Decision Support Systems XIII. Decision Support Systems in An Uncertain World: The Contribution of Digital Twins

Inhaltsverzeichnis

1. Frontmatter

2. DSS Models, Methods and Tools

   1. Frontmatter

   2. Multi-actor VIKOR Method for Highway Selection in Montenegro

      Boris Delibašić, Draženko Glavić, Sandro Radovanović, Andrija Petrović, Marina Milenković, Milija Suknović

      Abstract

      Nowadays, decision-making systems in modern infrastructural planning greatly impact everyday life. This paper proposes a novel modification of the multi-criteria decision analysis (MCDA) method VIKOR that can be successfully applied to infrastructural decision-making systems. Our contributions are twofold: We first solve a highway section selection on the Montenegro A1 highway. Secondly, we modify the VIKOR method for the multi-actor (MA) setting. Although the original VIKOR method recognized multi-actor preferences through the selection of the value of the compromise parameter v, it did not explicitly include multiple actors in the decision-making process. Moreover, we show how the multi-actor (MA) VIKOR method can serve as a decision support system for making important infra-structural decision problems, improve the transparency of the decision-making process with the rising need to include citizens in the decision-making process, and how it successfully solves the distortion in social choice problem.

   3. A Tool to Support the Decisions for the Trace Clustering Problem with a Non-compensatory Approach

      Nikolaos Zapoglou, Pavlos Delias

      Abstract

      Process Discovery and Trace Clustering are used to extract business process-related knowledge from event logs and create models of processes. A non-compensatory approach, involving concordance and discordance settings, can be used to assess trace similarity and form groups. Previous research demonstrated the effectiveness of that approach, but it is time-consuming and requires a deep understanding of the technique’s parameters and desired outcomes. To make the process more efficient, we developed a software tool to assist with parameter definition and analysis of results. The tool provides a user-friendly interface, visual aids, and the ability to adjust parameters to ensure the solution reflects user preferences, allowing users to make more informed decisions. The publicly available tool combines the power and versatility of the R language with the friendly interfaces implemented using the Shiny libraries.

   4. An Asset-Based Causal Loop Model to Improve Corporate Value

      Romain Ben Taleb, Matthieu Lauras, Mathieu Dahan, Aurélie Montarnal, Romain Miclo

      Abstract

      Assessing a company’s value is an important leverage for decision-making. Indeed, all decisions made within a company are generally aimed at maximizing the company’s value. In accounting, almost all models for assessing the value of a company are related to the amount of cash the company is able to generate. As a result, maximizing the value of a firm is usually about maximizing cash flow. However, beyond the usual accounting models, several recent initiatives have highlighted the key role of managing all assets, and not only the ones considered by the general accountability, in decision making and cash generation. Unfortunately, these methods are currently limited to conceptual and qualitative proposals that do not lead to a real decision support system thus far. This paper proposes a first step of formalization aiming at providing tools for these asset-oriented decision support approaches. The contribution is a Causal Loop Diagram to Support Decision-Making in order to maximize the value of companies. Based on a dedicated literature review, we design an asset-based causal loop model that formalizes the qualitative results of the literature review. We then develop an instantiation on an illustrative case via simulation in order to verify the relevance of the proposal and to show how such a model can be used in practice for decision making. This will support next research to develop an asset-based decision support system to maximize value of companies.

   5. Time-Aware Optimisation Models for Hospital Logistics

      Herwig Zeiner, Roland Unterberger, Julia Tschuden, Mohammad Yusuf Quadri

      Abstract

      Our healthcare system must become more efficient! Cost reduction in the medical sector usually means lowering the quality of patient care, which is socially unacceptable. Therefore, we need to identify areas of the healthcare system where cost reductions can be achieved without compromising on patient care. One area with significant potential for savings is hospital logistics, which still has far-reaching possibilities for optimization, especially in the planning and implementation of patient transport. The systems currently used for planning transport operations are mostly semi-automated and provide useful solutions for simple standard situations. However, such systems are incapable of exploiting the optimisation potential of complex logistics problems or of reacting independently to emergencies and combining patient transports in an optimal way. These disadvantages lead to the under-utilization of existing transportation capacities (vehicles, personnel) and delays in transport handling, which disrupt transportation logistics resources. In this paper, we present a novel, scale-able scheduling algorithm for patient transport in healthcare facilities and hospitals. We consider the time-aware aspects (e.g. short lead times in planning and opening hours of the stations).

