Widely Applicable Multi-Variate Decision Support Model for Market Trend Analysis and Prediction with Case Study in Retail

Chapter explains efficient ways of dealing with business problems of analyzing market environment and market trends under complex circumstances using heterogeneous data source. Under the assumption that used data can be expressed as time series, widely applicable multi variate model is explained together with case study in textile retail. This Chapter includes an overview of research conducted with a brief explanation of approaches and models available today. A widely applicable multi-variate decision support model is presented with advantages, limitations, and several variations for development. The explanation is based on textile retail case study with model wide range of possible applications in perspective. Complex business environment issues are simulated with explanation of several important global trends in textile retail in past seasons. Non-traditional approaches are revised as tools for a better understanding of modern market trends as well as references in relevant literature. A widely applicable multi-variate decision support model and its usage is presented through built stages and simulated. Model concept is based on specific time series transformation method in combination with Bayesian logic and Bayesian network as final business logic layer with front end interface built with open source Bayesian network tool. Explained case study provides one of the most challenging issue in textile retail: market trends seasonal/weather dependence. Separate outcomes for different scenario analysis approaches are presented on real life data from a textile retail chain located in Zagreb, Croatia. Chapter ends with a discussion about similar research’s, wide applicability of presented model with references for future research.

Fuzzy System Dynamics of Manpower Systems

Manpower recruitment and training in uncertain and turbulent environments is a challenge to decision makers in large organizations. In the absence of numerical precision on market growth and the ensuing manpower demand, designing manpower planning policies is vital. Often times, companies incur losses due to overstaffing and/or understaffing. For instance, organizations lose business when critical human resources leave. As a result, it is essential to develop robust effective dynamic recruitment and training policies, especially in a fuzzy and dynamic environment. In this chapter, a fuzzy systems dynamics modeling approach is developed to simulate and evaluate alternative dynamic policies relating skills recruitment, skills training, and available skills from a systems thinking perspective. Fuzzy system dynamics is implemented based on fuzzy logic and system dynamics concepts in order to arrive at robust strategies for manpower decision makers. It is anticipated that fuzzy system dynamics can help organizations to design effective manpower recruitment strategies in a dynamic and uncertain environment.

A Fuzzy Simulated Evolution Algorithm for Hard Problems

As problem complexity continues to increase in industry, developing efficient solution methods for solving hard problems, such as heterogeneous vehicle routing and integrated cell formation problems, is imperative. The focus of this chapter is to develop from the classical simulated evolution algorithm, a Fuzzy Simulated Evolution Algorithm (FSEA) that incorporates the concepts of fuzzy set theory, evolution, and constructive perturbation. The aim is to improve the search efficiency of the algorithm by enhancing the major phases of the algorithm through initialization, evaluation, selection, and reconstruction. Illustrative examples are provided to demonstrate the candidate application areas and to show the strength of the algorithm. Computational experiments are conducted based on benchmark problems in the literature. Results from the computational experiments demonstrate the strength of the algorithm. It is anticipated that the application of the FSEA metaheuristic can be extended to other hard large scale problems.

Data Mining Models as a Tool for Churn Reduction and Custom Product Development in Telecommunication Industries

This chapter represents the business case in the telecommunication company called Veza, in domain of churn prediction and churn mitigation. The churn project was divided into few stages. Due to limited budget and cost optimization, stage one was concentrated on prospective customer value calculation model based on fuzzy expert system. This helps Veza company to find most valuable telecom subscribers. It also helped company to better understand subscriber portfolio structure. Developed fuzzy expert system also helped Veza company in detection of soft churn. Stage two is profiling and customer segmentation based on time series analysis which provided potential predictors for predictive churn model. The central stage was concentrated on developing traditional predictive churn model based on logistic regression. This calculated probability that subscribers will make churn in next few months. The final stage was dedicated to SNA (Social Network Analysis) model development which found out the most valuable customers from the perspective of existing subscriber network. This model gave the answer that subscribers have the greatest influence on other subscribers in a way what is dangerous if they leave Veza company because they will motivate other subscribers to do the same thing. All three stages made complete churn detection/mitigation solution which take into consideration past behaviour of subscribers, their prospective value, and their strength of influence on other subscribers. This project helped Veza company to decrease churn rate and it gave directions for better understanding customer needs and behaviour which were the base for new product development.

