Negotiation mechanism for self-organized scheduling system with collective intelligence

Current Manufacturing Systems challenges due to international economic crisis, market globalization and e-business trends, incites the development of intelligent systems to support decision making, which allows managers to concentrate on high-level tasks management while improving decision response and effectiveness towards manufacturing agility. This paper presents a novel negotiation mechanism for dynamic scheduling based on social and collective intelligence. Under the proposed negotiation mechanism, agents must interact and collaborate in order to improve the global schedule. Swarm Intelligence (SI) is considered a general aggregation term for several computational techniques, which use ideas and inspiration from the social behaviors of insects and other biological systems. This work is primarily concerned with negotiation, where multiple self-interested agents can reach agreement over the exchange of operations on competitive resources. Experimental analysis was performed in order to validate the influence of negotiation mechanism in the system performance and the SI technique. Empirical results and statistical evidence illustrate that the negotiation mechanism influence significantly the overall system performance and the effectiveness of Artificial Bee Colony for makespan minimization and on the machine occupation maximization.

Estudo e Avaliação de Plataformas de Business Intelligence em contexto empresarial

O crescente interesse pela área de Business Intelligence (BI) tem origem no reconhecimento da sua importância pelas organizações, como poderoso aliado dos processos de tomada de decisão. O BI é um conceito dinâmico, que se amplia à medida que são integradas novas ferramentas, em resposta a necessidades emergentes dos mercados. O BI não constitui, ainda, uma realidade nas pequenas e médias empresas, sendo, até, desconhecido para muitas. São, essencialmente, as empresas de maior dimensão, com presença em diferentes mercados e/ou áreas de negócio mais abrangentes, que recorrem a estas soluções. A implementação de ferramentas BI nas organizações depende, pois, das especificidades destas, sendo fundamental que a informação sobre as plataformas disponíveis e suas funcionalidades seja objetiva e inequívoca. Só uma escolha correta, que responda às necessidades da área de negócio desenvolvida, permitirá obter dados que resultem em ganhos, potenciando a vantagem competitiva empresarial. Com este propósito, efectua-se, na presente dissertação, uma análise comparativa das funcionalidades existentes em diversas ferramentas BI, que se pretende que venha auxiliar os processos de seleção da plataforma BI mais adaptada a cada organização e/ou negócio. As plataformas BI enquadram-se em duas grandes vertentes, as que implicam custos de aquisição, de índole comercial, e as disponibilizadas de forma livre, ou em código aberto, designadas open source. Neste sentido, equaciona-se se estas últimas podem constituir uma opção válida para as empresas com recursos mais escassos. Num primeiro momento, procede-se à implementação de tecnologias BI numa organização concreta, a operar na indústria de componentes automóveis, a Yazaki Saltano de Ovar Produtos Eléctricos, Ltd., implantada em Portugal há mais de 25 anos. Para esta empresa, o desenvolvimento de soluções com recurso a ferramentas BI afigura-se como um meio adequado de melhorar o acompanhamento aos seus indicadores de performance. Este processo concretizou-se a partir da stack tecnológica pré-existente na organização, a plataforma BI comercial da Microsoft. Com o objetivo de, por um lado, reunir contributos que possibilitem elucidar as organizações na escolha da plataforma BI mais adequada e, por outro, compreender se as plataformas open source podem constituir uma alternativa credível às plataformas comerciais, procedeu-se a uma pesquisa comparativa das funcionalidades das várias plataformas BI open source. Em resultado desta análise, foram selecionadas duas plataformas, a SpagoBI e a PentahoBI, utilizadas na verificação do potencial alternativo das open source face às plataformas comerciais. Com base nessas plataformas, reproduziu-se os processos e procedimentos desenvolvidos no âmbito do projeto de implementação BI realizado na empresa Yazaki Saltano.

From a NoSQL data source to a business intelligence solution: An experiment

We are living in the era of Big Data. A time which is characterized by the continuous creation of vast amounts of data, originated from different sources, and with different formats. First, with the rise of the social networks and, more recently, with the advent of the Internet of Things (IoT), in which everyone and (eventually) everything is linked to the Internet, data with enormous potential for organizations is being continuously generated. In order to be more competitive, organizations want to access and explore all the richness that is present in those data. Indeed, Big Data is only as valuable as the insights organizations gather from it to make better decisions, which is the main goal of Business Intelligence. In this paper we describe an experiment in which data obtained from a NoSQL data source (database technology explicitly developed to deal with the specificities of Big Data) is used to feed a Business Intelligence solution.

