A linked data-driven & service-oriented architecture for sharing educational resources

The two fundamental aims of managing educational resources are to enable resources to be reusable and interoperable and to enable Web-scale sharing of resources across learning communities. Currently, a variety of approaches have been proposed to expose and manage educational resources and their metadata on the Web. These are usually based on heterogeneous metadata standards and schemas, such as IEEE LOM or ADL SCORM, and diverse repository interfaces such as OAI-PMH or SQI. Also, there is still a lack of usage of controlled vocabularies and available data sets that could replace the widespread use of unstructured text for describing resources. On the other hand, the Linked Data approach has proven that it offers a set of successful principles that have the potential to alleviate the aforementioned issues. In this paper, we introduce an architecture and prototype which is fundamentally based on (a) Linked Data principles and (b) Service-orientation to resolve the integration issues for sharing educational resources

Data-driven Service Operations Management

This dissertation concerns data driven service operations management and includes three projects. An important aim of this work is to integrate the use of rigorous and robust statistical methods into the development and analysis of service operations management problems. We develop methods that take into account demand arrival rate uncertainty and workforce operational heterogeneity. We consider the particular application of call centers, which have become a major communication channel between modern commerce and its customers. The developed tools and lessons learned have general appeal to other labor-intensive services such as healthcare. The first project concerns forecasting and scheduling with a single uncertain arrival customer stream, which can be handled by parametric stochastic programming models. Theoretical properties of parametric stochastic programming models with and without recourse actions are proved, that optimal solutions to the relaxed programs are stable under perturbations of the stochastic model parameters. We prove that the parametric stochastic programming approach meets the quality of service constraints and minimizes staffing costs in the long-run. The second project considers forecasting and staffing call centers with multiple interdependent uncertain arrival streams. We first develop general statistical models that can simultaneously forecast multiple-stream arrival rates that exhibit inter-stream dependence. The models take into account several types of inter-stream dependence. With distributional forecasts, we then implement a chance-constraint staffing algorithm to generate staffing vectors and further assess the operational effects of incorporating such inter-stream dependence, considering several system designs. Experiments using real call center data demonstrate practical applicability of our proposed approach under different staffing designs. An extensive set of simulations is performed to further investigate how the forecasting and operational benefits of the multiple-stream approach vary by the type, direction, and strength of inter-stream dependence, as well as system design. Managerial insights are discussed regarding how and when to take operational advantage of the inter-stream dependence. The third project of this dissertation studies operational heterogeneity of call center agents with regard to service efficiency and service quality. The proxies considered for agent service efficiency and service quality are agents' service times and issue resolution probabilities, respectively. Detailed analysis of agents' learning curves of service times are provided. We develop a new method to rank agents' first call resolution probabilities based on customer call-back rates. The ranking accuracy is studied and the comparison with traditional survey-driven methods is discussed.Doctor of Philosoph

Holistic, data-driven, service and supply chain optimisation: linked optimisation.

The intensity of competition and technological advancements in the business environment has made companies collaborate and cooperate together as a means of survival. This creates a chain of companies and business components with unified business objectives. However, managing the decision-making process (like scheduling, ordering, delivering and allocating) at the various business components and maintaining a holistic objective is a huge business challenge, as these operations are complex and dynamic. This is because the overall chain of business processes is widely distributed across all the supply chain participants; therefore, no individual collaborator has a complete overview of the processes. Increasingly, such decisions are automated and are strongly supported by optimisation algorithms - manufacturing optimisation, B2B ordering, financial trading, transportation scheduling and allocation. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems like supply chains. It is well-known that decisions made at one point in supply chains can have significant consequences that ripple through linked production and transportation systems. Recently, global shocks to supply chains (COVID-19, climate change, blockage of the Suez Canal) have demonstrated the importance of these interdependencies, and the need to create supply chains that are more resilient and have significantly reduced impact on the environment. Such interacting decision-making systems need to be considered through an optimisation process. However, the interactions between such decision-making systems are not modelled. We therefore believe that modelling such interactions is an opportunity to provide computational extensions to current optimisation paradigms. This research study aims to develop a general framework for formulating and solving holistic, data-driven optimisation problems in service and supply chains. This research achieved this aim and contributes to scholarship by firstly considering the complexities of supply chain problems from a linked problem perspective. This leads to developing a formalism for characterising linked optimisation problems as a model for supply chains. Secondly, the research adopts a method for creating a linked optimisation problem benchmark by linking existing classical benchmark sets. This involves using a mix of classical optimisation problems, typically relating to supply chain decision problems, to describe different modes of linkages in linked optimisation problems. Thirdly, several techniques for linking supply chain fragmented data have been proposed in the literature to identify data relationships. Therefore, this thesis explores some of these techniques and combines them in specific ways to improve the data discovery process. Lastly, many state-of-the-art algorithms have been explored in the literature and these algorithms have been used to tackle problems relating to supply chain problems. This research therefore investigates the resilient state-of-the-art optimisation algorithms presented in the literature, and then designs suitable algorithmic approaches inspired by the existing algorithms and the nature of problem linkages to address different problem linkages in supply chains. Considering research findings and future perspectives, the study demonstrates the suitability of algorithms to different linked structures involving two sub-problems, which suggests further investigations on issues like the suitability of algorithms on more complex structures, benchmark methodologies, holistic goals and evaluation, processmining, game theory and dependency analysis

