Chapter 9 Moving frontier analysis: An application of Data Envelopment Analysis for competitive analysis of a high-technology manufacturing plant

Competitive analysis of plants is fundamental to enhancing the competitiveness of manufacturing firms. Moving Frontier Analysis (MFA), proposed in this paper, is a new application of Data Envelopment Analysis (DEA) to conduct competitive analysis of a high-technology manufacturing plant. The development and application of MFA was done in collaboration with the management of a US merchant semiconductor manufacturing firm. A wafer fabrication plant of the firm served as the research site for this study. MFA provides a means to determine (i) the gap between a plant's best performance and the best of competition, and (ii) whether or not it will be possible to close this performance gap, and if so, the time it will take to do so. From an implementation standpoint, competitive analysis using MFA can be conducted even if (i) accurate and detailed data on comparable plants of competitor companies are not available, and (ii) the operations of competitors' plants are dynamically changing. An application of MFA for conducting competitive analysis of the wafer fabrication plant is presented. This application illustrates how MFA makes it possible to estimate (i) the unit cost of production for the best of competitors' plants using aggregate operational information on industry best practices, and (ii) the time it will take for the plant's unit cost of production to catch up with the best of competition using the technologies in operation.     

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

Three levels of competitiveness affect the success of business enterprises in a globally competitive environment: the competitiveness of the company, the competitiveness of the industry in which the company operates and the competitiveness of the country where the business is located. This study analyses the competitiveness of the automotive industry in association with the national competitiveness perspective using a methodology based on Bayesian Causal Networks. First, we structure the competitiveness problem of the automotive industry through a synthesis of expert knowledge in the light of the World Economic Forum’s competitiveness indicators. Second, we model the relationships among the variables identified in the problem structuring stage and analyse these relationships using a Bayesian Causal Network. Third, we develop policy suggestions under various scenarios to enhance the national competitive advantages of the automotive industry. We present an analysis of the Turkish automotive industry as a case study. It is possible to generalise the policy suggestions developed for the case of Turkish automotive industry to the automotive industries in other developing countries where country and industry competitiveness levels are similar to those of Turkey.     

On Multi-threaded Metrical Task Systems

Traditionally, on-line problems have been studied under the assumption that there is a unique sequence of requests that must be served. This approach is common to most general models of on-line computation, such as Metrical Task Systems. However, there exist on-line problems in which the requests are organized in more than one independent thread. In this more general framework, at every moment the first unserved request of each thread is available. Therefore, apart from deciding how to serve a request, at each stage it is necessary to decide which request to serve among several possibilities.In this paper we introduce Multi-threaded Metrical Task Systems, that is, the generalization of Metrical Task Systems to the case in which there are many threads of tasks. We study the problem from a competitive analysis point of view, proving lower and upper bounds on the competitiveness of on-line algorithms. We consider finite and infinite sequences of tasks, as well as deterministic and randomized algorithms. In this work we present the first steps towards a more general framework for on-line problems which is not restricted to a sequential flow of information.     

On the multiplicity of solutions in generation capacity investment models with incomplete markets: a risk-averse stochastic equilibrium approach

Investment in generation capacity has traditionally been evaluated by computing the present value of cashflows accruing from new equipment in a market with globally optimized capacity mix. The competition and risk that now prevail in the sector may require a more refined analysis. We consider a competitive market with agents investing in some mix of capacities: the risk exposure of a plant and the attitude towards risk of the owner depend on the plant and the portfolio of its capacities. They may also depend on hedging contracts acquired by the investor on the market if such contracts exist. We represent these effects through equilibrium models of generation capacity in incomplete markets. The models come in different versions depending on the portfolio of physical plants and hedging contracts. These modify the long-term risk of the plants, the attitude of the owners towards risk, and hence the incentive to invest. The models involve risk-averse producers and consumers, and their behavior is represented by convex risk measures. We use degree theory to prove existence and explore multiplicity of equilibrium solutions.     

An Empirical Study on Measuring Industrial Competitiveness

This paper discusses the methodology developed for the Industrial Development Bank of Turkey for the purpose of measuring the competitiveness of local manufacturing firms in foreign markets. First, an industrial competitiveness model which incorporates both the firm's and foreign competititors' input costs, technological characteristics and political-economic environments is summarized. Then an application of this model to 30 selected local firms in textiles, stone and soil, and food industries is described. The results indicate that some of the selected firms are quite competitive in European, North African and Middle Eastern countries, and can export to these markets without much difficulty. Moreover, the methodology employed in this study promises to be useful in identifying the weaknesses and strengths of industries, albeit in general terms, to formulate government policies.     

Linking industrial competitiveness and productivity at the firm level

The present paper analytically demonstrates that the competitiveness and productivity levels of a firm are related to one another. An industrial competitiveness model is presented first and then an analytical linkage between the competitiveness and productivity levels is established. Also discussed are the managerial implications of this analytical linkage for competitive strategy formulation.     

