Issues in end-of-life product recovery and reverse logistics

There is a fundamental shift in waste management responsibility from the private waste management industry and local governments towards manufacturers, distributors, and retailers. In recent years the responsibility of manufacturers has been extended to cover the entire life of certain products. This also includes responsibility for the products safe disposal. As the enforcement of environmental legislation becomes more stringent and an increasing number of customers are demanding take-back of their old products, companies are beginning to focus on possible distribution channels for the return of their products i.e. reverse logistics. The aim of this paper is to examine the emerging issues in reverse logistics, in particular the information requirements for reverse logistics within the Extended Enterprise. A study of end-of-life vehicles (ELVs) will illustrate the specific information flow between the key players within the automotive industry. This paper addresses the initial development of possible distribution channels, their key operational decisions and supporting information systems for the recycling of end of life products.     

Multiproduct cost-volume-profit model with product limits

A multiproduct cost-volume-profit model is extended to incorporate sales or product capacity limits for each product, required rate of return on sales, and the tax rate depending on profit levels as determined by the government. The model also deals with overhead costs that are traceable to a group of products but which cannot be allocated to individual products within the group to any reasonable accuracy. To solve the model, an algorithm is constructed to determine the required volume for each product that will achieve the best possible rate of return on sales revenue. Based upon the dBase Database Management System and the CVP model, a user-friendly menu-driven interactive decision support system is developed. The model algorithm and decision support system is illustrated with an example consisting of five products. Various reports generated by the interactive decision support system are also presented.     

Modelling end-of-life product recovery chains - a case study

European Union legislation, and existing national legislation of many European countries, mandates that manufacturers take back and recover their electronic and electrical equipment. If manufacturers are to comply with legislation, models need to be developed for these activities. Whilst infrastructural and technological deficits exist and must be addressed, so too must the organization of recovery, from a business perspective. In this paper, models of resource recovery are presented and a case study described.     

A multicriteria decision making model for reverse logistics using analytical hierarchy process

Product recovery activities such as recycling, refurbishing and direct reuse are becoming integral to manufacturing supply chains. This study presents a multicriteria decision making model for reverse logistics using analytical hierarchy process (AHP). The AHP model evaluates a hierarchy of criteria and subcriteria, including costs and business relations, for critical decisions regarding network design. Using sensitivity analysis with AHP, the work provides insights into the preference ordering among eight alternative network configurations. For instance, the choice of test sites is largely dependent on the potential for cost savings on testing procedures and transportation of scrap, and this decision is not sensitive to the importance of business relations. By contrast, the choice of collection sites is largely determined by the relative importance of business relations considerations vs. cost considerations. As well, the processing location decision favors a third-party reprocessor if there is little need to protect proprietary product knowledge and cost savings is very important. The model is demonstrated using three case studies of real-world applications.     

Multicriteria decision making applied to waste recycling in Brazil

Waste management, like other environmental issues, tends to be a suitable topic for problem solving using multicriteria decision-making techniques when uncertainty is involved. This paper presents two cases where the decision makers had different preferences. In the first case, social agents required an evaluation of different disposal alternatives for plastic waste. In the second case, existing construction and demolition waste recycling facilities required a performance evaluation.     

控股股东代理成本、纳税筹划方案抉择及其市场价值:基于J有限公司的案例研究

纳税筹划是实务界与学术界共同关心的领域,现有研究大量讨论了公司纳税筹划行为的决定因素及其经济后果,但仍存在巨大争议。一方面,纳税筹划节约了现金流,提升股东价值;另一方面,代理问题使企业的纳税筹划方案更可能有利于控股股东或管理层,而非上市公司利益最大化,从而可能降低企业价值,最终纳税筹划方案的经济后果取决于上述两方面的共同作用。本文基于某上市公司(J有限公司)收购大股东土地案例,分析了代理成本影响公司税务决策的路径及其经济后果。本文发现,控股股东构造了较为复杂的股权转移交易方式,为上市公司和控股股东节约了大量税务成本。但由于代理成本的存在,在多种纳税筹划方案中,控股股东并未选择对上市公司最为有利的方案,而选择了能够取得现金最多,控股股东利益最大化的方案。进一步的研究发现,市场非常关注该企业的纳税筹划行为,与本文搜集的对照样本相比,尽管J公司的控股股东未选择最优的方案,但投资者仍给予了该纳税筹划行为以积极反应,提升了企业价值。本文为研究代理成本影响公司税务决策的机理提供了重要参考。     

