International Journal of RF Technologies

Big data are a commonly discussed topic, and their value in marketing and sales has been thoroughly investigated. Although some authors have also discussed their relevance to purchasing and supply management, the literature on this topic remains scant. This paper aims to investigate the role of big data in supporting the procurement process and the implementation of procurement practices. The main benefits of such procurement practices for companies are highlighted.

By presenting the results of a focus group of procurement professionals and experts and discussing four exploratory case studies (supported by secondary data analysis), the paper addresses 1) how (and in which format) big data affects different activities in the procurement process and 2) which benefits procurement professionals can expect from adopting big data.

In this work, a framework is developed to integrate IoT-based energy management and company’s existing information systems. This framework is a multi-layer model that includes three layers: 1) data collection layer, 2) data management layer and 3) data analytics layer. In order to test the proposed approach and assess its impact on improving energy efficiency, a pilot study was carried out in an Italian manufacturing company. Several smart meters have been installed at machine level to collect energy consumption data in real time, and then this data have been analyzed and provided to decision makers to improve energy efficiency by integrating them in production management decisions. When a company aims at analyzing the energy characteristics of its production system, data provided by different sources and geographically dispersed repositories must be taken into consideration. These characteristics bring several problems to develop a data analytic architecture. In this paper, we propose a data analytic model for IoT, in order to integrate the data collected from different sources and to improve energy-aware decision-making. Improving the overall equipment effectiveness of machine tools will improve resource-efficiency and productivity in manufacturing and support the development of smart factories from an energy point of view.

Wireless sensor technologies open up new possibilities for optimizing product quality and spoilage in the perishables supply chain. In particular, the processing of sensor data such as temperature information allows for the construction of novel issuing policies in retail distribution centres for fresh or frozen goods (e.g., meat, fish, fruits, vegetables, flowers). The objective of the present study is to investigate the potential of sensor-based issuing policies in comparison to classical policies based on identity and expiry information. In a simulation study, we consider a three-echelon supply chain model, including a manufacturer, a distribution centre, and a retail store. We investigate the impact of different combinations of issuing policies and customer withdrawal behaviours on the quality of sold goods and total spoilage. Our results show that sensor-based issuing policies may pose an effective complement to conventional issuing policies. However, they also indicate that wireless sensors are no one-size-fits-all solution and that important trade-offs must be taken into account in the selection of a specific policy.