Predicting Uncertain Behavior and Performance Analysis of the Pulping System in a Paper Industry using PSO and Fuzzy Methodology

The main objective of the present study is to permit the reliability analyst or system manager to analyze the failure behavior of the system in a more consistent and logical manner. As the collected or available data from various resources are uncertain and imprecise due to various practical constraints and hence the performance of the system cannot be made up to desired levels. To cope with such situations and subjective information in a consistent and logical manner, fuzzy methodology is one of the most vital and effective tool. To this effect a structural framework has been developed by the authors for analyzing and predicting the system behavior. The pulping unit of paper industry has been taken as an illustration. The failure rates and repair times for all the constituent components are obtained by solving availability-cost optimization model using particle swarm optimization and genetic algorithm. To increase the performance of the system, various reliability parameters are computed with the obtained results using a confidence interval based fuzzy lambda-tau methodology. Sensitivity as well as performance analysis of the system performance has been done for ranking the critical component of the system as per preferential order. The computed results are compared with existing fuzzy lambda-tau and traditional (crisp) methodology results.

Non Linear and Non Gaussian States and Parameters Estimation using Bayesian Methods-Comparatives Studies

This chapter deals with the problem of non-linear and non-Gaussian states and parameters estimation using Bayesian methods. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). In the current work, the authors consider two systems (biological model and power system) to perform evaluation of estimation algorithms. The results of the comparative studies show that the UKF provides a higher accuracy than the EKF due to the limited ability of EKF to accurately estimate the mean and covariance matrix of the estimated states through lineralization of the nonlinear process model. The results also show that the PF provides a significant improvement over the UKF because, unlike UKF, PF is not restricted by linear-Gaussian assumptions which greatly extends the range of problems that can be tackled.

Metamodel-Based Optimum Design Examples of Structures

In order to achieve greater fuel efficiency and energy conservation, the reduction of weight and enhancement of the performance of structures has been sought. In general, there are two approaches to reducing structural weight. One of which is to use materials that are lighter than steel and the other is to redesign the structure. However, conventional structural optimization methods using gradient-based algorithm directly have difficulties in defining complex shape design variables and preventing mesh distortions. To overcome these difficulties a metamodel-based optimization method is introduced in order to replace the true response by an approximate one. This research presents four case studies of structural design using a metamodel-based approximation model for weight reduction or performance enhancement.

Multi-Objective Generation Scheduling Using Genetic-Based Fuzzy Mathematical Programming Technique

This chapter presents a solution for multi-objective Optimal Power Flow (OPF) problem via a genetic fuzzy formulation algorithm (GA-FMOPF). The OPF problem is formulated as a multiple objective problem subject to physical constraints. The objectives and constraints are modelled as fuzzy mathematical programming problems involving the minimization of the objective function with fuzzy parameters and uncertainties in set of constraints. So the method is capable of representing practical situations in power system operation where the limits on specific variables are soft and the small violations of these limits may be tolerable. Then, genetic algorithm is used in order to seek a feasible optimal solution to the environmental/economic dispatch problem. Illustrative examples are given to clarify the proposed method developed in this manuscript and the performance of this solution approach is evaluated by comparing its results with that of their existing methods.

Quantification of Corporate Performance Using Fuzzy Analytic Network Process

Performance Measurement (PM) is a combination of a company’s characteristics that can be numerically expressed. The aim of the PM is to provide feedback about the success of current activities and give insight about future performance. Performance of a company depends on its vision and goals so the definition of performance can vary with time. While PM literature provides various models for PM, the most accepted model is Balanced ScoreCard (BSC). BSC supplies four inter-related perspectives that the companies can identify as indicators for performance. These perspectives are: financial, internal business processes, customer, and learning and growth perspectives. In this study, PM is formulated as a Multi Attribute Decision Making (MADM) problem and a Fuzzy Analytical Network Process (FANP) based performance measurement model is proposed. The performance measurement criteria are built based on four perspectives of BSC. The proposed model utilizes FANP in order to determine the relative importance of perspectives and indicators. The performance scores for each indicator are determined based on the predefined goals and these scores are aggregated to reach an overall performance score.

Fuzzy System Dynamics

In the presence of fuzzy or linguistic and dynamic variables, dynamic modeling of real-world systems is a challenge to many decision makers. In such environments with fuzzy time-dependent variables, the right decisions and the impacts of possible actions are not precisely known. The presence of linguistic variables in a dynamic environment is a serious cause for concern to most practicing decision makers. For instance, in a demand-driven supply chain, demand information is inherently imprecise, leading to unwanted fluctuations throughout the supply chain. This chapter integrates, from a systems perspective, fuzzy logic and system dynamics paradigms to model a typical supply chain in a fuzzy environment. Based on a set of performance indices defined to evaluate supply chain behavior, results from comparative simulation experiments show the utility of the fuzzy system dynamics paradigm: (1) the approach provides a real-world picture of a fuzzy dynamic supply chain, (2) expert opinion can be captured into a dynamic simulation model with ease, (3) the fuzzy dynamic policies yield better supply chain performance, and (4) “what-if analysis” show the robustness of the fuzzy dynamic policies even in turbulent demand situations. Managerial insights and practical evaluations are provided.