Forecasting Failure Rates of Electronic Goods by Using Decomposition and Fuzzy Clustering of Empirical Failure Rate Curves

In this paper a novel methodology founded on the joint application of analytic decomposition of empirical failure rate time series and soft computational techniques is introduced in order to predict bathtub-shaped failure rate curves of consumer electronic goods. Empirical failure rate time series are modeled by a flexible function the parameters of which have geometric interpretations, and so the model parameters grab the characteristics of bathtub-shaped failure rate curves. The so-called typical standardized failure rate curve models, which are derived from the model functions through standardization and fuzzy clustering processes, are applied to predict failure rate curves of consumer electronics in a method that combines analytic curve fitting and soft computing techniques. The forecasting capability of the introduced method was tested on real-life data. Based on the empirical results from practical applications, the introduced method can be considered as a new, alternative reliability prediction technique the application of which can support the electronic repair service providers to plan their resources in the long run

FEASIBILITY OF B2C CUSTOMER RELATIONSHIP ANALYTICS IN THE B2B INDUSTRIAL CONTEXT

Abstract The purpose of the paper is to evaluate the feasibility of business-to-consumer (B2C) customer relationship analytics in the industrial business-to-business (B2B) context, in particular spare part sales. The contribution of the paper is twofold; the article identifies analytics approaches with value potential for B2B decision-making, and illustrates their value in use. The identified analytics approaches, customer segmentation, market basket analysis and target customer selection, are common in the B2C marketing and e-commerce. However, in the industrial B2B marketing, the application of these approaches is not yet common.. The different kinds of analytics under examination in this paper use machine learning (ML) techniques. The examination takes into account the applicability and usefulness of the techniques as well as implementation challenges. The research suggests that the identified analytics may serve different business purposes and may be relatively straightforward to implement. This requires careful examination of the desired purposes of use in a particular business context. However, the continuous and real-time use of such analyses remains a challenge for further examination also in information systems research. Keywords: Business analytics, B2B decision-making, Machine learning, Data mining, Artificial intelligence, CR

BARCH: a business analytics problem formulation and solving framework

The BARCH framework is a business framework that is specifically formulated to help analysts and management who want to identify and formulate a scenario to which Analytics can be applied and the outcome will have a direct impact on the business. This is the overarching public work that I have used extensively in various projects and research. This framework has been developed initially in the banking sector and has evolved progressively with successive projects. The framework’s name represents five aspects for the formulation and identification of an area that one can use Analytics to answer. The five aspects are Business, Analytics, Revenue, Cost and Human. The five aspects represent the entire system and approach to the identification, formulation, understanding and modelling of Analytic problems. The five aspects are not necessarily sequential but are interrelated in some ways where certain aspects are dependent on the other aspects. For example, revenue and cost are related to business and depend on the business from which they are derived. However, in most practices involving Analytics, Analytics are conducted independent of business and the techniques in Analytics are not derived from business directly. This lack of harmony between business and Analytics creates an unfortunate combination of factors that has led to the failure of Analytics projects for many businesses. In intensely practising Analytics and critically reflecting on every piece of work I have done, I have learned the importance of combining knowledge with skills and experience to come up with new knowledge and a form of practical wisdom. I also realize now the importance of understanding fields that are not directly related to my field of specialization. Through this context statement I have been able to increase the articulation of my thinking and the complexities of practice through approaches to knowledge such as transdisciplinarity which further supports the translation of what I can do and what needs to be done in a way that business clients can understand. Having the opportunity to explore concepts new to me from other academic fields and seeking their relevance and application in my own area of expertise has helped me considerably in the ongoing development of the BARCH framework and successful implementation of Analytics projects. I have selected the results of three projects published in papers that are listed in Appendices A-C to demonstrate how the model can be applied to solve problems successfully compared to other frameworks. The evolution of the model involves a continual feedback loop of learning from each successive project which contributes to the BARCH model being able to not only continuously demonstrate its applicability to various problems but to consistently produce better and more refined results. The majority of analytical models applied to the many problems in the business environment address the problems only superficially (Bose, 2009; Krioukov et. al., 2011), that is without understanding the impact on the business as a whole. Many Analytics projects have not delivered the promised impact because the models applied are overly complicated (Stubbs, 2013) to solve the root causes of the business problem. This situation is compounded by an increasing number of analysts applying Analytics to business problems without a proper understanding of the context, technique and environment (Stubbs, 2013). While many experts in the field interpret the problem as a multidisciplinary problem, the problem is in my opinion transdisciplinary in nature

Modelling and simulation of paradigms for printed circuit board assembly to support the UK's competency in high reliability electronics

The fundamental requirement of the research reported within this thesis is the provision of physical models to enable model based simulation of mainstream printed circuit assembly (PCA) process discrete events for use within to-be-developed (or under development) software tools which codify cause & effects knowledge for use in product and process design optimisation. To support a national competitive advantage in high reliability electronics UK based producers of aircraft electronic subsystems require advanced simulation tools which offer model based guidance. In turn, maximization of manufacturability and minimization of uncontrolled rework must therefore enhance inservice sustainability for ‘power-by-the-hour’ commercial aircraft operation business models. [Continues.