Exploração de dados para soluções de retalho em business intelligence

Dissertação de mestrado integrado em Engenharia de Gestão e Sistemas de Informação

Pervasive business intelligence: a marketing intelligence framework proposal

Doctoral Thesis in Information Systems and Technologies Area of Information Systems and Technology

Ensuring the efficient utilization of Business Intelligence

In recent years, chief information officers (CIOs) around the world have identified Business Intelligence (BI) as their top priority and as the best way to enhance their enterprises competitiveness. Yet, many enterprises are struggling to realize the business value that BI promises. This discrepancy causes important questions, for example: what are the critical success factors of Business Intelligence and, more importantly, how it can be ensured that a Business Intelligence program enhances enterprises competitiveness. The main objective of the study is to find out how it can be ensured that a BI program meets its goals in providing competitive advantage to an enterprise. The objective is approached with a literature review and a qualitative case study. For the literature review the main objective populates three research questions (RQs); RQ1: What is Business Intelligence and why is it important for modern enterprises? RQ2: What are the critical success factors of Business Intelligence programs? RQ3: How it can be ensured that CSFs are met? The qualitative case study covers the BI program of a Finnish global manufacturer company. The research questions for the case study are as follows; RQ4: What is the current state of the case company’s BI program and what are the key areas for improvement? RQ5: In what ways the case company’s Business Intelligence program could be improved? The case company’s BI program is researched using the following methods; action research, semi-structured interviews, maturity assessment and benchmarking. The literature review shows that Business Intelligence is a technology-based information process that contains a series of systematic activities, which are driven by the specific information needs of decision-makers. The objective of BI is to provide accurate, timely, fact-based information, which enables taking actions that lead to achieving competitive advantage. There are many reasons for the importance of Business Intelligence, two of the most important being; 1) It helps to bridge the gap between an enterprise’s current and its desired performance, and 2) It helps enterprises to be in alignment with key performance indicators meaning it helps an enterprise to align towards its key objectives. The literature review also shows that there are known critical success factors (CSFs) for Business Intelligence programs which have to be met if the above mentioned value is wanted to be achieved, for example; committed management support and sponsorship, business-driven development approach and sustainable data quality. The literature review shows that the most common challenges are related to these CSFs and, more importantly, that overcoming these challenges requires a more comprehensive form of BI, called Enterprise Performance Management (EPM). EPM links measurement to strategy by focusing on what is measured and why. The case study shows that many of the challenges faced in the case company’s BI program are related to the above-mentioned CSFs. The main challenges are; lack of support and sponsorship from business, lack of visibility to overall business performance, lack of rigid BI development process, lack of clear purpose for the BI program and poor data quality. To overcome these challenges the case company should define and design an enterprise metrics framework, make sure that BI development requirements are gathered and prioritized by business, focus on data quality and ownership, and finally define clear goals for the BI program and then support and sponsor these goals.

On advancing business intelligence in the electricity retail market

In recent decades, business intelligence (BI) has gained momentum in real-world practice. At the same time, business intelligence has evolved as an important research subject of Information Systems (IS) within the decision support domain. Today’s growing competitive pressure in business has led to increased needs for real-time analytics, i.e., so called real-time BI or operational BI. This is especially true with respect to the electricity production, transmission, distribution, and retail business since the law of physics determines that electricity as a commodity is nearly impossible to be stored economically, and therefore demand-supply needs to be constantly in balance. The current power sector is subject to complex changes, innovation opportunities, and technical and regulatory constraints. These range from low carbon transition, renewable energy sources (RES) development, market design to new technologies (e.g., smart metering, smart grids, electric vehicles, etc.), and new independent power producers (e.g., commercial buildings or households with rooftop solar panel installments, a.k.a. Distributed Generation). Among them, the ongoing deployment of Advanced Metering Infrastructure (AMI) has profound impacts on the electricity retail market. From the view point of BI research, the AMI is enabling real-time or near real-time analytics in the electricity retail business. Following Design Science Research (DSR) paradigm in the IS field, this research presents four aspects of BI for efficient pricing in a competitive electricity retail market: (i) visual data-mining based descriptive analytics, namely electricity consumption profiling, for pricing decision-making support; (ii) real-time BI enterprise architecture for enhancing management’s capacity on real-time decision-making; (iii) prescriptive analytics through agent-based modeling for price-responsive demand simulation; (iv) visual data-mining application for electricity distribution benchmarking. Even though this study is from the perspective of the European electricity industry, particularly focused on Finland and Estonia, the BI approaches investigated can: (i) provide managerial implications to support the utility’s pricing decision-making; (ii) add empirical knowledge to the landscape of BI research; (iii) be transferred to a wide body of practice in the power sector and BI research community.

