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Industrial Dynamics

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... To that end, this paper proposes a novel hybrid simulation model, CrowdSim for task failure prediction in CSD. It integrates three different simulation techniques, i.e. system dynamics simulation (SDS) [17], discrete event simulation (DES) [18], and agent-based simulation (ABS) models [19] , to provide insights on the effects of various compounding factors [20] [21] for attracting reliable crowd workers on the task failure risk in CSD. While SDS methods represent the overall view of the system and dynamic relation among the stakeholders [17], DES models capture the actual process-level details in development [18], and ABS models simulate the behavior among individual workers and social groups [19]. ...
... It integrates three different simulation techniques, i.e. system dynamics simulation (SDS) [17], discrete event simulation (DES) [18], and agent-based simulation (ABS) models [19] , to provide insights on the effects of various compounding factors [20] [21] for attracting reliable crowd workers on the task failure risk in CSD. While SDS methods represent the overall view of the system and dynamic relation among the stakeholders [17], DES models capture the actual process-level details in development [18], and ABS models simulate the behavior among individual workers and social groups [19]. Moreover, simulation allows one to reenact a scenario in order to identify bottlenecks in the process and provide solutions [22]. ...
... Agents are assigned unique IDs upon their entrance into the simulation. As is displayed in Figure 3, the agent-based model contains: i) the agent environment, ii) a set of agents' attributes, and iii) and the agent decision-making process [17]. In any crowdsourcing platform, an individual agent has "agent's knowledge", which is based on his or her skillset, background, and the society s/he represents [32] [33]. ...
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A typical crowdsourcing software development(CSD) marketplace consists of a list of software tasks as service demands and a pool of freelancer developers as service suppliers. Highly dynamic and competitive CSD market places may result in task failure due to unforeseen risks, such as increased competition over shared worker supply, or uncertainty associated with workers' experience and skills, and so on. To improve CSD effectiveness, it is essential to better understand and plan with respect to dynamic worker characteristics and risks associated with CSD processes. In this paper, we present a hybrid simulation model, CrowdSim, to forecast crowdsourcing task failure risk in competitive CSD platforms. CrowdSim is composed of three layered components: the macro-level reflects the overall crowdsourcing platform based on system dynamics,the meso-level represents the task life cycle based on discrete event simulation, and the micro-level models the crowd workers' decision-making processes based on agent-based simulation. CrowdSim is evaluated through three CSD decision scenarios to demonstrate its effectiveness, using a real-world historical dataset and the results demonstrate CrowdSim's potential in empowering crowdsourcing managers to explore crowdsourcing outcomes with respect to different task scheduling options.
... The SD approach is defined as "the study of the information-feedback characteristic of industrial activity to show how organization structure, amplification, and time delays interact to influence the success of the enterprises" (Forrester, 1958;Forrester, 1961). It has been used since the 1950s in diverse applications including economics, sociology, ecology, and engineering. ...
... It has been used since the 1950s in diverse applications including economics, sociology, ecology, and engineering. It is based on the notion of system thinking (Forrester, 1961;Sterman, 2000) and allows to comprehensively review the structure and dynamics of complex systems (Tenza et al., 2017). It emerged as an innovative approach to facilitate holistic analysis of coupled human-environmental systems such as FEW systems (Kotir et al., 2016;Turner et al., 2016;Tenza et al., 2017;Xu and Szmerekovsky, 2017). ...
