Determination of Business Intelligence and Analytics-Based Healthcare Facility Management Key Performance Indicators

The use of digital technologies such as Internet of Things (IoT) and smart meters induces a huge data stack in facility management (FM). However, the use of data analysis techniques has remained limited to converting available data into information within activities performed in FM. In this context, business intelligence and analytics (BI&A) techniques can provide a promising opportunity to elaborate facility performance and discover measurable new FM key performance indicators (KPIs) since existing KPIs are too crude to discover actual performance of facilities. Beside this, there is no comprehensive study that covers BI&A activities and their importance level for healthcare FM. Therefore, this study aims to identify healthcare FM KPIs and their importance levels for the Turkish healthcare FM industry with the use of the AHP integrated PROMETHEE method. As a result of the study, ninety-eight healthcare FM KPIs, which are categorized under six categories, were found. The comparison of the findings with the literature review showed that there are some similarities and differences between countries’ FM healthcare ranks. Within this context, differences between countries can be related to the consideration of limited FM KPIs in the existing studies. Therefore, the proposed FM KPIs under this study are very comprehensive and detailed to measure and discover healthcare FM performance. This study can help professionals perform more detailed building performance analyses in FM. Additionally, findings from this study will pave the way for new developments in FM software and effective use of available data to enable lean FM processes in healthcare facilities.

Multicriteria Visual Approach to the Analysis of Water Quality—A Case Study of the Tisa River Basin in Serbia

The aim of this research is to provide the assessment of water quality with a wider scheme of interrelations between the water quality parameters and locations using a reliable visual approach of multicriteria PROMETHEE and GAIA methods. The case study of one of the largest and regionally most important catchment areas on the territory of the Republic of Serbia—the Tisa River Basin—was therein used. The analysis of water quality included scenarios for warm (summer), cold (winter), and average annual period. A partial and complete ranking of locations according to the quality of water was performed by applying the PROMETHEE method and expanded afterward by GAIA method analysis to point out critical locations with endangered water quality (M6, M4, and M11). Identified locations were then investigated in more detail using spider web graphs that revealed water quality variables of concern (PO4-P and N) and indicated the causes of its occurrence. The obtained results are in accordance with the results of physical and chemical tests that are regularly conducted by the official government agencies for environmental protection and the reports that are presented to the public. The presented approach can easily be applied to any water body to point out both the locations with reduced water quality and the specific parameters (causes) that affect the reduction of water quality at these locations, thereby enhancing and strengthening usual water quality assessments as well as water resources management in general.

Sistem Pendukung Keputusan untuk Evaluasi Tingkat Kesejahteraan Masyarakat Menggunakan Metode PROMETHEE

Salah satu faktor tercapainya kesejahteraan masyarakat yaitu rendahnya tingkat penduduk miskin. Pemerintah berperan penting dalam mensejahterakan masyarakat dan pengentasan kemiskinan. Seleksi tingkat kesejahteraan masyarakat adalah salah satu masalah yang memerlukan keputusan yang tepat agar bantuan disalurkan kepada masyarakat yang membutuhkan tepat sesuai target. Oleh karena itu, dibangun sistem decision support menggunakan metode Promethee. Data penelitian berupa 220 data sampel keluarga dan melibatkan 15 kriteria dalam mengevaluasi tingkat kesejahteraan masyarakat bersumber pada Survei Sosial Ekonomi Nasional oleh Badan Pusat Statistik Provinsi Kalimantan Timur. Sistem yang telah dibangun memberikan output berupa urutan prioritas kesejahteraan masyarakat yang dapat dijadikan pertimbangan bagi pemerintah atau pihak terkait dalam penyaluran bantuan agar tepat sasaran Abstract One of the factors in achieving community welfare is the low poverty level. The government has an essential duty in the welfare of society and alleviating poverty. The evaluation of the welfare level is one of the problems that require the right decision so that the social assistance provided to people in need can be right on target. The study aims to deploy a system that utilizes the Promethee method for decision-makers. There is 220 family as the sample data evaluated involves 15 criteria for assessing the community welfare level sourced from the National Socio-Economic Survey by the Statistics of East Kalimantan Province. The decision support system built was able to result in priority order of community welfare level so that it could be a consideration or reference to the government or related agencies in distributing aid to make it right on target.

