical traffic conditions based on analysis of traffic variables in a route allows the planners to react timely under such conditions. In this paper, a Decision Support System (DSS) is proposed to determine traffic condition in a route based on the data traffic variables received online in a route such as speed and flow. A knowledge based system is established, which consists of a series of rules (If-Then) to identify the critical traffic conditions when the traffic variables in a route decrease or increase as compared with the previous means, significantly. Statistical control charts are used to identify significant deviations of traffic variables from their means, which are usually caused by events such as accidents. The data for traffic variables, namely flow and speed in 60 hours was collected from one of the main highways of Tehran (Iran’s Capital) in peak hours (17 and 18). 35 observations were used to design control limits for two traffic variables, namely flow and speed and the remaining 25 observations were used to monitor traffic conditions in the future (execution of phase 2 of statistical control charts). The results of traffic monitoring in the intended route indicate that the decision support system has a good performance to identify critical traffic conditions.

Decision support system (DSS) normally consists of a set of related computer programs and the data required to help with analysis and decision-making within an organization. Many software vendors claim their decision support, analytics or business intelligence applications may provide a competitive advantage by providing different facilities such as business intelligence tools, business performance management software, data mining tools, etc. This paper presents an empirical investigation to determine different DSS factors influencing on development of export activities on selected firms which are active in Tehran Stock Exchange. The study designs a questionnaire consists of 35 questions and distributes it among 211 randomly selected managers who were involved in export activities. Cronbach alpha has been calculated as 0.817, which is well above the minimum desirable level. Using principle component analysis, the study has detected five factors including internal resources, management approach, quality of data, efficiency of data and organizational approach, which influence the most on export activities.

An innovative price plan monitoring and advisory system simulates subscriber usage consumption for offering suitable price plan. The aim of this paper is to develop the decision support system by using Statistical Process Control (SPC) to identify subscriber usage behavior and provide critical visibility into subscriber consumption to detect their inappropriate usage especially in exceeding usage. To explore subscriber usage behavior, a forecasting model and a regression is employed to identify related factors and predictive usage model. The innovative price plan monitoring and advisory system has been verified and validated with one of the largest telecommunication company in Thailand. Using decision support system with effective control chart and real subscriber behavior pattern help mobile network operator grow their revenues and profits by offering an appropriate price plan as well as improve subscriber experience with more flexible choice to meet their individual usage consumption needs.

Considering the importance and extensive range of decision-making, scientists from various fields have had many discussions on this issue. Various models have been proposed to facilitate decision-making and have had much utilization. In many site selection problems, multiple objectives must be obtained, simultaneously. This study uses a mathematical model to select a suitable location for the refinery in the multi attribute environment. The proposed model uses a large amount of qualitative and quantitative information in the frame of multi objective functions for the first time in the refinery site selection and is flexible enough to use decision makers’ opinions in order to achieve goals. For this reason, after a brief overview of the selected area characteristics, using analytic hierarchy process (AHP) for weighting the criteria, a mathematical operation research model is proposed to determine the best alternatives.

Hospital departments are continuously challenged to allocate their limited resources—beds, nursing staff, and operating room (OR) time—among patients with different clinical priorities on the waiting list. This paper presents a solution to this intricate planning problem by constructing a novel Integer Linear Programming (ILP) model to schedule elective patient admissions optimally. The goal is to increase the total priority score of the patients admitted, where the score indicates clinical urgency and waiting time, while at the same time honoring the restrictions on bed capacity, nursing hours, and OR availability through a multi-day planning horizon. The model uses binary decision variables for patient admission as well as pre-processed parameters to map the individual patient resource consumption over the time they are expected to stay in the hospital. We showcase the model's application through a realistic scenario with synthetic data. The outcomes reveal that the suggested framework not only effectively creates an optimum admission timetable but also considerably enhances the use of vital resources when compared to a first-come-first-served standard. The ILP model is not only potent but also clear and just decision-support tool for hospital management, according to the study's findings. By assigning priority to clinical criteria and making patient access more equitable, it provides a data-guided way to improve operational productivity, as well as to decide the trade-offs that come with the limitations of healthcare facilities in terms of resources. Besides, the model is computationally capable of meeting the needs for the inclusion of extra real-world constraints and adjustments.