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Interconnected food, energy, and water (FEW) nexus systems face many challenges to support human well-being (HWB) and maintain resilience, especially in arid and semiarid regions like New Mexico (NM), United States (US). Insufficient FEW resources, unstable economic growth due to fluctuations in prices of crude oil and natural gas, inequitable education and employment, and climate change are some of these challenges. Enhancing the resilience of such coupled socio-environmental systems depends on the efficient use of resources, improved understanding of the interlinkages across FEW system components, and adopting adaptable alternative management strategies. The goal of this study was to develop a framework that can be used to enhance the resilience of these systems. An integrated food, energy, water, well-being, and resilience (FEW-WISE) framework was developed and introduced in this study. This framework consists mainly of five steps to qualitatively and quantitatively assess FEW system relationships, identify important external drivers, integrate FEW systems using system dynamics models, develop FEW and HWB performance indices, and develop a resilience monitoring criterion using a threshold-based approach that integrates these indices. The FEW-WISE framework can be used to evaluate and predict the dynamic behavior of FEW systems in response to environmental and socioeconomic changes using resilience indicators. In conclusion, the derived resilience index can be used to inform the decision-making processes to guide the development of alternative scenario-based management strategies to enhance the resilience of ecological and socioeconomic well-being of vulnerable regions like NM.
... For better understanding the impact of different factors on the decline of lake level, a model should be presented that is dynamic, fully observes the feedback between the physical characteristics of the lake water balance, implements the combined effect of different factors simultaneously, and can present the result in a simple and understandable form. Among these, one of the best models is the System Dynamics (SD) model [4] [5] [6], which is specially designed for modeling and analyzing largescale social and economic systems. This method has been implemented in many aquatic and environmental studies. ...
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In the last decades, climate change, population growth, and agricultural development have caused environmental problems in various parts, including Iran. In northwestern Iran, Lake Urmia, the largest inland lake in Iran and the largest saltwater lake in the Middle East, and several species live there. This lake affects the economic, social, and ecological aspects of the northwestern region of Iran. Several studies have been conducted on human impacts on the lake for various reasons, such as successive droughts, improper agricultural development, and improper use of groundwater and surface water resources. Also, studies have been conducted to investigate the impact of various factors on each other and consider their cumulative effect on the lake water level. In this article, using the system dynamics method, we discuss the modeling of the impact of different factors and their combination and their cumulative role in reducing the lake level, and more specifically, about the role of agriculture.
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Switching from internal combustion engine cars to electric vehicles is envisioned as a cornerstone of decarbonization policy. This chapter explores how the vehicle technology mix could change with decarbonization and what role electric vehicles may play. The chapter provides a systematic review of decarbonization pathways for passenger vehicles. Published pathways indicate that the passenger vehicle fleet must be primarily comprised of zero‐emission vehicles by 2050, in a future consistent with keeping global warming below 2 or 1.5 °C. The fleet share of zero‐emission vehicles ranges between 22% and 90% (median of 62%) in 2 °C scenarios and between 67% and 100% (median of 96%) in 1.5 °C scenarios. Most scenarios envision battery electric vehicles accounting for all or nearly all zero‐emission vehicles in 2050. Battery electric vehicles represent 22–90% (median of 56%) and 67–100% (median of 87%) of the fleet in 2 and 1.5 °C scenarios, respectively. A simple quantitative stock‐and‐flow fleet turnover model developed for this chapter shows how the slow turnover of the vehicle fleet poses a significant challenge to such goals. An indicative quantitative analysis using this model shows that reaching a zero‐emission vehicle fleet consistent with the Paris Agreement goal of keeping global warming “well below” 2 °C requires increasing the adoption of battery electric vehicles beyond current trajectories. Achieving 1.5 °C‐consistent zero‐emission vehicle fleet shares requires both rapidly phasing out emitting vehicle sales and significantly accelerating fleet turnover. The scale of the challenge calls for greater consideration of additional transport mitigation measures. Finally, this chapter provides a case study of electric vehicle policy in Norway. This case shows that barriers to the adoption of battery electric vehicles, such as range, charging speed, and consumer awareness, do not prevent them from reaching most vehicle sales in the presence of sustained political commitment and a mix of support measures.
