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The earth’s carrying capacity cannot withstand the pace of consumption resulting from current economic models, mainly the linear economy (LE) built on a throwaway culture. In the last few decades, the concept of a circular economy (CE), aiming to design waste out of the economy and mimic ecosystems, emerged as a strong alternative to LE. Being at the heart of the economic landscape, supply chains (SCs) need to respond to the necessary shift to CE. In so doing, the planning and execution of circular supply chains (CSCs) require a broader comprehension of CE and more sophisticated and large-scale analytical decision models. This chapter surveys extant literature on available best practices and quantitative models for sustainable supply chains (SSCs) and offers a new definition of CSC. Mapping on the knowledge extracted from this classification, potential gaps and strengths in the literature are identified. Key research papers on the “closed-loop” and “open-loop” ends of CSCs are highlighted. Challenges in developing CSC performance indicators and prescriptive models are emphasized.

This chapter proposes a multiobjective model to design a Closed Loop Supply Chain (CLSC) network. The first objective is to minimize the total cost of the network, while the second objective is to minimize the carbon emission resulting from production, transportation, and disposal processes using carbon cap and carbon tax regularity policies. In the third objective, we maximize the service level of retailers by using maximum covering location as a measure of service level. To model the proposed problem, a physical programming approach is developed. This work contributes to the literature in designing an optimum CLSC network considering the service level objective and product substitution.

This chapter studies the integration of quantitative and qualitative attributes of a particular issue in the strategic “designing” level of the reverse supply chain (RSC) process in a multicriteria decision-making environment. The study employs an analytical network process (ANP) to determine the performance indices of the collection centers derived through qualitative criteria from the remanufacturing facilities that are interested in buying used products. The evaluating criteria are comprised as a four-level hierarchy: the first level contains the objective of evaluating the collection centers, the second level involves the main evaluation criteria taken from the perspective of a remanufacturing facility, the third level contains the subcriteria under the main evaluation criteria, and the fourth level has the collection centers. ANP is presented herein as a matrix that comprises a list of all facets listed horizontally and vertically. This particular method is of value when key elements of a decision are difficult to quantify and contrast, and thus the identification of important facets and their incorporation into a linear physical programing (LPP) environment is of value. To determine the quality of end-of-life (EOL) products for transport from collection centers to remanufacturing facilities, a physical programming approach is adopted. Four criteria and their satisfaction are focused upon: (1) maximizing the total value of purchase; (2) minimizing the total cost of transportation; (3) minimizing the disposal cost; and (4) minimizing the purchase cost. A numerical example is considered in which three collection center locations are evaluated to identify the optimal collection center.

Economic incentives, government regulations, and customer perspective on environmental consciousness (EC) are driving more and more companies into product recovery business, which forms the basis for a reverse supply chain. A reverse supply chain consists a series of activities that involves retrieving used products from consumers and remanufacturing (closed-loop) or recycling (open-loop) them to recover their leftover market value. Much work has been done in the areas of designing forward and reverse supply chains; however, not many models deal with the transshipment of products in multiperiods. Linear physical programming (LPP) is a newly developed method whose most significant advantage is that it allows a decision-maker to express his/her preferences for values of criteria for decision-making in terms of ranges of different degrees of desirability but not in traditional form of weights as in techniques such as analytic hierarchy process, which is criticized for its unbalanced scale of judgment and failure to precisely handle the inherent uncertainty and vagueness in carrying out pair-wise comparisons. In this chapter, two multiperiod models are proposed for a remanufacturing system, which is an element of a Reverse Supply Chain (RSC), and illustrated with numerical examples. The first model is solved using mixed integer linear programming (MILP), while the second model is solved using linear physical programming. The proposed models deliver the optimal transportation quantities of remanufactured products for N-periods within the reverse supply chain.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

This chapter shows how different recycling locations influence closed-loop supply chain (CLSC) cost and carbon dioxide equivalents (CO₂e), as well as reveal competitive recycling and manufacturing locations, including relevant distance- and location-related factors, for achieving very low cost and CO₂e CLSCs supporting circular economy. Exploratory data analysis is used to analyze results from simulations based on empirical data and market rates relating to textile and clothing CLSCs. The results show that most very low-cost and CO₂e CLSCs consist of fabric and garment manufacturing located at the same or nearby locations, and whose labor costs and electricity CO₂e are low, whether fiber recycling facilities are located in proximity to used garment sorting facilities or not. Scenario and sensitivity analyses of important cost and CO₂e factors for recycling location competitiveness reveal that increasing used garment prices makes locations with high import duties lose competitiveness, and that varying water freight CO₂e changes comparative location competitiveness.

