Table of contents(20 chapters)
With rapid social development and deepening division of labor, more and more complex projects are required to be carried out in a team form. When evaluating team performance, previous research has usually treated team as a united entity. However, the operating environment of the team has a significant impact on its members and the interaction between them greatly influences the team's efficiency. To better evaluate team performance, we propose a circle loop to illustrate the relationship between the operating environment of the team and its members. A two-stage DEA model with feedback is developed to evaluate the team performance, together with the efficiencies of the operating environment and team members as well as their impacts on overall efficiency. Various conditions of the team are discussed to illustrate that team performance depends on the assumption of the conditions.
As state funding for public higher education has declined, there is a rising demand for accountability. Past studies have relied on indicator ratios to look at the relationship between funding and performance measures. This approach has some inherent problems that make it difficult to identify inefficiencies. This chapter will study efficiency in state systems of higher education by applying data envelopment analysis (DEA). DEA methodology converts multiple variables into a single comprehensive measure of performance efficiency and has the ability to perform benchmarking for the purpose of establishing performance goals. The advantages of DEA modeling will be shown by comparing results with those from a recent study of higher education finance based on publicly available data. DEA is shown to be feasible and implementable for studying state systems of higher education, and provides useful information in identifying “best practice” state systems and guidance for improvement. The value of DEA modeling to state policy makers and education researchers is discussed.
The global financial meltdown of late 2008 threatened the survival of many banks, insurance companies, automakers, and other institutions, further contributing to the economic slowdown already underway in the United States and abroad. The ensuing recession has negatively impacted on the airline industry in the United States with losses running into billions. In this chapter, we illustrate the use of data envelopment analysis (DEA), an operations research technique, to analyze the operating efficiency of the US airline industry by benchmarking a set of ratios that assess the operating efficiency of a firm against its peers. DEA clearly brings out the airline(s) that is (are) operating more efficiently in comparison to other airlines in the industry, and points out the areas that poorly performing airlines need to improve.
This study develops a multidimensional framework using data envelopment analysis (DEA) as a benchmarking tool to assess the performance of the commercial banks in India. Using the DEA approach, this study compares the relative performance of 35 banks against one another with 8 variables as the benchmark parameters. This study finds that most of the banks are consistently performing well over a period from 2005 to 2009. The study also shows the areas in which inefficient banks are lagging behind and how they can improve their performance to bring them at par with the efficient commercial banks.
In today's world, all firms’ corporate social responsibility (CSR) actions are coming under increasing public scrutiny. This is especially true for large companies whose decisions can and do have impact on society. Public service advertisements (PSAs), a mass-media approach, are advertisements which inform audiences of a firm's CSR actions and enhance its public image. In this chapter, we focus on a supply chain system consisting of two firms and their coordination strategy for public service advertising. To describe the synergistic effect between a PSA and a normal commercial advertisement, a modified Nerlove–Arrow model is employed in this chapter. Using differential game theory, we calculate and compare the optimal advertising levels for each stage of the supply chain system under two different decision scenarios, i.e., (i) the two firms make decisions independently and (ii) the two firms make decisions as an integral system. A coordination mechanism on the public service advertising between the two firms has also been proposed for the supply chain system and has been proved effective.
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.
This chapter addresses quality management (QM) content on the process quality management (PQM) level in the high-technology industry of semiconductor manufacturing. Identifying critical components of a manufacturing or service process and improving them to ensure superior quality at economic costs is the overall goal of PQM. Deming was a prominent proponent of PQM as a means to optimize the performance of a product or process. In optimizing the performance of a product or process, good design practices require the evaluation of designs from a process perspective. Advanced design techniques, namely design of experiments (DOEs), are cornerstone to the optimization process, to design management, and in turn to PQM. This chapter investigates the use of DOEs in the manufacture of semiconductors. Specifically, two underlying assumptions impact operations managers using DOEs: solution differences/similarities in underlying DOE optimization methods and marginal rates of substitution. Perhaps unknown to the user, DOE optimization techniques carry strong assumptions regarding these characteristics. This chapter investigates two commonly used DOE optimization approaches applied to the operational control of semiconductor wafer production, and demonstrates that each method contains assumptions about these characteristics, which are not intuitively evident to a user.
In a decentralized supply chain with one supplier and one retailer, a properly designed contract can lead to supply chain coordination. In this chapter, we model the selection of an appropriate coordinating contract from a menu of contracts including wholesale price, buyback, and markdown money, while allowing both the supplier and the retailer to assume the roles of Stackelberg leader and/or supply chain captain. This work extends previous literature that assumes that the supplier is both the Stackelberg leader and the supply chain captain. In our models, either agent can make stocking and pricing decisions. Our findings suggest that the feasibility of a coordinating contract depends on the addition of Pareto-improving, profit-sharing conditions that motivate agents to take part in the contract. Further, the selection of an optimal contract is based not only on which agent holds the overstock liquidation advantage, but also on the decision structure of the supply chain. For instance, when the supplier is the Stackelberg leader and the retailer is the supply chain captain, as well as holds the inventory liquidation advantage, and controls the stocking level, then a wholesale price contract can coordinate the supply chain under the proposed Pareto-improving profit sharing, termed Pareto-improving coordination. Additional results and managerial implications are presented in the chapter.