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Fighting poverty and social inequality are baseline principles of social credit. This type of microcredit has thus increasingly attracted academics and practitioners’ attention, particularly after Muhammad Yunus was awarded the 2006 Nobel Peace Prize. Researchers acknowledge that social credit is extremely important and that it should be taken seriously. However, studies examining the dynamics of its determinants are still extremely rare. This gap needs to be filled because the objectives of social credit are quite different from those of other types of credit, going beyond profit and embracing social concerns. Adopting a constructivist, process‐oriented stance that combined fuzzy cognitive mapping and the system dynamics (SD) approach, the present research sought to develop and analyze a cognitive structure that elucidates the dynamics of social credit determinants over time. Face‐to‐face group work sessions were held with a panel of professional credit analysts, and the results reveal that the combined use of fuzzy cognitive mapping and SD provides a clearer understanding of the dynamics of these determinants over time. The advantages, limitations, and implications of the proposed methodology are discussed.
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This is the second of a series of papers on the stages of critical systems practice. Critical systems practice is a multimethodology that seeks to employ the ideas developed in critical systems thinking to intervene in and improve complex real‐world problem situations. It has four stages—Explore (the problem situation), Produce (an intervention strategy), Intervene (flexibly) and Check (on progress)—called to mind as EPIC. The aim is to set out where thinking has reached on the best way to carry out each of these stages and to invite comment on what more needs doing. This second stage, Produce, is concerned with the design of an appropriate multimethodological intervention strategy based on the outcomes of Explore. The first pass through the Produce stage concludes when it becomes possible to set objectives for the intervention and to structure and schedule its delivery.
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The Internet of things and services (IOT/IOS) as well as Industrial Internet and Industry 4.0 assume networked products, systems, and service in the future. The value proportion of electronics and software will continually increase with these kinds of products and embedded services. When products communicate with one another over the Internet, we refer Cyberphysical Systems or Cybertronic Systems. The development of these new systems will bring several consequences: interdisciplinary, regional, and organizationally distributed and integrated product development, a rethinking of current construction methods, processes, IT solutions, and organizational forms as well as the demand for consistent process chains based on digital models in the requirement definition, system architecture, product development, simulation, product planning, production, and service. Furthermore, planning and design procedures of all disciplines—mechanical, electronic, and software—must be put to the test and their suitability for a new process model for product, system, and service development checked in order to transit them to a common, integrated, and interdisciplinary method, process, and IT solution approach. This approach to the digitalization of product development is called Engineering 4.0. The methodologies of systems engineering (SE), model-based systems engineering (MBSE) and systems thinking form the foundations. Digitalization of products and product development means a transformation process which rearranges the classic limits of a fragmented and competitive IT solution world, a departure from silo thinking to a consistent, integrational solution approach for engineering. A lightweight and federated engineering backbone (→ System Lifecycle Management, SysLM) will take on the role of data and process integration for the entire product lifecycle, including operations. This chapter will present the foundations, framework conditions, and drivers of digitalization and derive an adjusted construction methodology from the results of this for the development of cybertronic products and systems.
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Competing and coexisting policies (CACP) may arise from the incompatibility of incentives, standards and regulatory models between a local state and a federal government, or between two government jurisdictions across which supply networks operate. Traditional studies of supply chain dynamics typically explore the impact of policy regimens as standalone instruments. This paper explores how the interplay between CACP regimens can affect the supply dynamics between producers, customers, and their intermediaries. We use a supply network configuration lens to assess implications for supply chain actors and system‐level outcomes. Our work is motivated by the federal‐state dissonance in the current dispute between India’s farmers and the federal government regarding new laws that impact agricultural supply chains in India. In this case, alternative and coexisting policy interventions, ostensibly aimed at modernising and transforming production and distribution, can lead to significant supply chain netting and inventory pooling reconfigurations in terms of material, information and financial flows among Indian agricultural stakeholders, along with inventory repositioning and market creation options. In addition, of significance is the consequent shift in the balance between state/nation and federal/supranational equity and bargaining power, an increasingly relevant context where supply chains operate across a common but multi‐jurisdictional territory, and implications for system‐level outcomes, in this particular case equity, welfare economics and food security. We conclude by pointing to the implications of CACP regimens, and their interplay, for the broader field of operations management and supply chain research.