A decision support system for the selection of a casting process using analytic hierarchy process

This paper presents an Analytic Hierarchy Process (AHP) based decision support system to select the most suitable casting process for a given product. The hierarchical structure of the proposed method allows the decision maker to compare the different casting processes using the material suitability and flexibility, geometrical complexity, dimensional tolerance and surface finish of the casting, and the cost as the criteria for selection. Judgemental inconsistency of the decision maker in selecting the casting process is taken care by ensuring that the value of consistency ratio is below (0.1). A numerical example is presented to illustrate the effectiveness of the proposed methodology for selecting the suitable casting process.     

The Effects of Memory Structure on Using Rule-Based Expert Systems for Training: A Framework and an Empirical Test*

One justification for eliciting and representing the judgment strategy of an expert in a rule-based expert system (RBES) is to facilitate knowledge transfer to individuals with less expertise. However, prior research suggests complexities and potential problems when using RBESs for training. In this paper, a conceptual framework of user learning from RBESs is presented. It is proposed that learning may be ineffective when the problem representation of the RBES is inconsistent with the user's mental representation of the task-domain knowledge. An experiment was conducted to examine the effects of consistency (inconsistency) between the problem-solving strategy of RBESs and individuals' memory structures. Groups of subjects whose memory structure either matched or did not match two RBESs' problem-solving strategies were examined using an internal control evaluation task. The results suggest that learning was facilitated only for groups with congruence between the RBES's problem-solving strategy and a subject's memory structure.     

An Information-Maximizing Interactive Procedure for Scenario Probability Elicitation

Various approaches have been proposed for determining scenario probabilities to facilitate long-range planning and decision making. These include microlevel approaches based on the analysis of relevant underlying events and their interrelations and direct macrolevel examination of the scenarios. The determination of a unique solution demands excessive consistency and time requirements on the part of the expert and often is not guaranteed by these procedures. We propose an interactive information maximizing scenario probability query procedure (IMQP) that exploits the desirable features of existing methods while circumventing their drawbacks. The approach requires elicitation of cardinal probability assessments and bounds for only marginal and first-order conditional events, as well as ordinal probability comparisons (probability orderings or rankings) of carefully selected scenario subsets determined using concepts of information theory. Guidelines for implementation based on simulation results are also developed. A goal program for handling inconsistent ordinal probability responses is also integrated into the procedure. The results of behavioral experimentation (which compared our approach to Expert Choice and showed that the IMQP was viable) compared favorably in terms of ease of use and time requirements, and works best for problems with a large number of scenarios. Design modifications to IMQP learned from the experiments, such as incorporating interactive graphics, are also in progress.     

The Development Manager's Advisory System: A Knowledge-Based DSS Tool for Project Assessment*

This research investigates whether the knowledge-based decision support system (KBDSS) paradigm provides the necessary supporting structure and developmental framework for product development evaluation. To address the research questions posed in this study, it is necessary to develop and implement KBDSS's at specific decision points along the product development cycle. This paper describes the design, development, and implementation of a KBDSS to support a product development manager's decision concerning full-scale development of a new product. From the systems design perspective, this paper addresses the integration and innovative use of a variety of techniques for knowledge acquisition, modeling, and processing. The approach utilized obtains the benefits of normative modeling as well as the flexibility and developmental advantages of knowledge-based systems. Since its implementation, the system has been successfully used by a development manager to support his recommendation for an ongoing project. His complete satisfaction with this system served as the impetus for the design and development of a multi-expert system which was implemented at the strategic level.     

