Thematische Bibliographien / Production support

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Auswahl der wissenschaftlichen Literatur zum Thema „Production support“

Autor: Grafiati

Veröffentlicht am 24. Juni 2021

Zuletzt aktualisiert am 12. Februar 2022

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Zeitschriftenartikel zum Thema "Production support"

Nakagawa, Yoshiaki. „Advanced IT to Support Production“. JAPAN TAPPI JOURNAL 73, Nr. 3 (2019): 188–93. http://dx.doi.org/10.2524/jtappij.73.188.

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HIBINO, Hironori, und Kentaro YANAGA. „Decision support for energy-saving idle production facility operations (Decision support in production line)“. Transactions of the JSME (in Japanese) 84, Nr. 868 (2018): 18–00133. http://dx.doi.org/10.1299/transjsme.18-00133.

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Avliyakulov, Nodir Nizomovich, und Sadriddin Ubaidilloevich Rukhilloev. „ACTIVITY OF ME Y OF METROLOGICAL SUPPOR OGICAL SUPPORT OF PRODUC T OF PRODUCTION PROVIDING RELEASE OF QU VIDING RELEASE OF QUALITY PRODUC Y PRODUCTS“. Scientific Reports of Bukhara State University 4, Nr. 1 (26.02.2020): 19–24. http://dx.doi.org/10.52297/2181-1466/2020/4/1/2.

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Improving the efficiency of production and quality of products is possible only with the organization of modern metrological support. The paper presents measures and tasks concerning metrological support of production at the stages of design, development, production and testing, contributing to the production of quality products. To fulfill the tasks assigned to the metrological service, it must have a position, structure, quality assurance system, personnel, necessary premises, conditions for the operation and storage of measuring instruments.

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Unsworth, Richard K. F., Lina Mtwana Nordlund und Leanne C. Cullen-Unsworth. „Seagrass meadows support global fisheries production“. Conservation Letters 12, Nr. 1 (21.05.2018): e12566. http://dx.doi.org/10.1111/conl.12566.

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Allan, Craig. „EL production support: procedures and tools“. Library Hi Tech 16, Nr. 3/4 (Dezember 1998): 132–37. http://dx.doi.org/10.1108/07378839810305981.

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WAN, Jiangping. „Research on Software Production Support Structure“. Journal of Software Engineering and Applications 02, Nr. 03 (2009): 173–94. http://dx.doi.org/10.4236/jsea.2009.23025.

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Matthies, Klaus. „Production cuts to support oil prices“. Intereconomics 36, Nr. 5 (September 2001): 272–76. http://dx.doi.org/10.1007/bf02928981.

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Bakhrankova, Krystsina. „Decision support system for continuous production“. Industrial Management & Data Systems 110, Nr. 4 (27.04.2010): 591–610. http://dx.doi.org/10.1108/02635571011039043.

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Renna, Paolo. „Coordination strategies to support distributed production planning in production networks“. European J. of Industrial Engineering 9, Nr. 3 (2015): 366. http://dx.doi.org/10.1504/ejie.2015.069342.

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Stoffels, Pascal, und Michael Vielhaber. „Decision Support for Energy Efficient Production in Product and Production Development“. Procedia CIRP 40 (2016): 530–35. http://dx.doi.org/10.1016/j.procir.2016.01.128.

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Dissertationen zum Thema "Production support"

Powell, Daryl. „Investigating ERP Support for Lean Production“. Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for produksjons- og kvalitetsteknikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-17429.

