Search results

The study aims to provide a quick-and-robust multifactorial screening technique for early detection of statistically significant effects that could influence a product's life-time performance.

The proposed method takes advantage of saturated fractional factorial designs for organizing the lifetime dataset collection process. Small censored lifetime data are fitted to the Kaplan–Meier model. Low-percentile lifetime behavior that is derived from the fitted model is used to screen for strong effects. A robust surrogate profiler is employed to furnish the predictions.

The methodology is tested on a difficult published case study that involves the eleven-factor screening of an industrial-grade thermostat. The tested thermostat units are use-rate accelerated to expedite the information collection process. The solution that is provided by this new method suggests as many as two active effects at the first decile of the data which improves over a solution provided from more classical methods.

To benchmark the predicted solution with other competing approaches, the results showcase the critical first decile part of the dataset. Moreover, prediction capability is demonstrated for the use-rate acceleration condition.

The technique might be applicable to projects where the early reliability improvement is studied for complex industrial products.

The proposed methodology offers a range of features that aim to make the product reliability profiling process faster and more robust while managing to be less susceptible to assumptions often encountered in classical multi-parameter treatments.

In surface mount assembly (SMA) process, small components are subjected to high temperature variations, which result in components’ deformation and cracking. Because of this phenomenon, cracks are formed in the body of carbonyl powder ceramic inductor (CPCI) in the preheat and cooling stages of the reflow oven. These cracks become the main cause of board failure in the ageing process. The purpose of this paper is to ascertain the thermal stress, thermal expansion of carbonyl iron ceramics and its effects on crack commencement and proliferation in the preheat stage of reflow oven. Moreover, this paper also categorized and suggested important parameters of reflow profile that could be used to eliminate these thermal shock failures.

In this paper, two different reflow profiles were studied that evaluate the thermal shock of CPCI during varying ΔT at the preheat zone of the reflow oven. In the first profile, the change in temperature ΔT at preheat zone was set to 3.26°C/s, which has resulted in a number of device failures because of migration of micro cracks through the CPCI. In the second profile, this ΔT at preheat stage is minimized to 2.06°C/s that eliminated the thermal stresses; hence, the failure rates were significantly reduced.

TMPC0618H series lead (Pb)-free CPCI is selected for this study and its thermal expansion and thermal shock are observed in the reflow process. It is inferred from the results that high ΔT at preheat zone generates cracks in the carbonyl powder-type ceramics that cause device failure in the board ageing process. Comparing materials, carbonyl powder ceramic components are less resistant to thermal shock and a lower rate of temperature change is desirable.

The proposed study presents an experimental analysis for mitigating the thermal shock defects. The realization of the proposed approach is validated with experimental data from the printed circuit boards manufacturing process.

Organization development is focused on implementing a planned process of positive humanistic change in organizations through the use of social science theory, action research, and data-based feedback methods. The role of personality in that change process, however, has historically been ignored or relegated to a limited set of interventions. The purpose of this chapter is to provide a conceptual overview of the linkages between personality and OD, discuss the current state of personality in the field including key trends in talent management, and offer a new multi-level framework for conceptualizing applications of personality for different types of OD efforts. The chapter concludes with implications for research and practice.

Addresses the subject of the entrepreneur, describes the significant and growing number of small and medium-sized enterprises in the UK, and outlines the author's development of an entrepreneurial competency profile (ECP).

Draws on statistics taken form the UK government-sponsored Young Report, Make Business Your Business, and the author's own research into entrepreneurial efficacy.

Describes how governments in developed countries and those in emerging nations are driving to create an environment that nurtures and sustains entrepreneurship. They believe it can rapidly create jobs, improve GDP and increase long-term productivity.

Will benefit those in senior management positions by discussing the use of predictive profiling techniques to facilitate wealth creation.

With so much attention being paid to entrepreneurship, and given our knowledge of predictive behavioral economics, can it be justifiably classified as a relatively distinct field in organizational sciences? Whilst there are many notable entrepreneurial successes, there are equally a large number of failures. How can we mitigate the failure rate?

The purpose of this paper is to identify the tools, methods and models that UK manufacturing companies adopt and apply in order to achieve resiliency and economic sustainability. The results of this work can assist in developing the foundations for defining a new joint resiliency/sustainability paradigm to assist industry.

