Simulation of an automotive supply chain using big data

Supply Chains (SCs) are dynamic and complex networks that are exposed to disruption, which have consequences hard to quantify. Thus, simulation may be used, as it allows the uncertainty and dynamic nature of systems to be considered. Furthermore, the several systems used in SCs generate data with increasingly high volumes and velocities, paving the way for the development of simulation models in Big Data contexts. Hence, contrarily to traditional simulation approaches, which use statistical distributions to model specific SC problems, this paper proposed a Decision-Support System, supported by a Big Data Warehouse (BDW) and a simulation model. The first stores and integrates data from multiple sources and the second reproduces movements of materials and information from such data, while it also allows risk scenarios to be analyzed. The obtained results show the model being used to reproduce the historical data stored in the BDW and to assess the impact of events triggered during runtime to disrupt suppliers in a geographical range. This paper also analyzes the volume of data that was managed, hoping to serve as a milestone for future SC simulation studies in Big Data contexts. Further conclusions and future work are also discussed.This work has been supported by FCT (Fundacao para a Ciencia e Tecnologia) within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH)

The development of a research framework for inventory performance improvement of the automotive manufacturing industry: review of literature from 2011-2020

The main purpose of this study is to examine existing literature on how inventory performance in the automotive manufacturing supply chain can be improved. Articles reviewed are from the last ten years that is related to automotive manufacturing industry and targeted to inventory performance. Transformational changes are happening in the automotive manufacturing supply chains with ubiquitous technology leading the transformation. Inherently, due to its outsourced manufacturing in dispersed locations globally, communication and coordination would be affected in some form. This results in companies in the automotive manufacturing supply chain resorting to hold more inventory to mitigate the uncertainty. Therefore, to address this issue, this study conceptualises a theoretical framework that is adapted from Galbraith’s (1974) Organisational Information Processing Theory. The recommended framework may provide a guide to practitioners on understanding the intricacies of supply chain integration and how technology tools may help in mitigating risks and improve inventory performance (Abstract by authors

Disruption Detection for a Cognitive Digital Supply Chain Twin Using Hybrid Deep Learning

Purpose: Recent disruptive events, such as COVID-19 and Russia-Ukraine conflict, had a significant impact of global supply chains. Digital supply chain twins have been proposed in order to provide decision makers with an effective and efficient tool to mitigate disruption impact. Methods: This paper introduces a hybrid deep learning approach for disruption detection within a cognitive digital supply chain twin framework to enhance supply chain resilience. The proposed disruption detection module utilises a deep autoencoder neural network combined with a one-class support vector machine algorithm. In addition, long-short term memory neural network models are developed to identify the disrupted echelon and predict time-to-recovery from the disruption effect. Results: The obtained information from the proposed approach will help decision-makers and supply chain practitioners make appropriate decisions aiming at minimizing negative impact of disruptive events based on real-time disruption detection data. The results demonstrate the trade-off between disruption detection model sensitivity, encountered delay in disruption detection, and false alarms. This approach has seldom been used in recent literature addressing this issue

Supply chain integration in the industry 4.0 era: a systematic literature review: Integração da cadeia de suprimentos na era industrial 4.0: uma revisão sistemática da literatura

From an Industry 4.0 perspective, supply chain actors (suppliers, manufacturers, retailers, and third-party logistics operators) are integrated into a collaborative network based on information-sharing to improve the overall supply chain performance. Real data can be captured and systematically processed into information, hence dealing with uncertainty. Poor integration may lead to supply chain disruptions. This paper examines the role of Industry 4.0 (I4.0) for integrating the supply chain, to which a systematic literature review (SLR) has been applied. First, according to the research questions, the I4.0 technologies adopted for supply chain integration were identified. Second, the approaches for integrating the supply chain at I4.0 were examined and classified by strategy (vertical, horizontal, and end-to-end integration). Third, the functional and cross-functional approaches for supply chain integration at I4.0 were also examined. Finally, it was discussed which traditional SCI approaches can be upgraded to the Industry 4.0 era and the future research directions

Simulation pulled by the need to reduce wastes and human effort in an intralogistics project

Lean Thinking is a well-known management philosophy that increases productivity and reduces cost by eliminating waste. To achieve this, it uses all suitable available tools. Simulation has been widely used to design and/or diagnose production systems and to propose effective better alternatives. In addition, Ergonomics approaches have also been applied to improve work conditions. The project described in this article applied these knowledge areas to a real case, in order to propose the use of AGVs for the materials handling and transport in certain shop-floor zones, instead of using milkrun. The developed simulation model greatly contributed for this analysis and revealed the reduction of the supply machines time and route time by half as well the reduction the human effort

An Environmental-Based Perspective Framework: Integrating IoT Technology into a Sustainable Automotive Supply Chain

Purpose - Over the next decade, humanity is going to face big environmental problems, and considering these serious issues, businesses are adopting environmentally responsible practices. To put forward specific measures to achieve a more prosperous environmental future, this study aims to develop an environment-based perspective framework by integrating the Internet of Things (IoT) technology into a sustainable automotive supply chain (SASC). Design/methodology/approach - The study presents a conceptual environmental framework - based on 29 factors constituting four stakeholders’ rectifications - that holistically assess the SASC operations as part of the ReSOLVE model utilizing IoT. Then, experts from the SASC, IoT, and sustainability areas participated in two rigorous rounds of a Delphi study to validate the framework. Findings – The results indicate that the conceptual environmental framework proposed would help companies enhance the connectivity between major IoT tools in SASC, which would help develop congruent strategies for inducing sustainable growth. Originality/value - This study adds value to existing knowledge on SASC sustainability and digitalization in the context where the SASC is under enormous pressure, competitiveness, and increased variability

