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Green supply chain management and product innovation both have become sources of competitive advantage for companies from different industries. However, research on the configuration of green supply chains for new products represents a comparably new trend. In this paper, a goal programming approach is suggested to optimise the supply chain (SC) configuration for a new consumer product under consideration of economic and environmental criteria. The approach is illustrated by the case example of a fast moving consumer goods manufacturer. In this context, trade-offs between the ecologic factor of carbon emission, on the one hand, and financial value creation and customer service level on the other hand, are assessed in the deterministic analyses. The influence of long-term demand uncertainties is modelled in a scenario approach. It is observed that decentralised SC configurations enable carbon emission reduction without deteriorating the economic SC performance. Furthermore, it is detected that a focused economic optimisation strongly amplifies negative environmental impacts of demand uncertainties.

Stakeholder influences on sustainable supply chain management (SSCM) are of increasing interest for researchers to take into account economic, environmental, and social risks. While extant literature on stakeholder influences or risks in SSCM concentrates on selected issues, a comprehensive review of both stakeholder and risk constructs is missing. Hence, this paper examines stakeholder influences and risks in SSCM, as addressed by conceptual frameworks, empirical studies, and formal models to shed light on the trends and gaps in qualitative and quantitative SSCM research. Based on a content analysis of systematically selected journal publications, the commonalities and differences between the research designs are identified. The findings suggest that the integration of economic risks prevails over the consideration of environmental and social risks. Qualitative studies frequently focus on customers or multiple stakeholders that trigger SSCM and relate to supply, demand, and particularly reputational risks. In contrast, quantitative models rather concentrate on formalizing governmental triggers and operational risks. Thus, mutual stimuli between conceptual, empirical, and model-based SSCM research and their implications for future research directions are derived.

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