Search results

The future construction site will be pervasive, context aware and embedded with intelligence. The purpose of this paper is to explore and define the concept of the digital skin of the future smart construction site.

The paper provides a systematic and hierarchical classification of 114 articles from both industry and academia on the digital skin concept and evaluates them. The hierarchical classification is based on application areas relevant to construction, such as augmented reality, building information model-based visualisation, labour tracking, supply chain tracking, safety management, mobile equipment tracking and schedule and progress monitoring. Evaluations of the research papers were conducted based on three pillars: validation of technological feasibility, onsite application and user acceptance testing.

Technologies learned about in the literature review enabled the envisaging of the pervasive construction site of the future. The paper presents scenarios for the future context-aware construction site, including the construction worker, construction procurement management and future real-time safety management systems.

Based on the gaps identified by the review in the body of knowledge and on a broader analysis of technology diffusion, the paper highlights the research challenges to be overcome in the advent of digital skin. The paper recommends that researchers follow a coherent process for smart technology design, development and implementation in order to achieve this vision for the construction industry.

To determine how to best deploy the Triple-A supply chain (SC) capabilities (AAA-agility, adaptability and alignment) to improve competitive advantage (CA) by identifying the Triple-A SC model with the highest CA predictive capability.

Assessment of in-sample and out-of-sample predictive capacity of Triple-A-CA models (considering AAA as individual constructs) to find which has the highest CA predictive capacity. BIC, BIC-Akaike weights and PLSpredict are used in a multi-country, multi-informant, multi-sector 304 plant sample.

Greater direct relationship model (DRM) in-sample and out-of-sample CA predictive capacity suggests DRM's greater likelihood of achieving a higher CA predictive capacity than mediated relationship model (MRM). So, DRM can be considered a benchmark for research/practice and the Triple-A SC capabilities as independent levers of performance/CA.

DRM emerges as a reference for analysing how to trigger the three Triple-A SC levers for better performance/CA predictive capacity. Therefore, MRM proposals should be compared to DRM to determine whether their performance is significantly better considering the study's aim.

Results with our sample justify how managers can suitably deploy the Triple-A SC capabilities to improve CA by implementing AAA as independent levers. Single capability deployment does not require levels to be reached in others.

First research considering Triple-A SC capability deployment to better improve performance/CA focusing on model's predictive capability (essential for decision-making), further highlighting the lack of theory and contrasted models for Lee's Triple-A framework.

In the context of the Industry 4.0, this paper aims to investigate the state of the art of Italian manufacturing, focusing the attention on the implementation of intelligent predictive maintenance (IPdM) and 4.0 key enabling technologies (KETs), analyzing advantages and limitations encountered by companies.

A survey has been developed by the University of Parma in cooperation with the Italian Workers' Compensation Authority (INAIL) and was submitted to a sample of Italian companies. Overall, 70 answers were collected and analyzed.

Results show that the 54% of companies implemented smart technologies, increasing quality and safety, reducing the operating costs and sometimes improving the process' sustainability. However, IPdM was implemented only by the 37% of respondents: thanks to big data collection and analytics, Internet of Things, machine learning and collaborative robots, they reduced downtime and maintenance costs. These changes were implemented mainly by large companies, located in northern Italy. To spread the use of IPdM in Italian manufacturing, the high initial investment, lack of skilled labor and difficulties in the integration of new digital technologies with the existing infrastructure are the main obstacles to overcome.

The article gives an overview on the current state of the art of 4.0 technologies implementation in Italy: it is useful not only for companies that want to discover the implementations' advantages but also for institutions or research centres that could help them to solve the encountered obstacles.

The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.

In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.

The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.

A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.

The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.

The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

This study aims to examine factors that determine the adoption of additive manufacturing by small- and medium-sized industries. It provides insights with regard to benefits, challenges and business factors that influence small- and medium-sized industries when adopting this technology. The study also aims to expand the domain of additive manufacturing by including a broader range of challenges and benefits of additive manufacturing in literature.

Using data collected from 175 small- and medium-sized industries, the study has examined through Mann–Whitney test to understand the difference between owners and design engineers on additive manufacturing technology adoption in small- and medium-sized companies.

This study suggests contribution to academic discussion by providing associated factors that have significant impact on the adoption of additive manufacturing technology. Related advantages of additive manufacturing are reduction in inventory cost, lowering the wastage in production and customization of products. The study also indicates that factors such as cost of machinery, higher level of cost in integrating metal components have a negative impact on the adoption of this technology in small- and medium-sized industries.

Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further in the field of challenges and growth in other areas of application of additive manufacturing, for instance, medical sciences, fabric and aerospace.