Antecendents and effects of decision comprehensiveness: The role of decision quality and perceived uncertainty

On the basis of a sample of 184 top executives, we investigated the roles of decision quality and perceived uncertainty in the relationship between decision comprehensiveness and performance. Our results show that decision quality mediates a large proportion of the comprehensiveness–performance relationship and may thus provide a more proximate outcome measure of the effect of comprehensiveness. In addition, we found that perceived uncertainty directly affects the level of comprehensiveness in organizations rather than moderating its effect on performance as conceptualized by previous research. Based on the integration of behavioral and information processing theories we suggest that more process-oriented measures such as decision quality and perceived uncertainty may overcome conflicting empirical results in the field.     

The Application of Neural Networks and a Qualitative Response Model to the Auditor's Going Concern Uncertainty Decision*

An auditor gives a going concern uncertainty opinion when the client company is at risk of failure or exhibits other signs of distress that threaten its ability to continue as a going concern. The decision to issue a going concern opinion is an unstructured task that requires the use of the auditor's judgment. In cases where judgment is required, the auditor may benefit from the use of statistical analysis or other forms of decision models to support the final decision. This study uses the generalized reduced gradient (GRG2) optimizer for neural network learning, a backpropagation neural network, and a logit model to predict which firms would receive audit reports reflecting a going concern uncertainty modification. The GRG2 optimizer has previously been used as a more efficient optimizer for solving business problems. The neural network model formulated using GRG2 has the highest prediction accuracy of 95 percent. It performs best when tested with a small number of variables on a group of data sets, each containing 70 observations. While the logit procedure fails to converge when using our eight variable model, the GRG2 based neural network analysis provides consistent results using either eight or four variable models. The GRG2 based neural network is proposed as a robust alternative model for auditors to support their assessment of going concern uncertainty affecting the client company.     

The Determinants of DSS Success: An Integrated Model*

The results of a field study investigating the determinants of decision support systems (DSS) success are presented. A multivariate model was developed and tested using multiple regression hierarchical analysis on responses from 118 DSS users. Several specific hypotheses are also proposed and tested. Data analyses indicate that DSS success, as measured by DSS satisfaction and perceived benefits, depends on several factors: previous user experience with DSS, user involvement, user training, top management support, information sources, the level of managerial activity, and task structure, difficulty and interdependence. Based on the research results, several suggestions are made to improve the likelihood of successful DSS implementation.     

An Empirical Investigation of Information Search Strategies with Implications for Decision Support System Design

Understanding how people search through and combine information before making decisions is an important concern in the study of decision making and in the design of decision support systems (DSS). The purpose of this study is to examine DSS search strategies in relation to the body of empirical research on information load. Prior research is examined in relation to basic empirically-testable hypotheses and compared to the results of this study as a way of validating the procedures used here. The transfer of information load empirical research to a DSS scenario is confirmed statistically. Then, results of an analysis of information search patterns under DSS conditions are described. Finally, implications for DSS design are discussed in terms of possible search support mechanisms for specific search strategies.     

The Effectiveness of Graphical Presentation for Information Extraction: A Cumulative Experimental Approach*

Three laboratory experiments were conducted to assess the relative strengths and weaknesses of bar, symbol, and line graphs for performing a variety of elementary information extraction tasks using two dependent variables, time and accuracy. The findings indicate that the degree of support provided by a particular graph format for a particular data extraction task depends on the matching of format and task in terms of their anchoring characteristics. Anchoring, in this context, refers to the phenomenon that specific and diverse parts of a graph are segmented by the reader to act as salient and relevant cues, or anchors, when different classes of information are to be extracted from the graph. A data extraction task has high x-value (y-value) anchoring if the x-axis (y-axis) component is represented in the question as either a given value or an unknown value. Conversely, a task has low x-value (y-value) anchoring if the x-axis (y-axis) component is not represented in the question as either a given value or as an unknown value. Data extraction accuracy was not significantly affected by presentation format. Bars provided the best time performance for data extraction tasks having high anchoring on both axes but were not appropriate for tasks having low anchoring on either the y axis or both the x and y axes. Line graphs tended to be worse in terms of time performance for tasks having high anchoring on both axes although they were as fast or better than other representations for tasks having low anchoring on both axes. Symbol plots appeared to possess anchoring characteristics associated with both bars and line graphs. Symbols (as with bars) tended to produce a time performance superior to that of line graphs for tasks having high anchoring on both axes; and (as with line graphs) symbols allowed faster results than bar graphs for tasks having low anchoring on either the y axis or both the x and y axes.     

