Information and communication technology adoption in the Italian road freight haulage industry

Despite the importance of information and communication technology (ICT) in the management of transport and logistics systems, there is a shortage of studies in the road freight haulage sector. This paper is aimed at filling this void through an exploratory survey on ICT adoption and the influencing factors carried out in the Italian road transport market. The paper provides a review of the previous research on this topic that allows the identification of research gaps that have been addressed through a questionnaire survey. The findings provide evidence of a passive stance on ICT usage characterised by the adoption of isolated applications. The financial risk associated with technology investment and human resources are the main barriers to ICT adoption, while the improvement of service level and the reliability of transport operations emerge as stimulating factors. The results suggest that the potential benefits of technology have not been fully exploited and a risk-sensitive stance on ICT is evident preventing the full incorporation of ICT into business processes.

Enhancing competitive advantage with radio-frequency identification (RFID) enabled returnable transport equipment (RTE)

Fierce competition within the third party logistics (3PL) market has developed as providers compete to win customers and enhance their competitive advantage through cost reduction plans and creating service differentiation. 3PL providers are expected to develop advanced technological and logistical service applications that can support cost reduction while increasing service innovation. To enhance competitiveness, this paper proposes the implementation of radio-frequency identification (RFID) enabled returnable transport equipment (RTE) in combination with the consolidation of network assets and cross-docking. RFID enabled RTE can significantly improve network visibility of all assets with continuous real-time data updates. A four-level cyclic model aiding 3PL providers to achieve competitive advantage has been developed. The focus is to reduce assets, increase asset utilisation, reduce RTE cycle time and introduce real-time data in the 3PL network. Furthermore, this paper highlights the need for further research from the 3PL perspective. Copyright © 2013 Inderscience Enterprises Ltd.

Probabilistic topographic information visualisation

The focus of this thesis is the extension of topographic visualisation mappings to allow for the incorporation of uncertainty. Few visualisation algorithms in the literature are capable of mapping uncertain data with fewer able to represent observation uncertainties in visualisations. As such, modifications are made to NeuroScale, Locally Linear Embedding, Isomap and Laplacian Eigenmaps to incorporate uncertainty in the observation and visualisation spaces. The proposed mappings are then called Normally-distributed NeuroScale (N-NS), T-distributed NeuroScale (T-NS), Probabilistic LLE (PLLE), Probabilistic Isomap (PIso) and Probabilistic Weighted Neighbourhood Mapping (PWNM). These algorithms generate a probabilistic visualisation space with each latent visualised point transformed to a multivariate Gaussian or T-distribution, using a feed-forward RBF network. Two types of uncertainty are then characterised dependent on the data and mapping procedure. Data dependent uncertainty is the inherent observation uncertainty. Whereas, mapping uncertainty is defined by the Fisher Information of a visualised distribution. This indicates how well the data has been interpolated, offering a level of ‘surprise’ for each observation. These new probabilistic mappings are tested on three datasets of vectorial observations and three datasets of real world time series observations for anomaly detection. In order to visualise the time series data, a method for analysing observed signals and noise distributions, Residual Modelling, is introduced. The performance of the new algorithms on the tested datasets is compared qualitatively with the latent space generated by the Gaussian Process Latent Variable Model (GPLVM). A quantitative comparison using existing evaluation measures from the literature allows performance of each mapping function to be compared. Finally, the mapping uncertainty measure is combined with NeuroScale to build a deep learning classifier, the Cascading RBF. This new structure is tested on the MNist dataset achieving world record performance whilst avoiding the flaws seen in other Deep Learning Machines.