Business-driven IT Management

Business-driven IT management (BDIM) aims at ensuring successful alignment of business and IT through thorough understanding of the impact of IT on business results, and vice versa. In this dissertation, we review the state of the art of BDIM research and we position our intended contribution within the BDIM research space along the dimensions of decision support (as opposed of automation) and its application to IT service management processes. Within these research dimensions, we advance the state of the art by 1) contributing a decision theoretical framework for BDIM and 2) presenting two novel BDIM solutions in the IT service management space. First we present a simpler BDIM solution for prioritizing incidents, which can be used as a template for creating BDIM solutions in other IT service management processes. Then, we present a more comprehensive solution for optimizing the business-related performance of an IT support organization in dealing with incidents. Our decision theoretical framework and models for BDIM bring the concepts of business impact and risk to the fore, and are able to cope with both monetizable and intangible aspects of business impact. We start from a constructive and quantitative re-definition of some terms that are widely used in IT service management but for which was never given a rigorous decision: business impact, cost, benefit, risk and urgency. On top of that, we build a coherent methodology for linking IT-level metrics with business level metrics and make progress toward solving the business-IT alignment problem. Our methodology uses a constructive and quantitative definition of alignment with business objectives, taken as the likelihood – to the best of one’s knowledge – that such objectives will be met. That is used as the basis for building an engine for business impact calculation that is in fact an alignment computation engine. We show a sample BDIM solution for incident prioritization that is built using the decision theoretical framework, the methodology and the tools developed. We show how the sample BDIM solution could be used as a blueprint to build BDIM solutions for decision support in other IT service management processes, such as change management for example. However, the full power of BDIM can be best understood by studying the second fully fledged BDIM application that we present in this thesis. While incident management is used as a scenario for this second application as well, the main contribution that it brings about is really to provide a solution for business-driven organizational redesign to optimize the performance of an IT support organization. The solution is quite rich, and features components that orchestrate together advanced techniques in visualization, simulation, data mining and operations research. We show that the techniques we use - in particular the simulation of an IT organization enacting the incident management process – bring considerable benefits both when the performance is measured in terms of traditional IT metrics (mean time to resolution of incidents), and even more so when business impact metrics are brought into the picture, thereby providing a justification for investing time and effort in creating BDIM solutions. In terms of impact, the work presented in this thesis produced about twenty conference and journal publications, and resulted so far in three patent applications. Moreover this work has greatly influenced the design and implementation of Business Impact Optimization module of HP DecisionCenter™: a leading commercial software product for IT optimization, whose core has been re-designed to work as described here

Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.This work has been co-funded by the ECLIPSE-UA (RTI2018-094283-B-C32) project funded by Spanish Ministry of Science, Innovation, and Universities and the DQIoT (INNO-20171060) project funded by the Spanish Center for Industrial Technological Development, approved with an EUREKA quality seal (E!11737DQIOT). Ana Lavalle holds an Industrial PhD Grant (I-PI 03-18) co-funded by the University of Alicante and the Lucentia Lab Spin-off Company