The resource constrained single-item inventory problem with demand-dependent unit cost

In this paper, we present an economic order quantity (EOQ) with both demand-dependent unit cost and restrictions. An analytical solution of the EQO is derived using a recent and simple method, which isthe geometric programming approach. The EOQ inventory model with demand-dependent unit cost without any restriction and the classical EOQ inventory model are obtained.     

Automated Discovery in Managerial Problem Formulation: Formation of Causal Hypotheses for Cognitive Mapping

Development of knowledge acquisition techniques known as automated discovery systems has occurred in deep and narrow domains of knowledge. Automated discovery is the generation of new knowledge by a computer system on its own, without the help of another knowledge source. This paper describes research and validation of an automated discovery system for a wide and shallow domain—business management. The system continues recent advances in expert systems research which have enhanced cognitive mapping, a problem formulation tool. The system perceives the behavior of distal variables in the environment through probabilistic cues-to-causality, and generates previously unknown hypotheses by aggregating the probabilities into a single criterion of causal relatedness. The system is validated against the source code of a simulated managerial environment, and causal relationships posited by decision makers experienced in the play of the gaming simulator.     

Strategic Assessment Model (SAM): A Multiple Criteria Decision Support System for Evaluation of Strategic Alternatives*

The evaluation of strategic alternatives is a particularly difficult task. This difficulty is due to the complexities inherent in the evaluation process and the lack of structured information. The evaluation process must consider a multitude of relevant information from both the internal and external environments of the organization. Various analytical and normative models have helped decision makers utilize large volumes of information in strategic evaluation; however, most of these models have some limitations. We present a multiple criteria decision support system, called strategic assessment model (SAM), that addresses some of the limitations inherent in the existing models. SAM captures the decision maker's beliefs through a series of sequential, rational, and analytical processes. The environmental forces—decomposed into internal, task, general opportunities, and threats—are used along with the analytic hierarchy process (AHP), subjective probabilities, the entropy concept, and utility theory to enhance the decision maker's intuition in evaluating a set of strategic alternatives.     

Mixed integer (0–1) fractional programming for decision support in paper production industry

This paper presents an effective and efficient method for solving a special class of mixed integer fractional programming (FP) problems. We take a classical reformulation approach for continuous FP as a starting point and extend it for solving a more general class of mixed integer (0–1) fractional programming problems.To stress the practical relevance of the research we focus on a real-life application in paper production industry. The constantly advancing physical knowledge of large scale pulp and paper production did have a substantial impact on an existing DSS in which mixed integer (0–1) fractional programming is introduced. We show that the motivation to solve a real-life fractional programming problem can provide the basis for a new approach in a new context that has an added value of its own, even outside the given application area. We describe the main characteristics of the DSS, the necessity to develop a non-iterative solution procedure and demonstrate both the effectiveness and efficiency of the proposed approach from practical data sets.     

The Influences of User Characteristics on Performance in a Logistics DSS Application

Decision Support Systems (DSS) are widely used in logistics decision applications, and a large number and variety of systems are commercially available. We investigate the contributions of user characteristics including experiences, data preferences, intuition, and effort to decision performance in a logistics DSS context. The study includes a laboratory experiment in which decision makers with varied experiences used a DSS to make facility network design decisions for problems of varying complexity. Two variants of the DSS are utilized in order to examine the interactions of a DSS decision aid with user characteristics. We find that intuition and effort are associated with decision-making performance. High analytic ability is not related to intuition, however. Education and previous experience are associated with performance. Yet these characteristics are also unrelated to intuition. Decision makers who highly value disaggregated data provided by the DSS tend to perform poorly. Also, the results suggest that the effects of users' experiences and preferences on performance are influenced by an analytical decision aid.