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The purpose of this paper is twofold: first, to discuss key challenges associated with the use of either simulation or real-world application of intelligent autonomous vehicles (IAVs) in supply network operations; and second, to provide a theoretical and empirical evidence-based methodological framework that supports the integrated application of conceptualisation, simulation, emulation and physical application of IAVs for the effective design of digital supply networks.

First, this study performs a critical review of the extant literature to identify major benefits and shortcomings related to the use of either simulation modelling or real-word application of physical IAVs. Second, commercial and bespoke software applications, along with a three-dimensional validation and verification emulation tool, are developed to evaluate an IAV’s operations in a conceptual warehouse. Third, a commercial depth-sensor is used as a test bed in a physical setting.

The results demonstrate that conceptual and simulation modelling should be initially used to explore alternative supply chain operations in terms of ideal performance while emulation tools and real-world IAV test beds are eminent in validating preferred digital supply chain design options.

The provided analysis framework was developed using literature evidence along with experimental work and research experience, without consulting any industry experts. In addition, this study was developed based on the application of a single physical device application as a test bed and, thus, the authors should further progress with the testing of a physical IAV in an industrial warehouse.

The study provides bespoke simulation modelling and emulation tools that can be useful for supply chain practitioners in effectively designing network operations.

This work contributes in the operations management field by providing both a multi-stage methodological framework and a practical “toolbox” for the proactive assessment and incorporation of IAVs in supply network operations.

This paper aims to present a general review of the circumstances of America and Japan's rapid corporate, economic and industrial development in the twentieth century.

The approach considered and evaluated how the circumstances of America and Japan's growth might apply to China and India, two of the fastest growing economies of the twenty‐first century.

The findings suggest that both America and Japan might be considered exceptional cases and, as such, neither one might be regarded as a good model for emulation. However, the circumstances of Japan's rapid growth appear closer to those of contemporary China and India and on that basis the authors suggest that Japan might be a better model for emulation.

The American model is too novel and unlikely to be imitated, replicated or repeated whereas Japan's high population density, agrarian origins, state assisted and administered development, adaptation and hybridization of local and imported methods and technologies, kinship, pseudo‐kinship and locality based business groupings, and rapid, come‐from‐behind charge toward industrialization, urbanization and international emergence, all suggest that Japan offers a more relevant and useful development model.

This study aims to examine changes in the strength of attachment to role models (strong, weak) and in the levels of commitment to the brands endorsed by role models (high, moderate, low) by exposing young athletes emulating sport stars to celebrity-engendered negative messages (CeNM).

Two studies were conducted with footballers ranging in age from 13 to 18 years. Study 1 explored emulated football stars, attachment strength and commitment levels (n = 1,425). Study 2 exposed purposively selected 853 young footballers to four types of CeNM by content (social attribute, moral conduct, game performance, physical condition). A combination of non-/parametric t-tests was run for the paired comparisons of before–after exposure to CeNM.

All CeNM contents weakened the ties to emulated football stars across the entire sample. Similarly, commitment to endorsed brands was diminished in all three categories after exposure to CeNM. When further examined, it appeared that the message about sport stars’ bad health did not affect the highly and moderately committed subjects. Overall, not just CeNM comprising personality-related topics (e.g. illicit affair, unfair play) but also those covering uncontrollable (e.g. injuries) and transient issues (e.g. penalty-kick misses) damaged young people’s attachment to their celebrity role models and tarnished, to a great extent, their evaluations of the brands the role models endorse.

Besides echoing the common tendency that attachment to celebrity role models is strong among the youth, findings also indicate that young people, unlike adults, react naively to CeNM.

The purpose of this paper is to obtain the characteristic parameters of dynamic trend for hydro turbine governors, and take these as the inputs of the neural network to realize the diagnosis of the system.

Computer simulation technique has become an important tool in the science research and development. Because of the different degree defaults of the traditional modeling method, a distributed bond graph modeling (BGD) method is presented in this paper. Fault diagnosis and identification have been widely developed in recent years, while many kinds of diagnosis methods have been used in this area. Since it is difficult to get the characteristic parameters of dynamic trend, a new methodology is proposed which integrates BGD and neural network. It gets the characteristic parameters by simulation to the system, and takes these as the inputs of the neural network to realize the diagnosis of the system.

The paper presents the need for application of two models – conventional procedure and bond graph model approaches.

The paper is a very useful diagnosis tool for operators.

A new methodology is proposed which integrates BGD and neural network. The paper is aimed at operational researches and engineers, the results from simulations are reported and commented in the paper.

The purpose of this paper is to develop a method for the discovery of knowledge in emergency response databases based on police incident reports, generating information that identifies local criminal demands that allow the selection of the appropriate policing strategies portfolio to solve the problem.

The developed model uses a methodology for the discovery of knowledge involving text mining techniques using Latent Dirichlet Allocation (LDA) integrated with the ELECTRE I multicriteria method.

The developed method allowed the identification of the most common criminal demands that occurred from January 1 to December 31, 2016, in the policing areas studied. One of the crimes does not occur homogeneously in a particular locality. In this study, it was initially observed that 40 per cent of the crimes identified in the Integrated Public Safety Area 5, or AISP-5, (historical city center of RJ) had no correlation with AISP-19 (Copacabana - RJ), and 33 per cent of crimes crimes in AISP-19 were not identified in AISP-5. This finding guided the second part of the method that sought to identify which portfolio of policing strategies would be most appropriate for the identified demands. In this sense, using the ELECTRE I method, eight different scenarios were constructed where it can be identified that for each specific criminal demand set there is a set of policing strategies to be applied.

