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Purpose: Demand-Driven Distribution Resource Planning (DDDRP) has recently been proposed in the literature to deal with higher supply networks complexity, shorter customer tolerance times, and inaccurate forecasts. The DDDRP requires to position inventory buffers in critical network nodes, where the inventory level in each buffer is replenished based on actual demands rather than on demand forecasts. This paper aims to identify optimal buffer positions in a distribution network driven by the DDDRP approach and to assess the performance of the DDDRP approach compared to the conventional Distribution Resource Planning (DRP) approach. Design/methodology/approach: First, a mixed-integer non-linear model is proposed to optimize buffer positioning under supply network constraints and with the objective of minimizing supply chain holding costs. Then, a case study is investigated to validate the optimization model and to evaluate the performance of the optimized distribution network driven by the DDDRP approach, compared to the DRP approach. Findings: Results of the considered case study demonstrate that the distribution network optimized and driven by the DDDRP approach achieves savings of 75% in terms of total holding costs and 67% in terms of inventory amounts, compared to a distribution network driven by the DRP approach. Research limitations/implications: Results of this paper cannot be generalized since several assumptions have been considered. Thus, addressing real case studies in different industrial contexts may be of theoretical and practical interest. Originality/value: This paper is the first to propose a mathematical model to optimize buffer positioning in a distribution network driven by the DDDRP approach.

The resource curse haunts countries whose economies have become dangerously specialized in the exploitation of a single resource. This curse threatens countries whose economies are poorly diversified and oriented mainly towards the export of their non-renewable natural resources, such as oil. What about the exploitation of an abundant renewable natural resource such as solar energy? Based on a case study of six solar power plants in six African countries (Burkina Faso, Madagascar, Morocco, Rwanda, Senegal, and South Africa), this paper analyzes the extent to which the impacts of the exploitation of these energy systems contribute to this curse. The research method is qualitative (296 interviews) and quantitative (use of a sustainability index), making it possible to analyze the impacts of solar power plants on four levels (local, regional, national, and international). Our results reveal four findings symptomatic of the resource curse: (i) the emergence of conflict situations, (ii) fragile local development, (iii) latent financial risk, and (iv) limited economic development leverage. In short, the resource curse linked to the use of renewable energies seems to bring another challenge to the landscape of energy transition.

Purpose: Demand Driven Material Requirements Planning (DDMRP) aims to deal with variability by adjusting inventory levels while maintaining, or even increasing, customer service levels. This approach bridges the push and pull approaches. Even though it first made its appearance in 2011, research in this field remains relatively limited. This paper aims to measure the spatiotemporal evolution of the DDMRP, its scope and context of implementation, and the research lines studied in that field in order to identify areas that still need to be addressed by future researchers. Design/methodology/approach: The systematic literature review approach adopted in this paper examines research dealing with the DDMRP approach published in different languages between 2009 and 2020. To-date papers focused on the performance analysis and comparison, what differentiates this study is the focus on the scientific evolution level of DDMRP, the parameters, and contexts that should be more studied. Findings: The results show that DDMRP is not yet a mature method and that the robustness of the approach still needs to be tested. More research is also required to determine scientifically some setting parameters, how the proposed DDMRP could be implemented in different industrial contexts with existing information systems. Originality/value: Based on the evolution analysis of DDMRP, this study outlines its current state of maturity and its different shortcomings under a broader vision to make this method more complete on the scientific and industrial level.

The success of modular multilevel converters (MMCs) in high-voltage direct current (HVDC) applications has fueled the research on modular converter topologies. New modular converter topologies are often proposed, discussed, and sometimes applied in HVDC, as well as other industrial application such as STATCOMs, DC/DC HVDC, medium-voltage direct current (MVDC), etc. The performance evaluation of new modular converter topologies is a complex and time-consuming process that typically involves dynamic simulations and the design of a control system for the new converter topology. Sadly, many topologies do not progress to the implementation stage. This paper proposes a set of key performance indicators (KPIs) related to the cost and footprint of the converter and a procedure designed to rapidly evaluate these indicators for new converter topologies. The proposed methodology eliminates the need for dynamic simulations and control-system design, and is capable of identifying whether a particular converter is worth considering or not for further studies of a specific application, depending on the operating requirements. Thanks to the method outlined in this work and via the key parameters quantifying the “relevance” of the analyzed converters, promising topologies were easily identified, while the others could be rapidly discarded, resulting in saving valuable time in the study of the solutions that have a real potential. The proposed method is first described from a general point of view and then applied to a case study of the new converter topology—Open-Delta CLSC—and its application in two use cases.