Stream processing data decision model for higher environmental performance and resilience in sustainable logistics infrastructure

Purpose: As the global freight transport network has experienced high vulnerability and threats from both natural and man-made disasters, as a result, a huge amount of data is generated in freight transport system in form of continuous streams; it is becoming increasingly important to develop sustainable and resilient transport system to recover from any unforeseen circumstances quickly and efficiently. The aim of this paper is to develop a stream processing data driven decision-making model for higher environmental performance and resilience in sustainable logistics infrastructure by using fifteen dimensions with three interrelated domains. Design/methodology/approach: A causal and hierarchical stream processing data driven decision-making model to evaluate the impact of different attributes and their interrelationships and to measure the level of environmental performance and resilience capacity of sustainable logistics infrastructure are proposed. This work uses fuzzy cognitive maps (FCMs) and fuzzy analytic hierarchy process (FAHP) techniques. A real-life case under a disruptive event scenario is further conducted. Findings: The result shows which attributes have a greater impact on the level of environmental performance and resilience capacity in sustainable logistics infrastructure. Originality/value: In this paper, causal and hierarchical stream processing data decision and control system model was proposed by identified three domains and fifteen dimensions to assess the level of environmental performance and resilience in sustainable logistics infrastructure. The proposed model gives researchers and practitioners insights about sustainability trade-offs for a resilient and sustainable global transport supply chain system by enabling to model interdependencies among the decision attributes under a fuzzy environment and streaming data

Business Intelligence and Analytics in Small and Medium-Sized Enterprises

This thesis presents a study of Business Intelligence and Analytics (BI&A) adoption in small and medium-sized enterprises (SMEs). Although the importance of BI&A is widely accepted, empirical research shows SMEs still lag in BI&A proliferation. Thus, it is crucial to understand the phenomenon of BI&A adoption in SMEs. This thesis will investigate and explore BI&A adoption in SMEs, addressing the main research question: How can we understand the phenomenon of BI&A adoption in SMEs? The adoption term in this thesis refers to all the IS adoption stages, including investment, implementation, utilization, and value creation. This research uses a combination of a literature review, a qualitive exploratory approach, and a ranking-type Delphi study with a grounded Delphi approach. The empirical part includes interviews with 38 experts and Delphi surveys with 39 experts from various Norwegian industries. The research strategy investigates the factors influencing BI&A adoption in SMEs. The study examined the investment, implementation, utilization, and value creation of BI&A technologies in SMEs. A thematic analysis was adopted to collate the qualitative expert interview data and search for potential themes. The Delphi survey findings were further examined using the grounded Delphi method. To better understand the study’s findings, three theoretical perspectives were applied: resource-based view theory, dynamic capabilities, and IS value process models. The thesis’ research findings are presented in five articles published in international conference proceedings and journals. This thesis summary will coherently integrate and discuss these results.publishedVersio

Integrating enterprise resource planning into electronic content management in a South African water utility company

Bibliography: pages 184-207Digital records are either stored in an enterprise resource planning (ERP) system or electronic content management (ECM), or managed without the benefit of either system. In many countries, public and private organisations have implemented ECM systems, some have implemented ERP systems and others generate digital records without the benefit of any controlled system. In most organisations such systems are not integrated resulting in duplication and fragmentation of records. The South African Water Utility company, Rand Water, has implemented both ERP and ECM systems. Investing in these systems as an organisation comes at a cost but it can add value when used optimally to improve the organisation’s productivity and efficiency. To achieve high productivity and efficiency, integration of an ERP system into an ECM system is a requirement but remains lacking. This qualitative study utilised the Actor Network Theory to explore the integration of ERP into ECM at the South African Water Utility company, Rand Water, with a view to developing a framework for integration of the systems. The study utilised a system analysis case design with fourteen interviews conducted at different levels in the organisation and diverse business units using ERP and ECM to perform their operational deliverables in line with the organisation’s business objectives. The interviews were augmented with data from document analysis of policies, specifications and functionalities of the systems to determine the feasibility of integration. The study established that the water utility company has implemented ERP systems (SAP) since 1994 and ECM system since 1991 (Papertrail and later IBM FileNet) with only information flow module integrated. The study suggested that to integrate ERP into ECM, human and non-human actors need to collaborate to ensure that the actor network being integrated is achieved. The study also presents a strategy discussion for integrating ERP into ECM. A further study on the transfer of digital records in ECM into archival custody is recommended.Information ScienceD. Phil. (Information Science