Abstract
The emergence of complex supply chains is one of the most important consequences of globalization. The management of these supply chains requires increased efforts by organizations that, on one hand, are increasingly pressured by customers in terms of service levels, on the other hand, must manage their suppliers from various locations and with different local requirements. In this context, an appropriate management of information flows is needed to create the adequate visibility level for managing supply chain risk. This chapter presents an overview on the concepts of risk management, visibility and information management in supply chains. This study proposes a conceptual framework for the selection of risk mitigation strategies in the supply chain and characterizes the external and internal information flows decision makers need to implement two categories of risk mitigation strategies: redundancy and flexibility. © 2017, IGI Global.

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Social innovation is presented as a viable alternative to incite systemic changes related to sustainability in its three dimensions (social, environmental and economic), to involve the government, companies and, above all, civil society. In order to understand the development of social innovation in Brazil and Portugal, and to promote cross-sector collaboration, we have conducted several case studies involving centers for social innovation in both countries. The data analysis demonstrates that collaboration between sectors and the construction of collaborative networks is as a key element in the development and sustainability of social innovation. In spite of this, in practice the construction of these networks is not trivial, there is the need to manage these networks innovatively. The literature review points out the key enablers for cross-sector collaboration in the context of social innovation, the alignment of values and goals, mutual trust, commitment and bridge leadership.

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Evaluating the role of logistics and supply chain management skills or applications is necessary for the success of any organization or business. As market competition becomes more aggressive, it is crucial to evaluate ways in which a business can maintain a strategic edge over competitors. The Handbook of Research on Information Management for Effective Logistics and Supply Chains highlights strategies, tools, and skills necessary for supply management within organizations and companies. Featuring best practices and empirical research within the field, this handbook is a critical reference source for scholars, practitioners, researchers, information systems and telecommunication specialists, and managers.

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Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail supply chains. For profitable retail businesses, accurate sales forecasting is crucial in organizing and planning purchasing, production, transportation and labor force. Retail sales series belong to a special type of time series that typically contain strong trend and seasonal patterns, presenting challenges in developing effective forecasting models. This paper compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. An approach based on cross-validation is used to identify automatically appropriate state space and ARIMA models. The forecasting performance of these models is also compared by examining the out-of-sample forecasts. The results indicate that the overall out-of-sample forecasting performance of ARIMA models evaluated via RMSE, MAE and MAPE is better than state space models. The performance of both forecasting methodologies in producing forecast intervals was also evaluated and the results indicate that ARIMA produces slightly better coverage probabilities than state space models for the nominal 95% forecast intervals. For the nominal 80% forecast intervals the performance of state space models is slightly better.