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![Development of Production Volume and Variety of Variants [3].](publication/333468163/figure/fig1/AS:872318272294919@1584988291536/Development-of-Production-Volume-and-Variety-of-Variants-3.png)
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![Fig. 1. Development of Production Volume and Variety of Variants [3].](publication/333468163/figure/fig1/AS:872318272294919@1584988291536/Development-of-Production-Volume-and-Variety-of-Variants-3_Q320.jpg)
![Fig. 2. The Control Loop of Production Planning and Control [6].](publication/333468163/figure/fig2/AS:872318272303104@1584988291568/The-Control-Loop-of-Production-Planning-and-Control-6_Q320.jpg)
![Fig. 3. Polylemma of Production [6].](publication/333468163/figure/fig3/AS:872318272299008@1584988291605/Polylemma-of-Production-6_Q320.jpg)
Production optimization can be done on different levels, ranging from improving individual machines, over organizational aspects like Kanban, to optimizing the overarching production planning. The last approach may inherit the greatest potential. Still, it is also the most challenging since it requires a holistic systems engineering approach. One o...
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Citations
... Manufacturers need to constantly analyse and optimize their value generating processes, in order to save costs and stay competitive [1]. Traditionally, they deploy a variety of tools and methodologies to achieve this, such as lean management [1,2]. ...
... Manufacturers need to constantly analyse and optimize their value generating processes, in order to save costs and stay competitive [1]. Traditionally, they deploy a variety of tools and methodologies to achieve this, such as lean management [1,2]. More recently, digitalization has proven to be a viable optimization possibility [3]. ...
Manufacturers install and rely on a large number of sensors to operate and control their processes. However, the collected sensor data is rarely used to analyse and improve the higher-level, aggregated business processes. Process mining (PM) appears to be a promising solution, with the ability to automatically generate and analyse business process models based on data. However, the atomic events of sensor measurements need to be refined, aggregated, and enriched to properly represent a business process. In this paper, we propose a novel framework to make manufacturing sensor data analysable with PM. The framework allows manufacturers with batch and continuous processes (BCP) to systematically enrich their sensor data to use it for optimization purposes. Following the action design research, we demonstrate the applicability of the framework in a use case study using sensor data from a BCP beverage production.
... Getting to know the issue is an essential part of the job. It will ensure that the analysis, design, and implementation of solutions lead to the desired results with an emphasis on the elimination of inaccuracies and errors [1][2][3][4][5][6]. ...
The paper deals with the process of optimizing the creation of the output product, which is the Cobas HIV-1 blood plasma separation card. The capabilities of the Tecnomatix Plant Simulation software will be used in this process. The application of simulation software will help to reveal the possibilities of improvement during the entire production process of the said product and to debug all potential improvement errors on the digital model without physically interfering with the current production process. The paper contains a basic theoretical clarification of some basic terms. The following content describes individual parts of the production process in question. An analysis will be carried out, on the basis of which the most significant bottlenecks will be revealed. The data used to create the simulation were collected based on the recording of working time and its use on the given production line. This kind of observation also revealed initial bottlenecks. For these bottlenecks, a methodology for their effectiveness and criteria for their optimization is subsequently proposed. Subsequently, an optimization solution is proposed, which is verified and evaluated by simulation. The collected data were then transferred to a digital model in the form of a simulation. Based on the results of the simulation, the given optimization proposals will be evaluated at the end. Production on the line has been optimized gradually and thanks to this we can see several variants that can be compared, and the best one can be chosen. The development of the entire optimization process is the possibility to increase the production output from the original 350 produced pieces up to 699 pieces in the same time horizon. In the process of optimization, methodologies were created that followed it and gave instructions for the optimization of productions of a similar nature. In addition to the mentioned optimization simulation variants, the paper also contains a design solution proposal in the form of 3D models, when several workstations are united into one workstation.
... According to Joppen et al. (2019), efficient and flexible production can be achieved in various ways: comprehensive production replanning, equipment parameter optimization or process improvement tools (Joppen et al., 2019). Which will be covered in more detail in the following sections. ...
... According to Joppen et al. (2019), efficient and flexible production can be achieved in various ways: comprehensive production replanning, equipment parameter optimization or process improvement tools (Joppen et al., 2019). Which will be covered in more detail in the following sections. ...
In 2020, Europe reached 76.7 billion people. EUR of cosmetics sales in the retail market and according to this indicator is the largest market for cosmetic products in the world. 500 million European consumers use cosmetics and personal care products every day to protect their health and improve their well-being. With the high demand for cosmetic products, companies must ensure that products are produced at the right time and in the right quantities. However, in cosmetic manufacturing processes, inevitable losses occur in the supply chain and various losses in production that slow the shipments of cosmetic products. Continuous improvement of production processes, cost reduction measures, and quality assurance are necessary measures to obtain maximum profit and remain competitive. The object of the study is to improve production processes in the cosmetics sector. The purpose of the article is to identify the main problems of production processes in the cosmetics sector and to create a model for improving production processes. To achieve the goal, the following tasks are set: perform an analysis of scientific literature related to the improvement of the production process of the cosmetics sector; to choose appropriate research methods that will help identify the problems of improving production processes in the cosmetics sector; to present a model for improving cosmetic product manufacturing processes in cosmetic manufacturing companies. Research methods: analysis of scientific literature, observation method, expert assessment.
