Chingter (2015) Harvesting big data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph. International Journal of Production Economics, Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/31811/4/IJPE_BIG%20DATA_New%20Version.pdf The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Thus, managers increasingly view data as an important driver of innovation and a significant source of value creation and competitive advantage. To get the most out of the big data (in combination with a firm's existing data), a more sophisticated way of handling, managing, analysing and interpreting data is necessary. However, there is a lack of data analytics techniques to assist firms to capture the potential of innovation afforded by data and to gain competitive advantage. This research aims to address this gap by developing and testing an analytic infrastructure based on the deduction graph technique. The proposed approach provides an analytic infrastructure for firms to incorporate their own competence sets with other firms. Case studies results indicate that the proposed data analytic approach enable firms to utilise big data to gain competitive advantage by enhancing their supply chain innovation capabilities.

Although many firms are actively deploying various digital technology (DT) assets across their supply chains to mitigate the negative impact of the COVID-19 pandemic on operations, whether these DT assets are truly helpful remains unclear. To disentangle this puzzle, we investigate whether firms that have higher levels of DT asset deployment achieve better supply chain performance in the COVID-19 crisis than firms with lower levels. From an asset orchestration perspective, we focus on two dimensions of DT asset deployment: breadth and depth, which reflect the scope and scale of DT assets, respectively. The empirical results from 175 Chinese firms that have deployed DT assets to varying degrees reveal that both the breadth and the depth of DT asset deployment show positive relationships with supply chain visibility. In contrast, the depth but not the breadth of DT asset deployment poses a positive relationship with supply chain agility. Most importantly, high levels of supply chain visibility and supply chain agility were prerequisites for excellent supply chain performance in the COVID-19 crisis. We contribute to the digital supply chain management literature by uncovering the mechanism through which DT asset deployment generates impacts on supply chain performance from an asset orchestration perspective. Our study also assists firms in improving their digital transformation strategies to combat the COVID-19 pandemic.

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Globalisation has created both drivers and pressure for Chinese organisations to enhance their business performance as well as environmental performance. Green and lean practice is emerging as a critical approach for Chinese organisations to achieve sustainable development and improve organisational performance. By conducting empirical studies from 172 respondents on green and lean practice in different Chinese organisations, this research shows how green and lean practice affects organisational performance and how this association is affected by guanxi. The findings explain that guanxi between organisational partners improves the positive effect of green and lean practice on organisational performance. The results of this paper offer helpful insights into how managers should enhance their guanxi initiatives, in order to improve environmental and business performance over their supply chains. The paper also suggests the limitations of this research, as well as directions for future research.

© 2016 The Author(s) This study explores how big data can be used to enable customers to express unrecognised needs. By acquiring this information, managers can gain opportunities to develop customer-centred products. Big data can be defined as multimedia-rich and interactive low-cost information resulting from mass communication. It offers customers a better understanding of new products and provides new, simplified modes of large-scale interaction between customers and firms. Although previous studies have pointed out that firms can better understand customers’ preferences and needs by leveraging different types of available data, the situation is evolving, with increasing application of big data analytics for product development, operations and supply chain management. In order to utilise the customer information available from big data to a larger extent, managers need to identify how to establish a customer-involving environment that encourages customers to share their ideas with managers, contribute their know-how, fiddle around with new products, and express their actual preferences. We investigate a new product development project at an electronics company, STE, and describe how big data is used to connect to, interact with and involve customers in new product development in practice. Our findings reveal that big data can offer customer involvement so as to provide valuable input for developing new products. In this paper, we introduce a customer involvement approach as a new means of coming up with customer-centred new product development