Analyzing Performance of a Global Help Desk Team Operation – Country Handoffs, Efficiencies and Costs

In this paper, we study the characteristics of a global help desk operation using Volvo IT support help desk. We analyze the incidents log produced by the Volvo IT department that gives the full path of an incident and the participating country and worker at each step. Our main goal is to gain a better understanding of the nature of international traffic flows that occur during the resolution of incidents. We find that increasing the number of participating countries negatively affects IT service efficiency metrics. Further, we breakdown international traffic flows (by high- and low-efficiency countries) and examine the country effects~and their implications in terms of efficiency and cost in considerable detail. The results and insights gained are discussed at length and can help in optimizing incident resolution workflows from a cost, efficiency and resource allocation perspective

Continuous Process Auditing (CPA): an Audit Rule Ontology Approach to Compliance and Operational Audits

Continuous Auditing (CA) has been investigated over time and it is, somewhat, in practice within nancial and transactional auditing as a part of continuous assurance and monitoring. Enterprise Information Systems (EIS) that run their activities in the form of processes require continuous auditing of a process that invokes the action(s) speci ed in the policies and rules in a continuous manner and/or sometimes in real-time. This leads to the question: How much could continuous auditing mimic the actual auditing procedures performed by auditing professionals? We investigate some of these questions through Continuous Process Auditing (CPA) relying on heterogeneous activities of processes in the EIS, as well as detecting exceptions and evidence in current and historic databases to provide audit assurance

Systematic Analysis of Engineering Change Request Data - Applying Data Mining Tools to Gain New Fact-Based Insights

Large, complex system development projects take several years to execute. Such projects involve hundreds of engineers who develop thousands of parts and millions of lines of code. During the course of a project, many design decisions often need to be changed due to the emergence of new information. These changes are often well documented in databases, but due to the complexity of the data, few companies analyze engineering change requests (ECRs) in a comprehensive and structured fashion. ECRs are important in the product development process to enhance a product. The opportunity at hand is that vast amount of data on industrial changes are captured and stored, yet the present challenge is to systematically retrieve and use them in a purposeful way.This PhD thesis explores the growing need of product developers for data expertise and analysis. Product developers increasingly refer to analytics for improvement opportunities for business processes and products. For this reason, we examined the three components necessary to perform data mining and data analytics: exploring and collecting ECR data, collecting domain knowledge for ECR information needs, and applying mathematical tools for solution design and implementation.Results from extensive interviews generated a list of engineering information needs related to ECRs. When preparing for data mining, it is crucial to understand how the end user or the domain expert will and wants to use the extractable information. Results also show industrial case studies where complex product development processes are modeled using the Markov chain Design Structure Matrix to analyze and compare ECR sequences in four projects. In addition, the study investigates how advanced searches based on natural language processing techniques and clustering within engineering databases can help identify related content in documents. This can help product developers conduct better pre-studies as they can now evaluate a short list of the most relevant historical documents that might contain valuable knowledge.The main contribution is an application of data mining algorithms to a novel industrial domain. The state of the art is more up for the algorithms themselves. These proposed procedures and methods were evaluated using industrial data to show patterns for process improvements and cluster similar information. New information derived with data mining and analytics can help product developers make better decisions for new designs or re-designs of processes and products to ensure robust and superior products

A Systematic Literature Review on Automotive Digital Forensics: Challenges, Technical Solutions and Data Collection

