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In the age of the Fourth Industrial Revolution, Industry 4.0 can increase the productivity and competitiveness of companies in the international marketplace. The purpose of this article is to investigate the drivers for and outcomes of the adoption of Industry 4.0 technologies in the case of a food processing company located in Italy.

The present work adopted a case study approach by investigating an Italian food processing company to investigate the drivers, challenges and outcomes of Industry 4.0 adoption in the agri-food sector.

This research highlighted drivers and challenges related to the adoption of different Industry 4.0 technologies. Secondly, it underlined the impacts of Industry 4.0 in terms of firm performance, operations management, human resource management and strategy.

Industry 4.0 technologies remain underexplored from the strategic perspective in the agri-food sector. This article provides preliminary evidence on the digital transformation of food processing companies, with a focus on Industry 4.0. Practical implications for managers, CEOs and entrepreneurs are discussed.

This paper examines how the altering nature of planning decisions affects operational efficiency in seaport container terminals. The uncertainty and the role of the planner were investigated considering the dynamic integrated planning function of the quay to yard interface.

A system dynamics model has been built to illustrate the integrated dynamic environment. Data collection was conducted at a leading container terminal at a hub port. The model was simulated for different scenarios to derive findings.

The planner has been identified as the agent who makes alterations between the initial operational plan and the actual plan. The initial plan remains uncertain even when there is no impact from crane breakdowns, requiring a significant number of alterations to be made. The planner who had worked on the yard plan had altered (approximately 45%) the initial plan than the alterations done by the planner who had worked on the vessel plan. As a result, the feedback loop that is created by the remaining moves at each hourly operation influences the upcoming operation as much as crane breakdowns influence.

The uncertainty and the role of the planner were investigated considering the dynamic integrated planning function of the quay to yard interface. The findings of this study are significant since terminal efficiency is examined considering the quayside and landside as an integrated system.

The literature on warehouse performance assessments is mainly focussed on the efficiency and effectiveness of an action or activity due to customer demand and tailored fulfilment, with less attention being given to the performance measurement of each function of the warehouse and its overall productivity. Therefore, this study was aimed at revising the key warehouse performance metrics to a set of productivity measurement indicators that can be adopted internationally for benchmarking productivity performance.

A literature review and semi-structured survey questionnaire were used for this study. The importance of warehouse productivity performance was reviewed to revamp the measurement indicators. Through the use of a directed content analysis and descriptive analysis, an extensive study was carried out to analyze existing warehouse productivity indicators.

The findings of this study provide comprehensive references for practitioners and academicians for improving the classification of productivity measurements from existing key performance metrics for warehousing. Also, this paper highlights the warehouse resources related to the respective warehouse operation activities.

The study was limited to productivity performance indicators adapted from Staudt et al. (2015). Furthermore, the samples for this study comprised Malaysian academicians and practitioners in the related field. The findings can be adapted on a global scale as this study implemented general warehouse operation processes.

Consequently, the contributions of this study are that it provides relevant benchmarks for key productivity performance indicators in the warehousing sector that has worldwide applicability and the developed model provides a conceptual platform from which further theoretical and empirical developments can be carried out.

The purpose of this paper is to examine the role of innovative HR practices as an important mechanism through which strategic flexibility affects firm performance as well as the role of female leadership in this relationship.

Data were gathered from a sample of 113 firms in China. The authors collected information on organizational strategy, HR practices, CEO information, corporate social responsibility and other firm characteristics in terms of firm age, location, and financial performance. Conditional procedural analysis was conducted to test the model.

The authors found strong evidence in support of the mediation relationship in which organizations with a strong focus on strategic flexibility are more likely to adopt Innovative HR Practices. Furthermore, the authors found that the extent to which firms have adopted innovative HR practices has a strong effect on employee productivity. In addition, the authors found that female leadership enhances strategic flexibility-performance relationship.

Information on strategic flexibility, HR practices and firm performance was collected at the same time. Future studies based on panel data would be helpful to establish the causal relationships in the model.

The authors’ findings suggest that practitioners should put more emphasis on developing innovative HR practices, as they are required by strategic flexibility.

Firms pursuing strategic flexibility should feel more confident when appointing a female CEO, because the results show that female leadership may enhance the positive impact of strategic flexibility on firm performance.

