Complex Production Systems Research Articles

Production system of apartment house as an enterprise

The specific problems of apartment building management in Ukraine, which arose as a result of housing privatization using the approaches of civil rather than commercial law, are considered. The recognition of the apartment as real estate, the organization of house management through the voluntary creation of condominiums in the form of a public organization; absence of land and house as real estate in the account of the subject of ownership and management are criticized. The analysis of the literature showed the need for a profound rethinking of ways to improve home management. 
 The purpose of the article is to consider an apartment building as a primary object of ownership and as a production system, an economic and not public organization, enterprise. The methodological approach in substantiating the ways to solve the problem and in formulating conclusions is to compare these alternatives of joint ownership with the use of the provisions of system analysis, models and organizational and legal forms of enterprises. 
 It is shown that in the housing sector as a production subsystem of the sphere of social production, and in it - in the multi-apartment housing stock as a subsystem, the house is their element. Home management is mostly organized by apartment owners as a joint household or condominium. However, a modern apartment building in a business environment is a complex production system that has all the functional features of a business organization-enterprise, and the ownership structure can function most effectively on a corporate model in the form of a joint stock company.
 Conclusions on the incapacity of the current model of ownership and management of an apartment building, the need to improve the model of condominiums as a subject of management of the object - a condominium; formation of a new segment of corporate-owned houses in joint households are formulated.

Service production in high captivity service firms

Service firms need to handle customer-induced uncertainty during service production. The literature argues for several mechanisms that organizations utilize to handle customer-induced uncertainty in service production. Specifically, the literature argues that involving customers as coproducers, or quasi-employees, in the production system could decrease such uncertainty. This reduction in turn decreases the need for a complex production system and high levels of human capital. Moreover, research suggests service production complexity and human capital to be interchangeable mechanisms in handling customer-induced uncertainty. In contrast to these past findings, we argue that such relationships might not hold in service firms exhibiting high captivity; firms where the customer has difficulty exiting the system once service delivery begins. Our empirical analysis provides support to our arguments leading us to identify an important boundary condition to existing theory regarding service production mechanisms.

Changing Path and Curbing Cost Escalation: Lessons Learnt from the Gripen Case

ABSTRACT Extant theory suggests that paths of development, although identified as being unsustainable, might be difficult to depart from. The aim of this study has therefore been to explore and understand how a path change can be facilitated in a setting of complex product systems. A unique single case of product development that managed to curb an unsustainable intergenerational cost escalation for complex defence equipment has been studied. The study shows a relation between a path change and a challenging opportunity, and also indicates how this relation can be moderated by the company management and the customers. The study provides both theoretical and practical implications, supporting understanding and facilitation of path changes.

Organising strategy decisions for complex product system innovation, influencing factors, and their impact on learning

This comparative case study tried to examine the thread of organising strategy decisions, influential factors and their decisional impacts on learning in complex product system (CoPS) innovation. I...

Scope and delimitation of game engine simulations for ultra-flexible production environments

Game engines are middleware that allows the integration of multiple resources turning them into assets with graphic information, programmed content and physical simulation parameters. These features meet the requirements for modelling, visualizing, simulating and controlling complex production systems. Therefore, an approach based on game engines to manage the user-perceived complexity was proposed. This paper compares 24 game engines and identifies suitable modelling environments for production based on their technical and market-development features. Then, a modular machine was integrated in two game engines. The results of scopes and limitations regarding functionality, scale, physics simulation accurateness, resource redundancies and visualization are presented.

Organising strategy decisions for complex product system innovation, influencing factors, and their impact on learning

Organising strategy decisions for complex product system innovation, influencing factors, and their impact on learning

Text mining for AI enhanced failure detection and availability optimization in production systems

The success of data-driven maintenance is strongly dependant on effective use of AI and multi-structured data sources. Introducing and integrating an AI-enhanced methodology in reliability-centred maintenance study of complex production systems leads to reducing failure rates and optimizing availability. In manufacturing enterprises, information about machine failures and expert knowledge are often stored in digital shift books (DSB). This paper introduces a transferable and scalable AI-enhanced methodology for DSB in automotive industry, which enhances Overall Equipment Efficiency (OEE) by optimizing availability through reducing the Mean Failure Detection Time (MFDT). Experimental investigations in the use-case suggest an OEE increase by over 5%.

