Stakeholder engagement as a facilitator of organizational learning

This paper examines the relationship between stakeholder engagement and competence building. Following the dual perspective of the firm, which indicated that managers deal with both transactions and competences concurrently, we argue that stakeholder interactions also concern both transaction cost reduction and value creation. Based on a review of the extant literature, we incorporated a micro-macro connection between organizational learning and competence building. Further to this, we developed a conceptual framework by linking stakeholder engagement and organizational learning. This framework demonstrates that stakeholder relations may have significant effects on organizational learning and thus stakeholder engagement can play the role of facilitator in building firm competences

Machine prognostics based on health state estimation using SVM

The ability to accurately predict the remaining useful life of machine components is critical for continuous operations in machines which can also improve productivity and enhance system safety. In condition-based maintenance (CBM), effective diagnostics and prognostics are important aspects of CBM which provide sufficient time for maintenance engineers to schedule a repair and acquire replacement components before the components finally fail. All machine components have certain characteristics of failure patterns and are subjected to degradation processes in real environments. This paper describes a technique for accurate assessment of the remnant life of machines based on prior expert knowledge embedded in closed loop prognostics systems. The technique uses Support Vector Machines (SVM) for classification of faults and evaluation of health for six stages of bearing degradation. To validate the feasibility of the proposed model, several fault historical data from High Pressure Liquefied Natural Gas (LNG) pumps were analysed to obtain their failure patterns. The results obtained were very encouraging and the prediction closely matched the real life particularly at the end of term of the bearings

The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems

Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures

Cloud controllers support the operation and quality management of dynamic cloud architectures by automatically scaling the compute resources to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of architecture adaptation rules. However, for a cloud provider, deployed application architectures are black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. We propose the dynamic learning of adaptation rules for deployed application architectures in the cloud. We introduce FQL4KE, a self-learning fuzzy controller that learns and modifies fuzzy rules at runtime. The benefit is that we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to configure cloud controllers by simply adjusting weights representing priorities for architecture quality instead of defining complex rules. FQL4KE has been experimentally validated using the cloud application framework ElasticBench in Azure and OpenStack. The experimental results demonstrate that FQL4KE outperforms both a fuzzy controller without learning and the native Azure auto-scalin

Adaptation for a Changing Environment: Developing learning and teaching with information and communication technologies

This article examines the relationship between the use of information and communication technologies (ICT) and learning and teaching, particularly in distance education contexts. We argue that environmental changes (societal, educational and technological) make it necessary to adapt systems and practices that are no longer appropriate. However, the need to adapt can be perceived as being technology-led and primarily concerned with requiring academic staff to develop their skills in using ICT. We provide a critique of continuing professional development (CPD) for using ICT in teaching and learning that does not entail examining the impact of environmental changes upon the assumptions, goals and strategies which underlie and shape an organisation's educational practices. In particular, we oppose CPD that concentrates on the individual teacher and their use of ICT. Instead, we contend that professional development should focus upon the scholarship of teaching and learning and must also reflect the wider organisational context within which ICT is managed and used

S-Net for multi-memory multicores

Copyright ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 5th ACM SIGPLAN Workshop on Declarative Aspects of Multicore Programming: http://doi.acm.org/10.1145/1708046.1708054S-Net is a declarative coordination language and component technology aimed at modern multi-core/many-core architectures and systems-on-chip. It builds on the concept of stream processing to structure dynamically evolving networks of communicating asynchronous components. Components themselves are implemented using a conventional language suitable for the application domain. This two-level software architecture maintains a familiar sequential development environment for large parts of an application and offers a high-level declarative approach to component coordination. In this paper we present a conservative language extension for the placement of components and component networks in a multi-memory environment, i.e. architectures that associate individual compute cores or groups thereof with private memories. We describe a novel distributed runtime system layer that complements our existing multithreaded runtime system for shared memory multicores. Particular emphasis is put on efficient management of data communication. Last not least, we present preliminary experimental data

'Transformations towards sustainability':Emerging approaches, critical reflections, and a research agenda

Over the last two decades researchers have come to understand much about the global challenges confronting human society (e.g. climate change; biodiversity loss; water, energy and food insecurity; poverty and widening social inequality). However, the extent to which research and policy efforts are succeeding in steering human societies towards more sustainable and just futures is unclear. Attention is increasingly turning towards better understanding how to navigate processes of social and institutional transformation to bring about more desirable trajectories of change in various sectors of human society. A major knowledge gap concerns understanding how transformations towards sustainability are conceptualised, understood and analysed. Limited existing scholarship on this topic is fragmented, sometimes overly deterministic, and weak in its capacity to critically analyse transformation processes which are inherently political and contested. This paper aims to advance understanding of transformations towards sustainability, recognising it as both a normative and an analytical concept. We firstly review existing concepts of transformation in global environmental change literature, and the role of governance in relation to it. We then propose a framework for understanding and critically analysing transformations towards sustainability based on the existing ‘Earth System Governance’ framework (Biermann et al., 2009). We then outline a research agenda, and argue that transdisciplinary research approaches and a key role for early career researchers are vital for pursuing this agenda. Finally, we argue that critical reflexivity among global environmental change scholars, both individually and collectively, will be important for developing innovative research on transformations towards sustainability to meaningfully contribute to policy and action over time