Building a Roadmap for a Market Intelligence function in a Global B2B company

Tämän diplomityötutkimuksen tarkoituksena on luoda markkinaälyyn (MI) erikoistunut funktio suurelle, globaalisti toimivalle B2B-yritykselle. Tämän päivän muut-tuvilla markkinoilla, teollisuusyrityksen on oltava markkinalähtöinen selviytyäkseen. Markkinatiedon tehokas hyödyntäminen ei pelkästään luo tietoa markkinoista, vaan tuottaa kilpailukykyistä tietoa ja toimii strategisen päätöksenteon tukena pitkällä aikavälillä. Tämä tutkimus on kvalitatiivinen toimintatutkimus, joka sisältää kirjallisuuskat-sauksen, yritystapaustutkimuksen sekä syväanalyysin yrityksen MI-ympäristöstä. Kirjallisuuskatsaus pitää sisällään teoriaa liittyen markkinaälyyn useassa eri kon-tekstissa, asiakassuhteeseen, sekä prosessinmallintamiseen. Empiiriseen osaa seuraa tutkimusmenetelmäkappale, joka sisältää kaksivaiheisen tutkimuksen mukaan lu-kien 20 päällikkötason haastattelua sekä yhden laaja-alaisen työryhmätapaamisen. Työn tuloksena syntyy kolmivaiheinen tiekartta, jonka tarkoitus on toimia pohjana uuden MI-funktion rakentamiselle Case-yrityksessä. Tuloksen mukaan MI-funktio tulisi sijoittaa yrityksen asiakasrajapintaan sekä tukea yksiköiden välistä integraa-tiota. Markkinaälyn jakaminen yrityksen sisällä vaatii käytäntöjen, tarpeiden ja ta-voitteiden systemaattista viestintää eri organisaatiotasoille, jotta yritys voi edelleen saada asiakkaalta tarpeeseen vastaavaa tietoa. Viestintä yrityksen ja asiakkaan välil-lä on oltava molemminpuolista, jotta tulokset voisivat parantaa asiakassuhdetta. Kun asiakassuhde paranee, yritys voi oppia asiakkaalta arvokasta tietoa, markkinaälyä.

Business intelligence – enabling self-service: A case study in a global manufacturing company

Business intelligence (BI) is an information process that includes the activities and applications used to transform business data into valuable business information. Today’s enterprises are collecting detailed data which has increased the available business data drastically. In order to meet changing customer needs and gain competitive advantage businesses try to leverage this information. However, IT departments are struggling to meet the increased amount of reporting needs. Therefore, recent shift in the BI market has been towards empowering business users with self-service BI capabilities. The purpose of this study was to understand how self-service BI could help businesses to meet increased reporting demands. The research problem was approached with an empirical single case study. Qualitative data was gathered with a semi-structured, theme-based interview. The study found out that case company’s BI system was mostly used for group performance reporting. Ad-hoc and business user-driven information needs were mostly fulfilled with self-made tools and manual work. It was felt that necessary business information was not easily available. The concept of self-service BI was perceived to be helpful to meet such reporting needs. However, it was found out that the available data is often too complex for an average user to fully understand. The respondents felt that in order to self-service BI to work, the data has to be simplified and described in a way that it can be understood by the average business user. The results of the study suggest that BI programs struggle in meeting all the information needs of today’s businesses. The concept of self-service BI tries to resolve this problem by allowing users easy self-service access to necessary business information. However, business data is often complex and hard to understand. Self-serviced BI has to overcome this challenge before it can reach its potential benefits.