Historically, public and private sector enterprises have been viewed as existing on opposing ends of the performance measurement spectrum, due to seemingly incompatible worldviews. Private sector enterprises are traditionally viewed as profit-driven and focused on a return on investment paradigm, while public sector enterprises are seen as mission-oriented and answerable to a paradigm less focused on investment and more on improving enterprise capability. The authors propose that, in fact, these worldviews are not mutually exclusive, as private and public sector enterprises must both account for investment and mission concerns. In order to leverage real synergy to be gained from distinct but complementary viewpoints, a systemic approach to evaluating organization performance through the novel fusion of operational test and evaluation and multi-criteria decision analysis is developed. Use of this framework is demonstrated within an enterprise that involves consideration of public and private sector concerns. The authors hope that the approach proposed in this chapter will enable public and private sector enterprises to comprehensively address performance.
Two-attribute warranty policies are considered that incorporate, for example, the time elapsed since sale of the product and product usage at a given point in time. Such policies occur in consumer products, such as automobiles, where warranty may be exercised if both time and usage are within specified warranty parameters when a product failure occurs. In this chapter, it is assumed that usage and product age are related through a random variable, the usage rate, which may have a certain probabilistic distribution as influenced by consumer behavior patterns. Product quality is modeled through the product failure rate, which is influenced by unit research and development expenditures as well as the usage rate and product age. The attained market share of the product is modeled as a function of the warranty policy parameters of price, warranty time, and usage limit, with product quality also having an influence. Attainment of single and multiple objectives are explored. Such objectives encompass expected total unit costs as a proportion of unit product price and market share.
In many educational and professional environments, diversely talented teams are created to solve problems requiring different skill sets. In the educational setting teams may be used to conduct a learning project; in a work setting teams may be used to develop a new product. Teams are usually constructed from “players” in different functional departments. Because the “best” player in each department can’t be on all teams, constructing teams so that all teams function optimally is a challenging and often arbitrary process. This chapter describes a multiple criteria model for team selection that balances skill sets among the groups and varies the composition of the teams from period to period. The results of applying this team selection model to a cohort-structured Executive MBA (EMBA) program and to team selection in a Fortune 100 corporation are presented. The results of this project suggest that an implantation of a quantitative method, such as our Model III, markedly improves team performance and achieves that improvement in a timelier manner.
Deciding the country or region of the world to expand and/or to continue a firm's direct foreign investment is a decision fraught with risks. Multinational firms are faced with making the decision of expanding their business without full knowledge of what will occur in the months or years ahead in the region with which they are proposing expansion. Many of the factors which will affect the decision are nonquantitative in nature and make the decision more difficult for the firm to undertake. This chapter uses an analytical hierarchy process to help analyze both quantitative and qualitative factors that will affect a country's or region of the world's risk level. The model can be used to evaluate country risk in assessing potential direct foreign investments and can lead to a better allocation of the firm's scarce resources to more profitable areas.
This chapter presents a multi-criteria portfolio model with the expected return as a performance measure and the expected worst-case return as a risk measure. The problems are formulated as a single-objective linear program, as a bi-objective linear program, and as a triple-objective mixed integer program. The problem objective is to allocate the wealth on different securities to optimize the portfolio return. The portfolio approach has allowed the two popular financial engineering percentile measures of risk, value-at-risk (VaR) and conditional value-at-risk (CVaR) to be applied. The decision-maker can assess the value of portfolio return, the risk level, and the number of assets, and can decide how to invest in a real-life situation comparing with ideal (optimal) portfolio solutions. The concave efficient frontiers illustrate the trade-off between the conditional value-at-risk and the expected return of the portfolio. Numerical examples based on historical daily input data from the Warsaw Stock Exchange are presented and selected computational results are provided. The computational experiments prove that both proposed linear and mixed integer programming approaches provide the decision-maker with a simple tool for evaluating the relationship between the expected and the worst-case portfolio return.
Multi-criteria optimization by meta-goal programming of a portfolio of asset allocation mutual funds is the focus of this chapter. Asset allocation is generally defined as the allocation of an investor's portfolio across a number of different asset classes. The standard classical portfolio model uses the nonlinear model of quadratic programming to minimize risk and maximize return by mean absolute deviation. Instead of the variance measure of the risk of the rate of return, the mean absolute deviation is used as a measure of risk. In this chapter, three types of meta-goals are Type 1: a meta-goal relating to other percentage sum of unwanted deviations, Type 2: a meta-goal relating to the maximum percentage deviation, and Type 3: a meta-goal relating to the percentage of L∞ goals.