A Tale of Evaluation and Reporting in UK Smart Cities

Global trends towards urbanisation are associated with wide-ranging challenges and opportunities for cities. Smart technologies create new opportunities for a range of smart city development and regeneration programmes designed to address the environmental, economic and social challenges concentrated in cities. Whilst smart city programmes have received much publicity, there has been much less discussion about evaluation of smart city programmes and the measurement of their outcomes for cities. Existing evaluation approaches have been criticised as non-standard and inadequate, focusing more on implementation processes and investment metrics than on the impacts of smart city programmes on strategic city outcomes and progress. To examine this, the SmartDframe project conducted research on city approaches to the evaluation of smart city projects and programmes, and reporting of impacts on city outcomes. This included the ‘smarter’ UK cities of Birmingham, Bristol, Manchester, Milton Keynes and Peterborough. City reports and interviews with representative local government authorities informed the case study analysis. The report provides a series of smart city case studies that exemplify contemporary city practices, offering a timely, insightful contribution to city discourse about best practice approaches to evaluation and reporting of complex smart city projects and programmes

Big Data Analytics for QoS Prediction Through Probabilistic Model Checking

As competitiveness increases, being able to guaranting QoS of delivered services is key for business success. It is thus of paramount importance the ability to continuously monitor the workflow providing a service and to timely recognize breaches in the agreed QoS level. The ideal condition would be the possibility to anticipate, thus predict, a breach and operate to avoid it, or at least to mitigate its effects. In this paper we propose a model checking based approach to predict QoS of a formally described process. The continous model checking is enabled by the usage of a parametrized model of the monitored system, where the actual value of parameters is continuously evaluated and updated by means of big data tools. The paper also describes a prototype implementation of the approach and shows its usage in a case study.Comment: EDCC-2014, BIG4CIP-2014, Big Data Analytics, QoS Prediction, Model Checking, SLA compliance monitorin

Towards an integrated perspective on fleet asset management: engineering and governance considerations

The traditional engineering perspective on asset management concentrates on the operational performance the assets. This perspective aims at managing assets through their life-cycle, from technical specification, to acquisition, operation including maintenance, and disposal. However, the engineering perspective often takes for granted organizational-level factors. For example, a focus on performance at the asset level may lead to ignore performance measures at the business unit level. The governance perspective on asset management usually concentrates on organizational factors, and measures performance in financial terms. In doing so, the governance perspective tends to ignore the engineering considerations required for optimal asset performance. These two perspectives often take each other for granted. However experience demonstrates that an exclusive focus on one or the other may lead to sub-optimal performance. For example, the two perspectives have different time frames: engineering considers the long term asset life-cycle whereas the organizational time frame is based on a yearly financial calendar. Asset fleets provide a relevant and important context to investigate the interaction between engineering and governance views on asset management as fleets have distributed system characteristics. In this project we investigate how engineering and governance perspectives can be reconciled and integrated to enable optimal asset and organizational performance in the context of asset fleets

Key Performance Indicators for Business Models: A Review of Literature

To support decision-making during the business model innovation process, researchers have investigated approaches for evaluating business models. Key Performance Indicators (KPIs) related to business models can play an important role in evaluating the performance of business models, as they reflect the decisions and activities that drive the critical aspects of the organization. To date, there has been considerable research on business model KPIs. However, current research lacks an overall understanding of how business model KPIs are managed. Therefore, this paper aims to contribute with a classification of existing studies on business model KPIs in five categories relevant to KPI management, as well as future research avenues. In particular, we identify the development of methods and software tools to support selection, concretization, and reporting of business model KPIs, and the design of an integrated approach for business model KPI management as important areas for further research

Digital Innovation in Corporations: Deriving a Practical Framework for the Measurement of Success of Digital Innovation Units