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This thesis presents work from a three-year PhD project within the research program SFI Norman: Centre for Research-based Innovation – Norwegian Manufacturing Future. SFI Norman is an eight year research program with the vision to develop new and multi-disciplinary research on next-generation manufacturing, and create theories, methods, models, and management tools that enable Norwegian manufacturers to thrive in global competition. SFI Norman has two main research partners – NTNU and SINTEF – and also consists of a number of industrial partners, including Kongsberg Automotive, Benteler Aluminium, and Pipelife Norway. This research project began in 2009 as part of the SFI Norman research area “Demand Driven Value Chains” (DRIVE). After the mid-term evaluation of Norman, the research areas were reclassified, and in 2011 this project became part of the new research area “Operations Management in Norwegian Manufacturing”. A major research topic in this research area is the relationship between lean production and information technology (IT). For example, though the lean principles are nowadays well understood, the relationship between IT and lean production remains a controversial and far less explored topic. Some would even suggest that the two approaches are contradictory in nature, stating that whilst lean is often characterized by decentralized coordination and control, IT is typically best suited to support centralized production planning. This thesis aims to provide illustrative frameworks in order to explore the topic in more detail. Lean production and enterprise resource planning (ERP) systems have for many years been recognised in the scientific literature and industrial trade journals as enablers of world-class manufacturing operations. Though many companies have undertaken the implementation of either or both of these approaches in order to achieve greater competitive advantage; in the traditional sense, IT such as ERP has often been viewed as a contributor to waste within lean production, for example through the generation of excessive data and unnecessary transactions, and by encouraging overproduction and excessive safety stocks, resulting in high inventory levels. However, as the business world changes and competition from low-cost countries increases, new models must be developed which deliver competitive advantage by combining modern-day technological advances with the lean paradigm. This PhD project set out to investigate the “contradictory” nature of ERP systems and lean production. Having first carried out an extensive literature review, it was identified that contrary to the traditional view, there appeared to be a potential synergy to be realised in combining both approaches. Therefore, the support functionality of ERP systems for lean production was subsequently evaluated by closely examining the capabilities of a contemporary ERP system in the context of lean production principles. This work was carried out by applying an action research methodology over a twelve month period at a Norwegian SME located in Trondheim, Norway. The company was involved in a concurrent implementation process – applying both a new ERP system and lean production practices. This resulted in two outcomes for the project – a framework for ERP support for lean production; and a model for an ERP-based lean implementation process. One of the fundamental reasons for the contradictory view of lean and ERP has been the discussion of pull vs. push. Whilst it is common knowledge that lean manufacturing intends to function as a pull system, environments which use ERP- and its associated material requirements planning (MRP) logic have typically been classed as push systems. Therefore, in order to strengthen the validity of this research and to mitigate any bias from the action research, the real-time, participatory research was supplemented by retrospective case study research, and four case studies were carried out in the Netherlands in order to investigate specific ERP support for pull production. This resulted in the development of a capability maturity model (CMM) for ERP support for pull production, which not only identifies the support mechanisms of an ERP system for pull production, but categories them into various levels of maturity. The outcomes of this project have implications to both theory and practice. The results of the investigation indicate a trend towards the combination of lean and ERP in manufacturing organisations. This has led to a number of contributions to theory and to practice. For example, the framework for ERP support for lean production can be used by researchers and practitioners in applying ERP systems and lean production together in order to increase the competitiveness of manufacturing companies. Secondly, the capability maturity model for ERP support for pull production makes a contribution to knowledge in that it identifies the functionality of ERP systems that can be applied to support pull production, and to practice, allowing manufacturers to benchmark the level of integration between its ERP- and pull systems, providing incentives to continuously improve. These contributions suggest a movement away from the traditional viewpoint of the contradictory nature of lean and ERP, and offer a solution to the recurring debate in the scientific literature as to whether or not lean and ERP are complementary technologies. Thirdly, the framework for an ERP-based lean implementation process also contributes to the field of knowledge within lean and ERP, and can be used by practitioners for the concurrent and synergetic application of lean and ERP.

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Woerlee, Auke Peter. „Decision support systems of production scheduling /“. Rotterdam : Erasmus universiteit, 1991. http://catalogue.bnf.fr/ark:/12148/cb37438055v.

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Proefschrift--Rotterdam--Erasmus universiteit, 1991.
Mention parallèle de titre ou de responsabilité : Beslissingsondersteunende systemen voor korte termijn produktieplanning. Résumé en néerlandais, 4 p. Ill. par l'auteur. Bibliogr. p. 151-165. Index.

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Yang, Zhenyu, und Zhitie Zhao. „Simulation Model to Support Production Transition“. Thesis, KTH, Skolan för industriell teknik och management (ITM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264415.

