Supply Chain Risk Management Frameworks and Models: A Review

Supply chain risk management (SCRM) is a relatively new scientific discipline aiming to support management in its everyday struggle against the inherent uncertainty of supply chain operations propagated mostly by demand and supply fluctuations, in terms of yields, capacity, costs and lead times. This paper focuses on a literature review of available SCRM frameworks and models. Using an appropriate combination of keywords, three established academic databases and a hard inclusion criterion, a final sample of 16 (starting from 922) relevant and above all, empirically validated SCRM frameworks/models papers are retrieved and studied in full. Following a systematic literature review approach and supported by a content analysis tool, the authors produce some useful results on the current research status and identify some of its shortcomings, which have to be addressed by researchers in the future, i.e. the immaturity of research in the field, the absence of a holistic approach for SCRM and finally the lack of a systematic approach to successfully identify risk propagation across contemporary and complex supply chain networks

A machine learning approach to enable bulk orders of critical spare-parts in the shipping industry

Purpose: The main purpose of this paper is to propose a methodological approach and a decision support tool, based on prescriptive analytics, to enable bulk ordering of spare parts for shipping companies operating fleets of vessels. The developed tool utilises Machine Learning (ML) and operations research algorithms, to forecast and optimize bulk spare parts orders needed to cover planned maintenance requirements on an annual basis and optimize the company’s purchasing decisions. Design/methodology/approach: The proposed approach consists of three discrete methodological steps, each one supported by a decision support tool based on clustering and Machine Learning (ML) algorithms. In the first step, clustering is applied in order to identify high interest items. Next, a forecasting tool is developed for estimating the expected needs of the fleet and to test whether the needed quantity is influenced by the source of purchase. Finally, the selected items are cost-effectively allocated to a group of vendors. The performance of the tool is assessed by running a simulation of a bulk order process on a mixed fleet totaling 75 vessels. Findings: The overall findings and approach are quite promising Indicatively, shifting demand planning focus to critical spares, via clustering, can reduce administrative workload. Furthermore, the proposed forecasting approach results in a Mean Absolute Percentage Error of 10% for specific components, with a potential for further reduction, as data availability increases. Finally, the cost optimizer can prescribe spare part acquisition scenarios that yield a 9% overall cost reduction over the span of two years. Originality/value: By adopting the proposed approach, shipping companies have the potential to produce meaningful results ranging from soft benefits, such as the rationalization of the workload of the purchasing department and its third party collaborators to hard, quantitative benefits, such as reducing the cost of the bulk ordering process, directly affecting a company’s bottom linePeer Reviewe

Evaluating the Effects of Gamification in Behavioural Change: A Proposed SEM-Based Approach

The purpose of this study is two-fold. Firstly, it aims to investigate the available papers on the effect of gamification elements to explain behavioural changes through a Systematic Literature Review (SLR). Secondly, based on the SLR, it proposes a four-step SEM (Structural Equation Model)-based approach that can be used to validate the effects of gamification on behavioural change and can be further applied in the context of a research project that aims to lower maritime plastic pollution in coastal areas. The SLR approach provides an overview of empirical studies that successfully measure the three identified objectives, i.e., increased (O1) usage of a web platform, (O2) awareness, and (O3) participation in behaviour, and it focuses on SEM to collect empirical results. Findings from the SLR highlight multiple research shortcomings, such as the lack of a unified taxonomy for gamification and motivational affordances, the absence of studies soundly linking gamification elements to psychological outcomes, and the tendency of researchers to measure the intention to conduct a behaviour rather than the long-term effect of actual behaviour changes. Finally, the created approach provides insights on which gamification elements to include and how to measure their behavioural effect based on a self-developed SEM and questionnaire, which can be applied in research projects utilising gamification, independent from the domain of activity

Identifying the knowledge supply chain: putting enterprise supply chain management disciplines to work

N/

Managing the risk of knowledge transfer in outsourcing organizations

N/

Industry 4.0 Technologies and Their Impact in Contemporary Logistics: A Systematic Literature Review

Even though the topic of Industry 4.0 in the last decade has attracted significant and multifarious attention from academics and practitioners, a structured and systematic review of Industry 4.0 in the context of contemporary logistics is currently lacking. This study attempted to address this shortcoming by performing a systematic review of the available literature of Industry 4.0 in the logistics context. To that end, and after a systematic inclusion/exclusion process, 65 carefully selected papers were addressed in the study. The results obtained from this study were illustrated and discussed in order to provide answers to two research questions pre-defined by the authors. In essence, this study identified emerging aspects and present trends in the area, addressed the main technological developments and evolution of Industry 4.0 and their impact for contemporary logistics, and finally pinpointed literature shortcomings and currently under-explored areas with a high potential for impactful future research. Findings of this review can hopefully be used as the basis for future research in the emerging Logistics 4.0 concept and related topics