Service quality has always been a tacit assumption within the delivery of academic library services, but since the 1990s demands for accountability from different stakeholders, including the clients, made service quality a highly debated and researched focus in academic libraries all over the world. The scope of the study covers a wide-ranging analysis of discourses underpinning service quality and its accompanying performance indicators in academic libraries. Using the academic library of the University of South Africa as an illustrative case study, this study examines the possible impact of inventory control on the service quality of the academic library in three areas, namely access to information resources, retrieval of information resources and positive implications for sound financial management. The study's findings all point to a positive enhancement of service quality in regard to the three areas mentioned. / Information Science / M. Inf.

Global consumption levels currently extend far beyond what planet Earth in terms of natural resources can regenerate in a sustainable manor and will by 2050 reach levels corresponding to what it would require three Earths to sustain. This overexploitation and unsustainable management of the Earth’s resources in combination with the necessity of mitigating climate change and reaching net zero CO2 emissions by 2050 require action across all sectors, not least the manufacturing industry. This thesis covers how large manufacturing enterprises can implement and utilize strategic targets and Key Performance Indicators (KPIs) to align with the principles of a Circular Economy (CE), and as a result, improve sustainability and business performance. Based on a case study conducted at Siemens Energy (SE) involving a literature study, interview study, and focus groups, a carefully selected set of strategic circularity targets and KPIs are presented to measure, evaluate, and drive circularity performance within large manufacturing enterprises. Since the thesis’ ambition was to provide valuable insights beyond SE, strategic circularity targets and KPIs specifically directed at SE were further generalized to be universally relevant for academia and other large manufacturing enterprises. Enterprises within the given sector share several key characteristics such as extensive material resource flows and complex value chains, hence strategic targets and KPIs emphasize material efficiency through decreasing virgin material dependency, increasing recirculation rates, and transitioning towards circular business models. While suggested targets and KPIs are universally directed at large manufacturing enterprises, individual organizations are recommended to conduct internal investigations and analyzes to further tailor and adapt strategic targets and KPIs towards the specific enterprise. In addition to strategic targets and KPIs, the thesis also presents an overview of opportunities, benefits, risks, and potential impacts for large manufacturing enterprises aspiring to increase circular initiatives, highlighting key principles to manage risk and capitalize on opportunities. The findings conclude that the main opportunity enabled by CE is to leverage synergies which align environmental, economic, and strategic corporate incentives, with key benefits including aspects such as decarbonization and reduced environmental impact, increased revenues and cost savings, risk management, and new business opportunities. Risks associated with CE include rebound effects, organizational insufficiencies, lack of material quality and safety, as well as a low product performance, which further can lead to potential impacts mitigating the positive effects of CE, or at worst setbacks causing a net negative output from implemented circular measures. In summary, the opportunities and benefits associated with CE are many, but implemented circular measures require risk awareness and continuous management to ensure efficiency.

The dairy industry is a complex system that involves many stakeholders and processes, from production to distribution. To measure and improve the performance of this system, a comprehensive and reliable Performance Measurement System (PMS) is needed. A PMS is a tool that helps to evaluate the efficiency and effectiveness of supply chain operations. One of the components of a PMS is the key performance indicators (KPIs), which reflect the performance of specific aspects of the supply chain. This thesis explores how KPIs can be used to enhance logistics performance in the dairy industry. The research is based on a single case study of a global company that operates in the dairy sector, which faces challenges such as high competition, customer demand variability, and food waste. The research uses a mix of literature review, interviews, and data analysis to identify and evaluate the relevant KPIs for logistics performance, as well as their interrelationships and impacts. The research applies the interpretive structural modeling (ISM) method to develop a hierarchical model of the KPIs and their causal relationships. The interpretive structural model of the indicators shows that the current indicators commonly used in the dairy industry today are not suitable for measuring performance, the indicators do not affect the current performance significantly but rather reflect the. This paper proposes a methodology for qualitatively evaluating performance measures, based on the integration of the ISM approach, the MICMAC analysis, and the seven criteria of a KPI. The ISM approach is a technique for identifying and analyzing the interrelationships among elements of a complex system. By applying this methodology, organizations and researchers can assess the suitability and importance of a set of KPIs. / Mejeriindustrin är ett komplext system som involverar många intressenter och processer, från produktion till distribution. För att mäta och förbättra systemets prestanda behövs ett heltäckande och tillförlitligt system för prestationsmätning (PMS). Ett PMS är ett verktyg som hjälper till att utvärdera effektiviteten i leveranskedjans verksamhet. En av komponenterna i ett PMS är nyckeltal (KPI:er), som är mått som återspeglar prestandan för specifika aspekter av leveranskedjan. Denna studie syftar till att undersöka hur KPI:er kan användas för att förbättra logistiken inom mejeriindustrin. Forskningen baseras på en fallstudie av ett globalt företag som är verksamt inom mejerisektorn, som står inför utmaningar som hög konkurrens, varierande kundefterfrågan och matsvinn. Forskningen använder en blandning av litteraturstudier, intervjuer och dataanalys för att identifiera och utvärdera relevanta KPI:er för logistikprestanda, samt deras inbördes relationer och påverkan. Forskningen tillämpar metoden ISM (Interpretive Structural Modeling) för att utveckla en hierarkisk modell av KPI:erna och deras orsakssamband. Den tolkande strukturella modellen av indikatorerna visar att de nuvarande indikatorerna som vanligtvis används i mejeriindustrin idag inte är lämpliga för att mäta prestanda, indikatorerna påverkar inte den nuvarande prestationen avsevärt utan återspeglar snarare resultaten. I denna rapport föreslås en metod för att utvärdera prestationsmått på ett kvalitativt sätt, baserat på integrationen av ISM-metoden, MICMAC-analysen och de sju kriterierna för en KPI. ISM-metoden är en teknik för att identifiera och analysera de inbördes relationerna mellan olika delar i ett komplext system. Genom att tillämpa denna metod kan organisationer och akademiker utvärdera lämpligheten och vikten av en uppsättning eller en KPI.

