When supply chain management (SCM) intersects with Big Data Analytics (BDA), uncountable opportunities for research emerge. Unfortunately, how analytics can be applied to supply chain processes is still unclear for both academics and industries. To better connect SC processes needs and what BDA offer, we present a structured review of academic literature that addresses BDA methods in SCM using the supply chain operations reference (SCOR) model. The literature since 2001 is reviewed to provide a taxonomy framework resulting in a nomenclature grids and a SCOR-BDA matrix. The most important result of this paper indicates a clear disparity and points to an urgent need to bring the efforts closer in a collaborative way for more intelligent use of BDA in SCM. Furthermore, this paper highlights a misalignment between data scientists and SC managers in BDA applicability. It also highpoints upcoming research tracks and the main gaps that need to be stunned.

The current intense competition forces enterprises to pay attention to supply chain collaboration with their upstream and downstream partners. Different collaborative strategies such as quick response (QR), efficient consumer response (ECR), vendor managed inventory (VMI) or collaborative planning, forecasting and replenishment (CPFR) have already been proposed. The key to ensuring that the supply chain partners are progressing on the right track of creating the best-in-class practice lays in their ability to choose the appropriate strategy. The current paper proposes a framework, based on analysis grids and graphical representations, which help to better characterize these strategies. The analysis grids use several characterization criteria to express the collaboration nature and its extent. For a better understanding, this framework is then applied to the CPFR strategy.

The transition to modern and ecologically oriented industries requires the application of new approaches and advanced models of logistics management. In this context, horizontal collaboration has recently become a promising and emerging practice for companies intending to improve their freight transportation efficiency and achieve environmental sustainability. This strategy is usually involved in the independent planning of decision-making problems with economic objectives. However, integrated optimisation of planning decisions with environmental considerations is nowadays considered as one of the major challenges to enhance the competitiveness of logistics networks. In this paper, the integrated planning problem of location, inventory and routing to design two-echelon green logistics networks is studied in collaborative and non-collaborative scenarios. The main purpose of this study is to assess the benefits of horizontal collaboration between suppliers in integrated freight transportation optimization by proposing a new decision support tool. A bi-objective Mixed Integer Linear Programming is proposed and adapted to address both distribution strategies. First, a resolution of the several scenarios with a single objective and a comparison of the results are performed. Second, a multi-objective optimization using an aggregation approach is conducted to find a good trade-off between the economic objectives and environmental ones. The experimental results show that horizontal collaboration among suppliers may decrease the total aggregated cost and emissions in the logistics system.

Smart technologies have dramatically improved environmental risk perception and altered the way organizations share knowledge and communicate. As a result of the increasing amount of data, there is a need for using business intelligence and data mining (DM) approaches to supply chain risk management. This paper proposes a novel environmental supply chain risk management (ESCRM) framework for Industry 4.0, supported by data mining (DM), to identify, assess, and mitigate environmental risks. Through a systematic literature review, this paper conceptualizes Industry 4.0 ESCRM using a DM framework by providing taxonomies for environmental risks, levels, consequences, and strategies to address them. This study proposes a comprehensive guide to systematically identify, gather, monitor, and assess environmental risk data from various sources. The DM framework helps identify environmental risk indicators, develop risk data warehouses, and elaborate a specific module for assessing environmental risks, all of which can generate useful insights for academics and practitioners.