The Investigation of the Characteristics of Conflicts in Industrial Organizations Based on Modeling

Abstract The paper discusses the features of managing complex production systems, taking into account the conflicts occurring in them. This problem is very relevant in the context of the impact of external, not always stable economic conditions. As the basis of operational management, the decision is considered by the head of a department or organization, who will be the decision-maker. Characteristics of a fuzzy model that allows to determine the features of conflicts in production systems are considered. The block diagram of according algorithm is demonstrated. The decision-based system used expert information in the analysis is constructed. In total, 10 parameters in model were considered that could affect the form of the conflict. The results of the paper are useful for using in the management of industrial organizations while taking into account possible conflicts.

Achieving lean through value stream mapping with constraint programming and simulation technique for complex production systems

Achieving lean through value stream mapping with constraint programming and simulation technique for complex production systems

A framework for achieving lean orientation in complex production value streams

The purpose of this paper is to present a framework for achieving lean in manufacturing organisations. Value stream mapping (VSM) is a commonly used tool for achieving lean orientation in a manufacturing setup. It is a very useful preliminary tool to gain useful insight to identify lean wastes. However, there are many cases, where VSM has to be supported by some additional tools for analysis. Examples of such production systems include complex production systems such as converging, diverging flows or production systems where complete products (assemblies made up of several components) are getting manufactured. There are also situations where lean practitioners need additional insights into the financial health of the value stream in terms of monetary measures, e.g., revenue, costs, profitability, etc. This research paper suggests a framework for such production scenarios. The proposed framework combines value stream mapping with discrete event simulation (DES) and lean box score. The framework is demonstrated through a case of a capacitor manufacturer.

Combining LCA and circularity assessments in complex production systems: the case of urban agriculture

Local food production through urban agriculture (UA) is promoted as a means to make cities more sustainable. However, UA does not come free of environmental impacts. In this sense, optimizing urban resources through circular economy principles offers the opportunity to close loops and improve production systems, but an assessment of these systems through a combination of circularity and environmental tools is missing from the literature. The goal of our study is to analyse the environmental and circularity performance of applying circular strategies in UA systems. We use Life Cycle Assessment (LCA) and the Material Circularity Indicator (MCI) to assess the baseline scenario of a Mediterranean rooftop greenhouse and the application of 13 circular strategies. The results show that the MCI score for all strategies was biased by overweighting of the water subsystem in the mass balance. Based on this finding, we propose a series of modifications to the circularity assessment, calculating specific MCI scores for every subsystem before coupling them with environmental life cycle indicators. The outcome is a set of indicators that use the Linear Flow Index (LFI), where decreasing the values as much as possible will correspond to a decrease both in environmental impact and linearity of the system (the inverse of circularity). The use of these indicators provides a simple understanding of the circular and environmental performance of these systems while being fully adaptable. With these indicators, the uses of nutrient recirculation, struvite fertilizer or recycled materials were the best strategies to improve urban agriculture.

Innovative Project Methods and Procedures for Implementation of Companies

Designing of today's complex and diverse production systems requires a comprehensive analysis of their operations in three spatial dimensions and over time. Only this approach is a guarantee of identification, optimization, respectively elimination of project disadvantages before their future physical implementation. The designer of tomorrow's production groups must be a professional and a specialist. He must have the creativity and professional theoretical and practical knowledge of the issues that are solved in the project. In technical project solutions, he must have a sense of precision and addressing details as a limiting requirement for the success and implementation of a future project. He must follow the development trends of designing new production structures and the direction of their development and he can´t stay at the level of selection the outdated, time-consuming technical solutions and applied technologies.

Immunogenicity of Plant-Produced Human Papillomavirus (HPV) Virus-Like Particles (VLPs).