A Linear Programming Based DEA-PROMETHEE Approach for Performance Evaluation

Companies follow their objectives with some critical success factors (CSF), and they know their bottlenecks and strong points. This provides decision support for them, but this method ignores overall performance and ranking issues. In this study, a comprehensive methodology is recommended to find out an effective solution to the performance evaluation problem for making strategic performance management. Two methods are used from different areas as a framework. To select the higher-performing departments, Data Envelopment Analyses (DEA) is used as a linear programming-based main method. Moreover, a Multi-Criteria Decision Making (MCDM) method is proposed, Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), to increase the discrimination power of DEA and eliminate undesirable results because of determining weight bounds. These two methods are combined, and a comprehensive solution model is presented in the study. In the end, a case study is given for a real-life example, an integrated DEA-PROMETHEE method is applied to the case. When the case results are examined, the proposed model produces more logical weight values and better results.

An integrated approach to site selection for a big data center using PROMETHEE-MCGP methodology

In today’s economy, information technology (IT) is vitally important, and the increasing use of the Internet, telecommunications services, and internal IT networks in organizations have led to rapid growth in the demands on big data processing. In general, site selection is a fundamental part of the design of a big data center (BDC), and a poor site decision can affect the sustainability of the facility. To construct a comprehensive assessment framework for a BDC, the following three categories of indicators are determined based on the “Specification for Design of Data Center” in GB50174-2017 of China: economic factors, natural climate environment factors, and energy resources factors. After explaining the rationality of choosing these indicators in detail, an integrated method that combines the multi-criteria decision-making (MCDM) method and the multi-choice goal programming (MCGP) model is proposed. The proposed approach uses two phases to conduct the decision procedure. First, the preference ranking organization method for enrichment evaluation (PROMETHEE) method is applied to evaluate the economic factors. Then, the evaluation results are added to the MCGP model as one of the goals of multi-objective programming. Second, the remaining five sub-indicators and the evaluation results generated from the first phase are formulated as a complete MCGP model. Finally, an empirical study on the site selection for the BDC is implemented based on the proposed method. The result shows that Guiyang is the most suitable place for locating a BDC in China.

Decision Support System for Millennial Generation Softskill Competency Assessment using AHP and Eliminate Promethee Method

The current millennial generation has the soft skills needed to follow the trends and technology of the industrial generation 4.0. It is clear that many Millennials look more energetic and always synergistic with destructive situations and conditions. Industry 4.0 generation makes the business world switch to always using advanced technology in various sectors so that technological progress is felt faster than before, and human power is starting to be replaced by machine power, robotics, and even artificial intelligence. Thus, soft skills for the millennial generation are needed to get job opportunities in conditions where the need for human labour has begun to be eliminated in their work. The purpose of this paper is to assess the soft skills competencies possessed by the millennial generation, who are always involved with technological advances in the very fast business industry world. There are eight soft skills that the millennial generation must possess, namely critical thinking, communication, analyzing, creative and innovation, leadership, adaptation, cooperation and public speaking. The method used to select soft skills competencies for job opportunities for the millennial generation is the Analytic Hierarchical Process (AHP) method in collaboration with the Promethee elimination method. The final result of the decision support for soft-skill competency selection from 23 millennial generations, who passed the selection, was 43% (10 users) with a positive score and 57% (13 users) who experienced selection failure. This failure was due to having a negative score. Thus, the collaboration of the AHP and Promethee Elimination methods can provide optimal results for decision-making support.

An objectıve criteria proposal for the comparison of MCDM and weighting methods in financial performance measurement: An application in Borsa Istanbul

Financial performance research with multi-criteria decision making (MCDM) methods, is a common subject of study not only for researchers in the finance literature but also in the applied sciences. Financial performance manifests itself in an internal universe that a firm can directly control, while the share return of the same firm is shaped synchronically in an external universe that cannot be controlled directly. On the other hand, preferring the most suitable MCDM and weighting method to use in measuring financial performance is often regarded as a source of uncertainty. In this study, the share price is used as an external proxy and a tool for comparing MCDM methods, completely different from the previously proposed approaches based on the superiority of internal features. This study was conducted on 131 manufacturing companies in Borsa Istanbul, covering entire 20-quarter period between 2014 and 2018. The experimental findings of the study provide valid solutions for the MCDM and weighting selection problem, that can be proposed as a practical and indirect solution. The results show that preference ranking organization method for enrichment of evaluations (PROMETHEE) method used with hybrid weighting technique produced by far the best performance rankings in 19 out of 20 quarterly periods when compared to the technique for order preference by similarity to ideal solution (TOPSIS) and weighted sum approach (WSA).