A modern vehicle has a complex internal architecture and is wirelessly connected to the Internet, other vehicles, and the infrastructure. The risk of cyber attacks and other criminal incidents along with recent road accidents caused by autonomous vehicles calls for more research on automotive digital forensics. Failures in automated driving functions can be caused by hardware and software failures and cyber security issues. Thus, it is imperative to be able to determine and investigate the cause of these failures, something which requires trustable data. However, automotive digital forensics is a relatively new field for the automotive where most existing self-monitoring and diagnostic systems in vehicles only monitor safety-related events. To the best of our knowledge, our work is the first systematic literature review on the current research within this field. We identify and assess over 300 papers published between 2006 - 2021 and further map the relevant papers to different categories based on identified focus areas to give a comprehensive overview of the forensics field and the related research activities. Moreover, we identify forensically relevant data from the literature, link the data to categories, and further map them to required security properties and potential stakeholders. Our categorization makes it easy for practitioners and researchers to quickly find relevant work within a particular sub-field of digital forensics. We believe our contributions can guide digital forensic investigations in automotive and similar areas, such as cyber-physical systems and smart cities, facilitate further research, and serve as a guideline for engineers implementing forensics mechanisms

A Novel Business Process Prediction Model Using a DeepLearning Method

The ability to proactively monitor business pro-cesses is a main competitive differentiator for firms. Processexecution logs generated by process aware informationsystems help to make process specific predictions forenabling a proactive situational awareness. The goal of theproposed approach is to predict the next process event fromthe completed activities of the running process instance,based on the execution log data from previously completedprocess instances. By predicting process events, companiescan initiate timely interventions to address undesired devi-ations from the desired workflow. The paper proposes amulti-stage deep learning approach that formulates the nextevent prediction problem as a classification problem. Fol-lowing a feature pre-processing stage with n-grams andfeature hashing, a deep learning model consisting of anunsupervised pre-training component with stacked autoen-coders and a supervised fine-tuning component is applied.Experiments on a variety of business process log datasetsshow that the multi-stage deep learning approach providespromising results. The study also compared the results toexisting deep recurrent neural networks and conventionalclassification approaches. Furthermore, the paper addressesthe identification of suitable hyperparameters for the pro-posed approach, and the handling of the imbalanced nature ofbusiness process event datasets

Greening Business Information Systems

Efficient and effective environmental information processing is a key element for the improvement of an organization‘s environmental performance. Business Information Systems (BIS) are recognized as a tool for the facilitation of collecting, storing, processing and communicating any kind of information. However, there are other benefits that BIS can provide for a company. These benefits are related to the effective use of information, such as support of informed decision-making, increase of overall environmental awareness in an organization and, as a consequence, behavioral change. Therefore, the purpose of study is related to the extension of BIS‘ functionalities through the integration of the environmental information flow. The efficient and effective integration of environmental information flow is pursued by means of application of the multi-disciplinary approach where organizational and cybernetic science and environmental management are combined. Beer‘s Viable System Model and Organizational Information Processing Theory are the main theories used for the assessment of studied information systems and information technology solutions in the purchasing process. The studied IT solutions are deployed in the areas of supplier evaluation, logistics and business traveling. The Environmental Information System Evaluation Framework (EISEF) is the principal outcome of the research work. The research implications are two-fold. The scientific implication is based on the application of the aforementioned theories for the environmental information processing by means of Business Information Systems. According to the results of literature review, a similar approach has not been used before in this field. The practical implications are EISEF itself and recommendations for its use that are also the principal outcomes of research

Extra Financial Analysis – EFA: Environmental and financial performances of ABB, Akzo-Nobel and SCA: Picturing the business opportunities and risks associated to stakeholder perceptions and environmental and social prerequisites

