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This paper aims to develop a method based on artificial intelligence capable of predicting the due date (DD) of job shops in real-time, aiming to assist in the decision-making process of industries.

This paper chooses to use the methodological approach Design Science Research (DSR). The DSR aims to build solutions based on technology to solve relevant issues, where its research results from precise methods in the evaluation and construction of the model. The steps of the DSR are identification of the problem and motivation, definition of the solution’s objectives, design and development, demonstration, evaluation of the solution and the communication of results.

Along with this work, it is possible to verify that the proposed method allows greater accuracy in the DD definition forecasts when compared to conventional calculations.

Some limitations of this study can be pointed. It is possible to mention questions related to the tasks to be informed by users, as they could lead to problems in the performance of the artifact as the input data may not be correctly posted due to the misunderstanding of the question by part of the users.

The proposed artifact is a method capable of contributing to the development of the manufacturing industry to improve the forecast of manufacturing dates, assisting in making decisions related to production planning. The use of real production data contributed to creating, demonstrating and evaluating the artifact. This approach was important for developing the method allowing more reliability.

Innovative work behaviour (IWB) has long been advocated as a crucial resource for organisations. Evidence that work characteristics stimulate the adoption of IWB is widespread. Yet, the relationship between knowledge characteristics and IWB has often been overlooked. This study aims to address this gap by examining this relationship.

Building on an integrative vision of innovation, this study analyses the effects of combinations in work characteristics on IWB through a configurational approach. Job autonomy, complexity, problem solving, specialisation and demand for constant learning were examined as determinants of IWB using fuzzy-set qualitative comparative analysis.

Based on a sample of 214 Belgium employees, the results highlight seven configurations of work characteristics to elicit high levels of IWB. For six of them, problem solving appears as a needed condition.

Presented findings offer insights for organisations aiming at evolving in a competitive context to generate optimal conditions for promoting employee innovation.

While most studies have tested the influence of work characteristics independently, this research investigates the joint influence of work characteristics and identifies how combinations of multiple variables lead to IWB.

The primary aim of this study is to gain insight into the entrepreneurial marketing strategy (EMS) decisions made by microenterprises in the craft sector and draw comparisons between the marketing strategy formulation and implementation of conventional businesses and those of craft-based microenterprises with a specific focus on the context of emerging markets, particularly India.

This paper follows a qualitative interpretivist paradigm using a multiple-case methodology approach. It tracks craft-based microenterprises that make furniture or home décor products in India. The study participants were the founders, principal designers, studio managers or craftspersons.

This study’s findings reveal that craft-based microenterprises implement an EMS that adopts a hybrid form of market orientation strategy. In this approach, the product or creative concept is at the centre of the decision-making, and the customer needs are factored in at a later stage for customisation. These microenterprises prioritise product positioning over segmentation and targeting strategies.

This study tries to understand marketing strategy decision-making processes among craft-based microenterprises in India. Given that study participants came from only two-product-based craft businesses, this limits the generalisability of the findings to similar or related contexts. This study provides a framework and methodology for replication in other contexts and industries to formulate a nuanced understanding of micro, context-specific, craft-based businesses.

This study uses qualitative analysis to understand EMS in craft-based businesses in India. This study contributes to this fledgling stream of literature at the interface of marketing and entrepreneurship to understand entrepreneurial marketing. This study analyses the marketing strategy of craft-based businesses using the framework of Morgan et al. (2019).

We studied the relationship between job engagement and systematic problem solving (SPS) among shop-floor employees and how lean production (LP) and Internet of Things (IoT) systems moderate this relationship.

We collected data from a sample of 440 shop floor workers in 101 manufacturing work units across 33 plants. Because our data is nested, we employed a series of multilevel regression models to test the hypotheses. The application of IoT systems within work units was evaluated by our research team through direct observations from on-site visits.

Our findings indicate a positive association between job engagement and SPS. Additionally, we found that the adoption of lean bundles positively moderates this relationship, while, surprisingly, the adoption of IoT systems negatively moderates this relationship. Interestingly, we found that, when the adoption of IoT systems is complemented by a lean management system, workers tend to experience a higher effect on the SPS of their engagement.

