Due to global competition modern organizations are paying particular attention to Global Sourcing. This practice has been embraced due to the cost savings it generates, the access to technologies and higher quality products in some cases. Organizations can choose suppliers from anywhere in the world, developing countries are becoming more competitive given their low labor and operating costs. Global supplier selection is riskier than the domestic supplier selection, consequently the decision making process is strongly affected by perceived risks. Suppliers with low price products can be offset by a history of delayed deliveries, or suppliers with state of the art technology can be undermined by excessive tariffs and costs.Risk can originate from economics and political uncertainty in the supplier's country. Natural disasters are also within the risk
factor, any natural disaster can have catastrophic consequences in today's interconnected global supply network. All of these types of disruptions can damage profitability, stock price, and market reputation for the organization with significant long lasting consequences.
In Sawik affirms that taking risk into consideration will allow the buyer to decide whether it should cooperate with a low cost yet risky supplier, over a more expensive but possibly more reliable supplier. There is a crucial need to identify these risk factors and take them into consideration when selecting a supplier. However the supplier selection team struggles to obtain precise, complete and up to date information. Data resources are usually only available at a low frequency of monthly or quarterly levels. The data is sparse through reports, external databases, ERP and MIS systems which are limited and not able to provide sufficient information regarding the risks and uncertainty from the suppliers. The supplier selection process demands more transparency and up to date information.
Given the new Big Data era we are in, gathering data is not a problem anymore and we can utilize global insight and knowledge to assess the risk and uncertainty for each supplier. In regards of supply chain management some researchers have already used the data in social media to revolutionize their organizations. For instance, social media has played an important role in demand prediction for supply chain management , according to social media offers insight on preferences and consumer behavior. The information in social media is updated rapidly and spreads virally at an exceptional speed; this provides us with first-hand information. We now have the opportunity to analyze this vast portfolio of information to assist the supplier selection process. In this research, we focus on social media rather than conventional online data due to its ability to be generated and diffused in a quicker manner; we mainly focus on the microblogging tool Twitter. This is because up to date and relevant information is required due to the nature of the risk and uncertainty criteria. Tweets are compact and fast. This is why it has become widely used to spread and share breaking news.
The main objective of this research is to provide a tool called Twitter Enabled Supplier Status Assessment (TESSA) that can assist the procurement team when selecting a supplier, TESSA can provide information on the risk and uncertainty for each supplier helping the decision team to reduce their potential supplier list and make a final decision. As a secondary objective we aim to open a new window on the research field regarding supplier selection and the use of social networks, as a result, companies can be more prone to exploit the extensive data social networks has to offer and utilize it in the decision making process.
The remaining of the paper is organized as follows: Section 2 reviews related literature on global supplier selection, text-classification methodologies, and ontology. Section 3 presents the system architecture and methodology of TESSA. Section 4 describes the implementation with the usage scenarios. Finally, Section 5 provides the contributions and conclusion of the paper.
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