Critical evaluation of paradigms for modelling integrated supply chains

Dam, Koen H. van; Adhitya, Arief; Srinivasan, Rajagopalan; Lukszo, Zofia

doi:10.1016/j.compchemeng.2009.01.023

Cited by 35 publications

(15 citation statements)

References 30 publications

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“…An ABDS system can scale much more freely than system dynamics, especially if the model can be scaled using databases. This corresponds to the findings of van Dam et al (2009). However, constructing a similar kind of system dynamics model would have taken less time.…”

Section: Discussionmentioning

confidence: 99%

Agent based decision support in the supply chain context

Hilletofth

Lättilä

2012

Industrial Management &Amp; Data Systems

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Purpose: The purpose of this paper is to investigate the benefits and the barriers of agent based decision support (ABDS) systems in the supply chain context. Design/methodology/approach: Two ABDS systems have been developed and evaluated. The first system concerns a manufacturing supply chain while the second concerns a service supply chain. The systems are based on actual case companies. Findings:This research shows that the benefits of ABDS systems in the supply chain context include the possibility to increase versatility of system architecture, to improve supply chain visibility, to conduct experiments and what-if analyses, to improve the understanding of the real system, and the possibility to improve communication within and between organizations in the supply chain. The barriers of ABDS systems in the supply chain context include the difficulty to access data from partners in the supply chain, the difficulty to access data on a higher level of granularity, and the difficulty to retrieve data from other information systems. Research limitations/implications:The research is explorative in nature therefore empirical data from similar and other research settings should be gathered to reinforce the validity of the findings. Practical implications:This research provides knowledge and insights on how ABDS systems may be developed and used in the supply chain context and demonstrates its main benefits and barriers. Originality/value:This research expands the current research of benefits of ABDS systems to the supply chain domain and also addresses the barriers of ABDS systems to a larger extent than previous research. Comparisons to other simulation based decision support systems are also given.

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Section: Discussionmentioning

confidence: 99%

Agent based decision support in the supply chain context

Hilletofth

Lättilä

2012

Industrial Management &Amp; Data Systems

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“…For stochastic models, when multiple runs of the models are averaged out, it still cannot be concluded that the output will also be the same. But after looking at the definition of the models and how the results are produced, it is possible to get a better understanding of how models will behave under different inputs and different scenarios [24].…”

Section: Implementation Results and Discussionmentioning

confidence: 99%

Development and implementation of a green logistics-oriented framework for batch process industries: two case studies

El-Berishy

Scholz‐Reiter

2016

Logist. Res.

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In recent years, green logistics has received increasing interest due to the rise in greenhouse gases emission from transport operations. It is a promising approach to managing supply chain decisions to reduce environmental damages. Many models have been developed in the literature to study the environmental aspects of routing problems. However, these models have not been implemented in practice, particularly in batch processing industries. In this paper, we developed a two-stage stochastic model which integrates the tactical and operational decision levels to reduce the economic and environmental impacts of transport activities. This model generates optimum vehicle routes and their delivery velocities with the objective of minimizing the total distribution cost and the related emissions. We implemented the proposed model in two case studies drawn from the fast-moving consumer goods industry. This resulted in a cost reduction of up to 13 % of the total related economic and environmental costs compared to the actual situation at the test sites. These results also show the potential of green logistics-based models to improve the current modelling capabilities for batch distribution planning.

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“…A benchmarking study has been executed in which the agent-based model was compared with an equation-based model of the same supply chain [21]. The aim of the benchmarking study was to compare the modeling paradigms (i.e., the agent-based and equation-based paradigms) and to learn about the advantages and disadvantages of the different approaches.…”

Section: Verification and Validation Of The Modelmentioning

confidence: 99%

“…Modeling the behavior of the agents was based on existing behavioral rules for trading (as also used in the freight hub model from Section 2.3.1), but additional rules had to be implemented for various procurement policies (e.g., forecasting of demand deciding on procurement), scheduling which OperationalConfiguration to use and for the activities of the jetty (which had not been used in earlier models), for example [21]. The new elements can now be shared with other modelers or be re-used in new case studies and as such the new modeling effort has contributed to a larger set of building blocks.…”

Section: Implementation Of the Behavior Of The Agentsmentioning

confidence: 99%

“…This model enables decision making for supply chain management by allowing the user to evaluate the impact of a particular decision on the supply chain performance, analyze different supply chain policies, and identify the consequences of a disruption, through simulation. The model is based on the system description from [18] and is described in more detail in [21].…”

Section: An Oil Refinery Supply Chainmentioning

confidence: 99%

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Model Factory for Socio-Technical Infrastructure Systems

Dam

Lukszo

2009

Intelligent Infrastructures

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Decision makers often rely on models and simulations for support in the decision making process. Using insights gained this way, better-informed decisions can be made. Model makers design and build models that can be used to test different scenarios and to gain insight in the possible consequences and results of possible actions using simulations. This chapter presents a "model factory" for socio-technical infrastructure systems, which can be used to set up new models of infrastructures by following a number of modeling steps and re-using already existing building blocks from other models. The approach is demonstrated here through application to two case studies: a decision problem for the location of an intermodal freight hub and disturbance management in an oil refinery supply chain, both inspired by the real-life problems. Application of the framework by others confirms that it is widely applicable in various infrastructure domains. Based on these results and the literature study it can be concluded that the presented "model factory" is clearly rewarding for new application for Intelligent Infrastructures problems and strategic decision making.

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Critical evaluation of paradigms for modelling integrated supply chains

Cited by 35 publications

References 30 publications

Agent based decision support in the supply chain context

Agent based decision support in the supply chain context

Development and implementation of a green logistics-oriented framework for batch process industries: two case studies

Model Factory for Socio-Technical Infrastructure Systems

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