KPI-related monitoring, analysis, and adaptation of business processes

In today's companies, business processes are increasingly supported by IT systems. They can be implemented as service orchestrations, for example in WS-BPEL, running on Business Process Management (BPM) systems. A service orchestration implements a business process by orchestrating a set of services. These services can be arbitrary IT functionality, human tasks, or again service orchestrations. Often, these business processes are implemented as part of business-to-business collaborations spanning several participating organizations. Service choreographies focus on modeling how processes of different participants interact in such collaborations. An important aspect in BPM is performance management. Performance is measured in terms of Key Performance Indicators (KPIs), which reflect the achievement towards business goals. KPIs are based on domain-specific metrics typically reflecting the time, cost, and quality dimensions. Dealing with KPIs involves several phases, namely monitoring, analysis, and adaptation. In a first step, KPIs have to be monitored in order to evaluate the current process performance. In case monitoring shows negative results, there is a need for analyzing and understanding the reasons why KPI targets are not reached. Finally, after identifying the influential factors of KPIs, the processes have to be adapted in order to improve the performance. %The goal thereby is to enable these phases in an automated manner. This thesis presents an approach how KPIs can be monitored, analyzed, and used for adaptation of processes. The concrete contributions of this thesis are: (i) an approach for monitoring of processes and their KPIs in service choreographies; (ii) a KPI dependency analysis approach based on classification learning which enables explaining how KPIs depend on a set of influential factors; (iii) a runtime adaptation approach which combines monitoring and KPI analysis in order to enable proactive adaptation of processes for improving the KPI performance; (iv) a prototypical implementation and experiment-based evaluation.Die Ausführung von Geschäftsprozessen wird heute zunehmend durch IT-Systeme unterstützt und auf Basis einer serviceorientierten Architektur umgesetzt. Die Prozesse werden dabei häufig als Service Orchestrierungen implementiert, z.B. in WS-BPEL. Eine Service Orchestrierung interagiert mit Services, die automatisiert oder durch Menschen ausgeführt werden, und wird durch eine Prozessausführungsumgebung ausgeführt. Darüber hinaus werden Geschäftsprozesse oft nicht in Isolation ausgeführt sondern interagieren mit weiteren Geschäftsprozessen, z.B. als Teil von Business-to-Business Beziehungen. Die Interaktionen der Prozesse werden dabei in Service Choreographien modelliert. Ein wichtiger Aspekt des Geschäftsprozessmanagements ist die Optimierung der Prozesse in Bezug auf ihre Performance, die mit Hilfe von Key Performance Indicators (KPIs) gemessen wird. KPIs basieren auf Prozessmetriken, die typischerweise die Dimensionen Zeit, Kosten und Qualität abbilden, und evaluieren diese in Bezug auf die Erreichung von Unternehmenszielen. Die Optimierung der Prozesse in Bezug auf ihre KPIs umfasst mehrere Phasen. Im ersten Schritt müssen KPIs durch Monitoring der Prozesse zur Laufzeit erhoben werden. Falls die KPI Werte nicht zufriedenstellend sind, werden im nächsten Schritt die Faktoren analysiert, die die KPI Werte beeinflussen. Schließlich werden auf Basis dieser Analyse die Prozesse angepasst um die KPIs zu verbessern. In dieser Arbeit wird ein integrierter Ansatz für das Monitoring, die Analyse und automatisierte Adaption von Prozessen mit dem Ziel der Optimierung hinsichtlich der KPIs vorgestellt. Die Beiträge der Arbeit sind wie folgt: (i) ein Ansatz zum Monitoring von KPIs über einzelne Prozesse hinweg in Service Choreographien, (ii) ein Ansatz zur Analyse von beeinflussenden Faktoren von KPIs auf Basis von Entscheidungsbäumen, (iii) ein Ansatz zur automatisierten, proaktiven Adaption von Prozessen zur Laufzeit auf Basis des Monitorings und der KPI Analyse, (iv) eine prototypische Implementierung und experimentelle Evaluierung

Machine Learning-Powered Management Architectures for Edge Services in 5G Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

An information model for lean, agile, resilient and green supply chain management

Dissertação para a obtenção de Grau de Mestre em Engenharia e Gestão IndustrialIn modern business environments, an effective Supply Chain Management (SCM) is crucial to business continuity. In this context, Lean, Agile, Resilient and Green (LARG), are advocated as the fundamental paradigm for a competitive Supply Chain (SC) as a whole. In fact, competition between supply chains (SC) has replaced the traditional competition between companies. To make a supply chain more competitive, capable of responding to the demands of customers with agility, and capable of responding effectively to unexpected disturbance, in conjugation with environmental responsibilities, and the necessity to eliminate processes that add no value, companies must implement a set of LARG SCM practices and Key Performance Indicators (KPI) to measure their influence on the SC performance. However, the selection of the best LARG SCM practices and KPIs is a complex decision-making problem, involving dependencies and feedbacks. Still, any decision-making must be supported by real and transparent data. This dissertation intends to provide two integrated models to assist the information management and decision-making. The first is an information model to support a LARG SCM, allowing the exchange and storage of data/information through a single information platform. In this model three types of diagrams are developed, Business Process Diagram (BPD), Use Cases Diagram and Class Diagram to assist the information platform design. The second is a decision-making model, designated LARG Analytical Network Process (ANP) to select the best LARG SCM practices/KPI to be implemented in SCs. Both models are developed and validated within the automotive SC, namely in Volkswagen Autoeuropa