... Pohon atau tumbuhan sendiri merupakan produk engineering yang luar biasa, seperti carnivore plant dan bentuk duri pada tumbuhan di daerah kering. Secara singkat: engineering sudah diterapkan terlebih dahulu oleh alam dan dapat dijadikan contoh oleh manusia [8]. Gambar 1.5 Peran utama engineering pada pemenuhan unsur kehidupan Integrasi konsep 5E dengan engineering menjadi siklus yang berkelanjutan. ...
... Es así como, los sistemas de producción tradicionalmente suelen estar diseñados para un proceso de producción específico (Yadav & Jayswal, 2018). Por lo tanto, la intervención de pequeños cambios para atender especificas necesidades, requiere de ajustes manuales severos de los parámetros de control y, en algunas situaciones, pueden necesitar la reconfiguración de todo el equipo (Joppen et al., 2019;Polotski, 2019). En este sentido, la aplicación de un enfoque productivo integrador, permite la mejor programación y distribución de las operaciones en el proceso (Tsutsumi et al., 2020). ...
Esta investigación tiene como objetivo evaluar la eficiencia en los sistemas productivos de bienes y servicios de las pequeñas y medianas empresas (Pymes) en el Departamento de Bolívar-Colombia. Para este propósito se utilizó la técnica de Análisis Envolvente de Datos (DEA), en la cual se determinó las eficiencias técnicas de las 120 Pymes formalmente registradas en la Cámara de Comercio de Cartagena para los años 2017 a 2020. Se contrasta con otros estudios cuya técnica no paramétrica fue aplicada en sectores productivos similares que, el grupo de pequeñas y medianas empresas evaluadas mostraron resultados análogos en sus procesos operacionales. Se concluye que las Pymes evaluadas presentaron un desempeño productivo exiguo en sus actividades operacionales debido a factores relacionados con el bajo aplacamiento financiero y deficiente gestión de la innovación.
... . Mejora del proceso: el rendimiento y los resultados del proceso deben ser constantemente evaluados. Los cambios para mejorar el proceso deben ser cada que sea necesario.Lo anterior lo complementanJoppen et al. (2019), quienes explican que los procesos están ...
Knowledge management and process management are fundamental developments for today's companies. In this sense, companies in the tourism sector in the Department of Caldas can guarantee their permanence and success by using and incorporating these concepts appropriately. For this case, the objective was defined as to establish the relationship between knowledge management and process management in these companies. For this purpose, the life cycle of knowledge was studied and analyzed with the missionary, strategic and operational processes of these companies. Qualitative research with a descriptive, explanatory approach was developed with a population of 61 tour operators. The main finding corresponds to the significant correlation between the knowledge life cycle and the process management of the companies, which guarantees an adequate knowledge management.
... YearUnderstanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry[34] Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges[35] IEEE Access Architecture 2017 Topological Data Analysis to Solve Big Data Problem in Reservoir Engineering:Application to Inverted 4D Seismic Data[46] The potential value of digitization for Business -Insights from German-speaking experts[37] 2012BIOSIG -Proceedings of the International Conference of Biometrics Special Interest Group Data Mining-Driven Manufacturing Process Optimization [47] World Congress on Engineering 2012 Architecture 2012 The future of manufacturing industry: a strategic roadmap toward Industry 4.0 [31] Journal of Manufacturing Technology Management Time Monitoring System to Lean Manufacturing [48] Procedia Manufacturing 2018 Case Study 2018 Digitalization: Opportunity and Challenge for the Business and Information Systems Engineering Community [49] The expected contribution of Industry 4.0 technologies for industrial performance International Journal of Production Economics Framework 2018 Digitization of German Enterprises in the Production Sector-Do they know how " digitized " they are? [50] Six Sigma based approach to optimize radial forging operation variables [51] Journal of Materials Processing Technology Case Study 2008 Change through digitization-value creation in the age of industry 4.0 [38] Management of Permanent Change Theory 2015 Digitization as a Catalyst for Business Model Innovation A Three-Step Approach to Facilitating Economic Success [52] Digitization of Industrial Work: developments paths and prospects [40] Journal for Labour Market Research Theory 2016 Industrial revolution -Industry 4.0: Are German manufacturing SMEs the first victims of this revolution? ...
