Implications of Onshore Development on Global Software Engineering

Recently software industry has paid significant attention to customizing software products across distributed boundaries. Communicating the requirements of multiple clients across distributed borders is a crucial challenge for the software customization process. Local decision-making and local development at the client site are considered methods for reducing difficulties in communicating the requirements of multiple clients across distributed boundaries. This paper introduces a new model called the onshore development model (ODM) for accomplishing the customization requests in the distributed development process of software. This model presents a scenario for enhancing the onsite development of specific requirements to reduce delays and misunderstandings between the clients and the team involved. This model depends on moving the development process to the client’s location. Three empirical studies were conducted to evaluate the proposed model to measure its productivity, time performance, and cost reduction. The proposed model has been compared with two other models: the basic model (BM), which allocates the decision-making process and the development process for teams at the vendor’s location, and the local decision-making model (LDec), which assigns the decision-making process for team at the client’s location. The results of the empirical studies showed significant outperforming of the proposed model over the basic model and local decision-making model in productivity, time performance, and cost reduction. The productivity of the proposed model improved by 39% and 10% more than the basic model and the local decision-making model, respectively. In addition, the time performance of the proposed model became faster by 49% and 20.8% than the basic model and the local decision-making model, respectively. Also, it reduced the total cost of the development process by 31% in terms of the salaries of all persons involved in requirements collecting, decision-making, and development.     

Science and sustainability: who knows best?

Sustainability is a worthy ideal. Deciding between the priorities associated with the sustainable development needed to work towards sustainability, specific objectives to be met, methods for meeting them and criteria for success will be governed primarily by politics, perspective and probably power. Effective decision-making, however, involves identifying and deciding between a variety of options, assessing the existing state of knowledge, uncertainty and ignorance and the time that might be needed usefully to improve knowledge and reduce uncertainty. It also requires objectivity, open-mindedness, scepticism and clarity of thought. Knowledge is necessary (but not sufficient) for rational decision-making. Some knowledge (but not all) relates to a developing understanding of the physical and natural world, i.e. scientific knowledge. Are there absolutes in this knowledge? With what authority can (or should) science speak? Is science too much confused with technology and its application? How should critical scientific and technological controversies be resolved? To what extent should decision-making/adjudication be based on a democratic process or on rational judgement by experts? How can a balance between these be achieved and be made acceptable? What happens when scientists become activists? How can science and democracy be reconciled? The issues raised by these questions will be discussed.     

The Recent Growth Performance of US Firms in the Industrial Design Sector: An Exploratory Study

This paper assesses the competitive factors associated with company growth in the US industrial design sector. This small but technologically advanced sector delivers critical innovation inputs to firms that produce durable goods. Evidence from a survey of 85 US design companies suggests that competitive success hinges upon service diversity. Specifically, the most commercially buoyant companies have diversified their service offerings beyond product or component design. These firms have developed strategic competencies in fields such as contract research, prototype development, product testing, technological forecasting, market analysis and even advertising. Although most US design companies are small-to-medium-sized enterprises, successful firms do not differ from their less successful counterparts in terms of employment size, occupational structure, regional location or market focus (client sectors). Instead, the key differences lie in service diversity and the quality of human capital. The paper concludes with a brief discussion of the implications of the empirical findings for future research on the dynamics of the design industry.     

The state of multivariate thinking for scientists in industry: 1980–2000

Chemometrics has enjoyed tremendous success in the areas related to calibration of spectrometers and spectroscopy-based measurements. These chemometric-based spectrometers have been widely applied for process monitoring and quality assurance. However, chemometrics has the potential to revolutionize the very intellectual roots of problem solving. Are there barriers to a more rapid proliferation of chemometric-based thinking, particularly in industry? What are the potential effects of chemometrics technology and the New Network Economy (NNE) working in concert? Who will be the winners in the race for faster, better, cheaper systems and products? These questions are reviewed in terms of the principles of the NNE and in the promise of chemometrics for industry. What then is the state of multivariate thinking in industry? Several powerful principles are derived from an evaluation of the NNE and chemometrics which could allow chemometrics to proliferate much more rapidly as a key general problem-solving tool.     

Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020

Having a new technology opportunity is a significant variable that can lead to dominance in a competitive market. In that context, accurately understanding the state of development of technology convergence and forecasting promising technology convergence can determine the success of a firm. However, previous studies have mainly focused on examining the convergence paths taken in the past or the current state of convergence rather than projecting the future trends of convergence. In addition, few studies have dealt with multi-technology convergence by taking a pairwise-analysis approach. Therefore, this research aimed to propose a forecasting methodology for multi-technology convergence, which is more realistic than pairwise convergence, based on a patent-citation analysis, a dependency-structure matrix, and a neural-network analysis. The suggested methodology enables both researchers and practitioners in the convergence field to plan their technology development by forecasting the technology combination that will occur in the future.     

The control function of management

The control function of management can be a critical determinant of organizational success. Most authors discuss control only through feedback and adjustment processes. This article takes a broader perspective on control and discusses the following questions: What is good control? Why are controls needed? How can good control be achieved? If multiple control strategies are feasible, how should the choice among them be made?     

Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail

This paper proposes a novel forecasting method that combines the deep learning method – long short-term memory (LSTM) networks and random forest (RF). The proposed method can model complex relationships of both temporal and regression type which gives it an edge in accuracy over other forecasting methods. We evaluated the new method on a real-world multivariate dataset from a multi-channel retailer. We benchmark the forecasting performance of the new proposition against neural networks, multiple regression, ARIMAX, LSTM networks, and RF. We employed forecasting performance metrics to measure bias, accuracy, and variance, and the empirical evidence suggests that the new proposition is (statistically) significantly better. Furthermore, our method ranks the explanatory variables in terms of their relative importance. The empirical evaluations are replicated for longer forecasting horizons, and online and offline channels and the same conclusions hold; thus, advocating for the robustness of our forecasting proposition as well as the suitability in multi-channel retail demand forecasting.     

The relationship between operating efficiency and service quality: are they compatible?

Transferring and applying manufacturing principles and practices to improve both service efficiency and service quality is an important area of research in service operations. This paper advances this stream of research by examining the compatibility of operating efficiency and service quality. Specifically, our research addresses the following questions: (1) Do operating efficiency and service quality have to be traded off, or can they exist in unison (are they compatible)? and (2) What aspects of service quality have a stronger association with operating efficiency? In disentangling these important issues, we utilise a combination of data envelopment analysis (DEA) and survey-based empirical research methods. Additionally, our focus lies on a novel type of service industry where such research has been sparse. We evaluate the relative operating efficiencies of service driving agencies based on actual transaction data and subsequently analyse differences in service quality dimensions based on efficiencies. Overall, our analyses suggest important strategic decision-making implications for service operations managers and provide novel insights for academic research.     

Large-scale software engineering questions ? expert opinion or empirical evidence?

A recent report on the state of the UK information technology (IT) industry based most of its findings and recommendations on expert opinion. It is surprising that the report was unable to incorporate more empirical evidence. This paper aims to assess whether it is necessary to base IT industry and academic policy on expert opinion rather than on empirical evidence. Current evidence related to the rate of project failure is identified and the methods used to accumulate that evidence discussed. This shows that the report failed to identify relevant evidence and most evidence related to project failure is based on convenience samples. The status of empirical research in the computing disciplines is reviewed showing that empirical evidence covers a restricted range of subjects and seldom addresses the 'Society' level of analysis. Other more robust designs that would address large-scale IT questions are discussed. We recommend adopting a more systematic approach to accumulating and reporting evidence. In addition, we propose using quasi-experimental designs developed and used in the social sciences to improve the methodology used for undertaking large-scale empirical studies in software engineering.     