One limitation of this research is the reliance on the self-reported data collected from both workers (job engagement, SPS and control variables) and supervisors (lean bundles). Furthermore, our study was conducted in a specific country, Italy, which might have limitations on the generalizability of the results since cross-cultural differences in job engagement and SPS have been documented.

Our findings highlight that employees’ strong engagement in SPS behaviors is shaped by the managerial and technological systems implemented on the shop floor. Specifically, we point out that implementing IoT systems without the appropriate managerial practices can pose challenges to fostering employee engagement and SPS.

This paper provides new insights on how lean and new technologies contribute to the development of learning-to-learn capabilities at the individual level by empirically analyzing the moderating effects of IoT systems and LP on the relationship between job engagement and SPS.

This study aims to investigate the impact of job rotation on employee performance and explores the mediating role of human resources (HR) strategy and training effectiveness on this relationship, within the petrochemical industry, which represents a highly specialist and hazardous industrial context.

Data was collected through a questionnaire which was distributed among the experts working in an Iranian petrochemical organization. Previously validated scales were used to measure job rotation, employee performance, HR strategy and training effectiveness, and partial least squares structural equation modeling was used for hypothesis testing.

The research findings indicated that job rotation had a negative effect on employee performance, while training effectiveness and HR strategy positively mediated the relationship between job rotation and employee performance. This highlights the importance of ensuring effective training and a HR strategy to support job rotation of skilled and specialist employees.

Managers of employees in specialist and hazardous industries, such as petrochemical workers, interested in job rotation to support employee career development, should be mindful of potential negative implications on employee performance. To support and improve employee performance, job rotation should be considered alongside HR strategy and training.

Previous research has largely focused on the value of job rotation to develop managers’ organizational understanding and to reduce injury within blue-collar work, which has led to a paucity of research into job rotation within highly skilled and specialist industrial roles. It is highlighted within the literature that it remains unclear what supports effective job rotation. This study addresses this lacuna by investigating how job rotation affects employee performance in a highly skilled and specialized industry and how strategy and training effectiveness mediate this effect.

The purpose of the study is to identify the soft skills and abilities that are crucial to success in the fields of operations management (OM) and supply chain management (SCM), using the O*NET database and the classification of a set of professional figures integrating values for task skills and abilities needed to operate successfully in these professions.

The study used the O*NET database to identify the soft skills and abilities required for success in OM and SCM industries. Correlation analysis was conducted to determine the tasks required for the job roles and their characteristics in terms of abilities and soft skills. ANOVA analysis was used to validate the findings. The study aims to help companies define specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the job position.

As a result of the work, a set of soft skills and abilities was defined that allow, through correlation analysis, to explain a large number of activities required to work in the operations and SCM (OSCM) environment.

The work is inherently affected by the database used for the professional figures mapped and the scores that are attributed within O*NET to the analyzed elements.

The information resulting from this study can help companies develop specific assessments and tests for the roles of OM and SCM to measure individual attitudes and correlate them with the requirements of the job position. The study aims to address the need to identify soft skills in the human sphere and determine which of them have the most significant impact on the OM and SCM professions.

The originality of this study lies in its approach to identify the set of soft skills and abilities that determine success in the OM and SCM industries. The study used the O*NET database to correlate the tasks required for specific job roles with their corresponding soft skills and abilities. Furthermore, the study used ANOVA analysis to validate the findings in other sectors mapped by the same database. The identified soft skills and abilities can help companies develop specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the requirements of the job position. In addressing the necessity for enhanced clarity in the domain of human factor, this study contributes to identifying key success factors. Subsequent research can further investigate their practical application within companies to formulate targeted growth strategies and make appropriate resource selections for vacant positions.

The purpose of this paper is to explore and disseminate knowledge about quantum-inspired computing technology's potential to solve complex challenges faced by the operational agility capability in Industry 4.0 manufacturing and logistics operations.

A multi-case study approach is used to determine the impact of quantum-inspired computing technology in manufacturing and logistics processes from the supplier perspective. A literature review provides the basis for a framework to identify a set of flexibility and agility operational capabilities enabled by Industry 4.0 Information and Digital Technologies. The use cases are analyzed in depth, first individually and then jointly.

