Search results

Much of the recent research on supply chain uncertainty has focused on relationships between manufacturers and suppliers and existing models have therefore been based on this dyadic structure. The aim is to establish a supply chain uncertainty model that explicitly incorporates transport operations and hence the logistics triad; supplier, customer and transport carrier.

This is a literature‐based activity that synthesises and extends existing models of supply chain uncertainty.

The paper develops a new model to reflect the nature of transport operations. Consequently, it identifies five main categories of uncertainty, namely from the points of view of the supplier, the customer and the carrier, respectively, the control systems used in the supply chain and external factors. The interfaces between the uncertainty categories involving all three parties of the logistics triad are identified, so as to develop a more holistic perspective on supply chain uncertainty and how it can be reduced.

This paper is conceptual in nature and empirical research into the area of transport uncertainty will be required to validate its findings. Following this, the model can be used to investigate and evaluate improvements in the economic and/or environmental performance of freight transport within supply chains.

The model is intended to provide a framework within which organisations, including logistics providers, can develop a supply chain strategy to mitigate the effects of uncertainty. By categorising uncertainty into the types described, organisations may determine where the greatest uncertainties lie and hence develop a prioritised plan for supply chain re‐engineering by initially targeting those uncertainties with the most significant implications for supply chain efficiency.

Little research has been undertaken on the impact of uncertainties on transport in the context of collaborative supply chain management. The model rationalises uncertainties into various types taking into account the nature of the logistics triad.

The value of big data in supply chain management (SCM) is typically motivated by the improvement of business processes and decision-making practices. However, the aspect of value associated with big data in SCM is not well understood. The purpose of this paper is to mitigate the weakly understood nature of big data concerning big data’s value in SCM from a business process perspective.

A content-analysis-based literature review has been completed, in which an inductive and three-level coding procedure has been applied on 72 articles.

By identifying and defining constructs, a big data SCM framework is offered using business process theory and value theory as lenses. Value discovery, value creation and value capture represent different value dimensions and bring a multifaceted view on how to understand and realize the value of big data.

This study further elucidates big data and SCM literature by adding additional insights to how the value of big data in SCM can be conceptualized. As a limitation, the constructs and assimilated measures need further empirical evidence.

Practitioners could adopt the findings for conceptualization of strategies and educational purposes. Furthermore, the findings give guidance on how to discover, create and capture the value of big data.

Extant SCM theory has provided various views to big data. This study synthesizes big data and brings a multifaceted view on its value from a business process perspective. Construct definitions, measures and research propositions are introduced as an important step to guide future studies and research designs.

A large number of systematic reviews (SRs) studies have been performed in the cloud computing field, demonstrating miscellaneous outcomes and utilizing different approaches. Accordingly, a meta-review of cloud SRs is needed to appraise the results of such studies and create an integrated understanding. The paper aims to discuss these issues.

A tertiary study was conducted using a systematic method to analyze SRs including two stages: searching and screening the SRs and thematic synthesis of results. As a qualitative data management tool, Nvivo software was used to support the research process, for data coding and synthesis.

First, by searching electronic sources between the year of 2011–2016, out of a total of 142 identified articles, 94 articles were included according to pre-determined criteria, of which 76 articles were approved after qualitative evaluation. In the second stage, identifying the research themes, a map of the concepts and issues related to each theme was drawn up. The analysis shows that the quality of articles has improved but can be further enhanced using methodological guidelines as well as supporting tools. The research has focused more on the technical aspect, although there is an equal demand for synthesizing of cloud governance concepts.

This is the first tertiary study which presents the main research themes and concepts of cloud SRs in form of thematic maps by using the thematic synthesis and SR methods. This paper also provides some recommendations to improve reviews after evaluating the quality of papers. This study can support reviewers for future SRs in the field and also helps practitioners and managers to have a better understanding of different aspects of cloud computing.