The founding of new firms and efficient decision-making structures in localized production networks. The example of television production in the Cologne media cluster (Germany)

Much literature often discusses localized production networks as possible engines of prosperous regional development. In this literature, vertical disintegration and corresponding further specialization of individual firms within a commodity chain is seen as an essential factor in the success of such localized production networks. Additionally, this literature emphasizes the importance of information intensive economic transactions and the resulting interdependence between complementary firms in the production network. In turn, this type of interdependence makes the specific, specialized competencies of a single firm available to other firms in the commodity chain. Furthermore, such a production system is regarded as highly flexible. Firstly, thanks to their specialization in a small subset of the production chain, the single enterprises are in a position to develop a wide range of special solutions in their field of production. As a result, they are able to react very quickly to changing and unstable market conditions. Secondly, there is an enormous flexibility and adaptability because of the production chain's frequent renewal. In extreme cases, a new chain is built specifically to meet the demands of a specific project and is dissolved after the project is completed. Thus, production could take place with almost completely new partners (i.e., firms) from one project to the next. The numerous interactions required to sustain the reorganization of production for each new project in turn depend on spatial proximity, trust and embeddedness within a common socio-institutional context. In turn, these features are further reasons for the success of such a production network. These regionally-anchored, flexibly-specialized networks are often called "industrial districts". These networks not only operate in small niches of certain industries, but are also found in some parts of the sevice sector. The proposed presentation analyses the decision structures operating inside the production network of TV-programs in Cologne´s TV-production dominated Media-cluster. This research empirically confirms that competencies for decision-making are very unevenly distributed among the single units of the production chain. The research goes on to argue that the co-ordination of a highly disintegrated production network is possible only with a clear and plain hierarchy for decision making, execution and control processes. For this reason the network producing a TV-program is mainly designed and steered by only a handful actors. Although the competencies for decision-making are distributed unevenly through the production network, selection of subordinate partners by dominant firms is shaped by strong socio-institutional relations like trust based on previous positive working relations as well as common conventions, rules and routines. The final goal of the actual (December 2001) empirical research in Cologne is to analyse the decision-making structures affiliated with specific TV programs. This leads to a deeper understanding of how localized networks with uneven power structures work. In this context the question arises of how spatial concentration determines the respective decision-making process. At the same time there are still some indications that - besides some evolutionary factors - these special decision-making processes are a crucial factor in explaining the spatial concentration of TV-production in Cologne´s Media Cluster.

Advances and Future Perspectives

Agharafeie , R., Ramos, J. R. C., Mendes, J. M., & Oliveira, R. M. F. (2023). From Shallow to Deep Bioprocess Hybrid Modeling: Advances and Future Perspectives. Fermentation, 9(10), 1-22. [922]. https://doi.org/10.20944/preprints202310.0107.v1, https://doi.org/10.3390/fermentation9100922--- This work was supported by the Associate Laboratory for Green Chemistry - LAQV which is financed by national funds from FCT/MCTES (UIDB/50006/2020 and UIDP/50006/2020). This work received funding from the European Union’s Horizon 2020 research and innovation program under the grant agreement no. 101099487- BioLaMer-HORIZON-EIC-2022-PATHFINDEROPEN-01 (BioLaMer)Deep learning is emerging in many industrial sectors in hand with big data analytics to streamline production. In the biomanufacturing sector, big data infrastructure is lagging comparatively to other industries. A promising approach is to combine Deep Neural Networks (DNN) with prior knowledge in Hybrid Neural Network (HNN) workflows that are less dependent on the quality and quantity of data. This paper reviews published articles over the past 30 years on the topic of HNN applications to bioprocesses. It revealed that HNNs were applied to various bioprocesses, including microbial cultures, animal cells cultures, mixed microbial cultures, and enzyme biocatalysis. HNNs were mainly applied for process analysis, process monitoring, development of software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, quality-by-design, and recently for the development of digital twins. Most previous HNN studies combined shallow Feedforward Neural Networks (FFNNs) with physical laws, such as macroscopic material balance equations, following the semiparametric design principle. Only recently, deep HNNs based on deep FFNNs, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM) networks and Physics Informed Neural Networks (PINNs) have been reported. The biopharma sector is currently a major driver but applications to biologics quality attributes, new modalities, and downstream processing are significant research gaps.publishersversionpublishe