As a standard procedure of human factors engineering, the design of complex systems (e.g., operator interfaces) starts with analyses of system objectives, missions, functions, and tasks. Perceptual Control Theory (PCT) provides a theoretical framework for guiding this process. PCT is founded on notions from control theory, in which closed-loop, negative-gain, feedback systems can be used to build powerful models of goal-directed behavior and for implementing complex systems (Powers, 1973). One of the strengths of PCT over competing human behavior theories is that it explains how humans can control systems that are subject to a wide variety of external influences. UAV control is through the operators’ interaction with the interfaces in remote control stations. A closed-loop feedback system is crucial for both operators and control systems to understand the states and goals of each other. It is likely that advanced UAV control systems will require operators to interact with automated systems such as IAIs. IAIs are sophisticated and will require knowledge about mission goals, the operators’ goals, and states, as well as the UAV and environmental states. Thus, the methods of analysis used in this research were based on PCT given its engineering origins in control theory and advantages accommodating various external disturbances.

Purpose – This paper focuses on a unique historical case study of industry evolution in order to develop a road map where historical and strategic research could develop a common ground for trans-disciplinary inquiry.

Design/methodology/approach – The industry I explore is the Universal Credit Card Industry since its inception with the Diners Club in 1949 until its maturity in late 1990s. My empirical objective here is to develop a historically detailed and theoretically rich case study in which evolutionary processes are discovered as a result of the historical narrative.

Findings – The historical account of the industry demonstrates how the evolution of alternative business models as organizing forms has led to the establishment of interorganizational platforms with unique ecosystems. These alternative business models, through various experimentations, have ultimately produced two critical interorganizational organizations, one based on an open-loop system represented by Visa and MasterCard, and the other based on a closed-loop system represented by Diners Club and the American Express. The historical account also shows that in a given industry competition is not only among specific firms in the industry but also among the business models and the platforms created by these models.

Originality/value – I conclude that historical analyses reveal the nature of competition not only among firms but also among alternative business models where traditional strategy research rarely covers.

Reverse supply chain (RSC) is an extension of the traditional supply chain (TSC) motivated by environmental requirements and economic incentives. TSC management deals with planning, executing, monitoring, and controlling a collection of organizations, activities, resources, people, technology, and information as the materials and products move from manufacturers to the consumers. Except for a short warranty period, TSC excludes most of the responsibilities toward the product beyond the point of sale. However, because of growing environmental awareness and regulations (e.g. product stewardship statute), TSC alone is no longer an adequate industrial practice. New regulations and public awareness have forced manufacturers to take responsibilities of products when they reach their end of lives. This has necessitated the creation of an infrastructure, known as RSC, which includes collection, transportation, and management of end-of-life products (EOLPs). The advantages of implementing RSC include the reduction in the use of virgin resources, the decrease in the materials sent to landfills and the cost savings stemming from the reuse of EOLPs, disassembled components, and recycled materials. TSC and RSC together represent a closed loop of materials flow. The whole system of organizations, activities, resources, people, technology, and information flowing in this closed loop is known as the closed-loop supply chain (CLSC).

In RSC, the management of EOLPs includes cleaning, disassembly, sorting, inspecting, and recovery or disposal. The recovery could take several forms depending on the condition of EOLPs, namely, product recovery (refurbishing, remanufacturing, repairing), component recovery (cannibalization), and material recovery (recycling). However, neither the quality nor the quantity of returning EOLPs is predictable. This unpredictable nature of RSC is what makes its management challenging and necessitates innovative management science solutions to control it.

In this chapter, we address the order-driven component and product recovery (ODCPR) problem for sensor-embedded products (SEPs) in an RSC. SEPs contain sensors and radio-frequency identification tags implanted in them at the time of their production to monitor their critical components throughout their lives. By facilitating data collection during product usage, these embedded sensors enable one to predict product/component failures and estimate the remaining life of components as the products reach their end of lives. In an ODCPR system, EOLPs are either cannibalized or refurbished. Refurbishment activities are carried out to meet the demand for products and may require reusable components. The purpose of cannibalization is to recover a limited number of reusable components for customers and internal use. Internal component demand stems from the component requirements in the refurbishment operation. It is assumed that the customers have specific remaining-life requirements on components and products. Therefore, the problem is to find the optimal subset and sequence of the EOLPs to cannibalize and refurbish so that (1) the remaining-life-based demands are satisfied while making sure that the necessary reusable components are extracted before attempting to refurbish an EOLP and (2) the total system cost is minimized. We show that the problem could be formulated as an integer nonlinear program. We then develop a hybrid genetic algorithm to solve the problem that is shown to provide excellent results. A numerical example is presented to illustrate the methodology.