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Due to the increase of the market demand, Senseair needs to increase the throughput from 4,000 to 10,000 and to 100,000 in the future. The market is expanding greatly, which requires production line to be improved at a faster pace. To seize the opportunity, effective improvements need to be implemented. But the complexity of the production line makes it particularly difficult to predict the performance of the production line. Hence, computer simulation tool, Plant Simulation, is utilized to simulate the performance of the production line. Based on the collected data from the actual production system, a digital twin is built in Plant Simulation. Various experiments are conducted to examine how to increase the throughput in an effective and efficient manner. The result shows the possibilities to reach 10,000 throughput goal but the extreme difficulties to reach 100,000 throughput goal. Consequently, an automated assembly production line is designed based on the manual assembly production line. After tests, the automated production line is proved to be able to meet the higher demand. Improvement suggestions are provided to Senseair for both the current manual assembly production line and the new automated assembly production line.
På grund av den ökande efterfrågan på marknaden måste Senseair öka kapaciteten, först från 4 000 till 10 000 och därefter upp till 100 000. Marknaden expanderar kraftigt, vilket kräver att produktionslinan förbättras i snabbare takt. För att ta till vara möjligheten måste effektiva förbättringar genomföras. Komplexiteten i produktionslinan gör det dock särskilt svårt att förutsäga produktionslinans prestanda. Datorsimuleringsverktyget, Plant Simulation, används därför för att simulera produktionslinans prestanda. Baserat på insamlade data från det faktiska produktionssystemet byggdes en digital tvilling i Plant Simulation. Olika experiment genomfördes för att undersöka hur man kan öka genomströmningen på ett optimalt och effektivt sätt. Resultaten visar att det är möjligt att nå genomströmningsmålet på 10000 men att det är extremt svårt att nå genomströmningsmålet på 100 000. En automatiserad produktionslina baserad på den manuella monteringsproduktionslinan konstrueras därför. Efter tester har den automatiserade produktionslinan visat sig kunna möta den högre efterfrågan. Förslag på förbättringar presenteras för Senseair för både den aktuella manuella produktionslinan och den nya automatiska produktionslinan.

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Fitz-Rodriguez, Efren. „Decision Support Systems for Greenhouse Tomato Production“. Diss., The University of Arizona, 2008. http://hdl.handle.net/10150/195798.

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The purpose of greenhouse crop systems is to generate a high quality product at high production rates, consistently, economically, efficiently and in a sustainable way. To achieve this level of productivity, accurate monitoring and control of some processes of the entire biophysical system must be implemented. In addition, the proper selection of actions at the strategic, tactical and operational management levels must be implemented.Greenhouse management relies largely on human expertise to adjust the appropriate optimum values for each of the production and environmental parameters, and most importantly, to verify by observation the desired crop responses. The subjective nature of observing the plant responses, directly affects the decision-making process (DMP) for selecting these `optimums'. Therefore, in this study several decision support systems (DSS) were developed to enhance the DMP at each of the greenhouse managerial levels.A dynamic greenhouse environment model was implemented in a Web-based interactive application which allowed for the selection of the greenhouse design, weather conditions, and operational strategies. The model produced realistic approximations of the dynamic behavior of greenhouse environments for 28-hour simulation periods and proved to be a valuable tool at the strategic and operational level by evaluating different design configurations and control strategies.A Web-based crop monitoring system was developed for enhancing remote diagnosis. This DSS automatically gathered and presented graphically environmental data and crop-oriented parameters from several research greenhouses. Furthermore, it allowed for real-time visual inspection of the crop.An intelligent DSS (i-DSS) based on crop records and greenhouse environment data from experimental trials and from commercial operations was developed to characterize the growth-mode of tomato plants with fuzzy modeling. This i-DSS allowed the discrimination of "reproductive", "vegetative" and "balanced" growth-modes in the experimental systems, and the seasonal growth-mode variation on the commercial application.An i-DSS based on commercial operation data was developed to predict the weekly fluctuations of harvest rates, fruit size and fruit developing time with dynamic neural networks (NN). The NN models accurately predicted weekly and seasonal fluctuations of each variable, having correlation coefficients (R) of 0.96, 0.87 and 0.94 respectively, when compared with a dataset used for independent validation.

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Amado, António Correia de Campos Jordão. „An ontology to support evolvable production systems“. Master's thesis, Faculdade de CIências e Tecnologia, 2008. http://hdl.handle.net/10362/3662.

