745,050 research outputs found

    Research on IT tools used in flow planning in supply chains

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    The processes of globalization and increased competitiveness caused that the company have begun to look for solutions and strategies that give opportunities for development and further market expansion. The intensification of competition on the global scale in the 1980s, forced companies to offer low cost, high quality and robust products with greater design flexibility. Various management concepts have been used to improve production efficiency and speed up the cycle. In the 1990s, many manufacturers and service providers began to focus on improving collaboration with suppliers and improving purchasing and supply management. Initially the cooperation was mainly developed in the area of purchasing policies and supply management in factories, but as time went on, it became increasingly popular among wholesalers and retailers who also decided to integrate their transport and logistics functions in a supply chain with a view to gain competitive advantage (Choon T.). The increase in customer requirements with the same pressures to reduce costs and accelerate time service delivery resulted in the growing importance of planning and anticipation of future events. The increasing amount of data makes management depended on systems that allow for rapid decision-making. Apart from ERP systems, which mainly support the management of individual businesses, more and more companies invest in systems APS (Advanced Planning System), which provide  better support in planning processes throughout the supply chain. The aim of the article is to present the idea of advanced supply chain planning, constraints and challenges facing the managing the flow of products and information. The study also addressed the role of Advanced Planning Systems in supply chain management and identify main problems and obstacles that users and decision makers come across while implementing and using IT system

    Event Management for Sensing Enterprises with Decision Support Systems

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    The final publication is available at Springer via http://dx.doi.org/10.1007/s40745-015-0034-z[EN] Sensing enterprises make use of new technologies to capture real-time information and fed constantly the decision making process. Decision support systems (DSS) are exposed to these real-time events and it is possible to start the decision process from scratch in case any unexpected internal and external events take place. Thus, an event monitoring and management system should interact with the DSS to manage events that might affect their decisions. It should act as a supra-system to identify when decisions made are still valid or need to be reanalysed. The traditional configuration of DSS (where they collect internal and external information of the organization and the decision-maker is involved in the decision-making process) should be extended to treat event management using a monitoring and management system, which monitors internal and external information and facilitate the introduction of no monitored events. This monitor and manager systems become more and more necessary due to the incessant incorporation of new technologies that enables the companies to be more context-sensitive. Furthermore, this new and/or more accurate information, which is obtained for the organization, requires a proper management.This research has been carried out in the framework of the project PAID-06-21Universitat Politècnica de València (Sistema de ayuda a la toma de decisiones ante decisiones no programadas en la planificación jerárquica de la producción) and GV/2014/010 Generalitat Valenciana (Identificación de la información proporcionada por los nuevos sistemas de detección accesibles mediante internet en el ámbito de las “sensing enterprises” para la mejora de la toma de decisiones en la planificación de la producción).Boza, A.; Alemany Díaz, MDM.; Cuenca, L.; Ortiz Bas, Á. (2015). Event Management for Sensing Enterprises with Decision Support Systems. Annals of Data Science. 