Search results

The e-commerce boom presents new challenges for last-mile delivery (LMD), which may be mitigated by new delivery technologies. This paper evaluates the impact of mobile parcel lockers (MPL) on costs and CO₂ equivalent (CO₂e) emissions in existing LMD networks, which include home delivery and shipments to stationary parcel lockers.

To describe customers’ preferences, we design a multinomial logit model based on recipients’ travel distance to pick-up locations and availability at home. Based on route cost estimation, we define the operating costs for MPLs. We devise a mathematical model with binary decision variables to optimize the location of MPLs.

Our study demonstrates that integrating MPLs leads to additional cost savings of 8.7% and extra CO₂e emissions savings of up to 5.4%. Our analysis of several regional clusters suggests that MPLs yield benefits in highly populous cities but may result in additional emissions in more rural areas where recipients drive longer distances to pick-ups.

This paper designs a suitable operating model for MPLs and demonstrates environmental and economic savings. Moreover, it adds recipients’ availability at home to receive parcels improving the accuracy of stochastic demand. In addition, MPLs are evaluated in the context of several regional clusters ranging from large cities to rural areas. Thus, we provide managerial guidance to logistics service providers how and where to deploy MPLs.

This study aims to address the critical challenge of implementing lean manufacturing (LM) in emerging economies, where sustainability complexities on the production floor hinder production efficiency and the transition towards a circular economy (CE). Addressing a gap in existing research, the paper introduces a path analysis model to systematically identify, prioritize and overcome LM implementation barriers, aiming to enhance performance through strategic removal.

The authors used a mixed-method approach, combining empirical survey data with literature reviews to pinpoint key LM barriers. Using the grey-based Decision-Making Trial and Evaluation Laboratory (DEMATEL) along with the Network Knowledge (NK) method, they mapped causal relationships and barrier intensities. This formed the basis for developing a path simulation algorithm, integrating heuristic considerations for practical decision-making.

This analysis reveals that the primary barriers to LM adoption is the negative perception and inadequate understanding of lean tools and CE principles. The study provides a strategic framework for managers, offering new insights into barrier prioritization and overcoming strategies to facilitate successful LM adoption.

This research provides a strategic pathway for overcoming LM implementation barriers, empowering managers in emerging economies to enhance sustainability and competitive advantage through LM and CE integration. It emphasizes the significance of structured barrier management in the manufacturing sector.

This research pioneers a systematic exploration of LM implementation barriers in the CE context, making a significant contribution to the literature. It identifies, evaluates barriers and proposes a practical model for overcoming them, enriching sustainable manufacturing practices in emerging markets.

This paper investigates the constraints an organisation faces when using recruitment agencies and having to trade-off between the speed of hiring a candidate, the cost of a candidate and the match of the candidate against the job requirements across different job seniorities. We analyse how technology can shift the cost and hiring speed in spite of these constraints.

The research design is exploratory, quantitative and cross-sectional. The study employed a two-factor, unbalanced class Analysis of Variance (ANOVA) including interaction effects to test the difference between the means of the class of interest and a control class.

Our empirical findings confirm that (1) the technological innovation of a recruitment agency marketplace can liberate organisations from their time, cost and quality hiring constraints, accelerating the time to hire by four times and reducing costs by over 12%, and (2) these results hold across varying role seniority levels.

This study contributes to the existing literature in three ways: (1) it introduces the recruitment triangle from project management into the recruitment literature; (2) it demonstrates how technological innovations such as recruitment agency marketplaces are able to provide a shift in the constraints posed by the recruitment triangle.

This study aims to investigate the interaction effect between offering supply chain finance (SCF) programmes and sustainability ratings on the liquidity performance of buyers and suppliers.

The study uses a unique sample of buyers that each have an SCF programme. The sample is complemented with financial information and sustainability scores. The data is analysed through a random effects model.

Aligning with recent advances in SCF literature, the results confirm a tendency for SCF programmes to favour buyers over suppliers. However, the relationship between SCF programme adoption and liquidity performance for buyers and suppliers is positively moderated by the strong sustainability performance of both parties.

Buyers and suppliers are advised to implement and adopt effective SCF programmes that are beneficial for both parties. For buyers, the authors suggest leveraging on SCF programmes as incentives to foster sustainable behaviour among suppliers. For suppliers, the authors recommend caution before joining programmes offered by buyers that do not perform well on sustainability.

Enhancing sustainability within global supply chains and fostering favourable payment practices towards suppliers are crucial for policy development and regulation. The findings clarify the connection between both components, offering valuable insights for policymakers in this domain.

The study is built on a manually picked, unique database of buyers offering SCF programmes to their suppliers. This allows, across a large sample, an evaluation of the differences between buyers that offer SCF programmes and those that do not.

In developing countries like Tanzania, gems and jewellery industry mainly consists of disintegrated and unstable micro and small workshops which operate in a way that misalign value addition processes. This study is aimed to bridge gap by focussing on exploitation of industrial clusters in social normalisation and economic resilience to developing countries. The world economic shocks has been not only individually experienced but also globally shared while disrupted lives across all countries and communities and negatively affected global socio-economic growth.

Furthermore, the explorative design was adopted in this study in order to explore needs of respondents, and with the aim to direct the study towards a descriptive design. The sample frame consists of participants in gems and jewellery activities in Tanzania whereby sample was drawn from Dar es Salaam and Arusha. Semi-structured interview was used to collect quantitative data to establish evidence of Tanzanians’ SSJs linked to global value chains (GVCs).

