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

The Modeling of Multicriteria Assessment Activity in Enterprise Management

A lot of functions of enterprise management are grounded on the analytical basis that include the models of activity assessment which appears to be multicriteria under the complicated conditions of globalistics. The aim of the study is to improve the mathematical instrument for modeling the multicriteria assessment of enterprise activity. Taking into account the positive practice of using the Balanced Scorecard for assessing enterprise performance, its criteria are structured according to four components and serve for content assessment model. The assessment of enterprise activity based on the Balanced Scorecard mostly accounted for cause and effect interconnections, but this is just its level that influences the assessment. The paper presents the improved function of transforming the assessment criteria values and the formula of value calibration. The advantages of this function are flexibility and taking account the regular tendencies of changes in the criteria values. The level of enterprise performance is determined by an integrated index obtained as partial desirability functions folding by gmean. This generalizing desirability function is sensitive to small transformed criteria values that realizes tough requirements to assessment. The developed methodological approach in the modeling of multicriteria assessment of enterprise activity provides for taking into account the main criteria and possibility of their hierarchical systematization. The results of such modeling can be used in the processes of control, controlling and monitoring of this activity

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

Agent Based Modeling and Simulation Framework for Supply Chain Risk Management

This research develops a flexible agent-based modeling and simulation (ABMS) framework for supply chain risk management with significant enhancements to standard ABMS methods and supply chain risk modeling. Our framework starts with the use of software agents to gather and process input data for use in our simulation model. For our simulation model, we extend an existing mathematical framework for discrete event simulation (DES) to ABMS and then implement the concepts of variable resolution modeling from the DES domain to ABMS and provide further guidelines for aggregation and disaggregation of supply chain models. Existing supply chain risk management research focuses on consumable item supply chains. Since the Air Force supply chain contains many reparable items, we fill this gap with our risk metrics framework designed for reparable item supply chains, which have greater complexity than consumable item supply chains. We present new metrics, along with existing metrics, in a framework for reparable item supply chain risk management and discuss aggregation and disaggregation of metrics for use with our variable resolution modeling

An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success

Optimizing strategic sourcing in the healthcare supply chain with consideration of physician preference and vendor scorecards

This research focuses on the design of a procurement model for expensive medical supplies in a healthcare supply chain. A deterministic optimization model generates recommendations for optimal purchases of products in a given planning period. The model combines common concepts of supply chain procurement such as leveraging tiered pricing, ensuring supply base diversity with phenomena unique to healthcare supply chain such as consideration of physician preference for products. The deterministic optimization model minimizes total spend over a chosen planning period with consideration of four key decision parameters: Physician preference requirements (which are imposed as rules on product substitutability), Upper limits on vendor market share to ensure a suitably diverse supply base Vendors’ performance scores to impose standards for product pricing, quality, service, etc. Quantity discount rebate parameters for bulk purchasing to help contain medical costs The optimization model reveals the extent to which higher product substitutability and lower supply base diversity may help hospitals reduce total procurement costs. Experiments with the optimization model also reveal the potential consequences of rater biases in vendor scorecards on procurement cost. The various parameter combinations listed above may be used in negotiating contracts for better pricing. In summary, this research addresses questions pertinent to healthcare supply chains concerning the possible cost of physician preference for products, the impact of subjective scorecards on procurement costs, the effect of planning period on procurement plans, and the cost of vendor diversity

