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Article
Publication date: 2 January 2024

Wenlong Cheng and Wenjun Meng

This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

Abstract

Purpose

This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

Design/methodology/approach

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

Findings

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

Originality/value

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

Details

Robotic Intelligence and Automation, vol. 44 no. 1
Type: Research Article
ISSN: 2754-6969

Keywords

Open Access
Article
Publication date: 12 January 2024

Patrik Jonsson, Johan Öhlin, Hafez Shurrab, Johan Bystedt, Azam Sheikh Muhammad and Vilhelm Verendel

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

Abstract

Purpose

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

Design/methodology/approach

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

Findings

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

Practical implications

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

Originality/value

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

Details

International Journal of Operations & Production Management, vol. 44 no. 13
Type: Research Article
ISSN: 0144-3577

Keywords

Article
Publication date: 1 April 2024

Gianluca Elia, Gianpaolo Ghiani, Emanuele Manni and Alessandro Margherita

This study aims to present a methodology and a system to support the technical and managerial issues involved in anomaly detection within the reverse logistics process of an…

Abstract

Purpose

This study aims to present a methodology and a system to support the technical and managerial issues involved in anomaly detection within the reverse logistics process of an e-commerce company.

Design/methodology/approach

A case study approach is used to document the company’s experience, with interviews of key stakeholders and integration of obtained evidence with secondary data.

Findings

The paper presents an algorithm and a system to support a more efficient and smart management of reverse logistics based on a set of anticipatory actions, and continuous and automatic monitoring of returned goods. Improvements are described in terms of a number of key performance indicators.

Research limitations/implications

The analysis and the developed system need further applications and validations in other organizational contexts. However, the research presents a roadmap and a research agenda for the reverse logistics transformation in Industry 4.0, by also providing new insights to design a multidimensional performance dashboard for reverse logistics.

Practical implications

The paper describes a replicable experience and provides checklists for implementing similar initiatives in the domain of reverse logistics, in the aim to increase the company’s performance along four key complementary dimensions, i.e. time savings, accuracy, completeness of data analysis and interpretation and cost efficiency.

Originality/value

The main novelty of the study stays in carrying out a classification of anomalies by type and product category, with related causes, and in proposing operational recommendations, including process monitoring and control indicators that can be included to design a reverse logistics performance dashboard.

Details

Measuring Business Excellence, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1368-3047

Keywords

Article
Publication date: 25 January 2024

Anil Kumar Inkulu and M.V.A. Raju Bahubalendruni

In the current era of Industry 4.0, the manufacturing industries are striving toward mass production with mass customization by considering human–robot collaboration. This study…

Abstract

Purpose

In the current era of Industry 4.0, the manufacturing industries are striving toward mass production with mass customization by considering human–robot collaboration. This study aims to propose the reconfiguration of assembly systems by incorporating multiple humans with robots using a human–robot task allocation (HRTA) to enhance productivity.

Design/methodology/approach

A human–robot task scheduling approach has been developed by considering task suitability, resource availability and resource selection through multicriteria optimization using the Linear Regression with Optimal Point and Minimum Distance Calculation algorithm. Using line-balancing techniques, the approach estimates the optimum number of resources required for assembly tasks operating by minimum idle time.

Findings

The task allocation schedule for a case study involving a punching press was solved using human–robot collaboration, and the approach incorporated the optimum number of appropriate resources to handle different types of proportion of resources.

Originality/value

This proposed work integrates the task allocation by human–robot collaboration and decrease the idle time of resource by integrating optimum number of resources.

Details

Robotic Intelligence and Automation, vol. 44 no. 1
Type: Research Article
ISSN: 2754-6969

Keywords

Article
Publication date: 26 March 2024

Keyu Chen, Beiyu You, Yanbo Zhang and Zhengyi Chen

Prefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction…

Abstract

Purpose

Prefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).

Design/methodology/approach

Firstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.

Findings

The proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.

Originality/value

In this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

Keywords

Open Access
Article
Publication date: 31 October 2023

Emilia Kääriä and Ahm Shamsuzzoha

This study is focused to support an ongoing development project of the case company's current state and the challenges of the order-to-cash (O2C) process. The O2C process is the…

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Abstract

Purpose

This study is focused to support an ongoing development project of the case company's current state and the challenges of the order-to-cash (O2C) process. The O2C process is the most visible process to the customer, and therefore, its punctual and fluent order management is vital. It is observed that the high degree of manual work in the O2C process causes mistakes, delays and rework in the process. The purpose of this article is therefore to analyze the case company's current state of the O2C process as well as to identify the areas of development in this process by deploying the means of Lean Six Sigma tools such as value stream mapping (VSM).

