This paper has proposed a new approach QSDSS, to provide
supply chain quality assurance solutions in food supply chains.
This infrastructural framework, supported by association rule
mining and Dempster0
s rule, also involves the development of a
decision support system of mining logistics solutions with special
features to cope with tough quality assurance requirements in
food product activities. The major contribution of the proposed
system is to improve supply chain quality sustainability by
providing proper logistics solutions plans and continuously data
mining the logistics settings to ensure the food product quality
during transit. The feature of continual data mining means that
potential quality problems are not overlooked, and the same
mistakes are not repeated in transporting similar batches of
product. A large number of useful association rules concerning
environmental parameters and quality within a logistics network
can be easily extracted. Compared to the traditional food quality
assurance process, this paper also introduces a new ranking
measurement for assigning a likelihood ratio for each quality
assurance setting extracted from the cases. Table 3 highlights the
advantages of adopting the data mining approach in food supply
chain management in terms of the practical aspects of food quality
assurance.