Organizations use Business Intelligence (BI) systems to monitor how well they are meeting their goals and objectives. Yet, very often BI systems do not include clear models of the organization’s goals or of how to measure whether they are satisfied or not. Several researchers now attempt to integrate goal models into BI systems, but there are still major challenges related to how to get access to the BI data to populate the part of the goal model (often indicators) used to assess goal satisfaction. This thesis explores a new approach to integrate BI systems with goal models. In particular, it explores the integration of IBM Cognos Business Intelligence, a leading BI tool, with an Eclipse-based goal modeling tool named jUCMNav. jUCMNav is an open source graphical editor for the User Requirements Notation (URN), which includes the Use Case Map notation for scenarios and processes and the Goal-oriented Requirement Language for business objectives. URN was recently extended with the concept of Key Performance Indicator (KPI) to enable performance assessment and monitoring of business processes. In jUCMNav, KPIs are currently calculated or modified manually. The new integration proposed in this thesis maps these KPIs to report elements that are generated automatically by Cognos based on the model defined in jUCMNav at runtime, with minimum effort. We are using IBM Cognos Mashup Service, which includes web services that enable the retrieval of report elements at the most granular level. This transformation provides managers and analysts with useful goal-oriented and process-oriented monitoring views fed by just-in-time BI information. This new solution also automates retrieving data from Cognos servers, which helps reducing the high costs usually caused by the amount of manual work required otherwise. The novel approach presented in this thesis avoids manual report generation and minimizes any contract with respect to the location of manually created reports, hence leading to better usability and performance. The approach and its tool support are illustrated with an ongoing example, validated with a case study, and verified through testing.

Organizations use Business Intelligence (BI) systems to monitor how well they are meeting their goals and objectives. Yet, very often BI systems do not include clear models of the organization’s goals or of how to measure whether they are satisfied or not. Several researchers now attempt to integrate goal models into BI systems, but there are still major challenges related to how to get access to the BI data to populate the part of the goal model (often indicators) used to assess goal satisfaction. This thesis explores a new approach to integrate BI systems with goal models. In particular, it explores the integration of IBM Cognos Business Intelligence, a leading BI tool, with an Eclipse-based goal modeling tool named jUCMNav. jUCMNav is an open source graphical editor for the User Requirements Notation (URN), which includes the Use Case Map notation for scenarios and processes and the Goal-oriented Requirement Language for business objectives. URN was recently extended with the concept of Key Performance Indicator (KPI) to enable performance assessment and monitoring of business processes. In jUCMNav, KPIs are currently calculated or modified manually. The new integration proposed in this thesis maps these KPIs to report elements that are generated automatically by Cognos based on the model defined in jUCMNav at runtime, with minimum effort. We are using IBM Cognos Mashup Service, which includes web services that enable the retrieval of report elements at the most granular level. This transformation provides managers and analysts with useful goal-oriented and process-oriented monitoring views fed by just-in-time BI information. This new solution also automates retrieving data from Cognos servers, which helps reducing the high costs usually caused by the amount of manual work required otherwise. The novel approach presented in this thesis avoids manual report generation and minimizes any contract with respect to the location of manually created reports, hence leading to better usability and performance. The approach and its tool support are illustrated with an ongoing example, validated with a case study, and verified through testing.

Organizations use Business Intelligence (BI) systems to monitor how well they are meeting their goals and objectives. Yet, very often BI systems do not include clear models of the organization’s goals or of how to measure whether they are satisfied or not. Several researchers now attempt to integrate goal models into BI systems, but there are still major challenges related to how to get access to the BI data to populate the part of the goal model (often indicators) used to assess goal satisfaction. This thesis explores a new approach to integrate BI systems with goal models. In particular, it explores the integration of IBM Cognos Business Intelligence, a leading BI tool, with an Eclipse-based goal modeling tool named jUCMNav. jUCMNav is an open source graphical editor for the User Requirements Notation (URN), which includes the Use Case Map notation for scenarios and processes and the Goal-oriented Requirement Language for business objectives. URN was recently extended with the concept of Key Performance Indicator (KPI) to enable performance assessment and monitoring of business processes. In jUCMNav, KPIs are currently calculated or modified manually. The new integration proposed in this thesis maps these KPIs to report elements that are generated automatically by Cognos based on the model defined in jUCMNav at runtime, with minimum effort. We are using IBM Cognos Mashup Service, which includes web services that enable the retrieval of report elements at the most granular level. This transformation provides managers and analysts with useful goal-oriented and process-oriented monitoring views fed by just-in-time BI information. This new solution also automates retrieving data from Cognos servers, which helps reducing the high costs usually caused by the amount of manual work required otherwise. The novel approach presented in this thesis avoids manual report generation and minimizes any contract with respect to the location of manually created reports, hence leading to better usability and performance. The approach and its tool support are illustrated with an ongoing example, validated with a case study, and verified through testing.