Using boosting for automated planning and trading systems

The problem: Much of finance theory is based on the efficient market hypothesis. According to this hypothesis, the prices of financial assets, such as stocks, incorporate all information that may affect their future performance. However, the translation of publicly available information into predictions of future performance is far from trivial. Making such predictions is the livelihood of stock traders, market analysts, and the like. Clearly, the efficient market hypothesis is only an approximation which ignores the cost of producing accurate predictions. Markets are becoming more efficient and more accessible because of the use of ever faster methods for communicating and analyzing financial data. Algorithms developed in machine learning can be used to automate parts of this translation process. In other words, we can now use machine learning algorithms to analyze vast amounts of information and compile them to predict the performance of companies, stocks, or even market analysts. In financial terms, we would say that such algorithms discover inefficiencies in the current market. These discoveries can be used to make a profit and, in turn, reduce the market inefficiencies or support strategic planning processes. Relevance: Currently, the major stock exchanges such as NYSE and NASDAQ are transforming their markets into electronic financial markets. Players in these markets must process large amounts of information and make instantaneous investment decisions. Machine learning techniques help investors and corporations recognize new business opportunities or potential corporate problems in these markets. With time, these techniques help the financial market become better regulated and more stable. Also, corporations could save significant amount of resources if they can automate certain corporate finance functions such as planning and trading. Results: This dissertation offers a novel approach to using boosting as a predictive and interpretative tool for problems in finance. Even more, we demonstrate how boosting can support the automation of strategic planning and trading functions. Many of the recent bankruptcy scandals in publicly held US companies such as Enron and WorldCom are inextricably linked to the conflict of interest between shareholders (principals) and managers (agents). We evaluate this conflict in the case of Latin American and US companies. In the first part of this dissertation, we use Adaboost to analyze the impact of corporate governance variables on performance. In this respect, we present an algorithm that calculates alternating decision trees (ADTs), ranks variables according to their level of importance, and generates representative ADTs. We develop a board Balanced Scorecard (BSC) based on these representative ADTs which is part of the process to automate the planning functions. In the second part of this dissertation we present three main algorithms to improve forecasting and automated trading. First, we introduce a link mining algorithm using a mixture of economic and social network indicators to forecast earnings surprises, and cumulative abnormal return. Second, we propose a trading algorithm for short-term technical trading. The algorithm was tested in the context of the Penn-Lehman Automated Trading Project (PLAT) competition using the Microsoft stock. The algorithm was profitable during the competition. Third, we present a multi-stock automated trading system that includes a machine learning algorithm that makes the prediction, a weighting algorithm that combines the experts, and a risk management layer that selects only the strongest prediction and avoids trading when there is a history of negative performance. This algorithm was tested with 100 randomly selected S&P 500 stocks. We find that even an efficient learning algorithm, such as boosting, still requires powerful control mechanisms in order to reduce unnecessary and unprofitable trades that increase transaction costs

Diseño de herramientas para el control de gestión por indicadores

El proyecto es una actividad de investigación y transferencia con aportes científicos en el campo de la mejora de la gestión por indicadores. Cuenta con colaboradores de grupo de investigación en Informática de gestión Facultad de Ciencias Exactas, Universidad Nacional del Centro de la provincia de Buenos Aires y con miembros de Universidades extranjeras. Es una actividad de investigación y transferencia con aportes científicos en el campo de la mejora de la gestión por indicadores, en organizaciones conscientes de la importancia de tomar decisiones estratégicas a partir de la evolución de los indicadores que marcan el rumbo y el estado de salud de la organización. Los principales aportes de este proyecto se pueden resumir como una propuesta de solución integral a la metodología de gestión por indicadores compuesta por el marco metodológico, el marco tecnológico, el ciclo de vida y los procedimientos que se desarrollan en cada uno de estos aspectos.Eje: Innovación en Sistemas de SoftwareRed de Universidades con Carreras en Informática (RedUNCI

Diseño de herramientas para el control de gestión por indicadores

El proyecto es una actividad de investigación y transferencia con aportes científicos en el campo de la mejora de la gestión por indicadores. Cuenta con colaboradores de grupo de investigación en Informática de gestión Facultad de Ciencias Exactas, Universidad Nacional del Centro de la provincia de Buenos Aires y con miembros de Universidades extranjeras. Es una actividad de investigación y transferencia con aportes científicos en el campo de la mejora de la gestión por indicadores, en organizaciones conscientes de la importancia de tomar decisiones estratégicas a partir de la evolución de los indicadores que marcan el rumbo y el estado de salud de la organización. Los principales aportes de este proyecto se pueden resumir como una propuesta de solución integral a la metodología de gestión por indicadores compuesta por el marco metodológico, el marco tecnológico, el ciclo de vida y los procedimientos que se desarrollan en cada uno de estos aspectos.Eje: Innovación en Sistemas de SoftwareRed de Universidades con Carreras en Informática (RedUNCI