The Role of Knowledge Management in Supply Chain Management: A Literature Review

Purpose: The aim of this paper is to examine the state of knowledge management research in supply chain management from three standpoints, methodological approach, supply chain management area, and knowledge management processes. Design/methodology/approach: To achieve this, a systematic review is conducted over the period 2000-2014 on the basis of a qualitative content analysis. Findings: Major results showed that knowledge management can be viewed as a leverage mechanism for: (i) supply chain integration; (ii) the enhancement of intra and inter-relations across the supply chain; (iii) supply chain strategy alignment; and (iv) the reinforcement of knowledge transfer in product development. Some supply chain management areas such as reverse logistics, inventory management, forecasting/demand planning, outsourcing, and risk management have been explored only to some extent. Furthermore, knowledge transfer is being studied in the majority of the articles, mainly by both case study and survey approach; mathematical models and simulation techniques are used in very limited articles. Findings concerning theoretical perspectives and managerial issues are also described. Research limitations/implications: The limitation of our study encompasses the aspects of search period (2000-2014), selection of search databases (Web of Science and SCOPUS and language selection (English). Practical implications: The exhibition of the KM processes within the SC context may help practitioners and managers interested in implementing KM initiatives to replicate the methodologies in order to increase the possibilities of a successful KM adoption. Originality/value: The systematic review will contribute to the understanding of the present state of research in the knowledge management theory, with focus on the supply chain, as there are no state-of-knowledge studies that report a systematic literature review approach.Peer Reviewe

Risk pricing practices in finance, insurance and construction

A review of current risk pricing practices in the financial, insurance and construction sectors is conducted through a comprehensive literature review. The purpose was to inform a study on risk and price in the tendering processes of contractors: specifically, how contractors take account of risk when they are calculating their bids for construction work. The reference to mainstream literature was in view of construction management research as a field of application rather than a fundamental academic discipline. Analytical models are used for risk pricing in the financial sector. Certain mathematical laws and principles of insurance are used to price risk in the insurance sector. construction contractors and practitioners are described to traditionally price allowances for project risk using mechanisms such as intuition and experience. Project risk analysis models have proliferated in recent years. However, they are rarely used because of problems practitioners face when confronted with them. A discussion of practices across the three sectors shows that the construction industry does not approach risk according to the sophisticated mechanisms of the two other sectors. This is not a poor situation in itself. However, knowledge transfer from finance and insurance can help construction practitioners. But also, formal risk models for contractors should be informed by the commercial exigencies and unique characteristics of the construction sector

Exploring an FET mathematical literacy professional learning community (PLC) as a space that contributes to teacher knowledge.

Masters Degree. Universityof KwaZulu-Natal, Pietermaritzburg.According to the Trends in International Mathematics and Science Study document, there was a high failure rate in grade 9 Mathematics, and the new subject Mathematical Literacy was introduced in grade 10 to grade12 as an alternative for learners who did not do well in Mathematics. Since this was a new subject, teachers of Mathematical Literacy were encouraged to work collaboratively as clusters in order to be able to face challenges of teaching this new subject and to review their classroom practice as Mathematical Literacy teachers collectively. This idea of working as a cluster was drawn from the Integrated Strategic Planning Framework for Teacher Development which encouraged the formation of professional learning communities for teachers teaching the same subject. The objective of this study is to explore the types of teacher knowledge acquired by Mathematical Literacy teachers participating in the cluster and to further explore if this cluster reflected the characteristics of an effective professional learning community. The study is located within the interpretive paradigm and adopts a qualitative case study approach. Purposive sampling was used to select four Mathematical Literacy teachers to serve as participants of this study. Semi-structured interviews with the participants were conducted and two Mathematical Literacy cluster meetings were observed. The study is based in uMgungundlovu district in KwaZulu-Natal. The conceptual frameworks that underpin this study are Shulman’s domains of teacher knowledge which identified the types of teacher knowledge teachers need to have to be efficient in their practice, and Brodie’s characteristics of an effective professional learning community. The findings of this study show that participants mainly acquired general pedagogical knowledge, pedagogical content knowledge, content knowledge, and curriculum knowledge during cluster meetings. General pedagogical knowledge and pedagogical content knowledge were mentioned most often because teachers focussed mainly on classroom management and teaching methods to make the subject matter understandable to learners. Knowledge of learners and their characteristics was not mentioned often, and knowledge of context and Knowledge of educational ends, purposes and values were the knowledge domains least mentioned or acquired by participants. In addition it was also noted that four of the characteristics of an effective professional learning community were identified during cluster meetings: collegiality, professional collaboration, shared trust and shared values, goals and visions. Therefore, the Mathematical Literacy professional learning community can, to some extent, is regarded as an effective professional learning community. This study recommends that more time should be allocated for Mathematical Literacy teachers to meet at least once every month. A further recommendation is that subject advisors facilitate learning activities that focus on and develop all seven domains of teacher knowledge