The role of artificial intelligence in business transformation: A case of pharmaceutical companies

Artificial intelligence (AI) is of great interest to researchers and practitioners as a means of achieving the necessary progress in the pharmaceutical industry. However, the role of AI and ways of transforming companies are not well studied. The purpose of the paper is to identify exactly how AI affects the key and support business processes of pharmaceutical companies. We offer a qualitative interview study of five large, five medium, and five small pharmaceutical companies. Based on scarce literature on the role of AI in the pharmaceutical industry, we considered which business processes are subject to transform within it and how they do so. We determine that small pharma companies significantly change research and development, master data management, analysis and reporting, and human resource business processes under the influence of AI. Large pharma companies use AI to transform production, sales, marketing, and analysis business processes. In turn, medium-sized companies are in the middle and individually transform their business processes depending on their specialization.     

The Quest for Relevance: Roles for Academia and Industry in Japan and the U.S.

While U.S. industry is making headway in worldwide markets, much remains to be done. Some have proposed that improving the relevance of engineering education can have a marked influence on the future success of U.S. manufacturing. Some in academia have heard industries' cries for help to improve relevancy in engineering education and have responded with various solutions. Is this the situation in Japan? What is the state of engineering education in Japan? Where is it headed and how does the U.S. compare? We found that industry-academia relationships like those being strengthened in the U.S. are minimal in Japan. Surprisingly, Japanese industry and academia appear not to be headed in a collaborative direction and are even more detached from one another than in the United States. This paper presents some differences in the way Japan and the U.S. view their roles for academia and industry and their interrelationships. Our objective is to further motivate U.S. educators to collaborate with industry and continue to integrate greater relevancy into engineering education.     

The success of the prediction of Haicheng earthquake and the negligence of the Tangshan earthquake

The success of the prediction of Haicheng earthquake and the failure of the prediction of Tangshan earthquake were both well known in the world. What happened, why such a strong earthquake as occurred in Haicheng had been predicted successfully and with a small loss of lives and property? Why a successively strong earthquake about a year later in a region not so further was failure in the imminent stage of prediction and there were so many fatalities and a great degree of property? The author addresses these points based on these true experiences including the first hand experiences leading up to, during, and following these two earthquarter. In addition, he also introduced some seimic phenomena which he had seen after Chichi earthquake in Taiwan.     

Critical success factors influencing wearable sensing device implementation in AEC industry

The present study investigates the success factors (SFs) for implementing wearable sensing devices (WSDs) for safety and health monitoring within the construction industry. A questionnaire survey was designed and administered to stakeholders in the Architectural, Engineering, and Construction (AEC) industry in the United States. A total of 416 valid responses were received from practitioners with experience using WSDs on a construction project. The relative importance and criticality of 25 SFs for WSDs implementation were examined, and survey responses were grouped into general success factors, which were then tested for distinctions across four contingency variables: type of corporation, organization size, levels of Internet of Things (IoT) maturity, and organization experience using WSDs. The results of the statistical analysis revealed the most important SFs for WSDs implementation. The findings of the overall investigation indicate that the critical success factors (CSFs) typically differs depending on the contingency. However, educating and training workers, promoting personalized WSDs, and conducting detailed and continuous assessments of WSDs were identified as key strategies to improve the implementation of WSDs regardless of contingency. Overall, the present study provides a richer understanding of the essential elements required to successfully implement WSDs for safety and health monitoring in construction. Moreover, the present study provides vital information that should inform the development of research frameworks, guidelines, and strategies for improving WSDs integration into construction work processes.     

Quantum catalysis in enzymes: beyond the transition state theory paradigm. A Discussion Meeting held at the Royal Society on 14 and 15 November 2005.

How do enzymes work? What is the physical basis of the phenomenal rate enhancements achieved by enzymes? Do we have a theoretical framework that accounts for observed catalytic rates? These are the foremost questions-with particular emphasis on tunnelling phenomena-debated at this Discussion Meeting by the leading practitioners in the field.     

