Purpose: The aim of this paper is to examine the state of knowledge management research in supply chain management from three standpoints, methodological approach, supply chain management area, and knowledge management processes.Design/methodology/approach: To achieve this, a systematic review is conducted over the period 2000-2014 on the basis of a qualitative content analysis.Findings: Major results showed that knowledge management can be viewed as a leverage mechanism for: (i) supply chain integration; (ii) the enhancement of intra and inter-relations across the supply chain; (iii) supply chain strategy alignment; and (iv) the reinforcement of knowledge transfer in product development. Some supply chain management areas such as reverse logistics, inventory management, forecasting/demand planning, outsourcing, and risk management have been explored only to some extent. Furthermore, knowledge transfer is being studied in the majority of the articles, mainly by both case study and survey approach; mathematical models and simulation techniques are used in very limited articles. Findings concerning theoretical perspectives and managerial issues are also described.Research limitations/implications: The limitation of our study encompasses the aspects of search period (2000-2014), selection of search databases (Web of Science and SCOPUS and language selection (English).Practical implications: The exhibition of the KM processes within the SC context may help practitioners and managers interested in implementing KM initiatives to replicate the methodologies in order to increase the possibilities of a successful KM adoption.Originality/value: The systematic review will contribute to the understanding of the present state of research in the knowledge management theory, with focus on the supply chain, as there are no state-of-knowledge studies that report a systematic literature review approach.

During the last 10 years, important contributions about knowledge management (KM) issues in supply chain management (SCM) have been published. The current paper aims to build upon previous literature reviews focused on KM in supply chains (SCs) from an integrative perspective, particularly recognising the studies conducted by Bhosale and Kant (2016. “Metadata Analysis of Knowledge Management in Supply Chain: Investigating the Past and Predicting the Future.” Business Process Management Journal 22 (1): 140–172) and Cerchione and Esposito (2016. “A Systematic Review of Supply Chain Knowledge Management Research: State of the Art and Research Opportunities.” International Journal of Production Economics 182: 276–292) as an effort to discuss the evolution of KM in the SC field. To this end, a systematic literature review including 210 papers is conducted over the period 2008–2017 from three positions previously not addressed jointly within the supply chain (SC) context: research methods employed by the authors; KM processes applied in the business processes across the SC; and intra and inter- organisational performance metrics linked with KM initiatives. Results exhibited that: (i) ‘Survey’ and ‘Case study’ are the two research methodologies mostly employed by authors (ii) the knowledge transfer is discussed in the majority of the studies reviewed, (iii) 114 intra and inter-organisational performance metrics are reported within the SC context from an empirical data approach. Findings concerning previous identified gap analysis and future lines of research are described.

The cane sugar industry in Mexico depends heavily on the supply of energy, fossil fuels and material resources for its proper operation. The overuse of these resources plus the technical and technological deficiency causes severe environmental consequences. This scientific work aims to analyze the environmental damage attributable to cane sugar production following the life cycle assessment (LCA) methodology. System boundaries include sugarcane growing and harvesting, sugarcane transportation, sugar milling and electricity cogeneration from bagasse. The associated emissions were acquired from the SimaPro-Ecoinvent database, the Roundtable on Sustainable Biofuels (RSB) and the Agroscope Reckenholz-Tänikon Research Station (ART). The life cycle impact assessment (LCIA) was carried out by SimaPro 8.3.0 software and the characterization method used was IMPACT 2002+. The results show that sugarcane growing and harvesting stage provides the most harmful environmental impacts (52%) followed by electricity cogeneration (25.7%), sugarcane transportation (12.1%) and finally, sugar milling (10.2%). Regarding the environmental contributions at the endpoint categories, the highest percentage of impacts is found in the Human health category (53%), followed by Climate change (21%), Ecosystem quality (16%) and Resources (10%). The LCA in cane sugar production can support the decision-making process to deal with this environmental problem.

This paper presents an optimization modeling approach to support strategic planning for designing hydrogen supply chain (HSC) networks. The energy source for hydrogen production is proposed to be electricity generated at Mexican sugar factories. This study considers the utilization of existing infrastructure in strategic areas of the country, which brings several advantages in terms of possible solutions. This study aims to evaluate the economic and environmental implications of using biomass wastes for energy generation, and its integration to the national energy grid, where the problem is addressed as a mixed-integer linear program (MILP), adopting maximization of annual profit, and minimization of greenhouse gas emissions as optimization criteria. Input data is provided by sugar companies and the national transport and energy information platform, and were represented by probability distributions to consider variability in key parameters. Independent solutions show similarities in terms of resource utilization, while also significant differences regarding economic and environmental indicators. Multi-objective optimization was performed by a genetic algorithm (GA). The optimal HSC network configuration is selected using a multi-criteria decision technique, i.e., TOPSIS. An uncertainty analysis is performed, and main economic indicators are estimated by investment assessment. Main results show the trade-off interactions between the HSC elements and optimization criteria. The average internal rate of return (IRR) is estimated to be 21.5% and average payback period is 5.02 years.