Design/methodology/approach

The study was conducted as a mix of quantitative and qualitative analysis. Based on both the quantitative and qualitative data, a workshop on VSM was organized to analyze the current state of the O2C process of a case company, engaged in the energy and environment sector in Finland.

Findings

The results found that excessive manual work was highly connected to inadequate or incorrect data in pricing and invoicing activities, which resulted in canceled invoices. Canceled invoices are visible to the customer and have a negative impact on the customer experience. This study found that by improving the performance of the O2C process activities and improving communication among the internal and external stakeholders, the whole O2C process can perform more effectively and provide better customer value.

Originality/value

The O2C process is the most visible process to the customer and therefore its punctual and fluent order management is vital. To ensure that the O2C process is operating as desired, suitable process performance metrics need to be aligned and followed. The results gathered from the case company's data, questionnaire interviews, and the VSM workshop are all highlighted in this study. The main practical and managerial implications were to understand the real-time O2C process performance, which is necessary to ensure strong performance and enhance continuous improvement of the O2C process that leads to operational excellence and commercial competitiveness of the studied case company.

Details

International Journal of Productivity and Performance Management, vol. 73 no. 11
Type: Research Article
ISSN: 1741-0401

Keywords

Article
Publication date: 27 March 2023

Omaima Hajjami and Oliver S. Crocco

The purpose of this study is to investigate the factors that influenced employee engagement in the context of remote work as a result of the COVID-19 pandemic and compare them…

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Abstract

Purpose

The purpose of this study is to investigate the factors that influenced employee engagement in the context of remote work as a result of the COVID-19 pandemic and compare them with antecedents of employee engagement in traditional workplaces.

Design/methodology/approach

This study adopted an integrative literature review design of 27 empirical and conceptual peer-reviewed journal articles from a host of academic databases. Data were analyzed via a matrix and mapped onto individual and organizational antecedents of employee engagement.

Findings

This study identified 18 antecedents of remote work, which were categorized into individual antecedents, for example, mindfulness and digital literacy, as well as organizational antecedents, for example, job autonomy and supportive leadership. These findings were compared with antecedents of employee engagement in traditional workplaces to generate new knowledge about the impact of remote work on employee engagement as a result of the large shift to remote work in 2020.

Originality/value

This study synthesizes the most recent literature on antecedents of employee engagement in remote work settings as the result of the pandemic and contrasts these new approaches with previously identified antecedents of employee engagement in traditional workplaces.

Details

European Journal of Training and Development, vol. 48 no. 3/4
Type: Research Article
ISSN: 2046-9012

Keywords

Article
Publication date: 25 December 2023

Luz Esmeralda Hernández Martínez and Zeidy Edith Chunga-Liu

This research aims to determine the influence that work flexibility (WF) has on the happiness of workers through the work and personal life balance, work-life balance (WLB), as a…

Abstract

Purpose

This research aims to determine the influence that work flexibility (WF) has on the happiness of workers through the work and personal life balance, work-life balance (WLB), as a mediating variable, as well as the moderating role of gender between WLB and job happiness (JH). A structural model that describes the interactions between these study variables is proposed.

Design/methodology/approach

A quantitative approach was used. The data were collected by non-probabilistic sampling, surveying 200 mid-level employees in small and medium industrial enterprises (industrial SMEs). The proposed hypotheses were analyzed and tested using partial least squares structural equation modeling.

Findings

The results confirmed the hypotheses presented. In the relation of WLB and JH, positive work-family and family-work interactions would result in a greater increase in JH in the women group compared to men, and special characteristics were found in the variables in the Mexican context.

Practical implications

This study will provide information to those responsible for the human resources departments of companies to design and implement good practices in which importance can be given to labor agreements involving WF and the implementation of WLB policies differentiated by gender to generate happiness at work.

Originality/value

The JH construct proposed by Fisher (2010) was applied, and its relationship with WF and WLB in a post-pandemic context was studied. The research applied to supervisors and area managers of industrial SMEs reflects the importance of considering the balance between their life and work to achieve JH, understanding it as job satisfaction and more commitment to work, in addition to considering the differences by gender.

Details

Journal of Management Development, vol. 43 no. 2
Type: Research Article
ISSN: 0262-1711

Keywords

Open Access
Article
Publication date: 22 March 2024

Ambra Galeazzo, Andrea Furlan, Diletta Tosetto and Andrea Vinelli

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Details

International Journal of Operations & Production Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0144-3577

Keywords

Article
Publication date: 26 March 2024

Léa Fréour, Adalgisa Battistelli, Sabine Pohl and Nicola Cangialosi

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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

Originality/value

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.

Details

International Journal of Organizational Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1934-8835

Keywords

1 – 10 of 46