组合模型对我国软件人才预测的应用实例

随着软件行业的快速发展,软件人才已经成为该行业重要的战略资源.研究目的便是通过对软件人才预测进行研究,使政府部门以及软件企业更好地了解和管理软件人才.根据人才预测的特点,引用灰色GM(1,1)模型预测法和线性回归预测法,利用组合模型技术建立了需求预测综合模型.应用此模型对我国未来5年软件人才需求进行了预测.     

Human performance reliability: on-line assessment using fuzzy logic

This paper presents an on-line approach to monitoring human performance in terms of conditional reliability when one is performing a task. Unlike traditional human reliability analysis, this approach develops a dynamic model that is able to cope with constantly changing conditions that affect operator performance. A fuzzy knowledge-based assessment approach is developed in order to deal with uncertainty and subjectivity associated with human performance assessment. This technology includes three main parts/functions: (i) on-line performance monitoring; (ii) real-time performance forecasting; and (iii) performance reliability assessment. The technology is demonstrated in real-time and provides timely conditioned reliability information regarding task success/failure. In general, this technology offers human reliability assessment under highly dynamic circumstances.     

Cryocooler reliability issues for space applications

Analyzing cryocooler reliability has been problematic from the beginning. Classic reliability analyses rely on statistical sampling and comparing failure modes to other similar systems where statistical results are available. These approaches do not apply to cryocoolers, particularly cryocoolers for aerospace applications. The industry has not built enough total cryocoolers, let alone a single type of cooler, to provide any meaningful statistical sample. This forces us to rely on comparing failure modes of similar systems to that of the cryocooler, which leads to the next problem; today's aerospace cryocooler is designed to have no failure modes. What can it be compared to? Any classic reliability study performed on a cryocooler makes several critical assumptions that completely dominate the results. Change the assumptions and you get a different answer; the results are dictated by the assumptions not the hardware. There are no easy answers to these problems. This paper attempts to show why classic reliability studies do not apply to cryocoolers and that as an industry we must work together to show that all cryocoolers have high levels of reliability.     

Procedure for the analysis of errors of commission during non-power modes of nuclear power plant operation

The continued, historical occurrence of human interactions which place nuclear power plants in a condition of potentially heightened risk is of increasing interest to regulators, utility management, and industry observers alike. These Errors of Commission (EOCs), as they are often called, lead to a variety of questions such as: ‘Can the event lead to a potentially dangerous condition such as core damage? By what failure mechanisms? With what frequency? What defences does the plant have to mitigate the event? Are these actions in the Probabilistic Risk Assessment (PRA) model of the plant?’. EOCs are often excluded from the bounds of a typical PRA model, yet they have the potential for being significant contributors to risk. This paper is the second of two describing procedures for the analysis of the potential for significant errors of commission. The first paper addressed operations at power while this paper describes the procedure for non-power operations. Each procedure describes a method for identifying the opportunities for error, identifying failures modes of functions, systems, or components that could arise from such errors (referred to in this paper as error expressions), and the identification of the most significant of these EOCs based on consideration of consequences, recovery potential, and likelihood.     

Intelligent Manufacturing for the Process Industry Driven by Industrial Artificial Intelligence

Based on the analysis of the characteristics and operation status of the process industry, as well as the development of the global intelligent manufacturing industry, a new mode of intelligent manufacturing for the process industry, namely, deep integration of industrial artificial intelligence and the Industrial Internet with the process industry, is proposed. This paper analyzes the development status of the existing three-tier structure of the process industry, which consists of the enterprise resource planning, the manufacturing execution system, and the process control system, and examines the decision-making, control, and operation management adopted by process enterprises. Based on this analysis, it then describes the meaning of an intelligent manufacturing framework and presents a vision of an intelligent optimal decision-making system based on human–machine cooperation and an intelligent autonomous control system. Finally, this paper analyzes the scientific challenges and key technologies that are crucial for the successful deployment of intelligent manufacturing in the process industry.