Supply chain hybrid simulation: From Big Data to distributions and approaches comparison

The uncertainty and variability of Supply Chains paves the way for simulation to be employed to mitigate such risks. Due to the amounts of data generated by the systems used to manage relevant Supply Chain processes, it is widely recognized that Big Data technologies may bring benefits to Supply Chain simulation models. Nevertheless, a simulation model should also consider statistical distributions, which allow it to be used for purposes such as testing risk scenarios or for prediction. However, when Supply Chains are complex and of huge-scale, performing distribution fitting may not be feasible, which often results in users focusing on subsets of problems or selecting samples of elements, such as suppliers or materials. This paper proposed a hybrid simulation model that runs using data stored in a Big Data Warehouse, statistical distributions or a combination of both approaches. The results show that the former approach brings benefits to the simulations and is essential when setting the model to run based on statistical distributions. Furthermore, this paper also compared these approaches, emphasizing the pros and cons of each, as well as their differences in computational requirements, hence establishing a milestone for future researches in this domain.This work has been supported by national funds through 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)

Sustainability in the Aerospace, Naval, and Automotive Supply Chain 4.0: Descriptive Review

The search for sustainability in the Supply Chain (SC) is one of the tasks that most concerns business leaders in all manufacturing sectors because of the importance that the Supply Chain has as a transversal tool and due to the leading role that it has been playing lately. Of all the manufacturing sectors, this study focuses on the aerospace, shipbuilding, and automotive sectors identified as transport. The present study carries out a descriptive review of existing publications in these three sectors in relation to the sustainability of the Supply Chain in its 4.0 adaptation as an update in matters that are in constant evolution. Among the results obtained, Lean practices are common to the three sectors, as well as different technologies focused on sustainability. Furthermore, the results show that the automotive sector is the one that makes the greatest contribution in this sense through collaborative programs that can be very useful to the other two sectors, thus benefiting from the consequent applicable advantages. Meanwhile, the Aerospace and Shipbuilding sectors do not seem to be working on promoting a sustainable culture in the management of the Supply Chain or on including training programs for their personnel in matters related to Industry 4.0

Development of model for logistics risk management in supply chains

Doktorska disertacija razmatra probleme upravljanja rizicima u lancima snabdevanja sa osnovnim ciljem razvoja modela za upravljanje određenom grupom rizika-logističkim rizicima. Na osnovu širokog pregleda postojeće literature predloženo je više originalnih konceptualnih okvira namenjenih razumevanju složene strukture koncepta rizika u lancima snabdevanja i principa njihove sistemske analize i upravljanja. Takođe, razvijen je originalni model identifikacije, ocene i tretiranja logističkih rizika, koji se zasniva na simulaciji diskretnih događaja i SCOR metodologiji. Testiranjem predloženih okvira i modela na realnom primeru pokazana je njihova praktična primeljivost.The thesis is dedicated to exploring the problems of supply chain risk management with the final aim of developing model for logistics risk management. Based on a broad literature review it is proposed a several original conceptual frameworks aimed to understanding the complex structure of the supply chain risks concept as well as principles of their system analysis and management. In addition, original model for identification, assessment and control is developed, based on discrete event simulation and SCOR methodology. Case study shows practical applicability of proposed frameworks and models

Gestão de Riscos Logísticos em Cadeias de Suprimentos: Otimização via Metamodelo de Simulação.

