Purchasing activities consume more than half of manufacturing and trading organizations sales capitals. Effective procurement is tied with efficient and highly accurate collection of data needed for purchasing the right material with the acceptable quality from appropriate suppliers. Supply chain management (SCM) consists of complex networks of distributed actors in which the problem of identifying the appropriate suppliers and allocating optimal order quantities based on the Triple Bottom Line (TBL) attributes is strategically important. However, implementation of an autonomous and automated assessment that can incorporate dynamics and uncertainty of the whole supply chain during the assessment period is not addressed. In the current research paper, a novel framework is designed and proposed to narrow the aforementioned gap. Agent technology has been incorporated in the developed framework to decrease the supplier chain uncertainty by decreasing human interactions and automating the process of supplier evaluation and order allocation.
['Pezhman Ghadimi', 'Cathal Heavey']
Masos: a multi-agent system simulation framework for sustainable supplier evaluation and order allocation