Confronted with entirely new challenges resulting from digital technologies, established corporations increasingly set up dedicated digital innovation units (DIUs) to foster digital innovation and to explore opportunities for the digital future. Although DIUs recently face criticism with regards to their performance and impact on the core organization, literature lacks in suitable approaches to assess the success of DIUs. Therefore, we derive a practical framework for the measurement of success of DIUs in the course of this research project. We develop this framework by identifying critical success factors (CSFs) and key performance indicators (KPIs). Subsequently, we merge our results with existing literature. To determine these CSFs and KPIs, we designed an explorative, qualitative-empirical case study research approach. The research design is based on a mixed-method approach that combines semi-structured interviews as core component with a supplementary survey. Conducting nine cross-industry case studies, we identified 16 CSFs and 38 objective related KPIs. Thus, the framework derived in this thesis contributes to practice and literature by addressing the existing gap in DIU and performance measurement research. Keywords: Digital innovation units; performance measurement; critical success factors; key performance indicators; qualitative case studies.Confronted with entirely new challenges resulting from digital technologies, established corporations increasingly set up dedicated digital innovation units (DIUs) to foster digital innovation and to explore opportunities for the digital future. Although DIUs recently face criticism with regards to their performance and impact on the core organization, literature lacks in suitable approaches to assess the success of DIUs. Therefore, we derive a practical framework for the measurement of success of DIUs in the course of this research project. We develop this framework by identifying critical success factors (CSFs) and key performance indicators (KPIs). Subsequently, we merge our results with existing literature. To determine these CSFs and KPIs, we designed an explorative, qualitative-empirical case study research approach. The research design is based on a mixed-method approach that combines semi-structured interviews as core component with a supplementary survey. Conducting nine cross-industry case studies, we identified 16 CSFs and 38 objective related KPIs. Thus, the framework derived in this thesis contributes to practice and literature by addressing the existing gap in DIU and performance measurement research. Keywords: Digital innovation units; performance measurement; critical success factors; key performance indicators; qualitative case studies

A Performance Assessment System incorporating indirect indicators and semantics

Measuring performance is key to reengineering and optimization of business processes. Although many of them cannot easilybe measured due to their quantitative or non-deterministic nature, most performance measurement systems rely on the usageof numeric parameters (Key Performance Indicators, KPIs). So, performance problems stay invisible that could be assessedby other indirect indicators like goals, complexity, maturity, relations or dependencies. In this paper, a Four-Box-Model ispresented that also includes internal process views, descriptive approaches and semantics in addition to KPIs. It offers a broadrange of possibilities to better identify performance problems and hence, to increase process performance

System-level key performance indicators for building performance evaluation

Quantifying building energy performance through the development and use of key performance indicators (KPIs) is an essential step in achieving energy saving goals in both new and existing buildings. Current methods used to evaluate improvements, however, are not well represented at the system-level (e.g., lighting, plug-loads, HVAC, service water heating). Instead, they are typically only either measured at the whole building level (e.g., energy use intensity) or at the equipment level (e.g., chiller efficiency coefficient of performance (COP)) with limited insights for benchmarking and diagnosing deviations in performance of aggregated equipment that delivers a specific service to a building (e.g., space heating, lighting). The increasing installation of sensors and meters in buildings makes the evaluation of building performance at the system level more feasible through improved data collection. Leveraging this opportunity, this study introduces a set of system-level KPIs, which cover four major end-use systems in buildings: lighting, MELs (Miscellaneous Electric Loads, aka plug loads), HVAC (heating, ventilation, and air-conditioning), and SWH (service water heating), and their eleven subsystems. The system KPIs are formulated in a new context to represent various types of performance, including energy use, peak demand, load shape, occupant thermal comfort and visual comfort, ventilation, and water use. This paper also presents a database of system KPIs using the EnergyPlus simulation results of 16 USDOE prototype commercial building models across four vintages and five climate zones. These system KPIs, although originally developed for office buildings, can be applied to other building types with some adjustment or extension. Potential applications of system KPIs for system performance benchmarking and diagnostics, code compliance, and measurement and verification are discussed