As supply chains continue to globalize, the need for robust Third Party Logistics (3PL) provider qualification, selection, and evaluation programs becomes increasingly critical. In this context, this chapter aims to present a methodological approach for the optimization of this specific type of outsourcing operations in today’s globalized supply chains. More specifically, the authors first present an analytical literature review of the criteria and the methods that are employed in this field of decision-making, and then propose a generic methodological framework for the 3PL partner selection and evaluation problem. This framework is constituted by a nine-phased conceptual decision-making methodology that outlines the whole life cycle of the 3PL provider selection and continuous evaluation processes.

As supply chains continue to globalize, the need for robust Third Party Logistics (3PL) provider qualification, selection, and evaluation programs becomes increasingly critical. In this context, this chapter aims to present a methodological approach for the optimization of this specific type of outsourcing operations in today's globalized supply chains. More specifically, the authors first present an analytical literature review of the criteria and the methods that are employed in this field of decision-making, and then propose a generic methodological framework for the 3PL partner selection and evaluation problem. This framework is constituted by a nine-phased conceptual decision-making methodology that outlines the whole life cycle of the 3PL provider selection and continuous evaluation processes.

We propose generic single period (newsvendor-type) inventory models for capturing the trade-off between inventory policies and disruption risks in a dual-sourcing supply chain network both unconstrained and under service level constraints, where both supply channels are susceptible to disruption risks. The models are developed for both risk neutral and risk-averse decision-makers and can be applicable for different types of disruptions related among others to the supply of raw materials, the production process, and the distribution system, as well as security breaches and natural disasters. Analytical closed-form solutions are obtained and interesting managerial insights on the merit of contingency strategies in managing uncertainties and risks in dual-sourcing supply chains are discussed.

The goal of this paper is to present a new planning optimisation framework for the disassembly processes of an end-of-life (EOL) building, by explicitly taking into account, as an input, its bill of materials. When disassembling an EOL building, there is the option either to selectively deconstruct specific components and materials for reuse and recycling, respectively, while demolishing the remainder of the building, or to further continue the deconstruction process and postpone the demolition of the building for a later stage. In this context, we present a novel, integrated, decision-making model for the entire construction and demolition supply chain, starting from the optimisation of the on-site deconstruction and demolition decisions till the delivery of the collected components and materials to potential recyclers/customers and landfills. An application of the developed methodology is demonstrated via a specific case study, while the relative obtained managerial insights, regarding the behaviour of the optimal solution, are presented.

In this work, we propose a single period stochastic inventory decision-making model that captures the trade-off between inventory policies and disruption risks for unreliable (both uncapacitated and capacitated) dual-sourcing reverse supply chain networks. Risk-management has emerged high at the corporate agenda as globalised supply chain networks are more stretched than ever due to offshoring and thus are more exposed to disruptions, while reverse logistics has been proven to constitute a profit center. In this environment, global companies have to scrutinize especially the role of major Asian economies (with large manufacturing capacities and huge markets), while conducting their strategic procurement planning. The developed model can be applied to a number of different scenario types encompassing various instances of disruptions to the collection of the end-of-life products, of the transportation system, and of the remanufacturing yield and capacity. Analytical closed-form solutions are obtained and important managerial insights on the merit of contingency strategies in managing uncertainties for reverse logistics networks are discussed.

Reverse logistics is a key supply chain management discipline addressing the need for environmentally conscious manufacturing and processing of the end-of-life products. As the recovery processes are being recognised as a new value-added profit centre, the design of reverse logistics is receiving increased attention and scrutiny. In this chapter, we first present a comprehensive up-to-date literature review on the optimal design of reverse logistics and closed-loop supply chain networks. The chapter builds upon the general concepts that were developed by previous works, while extending them by presenting an integrated decision-support methodological approach for the optimal configuration of reverse supply chain networks in support or agile closed-loop supply chains. The proposed decision-making methodology provides a valuable strategic generalised model to decision makers that can be applied to various business environments. Finally, useful managerial insights regarding the implementation of the proposed solution methodology and sensitivity analysis are discussed, while specific directions for future research are provided.

Today, an ever increasing number of enterprises undertake significant steps in order to maximise the financial value recovered from End-of-Life (EOL) products, while ensuring sustainable development. In this context, this work deals with the optimal configuration of efficient reverse logistics networks. More specifically, we first present a five-phased strategic methodological model for the development of reverse supply chains. Then, we provide a new mathematical programming model for enterprises that have already structured their forward supply chain and aim at being involved in EOL products' recovery processes. An application of the optimisation model is demonstrated, while obtained managerial insights are discussed.

Modern supply chains need to be agile and flexible enough so that they can be responsive to sources of variability and unpredictability, such as, demand fluctuations, supply and transportation disruptions and lengthy and variable order lead times. At the core of the appropriate response to this challenge, resides the effective collaboration among all stakeholders of the extended enterprise; this is further realized though the proper supply chain contractual agreements, information sharing and a system-wide cost and profitability optimization paradigm. In this manuscript, we present a control theory-based optimization framework that can be employed at an operational level, determining the optimal policy for multi-product, multi-echelon supply chain network resilience. The decision variables include production rates, transportation loads, and product inventory levels. The objective function is the minimization of total supply chain cost over a specified rolling time horizon subject to production, storage and transportation capacities and service (fill-rate) constraints. Having selected a suitable, based on the dynamics of the supply chain, time interval, a detailed difference model is utilized for the prediction of the dynamic behavior of the system. In addition, a stochastic model is used for the forecast of future product demand variation based on the calculated prediction error at each time period. The response of the system to abrupt demand changes and to disruptions in production and transportation is investigated and analyzed. This analysis quantifies the impact of system shocks such as changes and disruptions, while further providing useful managerial insights for improving the resiliency of the entire supply chain.

As companies continue to globalise their operations and outsource significant portion of their value chain activities, they often end up relying heavily on order replenishments from distant suppliers. The explosion in long-distance sourcing is exposing supply chains and shareholder value at ever increasing operational and disruption risks. It is well established, both in academia and in real-world business environments, that resource flexibility is an effective method for hedging against supply chain disruption risks. In this contextual framework, we propose a single period stochastic inventory decision-making model that could be employed for capturing the trade-off between inventory policies and disruption risks for an unreliable dual sourcing supply network for both the capacitated and uncapacitated cases. Through the developed model, we obtain some important managerial insights and evaluate the merit of contingency strategies in managing uncertain supply chains.

Managing risk and disruptions have emerged as issues of critical importance for today's globalised supply chains. In this work, we first provide an up-to-date taxonomy of the risks that supply chains are exposed to along with the appropriate solutions that can be employed to improve their resiliency. We then proceed by outlining an analytical methodological framework for supporting the design and operations of efficient supply chains in the new business environment by taking into account the stochasticity of various factors that can lead to disruptions. More specifically, we present a novel, to our knowledge, stochastic single period quantitative model that can assist in the decision-making process regarding the optimal security protection-based configuration of a supply chain both for single and multiple disruption cases. We obtain closed-form solutions in all cases allowing for capturing the optimal trade-off between inventory policies and supply chain protection levels. Finally, we discuss additional useful managerial insights that were obtained.