Abstract
In the context of today ’s pattern of globalization and a huge amount of information, a smart supply management chain is required. Naturally, statistics and operations research are used for optimizing supply and demand objectives. However, the new context brings out new opportunities at descriptive, predictive and prescriptive levels for supply chain network design, logistics and distribution and strategic sourcing. The key question is still how to capture and to use information. One striking example can be taken from social media, where their use allow to gain insight into the perception of consumers and to capture a real time overview of consumer reactions, regarding one or more specific events. In this regard, different modern approaches, such as IoT or Quantum neural network, are developed. In the same line of thought, we propose an analytic approach, based on KNN, Logistic Regression and SVM with the use of Twitter data in chicken supply chain management. Results identify the main concerns related to chicken products and allow to the development of a consumer-centric supply chain. The proposed approach can be extended to other topics such as anomaly detection and codification of customer intelligence.
Highlights
Chain management is presented as the field which consists, in parts, to provide the right quantities of goods most efficiently at the right place in the right order within the right time
Planning process can be separated into strategic planning which generates an initial evaluation for feasibility of different plant and supplier locations to integrate new products into production network, tactical inbound logistics planning which focuses on the engineering of logistics process alternatives and their evaluation and operational planning of logistics before start of production and where all preselected logistics process and resources will be continuously detailed and integrated into the production plant by pre-series processes during the ramp up
We propose an analytic approach, based on a comparison between three machine learning models: KNearest Neighbours (KNN), Logistic Regression, and Support Vector Machine (SVM) with the use of Twitter data related to chicken supply chain
Summary
Chain management is presented as the field which consists, in parts, to provide the right quantities of goods most efficiently at the right place in the right order within the right time. Meeting these demands requires planning the inbound logistics. The term supply chain analytics can be used to define the advanced big data analytics in supply chain management [3]. This analytics can be categorized into descriptive, predictive and prescriptive analytics [4]
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