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Introduction and motivation
Back in 2013, DHL prophesied that Big Data would improve logistics operational efficiency and customer experience, and create useful new business models, adding, “Big Data has much to offer the world of logistics. Sophisticated data analytics can consolidate this traditionally fragmented sector, and these new capabilities put logistics providers in pole position as search engines in the physical world” (Jeske et al., 2013, p. 1). However, the following year, Accenture (2014) conducted a web-based survey of 1,014 supply chain professionals, and concluded that the “actual use of Big Data Analytics (BDA) is limited” (p. 7). They found that, despite the acknowledged benefits of BDA, most companies experienced difficulties in adopting it, and were also worried about the level of investment required, security risks, and the lack of available business cases for analytics (Accenture, 2014).
More recently, a KPMG (2017) report described a number of emerging case examples that reveal how logistics operations are utilising real-life Big Data solutions to reduce delivery delays through the availability of GPS, traffic, and weather data. However, academic examples describing “real-life” cases of BDA utilisation in the logistics industry are limited, and when Wang et al. (2016, p. 107) conducted a systematic review of Big Data business analytics literature with a logistics and supply chain management context, a “gap between academic theory and supply chain practices” was confirmed.
Similarly, the Internet of Things (IoT) has also been predicted to play an important role in the future of the logistics industry, as an increasing number of objects start to carry bar codes, RFID tags, and sensors, generating geospatial data that enable accurate, real-time, tracking of physical objects across an entire supply chain (Atzori et al., 2010; Da et al., 2014; Razzaq Malik et al., 2017; Swaminathan, 2012).
The motivation for undertaking this programme of research was, therefore, to gain a better understanding of how BDA and IoT are being utilised in today’s logistics industry. It strives to present an interesting new case example, to benefit industry practitioners and academic researchers alike, which contributes towards a closing of the gap between theory and supply chain practices.
Research objectives and questions
The objective of the research is to collect data from a “real-life” situation and create...