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Chapter
Das Internet der Dinge und Services (IOT/IOS) sowie Industrie 4.0 gehen in der Zukunft von vernetzten Produkten, Systemen und Dienstleistungen aus. Der wertmäßige Anteil an Elektronik und Software wird bei dieser Art von Produkten und eingebetteten Dienstleistungen kontinuierlich steigen. Kommunizieren Produkte miteinander über das Internet, wird von Cyber-Physical Systems bzw. Cybertronischen Systemen gesprochen. Die Entwicklung dieser neuen Systeme wird mehrere Konsequenzen nach sich ziehen: interdisziplinäre, regional und organisatorisch verteilte sowie integrierte Produktentwicklung, ein Überdenken heutiger Konstruktionsmethoden, Prozesse, IT-Lösungen und Organisationsformen sowie die Forderung nach durchgängigen Prozessketten, basierend auf digitalen Modellen in der Anforderungsdefinition, Systemarchitektur, Produktentwicklung, Simulation, Produktionsplanung, Produktion und Service. Weiterhin müssen Planungs- und Entwurfsmethoden aller Disziplinen – Mechanik, Elektronik und Software – auf den System-Prüfstand gestellt und ihre Tauglichkeit für ein neues Vorgehensmodell der Produkt-, System und Serviceentwicklung überprüft werden, um diese in einen gemeinsamen, integrierten und interdisziplinären Methoden-, Prozess- und IT-Lösungsansatz zu überführen. Dieser Ansatz der Digitalisierung der Produktentwicklung wird Engineering 4.0 genannt. Der hier verwendete Begriff Produktenwicklung bezieht sich sowohl auf die eigentlichen Produkte als auch auf die Produktionsmittel, denn diese sind auch Produkte im eigentlichen Sinne. Diese Begriffsdefinition lehnt sich an die Begriffsbestimmung von Ehrlenspiel an [29]. Damit umfasst der in diesem Buch verwendete Begriff der Produktentwicklung den von vielen Autoren [78, 93] verwendeten Begriff der Produktentstehung. Die Grundlagen bilden Methodiken des Systems Engineering (SE), des Model Based Systems Engineering (MBSE) und des Systems Thinking. Die Digitalisierung der Produkte und der Produktentwicklung bedeutet einen Transformationsprozess, der die klassischen Grenzen einer fragmentierten und konkurrierenden IT-Lösungswelt neu ordnet. Weg vom Silodenken zu einem durchgängigen und integrativen Lösungsansatz für das Engineering. Ein leichtgewichtiger und föderierter Engineering Backbone (→ System Lifecycle Management, SysLM) wird die Rolle der Daten- und Prozessintegration über den gesamten Produktlebenszyklus inklusive des operativen Betriebes einnehmen. In diesem Kapitel werden Grundlagen, Randbedingungen und Treiber der Digitalisierung vorgestellt und eine daraus resultierende für die Entwicklung von cybertronischen Produkten und Systemen angepasste Konstruktionsmethodik abgeleitet.
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For the last several decades and recently amidst the COVID-19 pandemic, many in the global evaluation communities call for shifts from linear, reductionist ways of thinking and working to approaches that embrace systems and complexity. In this introductory chapter, we orient readers to key systems and complexity traditions and terms and how these have been put to use in the evaluation field. Doing so provides a foundation from which to engage with the subsequent chapters. We close this chapter with highlights from the case examples featured in this issue.
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This article examines how students learn about data‐driven decision‐making by creating and using a dashboard to play an online version of the familiar Beer Game. The objective is to apply data visualization skills to a business system in a way that leads to effective decisions. The students not only build a dashboard in Tableau, they also use it as they make ordering decisions while playing a round of the Beer Game that has a seasonal ramp up/ramp down demand pattern. Our assessment indicates that the exercise provides an effective application of Tableau skills and raises awareness of important aspects of dashboard design. We continue to improve the exercise to emphasize that analytics solutions can only be developed with a solid understanding of the business system.
