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This study is a reference for manufacturers who are promoting their product-service system (PSS) development. Currently, improvements in both digital customization and sustainability for various smart PSS categories have been considered rarely. This paper addresses this research gap by developing relevant models.

The development trends of customization-oriented PSS are described in a literature review. An in-depth multiple-case study methodology is adopted, and seven manufacturing companies are sampled. The goal is to identify digital customization measures that can be employed on representative smart PSS models and to explore how these models can create sustainable value.

This study provides valuable insights by uncovering a synthesis framework for achieving customization of the product/use/result-oriented smart PSSs, and the relevant representative smart functions are summarized. This identifies how digital customization capabilities can improve sustainability, including direct economic value for customers as well as additional social benefits and environmental improvements during customization.

Currently, the influence of digitalization on customized offerings and the relevant impact on sustainability development have not been fully addressed to date. This study provides comprehensive information with a reference value for digital customization transformation among the three main types of smart PSS.

Product Lifecycle Management (PLM) systems have gained wide popularity for their role in manufacturing organizations for creating, managing and distributing product data. These systems are one of various enterprise systems which are required for smooth functioning and meeting the scaling up requirements organization. However, with introduction of cloud technology and other industry 4.0 initiatives, there has been focus on moving the on-premises IT application to the cloud. Such a move needs to be carried out by identifying and evaluating various challenges. This paper aims to discuss the aforementioned objective.

The challenges identified through literature review have also been confirmed to be present via interview, system observation and documentation review through case study-based validation in an automotive component manufacturing industry.

The article identifies needs and challenges of cloud PLM systems and presents a generic framework for developing an approach for cloud PLM journey for an organization. The article also provides an approach for resolving the different challenges to realizing the designed system.

The simplified generic framework has been presented for use by industry professionals and researchers for designing cloud PLM systems that would fulfill expectations of different levels of stakeholders.

This paper aims to propose a new framework on prioritizing and deployment of design for additive manufacturing (DfAM) strategies to an industrial component using Fuzzy TOPSIS multiple criteria decision-making (MCDM) techniques. The proposed framework is then applied to an automotive component, and the results are discussed and compared with existing design.

Eight DfAM design alternatives associated with eight design criteria have been identified for framing new DfAM strategies. The prioritization order of the design alternatives is identified by Fuzzy TOPSIS MCDM technique through its closeness coefficient. Based on Fuzzy TOPSIS MCDM output, each of the design alternatives is applied sequentially to an automobile component as a case study. Redesign is carried out at each stage of DfAM implementation without affecting the functionality.

On successful implementation of proposed framework to an automotive component, the mass is reduced by 43.84%, from 0.429 kg to 0.241 kg. The redesign is validated by finite element analysis, where von Mises stress is less than the yield stress of the material.

The proposed DfAM framework and strategies will be useful to designers, R&D engineers, industrial practitioners, experts and consultants for implementing DfAM strategies on any industrial component without impacting its functionality.

To the best of the authors’ knowledge, the idea of prioritization and implementation of DfAM strategies to an automotive component is the original contribution.

Digital transformation has created an important framework for the commercial aviation industry. Aviation companies that develop a digital strategy or implement the strategies successfully are seeing improvements in their overall efficiency, cost, flexibility and security performances with the effect of digitalization. For aviation businesses, digitalization is seen as one of the important conditions of competition. For this reason, many aviation businesses prioritize digital investments and make the major strategic moves necessary to gain a competitive advantage. However, the digitization of aviation businesses cannot be achieved by investing in technology alone. Digital arguments need to be aligned with customer expectations and the goals of the business. The increase in the number of airline companies has caused airline companies to create new strategies that will increase cost efficiency. IATA (International Air Transport Association), which is a member of airline companies and works for the efficiency of commercial air transportation revealed its vision of a ‘Digital Airline’, which represents what an airline can look like in 2025. Digitalization and efficiency, reflections on human resources, digital applications of airlines, benefits of airline companies from digital transformation, airline mobile applications, online check-in and boarding services (self-check-in, kiosk), online travel assistant, digital baggage cards (RFID – radio frequency identification), digital cabin management systems, in-flight entertainment systems, cabin cleaning robots, digital loyalty programmes, a new perspective in aviation education, interactive virtual reality environments, big data technology and applications and disadvantages of digital transformation are evaluated in the chapter.

This article aims to understand where industry is in terms of digitalizing their operations, what features of this transformation are essential for practitioners, and what barriers they are facing during their journey. In addition, the authors aim to provide recommendations for organization to start their digital transformation.

Through literature review, the authors summarize the emerging tools and technologies in operations and supply chains to inform the practitioners. Then, the authors use surveys conducted on 183 operations and supply chain professionals, and use statistical tools to examine the association between variables of the data set. The authors present real-life case studies to explain important steps of a digital transformation project.

The survey results indicate that real-time monitoring and data analytics are viewed as the most important and needed tools for organizations. High cost, lack of stakeholder buy-in and lack of successful business use cases are major barriers for companies when starting a digital transformation.

The authors provide recommendations for practitioners based on the survey responses, and outline that starting small, focusing on stakeholder buy-in and implementation of software are the three key steps for a successful transformation journey.

Main contributions of this article are to understand practitioner perspectives in digitalization and provide guidelines for organizations to follow when transforming their operations. This research closes the gap between academic research and practice by collaborating with operations and supply chain professionals.

Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.

This study aims to explore how green supply chain management (GSCM) strategies can be effectively implemented for business supply chain operations, relationship management and product design to gain green competitive advantages.

An exploratory in-depth case study was conducted with one of the largest Chinese electronics manufacturers that is considered a leading GSCM adopter in the industry, to understand how the company adopts green supply chain practices across its multiple product lines.

The findings show that businesses can build different green focuses across GSCM elements of green operation, green relationship management and green product design to form diverse hybrid strategic solutions. They include green control, lean, leagile, agile and clean innovation while taking consideration of supply chain type and product lifespan. A taxonomy of four key GSCM strategic combinations is proposed based on the findings. The strategies align with green demand and supply chain characteristics balancing a series of business competitive objectives in terms of reducing pollution and waste, improving green cost efficiency, enhancing green demand innovation and building green service effectiveness.

This study lends insight into the strategic alignment relationships between product supply chain types and approaches to GSCM.

The findings of this study can support industry practitioners in formulating aligned GSCM strategies based on product types to achieve optimal results.

Optimised green supply chain design, operations and relationship management incorporating product attributes can help further minimise negative impacts of business activities on the environment.

This research provides a systematic understanding of how product supply chain types can influence GSCM strategy formulation. It gives a holistic picture of how hybrid choices of strategies with green supply chain operations, relationship management and product design can be formulated based on product and supply chain characteristics.

Spare Parts Management (SPM) and Industry 4.0 has proven their importance. However, employment of Industry 4.0 solutions for SPM is at emerging stage. To address the issue, this article is aimed toward a systematic literature review on SPM in Industry 4.0 era and identification of research gaps in the field with prospects.

Research articles were reviewed and analyzed through a content-based analysis using four step process model. The proposed framework consists of five categories such as Inventory Management, Types of Spares, Circularity based on 6Rs, Performance Indicators and Strategic and Operational. Based on these categories, a total of 118 research articles published between 1998 and 2022 were reviewed.

The technological solutions of Industry 4.0 concepts have provided numerous opportunities for SPM. Industry 4.0 hi-tech solutions can enhance agility, operational efficiency, quality of product and service, customer satisfaction, sustainability and profitability.

The review of articles provides an integrated framework which recognizes implementation issues and challenges in the field. The proposed framework will support academia and practitioners toward implementation of technological solutions of Industry 4.0 in SPM. Implementation of Industry 4.0 in SPM may help in improving the triple bottom line aspect of sustainability which can make significant contribution to academia, practitioners and society.

The examination uncovered a scarcity of research in the intersection of SPM and Industry 4.0 concepts, suggesting a significant opportunity for additional investigative efforts.

Observation of the animal world is an important component of nature surveys. It provides a number of different information concerning aspects such as population sizes, migration directions, feeding sites and many other data. The paper below presents the results from the flights of an unmanned aerial vehicle (UAV) aimed at detecting animals in their natural environment.

The drone used in the research was equipped with RGB and thermal infrared (TIR) cameras. Both cameras, which were mounted on the UAV, were used to take pictures showing the concentration of animals (deer). The overview flights were carried out in the villages of Podlaskie Voivodeship: Szerokie Laki, Bialousy and Sloja. Research flights were made in Bialousy and Sloja. A concentration of deer was photographed during research flights in Sloja. A Durango unmanned platform, equipped with a thermal imaging camera and a Canon RGB camera, was used for research flights. The pictures taken during the flights were used to create orthomaps. A multicopter, equipped with a GoPro camera, was used for overview flights to film the flight locations. A flight control station was also used, consisting of a laptop with MissionPlanner software.

Analysis of the collected images has indicated that environmental, organisational and technical factors influence the quality of the information. Sophisticated observation precision is ensured by the use of high-resolution RGB and TIR cameras. A proper platform for the cameras is an UAV provided with advanced positioning systems, which makes it possible to create high-quality orthomaps of the area. When observing animals, the time of day (temperature contrast), year season (leaf ascent) or flight parameters is important.

The paper introduces the conclusions of the research flights, pointing out useful information for animal observation using UAVs.

The purpose of this paper is to investigate and quantify the impact of product complexity, including architectural complexity, on operator learning, productivity and quality performance in both assembly and disassembly operations. This topic has not been extensively investigated in previous research.

An extensive experimental campaign involving 84 operators was conducted to repeatedly assemble and disassemble six different products of varying complexity to construct productivity and quality learning curves. Data from the experiment were analysed using statistical methods.

The human learning factor of productivity increases superlinearly with the increasing architectural complexity of products, i.e. from centralised to distributed architectures, both in assembly and disassembly, regardless of the level of overall product complexity. On the other hand, the human learning factor of quality performance decreases superlinearly as the architectural complexity of products increases. The intrinsic characteristics of product architecture are the reasons for this difference in learning factor.

The results of the study suggest that considering product complexity, particularly architectural complexity, in the design and planning of manufacturing processes can optimise operator learning, productivity and quality performance, and inform decisions about improving manufacturing operations.

While previous research has focussed on the effects of complexity on process time and defect generation, this study is amongst the first to investigate and quantify the effects of product complexity, including architectural complexity, on operator learning using an extensive experimental campaign.