External assessment of companies’ environmental aspects often focus on the existence of strategies, commitments, management systems and reporting of firms that concerns environmental aspects. Instead, in line with extra financial analysis, in order to play a role in decision-making, analysis of environmental aspects should incorporate the influence that stakeholders may have on future revenues of the assessed firm and how well advanced corporate strategies are in meeting these threats, turning them into business opportunities. Thereafter, the environmental information financial analysts’ use in their financial analyst reports as well as the relation between environmental and financial performance are illuminated. Three industry sectors, Chemicals, Electrical Equipment and Paper & Forest Products, are specially analysed in this report. Out of almost 4500 analyst reports about 36 percent contain environmental information, but when looking at industry sectors these numbers range from only 3 to up to 79 percent. The type of environmental information that the analysts focus on in their reports are on how firms’ products and product portfolios are adopted to Environmental regulations facing customers/markets, Customer demands and Eco-Efficiency. This product perspective is strongly related to discussions of business opportunities of the firm. In fact, a good 77 % of the financial analyst reports containing environmental information dealt with opportunities linked to environmental aspects. To a lower extent, financial analysts write about company specific risk issues like emissions and litigations while their reports is virtually absent from aspects like environmental strategies, policies, management systems, reporting and auditing. The correlation between corporate financial and environmental performances is illuminated through regression analyses. Industry environmental risk is found to be negatively correlated to corporate return on assets – ROA – (in an static model) while (when applying a dynamic model) corporate environmental performance and ROA have a positive correlation in the short term, which can find support by other studies using different data.Extra financial analysis; EFA; Financial analyst reports; Content analysis; ESG Framework; Return on assets; ROA; Environmental performance; Social performance; financial performance; Financial accounting; Non-financial information

Automated Process Discovery: A Literature Review and a Comparative Evaluation with Domain Experts

Äriprotsesside kaeve meetodi võimaldavad analüütikul kasutada logisid saamaks teadmisi protsessi tegeliku toimise kohta. Neist meetodist üks enim uuritud on automaatne äriprotsesside avastamine. Sündmuste logi võetakse kui sisend automaatse äriprotsesside avastamise meetodi poolt ning väljundina toodetakse äriprotsessi mudel, mis kujutab logis talletatud sündmuste kontrollvoogu. Viimase kahe kümnendi jooksul on väljapakutud mitmeidki automaatseid äriprotsessi avastamise meetodeid balansseerides erinevalt toodetavate mudelite skaleeruvuse, täpsuse ning keerukuse vahel. Siiani on automaatsed äriprotsesside avastamise meetodid testitud ad-hoc kombel, kus erinevad autorid kasutavad erinevaid andmestike, seadistusi, hindamismeetrikuid ning alustõdesid, mis viib tihti võrdlematute tulemusteni ning mõnikord ka mittetaastoodetavate tulemusteni suletud andmestike kasutamise tõttu. Eelpool toodu mõistes sooritatakse antud magistritöö raames süstemaatiline kirjanduse ülevaade automaatsete äriprotsesside avastamise meetoditest ja ka süstemaatiline hindav võrdlus üle nelja kvaliteedimeetriku olemasolevate automaatsete äriprotsesside avastamise meetodite kohta koostöös domeeniekspertidega ning kasutades reaalset logi rahvusvahelisest tarkvara firmast. Kirjanduse ülevaate ning hindamise tulemused tõstavad esile puudujääke ning seni uurimata kompromisse mudelite loomiseks nelja kvaliteedimeetriku kontekstis. Antud magistritöö tulemused võimaldavad teaduritel parandada puudujäägid meetodites. Samuti vastatakse küsimusele automaatsete äriprotsesside avastamise meetodite kasutamise kohta väljaspool akadeemilist maailma.Process mining methods allow analysts to use logs of historical executions of business processes in order to gain knowledge about the actual performance of these processes.One of the most widely studied process mining operations is automated process discovery.An event log is taken as input by an automated process discovery method and produces a business process model as output that captures the control-flow relations between tasks that are described by the event log.Several automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models.So far, automated process discovery methods have been evaluated in an ad hoc manner, with different authors employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of non-publicly available datasets.In this setting, this thesis provides a systematic review of automated process discovery methods and a systematic comparative evaluation of existing implementations of these methods with domain experts by using a real-life event log extracted from a international software engineering company and four quality metrics.The review and evaluation results highlight gaps and unexplored tradeoffs in the field in the context of four business process model quality metrics.The results of this master thesis allows researchers to improve the lacks in the automated process discovery methods and also answers question about the usability of process discovery techniques in industry