   6. Prevention and Detection of Network Attacks: A Comprehensive Study

      Paul Addai, Ryan Freas, Elnatan Mesfin Tesfa, Max Sellers, Tauheed Khan Mohd

      Abstract

      Cybersecurity is currently a topic of utmost significance in tech sectors. The ever-evolving landscape of this field makes it particularly difficult to navigate. This paper aims to help the reader understand the complexity of network attacks and also show how we may never ‘solve’ the problem of cyber attacks. Our paper may be accessible to the layman, but a basic understanding of networking fundamentals would be desirable. Computer security, cybersecurity, or information technology security may all be used interchangeably throughout the paper. An ‘attack’ will refer to a breach in security to an online system that may cause (but is not limited to) the following: unauthorized information disclosure, theft of technology, or disruption of services.

3. DSS for Business Performance and Stakeholders

   1. Frontmatter

   2. A Literature Review on the Contribution of Industry 4.0 Technologies in OEE Improvement

      Emna Masmoudi, Laurent Piétrac, Séverine Durieux

      Abstract

      Overall Equipment Effectiveness (OEE) has remained a valuable performance indicator over the decades. Yet, methods for improving equipment effectiveness have changed and advanced over time. This paper deals with the contribution of the Industry 4.0 in OEE improvement in the context of production systems monitoring and control through an analysis of the current literature. Industry 4.0 provides innovative technologies to enable new ways of tracking, taking decisions and acting upon production system health data. Internet of Things (IoT) when integrated into production systems, enables tracking of operational parameters remotely in real-time. Big Data and Artificial Intelligence enable analyzing historical and current operational data to using the results for predictive maintenance. Simulation and Digital Twins allow to test various production scenarios to measure their impact on production systems performance… This leads to better insights on production performance, identification and minimization of losses, and enhanced decision making in favor of increasing OEE values consistently. In this work, we give an overview of the Industry 4.0 technologies used in the literature. Then we identify and present different use cases that combine a number of these technologies to assure production monitoring and control.

   3. MAMCABM: A Data-Driven Stakeholder-Based Decision-Support System that Considers Uncertainties

      He Huang, Shiqi Sun, Lina Liu, Koen Mommens, Cathy Macharis

      Abstract

      In recent years, decision-making in mobility has increasingly relied on data support and consideration of uncertainty. However, conventional decision-making methods such as Multi-Criteria Decision Making (MCDM) and Multi-Criteria Group Decision Making (MCGDM) have limitations in accounting for the complexity and dynamics of real-world mobility situations. This has led to an interest in Agent-Based Modeling (ABM), which can capture the heterogeneity and interactions of individuals in a system. On the other hand, MCDM remains a legitimate method that allows for the consideration of conflicting interests simultaneously. Moreover, it is still valuable to involve stakeholders in the decision-making process, as they can provide important insights and perspectives that may not be captured by purely analytical methods.

      This paper presents a novel decision-support system (DSS) that combines Multi-Attribute Multi-Criteria Analysis (MAMCA) and ABM to support mobility decision-making under conditions of uncertainty, called MAMCABM. The DSS provide stakeholders with a comprehensive decision making tool to assess and compare alternative scenarios based on different criteria, where ABM provides rich data support. Furthermore, MAMCABM also accounts for uncertainties that are generated in different steps. MAMCABM is demonstrated on a real-world case study of a road adjacent to a university campus, where different types of vehicles, cyclists and pedestrians interact in complex ways. The results of the MAMCABM analysis highlight the importance of considering multiple criteria and uncertainty in mobility decision-making, and provide valuable insights for improving the road situation by taking into account the preferences of different stakeholders.