Study results suggest that quantum-inspired computing technology has the potential to harness and boost companies' operational flexibility to enhance operational agility in manufacturing and logistics operations management, particularly in the Industry 4.0 context. An exploratory model is proposed to explain the relationships between quantum-inspired computing technology and the deployment of operational agility capabilities.

This is study explores the use of quantum-inspired computing technology in Industry 4.0 operations management and contributes to understanding its potential to enable operational agility capability in manufacturing and logistics operations.

As an Internet fashion brand, HSTYLE has developed into an Internet enterprise with annual sales of 1.5 billion RMB within 10 years, establishing its position as the top industry performer in China. This case studies HSTYLES' innovation in business model and organizational management. HSTYLE's workgroups have achieved the balance of responsibilities and rights in a small team of three members at minimum, while mobilizing the enthusiasm and initiative of the line managers with the support of public service sector. At the same time, HSTYLE enriches its brand style, establishes a fashion cloud platform, and integrates individual and organizational consumers into its existing fashion design, manufacturing and sales system.

The occupational health risk associated with the production of prefabricated concrete components is often overlooked. This paper will use a damage assessment and cyclic mitigation (DACM) model to provide individualized exposure risk assessment and corresponding mitigation management measures for workers who are being exposed.

The DACM model is proposed based on the concept of life cycle assessment (LCA). The model uses Monte-Carlo simulation for uncertainty risk assessment, followed by quantitative damage assessment using disability-adjusted life year (DALY). Lastly, sensitivity analysis is used to identify the parameters with the greatest impact on health risks.

The results show that the dust concentration is centered around the mean, and the fitting results are close to normal distribution, so the mean value can be used to carry out the calculation of risk. However, calculations using the DACM model revealed that there are still some work areas at risk. DALY damage is most severe in concrete production area. Meanwhile, the inhalation rate (IR), exposure duration (ED), exposure frequency (EF) and average exposure time (AT) showed greater impacts based on the sensitivity analysis.

Based on the comparison, the DACM model can determine that the potential occupational health risk of prefabricated concrete component (PC) factory and the risk is less than that of on-site construction. It synthesizes field research and simulation to form the entire assessment process into a case-base system with the depth of the cycle, which allows the model to be continuously adjusted to reduce the occupational health damage caused by production pollution exposure.

This study aims to investigate the implementation of lean human resource management (HRM) practices in manufacturing small- and medium-sized enterprises (SMEs) and explore how various factors interact to influence their successful adoption. By exploring the interplay among these factors, the research seeks to identify key drivers affecting the adoption of lean HRM in manufacturing SMEs. Ultimately, the research intends to provide insights that can guide organisations, practitioners and policymakers in effectively implementing lean HRM practices to enhance operational efficiency, workforce engagement and competitiveness within the manufacturing SME sector.

The study combined total interpretive structural modelling (TISM) and Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis. TISM helped in understanding the hierarchical relationship among different factors influencing lean HRM implementation, whereas MICMAC analysis provided insights into the level of influence and dependence of each factor on others.

The research revealed that “top management support” emerged as the most independent factor, indicating that strong support from top management is crucial for initiating and sustaining lean HRM practices in manufacturing SMEs. On the other hand, “employee involvement and empowerment” was identified as the most dependent factor, suggesting that fostering a culture of employee engagement and empowerment greatly relies on the successful implementation of lean HRM practices.

While the study provided valuable insights, it has certain limitations. The research was conducted within the specific context of manufacturing SMEs, which might limit the generalizability of the findings to other industries. Expert opinions introduce subjectivity in data collection. Additionally, the study may not cover all critical factors, allowing room for further exploration in future research.

The findings have practical implications for manufacturing SMEs aiming to implement lean HRM practices. Recognising the pivotal role of top management support, organisations should invest in cultivating a strong leadership commitment to lean HRM initiatives. Furthermore, enhancing employee involvement and empowerment can lead to better adoption of lean HRM practices, resulting in improved operational efficiency and overall competitiveness.

This research contributes to the field by offering a comprehensive exploration of the interplay among factors influencing lean HRM implementation. The use of TISM and MICMAC analysis provides a unique perspective on the relationship dynamics between these factors, allowing for a nuanced understanding of their roles in the adoption of lean HRM practices in manufacturing SMEs. The identification of “top management support” as the most independent and “employee involvement and empowerment” as the most dependent factors adds original insights to the existing literature.