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.

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.

Purpose-This paper suggests how firms could use big data to facilitate the product innovation processes, by shortening the time to market, improving customers' product adoption and reducing costs. Design/methodology/approach-Our research is based on a two-step approach. First, this research summarised and identified four potential key success factors for organisations to integrate big data in accelerating their product innovation processes. The proposed factors were further examined and developed by conducting interviews with different organisation experts and academic researchers. Then, a framework was developed based on the interview outputs. The framework sets out the key success factors involved in leveraging big data to reduce lead times and costs in product innovation processes. Findings-The three determined key success factors are: a) Accelerated innovation process; b) Customer connection; and c) Ecosystem of innovation. The developed framework based on big data we believe represents a paradigm shift. It can help firms to make new product development dramatically faster and less costly. Research limitations/implications-The proposed accelerated innovation processes demands a shift in traditional organisational culture and practices. It is, though, meaningful only for products and services with short product life cycles. Moreover, the framework has not yet been widely tested. Practical implications-This paper points to the vital role of big data in helping firms to accelerate product innovation processes. First of all, it allows organisations to launch new products to market as quickly as possible. Secondly, it helps organisations to determine the weaknesses of the product earlier in the development cycle. Thirdly, it allows functionalities to be added to a product that customers are willing to pay a premium for, while eliminating features they don't want. Last but not least, it identifies and then prioritises customer needs for specific markets. Originality/value-The research shows that firms could harvest external knowledge and import ideas across organisational boundaries. An accelerated innovation processes based on Page 1 of 28 Business Process Management Journal 2 big data is characterised by a multidimensional process involving intelligence efforts, relentless data collection and flexible working relationships with team members.

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The question this special issue would like to address is how to harvest big data to help decision-makers to deliver better fact-based decisions aimed at improving performance or to create better strategy? This special issue focuses on the big data applications in supporting operations decisions, including advanced research on decision models and tools for the digital economy. Responds to this special issue was great and we have included many high quality papers. We are pleased to present 13 of the best papers. The techniques presented include data mining, simulation, and expert system with applications span across online reviews, food retail chain to E-health.

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