   4. BPR Assessment Framework: Staging Business Processes for Redesign Using Cluster Analysis

      George Tsakalidis, Nikolaos Nousias, Kostas Vergidis

      Abstract

      In response to increasingly competing environments, organizations are examining how their core business processes (BPs) may be redesigned to improve performance and responsiveness. However, there is a lack of approaches for evaluating Business Process Redesign (BPR) at design time and systematically applying BPR in the case of eligible models. The aim of this research is to demonstrate in practice how the BPR Assessment Framework evaluates the redesign capacity of process models prior to implementation. From the two discrete operation modes of the framework, the paper focuses on the Staging Mode that accounts for the classification of sets of organizational processes. The staging is supported with a clearly defined methodology that is based on partitional clustering and is demonstrated by using a process model repository from literature, initially containing 1000 process models. Based on the findings, the models have varying BPR capacity and the results are consistent to the rational claim that a rising structural complexity denotes a low capacity for BPR. The framework proved to be a convenient and straightforward method for classifying the process models of the repository to categories of low, moderate, and high plasticity and external quality. The contribution of the approach lies to the fact that it can be readily used by practitioners in the course of BPR decision making.

   5. A Lean Knowledge Management Processes Framework for Improving the Performance of Manufacturing Supply Chain Decisions in an Uncertain World

      Jiang Pan, Shaofeng Liu, Sarah Tuck, Aira Ong

      Abstract

      As a consequence of the COVID-19 pandemic and the ongoing Russian-Ukraine war, global supply chain has been disrupted, causing worldwide shortages and affecting consumer patterns. To combat this circumstance and build more resilient manufacturing supply chains, this paper develops a Lean Knowledge Management Processes framework to improve manufacturing companies’ knowledge management performance. Ultimately, it contributes to providing decision makers with sufficient and high-quality information and knowledge to make more accurate decisions in this uncertain world. The framework is empirically tested by partial least squares structural equation modelling approach with survey data using 359 responses from two types of manufacturing industries (i.e., machinery and electronics manufacturing and food and drink industry), two types of business sizes (i.e., SMEs and Large companies), and three countries (i.e., the US, China, and the UK).

   6. Integrating Existing Knowledge to Accelerate Buildings Renovation Rates in Europe

      Charikleia Karakosta, Zoi Mylona, Jason Papathanasiou, John Psarras

      Abstract

      Nowadays, boosting the implementation of energy efficiency measures in buildings and subsequently, mainstreaming energy efficiency financing is of paramount importance for the European Union towards achieving its goal of carbon-neutrality by 2050. Unfortunately, statistics have shown that a lot of effort is needed to achieve the Europe’s targets, since energy efficiency is not yet considered as an attractive investment by the financial sector. The lack of expertise and knowledge, as well as the different perspective of project developers and financing institutions are some indicative challenges that have to be overcome. Specific to energy efficiency in buildings this is reflected by the current insufficient trends observed in the renovation rates of buildings, which reveal the urgent need for action since this is the largest consumer of energy in Europe. Furthermore, a combination of public and private funding through innovative financing instruments is required to overcome current barriers that prevent mobilization of necessary investments. The aim of this paper is to set up a role-based methodological approach for the deployment of an integrated matchmaking mechanism on an ICT platform to boost energy efficiency investments in an easy-access and trust-worthy way. The methodology envisages to follow a multidisciplinary perspective which takes into account the interactions between various key factors, such as stakeholders and barriers, so as to facilitate the complex set of decision-making actions for building renovation. The core of concept centres around the definition of the roles of the potential users of a big data for buildings platform, their interdependency and requirements with the ultimate purpose of accelerating renovation rates.

   7. Equity-Based Allocation Criteria for Water Deficit Periods: A Case Study in South Africa

      Sinetemba Xoxo, Jane Tanner, Sukhmani Mantel, David Gwapedza, Bruce Paxton, Denis Hughes, Olivier Barreteau

      Abstract

      Managing water resources and preventing water-related disasters requires investing in tools that aid knowledge-based group decision-making at local levels. We contribute to this toolbox by demonstrating the utility of the Analytic Hierarchical Process (AHP) for establishing an expressed equity-based allocation criteria (called community weighting index) for deficit conditions. Preference for water supply during low-flow conditions in the Twee area supports the principle of proportionality and for multipurpose use. High endemism of fish species in the rivers draining the Twee sub-catchment, the socio-economic importance of farmers, the constitutional protection of the domestic water user, and the tourism sector’s importance ranking all justify the established index and revealed acceptance of proportionality. Using the AHP for community weighting can facilitate cooperative and inclusive water management decision-making while mitigating ongoing conflicts and ensuring community understanding. In the future, the results will be combined with hydrological and environmental flow estimates to determine the risk of water deficits.

