Explore the vast range of titles available.

Purpose The purpose of this study is to analyze cross-functional integration processes and their respective impacts on resilience capabilities, with particular emphasis on collaboration, in an automotive supply chain. Design/methodology/approach A case study was conducted in an automotive supply chain through 18 in-depth interviews with managers from six different companies, namely, two suppliers, one focal manufacturer and three customers. The automotive industry is one of the most vital industries in the Brazilian economy and is still recovering from the 2015 financial crisis, making it an interesting case for this research. Findings The findings reveal several links between resilience and cross-functional integration literatures through the analysis of three basic categories, namely, disruptions (lack of product, interruption of production, delivery delay, organizational bankruptcy and sales loss); capabilities (redundancy, flexibility, adaptability, collaboration, visibility and agility); and integration factors (cross-functional meetings, adequate communication, longevity of relationships, cross-functional training, recognition of interdependence and the consideration given to informal groups). Practical implications This research addresses several implications for practitioners. Managers should pay attention to the cross-functional teams, which may provide internal collaboration, and hence collaboration in the supply chain. Originality/value This paper suggests a new concept for the collaboration capability. According to the results, collaboration is the capability of dealing with formal and informal factors to integrate both the internal functions and supply chain members, which can provide visibility, agility and adaptability toward supply chain resilience.

This article provides a comprehensive overview of the digital transformation of the fashion industry and describes the opportunities and influences on supply chains, business models, and sustainability-oriented innovations that it offers. Desk research was performed to review emerging cases of companies that engage actively in using 3-dimensional virtual and digital (3DVD) technologies, such as 3D modeling, virtual and augmented reality (VR and AR), 2- and 3-dimensional (2D/3D) scanning, and digital twinning (DT). The analysis shows how the adoption of digital technologies provides opportunities to dematerialize the traditional fashion supply-chain model of garment production and distribution and maps the innovative shifts occurring in the fashion industry’s processes, products, and services. The adoption of 3DVD technologies by fashion companies unleashes new opportunities with respect to innovation in products/services and optimization of operational processes to streamline activities, shorten the lead time for designing, prototyping, manufacturing, marketing and retailing, and reorganizing the working phases. These capabilities also drive multicentred business-model innovations and thus affect value creation and delivery and capture changes. In addition, the analysis shows that digital transformation affects the four dimensions of sustainability that are interconnected intrinsically across supply-chain processes. Cultural sustainability is paramount, as fashion is a complex cultural system that is able to create products/services that influence the environment, economy, and society. In particular, 3DVD technologies promote cultural transformation of design processes to achieve a remix of skills and open knowledge, a behavioral shift from the consumer perspective in terms of diversity and self-expression, and a change in the organizational culture of companies that drive the digital transformation.

Industry 4.0 lets the industry build compact, precise, and connected assets and also has made modern industrial assets a massive source of data that can be used in process optimization, defining product quality, and predictive maintenance (PM). Large amounts of data are collected from machines, processed, and analyzed by different machine learning (ML) algorithms to achieve effective PM. These machines, assumed as edge devices, transmit their data readings to the cloud for processing and modeling. Transmitting massive amounts of data between edge and cloud is costly, increases latency, and causes privacy concerns. To address this issue, efforts have been made to use edge computing in PM applications., reducing data transmission costs and increasing processing speed. Federated learning (FL) has been proposed a mechanism that provides the ability to create a model from distributed data in edge, fog, and cloud layers without violating privacy and offers new opportunities for a collaborative approach to PM applications. However, FL has challenges in confronting with asset management in the industry, especially in the PM applications, which need to be considered in order to be fully compatible with these applications. This study describes distributed ML for PM applications and proposes two federated algorithms: Federated support vector machine (FedSVM) with memory for anomaly detection and federated long-short term memory (FedLSTM) for remaining useful life (RUL) estimation that enables factories at the fog level to maximize their PM models’ accuracy without compromising their privacy. A global model at the cloud level has also been generated based on these algorithms. We have evaluated the approach using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset to predict engines’ RUL Experimental results demonstrate the advantage of FedSVM and FedLSTM in terms of model accuracy, model convergence time, and network usage resources.