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Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores
Ontologias são cada vez mais um conceito fundamental no suporte à interoperabilidade. Além disso, elas também são fundamentais no suporte aos sistemas evolutivos de produção por duas razões principais. A primeira está relacionada com o facto de a clara identificação e formalização dos processos ser importante para a criação de módulos inteligentes. A segunda razão está relacionada com o facto de os sistemas evolutivos de produção (SEP) serem baseados em sistemas multi-agente que depende em muito, da construção das ontologias de modo a permitir a comunicação entre os agentes pertencentes ao sistema. Os principais conceitos por detrás da ontologia aqui desenvolvida serão os conceitos de processos, tarefas, produto e componentes de manufactura. Esta tese pretende mostrar não só a criação de uma ontologia, mas também de um agente de modo a ser possível a integração da ontologia num sistema multi-agente, no âmbito da manufactura inteligente respondendo às questões envolventes ao paradigma dos sistemas evolutivos de produção. Sabendo que os SEP são baseados em sistemas multi-agente, será também mostrado um agente que irá ter todo o controlo da ontologia e irá pertencer ao sistema de manufactura.

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Marghalany, Issam Kamal. „A decision support methodology for production systems optimisation“. Thesis, Cranfield University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.408907.

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Schulz, Joseph Edward. „A desision support system for session scheduling“. Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/25106.

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Leiva, Conrad M. „A decision support system for workforce preference scheduling“. Thesis, Georgia Institute of Technology, 1986. http://hdl.handle.net/1853/24361.

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Mirosavljevic, Dejan, und Mattias Augustsson. „Förbättringsarbete mot Lean Production på Tooling Support Halmstad AB“. Thesis, Halmstad University, School of Business and Engineering (SET), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1613.

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Nowdays an efficient production is critical to achieve for companies competing on the market for steel cutting tools. One of such companies is Halmstad based Tooling Support Halmstad AB that manufactures threading-taps, threading-dies and parting off tools, towards a centralized warehouse in Schiedam, Holland.

The large amount of different products, approximately 2500 is a big contributor to the problem along with a new order system that creates a demand of low setup times. Previously the company was forwarded orders through quarter based prognoses which enabled planning of the production over a longer time span. Currently the newer order system ZENIT is in use and the company thereby gets their orders weekly. This type of customer orders effects the production in terms of weekly orders having a variety of different products, which in turn creates a larger amount of setup work that prolongs the lead times even further. The purpose of this project has been to survey the present production in the end manufacturing state of the thread taps in order to come up with improvement proposals which will lead to a more flexible production and less sensitive to irregular demand.

The factory consists of several production lines. Our work has been limited to the end manufacturing state of the thread taps in one of the 9 production lines, line 435. Based on the limitation our work has followed the production and order use of thread taps from the local storage to precisely before the wash.

Value stream mapping and the SMED-method has been useful during the work process. With these tools proposals have been generated towards a production flow with a divided lead time compared to the current state and a decreased setup-time by 26%

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Engerberg, Malin. „Development of database support for production of doubled haploids“. Thesis, University of Skövde, Department of Computer Science, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-711.

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In this project relational and Lotus Notes database technology are evaluated with regard to their suitability in providing computer-based support in plant breeding in general and specifically in the production of doubled haploids. The two developed databases are compared based on a set of requirements produced together with the DH-group which is the main users of the databases. The results indicate that both Lotus Notes and the relational databases are able to fulfil all needs documented in this project, although both systems have their limitations. An often expressed opinion is that it is difficult to combine biology and databases. The experience gained in this project however suggests that it does not need to be the case in instances where data is not as complicated as often discussed. Observations made during this project indicate that data warehousing with integrated data mining and OLAP tools are surprisingly similar to how the DH-group at Svalöf Weibull works and could be a suitable solution for the production of doubled haploids.

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Bücher zum Thema "Production support"

MacKerron, D. K. L., und A. J. Haverkort, Hrsg. Decision support systems in potato production. The Netherlands: Wageningen Academic Publishers, 2004. http://dx.doi.org/10.3920/978-90-8686-527-7.

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Denston, Gerry. The production cell: An activity to support GNVQ manufacturing. Coventry: SCIP, 1994.

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Carl, Pegels C., Hrsg. Decision support systems for production and operations management (DSSPOM). 2. Aufl. Homewood, IL: Irwin, 1991.

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Ebert, Lee G. Special tooling disposition for aircraft entering post production support. Monterey, Calif: Naval Postgraduate School, 1992.

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Engineering information management systems: Beyond CAD/CAM, to concurrent engineering support. New York: Van Nostrand Reinhold, 1992.

