Information Exchange in Global Production Networks: Increasing Transparency by Simulation, Statistical Experiments and Selection of Digitalization Activities

Today, companies of all industries are part of global production networks. They have a variety of performance relationships with suppliers and customers. Digitalization offers the potential to exchange more information between the partners of global production networks. This may improve operational performance. Especially within the three business processes order management, quality problem solving and engineering change management, a targeted increase in transparency promises a better handling of disruptions and an increase in robustness. This paper presents a simulation-based methodology for modeling production and business processes as well as information exchange in global production networks. Following the principles of Design of Experiment (DoE), screening test plans first carve out the impact of disruptions and information exchange on the performance of the production network. This is followed by the determination of the disruption-robust information exchange using Taguchi-experiments. Starting from the actual state of information exchange, digitalization activities to increase transparency are finally determined. The activities consist of the implementation of digitalization technologies and the stronger linkage of information systems. The paper ends with an application of the methodology to a global production network for plastic-metal components in the automotive supplier industry

Supply Chain Management of Industrial Enterprise Based on the Participation in Network Architecture Holdings (Case Study: SPV)

Abstract— The modern industry of the Russian Federation is experiencing an acute shortage of long-term investment resources for technological re-equipment programs aimed at import substitution. The purpose of the study is to identify topical issues of the supply chain management at an industrial enterprise that has become an SPV in a business network. The study is mainly based on the methodology for modeling supply chain management of the recipient enterprise and the method of discounting its cash flows generated with the participation of the industrial enterprise in the leasing process. The proposed concept of systematic management of the leasing process based on business networks allows investors to focalize investment resources on the basis of a set of management methods in order to accelerate the launch of industrial production and increase capitalization. The proposed systematic supply chain management concept is of practical value for a wide range of organizations considering the possibility of using tools for technological re-equipment of industrial production in the context of platform-based business networks. Implementation of the supply chain in SPV is showed at creation of a continuous innovations flow, reduction of technological development costs and unfirming system for development

Production Allocation of Reservoir Layers using Data-Driven Reservoir Modeling

The pros of having a commingled layer scheme would be considered high with successful reservoir management. If not, the cons will impact the production drastically as unfortunate consequences may result in reservoir fluids communication, well integrity issues, and production termination. Although the plane requires optimizing production with minimal capital investments and operating expenses, it is an enormous challenge considering commingled layers frequent surveillance and workover requirements. As the value of information is a decision tool for the surveillance frequency, the oil industry often uses static assumptions as an economical replacement of dynamic measurements such as KH static modeling. However, the last is misleading for not considering the effect of dynamic attributes such as reservoir pressure and fluid properties. Simultaneously, the evolution of Artificial Intelligence (AI) and Machine Learning (ML) made the challenge of allocating commingled layers allocation possible since AI does not build assumptions based on static properties but rather pick the static and dynamic patterns associated with rock and fluid properties. Accordingly, AI and ML application was used in this research as a new approach for commingled layers allocation estimation, which is known technically as Top-Down Modeling (TDM). TDM features the entire acquired static and dynamic field measurements through Artificial Intelligence and Data Science that utilizes Machine Learning, Fuzzy and crisp Logic via Neural Networks to develop a reservoir model. TDM was tested on a synthetic heterogeneous reservoir model with three commingled layers across 63 wells in conjunction with multi-random comingling schemes throughout wells\u27 lifespan. As the static KH modeling proven ambiguous in picking the effect of reservoir pressure on production profile per layer, a high certainty TDM modeling was successfully achieved both horizontally and vertically on a layer basis which confirms the capability of TDM in allocating commingled layers production in terms of certainty, and operational cost

Cloud-based manufacturing-as-a-service environment for customized products

This paper describes the paradigm of cloud-based services which are used to envisage a new generation of configurable manufacturing systems. Unlike previous approaches to mass customization (that simply reprogram individual machines to produce specific shapes) the system reported here is intended to enable the customized production of technologically complex products by dynamically configuring a manufacturing supply chain. In order to realize such a system, the resources (i.e. production capabilities) have to be designed to support collaboration throughout the whole production network, including their adaption to customer-specific production. The flexible service composition as well as the appropriate IT services required for its realization show many analogies with common cloud computing approaches. For this reason, this paper describes the motivation and challenges that are related to cloud-based manufacturing and illustrates emerging technologies supporting this vision byestablishing an appropriate Manufacturing-as-a-Service environment based on manufacturing service descriptions

Analysis of Andean blackberry (Rubus glaucus) production models obtained by means of artificial neural networks exploiting information collected by small-scale growers in Colombia and publicly available meteorological data

The Andean blackberry (Rubus glaucus) is an important source of income in hillside regions of Colombia. However, growers have little reliable information on the factors that affect the development and yield of the crop, and therefore there is a dearth of information onhowto effectively manage the crop. Site specific information recorded by small-scale producers of the Andean blackberry on their production systems and soils coupled with publicly available meteorological data was used to develop models of such production systems. Multilayer perceptrons and Self-Organizing Maps were used as computational models in the identification and visualization of the most important variables for modeling the production of Andean blackberry. Artificial neural networks were trained with information from 20 sites in Colombia where the Andean blackberry is cultivated. Multilayer perceptrons predicted with a reasonable degree of accuracy the production response of the crop. The soil depth, the average temperature, external drainage, and the accumulated precipitation of the first month before harvest were critical determinants of productivity. A proxy variable of location was used to describe overall differences in management between farmers groups. The use of this proxy indicated that, even under essentially similar environmental conditions, large differences in production could be assigned to management effects. The information obtained can be used to determine sites that are suitable for Andean blackberry production, and to transfer ofmanagement practices from sites of high productivity to sites with similar environmental conditions which currently have lower levels of productivity