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In an air defence scenario decisions need to be taken with extreme precision and under high pressure. These decisions becomes even more challenging when the aircraft in question need to function as a team and coordinate their effort. Because of the difficulty of the task, and the amount of information that needs to be rapidly processed, fighter pilots can benefit greatly from computer-assisted decision making.  In this thesis this kind of decentralised multi-agent coordination problem is studied and mission assignment models, based on robust and stochastic optimisation, are evaluated. Since the information obtained by aircraft sensors often suffer from a notable amount of noise and the scenario state therefore is uncertain, a Poisson multi-Bernoulli mixture filter is implemented in order to model these noisy measurements and keep track of potential adversaries. The study finds that the filter used was more than capable of handling the scenario uncertainties and provided valuable task information to the mission assignment models. However, the preliminary robust optimisation models based entirely on the positional uncertainty of the adversaries were not sophisticated enough for such a complex coordination problem, indicating that further research is needed in this area.

As data availability increases so do the opportunities within businesses. Companies need to explore technologies that are able to exploit and capitalise on this vast amount of data in order to stay relevant in today’s competitive market. Artificial intelligence and simulation are two promising technologies that are able to manage and utilise these large amounts of data. This paper explores the opportunities and challenges that exist of combining artificial intelligence with simulation in order to achieve data-driven decisions within industries. Although these two technologies are well researched in isolation, their combination and synergetic effects remain largely unexplored. The aim of this study is to survey this existing vacuum by performing a literature review and producing a digitised AI and simulation ecosystem that encapsulates the opportunities and challenges enabled by these two technologies. This research explored this ecosystem by applying and developing it on a real case study of an automotive parts supplier’s production process. It was concluded that this modularised digitised ecosystem could act as an alternative to expensive and generic software solutions due to its high customisation, simple integration and cost-efficiency, especially for SMEs. The study also concluded that adding additional AI and simulation models to the ecosystem reduces the modules’ unit costs since they can share some high cost structures such as: databases, servers and user-interfaces; this idea was encapsulated in the term digitised economies of scale.

Data-driven approaches to decision making are today applied far and wide. With origins in the field of judgment and decision making (JDM), data-driven decision making (DDDM) has become an emergent topic within I-O psychology, especially within the fields of people analytics and human resource analytics. In light of the current AI revolution, it is evident that the next steps in JDM research include data- driven approaches. The purpose of this Master’s thesis was to compile the research on data-driven decision making conducted across disciplines into a comprehensive overview. Main research questions: based on systematic reviews and scoping reviews about implementations of DDDM affecting individuals, groups, or organizations, what areas of research can be identified? How and to what extent are they linked? To address these questions, this thesis utilizes a scoping meta-review design and bibliometrics. After rigorous search and screening processes, the final sample consisted of n = 1,008 systematic and scoping reviews. The results indicated that there are research areas within the included reviews that are isolated to a varying extent. Based on a multiple correspondence analysis (MCA), five areas of research were identified: business intelligence; learning analytics/education; mHealth/telemedicine; general decision making/decision support; and clinical decision support/diagnosis/healthcare. As a scoping meta-review encompassing a large number of scientific fields and methodologies, this thesis contributes to the progression of DDDM research at large. The results highlight the scattered nature of current research practices within DDDM and identify an opportunity for scientific advancement through interdisciplinary research.

Incident detection is a key component in real-time traffic management systems that allows efficient response plan generation and decision making by means of risk alerts at critical affected sections in the network. State-of-the-art incident detection techniques traditionally require: i) good quality data from closely located sensor pairs, ii) a minimum of two reliable measurements from the flow- occupancy-speed triad, and iii) supervised adjustment of thresholds that will trigger anomalous traffic states. Despite such requirements may be reasonably achieved in simulated scenarios, real-time downstream applications rarely work under such ideal conditions and must deal with low reliability data, missing measurements, and scarcity of curated incident labelled datasets, among other challenges. This paper proposes an unsupervised technique based on univariate timeseries anomaly detection for computationally efficient incident detection in real-world scenarios. Such technique is proved to successfully work when only flow measurements are available, and to dynamically adjust thresholds that adapt to changes in the supply. Moreover, results show good performance with low-reliability and missing data.