2(1):103-109. https://doi.org/10.1007/s40745-015-0034-zS10310921Estupinyà P (2010) El ladrón de cerebros: Compartiendo el conocimiento científico de las mentes más brillantes. Penguin Random House Grupo Editorial EspañaVicens E, Alemany ME, Andrés C, Guarch JJ (2001) A design and application methodology for hierarchical production planning decision support systems in an enterprise integration context. Int J Prod Econ 74:5–20. doi: 10.1016/S0925-5273(01)00103-7Van Wezel W, Van Donk DP, Gaalman G (2006) The planning flexibility bottleneck in food processing industries. J Oper Manag 24:287–300. doi: 10.1016/j.jom.2004.11.001Winter R (1994) Multi-stage production controlling based on continuous, flexible abstraction hierarchies. IEPMÖzdamar L, Bozyel MA, Birbil SI (1998) A hierarchical decision support system for production planning (with case study). Eur J Oper Res 104:403–422. doi: 10.1016/S0377-2217(97)00016-7FInES FIESC (2012) FInES Research Roadmap 2025Shamsuzzoha Ah, Rintala S, Cunha PF, Ferreira PS, Kankaanpää T, Maia Carneiro L (2013) Event monitoring and management process in a non-hierarchical business network. In: Poler R, Carneiro L, Jasinski T, Zolghadri rc, Pedrazzoli P (eds) Intelligent non-hierarchical manufacturing networks. Wiley, New York, pp 349–374Committee of Sponsoring Organizations of the Treadway Commission (2004) COSO enterprise risk management-integrated framework: application techniques. Committee of Sponsoring Organizations of the Treadway CommissionSantucci G, Martinez C, Vlad-Câlcic D (2012) The sensing enterprise. FInES workshop at FIA 2012Vargas A, Cuenca L, Boza A, Sacala I, Moisescu M (2014) Towards the development of the framework for inter sensing enterprise architecture, J Intell Manuf, 1–18Anthony RN (1965) Planning and control systems: a framework for analysis. Harvard University, CambridgeSimon HA (1960) The new science of management decision. Harper & Brothers, New YorkShim JP, Warkentin M, Courtney JF, Power DJ, Sharda R, Carlsson C (2002) Past, present, and future of decision support technology. Decis Support Syst 33:111–126. doi: 10.1016/S0167-9236(01)00139-7Peng Y, Kou G, Shi Y, Chen ZA (2008) Descriptive framework for the field of data mining and knowledge discovery. Int J Inf Technol Decis Mak 7:639–682. doi: 10.1142/S0219622008003204Alarcón F, Alemany MME, Lario FC, Oltra RF (2011) La falta de homogeneidad del producto (FHP) en las empresas cerámicas y su impacto en la reasignación del inventario. Boletín de la Sociedad Española de Cerámica y Vidrio 50:49–58. doi: 10.3989/cyv.072011Cegarra J, van Wezel W (2011) A comparison of task analysis methods for planning and scheduling. In: Fransoo JC, Waefler T, Wilson JR (eds) Behavioral operations in planning and scheduling. Springer, Berlin Heidelberg, pp 323–338FP7-ICT (2012) ICT: Information and Communication Technologies: work programme 2013Barash G, Bartolini C, Wu L (2007) Measuring and improving the performance of an IT support organization in managing service incidents. In: 2nd IEEE/IFIP international workshop on business-driven IT management, BDIM ’07, pp 11–18Bartolini C, Stefanelli C, Tortonesi M (2010) SYMIAN: analysis and performance improvement of the IT incident management process. IEEE Trans Netw Serv Manag 7:132–144. doi: 10.1109/TNSM.2010.1009.I9P0321Boza A, Alemany MME, Alarcón F, Cuenca L (2013) A model-driven DSS architecture for delivery management in collaborative supply chains with lack of homogeneity in products. Prod Plan Control 25:650–661. doi: 10.1080/09537287.2013.798085Boza A, Ortiz A, Vicens E, Poler R (2009) A framework for a decision support system in a hierarchical extended enterprise decision context. In: Poler R, van Sinderen M, Sanchis R (eds) Enterprise interoperability. Springer, Berlin Heidelberg, pp 113–124Grefen P, Dijkman R (2013) Hybrid control of supply chains: a structured exploration from a systems perspective. Int J Prod Manag Eng 1:39–5

