A cloud-based supply chain management system: effects on supply chain responsiveness

Purpose: Despite the ongoing calls for the incorporation of the cloud utility model, the effect of the cloud on elements of supply chain performance is still an evolving area of research. In this paper, we develop the architecture of a cloud-based supply chain management (C-SCM) ecosystem and explore how it enhances supply chain responsiveness. Design/methodology/approach: First, we discuss the potential benefits that cloud computing can yield compared to existing mature SCM information systems and solutions through a comprehensive literature review. We conceptualize SCR in terms of the level of visibility in the supply chain, supply chain flexibility, and rapid detection and reaction to changes and then we build the detailed architecture of a cloud based SCM system. The proposed ecosystem introduces a view of SCM and the associated practices when transferred to cloud environments. The potential to enhance SCR through the cloud is explored with scenarios on a case of supply chain operations in fashion retail industry. Findings: Our findings show that the proposed system can enhance all three dimensions of SCR. Implications for supply chain practice and how companies can migrate to a cloud supply chain are drawn. Originality/Value: Given that the development, creation, and delivery of goods and services is increasingly becoming a joint effort of several parties in a supply chain, we contribute to existing literature, by introducing a comprehensive cloud-based SCM system and show how companies can enhance their supply chain responsiveness

Enhancing the supply chain responsiveness through cloud manufacturing

With the substantial advances of Information Technology (IT) and digital communication platforms over the past decade, there is a growing awareness among stakeholders that their success is heavily dependent on early adoption of such technologies throughout their supply chain. The objective of this paper is to develop the computer architecture of a cloud based supply chain system and to explore the effect that its utilization has on supply chain responsiveness (SCR). SCR is conceptualized in terms of the level of visibility a company can in the supply chain, supply chain flexibility, and rapid detection and reaction to changes. The potential benefits that cloud can yield are discussed through a comprehensive literature review and compared to existing mature supply chain management (SCM) systems and solutions, in terms of several IT enabled supply chain capabilities. The detailed architecture of a cloud-based SCM (C-SCM) system is then developed, with the use of mature modules, to ensure its compatibility with existing technologies. The effect of the utilization of the cloud based SCM system on SCR is explored with case based scenarios, using data of a retail fashion company’s supply chain operations. Our findings suggest that the proposed system improves all the dimensions of SCR. Implications for supply chain practice and how companies can migrate to a cloud supply chain are drawn

Social media and sensemaking patterns in new product development: demystifying the customer sentiment

Artificial intelligence by principle is developed to assist but also support decision making processes. In our study, we explore how information retrieved from social media can assist decision-making processes for new product development (NPD). We focus on consumers’ emotions that are expressed through social media and analyse the variations of their sentiments in all the stages of NPD. We collect data from Twitter that reveal consumers’ appreciation of aspects of the design of a newly launched model of an innovative automotive company. We adopt the sensemaking approach coupled with the use of fuzzy logic for text mining. This combinatory methodological approach enables us to retrieve consensus from the data and to explore the variations of sentiments of the customers about the product and define the polarity of these emotions for each of the NPD stages. The analysis identifies sensemaking patterns in Twitter data and explains the NPD process and the associated steps where the social interactions from customers can have an iterative role. We conclude the paper by outlining an agenda for future research in the NPD process and the role of the customer opinion through sensemaking mechanisms