People analytics (PA) has experienced significant growth in recent years due to the increasing availability of employee data and the impact of digitalization on organizations. This data-driven approach utilizes inductive methods to predict various outcomes in the field of human resources. Nevertheless, concerns have emerged regarding the availability and reliability of the data used in PA. Surprisingly, the quality standards of these data-driven methods have not been evaluated in the PA literature, despite their widespread adoption. To address these gaps, nine research questions covering expertise areas, psychological constructs, patterns/trends, study types, data sources, reliability reporting, data-driven frameworks, prediction accuracy, and open science practices in PA were reviewed. A scoping review was conducted to extract relevant information from each piece of literature, while bibliometric analysis provides a structured analysis of trends, themes, and key contributors. A total of 3,103 records were identified from the Scopus (n = 449) and APA PsycINFO (n = 2,700) databases, with nine studies included in the review. Findings indicated a lack of consideration given to quality, reliability aspects, and open science practices within PA literature. The predominant emphasis of the research was on the evaluation of variables, particularly turnover intention. This study contributes to advancing the understanding of PA by emphasizing the importance of incorporating quality standards and open science practices to enhance the reliability and credibility of research findings. The classification of the PA literature and recommendations for future research directions are provided, highlighting the need for a hierarchy of knowledge in the field. / Scoping Review of People Analytics

Organizations are social systems established to make decisions. Modern organizational decision-making is complex and can easily overwhelm the capacity of individuals. Because of the complexity of multi-person decisions, there is a big risk for uncertainty in decision-making. In recent years, the rise of business intelligence has enabled organizations to base their decisions on data and minimize uncertainty in their decision-making. However, deployment of business intelligence systems is characterized by complexity, making many small and medium-sized organizations fail to use such a system effectively.This thesis aims to identify and describe variables that influence successful use of a business intelligence architecture to support small organizations in making data-based decisions, what small organizations need to become data-driven in decision-making, and what measures small organizations can take to use business intelligence systems efficiently. Eight semi-structured interviews were conducted with professionals from Vasaloppet, a small organization deploying a business intelligence system. The empirical data gathered have been analyzed with a thematic approach. The thematic analysis identified four themes’ Deficiencies in organizational governance, Deficiencies in data management, Perceived workload, and Degree of matching between processes, organization, and strategy. Findings in these themes and underlying codes within these themes revealed problem areas in organizational governance when making decisions. Respondents mentioned challenges with a lack of a decision model, clear business plan, and intra-organizational understanding. When it comes to becoming data-driven, respondents said deficiency of structure for communication, lack of access to data, lack of data in decision-making, general workload, deficiencies in project results, and deficiencies in degree of matching as problematic. Based on the results of this study, guidelines are presented for small organizations to become data-driven in their decision-making.Keywords: Data-driven decision-making, business intelligence, small organizations

Indiana University-Purdue University Indianapolis (IUPUI)

Manufacturers can develop next-generation production and service for their customers by the data gathered and analyzed from customers’ usage conditions. In this research, the operating condition of articular haulers is collected and analyzed through machine learning algorithms to predict the type of operational topographies and road surface. To achieve that, elevation data and satellite images, which were gathered from Microsoft Azure Maps, are used as data sources to identify the topography and road surface on which machines operated. In the end, two machine learning models are trained with machines’ inclination records and road roughness records, respectively, to classify the topography and road surface. For the topography classifier, the topography is categorized into four terrain labels, including "Low Hills", "Mountains", "Plains", and "Tablelands & High Hills". The road surface is classified into "Paved" and "Unpaved". A Convolutional Neural Network (CNN) image classification model is built for labeling satellite images instead of labeling manually. The results indicate that the prediction for topography labels "Plains" and "Tablelands & High Hills" has superior performance, which accounts for the majority of the raw dataset; on the contrary, the road surface classifier still needs further improvement in the future. In addition, an analysis and discussion regarding the imbalanced dataset are included, and it shows the limited effect on an extremely imbalanced dataset. Finally, the conclusion and future work are given.

During the past years an increased focus on the development of autonomous solutions has resulted in driverless vehicles being used in numerous industries. Volvo Construction Equipment is currently developing the TA15, an autonomous hauler part of a larger transport solution. The transition to autonomous haulers have further increased the need for improved system condition monitoring in the strive for increased operational time. A method aiming to identify and isolate faults in the hydraulic steering system on the TA15 was therefore investigated in this thesis. Using fault tree analysis, five faults considered to be of importance regarding steering performance were selected. Two different methods for detecting the faults were compared to each other, data-driven and model based. Out of the two, data-driven was selected as the method of choice due to high modularity and relative simplicity regarding implementation. The data-driven approach consisted of Feed-Forward and Long Short Term Memory networks where the suitable inputs were decided to be a combination of pressure and position signals. Utilizing a simulation model of the steering system validated against the TA15, the selected faults were induced in the simulated system with various severity. Training the networks to classify and estimate fault severity in the simulated model resulted in satisfactory results using both networks. It was however concluded that in contrary to the Feed-Forward network, the LSTM network could achieve good performance using less amount of sensors. Although the diagnostic method showed promising result on a simulation model, test on the real TA15 needs to be performed in order to properly evaluate the method. The advantage of using a data-driven approach was specially noticeable when comparisons were made to the model based approach. The data-driven approach relies on labeling data rather than complete system knowledge. Meaning that the method developed therefore could be applied on practically any hydraulic system in construction equipment by changing the training data.