Online Bounded Coloring of Permutation and Overlap Graphs

We consider a track assignment problem in a train depot leading to an online bounded coloring problem on permutation graphs or on overlap graphs. For permutation graphs we study the competitiveness of a First Fit-based algorithm and we show it matches the competitive ratio of the problem. For overlap graphs, even the unbounded case does not admit a constant competitive ratio.     

Using an economics model for teaching linear algebra

We present an approach for teaching linear algebra using models. In particular, we are interested in analyzing the modeling process under an APOS perspective. We will present a short illustration of the analysis of an economics problem related to production in a set of industries. This problem elicits the use of the concepts of linear combination, linear independence, among other linear algebra concepts related to vector space. We describe cycles of students’ work on the problem, present an analysis of the learning trajectory with emphasis on the constructions they develop, and discuss the advantages of this approach in terms of students’ learning.     

Dismantling of nuclear power plants at optimal NPV

Today, worldwide far more than 100 nuclear power plants, which have been decommissioned in the recent years, are waiting for their complete dismantling. Since the dismantling of a single reactor causes costs of up to one billion Euros and lasts up to 15 years, the elaboration of a scheduling approach helping to optimize the net present value of a dismantling project seems to be worthwhile. In this paper we present a resource-constrained project scheduling approach optimizing the total discounted disbursements of dismantling a nuclear power plant. For the corresponding NP-hard optimization problem, we introduce an appropriate project scheduling model with minimum and maximum time lags, renewable and cumulative resources as well as multiple execution modes. To solve this model, we introduce a relaxation-based enumeration approach that delivers optimal solutions for problem instances containing up to 50 activities.     

Online Gradient Descent Learning Algorithms

This paper considers the least-square online gradient descent algorithm in a reproducing kernel Hilbert space (RKHS) without an explicit regularization term. We present a novel capacity independent approach to derive error bounds and convergence results for this algorithm. The essential element in our analysis is the interplay between the generalization error and a weighted cumulative error which we define in the paper. We show that, although the algorithm does not involve an explicit RKHS regularization term, choosing the step sizes appropriately can yield competitive error rates with those in the literature.     

Enhancing the generality of fuzzy relational models for control

A promising area of research in fuzzy control is the model-based fuzzy controller. At the heart of this approach is a fuzzy relational model of the process to be controlled. Since this model is identified directly from process input-output data it is likely that ‘holes’ will be present in the identified relational model. These holes are real problems when the model is incorporated into a model-based controller since the model will be unable to make any predictions whatsoever if the system drifts into an unknown region. The present work deals with the completeness of the fuzzy relational model which forms the core of the controller. This work proposes a scheme of post-processing to ‘fiil in’ the fuzzy relational model once it has been built and thereby improve its applicability for on-line control. A comparative study of the post-processed model and conventional relational model is presented for Box-Jenkins data identification system and a real-time, highly non-linear application of pH control identification.     

New algorithm to construct a stabilizing controller for linear systems over integral domains

We are concerned with the coordinate-free approach to controlsystems, which is a factorization approach but does not requirethe coprime factorizations of plants, provided only that theset of stable causal transfer functions forms an integral domain.We first present a new criterion of the feedback stabilizability.This is a generalization of the criteria in terms of reducedminors given by Sule in 1994 and in terms of quotient idealsgiven by Shankar and Sule in 1992. We then present an algorithmto construct a stabilizing controller, which is derived fromthe result given by Lin in 1998. Sule's algorithm and its derivationsneed to convert some matrices over some ring of fractions toones over the set of stable causal transfer functions, whichmay raise the unnecessarily complicated calculations. On theother hand, the algorithm of this paper does not require sucha conversion. An illustrative example to construct a stabilizingcontroller is also presented.     

On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples

We consider the problem of assessing the number of clusters in a limited number of tissue samples containing gene expressions for possibly several thousands of genes. It is proposed to use a normal mixture model-based approach to the clustering of the tissue samples. One advantage of this approach is that the question on the number of clusters in the data can be formulated in terms of a test on the smallest number of components in the mixture model compatible with the data. This test can be carried out on the basis of the likelihood ratio test statistic, using resampling to assess its null distribution. The effectiveness of this approach is demonstrated on simulated data and on some microarray datasets, as considered previously in the bioinformatics literature.     

Lean principles,practices, and impacts: a study on small and medium-sized enterprises (SMEs)

Lean as a business strategy is used to improve quality and service, eliminate waste, reduce time and costs, and enhance overall organizational effectiveness. Heightening challenges in competition in recent years have prompted many small and medium-sized enterprises (SMEs) to adopt lean to enhance firms’ competitiveness. This paper attempts to present an all-inclusive study and it examines various factors associated with the implementation of lean in SMEs in the U.S. The findings suggest that most of SMEs have a relatively accurate understanding of lean concept and philosophy. The primary reasons to implement lean are mainly internal, including reduce cost, improve profit margin, improve utilization of plant/facility, and maintain competitive position. A hierarchical cluster analysis was conducted to investigate lean status. It was discovered that both advanced adopters and beginners of lean are discovered. ANOVA test results show that there exist quite significant differences in terms of the degrees of lean implementation in SMEs. Varied lean tools and programs have been applied and they are positively related with firms’ performance. Lastly, the paper provides evidences that major lean barriers are encountered by SMEs regarding management or people related factors as well as key knowledge and know-how.     