Through a detailed, triangulated secondary data analysis and industry survey, the authors initially identify and then analyse the key resiliency and sustainability characteristics surrounding manufacturing operations. This paper initially reviews key literatures around resiliency and sustainability models and frameworks and subsequently draws out their key features and weaknesses. The work then details the research survey undertaken in to manufacturing companies aimed at identifying the resiliency/sustainability approaches that are adopted in companies. A sample of 72 manufacturing companies are used in the survey and from which the results are based.

Through analysing the fundamental business data of sales and manufacturing costs for 72 manufacturing companies, the authors cluster the companies in to four key manufacturing profiles. The work then shows through a more detailed analysis of the profiles that companies which are sustainable and more resilient in nature are, better engaged and connected to the development and application of resiliency and sustainability models. It was found that companies who seem to struggle in achieving economic sustainability or lack the ability to bounce back from various set-backs either do not employ such models or at best apply tools and techniques in an ad hoc manner.

The paper provides key insights in to the adoption of tools, techniques and models surrounding the achievement of resiliency and sustainability in manufacturing companies. In so doing, the paper offers a new view on these issues and with the profiling exercise undertaken, companies will be able to identify their position in relation to the survey companies. This can be of benefit to the wider industrial and academic community. The development of a qualitative assessment around a relatively small sample size has its obvious limitations and it is crucial that further work with a range of companies in the area of manufacturing sustainability is key to developing (and also validating) a comprehensive set of resiliency and sustainability characteristics.

The paper highlights the issues surrounding existing academic resiliency/sustainability models and through the industry survey, it provides further information on where UK manufacturing companies are on adopting specific resiliency/sustainability models. The work suggests that the resiliency/sustainability landscape of UK manufacturing companies is much more complex and that a single strategic approach towards achieving improved manufacturing performance is somewhat dated and ineffective.

The development of a set of resiliency/sustainability profiles including the identification of the specific tools and techniques adopted by industry is aimed at tackling directly the issues of improving company performance and is considered by the authors as one of a kind. The results of the survey provide essential information on the resiliency/sustainability landscape of UK manufacturing companies.

Global trade depends on more complex, prolonged and larger port systems, where port logistics is a backbone for such operations. Ports are responsible for transferring more than 80 percent of the global trade. Port logistics are prone to being risk-oriented. The study proposes a model to study various port logistics barriers and their associated risks for emerging economies in the Industry 4.0 era.

The study develops a framework by integrating the fuzzy set theory, the evidential reasoning approach and the expected utility theorem for identifying the severity value of port logistics barriers under the Industry 4.0 era for emerging economies and prioritize them based on various perspectives. The study identifies multiple risks associated with the barriers, and intensity-based categorization of the risks is performed for risk profiling.

The study reveals that poor infrastructure, nonsupportive policy ecosystem, and lack of research and development are the top barriers that need immediate attention. A new approach has been proposed that changes the importance of perspectives, and 192 analytical experiments were done to study the changing behavior of barriers. The study also presents various types of risks associated with the selected barriers.

In future studies, other barriers can be discovered and studied to develop such models. To cover the entire spectrum of possibilities, belief degrees of the barriers could be used to study the barriers instead of changing the weights.

This study presents a quantification model to prioritize the barriers based on environmental, economic and operational perspectives. Further, the model helps create scenarios for decision-makers to improve port logistics performance and achieve sustainability. The study identifies various risks associated with port logistics barriers and allows decision-makers to take proactive actions.

This study contributes significantly to the literature on port logistics by developing a framework for determining the severity of the barriers in the Industry 4.0 era for emerging economies. Further, the study pinpoints various risks associated with port logistics, and risk profiling is carried out.

This paper presents an application of the physics‐of‐failure design philosophy to flip‐chip bonds in a microelectronic package. The physics‐of‐failure philosophy utilises knowledge of the life‐cycle load profile, package architecture and material properties to identify potential failure mechanisms and to prevent operational failures through robust design and manufacturing practices. The potential failure mechanisms and failure sites are identified in this paper for flip‐chip bonds, and an approach is presented to prevent the identified potential failure mechanisms by design. Finally, quality conformance issues are discussed to ensure a robust manufacturing process and qualification issues are addressed to evaluate the reliability of the designed flip‐chip bond.