LearnFCA: A Fuzzy FCA and Probability Based Approach for Learning and Classification

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering. This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of it’s applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems. We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success. Adviser: Dr Jitender Deogu

LEARNFCA: A FUZZY FCA AND PROBABILITY BASED APPROACH FOR LEARNING AND CLASSIFICATION

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering. This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of it’s applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems. We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success. Adviser: Jitender Deogu

Estimating fisheries-induced selection: traditional gear selectivity research meets fisheries-induced evolution

The study of fisheries-induced evolution is a research field which is becoming recognized both as an important and interesting problem in applied evolution, as well as a practical management problem in fisheries. Much of the research in fisheries-induced evolution has focussed on quantifying and proving that an evolutionary response has taken place, but less effort has been invested on the actual processes and traits underlying capture of a fish by a fishing gear. This knowledge is not only needed to understand possible phenotypic selection associated to fishing but also to help to device sustainable fisheries and management strategies. Here, we draw attention to the existing knowledge about selectivity of fishing gears and outline the ways in which this information could be utilized in the context of fisheries-induced evolution. To these ends, we will introduce a mathematical framework commonly applied to quantify fishing gear selectivity, illustrate the link between gear selectivity and the change in the distribution of phenotypes induced by fishing, review what is known about selectivity of commonly used fishing gears, and discuss how this knowledge could be applied to improve attempts to predict evolutionary impacts of fishing

Perspectives for systems biology in the management of tuberculosis

Standardised management of tuberculosis may soon be replaced by individualised, precision medicine-guided therapies informed with knowledge provided by the field of systems biology. Systems biology is a rapidly expanding field of computational and mathematical analysis and modelling of complex biological systems that can provide insights into mechanisms underlying tuberculosis, identify novel biomarkers, and help to optimise prevention, diagnosis and treatment of disease. These advances are critically important in the context of the evolving epidemic of drug-resistant tuberculosis. Here, we review the available evidence on the role of systems biology approaches - human and mycobacterial genomics and transcriptomics, proteomics, lipidomics/metabolomics, immunophenotyping, systems pharmacology and gut microbiomes - in the management of tuberculosis including prediction of risk for disease progression, severity of mycobacterial virulence and drug resistance, adverse events, comorbidities, response to therapy and treatment outcomes. Application of the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach demonstrated that at present most of the studies provide "very low" certainty of evidence for answering clinically relevant questions. Further studies in large prospective cohorts of patients, including randomised clinical trials, are necessary to assess the applicability of the findings in tuberculosis prevention and more efficient clinical management of patients.Publisher PDFPeer reviewe

Using LEL and scenarios to derive mathematical programming models. Application in a fresh tomato packing problem