Short-term demand forecast using a bank of neural network models trained using genetic algorithms for the optimal management of drinking water networks

Efficient management of a drinking water network reduces the economic costs related to water production and transport (pumping). Model predictive control (MPC) is nowadays a quite well-accepted approach for the efficient management of the water networks because it allows formulating the control problem in terms of the optimization of the economic costs. Therefore, short-term forecasts are a key issue in the performance of MPC applied to water distribution networks. However, the short-term horizon demand forecast in a horizon of 24 hours in an hourly based scale presents some challenges as the water consumption can change from one day to another, according to certain patterns of behavior (e.g., holidays and business days). This paper focuses on the problem of forecasting water demand for the next 24 hours. In this work, we propose to use a bank of models instead of a single model. Each model is designed for forecasting one particular hour. Hourly models use artificial neural networks. The architecture design and the training process are performed using genetic algorithms. The proposed approach is assessed using demand data from the Barcelona water network.Peer ReviewedPostprint (author's final draft

Geosensors to Support Crop Production: Current Applications and User Requirements

Sensor technology, which benefits from high temporal measuring resolution, real-time data transfer and high spatial resolution of sensor data that shows in-field variations, has the potential to provide added value for crop production. The present paper explores how sensors and sensor networks have been utilised in the crop production process and what their added-value and the main bottlenecks are from the perspective of users. The focus is on sensor based applications and on requirements that users pose for them. Literature and two use cases were reviewed and applications were classified according to the crop production process: sensing of growth conditions, fertilising, irrigation, plant protection, harvesting and fleet control. The potential of sensor technology was widely acknowledged along the crop production chain. Users of the sensors require easy-to-use and reliable applications that are actionable in crop production at reasonable costs. The challenges are to develop sensor technology, data interoperability and management tools as well as data and measurement services in a way that requirements can be met, and potential benefits and added value can be realized in the farms in terms of higher yields, improved quality of yields, decreased input costs and production risks, and less work time and load

Application of multivariable control using artificial neural networks in a debutanizer distillation column

LOPES, Jose Soares Batista et al. Application of multivariable control using artificial neural networks in a debutanizer distillation column.In: INTERNATIONAL CONGRESS OF MECHANICAL ENGINEERING - COBEM, 19, 5-9 nov. 2007, Brasilia. Anais... Brasilia, 2007Abstract. This work has as objective to develop a control strategy based on neural identification of a mutivariable input- mutivariable output (MIMO) process. The plant to control was simulated in software HYSYS as a classic debutanizer column. Debutanizer distillation column is used to remove the litht components from the gasoline stream to produce Liquefied Petroleum Gas (LPG). The quality control of the product taking away from the top of the tower is affected by the Outflow Control (FIC-100) and the Temperature Control (TIC-100).The process variables chosen are concentration of i-pentene existing in butanes stream and concentration of i-butene existing in C5+ stream. The manipulated variables chosen are reflux flow rate (the setpoint of FIC-100 in h/m3) and thermal load (the setpoint of TIC-100 in oC). The FIC- 100 is responsible for the control of reflux and the TIC-100 for the control of the temperature in the debutanizer column, changing its thermal load to keeping the C5+ production at acceptable level. The purpose is to substitute two physical controllers, FIC-100 and TIC-100, by a neural control system. An important feature of this work is the use of a control strategy composed by two neural network structures: Neuroidentifier and Neurocontroller, responsible respectively for identifying and controlling the process.The software implementation of the artificial neural networks is made using Borland C++ Builder, and its communication with HYSYS is carried through the Microsoft Component Object Model (COM