The study provides important implications that are of interest for both research and practitioners, related to technology management in small- and medium-sized industries, e.g. foundry and machining industries.

This work/study fulfills an identified need of the small- and medium-sized companies in adopting new technologies and contribute to their growth by understanding the need to accept and implement technology.

This paper fulfills an identified need to study how small- and medium-scale companies accept new technologies and factors associated with implementation in the manufacturing process of the organization.

Food supply chains are facing significant changes and challenges coming from the need to adopt inclusive business practices and foster sustainable development. The current research aims to provide insights related to the inclusiveness of sustainable business practices of a dairy production co-operative where the smallholders are engaging with a traditional business mind-set. The purpose of the research is to deliver insights about the linkages between accounting and business practices in the adoption of an inclusive business model.

This research is based on the qualitative case study of a medium-sized production cooperative company operating in the dairy industry supply chain.

This study answers the call for research on accounting and control systems in the food sector, by exploring how management and control system is practised within contemporary supply chains. The research aims to analyse the contextual insights of managerial and organizational practices that facilitate inter-organizational relationships and coordination among actors that engage along the supply chain.

The main limitation relates to the conditions of the time available and the limited number of interviews. Even if key actors were interviewed, a larger sample would have been more beneficial to the research. However, the lack of proper results for accounting tools and software access caused some loss to the research.

This paper emphasizes the effects of the “inclusiveness” of the sustainable business models between suppliers and leading companies by enhancing the sustainable business development within the supply chain, as part of a wider business model in the food sector from an inter-organizational perspective.

The aim of this study is to identify and rank the factors of innovation capability (IC) in Indian micro, small and medium enterprises (MSMEs). This study focuses on ascertaining the important factors that help in enhancing the IC with an emblematic focus on the MSME sector of India.

This paper proposes a multicriteria decision-making methodology, which is based on fuzzy analytic hierarchical process to prioritize the factors that enhance the IC of MSMEs. Finally, sensitivity analysis is conducted to examine the ranking stability.

Knowledge management is the most important enabler, followed by creativity and idea management and organizational culture.

Several organizations promote the strategic measures for enhancing the IC. To increase their capability to innovate, there is a need to identify, acknowledge and implement the drivers of IC into practice.

Prioritization done in the study facilitates the entrepreneurs to determine the most important factors that need crucial attention in dealing with sensitive issues of IC. Entrepreneurs can take several steps to implement the most important factors for enhancing the IC into practices for meeting the needs of the consumers, generating profits and enhancing the competitiveness.

The organizational digital transformation (ODT) in companies presents small and medium-sized enterprises (SMEs) – who remain at the beginning of this transformation – with the challenge of offering digital services based on sensor technologies. Against this backdrop, the present paper identifies ways SMEs can enable digital servitization through sensor technology and defines the possible scope of the organizational transformation process.

Around 21 semi-structured interviews were conducted with experts from different hierarchical levels across the German manufacturing SME ecosystem. Using the Gioia methodology, fields of action were identified by focusing on influencing factors and opportunities for developing these digital services to offer them successfully in the future.

The complexity of existing sensor offerings must be mastered, and employees' (data) understanding of the technology has increased. Knowledge gaps, which mainly relate to technical and organizational capabilities, must be overcome. The potential of sensor technology was considered on an individual, technical and organizational level. To enable the successful implementation of service offerings based on sensor technology, all relevant stakeholders in the ecosystem must network to facilitate shared value creation. This requires standardized technical and procedural adaptations and is an essential prerequisite for data mining.

Based on this study, current problem areas were analyzed, and potentials that create opportunities for offering digital sensor services to manufacturing SMEs were identified. The identified influencing factors form a conceptual framework that supports SMEs' future development of such services in a structured manner.

Stereotypes are simplified and widely shared visions held by a social group regarding a place, object, event or recognizable set of people united by certain characteristics or qualities. They are “dangerous” mental models because they are widely disseminated, devious and capable of acting even unconsciously in individuals, social groups and organizations altering the rationality of assessments and choices and producing discrimination and prejudice. Stereotypes acritically extend from a characteristic of a significant percentage of a category to the totality of individuals. The process of generalization triggered by a stereotype produces the error of discrimination and prejudice. There are numerous forms of stereotypes, but this study takes into account gender stereotypes because they act pervasively, often subtly, to reduce “productivity”. People who are aware of being discriminated perceive an unsatisfactory fulfillment of their motivations, which reduces their incentive to improve their performance. Since productivity measures the efficient use of energy from working in production processes, the author believes that wherever gender stereotypes are at play, there is a productive “waste of energy”, an inefficiency in work activity with harmful effects for organizations of all kinds, including families.

