Supply chain resilience (SCR) manifests when the network is capable to withstand, adapt, and recover from disruptions to meet customer demand and ensure performance. This paper conceptualizes and comprehensively presents a systematic review of the recent literature on quantitative modeling the SCR while distinctively pertaining it to the original concept of resilience capacity. Decision-makers and researchers can benefit from our survey since it introduces a structured analysis and recommendations as to which quantitative methods can be used at different levels of capacity resilience. Finally, the gaps and limitations of existing SCR literature are identified and future research opportunities are suggested.

Review of quantitative methods for supply chain resilience analysis

The increasingly global context in which businesses operate supports innovation, but also increases uncertainty around supply chain disruptions. The COVID-19 pandemic clearly shows the lack of resilience in supply chains and the impact that disruptions may have on a global network scale as individual supply chain connections and nodes fail. This cascading failure underscores the need for the network analysis and advanced resilience analytics we find lacking in the existing supply chain literature. This paper reviews supply chain resilience literature that focuses on resilience modeling and quantification and connects the supply chain to other networks, including transportation and command and control. We observe a fast increase in the number of relevant papers (only 47 relevant papers were published in 2007-2016, while 94 were found in 2017-2019). We observe that specific disruption scenarios are used to develop and test supply chain resilience models, while uncertainty associated with threats including consideration of "unknown unknowns" remains rare. Publications that utilize more advanced models often focus just on supply chain networks and exclude associated system components such as transportation and command and control (C2) networks, which creates a gap in the research that needs to be bridged. The common goal of supply chain modeling is to optimize efficiency and reduce costs, but trade-offs of efficiency and leanness with flexibility and resilience may not be fully addressed. We conclude that a comprehensive approach to network resilience quantification encompassing the supply chain in the context of other social and physical networks is needed to address the emerging challenges in the field. The connection to systemic threats, such as disease pandemics, is specifically discussed.

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Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic.

Digital twins-based smart manufacturing system design in Industry 4.0: A review

Lean manufacturing and sustainable performance: Trends and future challenges

A framework to achieve sustainability in manufacturing organisations of developing economies using industry 4.0 technologies’ enablers

https://researchportal.port.ac.uk/portal/files/12080714/A_hybrid_MCDM_fuzzy_multi_objective_programming_approach_for_a_G_Resilient_1_.pdf

A hybrid MCDM-fuzzy multi-objective programming approach for a G-resilient supply chain network design

In the past decade, there has been a growing concern about the environmental protection in public society as governments almost all over the world have initiated certain rules and regulations to promote energy saving and minimize the production of carbon dioxide (CO2) emissions in many manufacturing industries. The development of sustainable manufacturing systems is considered as one of the effective solutions to minimize the environmental impact. Lean approach is also considered as a proper method for achieving sustainability as it can reduce manufacturing wastes and increase the system efficiency and productivity. However, the lean approach does not include environmental waste of such as energy consumption and CO2 emissions when designing a lean manufacturing system. This paper addresses these issues by evaluating a sustainable manufacturing system design considering a measurement of energy consumption and CO2 emissions using different sources of energy (oil as direct energy source to generate thermal energy and oil or solar as indirect energy source to generate electricity). To this aim, a multi-objective mathematical model is developed incorporating the economic and ecological constraints ﻿aimed for minimization of the total cost, energy consumption, and CO2 emissions for a manufacturing system design. For the real world scenario, the uncertainty in a number of input parameters was handled through the development of a fuzzy multi-objective model. The study also addresses decision-making in the number of machines, the number of air-conditioning units, and the number of bulbs involved in each process of a manufacturing system in conjunction with a quantity of material flow for processed products. A real case study was used for examining the validation and applicability of the developed sustainable manufacturing system model using the fuz﻿zy multi-objective approach.

/pdf/a-sustainable-manufacturing-system-design-a-fuzzy-multi-1xuekrj3px.pdf

A sustainable manufacturing system design: A fuzzy multi-objective optimization model

A sustainable manufacturing system design can be defined as a process aimed at minimising the negative aspect of both economic and ecological costs. This may be partially achieved through the implementation of lean manufacturing methods in order to reduce production wastes, increase efficiency of manufacturing systems and minimise operational costs. Nevertheless, the concept of lean methods does not include environmental considerations in terms of such as energy consumption and CO2 (carbon dioxide) emissions, which are also important factors today for developing a sustainable manufacturing system. This paper addresses these issues involved in modelling a sustainable manufacturing system allowing an evaluation in energy consumption and CO2 emissions against the total cost using the multi-objective approach. In this work, a multi-objective mathematical model was developed based on a manufacturing system incorporating its economic and ecological parameters towards a minimisation of the total cost, the total energy consumption and CO2 emissions associated with relevant machines, air-conditioning units and lighting bulbs involved in each manufacturing process and material flow. The model was coded using LINGO11 to help gain optimal solutions using the e-constraint approach and the LP-metrics approach, respectively. The best solution among obtained optimal results was revealed using the max-min approach. Applicability of the proposed method was also examined using collected data from a real case study. The study concluded that the multi-objective mathematical model was useful as an aid for optimizing the manufacturing system design under the economic and ecological constraints.

/pdf/optimisation-of-a-sustainable-manufacturing-system-design-44o0dr63y7.pdf

Optimisation of a sustainable manufacturing system design using the multi-objective approach

https://researchportal.port.ac.uk/portal/files/14183170/Drafting_a_cost_effective_approach.pdf

Drafting a cost-effective approach towards a sustainable manufacturing system design

In the past decade, there has been a growing concern about the environmental protection in the public society as governments in many countries have been enforcing ever-stricter environmental policies and regulations in industry by promoting energy saving and low carbon dioxide (CO 2 ) emissions in manufacturing activities. Development of sustainable manufacturing systems is considered as one of effective solutions to minimize the environmental impact. Lean manufacturing can also be helpful for achieving a sustainable manufacturing system as it can reduce production wastes and increase manufacturing efficiency. Nevertheless, the lean approach does not include a consideration in energy consumption and CO 2 emissions when designing a lean manufacturing system. This paper addresses these issues by evaluating a sustainable manufacturing system design by considering a measurement of energy consumption and CO 2 emissions. To this aim, a multi-objective mathematical model is developed incorporating the economic and ecological constraints in terms of minimization of the total cost, energy consumption and CO 2 emissions for a manufacturing system design. The study also addresses a decision making in the number of machines, the number of air conditioning units and the number of bulbs involved in each process of the manufacturing system in conjunction with a quantity of material flow for processing the products. A real case study was used for examining the validation and applicability of the developed sustainable manufacturing system model.

/pdf/the-multi-objective-optimization-model-for-a-sustainable-3ywls691xn.pdf

Reda Nujoom

Papers

A hybrid MCDM-fuzzy multi-objective programming approach for a G-resilient supply chain network design

A sustainable manufacturing system design: A fuzzy multi-objective optimization model

Optimisation of a sustainable manufacturing system design using the multi-objective approach

Drafting a cost-effective approach towards a sustainable manufacturing system design

The multi-objective optimization model for a sustainable manufacturing system design