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

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

Towards the next generation of manufacturing: implications of big data and digitalization in the context of industry 4.0 [Editorial]

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“Real impact”: challenges and opportunities in bridging the gap between research and practice – making a difference in industry, policy, and society

Achieving impact from academic research is a challenging, complex, multifaceted, and interconnected topic with a number of competing priorities and key performance indicators driving the extent and reach of meaningful and measurable benefits from research. Academic researchers are incentivised to publish their research in high-ranking journals and academic conferences but also to demonstrate the impact of their outputs through metrics such as citation counts, altmetrics, policy and practice impacts, and demonstrable institutional decision-making influence. However, academic research has been criticized for: its theoretical emphasis, high degree of complexity, jargon-heavy language, disconnect from industry and societal needs, overly complex and lengthy publishing timeframe, and misalignment between academic and industry objectives. Initiatives such as collaborative research projects and technology transfer offices have attempted to deliver meaningful impact, but significant barriers remain in the identification and evaluation of tangible impact from academic research. This editorial focusses on these aspects to deliver a multi-expert perspective on impact by developing an agenda to deliver more meaningful and demonstrable change to how “impact” can be conceptualized and measured to better align with the aims of academia, industry, and wider society. We present the 4D model - Design, Deliver, Disseminate, and Demonstrate - to provide a structured approach for academia to better align research endeavors with practice and deliver meaningful, tangible benefits to stakeholders.</p