The work aims to demonstrate that wherever gender stereotypes are at play, a “waste of energy” manifests itself in terms of productivity, representing an inefficiency in work activity with harmful effects for organizations of all kinds, including families. To describe the negative effects stereotypes produce in organizations, some models are presented based on the methods and language of systems thinking. These models, although typically qualitative, are capable of exploring the most accepted theories in the literature: tournament theory, the Pygmalion effect, the Galatea effect, self-fulfilling prophecies, the Queen bee syndrome, the role congruency theory, the glass ceiling theory (“think manager, think male” and “family responsibilities wall”). The paper follows a predominantly organizational and corporate approach, although the copious literature on stereotypes belongs largely to the area of social psychology and organization studies.

The paper does not consider the psychological origin of stereotypes but highlights their use as routines-shortcuts for evaluations and decisions demonstrating that, when adopted in social systems and within organisations, stereotypes produce different forms of discrimination: in social rights, in work, in careers and in access to levels of education and public services, reducing performance and limit potential. The paper also examines some ways gender and culture stereotypes can be opposed, presenting a change management strategy and some concrete solutions proposed by the process–structure–culture model for social change (PSC model).

The main limitation of the work is that it focuses on gender stereotypes, choosing not to consider the “intersection effect” of these with other stereotypes: racial stereotypes, religious stereotypes, color stereotypes, age stereotypes, sex and sexual orientation stereotypes, and many others, whose joint action can cause serious inefficiencies in organizational work.

As stereotypes are a component of social culture and are handed down, by use and example, from generation to generation, the maintenance over time of stereotypes used by individuals to evaluate, judge and act can be seen as an effect of the typical action of a combinatory system of diffusion, which can operate for a long time if not effectively opposed. Il PSC model indicates the strategy for carrying out this opposition.

With regard to gender stereotypes, it should be emphasized that in organizations and social systems, “gender diversity” should be considered an opportunity and not as a discriminating factor and thus encouraged by avoiding harmful discrimination. In fact, this diversity, precisely because of the distinctive characteristics individuals possess regardless of gender, can benefit the organization and lead to an increase in organizational and social performance. The United Nations 2030 Agenda for Sustainable Development (2020) Goal 5: Achieving gender equality and empowering all women and girls is examined in this context.

This study views the action of gender stereotypes as especially harmful “mental models”, highlighting the distortions they cause in the allocation of productive energy in society, groups and organizations. The paper follows a predominantly organizational and corporate approach, although the copious literature on stereotypes belongs largely to the area of social psychology. Using the “logic” and “language” of systems thinking, theories and models that describe and interpret the distorting effects of organizational choices based on stereotypes rather than rational analysis are highlighted. The action of stereotypes and their persistence over time can also be described using combinatory systems theory. With this paper, the author hopes that by acting on the three wheels of change highlighted by the PSC model, through legal provisions, control tools and actions on the culture operated by educational and social aggregative institutions, it should not be impossible to change the prevailing culture so that it becomes aware of the harmful influence of gender stereotypes and other discriminatory mental models and come to reject them. The author hopes this paper will help to understand the need to make this change.

The purpose of this study was to use relational vision as a theoretical support for an investigation of how operational competencies are developed from the interaction of shared relational resources in the supply chain and to verify how these competencies allow the resources to be able to function, unity, integration and direction.

This multihull study was based on semi-structured interviews with 13 representatives of four dyads from companies in the steel, automotive and industrial applications, pulp processing and manufacturing and application of flexible tubes.

The results indicate that information, knowledge and learning are significant constructs to influence the development of operational skills in the supply chain because they represent the ability of the company to promote skills to efficiently use resources and create a barrier to imitation.

There are limitations in the use of four companies from different industrial segments because it is possible to generalize the results. However, given the cross-sectional nature of the research, new studies may adopt a longitudinal approach to verify the evolution in the area of operations. Future studies may also expand the unit of analysis to understand the role of the relationship between the focus business and its strategic suppliers from the viewpoint of the suppliers. Thus, new research can be expanded to dyads, triads and business networks by investigating the various stages in the supply chain.

This study contributes to the literature and adds the dimension of relational operational skills, which is hitherto little explored in previous studies.

This study contributes to the literature in the area of operations management, in collaborative relationships between buyers and sellers, focusing on the relational view of competitiveness.

The growing importance of organizations and the role of collaboration, based on mutual benefits and grouping of skills, tend to increase the competitive benefits of companies operating in this context. The management of this type of arrangement becomes a challenge for researchers, reinforcing the originality of this study.