Production Process Modelling Architecture to Support Improved Cyber-Physical Production Systems

With the proliferation of intelligent networks in industrial environments, manufacturing SME’s have been in a continuous search for integrating and retrofitting existing assets with modern technologies that could provide low-cost solutions for optimizations in their production processes. Their willingness to support a technological evolution is firmly based on the perception that, in the future, better tools will guarantee process control, surveillance and maintenance. For this to happen, the digitalization of valuable and extractable information must be held in a cost-effective manner, through contemporary approaches such as IoT, creating the required fluidity between hardware and software, for implementing Cyber-Physical modules in the manufacturing process. The goal of this work is to develop an architecture that will support companies to digitize their machines and processes through an MDA approach, by modeling their production processes and physical resources, and transforming into an implementation model, using contemporary CPS and IoT concepts, to be continuously improved using forecasting/predictive algorithms and analytics.authorsversionpublishe

NEURAL NETWORKS ARCHITECTURES FOR MODELING AND SIMULATION OF THE ECONOMY SYSTEM DYNAMICS

This research work investigates the possibility to apply several neural network architectures for simulation and prediction of the dynamic behavior of the complex economic processes. Therefore we will explore different neural networks architectures to build several neural models of the complex dynamic economy system. In future work we will use these architectures to be trained by well-known training algorithms, such as Levenberg-Marquardt back-propagation error and Radial Basic Function (RBF), to compare their results and to decide at the end, which one is the best among the different applications from the economy field. The results presented in this work are based on the experience accumulated by the authors in the field of identification, modeling and control of the industrial and economic processes, namely chemical, HVAC, automotive industry and satellites constellation. The neural networks are strongly recommended for the highly nonlinear processes for which an analytic description is almost impossible. It is well known that the single-index economic models and selection of leading indicator variables are normally based on linear regression methods. Moreover, in statisti- cal modeling of the business cycle, it has been well established that cycles are asymmetric; therefore it is doubtful that linear models can adequately describe them. With recent development in nonlinear time series analysis, several authors have begun to examine the forecasting properties of nonlinear models in economics. Probably the largest share of economic appli- cations of nonlinear models can be found in the field of prediction of time series capital markets. Furthermore recently, the neural network architectures use financial variables to forecast industrial production by estimating a nonlinear, non- parametric nearest-neighbor regression model, and are very useful for fault detection, diagnosis and isolation ( FDDI) of the models fault in the control systems.The simulation results reveal a high capability of the neural networks to capture more accurate the nonlinear dynamics behavior of the process and to yield high performance, comparable to the Kalman filters techniques and all other control strategies developed in literature. The nonlinear mapping and self-learning abilities of neural networks have been motivating factors for development of intelligent contol strategies. The neural networks approach is very interesting because don`t need the linear model of the process that means time consuming and increasing the risk to reduce the accuracy in capturing the appropriate dynamics of the process.Dynamic Systems, Kalman Filers, Neural Networks Architectures, ARMA Models, Estimation, Neural-Models, Neural-Control Strategy, Inverse Neural-Control Strategy, MIMO Control Strategies. Market-Oriented Economy

Challenges in Global Multi-Variant Serial Production - A Study of Manufacturing Engineering Processes