The purpose of this study is to examine the effect of service failure severity on brand forgiveness and to investigate the moderating effects of interpersonal attachment styles and thinking styles on the service failure severity–brand forgiveness relationship.

The authors used retrospective experience sampling to collect the data and structural equation modeling (AMOS 24) to analyze 570 responses collected via an online survey.

This study shows that the service failure severity–brand forgiveness relationship is not always negative, as different conditions may amplify or weaken it. Specifically, a secure attachment style and holistic thinking weaken the negative impact of service failure severity on brand forgiveness, whereas an anxious attachment style and analytic thinking negatively amplify the relationship. An avoidance attachment style did not appear to play a role.

This study should help hotels fine-tune their segmentation, targeting and positioning efforts and may also help in implementing more focused recovery strategies.

This study provides insights into the role of psychological traits in amplifying/reducing the negative impact of service failure severity on brand forgiveness, thus showing the importance of developing the psychological profiles of customers beyond demographic profiling. The emotional and cognitive typologies of consumers are key to understanding the dependence of forgiveness on service failure severity.

Mechanical products usually require deterministic finite element analysis in the design phase to determine whether their structures meet the requirements. However, deterministic design ignores the influence of uncertainties in the design and manufacturing process of mechanical products, leading to the problem of a lack of design safety or excessive redundancy in the design. In order to improve the accuracy and rationality of the design results, a robust design method for structural reliability based on an active-learning marine predator algorithm (MPA)–backpropagation (BP) neural network is proposed.

The MPA was used to obtain the optimal weights and thresholds of a BP neural network, and an active-learning function applicable to neural networks was proposed to efficiently improve the prediction performance of the BP neural network. On this basis, a robust optimization design method for mechanical product reliability based on the active-learning MPA-BP model was proposed. Random moving quadrilateral sampling was used to obtain the sample points required for training and testing of the neural network, and the reliability sensitivity corresponding to each sample point was calculated by subset simulated significant sampling (SSIS). The total mass of the mechanical product and the structural reliability sensitivity of the trained active-learning MPA-BP model output were taken as the optimization objectives, and a multi-objective reliability-robust optimization design model was constructed, which was solved by the second-generation non-dominated ranking genetic algorithm (NSGA-II). Then, the dominance function was used in the obtained Pareto solution set to make a dominance-seeking decision to obtain the final reliability-robust optimization design solution. The feasibility of the proposed method was verified by a reliability-robust optimization design example of the bogie frame.

The prediction error of the active-learning MPA-BP neural network was smaller than those of the particle swarm optimization (PSO)-BP, marine predator algorithm (MPA)-BP and genetic algorithm (GA)-BP neural networks under the same basic parameter settings of the algorithm, which indicated that the improvement strategy proposed in this paper improved the prediction accuracy of the BP neural network. To ensure the reliability of the bogie frame, the reliability sensitivity and total mass of the bogie frame were reduced, which not only realized the lightweight design of the bogie frame, but also improved the reliability and robustness of the bogie.

The MPA algorithm with a higher optimization efficiency was introduced to find the weights and thresholds of the BP neural network. A new active-learning function was proposed to improve the prediction accuracy of the MPA-BP neural network.

The purpose of this paper is to introduce a 3R (right time, right place, and right material) principle for characterizing failure in humanitarian/relief supply chains’ response to natural disasters, and describes a Bayesian methodology of the failure odds with regard to external factors that may affect the disaster-relief outcome, and distinctive supply chain proneness to failure.

The suggested 3Rs combine simplicity and completeness, enclosing all aspects of the 7R principle popular within business logistics. A fixed effects logistic regression model is designed, with a Bayesian approach, to relate the supply chains’ odds for success in disaster-relief to potential environmental predictors, while accounting for distinctive supply chains’ proneness to failure.

Analysis of simulated data demonstrate the model’s ability to distinguish relief supply chains with regards to their disaster-relief failure odds, taking into account pertinent external factors and supply chain idiosyncrasies.

Due to the complex nature of natural disasters and the scarcity of subsequent data, the paper employs computer-simulated data to illustrate the implementation of the proposed methodology.

The 3R principle offers a simple and familiar basis for evaluating failure in relief supply chains’ response to natural disasters. Also, it brings the issues of customer orientation within humanitarian relief and supply operations to the fore, which had only been implicit within the humanitarian and relief supply chain literature.