The contribution of industry 4.0 technologies to increase internal and external operational flexibility of production systems

Manufacturing flexibility is recognized as an essential competitive factor in the company's operational strategy as a response to market uncertainties and turbulence. Industry 4.0 emerges as a new industrial paradigm that allows meeting these types of needs of manufacturing companies, focusing on the creation of an intelligent system along the entire value chain that allows the achievement of flexible and adaptive processes. However, the academic literature has not yet presented empirical evidence on how each specific Industry 4.0 technology can contribute to operational flexibility requirements. Although Industry 4.0 is treated as a solution to this need, it is known that there are different types of implementations of Industry 4.0 depending on the operational objectives pursued and the characteristics of the companies. Therefore, the technological sets of Industry 4.0 can have different forms of contribution to achieve greater flexibility in production processes. The aim of this thesis is to create a framework to help companies implement flexible operations in the context of Industry 4.0. The study followed a mixed approach, combining qualitative and quantitative methods. In quantitative terms, the thesis presents two survey research. The first was conducted with 94 companies in the machinery and equipment sector, through which the effect that different operational objectives – including flexibility – have on the definition of technological arrangements in Industry 4.0, is analyzed. The second was conducted with 379 companies, with the objective of analyzing how the smart supply chain concept contributes to the flexibility of the supply chain, especially in the context of uncertainties.. On the other hand, in qualitative terms, the thesis presents a multi-case study in 11 leading manufacturing companies in the implementation of 4.0 technologies, aiming to understand how these technologies are implemented to achieve different operational flexibility requirements. The present thesis demonstrates that, in fact, 4.0 technologies contribute to operational flexibility, but also explores the limitations and nuances of these contributions in different situations. The main contribution of this study is to provide empirical evidence of the effectiveness of different technologies used in a combined way to increase operational flexibility at its different levels.A flexibilidade da manufatura é reconhecida como um fator competitivo essencial na estratégia operacional das empresas, como resposta a necessidades do mercado, especialmente diante de incertezas e turbulências. A Industria 4.0 surge como um novo paradigma industrial que permite atender esse tipo de necessidades das empresas manufatureiras, sendo seu foco a criação de um sistema inteligente ao longo de toda a cadeia de valor que possibilita a obtenção de processos flexíveis e adaptativos. Contudo, a literatura acadêmica ainda não tem apresentado evidências empíricas sobre a forma como cada tecnologia específica da Indústria 4.0 pode contribuir para os requisitos de flexibilidade operacional. Embora Industria 4.0 seja apresentada como uma solução para essa necessidade, é sabido que existem diferentes tipos de implementação da Indústria 4.0 que dependem dos objetivos operacionais almejados e das características das empresas. Portanto, os conjuntos tecnológicos da Indústria 4.0 podem ter diferentes formas de contribuição para alcançar uma maior flexibilidade dos processos de produção. O objetivo desta tese é criar um framework para auxiliar as empresas na implementação de operações flexíveis no contexto da Indústria 4.0. O estudo seguiu uma abordagem mista, combinando métodos qualitativos e quantitativo. Em termos quantitativos, a tese apresenta duas pesquisas survey. A primeira foi conduzida com 94 empresas do setor de máquinas e equipamentos, através da qual se analisa o efeito que diferentes objetivos operacionais dentre eles a flexibilidade possuem sobre a definição de arranjos tecnológicos da Indústria 4.0. A segunda foi conduzida com 379 empresas, com objetivo de analisar como o conceito de smart supply chain contribui para a flexibilidade da cadeia de suprimento, principalmente no contexto de incertezas. Por outro lado, em termos qualitativos, a tese apresenta um estudo multicasos em 11 empresas de manufatura líderes na implantação de tecnologias 4.0, visando entender a forma como essas tecnologias são implementadas para alcançar diferentes requisitos de flexibilidade operacional. A presente tese demonstra que, de fato, as tecnologias 4.0 contribuem para a flexibilidade operacional, mas também explora as limitações e nuances dessas contribuições em diferentes situações. A principal contribuição deste estudo é fornecer evidências empíricas da efetividade de diferentes tecnologias utilizadas de forma combinada para incrementar a flexibilidade operacional nos seus diferentes níveis

Marketing Strategies for Automotive Dealerships in Grenada

AbstractSome executive-level automotive dealership managers in Grenada lack successful marketing strategies to sell new U.S. or Japanese vehicles to increase dealership profits. Executive-level automotive dealership managers are concerned about how dealership marketing strategies can negatively impact the business’s bottom line. Grounded in the segmentation, targeting, and positioning model, the purpose of this qualitative multiple-case study was to explore the successful marketing strategies four executive-level dealership managers used to sell U.S. or Japanese vehicles to increase profit in the southern region of Grenada. Yin’s 5-step thematic analysis of semistructured interviews and company documents derived four themes: customer retention, product branding, media promotional strategy, and after-sales service. A key recommendation for the executive-level automotive dealership manager is to implement two-way communication with customers to maintain good customer relationships. The implications for positive social change include the potential for marketing strategies that promote company sustainability that could result in improved satisfaction of employees and enhanced lifestyles of their families and communities