Global manufacturing companies have hard times to manage their global production networks as dispersion in their networks increases. Such dispersion is caused by the increasing level of product variety and more parameters to control as the networks grow. Handling product variety becomes more difficult as products become more complex and integrated. Product variety and its impact on productivity have been studied for several years. Those studies show that product variety has negative impact on productivity. There are no signs that product variety will decrease in the future. Nor are there any signs showing that the size of global product networks will decrease over time. Therefore, it is important to understand how product variety affects global production networks. The aim of this thesis is to investigate how global manufacturing companies operate their production networks in terms of manufacturing engineering processes and operational performance with respect to product variety. Due to the size of global production networks, engineering processes and systems tend to be dispersed. Therefore, the focus of this thesis is on studying manufacturing engineering processes in terms of standards for creating assembly work instructions for manual assembly and the effects of high product variety on operational performance in the same production network. In this study similarities and dissimilarities in such processes are mapped within one global production network. Four different cases studies have been designed and conducted collecting important data defining the setup for the investigated production network. Questionnaires, interviews and production data are used to map current manufacturing engineering processes and to study the effects of high product variety on operational performance. Results from the case studies show that the studied production network handles high levels of product variety and that the manufacturing engineering processes are highly dispersed due to lack of global standards. The high level of product variety has negative impact on operational performance as operators are facing unfamiliar product variants on a daily basis. Furthermore, the high level of product variety makes it more difficult for manufacturing engineering to create better and more supportive assembly work instructions. Future research activities should focus more on early phases of the engineering process. By studying the engineering process in more detail, a mature information model can be created defining (1) what information is used, (2) by whom it is used, (3) where in the process it is used and (4) for what purpose the information is used. Such an information model is essential to be able to develop better methods to handle high product variety in global production networks

Agents modeling experience applied to control of semi-continuous production process

The lack of proper analytical models of some production processes prevents us from obtaining proper values of process parameters by simply computing optimal values. Possible solutions of control problems in such areas of industrial processes can be found using certain methods from the domain of artificial intelligence: neural networks, fuzzy logic, expert systems, or evolutionary algorithms. Presented in this work, a solution to such a control problem is an alternative approach that combines control of the industrial process with learning based on production results. By formulating the main assumptions of the proposed methodology, decision processes of a human operator using his experience are taken into consideration. The researched model of using and gathering experience of human beings is designed with the contribution of agent technology. The presented solution of the control problem coincides with case-based reasoning (CBR) methodology

Translating research into policy: lessons learned from eclampsia treatment and malaria control in three southern African countries

<p>Abstract</p> <p>Background</p> <p>Little is known about the process of knowledge translation in low- and middle-income countries. We studied policymaking processes in Mozambique, South Africa and Zimbabwe to understand the factors affecting the use of research evidence in national policy development, with a particular focus on the findings from randomized control trials (RCTs). We examined two cases: the use of magnesium sulphate (MgSO₄) in the treatment of eclampsia in pregnancy (a clinical case); and the use of insecticide treated bed nets and indoor residual household spraying for malaria vector control (a public health case).</p> <p>Methods</p> <p>We used a qualitative case-study methodology to explore the policy making process. We carried out key informants interviews with a range of research and policy stakeholders in each country, reviewed documents and developed timelines of key events. Using an iterative approach, we undertook a thematic analysis of the data.</p> <p>Findings</p> <p>Prior experience of particular interventions, local champions, stakeholders and international networks, and the involvement of researchers in policy development were important in knowledge translation for both case studies. Key differences across the two case studies included the nature of the evidence, with clear evidence of efficacy for MgSO₄and ongoing debate regarding the efficacy of bed nets compared with spraying; local researcher involvement in international evidence production, which was stronger for MgSO₄than for malaria vector control; and a long-standing culture of evidence-based health care within obstetrics. Other differences were the importance of bureaucratic processes for clinical regulatory approval of MgSO₄, and regional networks and political interests for malaria control. In contrast to treatment policies for eclampsia, a diverse group of stakeholders with varied interests, differing in their use and interpretation of evidence, was involved in malaria policy decisions in the three countries.</p> <p>Conclusion</p> <p>Translating research knowledge into policy is a complex and context sensitive process. Researchers aiming to enhance knowledge translation need to be aware of factors influencing the demand for different types of research; interact and work closely with key policy stakeholders, networks and local champions; and acknowledge the roles of important interest groups.</p