PurposeThis study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.Design/methodology/approachRooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.FindingsThe authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.Research limitations/implicationsThis review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.Practical implicationsThe authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.Originality/valueThis is one of the first SLRs on drone applications in LMD from a logistics management perspective.

The capability of estimating future maintenance needs in advance and in a timely manner is a prerequisite for reliable manufacturing with high availability in a production unit. Additionally, conducting planned maintenance efforts regularly and prematurely increases the service lifetimes and utilization rates of parts, which leads to more sustainable production. The benefits of predictive maintenance are obvious, but introducing it into a facility poses various challenges. In this study, digital twins of well-functioning machines are used for predictive maintenance. The discrepancies between each physical unit and its digital twin are used to detect the maintenance needs. A thorough evaluation of the method over a period of 18 months by comparing digital twin detection results with maintenance and control system logs shows promising results. The method is successful in detecting discrepancies, and the paper describes the techniques that are used. However, not all discrepancies are related to the maintenance needs, and the evaluation identifies and discusses the most common sources of error. These are often the results of human interaction, such as parameter changes, maintenance activities and component replacement.

The role of the Sustainability Manager (SM) is expanding. Whether SMs are turning into a new profession is under debate. Pointing to the need for a distinct professional logic to qualify as a profession, we identify what is contained within a professional logic of SMs. Through analyzing ambiguities present in the role of the SMs, we show that there is no specific distinct professional logic of SMs, but rather a meta-construct building on market, bureaucratic, and sustainability logics. In addition, we point to the complex configurations of and relationships between these underlying logics. The complexities also explain why the SMs differ from traditional professions and why it is problematic to talk about a ‘SM profession’. Rather, SMs are ‘organizational professionals’. The article builds on 21 interviews with SMs working for Swedish companies.

PurposeThe service recovery paradox (SRP) is the phenomenon that happens when customer satisfaction level post-service failure and recovery surpasses the customer satisfaction level achieved at error-free service. The aim of this study was to identify how large the size of compensation has to be at recovery for customer satisfaction to surpass that of error-free service (i.e. to identify a threshold value for SRP). The purpose of this is to inform managers how to restore customer satisfaction yet avoid overcompensation.Design/methodology/approachThe paper covers two studies. Study 1 used the novel approach of asking participants who had experienced a service failure in the hotel industry what amount of money (recovery) would make them more satisfied than in the case of error-free service. Study 2 then tested the compensation levels expressed by Study 1 participants to be sufficient for the service recovery paradox to occur.FindingsStudy 1 indicated that the threshold for the SRP was (on average) around 1,204 SEK, or just over 80% of the original room reservation price of 1,500 SEK (approx. $180). Study 2 found that (on average) the customer satisfaction of participants who received 1,204 SEK in compensation for service failure marked the point where it surpassed that of error-free service. Participants who received 633 SEK were less satisfied; participants who received 1,774 SEK were more satisfied.Research limitations/implicationsThe findings are context-specific. Future research should test the findings' generalizability.Practical implicationsThe approach used in this paper could provide managers with a tool to guide their service recovery efforts. The findings could help hotel managers to make strategic decisions to restore customer satisfaction yet avoid overcompensation, given a legitimate service failure in which the organization is at fault.Originality/valueNumerous previous studies have investigated the occurrence or absence of the SRP at predetermined compensation levels. This paper used a novel approach to find a quantitative threshold at which the magnitude of the recovery effort makes customer satisfaction surpass that of error-free service.

