In this paper, we introduce an approach to combine material flow analysis (MFA) with data envelopment analysis (DEA) in order to evaluate resource efficiency of different stages in the material cycles in various countries. The approach is exemplified for the end-of-life stage of the anthropogenic iron cycle, comparing data of 48 countries. The proposed performance criteria are defined through a common MFA system definition so that the model could also be expanded to indicate the overall efficiency of the system, while emphasizing the relative importance of the different stages. Steps and implementation of the proposed methodology are explained with the help of a case study for iron.
In recent years, relating organization's attitude towards sustainable development, environmental management is gaining an increasing interest among researchers in supply chain management. With regard to a long term requirement of a shift from a linear economy towards a cycle economy, businesses should be motivated to embrace change brought about by consumers, government, competition, and ethical responsibility. To achieve business goals and objectives, a company must reply to increasing consumer demand for "green" products and implement environmentally responsible plans. Reverse logistics is an activity within organizations delegated to the customer service function, where customers with warranted or defective products would return them to their supplier. Emergence of reverse logistics enables to provide a competitive advantage and significant return on investment with an indirect effect on profitability. Many organizations are hiring third-party providers to implement reverse logistics programs designed to retain value by getting products back. Reverse logistics vendors play an important role in helping organizations in closing the loop for products offered by the organizations. In this regard, the selection of third-party providers issue is increasingly becoming an area of reverse logistics concept and practice. This study aims to assist managers in determining which third-party logistics provider to collaborate in the reverse logistics process with an alternative approach based on an integrated model using neural networks and fuzzy logic. An illustrative case study is discussed and the best provider is identified through the solution of this model.
Maintaining competitiveness in an environment where price and quality differences between competing products are disappearing depends on the company's ability to reduce costs and supply time. Timely responses to rapidly changing market conditions require an efficient Supply Chain Management (SCM). Outsourcing logistics to third-party logistics service providers (3PLs) is one commonly used way of increasing the efficiency of logistics operations, while creating a more "core competency focused" business environment. However, this alone may not be sufficient. Due to recent environmental regulations and growing public awareness regarding environmental issues, 3PLs need to be not only efficient but also environmentally benign to maintain companies' competitiveness. Even though an efficient and environmentally benign combination of 3PLs can theoretically be obtained using exhaustive search algorithms, heuristics approaches to the selection process may be superior in terms of the computational complexity. In this paper, a hybrid approach that combines a multiple criteria Genetic Algorithm (GA) with Linear Physical Weighting Algorithm (LPPW) to be used in efficient and environmentally benign 3PLs is proposed. A numerical example is also provided to illustrate the method and the analyses.
Today, since customers are able to obtain similar-quality products for similar prices, the lead time has become the only preference criterion for most of the consumers. Therefore, it is crucial that the lead time, i.e., the time spent from the raw material phase till the manufactured good reaches the customer, is minimized. This issue can be investigated under the title of Supply Chain Management (SCM). An efficiently managed supply chain can lead to reduced response time for customers. To achieve this, continuous observation of supply chain efficiency, i.e., a constant performance evaluation of the current SCM is required. Widely used conventional performance measurement methods lack the ability to evaluate a SCM since the supply chain is a dynamic system that requires a more thorough and flexible performance measurement technique. Balanced Scorecard (BS) is an efficient tool for measuring the performance of dynamic systems and has a proven capability of providing the decision makers with the appropriate feedback data. In addition to SCM, a relatively new management field, namely reverse supply chain management (RSCM), also necessitates an appropriate evaluation approach. RSCM differs from SCM in many aspects, i.e., the criteria used for evaluation, the high level of uncertainty involved etc., not allowing the usage of identical evaluation techniques used for SCM. This study proposes a generic Balanced Scorecard to measure the performance of supply chain management while defining the appropriate performance measures for SCM. A scorecard prototype, ESCAPE, is presented to demonstrate the evaluation process.
The latest enhancements in industrial technologies, especially the ones in electronics industry, have provided organizations with the ability to manufacture faster and more economical products. This fact, coupled with the growing interest and demand for the latest technology, have led electronic equipment manufacturers to start producing “hightech” and “personalized” products at an increasing rate. This has led to a high rate of obsolescence for electronic
products worldwide, even though the majority of these “obsolete” products still function. In this paper, we investigate a product recovery facility where the end-of-life (EOL) products are taken back from the last users and are brought into the facility for processing. We assume that there are multiple types of EOL products and that a combination of these can be disassembled to provide for a sufficient number of demanded components and materials. We then present a data
envelopment analysis (DEA) algorithm to determine the number and types of the EOL products that will be required to fulfill the demand. A numerical example is presented to demonstrate the functionality of the methodology.
When a product reaches its end of life, there are several options available for processing it including reuse, remanufacturing, recycling, and disposing (the least desirable option). In almost all cases, a certain level of disassembly may be necessary. Thus, finding an optimal (or near optimal) disassembly sequence is crucial to increasing the efficiency of the process. Disassembly operations are labor intensive, can be costly, have unique characteristics and cannot be considered as reverse of assembly operations. Since the complexity of determining the best disassembly sequence increases with the increase in the number of parts of the product, it is extremely crucial that an efficient methodology for disassembly process planning be developed. In this paper, we present a genetic algorithm for disassembly process planning. A case example is considered to demonstrate the functionality of the algorithm.
The current trend of depletion of natural resources due to an ever-increasing number of consumer goods manufactured has led to an increase in the quantity ofused and outdated products discarded. From an environmental point ofview, it is not only desirable to disassemble, reuse, remanufacture and/or recycle the discarded products, in many cases it can also be economically justified. This situation being the motive, in recent years there have been several studies reported on disassembly, remanufacturing and/or recycling environments. Since "environmentally conscious manufacturing" is a relatively new concept that brings new costs and profits into consideration, its analysis cannot be provided by readily available techniques. This paper presents a quantitative methodology to determine the allowable tolerance limits of planned/unplanned inventory in a remanufacturing supply chain environment based on the decision-maker's unique preferences. To this end, an integer goal-programming model that provides a unique solution for the allowable inventoiy level is presented. The objective of the supply-chain model is to determine the number of a variety of components to be kept in the inventoiy while economically fulfilling the demand of a multitude of components, and yet have an environmentally benign policy of minimizing waste generation. A numerical example is presented to illustrate the methodology.
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