Reverse resource exchanges in service supply chains:the case of returnable transport packaging

Purpose: The purpose of this paper is to understand how reverse resource exchanges and resource dependencies are managed in the service supply chain (SSC) of returnable transport packaging (RTP). Design/methodology/approach: A single case study was conducted in the context of automotive logistics focusing on the RTP SSC. Data were collected through 16 interviews, primarily with managers of a logistics service provider (LSP) and document analysis of contractual agreements with key customers of the packaging service. Findings: Resource dependencies among actors in the SSC result from the importance of the RTP for the customer’s production processes, the competition among users for RTP and the negative implications of the temporary unavailability of RTP for customers and the LSP (in terms of service performance). Amongst other things, the LSP is dependent on its customers and third-party users (e.g. the customer’s suppliers) for the timely return of package resources. The role of inter-firm integration and collaboration, formal contracts as well as customers’ power and influence over third-party RTP users are stressed as key mechanisms for managing LSP’s resource dependencies. Research limitations/implications: A resource dependence theory (RDT) lens is used to analyse how reverse resource exchanges and associated resource dependencies in SSCs are managed, thus complementing the existing SSC literature emphasising the bi-directionality of resource flows. The study also extends the recent SSC literature stressing the role of contracting by empirically demonstrating how formal contracts can be mobilised to explicate resource dependencies and to specify, and regulate, reverse exchanges in the SSC. Practical implications: The research suggests that logistics providers can effectively manage their resource dependencies and regulate reverse exchanges in the SSC by deploying contractual governance mechanisms and leveraging their customers’ influence over third-party RTP users. Originality/value: The study is novel in its application of RDT, which enhances our understanding of the management of reverse exchanges and resource dependencies in SSCs.

How do engineering students develop practical competencies during a period of industrial placement? - Students' perceptions

This paper explores engineering students' perceptions of developing practical competencies as experienced in their industrial placements. In addition, it discusses the criticisms in the literature on Problem Based Learning, Project Based Learning and Conceive-Design-Implement-Operate in relation to the evaluation of effective learning and teaching during placements. The paper goes on to discuss a study which examines how undergraduate engineering students develop practical competencies during their industrial placements. A phenomenological research approach is adopted using in-depth interviews and document analysis. The research findings from this PhD study will contribute to the knowledge, theory and practice for the students, the industries and the institutions of higher education as students' practical competencies are developed and graduate employability rises. In conclusion, this study explores students' experiences of developing practical competencies during industrial placements. Hence, the study should be able to contribute to a set of evidence-based guidelines for higher education institutions and industry.

Handwritten and machine-printed text discrimination using a template matching approach

We propose a novel template matching approach for the discrimination of handwritten and machine-printed text. We first pre-process the scanned document images by performing denoising, circles/lines exclusion and word-block level segmentation. We then align and match characters in a flexible sized gallery with the segmented regions, using parallelised normalised cross-correlation. The experimental results over the Pattern Recognition & Image Analysis Research Lab-Natural History Museum (PRImA-NHM) dataset show remarkably high robustness of the algorithm in classifying cluttered, occluded and noisy samples, in addition to those with significant high missing data. The algorithm, which gives 84.0% classification rate with false positive rate 0.16 over the dataset, does not require training samples and generates compelling results as opposed to the training-based approaches, which have used the same benchmark.