Alguns tipos de riscos podem causar danos às cadeias de suprimentos, provocando rupturas nos fluxos de materiais e produtos acabados. Riscos logísticos se relacionam às falhas nos processos de transporte, armazenagem, produção e vendas. A gestão adequada desses riscos é fator crítico para a integração dos fluxos sob a responsabilidade da logística e operações, cujas atividades são frequentemente realizadas por provedores de serviços logísticos. Entretanto, observou-se a falta de procedimentos sistemáticos focados na gestão de riscos logísticos que melhor aproveitasse as vantagens da integração entre métodos de simulação e otimização. A pesquisa foi realizada em uma cadeia de suprimentos do segmento automotivo português, a partir de dados secundários disponíveis na literatura. Os problemas desse estudo foram: (a) quais os impactos dos riscos logísticos sobre o desempenho dessa cadeia? (b) sob a influência desses riscos, que ajustes no sistema logístico poderiam melhorar a resposta do arranjo aos impactos? Para solucionar tais questões, definiu-se como objetivo, mitigar os efeitos desses riscos a partir de um metamodelo de simulação para a otimização de parâmetros críticos. As atividades logísticas desempenhadas na cadeia de suprimentos foram escolhidas como objeto de estudo. Essa pesquisa foi classificada como aplicada, quantitativa e exploratória normativa, considerando, respectivamente, a sua natureza, a abordagem do problema e os objetivos. A simulação a eventos discretos, elaborada no ambiente Arena®, foi utilizada como método de pesquisa. A otimização Black Box, realizada através do software OptQuest®, foi empregada para projetar os parâmetros adequados para o sistema logístico. Um metamodelo de regressão baseado no método OLS foi desenvolvido a partir da projeção e implantação de experimentos, servindo para integrar as saídas do modelo de simulação às entradas do modelo de otimização. Inúmeras técnicas de verificação e validação foram empregadas para calibrar o modelo de otimização via simulação, tais como: implantação modular e análise de sensibilidade. Uma sistemática metodológica fundamentada na abordagem DMAIC foi elaborada para relacionar as etapas de gestão dos riscos logísticos e conduzir aos resultados dessa pesquisa, incluindo a identificação (Definir), avaliação (Mensuração), gestão (Melhoria e análise) e monitoramento (Controle) do risco logístico. Um evento de risco logístico foi inserido no modelo com o fim de reproduzir rupturas no fluxo físico de distribuição e permitir a avaliação dos seus impactos sobre o desempenho da cadeia. Os impactos foram medidos por meio do custo logístico total, do risco de ruptura e da taxa de nível de serviço. Estratégias de mitigação do risco logístico de transporte, como redundância e flexibilidade, foram testadas para minimizar simultaneamente custo e risco e maximizar a taxa de entrega. A solução sugerida pelo modelo multiobjetivo de otimização via simulação mostrou ser adequada e eficaz já que os ajustes no sistema logístico bloquearam as consequências da ruptura. A principal contribuição da pesquisa foi desenvolver procedimentos sistemáticos para melhorar a gestão de riscos logísticos no âmbito de cadeias de suprimentos a partir do uso combinado entre métodos de simulação e otimização

Robustheitssteigerung in Produktionsnetzwerken mithilfe eines integrierten Störungsmanagements

Manufacturing companies operating in global production networks face increasing susceptibilities to disruptions that may have far-reaching consequences for the entire network. To cope with disruptions and to maintain the network\u27s performance even if disruptions occur, companies are in need of a holistic, systematic disruption management, which includes all network actors in the identification of advantageous reaction measures and thus ensures the network\u27s robustness against disruptions. However, current implementations of operational disruption management are mostly exclusively based on experience or intuition and are limited to individual, production or logistics-related partners or areas, hence not forcing a holistically advantageous reaction. Therefore, the objective of the present thesis lies in the development of a methodology for increasing robustness in production networks by means of an integrated disruption management, taking both production and logistics perspectives into account. Based on the analysis and modelling of significant, production- and logistics-related disruptions, a simulation-based approach is used to identify (combinations of) countermeasures that are suitable both for the elimination of disruptions as well as the minimization of their consequences. The simulation thereby combines design of experiments with methods of metamodeling in order to obtain comprehensive statements about the interactions between disruptions, countermeasures and system performance and thus about the suitability of certain measures. Based on the knowledge about the suitability of certain measures, proactive strategies are derived, which promote the implementation of advantageous measures from a planning point of view by appropriately adjusting the respective capacities in the production network. This combined approach, which optimally coordinates the planning and control components of disruption management, allows to increase robustness in production networks. Within the scope of the research project FlexPLN, the developed methodology has been discussed and applied to a use case from the aviation industry. The results thereby do not only unveil that a joint consideration of production and logistics measures provides a promising means for a comprehensive understanding of disruptions and their consequences for production networks, but also indicate that a metamodeling-based approach might be meaningful to predict suitable countermeasures for the reaction to disruptions