This research study is the first empirical examination of the mediating influence of innovative HR practices on the relationship between strategic flexibility and firm performance. The study also shows that female leadership benefits an organization in implementing strategic flexibility. The results are of value to researchers, human resource management managers, employees, and executives who are seeking to develop practices that are flexible and innovative in order to stay competitive in dynamic environments.

Productivity increase is correlated with profitability, sustainability and competitiveness of the construction firms. Recent studies reveal that the primary causes of productivity decline are poor usage of scientific and technological advances, ineffective supervision strategies and poor apprenticeship facilities/opportunities. Accordingly, the purpose of this study was to evaluate how well construction supervisors can utilise fundamental science and technological concepts/ideas to increase the efficiency and productivity of construction activities.

A new strategic layout was designed with the use of potential training guide tools. Based on the designed layout, a new supervisory training programme was developed, and 62 construction supervisors were selected, trained and evaluated in line with six parts of competencies and the relevant learning domains. An assessment guide with different levels of descriptions and criteria was developed through literature analysis and expert interviews. The research tools were verified using comprehensive approaches.

The overall mean values of supervisors’ performance scores indicate proficient-level grades in the competency characteristics related to taking measurements, generating drawings/designs using manual techniques and computer-aided tools, involving Bill of Quantities (BOQ) preparations and preparing training plans/materials for improving the competencies of labourers on estimation, measurements and understanding drawings. Their proficiency was notably lower in the use of information and communication technology application tools in construction tasks compared to others. The findings point to a modern generalised guideline that establishes the ranges of supervisory attributes associated with science and technology-related applications.

The study outcomes produce conceptualised projections to restructure and revalue the job functions of various working categories by adding new definitions within the specified scope. This may result in constructive benefits to upgrading the current functions associated with urbanisation, sustainability and society. The implementation of the study’s findings/conclusions will have a significant impact on present and future practices in other developing nations and developing industries, even if they are directly applicable to the Sri Lankan construction industry.

Up to certain limits/stages, the study fills not only the knowledge gap in the field of creating protocols and application techniques connected to lifelong learning and skill enhancement/upgrading but also the existing gaps in work attributes and roles of construction supervisors associated with the utilisation of fundamental science and technological concepts/ideas towards reinforcing sustainable and productive site operations.

This paper aims to improve our understanding of performance measurement systems in the health and care sector, by focussing on employee reactions to core performance measurement practices. Targets and monitoring are hypothesised to be associated with employee perceptions of job control, supportive management and job demands, which in turn, are expected to be linked to employee-wellbeing and organisational commitment.

Matched employee workplace data are extracted from a nationally representative and publicly available survey. Structural equation models are estimated.

Performance measurement systems are neither perceived as resources nor additional demands. Setting many targets and a focus on productivity can lead to negative employee outcomes, since these positively correlate with perceptions of job demands, which negatively correlate with employee wellbeing. However, monitoring financial performance and monitoring employee performance may be helpful to managers, as these are positively associated with employee perceptions of job control and supportive management, which positively correlate with job satisfaction and organisational commitment and, negatively, with anxiety. Overall, common criticisms of performance measurement systems in healthcare are questioned.

Given the lack of consensus on how performance measurement systems can influence employee experiences and outcomes, this study combines theories that argue for performance measurement systems in managing operations with models developed by psychologists to describe how perceptions of the work conditions can affect employee attitude and wellbeing. A conceptual model is therefore developed and tested, and potential direct and indirect effects of performance measurement systems in the health sector are inferred.

Collaboration and operational capabilities are two strengths for managing supply chains to achieve operational performance. In this context, this study aims to analyze the mediating effect of operational capabilities on the relationship between collaborative supply chain management and operational performance.

The study design consisted of a theoretical framework to estimate the mediation paths by latent variable structural modeling methods. A survey of 138 respondents from Brazilian capital goods companies was conducted.

The study revealed that operational capabilities partially mediate the relationship between collaborative supply chain management and operational performance. The findings provide important guidance for managers to strengthen the relationship with suppliers to continuously improve operational capability.

As the sample size was made up of 138 respondents, it was impossible to revalidate the theoretical–empirical model. New data need to be collected to re-evaluate the structural model and expand them to other economic segments.

By examining the theoretical insights and empirical findings, the study expanded knowledge about collaborative management and the understanding of the importance of operational capabilities in the relationship between collaborative management and operational performance for management practices.

The study developed a theoretical–empirical measurement model, reliable and statistically validated, to test the mediating effect of operational capabilities in the relationship between collaborative management and operational performance.

In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.