Cervical cancer is ranked fourth among the top cancers in women and is the second most common cancer in low- and middle-income regions, with ~570,000 new cases reported in 2018, which attributed to 84% of worldwide cervical cancer cases. Three commercially available prophylactic Human papillomavirus (HPV) vaccines are effective at preventing HPV infections. However, these vaccines are expensive due to their complex production systems, therefore limiting their use in developing countries. Recently, the use of plants to produce vaccines has emerged as a cost-effective alternative to conventionally used expression systems. Here, L1 proteins of eight high-risk (HPV 16, 18, 31, 33, 35, 45, 52, and 58) and two low risk (HPV 6 and 34) HPV types were successfully expressed in Nicotiana benthamiana, and transmission electron microscopy (TEM) analysis showed the presence of VLPs and/or capsomeres. Immunogenicity studies were conducted in mice utilizing HPV 35, 52, and 58 and showed that type-specific L1-specific antibodies were produced which were able to successfully neutralize homologous HPV pseudovirions in pseudovirion-based neutralization assays (PBNAs). This work demonstrated the potential for using plant-based transient expression systems to produce affordable and immunogenic HPV vaccines, particularly for developing countries.

16 Opportunities and challenges from deep-phenotyping of dairy cattle

Abstract Genetic selection has been a very successful tool for the long-term improvement of livestock populations, and the rapid adoption of genomic selection over the last decade has doubled the rate of gain in some populations. However, the full expression of genetic potential requires that animals are placed in environments that support such performance. Increasingly complex dairy cattle production systems require that all aspects of animal performance are measured across individuals’ lifetimes. Selection emphasis is shifting away from traits related to animal productivity towards those related to efficient resource utilization and increased animal welfare. However, phenotypes for many of these new traits are difficult or expensive to measure, or both. This is driving interest in sensor-based systems that provide continuous measurements of the farm environment, individual animal performance, and detailed milk composition. The goal of phenomics is to provide information for making decisions related to on-farm management, as well as genetic improvement. However, many challenges accompany these new technologies, including a lack of standardization, the need for high-speed Internet connections, increased computational requirements, and training to integrate these tools with more traditional management tools. There also is a lack of translational research on the use of these data for real-time precision management. We will identify opportunities and challenges associated with phenomics and discuss on-farm applications of these new tools.

УПРАВЛІННЯ ФІНАНСОВИМИ РИЗИКАМИ ПІДПРИЄМСТВА

У статті розглянуто сутність фінансового ризику, виокремлено джерела невизначеності та установлено походження фінансового ризику. Проаналізовано чистий прибуток (збиток) підприємств України та підприємств Житомирської області за основними видами економічної діяльності 2014-2018 років. Встановлено, що частка прибуткових підприємств збільшилась, але кількість збиткових підприємств є досить великою. Визначено основні етапи управління ризиками, за допомогою яких підприємство стає більш резистентним до непередбачуваних обставин, особливо до різних видів ризиків. Побудовано блок-схему моніторингу фінансових ризиків підприємства, за допомогою якої суб'єкти господарської діяльності зможуть швидко реагувати на непередбачувані події. Врахування основних етапів управління ризиками та застосування блок-схеми моніторингу фінансового ризику підприємств допоможе суб'єктам господарювання попереджати, ідентифікувати та уникнути досліджуваного ризику.

Maintenance policy structure investigation and optimisation of a complex production system with intermediate buffers

Existing research about the maintenance optimisation of production systems with intermediate buffers largely assumed a series system structure. However, practical production systems often contain subsystems of ring structures, for example, rework and feedforward. The maintenance optimisation of these complex systems is difficult due to the complicated structure of maintenance policies and the large search space for optimisation. This paper proves the control limit property of the optimal condition-based maintenance policy. Based on the control limit property, approximate policy structures that incur a smaller policy space are proposed. Because the state space of a production system is often large, the objective function of the maintenance optimisation cannot be evaluated analytically. Consequently, a stochastic branch and bound (SB&B) algorithm embedding a sequential simulation procedure is proposed to determine a cost-efficient condition-based maintenance policy. Numerical studies show that the proposed maintenance policy structures can deliver a cost-efficient maintenance policy, and the performance of the SB&B algorithm is enhanced by the inclusion of a sequential simulation procedure.