4. DSS Applications for Sustainability - Health, Energy and Transportation

   1. Frontmatter

   2. Attracting Financing for Green Energy Projects: A City Readiness Index

      Aikaterini Papapostolou, Charikleia Karakosta, Filippos Dimitrios Mexis, John Psarras

      Abstract

      Standard population projections show that virtually all global growth will be in urban areas over the next thirty years. At the same time, the last decades of constant economic and population growth, the modern lifestyle of developed countries and the creation of new needs have led to an increase in energy use per capita and at an overall level resulting in environmental pollution and strengthening of the greenhouse effect. It is essential, therefore, not only at a county level but also at a city level to boost investments in green energy projects. However, important barriers hinder local authorities from investing in this kind of projects. Among others, these barriers include the lack of internal capacity to identify and implement innovative financing schemes, high cost of financing or lack of private financing. This paper presents a software tool for assessing cities’ readiness to receive the necessary financial assistance in order to implement green energy projects. The cities’ performance is evaluated along three axes (i) Investment Attractiveness, (ii) Utilisation of Financial Resources and (iii) Project Implementation, while the methodology is based on multicriteria analysis. A pilot application in ten European cities has been conducted, and fruitful outcomes have been derived from the comparative analysis.

   3. Building Risk Prediction Models for Diabetes Decision Support System

      Sarra Samet, Ridda Mohamed Laouar

      Abstract

      Diabetes mellitus early detection is one of the most important issues in the literature nowadays. It contributes to the development of many deadly conditions, including heart disease, coronary disease, eye disease, kidney disease, and even nerve damage. As a result, its prediction is critical. Over the years, several academics have attempted to build an accurate diabetes prediction model. However, due to a lack of relevant data sets and prediction methodologies, this area still has substantial outstanding research concerns. The study attempts to solve the challenges by investigating healthcare predictive analytics. This project employs supervised learning through the application of 3 classification algorithms to early anticipate diabetes with high performance. To train and evaluate the prediction models, we used a sizable diabetes dataset based on actual health data gathered from the Centers for Disease Control and Prevention, which was properly pre-processed in this study, such as how the imbalance was handled utilizing resampling technique. We went with the Logistic Regression Algorithm, Decision Tree Algorithm, and Random Forest Algorithm to analyze the dataset. Based on several evaluation matrices, the results reveal that the RF algorithm outperformed other machine learning algorithms with an F1score of 93.01%. The results of the trial indicate that our suggested model outperforms cutting-edge alternatives. This study's findings may be useful to health professionals, organizations, students, and researchers working in diabetes prediction research and development.

   4. AHP Method Applied to the Evaluation of Costs and Pollution Emitted by Combined Means of Transport, Case of SMMC Port Toamasina

      Jean Baptiste Rakotoarivelo

      Abstract

      This article is based on the application of multicriteria support methods to decision-making. Within the Conventional Goods Handling Company, we encourage decision-makers in society to adopt assessment methods and available techniques based on strong assumptions in line with today’s reality, to take sufficient account of the objectives of integrated development. Indeed, this aimed at all aspects of development, and to integrate into the analysis of many projects different aspects and their impact on the national economy. The problems presented call for the development of new instruments more appropriate to the particular context of combined transport in the case of the SMMC (SMMC: Conventional Goods Handling Company). The purpose of this work is to model a transport system within society. Modelling could be seen as a generalization of the system so that it is standard and applicable to similar systems, taking into account three criteria: ecological, economic and traffic We have estimated the performance of the following five alternatives: pollution, energy, noise, time and damage in which we can estimate the costs of expenditure and the amount of pollution emitted during the transport journey from the port to the final destination of the goods. This makes it possible to consider collective points of view and plan integral resources in a decision support system concerning port activities through the Hierarchical Analytical Decision-Making Process (AHP (AHP: Analytical Hierarchy Process)) method.