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CAM: Algorithmen und Decision Support für die Fertigungssteuerung. Berlin: Springer-Verlag, 1989.

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Pedersen, Gaylen. System level post production support: Tendencies, conditions, facts, and principles. Bountiful, Utah: Post Production Specialities, 1988.

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U.S. DEPT. OF THE ARMY. Publications that support the Carrier, Command Post, M577A2 (new production). Washington, DC: Headquarters, Dept. of the Army, 1985.

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Corsten, Hans. Artificial neural networks to support production planning and control systems. Ingolstadt: Wirtschaftswissenschaftliche Fakultät Ingolstadt, Katholische Universität Eichstätt, 1995.

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Dennis, Terry L. Decision support software/production and operations management: Software and text. Minneapolis/St. Paul: West Pub. Co., 1995.

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Buchteile zum Thema "Production support"

LaRock, Thomas. „Production Support“. In DBA Survivor, 81–94. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-2788-5_5.

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Almekinders, C. J. M., und N. P. Louwaars. „4. Support from the formal sector“. In Farmers’ Seed Production, 72–84. Rugby, Warwickshire, United Kingdom: Practical Action Publishing, 1999. http://dx.doi.org/10.3362/9781780442150.004.

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Monden, Yasuhiro. „Computer System for Kanban System Support“. In Toyota Production System, 291–301. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-9714-8_19.

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Damgrave, Roy, und Eric Lutters. „Distance Collaboration Support Environment“. In Lecture Notes in Production Engineering, 653–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30817-8_64.

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Powell, Daryl, Erlend Alfnes, Jan Ola Strandhagen und Heidi Dreyer. „ERP Support for Lean Production“. In Advances in Production Management Systems. Value Networks: Innovation, Technologies, and Management, 115–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33980-6_14.

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Weidner, Robert, Bernward Otten, Andreas Argubi-Wollesen und Zhejun Yao. „Support Technologies for Industrial Production“. In Biosystems & Biorobotics, 149–56. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01836-8_14.

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Powell, Daryl, Andreas Binder und Emrah Arica. „MES Support for Lean Production“. In IFIP Advances in Information and Communication Technology, 128–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40361-3_17.

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Reis, Paula, André S. Santos, João Bastos, Ana M. Madureira und Leonilde R. Varela. „A Production Scheduling Support Framework“. In Advances in Intelligent Systems and Computing, 869–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71187-0_80.

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Woerlee, Auke. „A Flexible Decision Support Framework for Production Scheduling“. In Modern Production Concepts, 353–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76401-1_23.

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Petermann, Joachim, und Siegfried Wirth. „Concept of an integrated decisionmaking support and assessment system“. In Global Production Management, 348–56. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-0-387-35569-6_43.

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Konferenzberichte zum Thema "Production support"

Vincent, A. R. „Automated Cargo Handling: From Concept to Production“. In Military Support Ships. RINA, 2007. http://dx.doi.org/10.3940/rina.mss.2007.02.

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Anezaki, Takashi. „Human-friendly Cell-production Support Robot“. In 2007 International Conference on Mechatronics and Automation. IEEE, 2007. http://dx.doi.org/10.1109/icma.2007.4303814.

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Kiss, T., G. Terstyanszky, G. Kecskemeti, S. Illes, T. Delaittre, S. Winter, P. Kacsuk und G. Sipos. „Legacy code support for production grids“. In The 6th IEEE/ACM International Workshop on Grid Computing, 2005. IEEE, 2005. http://dx.doi.org/10.1109/grid.2005.1542754.

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Lejon, Kjell, Karl Johnny Hersvik und Arild Boe. „Multi-asset Production Support Centre - Generating Values“. In SPE Intelligent Energy Conference and Exhibition. Society of Petroleum Engineers, 2010. http://dx.doi.org/10.2118/127730-ms.

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Kuznetsova, Elena. „STATE ANTI-CRISIS PROGRAM FOR PRODUCTION SUPPORT“. In Globalistics-2020: Global issues and the future of humankind. Interregional Social Organization for Assistance of Studying and Promotion the Scientific Heritage of N.D. Kondratieff / ISOASPSH of N.D. Kondratieff, 2020. http://dx.doi.org/10.46865/978-5-901640-33-3-2020-305-307.