    Event Monitoring System to Classify Unexpected Events for Production Planning

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    [EN] Production planning prepares companies to a future production scenario. The decision process followed to obtain the production plan considers real data and estimated data of this future scenario. However, these plans can be affected by unexpected events that alter the planned scenario and in consequence, the production planning. This is especially critical when the production planning is ongoing. Thus providing information about these events can be critical to reconsider the production planning. We herein propose an event monitoring system to identify events and to classify them into different impact levels. The information obtained from this system helps to build a risk matrix, which determines the significance of the risk from the impact level and the likelihood. A prototype has been built following this proposal.This research has been carried out in the framework of the project GV/2014/010 funded by the Generalitat Valenciana (Identificacion de la informacion proporcionada por los nuevos sistemas de deteccion accesibles mediante internet en el ambito de las "sensing enterprises" para la mejora de la toma de decisiones en la planificacion de la produccion).Boza, A.; Alarcón Valero, F.; Alemany Díaz, MDM.; Cuenca, L. (2017). Event Monitoring System to Classify Unexpected Events for Production Planning. Lecture Notes in Business Information Processing. 291:140-154. https://doi.org/10.1007/978-3-319-62386-3_7S140154291Barták, R.: On the boundary of planning and scheduling: a study (1999)Buzacott, J.A., Corsten, H., Gössinger, R., Schneider, H.M.: Production Planning and Control: Basics and Concepts. Oldenbourg Wissenschaftsverlag, München (2012)Özdamar, L., Bozyel, M.A., Birbil, S.I.: A hierarchical decision support system for production planning (with case study). Eur. J. Oper. Res. 104(3), 403–422 (1998)Van Wezel, W., Van Donk, D.P., Gaalman, G.: The planning flexibility bottleneck in food processing industries. J. Oper. Manag. 24(3), 287–300 (2006)Shamsuzzoha, A.H., Rintala, S., Cunha, P.F., Ferreira, P.S., Kankaanpää, T., Maia Carneiro, L.: Event monitoring and management process in a non-hierarchical business network. In: Intelligent Non-hierarchical Manufacturing Networks, pp. 349–374. Wiley, Hoboken (2013)Sacala, I.S., Moisescu, M.A., Repta, D.: Towards the development of the future internet based enterprise in the context of cyber-physical systems. In: 19th International Conference on Control Systems and Computer Science, CSCS 2013, pp. 405–412 (2013)Chen, K.C.: Decision support system for tourism development: system dynamics approach. J. Comput. Inf. Syst. 45(1), 104–112 (2004)Boza, A., Alemany, M.M.E., Vicens, E., Cuenca, L.: Event management in decision-making processes with decision support systems. In: 5th International Conference on Computers Communications and Control (2014)Liao, S.-H.: Expert system methodologies and applications–a decade review from 1995 to 2004. Expert Syst. Appl. 28(1), 93–103 (2005)ISO: 73: 2009: Risk management vocabulary. International Organization for Standardization (2009)Chan, F.T.S., Au, K.C., Chan, P.L.Y.: A decision support system for production scheduling in an ion plating cell. Expert Syst. Appl. 30(4), 727–738 (2006)Weinstein, L., Chung, C.-H.: Integrating maintenance and production decisions in a hierarchical production planning environment. Comput. Oper. Res. 26(10–11), 1059–1074 (1999)Poon, T.C., Choy, K.L., Chan, F.T.S., Lau, H.C.W.: A real-time production operations decision support system for solving stochastic production material demand problems. Expert Syst. Appl. 38(5), 4829–4838 (2011)SAP AG: SAP AG 2014. Next-Generation Business and the Internet of Things. Studio SAP | 27484enUS (14/03) (2014)Carneiro, L.M., Cunha, P., Ferreira, P.S., Shamsuzzoha, A.: Conceptual framework for non-hierarchical business networks for complex products design and manufacturing. Procedia CIRP 7, 61–66 (2013)Vargas, A., Cuenca, L., Boza, A., Sacala, I., Moisescu, M.: Towards the development of the framework for inter sensing enterprise architecture. J. Intell. Manuf. 26, 55–72 (2016)Barash, G., Bartolini, C., Wu, L.: Measuring and improving the performance of an IT support organization in managing service incidents, pp. 11–18 (2007)Liu, R., Kumar, A., van der Aalst, W.: A formal modeling approach for supply chain event management. Decis. Support Syst. 43(3), 761–778 (2007)Söderholm, A.: Project management of unexpected events. Int. J. Proj. Manag. 26(1), 80–86 (2008)Bearzotti, L.A., Salomone, E., Chiotti, O.J.: An autonomous multi-agent approach to supply chain event management. Int. J. Prod. Econ. 135(1), 468–478 (2012)Baron, M.M., Pate-Cornell, M.E.: Designing risk-management strategies for critical engineering systems. IEEE Trans. Eng. Manag. 46(1), 87–100 (1999)Bartolini, C., Stefanelli, C., Tortonesi, M.: SYMIAN: analysis and performance improvement of the IT incident management process. IEEE Trans. Netw. Serv. Manag. 7(3), 132–144 (2010)Cox Jr., L.A.: What’s wrong with risk matrices? Risk Anal. Int. J. 28(2), 497–512 (2008)Shim, J.P., Warkentin, M., Courtney, J.F., Power, D.J., Sharda, R., Carlsson, C.: Past, present, and future of decision support technology. Decis. Support Syst. 33(2), 111–126 (2002)Steiger, D.M.: Enhancing user understanding in a decision support system: a theoretical basis and framework (2015). http://dx.doi.org/10.1080/07421222.1998.11518214Turban, E., Aronson, J., Liang, T.-P.: Decision Support Systems and Intelligent Systems, 7th edn. Pearson Prentice Hall, Upper Saddle River (2005)Turban, E., Watkins, P.R.: Integrating expert systems and decision support systems, 10, 121–136 (1986)Cohen, D., Asín, E.: Sistemas de información para los negocios: un enfoque de toma de decisiones. McGraw-Hill, New York City (2001)Boza, A., Cortés, B., Alemany, M.M.E., Vicens, E.: Event monitoring software application for production planning systems. In: Cortés, P., Maeso-González, E., Escudero-Santana, A. (eds.) Enhancing Synergies in a Collaborative Environment. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-14078-0_14Boza, A., Alarcón, F., Alemany, M.M.E., Cuenca, L.: Event classification system to reconsider the production planning. In: Proceedings of the 18th International Conference on Enterprise Information Systems, pp. 82–88 (2016)Maximal Software: What is MPL? (2016). http://www.maximalsoftware.com/mpl/what.htm

    Strengthening America's Best Idea: An Independent Review of the National Park Service's Natural Resource Stewardship and Science Directorate