The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics.

The developed methodology contributes in a complementary way to the identification of criminal practices and their characteristics based on reports of police occurrences stored in emergency response databases. The knowledge generated through the identification of criminal demands allows law enforcement decision makers to evaluate and choose among the available policing strategies, which best suit the reality they study, and produce the reduction of criminal indices.

It is possible to infer that by choosing appropriate strategies to combat local crime, the proposed model will increase the population’s sense of safety through an effective reduction in crime.

The originality of the study lies in the integration of text mining techniques, LDA and the ELECTRE I method for detecting crime in a given location based on crime reports stored in emergency response databases, enabling identification and choice, from customized policing strategies to particular criminal demands.

Reports on the innovations process in American policing. Explores how information about innovation is communicated among police organizations and the factors which influence adoption. Reports the findings of a survey of police chiefs and executives. Develops a model with four elements: cosmopolitanism; risk mediation; peer emulation; and innovativeness. Finds that many police departments use informal networks of communication to support organizational decision making and are often influenced by executives’ participation in policy communities and by efforts to reduce the organization’s perceived risk of civil liability. Recommends that police organizations strengthen their networks of communication in a more formal manner.

The purpose of this paper is threefold: to present internet of things (IoT)-based cyber-physical system (CPS) architecture framework to facilitate the integration of IoT and CPS; to implement an IoT-based CPS prototype based on the architecture framework for a PL application scenario of in a case study; and to devise evaluation methods and conduct experimental evaluations on an IoT-based CPS prototype.

The design research method, case study, emulation experiment method, and cost-benefit analysis are applied in this research. An IoT-based CPS architecture framework is proposed, and followed by the development of prototype system and testbed platform. Then, the emulation and experimental evaluation of IoT-based CPS are conducted on the testbed, and the experimental results are analyzed.

The emulation experiment results show that the proposed IoT-based CPS outperforms current barcode-based system regarding labor cost, efficiency, and operational adaptiveness. The evaluation of the IoT-based CPS prototype indicates significant improvements in PL tasks and reduced part inventory under a dynamic changing shop-floor environment.

The case study shows that the proposed architecture framework and prototype system can be applied to many discrete manufacturing industries, such as automobile, airplane, bicycle, home appliance, and electronics.

The proposed IoT-based CPS is among the first to address the need to integrate IoT and CPS for PL applications, and to conduct experimental evaluations and cost-benefit analysis of adopting IoT-based CPS for PL. This paper also contributes to the IoT research by using diverse research methods to offer broader insights into understanding IoT and CPS.

The global spread of production makes companies relocate their manufacturing units frequently. Not only equipment, systems, and facilities, need to be moved, but the transfer of operational knowledge and experience seem to be a major challenge. However, discussions on knowledge transfer are derived predominantly from a cognitive perspective and normally focus on the organizational level and are rarely linked with outsourcing. Thus, from the perspective of operations management, the purpose of this paper is to explore how to transfer production know‐how on the shop‐floor level when manufacturing units are relocated and indicate which means can be used to support this intra‐firm transfer process.

Four cases are identified from research in three Danish companies. Observations and 77 semi‐structured interviews have been made over a time period of two years. Surveys of documents, questionnaires, and the Delphi method have been used to supplement the research.

Four cases are analyzed from four aspects. By doing so, different relocation situations are identified; different types of transferred knowledge are recognized; and different groups of means for knowledge transfer are introduced and classified according to their usage.

A framework is summarized to integrate all the elements discussed in this paper, which provides strong support and clear directions to industrial managers when they need to transfer knowledge in the relocation projects. Together with the framework, a process to help managers implement their knowledge transfer is also proposed.

Besides the framework and process, this paper also indicates that absorptive capacity of a production unit depends not only on the level of technical competence at its site, but also on whether real manufacturing environment has already existed or not. Two means for establishing virtual manufacturing environment are suggested.

Leaders model behaviors they want followers to emulate, and they use various technologies to enhance their message, but which tools are most effective? Using two studies, this paper sets out to compare the effectiveness of newer and older computer technologies used by leaders for describing and demonstrating desired behaviors.

The first study, an interdisciplinary experimental design, involved 110 students across two college campuses and data were analyzed using a 2 (modeling and no modeling)×2 (older and newer technology) between‐subjects ANOVA. The second study further explored modeling with both technologies on one campus, and data were analyzed with independent samples t‐tests.

Newer technology was more effective than older technology in increasing desired behaviors but only when coupled with modeling of those behaviors by the leader. However, after the novelty of the new technology had worn off, no significant difference in production of desired behaviors was observed.

Justifying the expense of purchasing new technology to replace functional older equipment is an important consideration for businesses and universities. Organizational leaders need factual, unbiased data to guide their decisions about allocating limited financial resources.

The studies were designed to provide decision‐makers with some much‐needed empirical data.

The marketer’s principal objective is typically to build a relationship with buyers, rather than merely to make a single sale. Ideally, the essence of that relationship consists of a strong bond between the buyer and the brand. Outlines six strategies for building that relationship: linking the brand to a particular need; associating it with a pleasant mood; appealing to subconscious motives; conditioning buyers to prefer the brand through reward; penetrating perceptual and cognitive barriers to create preference; and providing attractive models for buyers to emulate. The choice of an individual strategy or combination depends mainly on the nature of the branded product or service. The success of the strategy depends heavily on the marketer’s understanding of the preference building and bonding process.