Classification of production systems for industrialized building: a production strategy perspective

From automation toward integration of process planning: a state-of-the-art review

Various researchers have determined the different factors influencing farmers’ decisions to adopt certified organic production, without considering factors about the social and institutional environments of smallholder farmers in developing countries. In this paper, we examined the social, physical and institutional factors that affect farmers’ choice of production systems for pineapple in Ghana. A multinomial logit model was used to examine the factors influencing the pineapple farmers’ choice of a production system. Empirical findings indicate that apart from personal and attitudinal factors, the social, physical and institutional factors are also very important in individual farmer’s decisions to adopt certified organic production systems. Policy implications of these findings are that besides farmers’ personal and attitudinal characteristics, the social, physical and institutional features were also crucial in their decision to adopt certified organic production systems. The identified factors contribute to informing the government and other key players along the pineapple value chain on the elements to strive for when designing strategies and programmes to promote certified organic pineapple production. The study proposes that to encourage and sustain certified organic pineapple production systems, stakeholders in the pineapple sector should help farmers to consider the environmental sustainability in their production decision making and educate farmers on the potential cost and benefits of certifying their products organically. Effective policy and strategy design should, therefore, consider these factors to improve the adoption rate from conventional to certified organic production systems.

Typology analysis of sheep production, feeding systems and farmers strategies for livestock watering in Tunisia

Transmission of biological hazards capable of causing disease in livestock can occur through a wide variety of direct and indirect routes. In swine production, there are a large number of possible routes of exposure of a pathogen into a susceptible population. African swine fever virus (ASFV) has been a significant challenge for Southeast Asia since first detected in China in 2018 and has spread through many countries within the region. In order to understand potential transmission pathways within an ASFV endemic region, a diagnostic investigation was performed to determine the level of contamination on a wide variety of surface types within a live animal production, feed manufacturing, and feed distribution system located in Vietnam. All diagnostic testing was performed locally by the production system's internal diagnostic laboratory using real‐time polymerase chain reaction (rt‐PCR) analysis. Early in the diagnostic investigation, it became clear that feed trucks were a common site of ASFV surface contamination detection. This information resulted in biosecurity‐focused actions for feed trucks arriving back at the feed mill, including decontamination of interior truck cab surfaces and washing of exterior truck surfaces with high‐pressure water prior to application of surface disinfectants. Additionally, a low number of rt‐PCR positive samples were detected within the feed production system, with the greatest number coming from transient surfaces such as high traffic areas and worker clothing. This illustrates the importance of managing employee traffic through procedures such as zoning and separation between clean–dirty areas to reduce the likelihood of pathogen transmission. In conclusion, this report illustrates the importance of routine data capture regarding efficacy of biosecurity procedures which allows for real‐time updates and improvement as biosecurity gaps are identified.

Since a few years, an abundant literature has been published in order to proof the existence of chaotic behaviors both in the field of science and in the field of technique. Until now very few articles studied the conduct of manufacturing production systems. Apparently some production systems let us think that their behavior might be chaotic. Nevertheless, in our opinion, the proof of existing chaos in the production systems has not been totally confirmed. The works presented in this article are aimed to make obvious and to prove the existence of chaotic behaviors in manufacturing production systems. After the presentation of the interest of this study in a manufacturing production environment, we present our analysis method of the dynamic of non-planned production systems. We then justify the choices which have been made regarding in particular: the sub-system in which our study is made, the variable of interest (temporal average of the number of parts in a waiting line), the determinism of the system parameters, and the imposed balance conditions (in the sense that the number of parts is finished regardless of the considered instant). In the second part are presented the results obtained with two manufacturing systems, both very simple and very similar, although they give very different results. We then compare the results with the rules of assignment and management of different waiting lines. In the last part, we show that an actual system, under certain management conditions, can also present a chaotic behavior. This study has been realized from the modeling of a flexible assembly cell.

A Tactical Planning Approach using Genetic Algorithms and Process Chain Simulation for Closed-Loop Production Systems for high-value components

The second part of this article focuses on the transformation of the production systems in manufacturing and agriculture as a result of the convergence in the I4.0 technologies detailed in Part I. The category of general-purpose production machines is presented in detail. The interconnection of these machines and our ability to operate them remotely turn the stand-alone machines into production systems. The characteristics of these systems are studied separately for the cases of manufacturing and agriculture, showing the interrelation of the transformations in the two sectors. The I4.0 production systems give a tremendous boost in productivity as they allow the user of these systems to switch between different activities dynamically, cutting down costs and inefficiencies. The widespread deployment of such production systems fosters the emergence of a new social entity, the “prosumer.” The subsequent rationalization in the production creates the conditions where overproduction gradually dies out while it shifts the balance from mass consumption to mass customization.

Presents a classification for production systems in manufacturing and processing industries. The proposed classification is intended to enlarge the scope of production systems that can be meaningfully classified. This is accomplished by including production system properties used in previous classifications and incorporating new characteristics that describe major changes in emerging automated production systems. The proposed classification is intended to provide better understanding of the functioning of production systems and management approaches available to improving their processes.

Assessing sensory attributes and quality of lettuce from open field, greenhouse, and controlled environment production systems

Development of Programmable Precision Manufacturing Systems (PPMS) for small Lot Production

The increasing worldwide demand for lithium-ion batteries is no longer an estimated forecast, but a fact. Observable trends, such as the increasing variety of battery cell formats and materials, present enormous challenges for the design of production processes. Many cause-effect relationships can be seen in the individual manufacturing processes, but are not yet properly or only partially understood. To meet these requirements and challenges and to ensure effective manufacturing, intelligent processes are needed in battery cell production. Methods of digitization, artificial intelligence and the use of digital twins offer a high potential to optimize the processes both in commissioning and in operation. These methods can be applied within a virtual production system for process optimization in battery cell manufacturing. In this paper, the potential of a virtual production system for battery cell manufacturing is discussed. Further, the design of a suitable infrastructure consisting of standardized interfaces, data models and process models is provided. The result is a system that offers the possibility to virtually quantify cause-effect relationships, to test optimization approaches along the entire process chain of battery cell production and to apply recommendations for action to the real production system. The process step of cell assembly is considered as an example. Here, the simulation model and the associated data model are specified.

Scientific and structural base of manufacturing

In our days in different industrial areas it become very important to modeling and control the production systems. For this, the most used are SCADA system; these are used to control process of production in different areas, to collect information and to centralized data acquisition. This paper proposed an architecture for the SCADA system, used for making more efficient production in manufacturing system, in order to can integrate all the elements of a flexible manufacturing line. The main characteristic of this SCADA system is to supervise and to control the manufacturing process from a flexible manufacturing line.

Economic measure of productivity, quality and flexibility in advanced manufacturing systems

Logistic Modelling Of Order Realization In The Complex Parallel Manufacturing System