Advances in Reliability, Risk and Safety Analysis with Big Data: Proceedings of the 57th ESReDA Seminar: Hosted by the Technical University of Valencia, 23-24 October, 2019, Valencia, Spain

The publication presents 57th Seminar organized by ESReDA that took place at the Polytechnic University of Valencia/Universitat Politècnica de Valencia, Spain. The Seminar was jointly organized by ESReDA and CMT Motores Termicos, a research unit at the Polytechnic University of Valencia. In accordance with the theme proposed for the Seminar, communications were presented that made it possible to discuss and better understand the role of the latest big data, machine learning and artificial intelligence technologies in the development of reliability, risk and safety analyses for industrial systems. The world is moving fast towards wide applications of big data techniques and artificial intelligence is considered to be the future of our societies. Rapid development of 5G telecommunications infrastructure would only speed up deployment of big data analytic tools. However, despite the recent advances in the these fields, there is still a long way to go for integrated applications of big data, machine learning and artificial intelligence tools in business practice. We would like to express our gratitude to the authors and key note speakers in particular and to all those who shared with us these moments of discussion on subjects of great importance and topicality for the members of ESReDA. The editorial work for this volume was supported by the Joint Research Centre of the European Commission in the frame of JRC support to ESReDA activities.JRC.C.3-Energy Security, Distribution and Market

Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems

This book explores the critical role of acquisition, application, enhancement, and management of knowledge and human competence in the context of the largely digital and data/information dominated modern world. Whilst humanity owes much of its achievements to the distinct capability to learn from observation, analyse data, gain insights, and perceive beyond original realities, the systematic treatment of knowledge as a core capability and driver of success has largely remained the forte of pedagogy. In an increasingly intertwined global community faced with existential challenges and risks, the significance of knowledge creation, innovation, and systematic understanding and treatment of human competence is likely to be humanity's greatest weapon against adversity. This book was conceived to inform the decision makers and practitioners about the best practice pertinent to many disciplines and sectors. The chapters fall into three broad categories to guide the readers to gain insight from generic fundamentals to discipline-specific case studies and of the latest practice in knowledge and competence management

Feature selection strategies for improving data-driven decision support in bank telemarketing

The usage of data mining techniques to unveil previously undiscovered knowledge has been applied in past years to a wide number of domains, including banking and marketing. Raw data is the basic ingredient for successfully detecting interesting patterns. A key aspect of raw data manipulation is feature engineering and it is related with the correct characterization or selection of relevant features (or variables) that conceal relations with the target goal. This study is particularly focused on feature engineering, aiming at the unfolding features that best characterize the problem of selling long-term bank deposits through telemarketing campaigns. For the experimental setup, a case-study from a Portuguese bank, ranging the 2008-2013 year period and encompassing the recent global financial crisis, was addressed. To assess the relevance of such problem, a novel literature analysis using text mining and the latent Dirichlet allocation algorithm was conducted, confirming the existence of a research gap for bank telemarketing. Starting from a dataset containing typical telemarketing contacts and client information, research followed three different and complementary strategies: first, by enriching the dataset with social and economic context features; then, by including customer lifetime value related features; finally, by applying a divide and conquer strategy for splitting the problem in smaller fractions, leading to optimized sub-problems. Each of the three approaches improved previous results in terms of model metrics related to prediction performance. The relevance of the proposed features was evaluated, confirming the obtained models as credible and valuable for telemarketing campaign managers.A utilização de técnicas de data mining para a descoberta de conhecimento tem sido aplicada nos últimos anos a uma grande variedade de domínios, incluindo banca e marketing. Os dados no seu estado primitivo constituem o ingrediente básico para a deteção de padrões de informação. Um aspeto chave da manipulação de dados em bruto consiste na "engenharia de atributos", que compreende uma correta definição e seleção de atributos relevantes (ou variáveis) que se relacionem com o alvo da descoberta de conhecimento. Este trabalho foca-se numa abordagem de "engenharia de atributos" para definir as variáveis que melhor caraterizam o problema de vender depósitos bancários a prazo através de campanhas de telemarketing. Sendo um estudo empírico, foi utilizado um caso de estudo de um banco português, abrangendo o período 2008-2013, que inclui os efeitos da crise financeira internacional. Para aferir da importância deste problema, foi realizada uma inovadora análise da literatura recorrendo a text mining e ao algoritmo latent Dirichlet allocation, confirmando a existência de uma lacuna nesta matéria. Utilizando como base um conjunto de dados de contactos de telemarketing e informação sobre os clientes, três estratégias diferentes e complementares foram propostas: primeiro, os dados foram enriquecidos com atributos socioeconómicos; posteriormente, foram adicionadas características associadas ao valor do cliente ao longo do seu tempo de vida; finalmente, o problema foi dividido em problemas mais específicos, permitindo abordagens otimizadas a cada subproblema. Cada abordagem melhorou as métricas associadas à capacidade preditiva do modelo. Adicionalmente, a relevância dos atributos foi avaliada, confirmando os modelos obtidos como credíveis e valiosos para gestores de campanhas de telemarketing