Almost strict competitiveness in extensive games

We consider an extension of an almost strictly competitive game, introduced by Aumann (1961), inn-person extensive games by incorporating Selten's subgame perfection. We call it a subgame perfect weakly-almost (SPWA) strictly competitive game, in particular, an SPWA strictly competitive game in strategic form is simply called a WA strictly competitive game. We give some general results on the structure of this class of games. One result gives an easy way to verify almost strict competitiveness of a given extensive game. We show that a two-person weakly unilaterally competitive extensive game, introduced by Kats and Thisse (1992) for normal form games, is SPWA strictly competitive. We remark that some of our main results for SPWA strictly competitive games do not hold for the modification of almost strict competitiveness with trembling-hand perfection.The author is indebted to Mamoru Kaneko for valuable discussions, comments and criticism throughout the paper. He thanks N. Bose, M. Frascatore, R. Gilles, H. Haller, A. Kats, J. Kline and an anonymous referee for helpful comments on earlier drafts. The usual disclaimer applies.     

Publisher's Announcement

The main purpose of this paper is to emphasize the particularities of models needed for model-based fault detection and isolation (FDI) in contrast to the models used for control. Of special interest is the question of complexity of the model, which is of great importance for the practical implementation. This, of course, depends basically on the given situation such as the kind of plant, the measurements, the kind and number of faults to be detected and the demands for fault isolation and robustness. However, the paper shows that diagnostic models, in contrast to the wide-spread opinion that those have always to be more complex than the functional models for control, may be even less complex, because they are restricted to only those parts of the system in which the faults occur. The issue of model complexity is discussed in terms of different model-based FDI approaches analytical, knowledge-based and data-based. The ideas are illustrated in a case study, where several types of model-based FDI techniques are compared with the same plant, the amira three tank system.     

布朗随机流动形:白噪声分析方法

本文在经典白噪声分析框架下,用一种新的方法研究随机流动形. 首先使用布朗运动的Wick积分定义Wick型随机流动形.进一步, 用白噪声分析方法和S-变换证明：布朗随机流动形可视为Hida广义泛函.     

Evaluating the impact of average cost based contracts on the industrial sector in the European emission trading scheme

The inception of the emission trading scheme in Europe has contributed to power price increases. Energy intensive industries have reacted by arguing that this may affect their competitiveness and will induce them to leave Europe. Taking up a proposal of these industrial sectors, we explore the possible application of special contracts, where electricity is sold at average generation cost to mitigate the impact of CO₂ cost on power prices. The model supposes fixed generation capacities. We first consider a reference model representing a perfectly competitive market where all consumers (industries and the rest of the market) are price-takers and buy electricity at short-run marginal cost. We then change the market design by assuming that energy intensive industries pay power either at a regional or at a zonal average cost price. The analysis is conducted with simulation models applied to the Central Western European power market. The models are implemented in GAMS/PATH. This work has been financially supported by the Chair Lhoist Berghmans in Environmental Economics and Management and by the Italian project PRIN 2006, Generalized monotonicity: models and applications, whose national responsible is Prof. Elisabetta Allevi.     

Interactive model-based search with reactive resource allocation

We revisit the interactive model-based approach to global optimization proposed in Wang and Garcia (J Glob Optim 61(3):479–495, 2015) in which parallel threads independently execute a model-based search method and periodically interact through a simple acceptance-rejection rule aimed at preventing duplication of search efforts. In that paper it was assumed that each thread successfully identifies a locally optimal solution every time the acceptance-rejection rule is implemented. Under this stylized model of computational time, the rate of convergence to a globally optimal solution was shown to increase exponentially in the number of threads. In practice however, the computational time required to identify a locally optimal solution varies greatly. Therefore, when the acceptance-rejection rule is implemented, several threads may fail to identify a locally optimal solution. This situation calls for reallocation of computational resources in order to speed up the identification of local optima when one or more threads repeatedly fail to do so. In this paper we consider an implementation of the interactive model-based approach that accounts for real time, that is, it takes into account the possibility that several threads may fail to identify a locally optimal solution whenever the acceptance-rejection rule is implemented. We propose a modified acceptance-rejection rule that alternates between enforcing diverse search (in order to prevent duplication) and reallocation of computational effort (in order to speed up the identification of local optima). We show that the rate of convergence in real-time increases with the number of threads. This result formalizes the idea that in parallel computing, exploitation and exploration can be complementary provided relatively simple rules for interaction are implemented. We report the results from extensive numerical experiments which are illustrate the theoretical analysis of performance.