The manufacturing industry is currently undergoing a digital transformation as part of the mega-trend Industry 4.0. As part of this phase of the industrial revolution, traditional manufacturing processes are being combined with digital technologies to achieve smarter and more efficient production. To successfully digitally transform a manufacturing facility, the processes must first be digitized. This is the conversion of information from an analogue format to a digital format. The objective of this study was to explore the research area of digitizing manufacturing data as part of the worldwide paradigm, Industry 4.0. The formal methodology of a systematic mapping study was utilized to capture a representative sample of the research area and assess its current state. Specific research questions were defined to assess the key benefits and limitations associated with the digitization of manufacturing data. Research papers were classified according to the type of research and type of contribution to the research area. Upon analyzing 54 papers identified in this area, it was noted that 23 of the papers originated in Germany. This is an unsurprising finding as Industry 4.0 is originally a German strategy with supporting strong policy instruments being utilized in Germany to support its implementation. It was also found that the Fraunhofer Institute for Mechatronic Systems Design, in collaboration with the University of Paderborn in Germany, was the most frequent contributing Institution of the research papers with three papers published. The literature suggested future research directions and highlighted one specific gap in the area. There exists an unresolved gap between the data science experts and the manufacturing process experts in the industry. The data analytics expertise is not useful unless the manufacturing process information is utilized. A legitimate understanding of the data is crucial to perform accurate analytics and gain true, valuable insights into the manufacturing process. There lies a gap between the manufacturing operations and the information technology/data analytics departments within enterprises, which was borne out by the results of many of the case studies reviewed as part of this work. To test the concept of this gap existing, the researcher initiated an industrial case study in which they embedded themselves between the subject matter expert of the manufacturing process and the data scientist. Of the papers resulting from the systematic mapping study, 12 of the papers contributed a framework, another 12 of the papers were based on a case study, and 11 of the papers focused on theory. However, there were only three papers that contributed a methodology. This provides further evidence for the need for an industry-focused methodology for digitizing and analyzing manufacturing data, which will be developed in future research. F.R. Clancy is with the Civil Engineering
... editor@iaeme.com Robert Joppen [26] researched on the optimization of production management process. It describes how the conflict of objectives can be analysed systematically and a reasonable operational status can be derived. ...
... Die Optimierung des Systems Produktion kann in unterschiedlichen Ebenen und Bereichen des Unternehmens stattfinden [3]. Dabei kommt dem Humankapital eine wesentliche Rolle zu, denn die Steigerung der Mitarbeiterleistung gilt als Grund voraussetzung für eine höhere Produktivität [2]. ...
Die Sicherung und Steigerung der Produktivität ist eine Kernanforderung an Unternehmen, um auf dem Markt wettbewerbsfähig zu bleiben. Daran anknüpfend stellt dieser Beitrag den Lean Production Leader vor, ein Führungsnavigator, der Verschwendungen der Leitungsebene in Produktionsbetrieben aufdeckt und diese systematisch abbaut. Dazu wird die Einflussstärke von Führungsaktivitäten und -fähigkeiten auf die Leistung und damit die Produktivität der Produktionsmitarbeiter untersucht.
Assurance and increase of productivity is a fundamental requirement for companies to remain competitive in the market. This article presents the Lean Production Leader, a leadership navigator that identifies and systematically reduces waste at the leadership level in production companies by examining the influence of managerial activities and skills on the productivity of production employees.
... As an essential step in reservoir management development, modelbased production optimization refers to seeking an optimal wellcontrol scheme that can achieve improved RM strategies (Chen and Reynolds, 2016;Chen et al., 2010;Dehdari et al., 2012;Hanssen et al., 2017;Jansen et al., 2006;Joppen et al., 2019;Yang et al., 2020;Zhang et al., 2018). By optimizing the control scheme of each well, decision makers are able to obtain the maximal economic benefit which is generally measured as net present value (NPV). ...
Multi-objective optimization (MOO), which involves more than one conflicting objective to be optimized simultaneously, is expected to provide efficient and comprehensive reservoir management (RM) solutions. The multi-objective production optimization problems are considered to be expensive due to the difficulties and cost of operations. Surrogate-assisted evolutionary algorithms (SAEAs), which has proved to be an effective way to solve expensive problems, design computationally cheap function to approximate each objective function. Meanwhile, the optimization process involves a large number of decision variables. However, building a high-quality surrogate model has become difficult due to the “curse of dimensionality”. Base on characterization, an efficient multi-objective optimization framework called SA-RVEA-PCA is proposed to effectively deal with large-scale and computationally expensive simulation-based optimization problems, including three parts:1) Given a set of simulation results, a Gaussian process (GP) model based on Principal Component Analysis (PCA) for each objective function is trained so that the surrogate models can guide the optimization more accurately. 2) A reference vector guided evolutionary algorithm (RVEA) recently developed is employed as a multi-objective optimizer. 3) The information of uncertainty given by GP and angle-penalized penalized (APD) proposed in RVEA are used to update the surrogate models. To the best of our knowledge, the proposed algorithm is applied to a benchmark function, and two typical applications of MOO with synthetic reservoir models. Results show that the proposed method can provide more comprehensive and efficient RM with a higher convergence speed.