   5. Evaluate the Potential of the Physical Internet for Last Mile Delivery in Developing Countries

      Eva Petitdemange, Sam Ban, Matthieu Lauras, Sarot Srang

      Abstract

      Last mile delivery is a crucial component of the supply chain process, particularly in developing countries. However, traditional delivery methods are often characterized by inefficiencies, such as high costs, long delivery times, and poor delivery accuracy. The rise of e-commerce and the growth of online retail have added further pressure to last mile delivery in these countries. To address these challenges, Physical Internet (PI) has emerged as a promising solution. PI is a new paradigm for logistics and supply chain management that aims to increase the efficiency, sustainability, and resilience of the supply chain. This study aims to assess the impact of PI on last mile delivery in developing countries, using a digital model-based approach. By analyzing the potential benefits and limitations of PI, this study will contribute to the literature and provide insights and recommandations into the implementation of PI-based scenarios in last mile delivery in developing countries.

   6. Towards an Integrative Assessment Model for Port Sustainability Decisions: A Systematic Review

      Xiaofang Wu, Shaofeng Liu, Shaoqing Hong, Huilan Chen

      Abstract

      The slow pace of sustainability poses questions about what sustainability purposes are served and how to assess the status quo of sustainability for effective decision support. Having recognized the fuzzy concept of sustainability and the lack of sustainability assessments for ports that play key nodes of global logistic networks, this study applies a systematic review method to broadly collect theoretical and practical data from literature databases and relevant organizations, to identify sustainability requirements, port sustainability perceptions, and existing sustainability assessment approaches and methods. Results show that the sustainability concept is moving to eco-centric and context-specific thinking while the port sustainability still lies in traditional triple lines and the elements of the concept lack recognition of the business-environment nexus. Although dozens of specific methods have been available from the existing sustainability assessments, previous assessment approaches rely much on subjective expert judgments or quantitative data, which may affect the reliability and validity of assessments. As such, this study provides a new integrative assessment model for port sustainability decisions to meet ecological needs. The proposed model integrates the interactions between port activities and the environment. It is a data-driven, evidence-based approach to reducing subjectivity and saving time. The proposed assessment model contributes to the understanding the port sustainability situations and finding preferable options in terms of interaction mechanisms.

5. DSS Users and Successful Adoption

   1. Frontmatter

   2. An Investigation on Cloud ERP Adoption Using Technology-Organisation-Environment (TOE) and Diffusion of Innovation (DOI) Theories: A Systematic Review

      Sin Ting Cheung, Uchitha Jayawickrama, Femi Olan, Maduka Subasinghage

      Abstract

      The purpose of this study was to explore the important factors for the adoption of Cloud ERP systems. When organisations make decision on implementation of innovative technology such as Cloud ERP, there is a range of factors to be considered. This paper aims to identify the most significant 9 TOE and DOI factors which have positive influence towards Cloud ERP adoption by conducting a systematic literature review (SLR). A conceptual framework was proposed which is useful reference for potential Cloud ERP adopters who are making decisions on Cloud ERP adoption. The conceptual framework includes the identified 9 factors as independent variables; adoption of Cloud ERP as dependent variable; firm sizes and countries as the two moderating variables.

   3. Young Elderly DSS Users – Some Reasons for Sustained and Successful Adoption

      Christer Carlsson, Pirkko Walden

      Abstract

      There is consensus in health studies that regular physical activities of sufficient intensity and duration contribute to better health both in the short and long term. We have worked on getting young elderly, the 60–75 years age group, to adopt and include physical activities as part of their daily routines. One reason for addressing young elderly is large numbers – they are now 18–22% of the population in most EU countries (80–100 million citizens). A second reason is that regular health-enhancing physical activities (HEPA) can serve as preventive health care, which will improve and sustain quality of life and save health-care costs for the ageing population. We have learned that the adoption of digital services, which are modern implementations of DSS technology, can be instrumental for building sustainable HEPA programs. We also found out – a bit surprisingly – that digital applications on mobile phones are readily accepted and adopted by the young elderly (“no problems with understanding and learning to use the application”) when they are tailored to meaningful purposes and a context that is relevant for the young elderly.