[EN] Mathematical programming models are invaluable tools at decision making, assisting managers to uncover otherwise unattainable means to optimize their processes. However, the value they provide is only as good as their capacity to capture the process domain. This information can only be obtained from stakeholders, i.e., clients or users, who can hardly communicate the requirements clearly and completely. Besides, existing conceptual models of mathematical programming models are not standardized, nor is the process of deriving the mathematical programming model from the concept model, which remains ad hoc. In this paper, we propose an agile methodology to construct mathematical programming models based on two techniques from requirements engineering that have been proven effective at requirements elicitation: the language extended lexicon (LEL) and scenarios. Using the pair of LEL + scenarios allows to create a conceptual model that is clear and complete enough to derive a mathematical programming model that effectively captures the business domain. We also define an ontology to describe the pair LEL + scenarios, which has been implemented with a semantic mediawiki and allows the collaborative construction of the conceptual model and the semi-automatic derivation of mathematical programming model elements. The process is applied and validated in a known fresh tomato packing optimization problem. This proposal can be of high relevance for the development and implementation of mathematical programming models for optimizing agriculture and supply chain management related processes in order to fill the current gap between mathematical programming models in the theory and the practice.This work was supported by the European Commission, project RUC-APS, grant number 691249, funded by the European Union's research and innovation programme under the H2020 Marie SklodowskaCurie Actions; and the Argentinian National Agency for Scientific and Technical Promotion (ANPCyT), grant number PICT-2015-3000.Garrido, A.; Antonelli, L.; Martin, J.; Alemany Díaz, MDM.; Mula, J. (2020). Using LEL and scenarios to derive mathematical programming models. Application in a fresh tomato packing problem. Computers and Electronics in Agriculture. 170:1-14. https://doi.org/10.1016/j.compag.2020.105242S114170Alemany, M., Ortiz, A., & Fuertes-Miquel, V. S. (2018). 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System monitoring and maintenance policies: a review

In the industrial context, the main goal of the maintenance team is to avoid sudden failures that can cause the stoppage of the system with a consequent loss of production. This means that each maintenance action must be performed before the degradation level of a system exceeds a critical threshold beyond which the failure probability becomes high. The increasing importance given to maintenance is shown not only by the great deal of literature on the topic, but also by the interest in transforming this area from a managerial area to a branch of applied mathematics (Operational Research or Statistics). Maintenance is now considered as a subject and much research activity is concerned with its mathematical modeling rather than with the management processes relating to maintenance itself. In [1], Scarf evidences the great importance of the mathematical modeling of maintenance and the correlated strategic support given by the maintenance management information systems. Nevertheless, no model can be built without an exhaustive collection of data. By data, Author means not only specific figures regarding, for example, failure times, but all information related to the process under study. With the recent advent of condition monitoring and the development of appropriate decision models, critical components of a system can be tracked through appropriate variable(s) correlated to their degradation process, logistic support (for example, spares inventory) can be provided, maintenance history can be stored, predetermined maintenance activity can be alarmed and management reports can be produced. The use of condition monitoring techniques reduces the uncertainty operators feel about the current state of the plant. For example, knowledge about the vibration levels of a rotating bearing gives engineers confidence about its operation in the short term. Data acquired by monitoring systems, maintenance histories collected for specific components can be considered fundamental resources for the mathematical modeling of the maintenance activities. This paper is the first part of two [2], presenting the transition from preventive maintenance policy to the predictive one. In particular, the paper presents a brief review of the subject and some critical considerations about the two maintenance policies

Characterizing the use of mathematical knowledge in boundary crossing situations at work

The first aim of this paper is to present a characterisation of techno-mathematical literacies needed for effective practice in modern, technology-rich workplaces that are both highly automated and increasingly focused on flexible response to customer needs. The second aim is to introduce an epistemological dimension to activity theory, specifically to the notions of boundary object and boundary crossing. In this paper we draw on ethnographic research in a pensions company and focus on data derived from detailed analysis of the diverse perspectives that exist with respect to one symbolic artefact, the annual pension statement. This statement is designed to facilitate boundary crossing between company and customers. Our study showed that the statement routinely failed in this communicative role, largely due to the invisible factors of the mathematical-financial models underlying the statement that are not made visible to customers, or to the customer enquiry team whose task is to communicate with customers. By focusing on this artefact in boundary-crossing situations, we identify and elaborate the nature of the techno-mathematical knowledge required for effective communication between different communities in one financial services workplace, and suggest the implications of our findings for workplaces more generally