O. Felix Offodile and John Grznar
Presents the similarity coefficient method for group technology to alleviate the part family formation problem in flexible manufacturing systems (FMS). Part families are formed in…
Abstract
Presents the similarity coefficient method for group technology to alleviate the part family formation problem in flexible manufacturing systems (FMS). Part families are formed in FMS in order to take advantage of part similarities in design and manufacture. Parts coding and classification analysis (PCA) has constituted the bulk of part family formation techniques in practice. Using shape‐based features for grouping is very labour intensive at the coding and classification stages. As a means of alleviating the latter problem, presents an approach for converting the weighted codes of the PCA to similarity coefficient measures. Uses a clustering algorithm to identify the part families. Presents a numerical example that compares the single and average linkage clustering technologies. An experimental investigation of the two methods showed that the average linkage clustering (ALC) performs better than the single linkage clustering (SLC) technology in minimizing intercellular materials handling costs.
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Marc Roedenbeck and Petra Poljsak-Rosinski
This study investigates whether the artificial neural network approach, when used on a large organizational soft HR performance dataset, results in a better (R2/RMSE) model…
Abstract
Purpose
This study investigates whether the artificial neural network approach, when used on a large organizational soft HR performance dataset, results in a better (R2/RMSE) model compared to the linear regression. With the use of predictive modelling, a more informed base for managerial decision making within soft HR performance management is offered.
Design/methodology/approach
The study builds on a dataset (n > 43 k) stemming from an annual employee MNC survey. It covers several soft HR performance drivers and outcomes (such as engagement, satisfaction and others) that either have evidence of a dual-role nature or non-linear relationships. This study applies the framework for artificial neural network analysis in organization research (Scarborough and Somers, 2006).
Findings
The analysis reveals a substantial artificial neural network model performance (R2 > 0.75) with an excellent fit statistic (nRMSE <0.10) and all drivers have the same relative importance (RMI [0.102; 0.125]). This predictive analysis revealed that the organization has to increase six of the drivers, keep two on the same level and decrease one.
Originality/value
Up to date, this study uses the largest dataset in soft HR performance management. Additionally, the predictive results reveal that specific target values lay below the current levels to achieve optimal performance.
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The purpose of this paper is to present the design and implementation of a genetic algorithm (GA), using a large language model (LLM) for optimizing the delivery scheduling…
Abstract
Purpose
The purpose of this paper is to present the design and implementation of a genetic algorithm (GA), using a large language model (LLM) for optimizing the delivery scheduling process in warehouses of third-party logistics (3PL) companies, within the context of a simplified case study, and to highlight the main directions for implementing this methodology in business realities.
Design/methodology/approach
Using a simplified case study of an international 3PL company, this study applies a GA developed in RStudio by LLM to generate test scenarios and input data. The GA was optimized to minimize the time and distance of movement in the process of preparing goods for shipment, demonstrating its effectiveness in improving warehouse delivery scheduling.
Findings
The study confirms that the GA, supported by LLM, significantly improves the delivery planning process in the warehouse. Specifically, the implementation of the GA led to notable improvements in scheduling efficiency and a reduction in the distance traveled within the warehouse. These enhancements enable more efficient generation, evaluation and optimization of logistic scenarios. Additionally, the use of LLM greatly facilitates the creation and refinement of complex algorithms like GA, through automation and innovative approaches in logistics.
Research limitations/implications
The study highlights limitations related to data quality, the dynamic nature of logistic operations, computational complexity and the need for generalization of results. It also points out the lack of research in business realities that demonstrate the effectiveness of combining the benefits of LLM and GA in practice.
Originality/value
This paper makes a significant contribution to the literature by demonstrating the capabilities of advanced technologies such as GA and LLM in 3PL logistics. It presents an innovative approach to optimizing logistic processes, offering perspectives for further innovations and automation in supply chain management. It also indicates new opportunities for 3PL companies in terms of improving operational and cost efficiency, emphasizing the importance of continuously seeking innovative solutions in the face of increasing market demands.
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Donald C. Kerr and Jaydeep Balakrishnan
Many algorithms have been proposed to form manufacturing cells from component routeings. Most of these methods require specialized algorithms for implementation. Some others use…
Abstract
Many algorithms have been proposed to form manufacturing cells from component routeings. Most of these methods require specialized algorithms for implementation. Some others use well known procedures such as integer programming. However, these may be difficult for practising managers to comprehend. Proposes a simple method that can be implemented using spreadsheet software. The method is based on similarity coefficients and a pair‐wise interchange procedure. Describes the method and the spreadsheet implementation. Compares our procedure with many existing procedures using eight well‐known problems from the literature. Using three evaluation measures shows that the proposed procedure is effective. Given its simplicity and effectiveness, it may be useful to practitioners and researchers.
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Avraham Sless, Yohanan Arzi and Daniel Sipper
This paper deals with a systematic approach for deriving an object based information model for CIM systems. Owing to the complex nature of CIM environment it is often difficult to…
Abstract
This paper deals with a systematic approach for deriving an object based information model for CIM systems. Owing to the complex nature of CIM environment it is often difficult to identify data structures and functionality of such a system. An object based modeling methodology is presented, along with clustering based procedure for deriving a static structure model from functional requirements. Performance measures for evaluating the “quality” of the derived model are presented and discussed. A sample manufacturing application is provided in order to illustrate the approach.
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Fanar Shwedeh, Ahmad Aburayya, Ogbolu Gbemisola and Ayotunde Adetola Adelaja
This study aims to investigate the impact of augmented reality (AR) training modules on knowledge retention in corporate settings, focusing on the context of the United Arab…
Abstract
Purpose
This study aims to investigate the impact of augmented reality (AR) training modules on knowledge retention in corporate settings, focusing on the context of the United Arab Emirates (UAE). Drawing on the Technology Acceptance Model (TAM) and existing literature, the study examines the relationships between AR training modules, employees’ engagement, interactive learning environments and knowledge retention.
Design/methodology/approach
This study uses a quantitative technique by using a structured survey to collect data from participants in service sectors in the UAE. The survey gathers information on their attitudes, views and behaviors toward using AR in business training. This study used a stratified random selection to guarantee representation across several service sectors in the UAE, including hospitality, tourism, retail and finance. A SEM analysis tool was used to test the relationship that exists between the construct under investigation, that is, employees’ engagements (EE), AR training modules (ARTM), interactive learning environment (ILE), Fintech training content (FTC), the moderating role of technological aptitude (TA) on knowledge retention (KR).
Findings
Findings reveal a significant positive correlation between AR training modules and knowledge retention, emphasizing the potential of immersive technologies in enhancing learning outcomes. Moreover, the study underscores the importance of engaged employees, customized training materials and technological proficiency in shaping knowledge retention. Limitations and avenues for further research are also discussed.
Originality/value
Overall, this study contributes to understanding the factors influencing knowledge retention in corporate training contexts and provides practical insights for organizations seeking to optimize their training programs.