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Supply chain risk management processes are among the most important activities in the value chain of any industry. The supply chain risk management process includes different activities, focused on the identification, measurement, assessment, and mitigation of the main risk sources that can affect a supply chain. The increasing complexity facing global supply chains generates the need for suppliers to collaborate in different processes in a supply chain. In this context, Third Party Logistics Providers (3PL) have been widely promoted by the phenomenon of outsourcing, on which companies increasingly rely. The growth in logistics outsourcing is mainly attributed to the benefits it brings in terms of reducing costs, improving performance, allowing companies to focus on their core businesses and building virtual enterprises through strategic alliances. In this chapter we develop a model to identify the operational risk factors of a 3PL provider.

PurposeThe aim of this paper is to find and prioritise multiple critical success factors (CSFs) for the implementation of LSS in the oil and gas industry.Design/methodology/approachBased on a preselected list of possible CFSs, experts are involved in screening them with the Delphi method. As a result, 22 customised CSFs are selected. To prioritise these CSFs, the step-wise weight assessment ratio analysis (SWARA) method is applied to find weights corresponding to the decision-making preferences. Since the regular permutation-based weight assessment can be classified as NP-hard, the problem is solved by a metaheuristic method. For this purpose, a genetic algorithm (GA) is used.FindingsThe resulting prioritisation of CSFs helps companies find out which factors have a high priority in order to focus on them. The less important factors can be neglected and thus do not require limited resources.Research limitations/implicationsOnly a specific set of methods have been considered.Practical implicationsThe resulting prioritisation of CSFs helps companies find out which factors have a high priority in order to focus on them.Social implicationsThe methodology supports respective evaluations in general.Originality/valueThe paper contributes to the very limited research on the implementation of LSS in the oil and gas industry, and, in addition, it suggests the usage of SWARA, a permutation method and a GA, which have not yet been researched, for the prioritisation of CSFs of LSS.

Se presenta una metodología para la gestión de los riesgos operacionales en una compañía proveedora de servicios logísticos, particularmente para los servicios de transporte y almacenamiento. Según informes nacionales e internacionales, estos servicios son los que más se subcontratan en la actualidad. La metodología desarrollada utiliza una lista de chequeo para la identificación de los riesgos, y la matriz de probabilidad e impacto, además del análisis jerárquico difuso (fuzzy analytic hierarchy process, AHP) para la priorización de los mismos. La metodología ha sido validada en una empresa 3PL colombiana (servicios de logística tercerizados) encontrándose que los riesgos más relevantes son delitos como robos y actos terroristas. También aparecen otros problemas tales como las huelgas y los defectos técnicos en el transporte terrestre de las mercancías y el robo de la mercancía, desastres naturales y deterioro en el almacenamiento. Esta metodología le permite a la organización emprender acciones para minimizar los riesgos que mayor impacto tienen en los resultados globales de la organización.

<p class="Abstractandkeywordscontent">Industrial solid waste (ISW) is increasing in both quantity and complexity and it is a priority to establish strategies to manage it. Reverse Logistics (RL) is a strategy that enables material recovery and reuse avoiding the damage that ISW may cause; also RL organizes solid waste management activities and supports other activities such as ISW trading. Most of the research linking ISW and RL in developed countries is related to the electronics subsector because of the negative effects on the environment; however, research oriented towards plastics subsector waste is lower. This is the case in Colombia where the plastics subsector is composed mainly by small and medium-sized enterprises (SMEs) facing diverse constraints for their operation. Main facilitators and barriers that face SMEs in the Colombian plastics subsector to undertake RL programs were identified. An exploratory study was carried out in which business managers assessed the facilitators and barriers identified in the literature. The results showed that the availability of skilled people to perform RL activities is one of the most important internal facilitators and the lack of secondary markets for recovered materials is among the external barriers. The findings contribute to the body of knowledge in the field that is still maturing in Colombia.</p>

Generalized semi-infinite programming, extended Mangasarian-Fromovitz, Kuhn-Tucker and Abadie constraint qualification, Fritz-John condition, first and second order optimality conditions, optimal value function, directional differentiability, second order epiregularity, second order growth condition, 90C34, 90C30, 90C46, 65K05,