PurposeThe purpose of this study is to measure the gaps between the engagements of shippers (i.e. logistics buyers) and logistics service providers (LSPs) in different green logistics practices (GLPs) throughout the key phases of the logistics purchasing process: request for proposal, negotiations, contracting and execution.Design/methodology/approachA large-scale survey of shippers and LSPs in Sweden was conducted. Respondents were 331 firms (169 shippers, 162 LSPs). Mean values of the actors' perceptions were analysed using independent- and paired sample t-tests.FindingsWhile this study supports previous research indicating that LSPs engage more extensively in selling GLPs than shippers do in buying them, it shows that this conclusion does not uniformly apply to all GLPs nor all purchasing phases. Three patterns emerged for the gaps between the actors' buying-selling engagements throughout the purchasing process: (1) steady and wide gaps, (2) steady and narrow gaps and (3) emergent gaps. Distinct GLPs were associated with each pattern. It is also shown that the prioritisation of GLPs is fairly aligned between shippers and LSPs.Research limitations/implicationsThis study contributes to the green logistics purchasing literature by systematically and simultaneously creating three types of distinction, between (1) shippers and LSPs, (2) different GLPs and (3) different logistics purchasing phases. Future studies could replicate the analysis in countries other than Sweden.Practical implicationsManagers of shipper/LSP firms learn tips to spot the GLPs that their partners prioritise, enabling them to modify their purchasing/marketing strategies accordingly.Originality/valueThe three types of distinction represent a novel approach in the green logistics purchasing literature.

PurposeThis paper investigates the suitability of fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.Design/methodology/approachTwo fuzzy-logic-based support tools are developed together with experts from a Swedish manufacturing firm. The first uses a complete rule base and the second a reduced rule base. Sixteen inference settings are used in both of the support tools.FindingsThe findings show that fuzzy-logic-based support tools are suitable for initial screening of manufacturing reshoring decisions. The developed support tools are capable of suggesting whether a reshoring decision should be further evaluated or not, based on six primary competitiveness criteria. In contrast to existing literature this research shows that it does not matter whether a complete or reduced rule base is used when it comes to accuracy. The developed support tools perform similarly with no statistically significant differences. However, since the interpretability is much higher when a reduced rule base is used and it require fewer resources to develop, the second tool is more preferable for initial screening purposes.Research limitations/implicationsThe developed support tools are implemented at a primary-criteria level and to make them more applicable, they should also include the sub-criteria level. The support tools should also be expanded to not only consider competitiveness criteria, but also other criteria related to availability of resources and strategic orientation of the firm. This requires further research with regard to multi-stage architecture and automatic generation of fuzzy rules in the manufacturing reshoring domain.Practical implicationsThe support tools help managers to invest their scarce time on the most promising reshoring projects and to make timely and resilient decisions by taking a holistic perspective on competitiveness. Practitioners are advised to choose the type of support tool based on the available data.Originality/valueThere is a general lack of decision support tools in the manufacturing reshoring domain. This paper addresses the gap by developing fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

The emergence of Industry 4.0 has revolutionized the industrial sector, enabling the development of compact, precise, and interconnected assets. This transformation has not only generated vast amounts of data but also facilitated the migration of learning and optimization processes to edge devices. Consequently, modern industries can effectively leverage this paradigm through distributed learning to define product quality and implement predictive maintenance (PM) strategies. While computing speeds continue to advance rapidly, the latency in communication has emerged as a bottleneck for fast edge learning, particularly in time-sensitive applications such as PM. To address this issue, we explore Federated Learning (FL), a privacy-preserving framework. FL entails updating a global AI model on a parameter server (PS) through aggregation of locally trained models from edge devices. We propose an innovative approach: analog aggregation over-the-air of updates transmitted concurrently over wireless channels. This leverages the waveform-superposition property in multi-access channels, significantly reducing communication latency compared to conventional methods. However, it is vulnerable to performance degradation due to channel properties like noise and fading. In this study, we introduce a method to mitigate the impact of channel noise in FL over-the-air communication and computation (FLOACC). We integrate a novel tracking-based stochastic approximation scheme into a standard federated stochastic variance reduced gradient (FSVRG). This effectively averages out channel noise’s influence, ensuring robust FLOACC performance without increasing transmission power gain. Numerical results confirm our approach’s superior communication efficiency and scalability in various FL scenarios, especially when dealing with noisy channels. Simulation experiments also highlight significant enhancements in prediction accuracy and loss function reduction for analog aggregation in over-the-air FL scenarios.