DATA-DRIVEN SERVICE INNOVATION: A SYSTEMATIC LITERATURE REVIEW AND DEVELOPMENT OF A RESEARCH AGENDA

The potential created by ongoing developments in data and analytics permeates a multitude of research areas, such as the field of Service Innovation. In this paper, we conduct a Systematic Literature Review (SLR) to investigate the integration of data and analytics as an analytical unit into the field of Service Innovation – referred to as Data-Driven Service Innovation (DDSI). Overall, the SLR reveals three main research perspectives that span the research field of Data-Driven Service Innovation: Explorative DDSI, validative DDSI, and generative DDSI. This integrated theoretical framework describes the distinct operant roles of data analytics for Service Innovation, and thus contributes to the body of knowledge in the field of DDSI by providing three unified lenses, which researchers can use to describe and locate their existing and future research endeavors in this ample field. Building up on the insights from the SLR, a research agenda is proposed in order to trigger and guide further discussions and future research surrounding DDSI. Ultimately, this paper aims at contributing to the body of knowledge of Service Innovation in general and Data-Driven Service Innovation in particular by presenting a three-dimensional research space model structuring DDSI towards its advancement

Managing Customer Data in Data-driven Service Innovation: A Framework of Data Principles

While customer data has been collected in enterprise systems since decades, the emerging consumer technologies create new sources of data. Although the need to co-create services with customers has been recognized, a systematic approach of how to include this sensitive source of innovation in the service innovation process is still lacking. This research explores the potential of data governance practices for data-driven service innovation. Data principles for the governance of customer data are collected and assessed by practioners in order to provide conceptual support for organizations and to facilitate the service innovation process. The results of this research integrate data principles of different research streams and offer a framework of data principles that can be applied in the design and management of data-driven services

Identification of Data Representation Needs in Service Design

Organisations are looking for new service offers through innovative use of data, often through a Service Design approach. However, current Service Design tools conceal technological aspects of service development like data and datasets. Data can support the design of future services but is often not represented or rendered as a readily workable design material. This paper reports on an early qualitative study of the tools used to work with data and analytics in a medium-sized organisation. The findings identify the current representations of data and data analytics used in the case organisation. We discuss to which extend the available representations of data and data analytics support data-driven service innovation. A comparison of our findings and current Service Design representations show that Service Design lack to represent data as design material. We propose the notion of expansiveness as a criterion to evaluate future data representations for data-driven Service Design

Use Your Data: Design and Evaluation of a Card-Based Ideation Tool for Data-Driven Services

Using data can significantly improve service design and development. However, for businesses, developing data-driven services can be challenging. To address this, we have developed the Data Service Cards (DSCs), a card-based tool to inspire the design of data-driven services. This paper presents two cycles of a design science research (DSR) project, focusing on the second cycle of redesign and evaluation of the DSCs. We conducted a two-step evaluation, including surveys and external expert ratings of data-driven service ideas. Survey results indicate that the DSCs are a valuable tool for developing data-driven services and external experts consider services designed using DSCs to be of higher quality. With the DSCs, we provide practitioners with a tool that facilitates and improves service design and supports digital transformation. Further, we contribute to DSR literature with a rigorous experimental procedure and to service innovation by supporting the early stages of data-driven service innovation

Challenges Concerning Data-Driven Innovation

Digital transformation is highly relevant to most organisations in the business and the government sectors. One important aspect of digital transformation is the capability to exploit data in order to develop new services. For a number of businesses, this capability has become an imperative to their survival in an ever more competitive market. Today, data exploitation is of vital importance for innovation and economic growth. However, there is a lack of consolidated knowledge about the challenges of managing processes for service innovation. The purpose of this study is to elaborate on challenges concerning data-driven service innovation. We have used the Grounded Theory approach to identify such challenges which are: lack of a systematic process, problems with data access, distrust of data, lack of appropriate digital tools and insufficient competence. Our conclusions reveal that data is rarely used as a strategic resource in data-driven service innovation and that there is a lack of data management