   4. Behavioral Studies for the Use of Visualization in Holistic Evaluation for Multicriteria Decision Problems Decision

      Evanielle Barbosa Ferreira, Tarsila Rani Soares de Vasconcelos, Lucia Reis Peixoto Roselli, Adiel Teixeira de Almeida

      Abstract

      Several behavioral studies have been performed related to MCDM/A (Multi-Criteria Decision Making/Aiding) methods, although not many of them aim directly to modulate (transform) those methods. Some of the studies intended to modulate methods provide suggestions to improve the FITradeoff decision process and the design of its Decision Support System (DSS). In this context, this paper presents behavioral study which has been constructed during the Covid-19 Pandemic and has been applied until now. These studies are concerned with the use of visualization in holistic evaluation for multicriteria decision problems decision using online survey to compare bar graphics and tables during the holistic evaluation Although these studies are contextualized for the FITradeoff Method, their results can be applied to any other methods in the context of MAVT (Multi-Attribute Value Theory), with additive aggregation. This study tested how DMs use bar graphics and tables to perform the holistic evaluation of alternatives. The experiment considers two types of visualizations: bar graphics and tables. Also, it uses two decision processes: the selection of the best alternative and the elimination of the worst alternative. In the past, DMs can only select the best alternative during the decision process in the FITradeoff DSS. However now, the elimination process is also included in the DSS, providing flexibility for Decision-Makers. As result, the experiment suggests that for some types of visualizations, the DMs performed better on the elimination process than the selection process. Moreover, results also showed that most of DMs prefer to select the best alternative than to eliminate the worst, even performing better in the elimination process. Hence, this result reinforces the flexibility provided in the DSS, but recommend another experiment using neuroscience tools, permitting to compare cognitive efforts during both decision process.

   5. A Digital Distance Learning Critical Success Factors Model for Conducting Learning Analytics Research

      Sean Eom

      Abstract

      A recent EDUCAUSE horizon report describes that learning analytics is one of the leading technologies and practices that will impact the future of teaching and learning. The growing presence of online delivery modes has accelerated the advancement of learning analytics (LA) and the use of data in education. Despite huge volumes of LA research publications, a systematic literature review reveals that LA research faces several challenges, including a lack of good pedagogical models that will further advance theoretical development in understanding relationships between the effectiveness and learning outcomes and the complexity of learning processes.

      Recently, Guzmán‑Valenzuela et al. claimed that LA tends to underplay the complexity of Learning processes. Their bibliometric analysis of recent literature identified several critical concerns of LA research, including oversimplification of the learning process and lack of good pedagogical models to illuminate students’ learning processes and outcomes. This paper aims to tackle these critical concerns. The complexities of teaching and learning processes are due to multiple interdependent factors that affect learning outcomes directly and indirectly. This paper aims to provide an integrated, foundational pedagogical model that is complete and parsimonious for further advancing e-learning analytics research.

   6. Scientific Authorship in DSS Research: Past Trends and Future Opportunities

      Peter B. Keenan, Ciara Heavin

      Abstract

      Over a period of almost 60 years, Decision Support Systems (DSS) research has focused on supporting managerial decision-making, drawing on contributions from diverse fields including Economics, Operations Research/Management Science (OR/MS), Information Systems (IS), and Management. To better understand the DSS landscape, this article uses a bibliometric analysis to investigate current publishing trends in DSS as a research area, co-authorship by gender, and location. By leveraging Scopus, we identify notable patterns and developments in DSS research authorship from 2018 to 2022. We present initial recommendations to guide the future research efforts of both DSS academics and practitioners.

6. Correction to: Multi-actor VIKOR Method for Highway Selection in Montenegro

   Boris Delibašić, Draženko Glavić, Sandro Radovanović, Andrija Petrović, Marina Milenković, Milija Suknović

7. Backmatter