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The crisis is one of the most important elements of the mechanism for self-regulation of the market economy. The state is one of the most influential levers in financial growth regulation. State anti-crisis management is a part of the unified system of state management, contains elements of forecasting and current diagnostics of crisis phenomena, that research factors of their manifestation, as well as not only the definition of priority measures to contain crisis situations, but also measures aimed at development and implementation of projects to prevent crisis situations.

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Terstyanszky, Gabor, Tamas Kiss, Thierry Delaitre, Stephen Winter, Peter Kacsuk und Gabor Kecskemeti. „Service-Oriented Production Grids and User Support“. In 2006 7th IEEE/ACM International Conference on Grid Computing. IEEE, 2006. http://dx.doi.org/10.1109/icgrid.2006.311040.

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A. Ivanov, Maxim. „PROJECT FINANCE AS SUPPORT TO AGRICULTURAL PRODUCTION“. In FINIZ 2015. Belgrade, Serbia: Singidunum University, 2015. http://dx.doi.org/10.15308/finiz-2015-196-198.

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Verkasalo, Matti, und Ken Friesen. „A Decision Support System for Production Investments“. In The International Symposium on the Analytic Hierarchy Process. Creative Decisions Foundation, 1996. http://dx.doi.org/10.13033/isahp.y1996.004.

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Lysenko, Vitaliy, und Irina Chernova. „Intelligent Decision Support System in Entomophages Production“. In 2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S&T). IEEE, 2020. http://dx.doi.org/10.1109/picst51311.2020.9467999.

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Lukashenko, V. S., und Ye A. Ovseychick. „Organic poultry production. Prospections and realities“. In SCIENTIFIC AND TECHNICAL SUPPORT EFFICIENCY AND QUALITY PRODUCTION OF AGRICULTURAL PRODUCTS. VNIIPP, 2019. http://dx.doi.org/10.30975/978-5-9909889-2-7-2019-1-1-130-135.

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Berichte der Organisationen zum Thema "Production support"

Amy Wright. State Support of Domestic Production. Office of Scientific and Technical Information (OSTI), Dezember 2007. http://dx.doi.org/10.2172/940180.

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Daniel M. Ginosar. Capabilities to Support Thermochemical Hydrogen Production Technology Development. Office of Scientific and Technical Information (OSTI), Mai 2009. http://dx.doi.org/10.2172/1031648.

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Woloshun, Keith. Northstar Accelerator Based Mo99 Production Facility Design Support. Office of Scientific and Technical Information (OSTI), Februar 2021. http://dx.doi.org/10.2172/1766962.

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Balasco, A. A., R. C. Bowen, I. Bodek, C. A. DeSantos und R. F. Machacek. Ball Powder Production Wastewater Biodegradation Support Studies - With Nitroglycerine. Fort Belvoir, VA: Defense Technical Information Center, Februar 1989. http://dx.doi.org/10.21236/ada223499.

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PETERSON BUILDERS INC STURGEON BAY WI. Performance Measurement in Production and Support Areas of a Shipyard. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada458615.

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Woloshun, Keith Albert. Northstar Accelerator Based Mo99 Production Facility Design Support LANL FY20 Quarters 1 and 2 Facility Design Support Report. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1614821.

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Bollinger, Benjamin. Demonstration of Isothermal Compressed Air Energy Storage to Support Renewable Energy Production. Office of Scientific and Technical Information (OSTI), Januar 2015. http://dx.doi.org/10.2172/1178542.

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Kitchen, B. G. International technology exchange in support of the Defense Waste Processing Facility wasteform production. Office of Scientific and Technical Information (OSTI), August 1989. http://dx.doi.org/10.2172/10135720.

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Calonico Soto, Alicia, Xavier Lepro Chavez, Chantel Aracne-Ruddle, Daniel Malone, Ed Lindsey, Michael Stadermann und Mike Wilson. Production of carbon nanotube fibers as a capsule support for NIF-ICF experiments. Office of Scientific and Technical Information (OSTI), Dezember 2018. http://dx.doi.org/10.2172/1489441.

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Flores, P., L. Griego und J. Weeks. Production Capability Assurance Program (PCAP) data base support process/procedures and roles/responsibilities. Office of Scientific and Technical Information (OSTI), April 1994. http://dx.doi.org/10.2172/10158011.

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