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    NRSS requested that an independent panel of the National Academy conduct a review of its effectiveness in five core functions, its relationships with key internal stakeholders, and its performance measurement system. Among other things, the National Park Service's Natural Resource Stewardship and Science Directorate (NRSS) is responsible for providing usable natural and social science information throughout the National Park Service (NPS). NRSS leadership requested this review of the directorate's performance on five core functions, its relationships with key internal NPS stakeholders, and its performance measurement system.Main FindingsThe panel determined that NRSS is a highly regarded organization that provides independent, credible scientific expertise and technical information. The panel also found that NRSS and NPS have additional opportunities to advance natural resource stewardship throughout the Service. If implemented, the panel's eight major recommendations will: (1) help the Service respond to the parks' environmental challenges while raising public awareness about the condition of these special places; (2) strengthen NRSS as an organization; (3) promote scientifically based decision-making at the national, regional, and park levels; and (4) improve the existing performance measurement system

    Making Social Work Work: Improving social work for vulnerable families and children without parental care around the world: A literature review

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    This literature review calls for families and children in developing countries to be supported in ways that are appropriate to the conditions, culture and resources available rather than through approaches to social work that are common in the west. Children living without, or at risk of losing, parental care have wide and varied needs, this paper highlights the need for more thorough assessments of appropriate approaches, functions and support needs for social workers, and suggests elements of an assessment tool to explore these issues. This paper is the first part of a longer process for developing such an assessment tool, and plans are underway to further develop and test the tool in 2012.- See more at: http://www.everychild.org.uk/resources/reports-policies/making-social-work-work#sthash.4EF6qnzc.dpu

    Strengthening Integrated Primary Health Care in Sofala, Mozambique

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    Background: Large increases in health sector investment and policies favoring upgrading and expanding the public sector health network have prioritized maternal and child health in Mozambique and, over the past decade, Mozambique has achieved substantial improvements in maternal and child health indicators. Over this same period, the government of Mozambique has continued to decentralize the management of public sector resources to the district level, including in the health sector, with the aim of bringing decision-making and resources closer to service beneficiaries. Weak district level management capacity has hindered the decentralization process, and building this capacity is an important link to ensure that resources translate to improved service delivery and further improvements in population health. A consortium of the Ministry of Health, Health Alliance International, Eduardo Mondlane University, and the University of Washington are implementing a health systems strengthening model in Sofala Province, central Mozambique.Description of implementation: The Mozambique Population Health Implementation and Training (PHIT) Partnership focuses on improving the quality of routine data and its use through appropriate tools to facilitate decision making by health system managers; strengthening management and planning capacity and funding district health plans; and building capacity for operations research to guide system-strengthening efforts. This seven-year effort covers all 13 districts and 146 health facilities in Sofala Province.Evaluation design: A quasi-experimental controlled time-series design will be used to assess the overall impact of the partnership strategy on under-5 mortality by examining changes in mortality pre- and post-implementation in Sofala Province compared with neighboring Manica Province. The evaluation will compare a broad range of input, process, output, and outcome variables to strengthen the plausibility that the partnership strategy led to healthsystem improvements and subsequent population health impact.Discussion: The Mozambique PHIT Partnership expects to provide evidence on the effect of efforts to improvedata quality coupled with the introduction of tools, training, and supervision to improve evidence-based decision making. This contribution to the knowledge base on what works to enhance health systems is highly replicable for rapid scale-up to other provinces in Mozambique, as well as other sub-Saharan African countries with limitedresources and a commitment to comprehensive primary health care

    Lean Thinking: Theory, Application and Dissemination

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    This book was written and compiled by the University of Huddersfield to share the learnings and experiences of seven years of Knowledge Transfer Partnership (KTP) and Economic and Social Research Council (ESRC) funded projects with the National Health Service (NHS). The focus of these projects was the implementation of Lean thinking and optimising strategic decision making processes. Each of these projects led to major local improvements and this book explains how they were achieved and compiles the lessons learnt. The book is split into three chapters; Lean Thinking Theory, Lean Thinking Applied and Lean Thinking Dissemination

    Gender equity in disaster early warning systems

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    Capacities of societies, communities and individuals or a social-ecological system to deal with adverse consequences and the impacts of hazard events define the resilience. New and innovative Emergency Communications, Warning Systems (ECWS) technologies and solutions improve resilience of the nations. Research shows that different types of systems (e.g. decision support, resource management, early warning, communications, and inter-agency) are highly valued in emergency and disaster events reducing live losses. As many individuals have online access today and young women have increased their online communication and young men tend to explore technology resources, the potential of using user friendly third revolution digital technology such as semantic features and devices (e.g. SMART phones) have the potential to improve the access to early warning/risk in-formation supporting community decision making saving lives. These personal and social relations that reflect gender dimensions can certainly be examined improving resilience making communities more prepared for disasters with proactive decision making for early warning. Fostering awareness about gender equity which is the recognition of women and men as active participants in development can tailor made within the context of resilience and more specifically within early warning systems saving lives of the people at immediate risk including the dependence of mother’s care (children and older people). In this context, this paper attempts to synthesis literature on the topic of gender equity within disaster early warning systems
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