Food supply chains are confronted with increased consumer demands on food
quality and sustainability. When redesigning these chains the analysis of food
quality change and environmental load of new scenarios is as important as the
analysis of efficiency and responsiveness requirements. Simulation tools are often
used for supporting decision-making on supply chain (re)design when logistic
uncertainties are in place, building on their inherent modelling flexibility. Mostly,
the underlying assumption is that product quality is not influenced by or does not
influence chain design. Clearly, this is not true for food supply chains, as quality
change is intrinsic to the industry. We propose a new integrated approach
towards logistics, sustainability and food quality analysis, and implement the
approach by introducing a new simulation environment, ALADINTM. It embeds
food quality change models and sustainability indicators in discrete event
simulation models. A case example illustrates the benefits of its use relating to
speed and quality of integrated decision making, but also to creativity in terms of
alternative solutions.
Food supply chains are confronted with increased consumer demands on foodquality and sustainability. When redesigning these chains the analysis of foodquality change and environmental load of new scenarios is as important as theanalysis of efficiency and responsiveness requirements. Simulation tools are oftenused for supporting decision-making on supply chain (re)design when logisticuncertainties are in place, building on their inherent modelling flexibility. Mostly,the underlying assumption is that product quality is not influenced by or does notinfluence chain design. Clearly, this is not true for food supply chains, as qualitychange is intrinsic to the industry. We propose a new integrated approachtowards logistics, sustainability and food quality analysis, and implement theapproach by introducing a new simulation environment, ALADINTM. It embedsfood quality change models and sustainability indicators in discrete eventsimulation models. A case example illustrates the benefits of its use relating tospeed and quality of integrated decision making, but also to creativity in terms ofalternative solutions.
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