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System dynamics (SD) modeling studies aim to reveal the causes of problematic dynamic behaviors and eliminate them through policy design and analysis. The analyst conducts sensitivity/scenario analyses and what‐if experiments to reveal the input–output relationships during modeling. However, during these analyses and investigations, the identification of input‐parameter spaces that cause the generation of different SD model behavior patterns is time consuming and susceptible to human bias. Therefore, we propose a metamodel‐based procedure for SD models that considers the necessity for unbiased and automated analysis and insight generation. The approach uses the random forest algorithm for metamodel generation and extracts interpretable IF–THEN rules from the metamodel, thereby identifying input subspaces that generate different qualitative or numerical SD model outputs. We illustrate the proposed approach using two well‐established SD models. These case studies reveal how the model analyst can utilize the proposed method to capture input–output relationships.
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Dairy farming is an important branch of agricultural production for the supply of the Austrian population with domestic food. This paper examines the impact of the COVID-19 pandemic on the Austrian drinking milk supply chain. For this purpose, a tripartite approach was chosen consisting of a process description using Business Process Model and Notation (BPMN), a qualitative System Dynamics analysis with a Causal Loop Diagram (CLD), and the use case of the COVID-19 pandemic in the year 2020 in Austria. The results show that the drinking milk supply chain is complex and consists of many individual process steps. However, the number of locations that are passed through during drinking milk production is rather small. The CLD revealed that reinforcing feedback loops occur in the provision of packaging material and the availability of dairy staff. Analysis of the use case showed that the system of dairy production in Austria is stable in the tested scenario, and that the supply chains preserved their function also during the pandemic. Dairies with diverse product ranges were able to react more easily to the massively changed demand situation. The insights gained by this research may be used to increase the resilience of the drinking milk supply chain. Furthermore, the methodological approach can be transferred and used to analyse the supply chains of other foods.
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This article examines whether and how customers' tone in MD&A disclosure affects suppliers' inventory efficiency. Using an unbalanced panel dataset compiled from multiple archival sources of Chinese‐listed companies, we find that the more positive tone in MD&A disclosure of customers is associated with the higher efficient suppliers' inventory and the results still hold after a battery of robust tests. The customers' tone in MD&A disclosure affects supplier firms' inventory efficiency by reducing the bullwhip effect. In addition, we find that customers' tone in MD&A disclosure has a more pronounced effect on improving suppliers' inventory efficiency when the customers have more analyst coverage and when the suppliers have less bargaining power.
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Improving flow is a core Operations Management theme that is set to become even more important following contemporary developments in manufacturing, such as smart products and digital encapsulation that enable new control concepts such as multi‐agent holonic control. But, companies often struggle to realize flow improvements in practice, both with and without new technologies. While the literature agrees on the importance of flow, a structured and independent process that supports managers in identifying the root causes of why flow items wait in inventories instead of being processed is missing. Managers often use a single production management concept, such as lean production or the theory of constraints, when they seek to understand the reasons for a flow problem, which may lead to misdirected and unsuccessful interventions. In response, we use design science to develop a comprehensive approach to diagnosing flow problems that is independent from any production management concept. This diagnosis process results from successive iterations with five companies and supports the selection of appropriate analytical models and flow improvement solutions. It enables an organization to widen the focus of its flow improvement actions beyond the scope of a singular production management concept and complements the application of recent advances in technology, allowing smart products to quickly interpret what is happening in a location without first simulating and analyzing the whole system. Furthermore, the study expands buffer theories by showing that buffers have an internal hierarchy and can be absorbed by other buffers, while enhancing other theories related to coordination, material flow control, and lean improvement.
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