Location intelligence strategisen päätöksenteon tukena

Kilpailuetua tavoittelevan yrityksen pitää kyetä jalostamaan tietoa ja tunnistamaan sen avulla uusia tulevaisuuden mahdollisuuksia. Tulevaisuuden mielikuvien luomiseksi yrityksen on tunnettava toimintaympäristönsä ja olla herkkänä havaitsemaan muutostrendit ja muut toimintaympäristön signaalit. Ympäristön elintärkeät signaalit liittyvät kilpailijoihin, teknologian kehittymiseen, arvomaailman muutoksiin, globaaleihin väestötrendeihin tai jopa ympäristön muutoksiin. Spatiaaliset suhteet ovat peruspilareita käsitteellistää maailmaamme. Pitney (2015) on arvioinut, että 80 % kaikesta bisnesdatasta sisältää jollakin tavoin viittauksia paikkatietoon. Siitä huolimatta paikkatietoa on vielä huonosti hyödynnetty yritysten strategisten päätösten tukena. Teknologioiden kehittyminen, tiedon nopea siirto ja paikannustekniikoiden integroiminen eri laitteisiin ovat mahdollistaneet sen, että paikkatietoa hyödyntäviä palveluja ja ratkaisuja tullaan yhä enemmän näkemään yrityskentässä. Tutkimuksen tavoitteena oli selvittää voiko location intelligence toimia strategisen päätöksenteon tukena ja jos voi, niin miten. Työ toteutettiin konstruktiivista tutkimusmenetelmää käyttäen, jolla pyritään ratkaisemaan jokin relevantti ongelma. Konstruktiivinen tutkimus tehtiin tiiviissä yhteistyössä kolmen pk-yrityksen kanssa ja siihen haastateltiin kuutta eri strategiasta vastaavaa henkilöä. Tutkimuksen tuloksena löydettiin, että location intelligenceä voidaan hyödyntää strategisen päätöksenteon tukena usealla eri tasolla. Yksinkertaisimmassa karttaratkaisussa halutut tiedot tuodaan kartalle ja luodaan visuaalinen esitys, jonka avulla johtopäätöksien tekeminen helpottuu. Toisen tason karttaratkaisu pitää sisällään sekä sijainti- että ominaisuustietoa, jota on yhdistetty eri lähteistä. Tämä toisen tason karttaratkaisu on usein kuvailevaa analytiikkaa, joka mahdollistaa erilaisten ilmiöiden analysoinnin. Kolmannen eli ylimmän tason karttaratkaisu tarjoaa ennakoivaa analytiikkaa ja malleja tulevaisuudesta. Tällöin ohjelmaan koodataan älykkyyttä, jossa informaation keskinäisiä suhteita on määritelty joko tiedon louhintaa tai tilastollisia analyysejä hyödyntäen. Tutkimuksen johtopäätöksenä voidaan todeta, että location intelligence pystyy tarjoamaan lisäarvoa strategisen päätöksenteon tueksi, mikäli yritykselle on hyödyllistä ymmärtää eri ilmiöiden, asiakastarpeiden, kilpailijoiden ja markkinamuutoksien maantieteellisiä eroavaisuuksia. Parhaimmillaan location intelligence -ratkaisu tarjoaa luotettavan analyysin, jossa tieto välittyy muuttumattomana päätöksentekijältä toiselle ja johtopäätökseen johtaneita syitä on mahdollista palata tarkastelemaan tarvittaessa uudelleen.

Business intelligence – enabling self-service: A case study in a global manufacturing company

Business intelligence (BI) is an information process that includes the activities and applications used to transform business data into valuable business information. Today’s enterprises are collecting detailed data which has increased the available business data drastically. In order to meet changing customer needs and gain competitive advantage businesses try to leverage this information. However, IT departments are struggling to meet the increased amount of reporting needs. Therefore, recent shift in the BI market has been towards empowering business users with self-service BI capabilities. The purpose of this study was to understand how self-service BI could help businesses to meet increased reporting demands. The research problem was approached with an empirical single case study. Qualitative data was gathered with a semi-structured, theme-based interview. The study found out that case company’s BI system was mostly used for group performance reporting. Ad-hoc and business user-driven information needs were mostly fulfilled with self-made tools and manual work. It was felt that necessary business information was not easily available. The concept of self-service BI was perceived to be helpful to meet such reporting needs. However, it was found out that the available data is often too complex for an average user to fully understand. The respondents felt that in order to self-service BI to work, the data has to be simplified and described in a way that it can be understood by the average business user. The results of the study suggest that BI programs struggle in meeting all the information needs of today’s businesses. The concept of self-service BI tries to resolve this problem by allowing users easy self-service access to necessary business information. However, business data is often complex and hard to understand. Self-serviced BI has to overcome this challenge before it can reach its potential benefits.

Temperament and self-based correlates of cooperative, competitive and individualistic learning preferences

People vary in the extent to which they prefer cooperative, competitive or individualistic achievement tasks. In the present research, we conducted two studies designed to investigate correlates and possible roots of these social interdependence orientations, namely approach and avoidance temperament, general self-efficacy, implicit theories of intelligence, and contingencies of self-worth based in others’ approval, competition, and academic competence. The results indicated that approach temperament, general self-efficacy, and incremental theory were positively, and entity theory was negatively related to cooperative preferences (|r| range from .11 to .41); approach temperament, general self-efficacy, competition contingencies, and academic competence contingencies were positively related to competitive preferences (|r| range from .16 to .46); and avoidance temperament, entity theory, competitive contingencies, and academic competence contingencies were positively related, and incremental theory was negatively related to individualistic preferences (|r| range from .09 to .15). The findings are discussed with regard to the meaning of each of the three social interdependence orientations, cultural differences among the observed relations, and implications for practicioners.

Video target tracking by using competitive neural networks

A target tracking algorithm able to identify the position and to pursuit moving targets in video digital sequences is proposed in this paper. The proposed approach aims to track moving targets inside the vision field of a digital camera. The position and trajectory of the target are identified by using a neural network presenting competitive learning technique. The winning neuron is trained to approximate to the target and, then, pursuit it. A digital camera provides a sequence of images and the algorithm process those frames in real time tracking the moving target. The algorithm is performed both with black and white and multi-colored images to simulate real world situations. Results show the effectiveness of the proposed algorithm, since the neurons tracked the moving targets even if there is no pre-processing image analysis. Single and multiple moving targets are followed in real time.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.