Results revealed the benefits of exploitation of artisanal industrial clusters to Tanzanians’ SSJs when linked to global value chains (GVCs). Findings of the study demonstrate the importance of artisanal industrial clusters in facilitating Tanzanians’ SSJs to access GVCs. Further, insufficient education, trust and social protection directly affects inclusive GVCs, inferring that the impact of artisanal industrial clusters on inclusive GVCs in social normalisation and economic resilience.

Study findings reveals shortcomings in existing regulatory framework of linking Tanzanians’ SSJs to artisanal industrial clusters, for improvements to better support the inclusiveness in GVCs. Findings of this research invite interventions on institutional capabilities and entrepreneurial competencies to enhance the capabilities of small-scale jewellers (SSJs). Like other studies, this study involved cross-sectional data, limit targeted study population as representative of SSJs in industrial clusters and GVCs in economic crises at limited time.

The study findings makes important practical contributions to the Tanzania’s SSJs by examining mediating role of artisanal industrial clusters hence informing policymakers of mining sector how to improve accessibility on GVCs by focus on offering great institutional capabilities and entrepreneurial competencies. These findings will help SSJs and policy makers to get better understanding of the relationships in exploitation of artisanal industrial clusters when accessing GVCs. Therefore, they can make better decisions on implementing artisanal industrial clusters as well as management accessing GVCs, so that SSJs will attain the best possible performance.

This emphasises the importance of community empowerment in the GVCs process through artisanal industrial clusters. Study findings indicate the influence of industrial relations to social dynamics which are previously inadequately addressed and scantly researched. In actual fact study propose initiatives that ensure local communities benefit socially from the integration of SSJs into GVCs through artisanal industrial clusters. Findings suggest local communities that take into account inter-sectionality of artisanal industrial clusters and inclusive GVCs, by considering how factors like education, trust and social protection status intersect to influence the social inclusiveness of SSJs.

There is limited evidence of linking Tanzanians’ SSJs to GVCs in social normalisation and economic resilience and few researchers have explored this topic. This article leverages exploitation of industrial clusters in normalisation and economic resilience to developing countries such as Tanzania as way of improving shared prosperity, sustainability, inclusive growth, cohesion, value chain upgrading and financial inclusion to SSJs.

This paper is devoted to prepare micro-cone structure with variable cross-section size by Stereo Lithography Appearance (SLA)-based 3D additive manufacturing technology. It is mainly focused on analyzing the forming mechanism of equipment and factors affecting the forming quality and accuracy, investigating the influence of forming process parameters on the printing quality and optimization of the printing quality. This study is expected to provide a µ-SLA surface preparation technology and process parameters selection with low cost, high precision and short preparation period for microstructure forming.

The µ-SLA process is optimized based on the variable cross-section micro-cone structure printing. Multi-index analysis method was used to analyze the influence of process parameters. The process parameter influencing order is determined and validated with flawless micro array structure.

After the optimization analysis of the top diameter size, the bottom diameter size and the overall height, the influence order of the printing process parameters on the quality of the micro-cone forming is: exposure time (B), print layer thickness (A) and number of vibrations (C). The optimal scheme is A1B3C1, that is, the layer thickness of 5 µm, the exposure time of 3000 ms and the vibration of 64x. At this time, the cone structure with the bottom diameter of 50 µm and the cone angle of 5° could obtain a better surface structure.

This study is expected to provide a µ-SLA surface preparation technology and process parameters selection with low cost, high precision and short preparation period for microstructure forming.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief.

A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations.

A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained.

Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.

Vehicle allocation problems (VAPs), which are frequently confronted in many transportation activities, primarily including but not limited to full truckload freight transportation operations, induce a significant economic impact. Despite the increasing academic attention to the field, literature still fails to match the needs of and opportunities in the growing industrial practices. In particular, the literature can grow upon the ideas on sustainability, Industry 4.0 and collaboration, which shape future practices not only in logistics but also in many other industries. This review has the potential to enhance and accelerate the development of relevant literature that matches the challenges confronted in industrial problems. Furthermore, this review can help to explore the existing methods, algorithms and techniques employed to address this problem, reveal directions and generate inspiration for potential improvements.

This study provides a literature review on VAPs, focusing on quantitative models that incorporate any of the following emerging logistics trends: sustainability, Industry 4.0 and logistics collaboration.

In the literature, sustainability interactions have been limited to environmental externalities (mostly reducing operational-level emissions) and economic considerations; however, emissions generated throughout the supply chain, other environmental externalities such as waste and product deterioration, or the level of stakeholder engagement, etc., are to be monitored in order to achieve overall climate-neutral services to the society. Moreover, even though there are many types of collaboration (such as co-opetition and vertical collaboration) and Industry 4.0 opportunities (such as sharing information and comanaging distribution operations) that could improve vehicle allocation operations, these topics have not yet received sufficient attention from researchers.

The scientific contribution of this study is twofold: (1) This study analyses decision models of each reviewed article in terms of decision variable, constraint and assumption sets, objectives, modeling and solving approaches, the contribution of the article and the way that any of sustainability, Industry 4.0 and collaboration aspects are incorporated into the model. (2) The authors provide a discussion on the gaps in the related literature, particularly focusing on practical opportunities and serving climate-neutrality targets, carried out under four main streams: logistics collaboration possibilities, supply chain risks, smart solutions and various other potential practices. As a result, the review provides several gaps in the literature and/or potential research ideas that can improve the literature and may provide positive industrial impacts, particularly on how logistics collaboration may be further engaged, which supply chain risks are to be incorporated into decision models, and how smart solutions can be employed to cope with uncertainty and improve the effectiveness and efficiency of operations.