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A formal modeling approach for supply chain event management [An article from: Decision Support Systems]
Book Details
Author(s)R. Liu, A. Kumar, W. van der Aalst
PublisherElsevier
ISBN / ASINB000PDYQFW
ISBN-13978B000PDYQF2
AvailabilityAvailable for download now
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Decision Support Systems, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
As supply chains become more dynamic, there is a need for a sense-and-respond capability to react to events in a real-time manner. In this paper, we propose Petri nets extended with time and color (to represent case data) as a formalism for managing events. We designed seven basic patterns to capture modeling concepts that arise commonly in supply chains. These basic patterns may be used by themselves and also combined to create new patterns. We also show how to combine the patterns to build a complete Petri net and analyze it using dependency graphs and simulation. Dependency graphs can be used to analyze the various events and their causes. Simulation was, in addition, used to analyze various performance indicators (e.g., fill rates, replenishment times, and lead times) under different strategies. We showed it is possible to perform sensitivity analysis to study the effect of changing parameter values on the performance indicators. This approach thus makes a very complex problem tractable.
Description:
As supply chains become more dynamic, there is a need for a sense-and-respond capability to react to events in a real-time manner. In this paper, we propose Petri nets extended with time and color (to represent case data) as a formalism for managing events. We designed seven basic patterns to capture modeling concepts that arise commonly in supply chains. These basic patterns may be used by themselves and also combined to create new patterns. We also show how to combine the patterns to build a complete Petri net and analyze it using dependency graphs and simulation. Dependency graphs can be used to analyze the various events and their causes. Simulation was, in addition, used to analyze various performance indicators (e.g., fill rates, replenishment times, and lead times) under different strategies. We showed it is possible to perform sensitivity analysis to study the effect of changing parameter values on the performance indicators. This approach thus makes a very complex problem tractable.