Simulation in industry 4.0: A state-of-the-art review

Simulation is a key technology for developing planning and exploratory models to optimize decision making as well as the design and operations of complex and smart production systems. It could also aid companies to evaluate the risks, costs, implementation barriers, impact on operational performance, and roadmap toward Industry 4.0. Although several advances have been made in this domain, studies that systematically characterize and analyze the development of simulation-based research in Industry 4.0 are scarce. Therefore, this study aims to investigate the state-of-the-art research performed on the intersecting area of simulation and the field of Industry 4.0. Initially, a conceptual framework describing Industry 4.0 in terms of enabling technologies and design principles for modeling and simulation of Industry 4.0 scenarios is proposed. Thereafter, literature on simulation technologies and Industry 4.0 design principles is systematically reviewed using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. This study reveals an increasing trend in the number of publications on simulation in Industry 4.0 within the last four years. In total, 10 simulation-based approaches and 17 Industry 4.0 design principles were identified. A cross-analysis of concepts and evaluation of models’ development suggest that simulation can capture the design principles of Industry 4.0 and support the investigation of the Industry 4.0 phenomenon from different perspectives. Finally, the results of this study indicate hybrid simulation and digital twin as the primary simulation-based approaches in the context of Industry 4.0.

Product-requirement-model to approach the identification of uncertainties in battery systems development

Electric mobility is on the verge of becoming a mass market. Major automotive OEMs have initiated programs to electrify their product portfolio. This transition poses new challenges and requires new innovative concepts in automotive development processes, especially for battery systems as the key component within electric powertrains. Battery system costs account for up to 40% of the electric vehicle’s total costs. Additionally, development cycles of battery systems for automotive applications are characterized by long development periods. Hence, the initiatives to advance electrification result in numerous development projects affiliated with significant development expenses. Battery systems can be referred to as mechatronic and electrochemical systems. They require a complex interaction of diverse scientific and engineering disciplines. Fast innovation cycles have effects regarding product requirements and assumptions towards their allocation. Hereby, uncertainties can lead to risks within development projects, especially in terms of time and costs. In current development processes, necessary changes are only dealt with reactively, causing unplanned additional expenses and delays. Thus, there is need for handling potential changes proactively, i.e. managing uncertainties leading to those changes as early as possible. New methods are necessary to identify and handle uncertainties of complex product systems within requirements engineering. An approach towards comprehensive uncertainty management is taken within this publication.

Knowledge configuration model for fast derivation design of electronic equipment and its implementation

There has contradictions in timeliness and diversity in the design schemes of complex product systems. The traditional solidified knowledge template for the knowledge reuse purpose cannot meet the rapid design needs of complex products. This paper proposes a configurable knowledge ontology model (CKOM) and a corresponding design method for rapid derivation of complex products. Based on the combination of basic knowledge elements (BKE), customized knowledge elements (CKE), and assorted knowledge elements (AKE), a knowledge structural body (KSB) is constructed by unitizing knowledge ontology into units of knowledge. And a dynamic structured knowledge template configuration method based on physical form (Pf) configuration and virtual form (Vf) association is proposed. The knowledge configuration information is transmitted and parsed in XML form, and the virtual form is established to associate and dynamically configure the corresponding physical form knowledge structure. Based on the structured knowledge model configuration template, a rapid derivative design method for electronic equipment is proposed. The rapid derivation design process is divided into three stages: requirement analysis, index demonstration, and scheme formation. By establishing seven knowledge attributes, such as global, local, scope, retrieval, the required product knowledge elements, rule knowledge elements and tools are dynamically configured to realize the dynamic processing of design parameters based on configuration knowledge elements and then form a corresponding design scheme. Finally, a rapid derivation design system is developed and applied to electronic equipment, which verifies the rationality and effectiveness of the proposed model and method.

Designing an adaptive production control system using reinforcement learning

Modern production systems face enormous challenges due to rising customer requirements resulting in complex production systems. The operational efficiency in the competitive industry is ensured by an adequate production control system that manages all operations in order to optimize key performance indicators. Currently, control systems are mostly based on static and model-based heuristics, requiring significant human domain knowledge and, hence, do not match the dynamic environment of manufacturing companies. Data-driven reinforcement learning (RL) showed compelling results in applications such as board and computer games as well as first production applications. This paper addresses the design of RL to create an adaptive production control system by the real-world example of order dispatching in a complex job shop. As RL algorithms are “black box” approaches, they inherently prohibit a comprehensive understanding. Furthermore, the experience with advanced RL algorithms is still limited to single successful applications, which limits the transferability of results. In this paper, we examine the performance of the state, action, and reward function RL design. When analyzing the results, we identify robust RL designs. This makes RL an advantageous control system for highly dynamic and complex production systems, mainly when domain knowledge is limited.