scholarly journals EuProGigant – A Concept Towards an Industrial System Architecture for Data-Driven Production Systems

Procedia CIRP ◽  
2021 ◽  
Vol 104 ◽  
pp. 324-329
Author(s):  
Stefan Dumss ◽  
Markus Weber ◽  
Clemens Schwaiger ◽  
Clemens Sulz ◽  
Patrick Rosenberger ◽  
...  
Author(s):  
Daniel P. Roberts ◽  
Nicholas M. Short ◽  
James Sill ◽  
Dilip K. Lakshman ◽  
Xiaojia Hu ◽  
...  

AbstractThe agricultural community is confronted with dual challenges; increasing production of nutritionally dense food and decreasing the impacts of these crop production systems on the land, water, and climate. Control of plant pathogens will figure prominently in meeting these challenges as plant diseases cause significant yield and economic losses to crops responsible for feeding a large portion of the world population. New approaches and technologies to enhance sustainability of crop production systems and, importantly, plant disease control need to be developed and adopted. By leveraging advanced geoinformatic techniques, advances in computing and sensing infrastructure (e.g., cloud-based, big data-driven applications) will aid in the monitoring and management of pesticides and biologicals, such as cover crops and beneficial microbes, to reduce the impact of plant disease control and cropping systems on the environment. This includes geospatial tools being developed to aid the farmer in managing cropping system and disease management strategies that are more sustainable but increasingly complex. Geoinformatics and cloud-based, big data-driven applications are also being enlisted to speed up crop germplasm improvement; crop germplasm that has enhanced tolerance to pathogens and abiotic stress and is in tune with different cropping systems and environmental conditions is needed. Finally, advanced geoinformatic techniques and advances in computing infrastructure allow a more collaborative framework amongst scientists, policymakers, and the agricultural community to speed the development, transfer, and adoption of these sustainable technologies.


Author(s):  
Gleb Valerievich Larionov ◽  
Anton Julievich Nikitin

The article presents the main factors of the industrial system of the enterprise and their development in evolution of scientific organization of production. The main principles underlying the efficient production system have been described. The comparison of the main stages of the development of the science of production organization and management has been done.


Procedia CIRP ◽  
2019 ◽  
Vol 83 ◽  
pp. 814-818 ◽  
Author(s):  
Yongheng Zhang ◽  
Rui Zhang ◽  
Yizhong Wang ◽  
Hongfei Guo ◽  
Ray Y Zhong ◽  
...  

2020 ◽  
Vol 51 (3) ◽  
pp. 328-345 ◽  
Author(s):  
Juliano Denicol ◽  
Andrew Davies ◽  
Ilias Krystallis

This systematic literature review explores the megaproject management literature and contributes by improving our understanding of the causes and cures of poor megaproject performance. The review analyzes 6,007 titles and abstracts and 86 full papers, identifying a total of 18 causes and 54 cures to address poor megaproject performance. We suggest five avenues for future research that should consider examining megaprojects as large-scale, inter-organizational production systems: (1) designing the system architecture; (2) bridging the gap with manufacturing; (3) building and leading collaborations; (4) engaging institutions and communities; and (5) decomposing and integrating the supply chain.


Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1328
Author(s):  
Rebecca K. McGrail ◽  
David A. Van Sanford ◽  
David H. McNear

Most of the effort of crop breeding has focused on the expression of aboveground traits with the goals of increasing yield and disease resistance, decreasing height in grains, and improvement of nutritional qualities. The role of roots in supporting these goals has been largely ignored. With the increasing need to produce more food, feed, fiber, and fuel on less land and with fewer inputs, the next advance in plant breeding must include greater consideration of roots. Root traits are an untapped source of phenotypic variation that will prove essential for breeders working to increase yields and the provisioning of ecosystem services. Roots are dynamic, and their structure and the composition of metabolites introduced to the rhizosphere change as the plant develops and in response to environmental, biotic, and edaphic factors. The assessment of physical qualities of root system architecture will allow breeding for desired root placement in the soil profile, such as deeper roots in no-till production systems plagued with drought or shallow roots systems for accessing nutrients. Combining the assessment of physical characteristics with chemical traits, including enzymes and organic acid production, will provide a better understanding of biogeochemical mechanisms by which roots acquire resources. Lastly, information on the structural and elemental composition of the roots will help better predict root decomposition, their contribution to soil organic carbon pools, and the subsequent benefits provided to the following crop. Breeding can no longer continue with a narrow focus on aboveground traits, and breeding for belowground traits cannot only focus on root system architecture. Incorporation of root biogeochemical traits into breeding will permit the creation of germplasm with the required traits to meet production needs in a variety of soil types and projected climate scenarios.


2015 ◽  
Vol 63 (10) ◽  
Author(s):  
Oliver Niggemann ◽  
Christian Frey

AbstractDue to global competition and increasing product complexity, the complexity of production systems has grown significantly in recent years. This places an increasing burden on automation developers, systems engineers and plant constructors. Intelligent assistance systems and smart automation systems are a possible solution to face this complexity: The machines, i.e. the software and assistance systems, take over tasks that were previously carried out manually by experts. At the heart of this concept are intelligent anomaly detection approaches based on models of the system behaviors. Intelligent assistance systems learn these models automatically: Based on data, these systems extract most necessary knowledge about the diagnosis task. This paper outlines this data-driven approach to plant analysis using several use cases from industry.


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