The aim of this research paper is to explore and evaluate previous work focusing on the relationship and links between Lean and Green supply chain management practices. Several explanatory frameworks are explored and discussed. It is intended that evidence and insights can be developed and used: (a) to assist our understanding of where Lean practices are synergistic for Green; (b) to clarify if Green practices are synergistic for Lean; and (c) to identify opportunities for companies to use their Lean framework as a catalyst to making their processes Green. The paper provides evidence suggesting that Lean is beneficial for Green practices and the implementation of Green practices in turn also has a positive influence on existing Lean business practices.

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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.

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Evidence suggests that lean methods and tools have helped manufacturing organisations to achieve operational excellence, and in this way meet both traditional and contemporary organisational objectives such as profitability, efficiency, responsiveness, quality, and customer satisfaction. However, the effect of these methods and tools on environmental performance is still unclear, as limited empirical research has been conducted in this field. This paper therefore investigates the impact of five essential lean methods, i.e. JIT, autonomation, kaizen/continuous improvement, total productive maintenance (TPM) and value stream mapping (VSM), on four commonly utilised measures for the compliance of environmental performance, i.e. material use, energy consumption, non-product output, and pollutant releases. A correlation analysis modelled the relationship and effect of these lean methods on the environmental performance of 250 manufacturing organisations around the world. Structural equation modelling (SEM) was used as a second pronged verification approach to ensure the validity of the results. The results indicate that TMP and JIT have the strongest significance on environmental performance, whereas kaizen/continuous improvement only showed an effect on the use of materials and release of pollutants. Autonomation and VSM did not show any impact on environmental performance. The research holds important implications for industrialists, who can develop a richer knowledge on the relationship between lean and green. This will help them formulate more effective strategies for their simultaneous or sequential implementation. The paper extends our knowledge in the lean and green field by helping us to establish and explain the given relationships between five of the most important and commonly used lean methods and the environmental performance of manufacturing organisations. No previous research had considered the studied lean methods and environmental measures of performance.

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