Purpose
The purpose of this paper is to investigate the impact of cloud computing (CC) on supply chain management (SCM).
Design/methodology/approach
The paper is conceptual and based on a literature review and conceptual analysis.
Findings
Today, digital technology is the primary enabler of supply chain (SC) competitiveness. CC capabilities support competitive SC challenges through structural flexibility and responsiveness. An Internet platform based on CC and a digital ecosystem can serve as “information cross-docking” between SC stakeholders. In this way, the SC model is transformed from a traditional, linear model to a platform model with the simultaneous cooperation of all partners. Platform-based SCs will be a milestone in the evolution of SCM – here conceptualised as Supply Chain 3.0.
Research limitations/implications
Currently, SCs managed holistically in cyberspace are rare in practice, and therefore empirical evidence on how digital technologies impact SC competitiveness is required in future research.
Practical implications
This research generates insights that can help managers understand and develop the next generation of SCM with the use of CC, a modern and commonly available Information and Communication Technologies (ICT) tool.
Originality/value
The paper presents a conceptual basis of how CC enables structural flexibility of SCs through easy, real-time resource and capacity reconfiguration. CC not only reduces cost and increases flexibility but also offers an effective solution for disruptive new business models with the potential to revolutionise current SCM thinking.
How value is created through service has recently undergone massive changes. Centralized service provision with clear distinctions between service offerers and beneficiaries is increasingly being substituted by value creation within decentralized networks of distributed actors integrating digital resources. One of the drivers of this transformation is blockchain technology. Applying the lens of service-dominant logic and discussing examples of blockchain-based decentralized finance, we shed light on how properties of decentralized technology stimulate value creation in service ecosystems. With this conceptual research, we postulate five propositions of decentralized value creation along the axiomatic foundations of the service-dominant logic. We provide first definitions for decentralized service as well as decentralized service ecosystems. Thereby, we contribute with an extension of the service-dominant logic to the context of decentralized ecosystems. To our knowledge, this research is among the first to add to the growing literature on blockchain value creation from a service science perspective.
With the current revolution in Shipping 4.0, a tremendous amount of data is accumulated during vessel operations.
Data quality (DQ) is becoming more and more important for the further digitalization and effective decision-making
in shipping industry. In this study, a practical DQ assessment method for raw data in vessel operations is proposed.
In this method, specific data categories and data dimensions are developed based on engineering practice and existing
literature. Concrete validation rules are then formed, which can be used to properly divide raw datasets. Afterwards,
a scoring method is used for the assessment of the data quality. Three levels, namely good, warning and alarm,
are adopted to reflect the final data quality. The root causes of bad data quality could be revealed once the internal
dependency among rules has been built, which will facilitate the further improvement of DQ in practice. A case study
based on the datasets from a Danish shipping company is conducted, where the DQ variation is monitored, assessed
and compared. The results indicate that the proposed method is effective to help shipping industry improve the quality
of raw data in practice. This innovation research can facilitate shipping industry to set a solid foundation at the early
stage of their digitalization journeys.
The approach documented in this paper employs system identification (SI), or data-based modelling, techniques as an alternative to model determination from first principles for modelling a vented oscillating water column wave energy converter, using real wave tank data gathered at Danmarks Tekniske Universitet. In SI, the parameters of the model are obtained from the experimental input/output data by minimizing a cost function, related to model fidelity. The main advantage of SI is its simplicity, as well as its potential validity range, where the dynamic model is valid over the full range for which the identification data was recorded. Furthermore, SI models are somewhat flexible, since they can be solely based on data (black-box models), or else can incorporate some physics-based information (grey-box models). However, a suitable excitation signal is of primary importance for the parametric model to be representative over a wide range of operating conditions.
This article examines the theoretical and practical implications of artificial intelligence (AI) integration in supply chain management (SCM). AI has developed dramatically in recent years, embodied by the newest generation of large language models (LLM) that exhibit human-like capabilities in various domains. However, SCM as a discipline seems unprepared for this potential revolution, as existing perspectives do not capture the potential for disruption offered by AI tools. Moreover, AI integration in SCM is not only a technical but also a social process, influenced by human sensemaking and interpretation of AI systems. This article offers a novel theoretical lens called the AI Integration (AII) framework, which considers two key dimensions: the level of AI integration across the supply chain and the role of AI in decision-making. It also incorporates human meaning-making as an overlaying factor that shapes AI integration and disruption dynamics. The article demonstrates that different ways of integrating AI will lead to different kinds of disruptions, both in theory and practice. It also discusses the implications of AI integration for SCM theorizing and practice, highlighting the need for cross-disciplinary collaboration and sociotechnical perspectives.
Linear potential flow (LPF) models remain the tools-of-the-trade in marine and ocean engineering despite their well-known assumptions of small amplitude waves and motions. As of now, nonlinear simulation tools are still too computationally demanding to be used in the entire design loop, especially when it comes to the evaluation of numerous irregular sea states. In this paper we aim to enhance the performance of the LPF models by introducing a hybrid LPF-ML (machine learning) approach, based on identification of nonlinear force corrections. The corrections are defined as the difference in hydrodynamic force (viscous and pressure-based) between high-fidelity CFD and LPF models. Using prescribed chirp motions with different amplitudes, we train a long short-term memory (LSTM) network to predict the corrections. The LSTM network is then linked to the MoodyMarine LPF model to provide the nonlinear correction force at every time-step, based on the dynamic state of the body and the corresponding forces from the LPF model. The method is illustrated for the case of a heaving sphere in decay, regular and irregular waves – including passive control. The hybrid LPF model is shown to give significant improvements compared to the baseline LPF model, even though the training is quite generic.
This review article presents a summary of the main categories of models developed for modeling cavitation, a multiphase phenomenon in which a fluid locally experiences phase change due to a drop in ambient pressure. The most common approaches to modeling cavitation along with the most common modifications to said approaches due to other effects of cavitating flows are identified and categorized. The application of said categorization is demonstrated through an analysis of selected cavitation models. For each of the models presented, the various assumptions and simplifications made by the authors of the model are discussed, and applications of the model to simulating various aspects of cavitating flow are also presented. The result of the analysis is demonstrated via a visualization of the categorizations of the highlighted models. Using the preceding discussion of the various cavitation models presented, the review concludes with an outlook toward future improvements in the modeling of cavitation.
Havebrugsindustrien i nordiske lande er meget afhængig af drivhussystemer på grund af begrænsningen af det naturlige miljø og de strenge plantekrav for bestemte plantetyper. Kommercielle avlere i disse regioner støder på betydelige udfordringer med at garantere kvaliteten af planterne, mens de minimerer produktionsomkostningerne. På den ene side skal et drivhussystem forbruge en stor mængde energi for at give et tilfredsstillende klima for plantevækst. På den anden side, i de senere år, har energiprisen stigende i Europa ført til en stigning i produktionsomkostningerne for drivhuse, hvilket gør energibesparelse og optimering imperativ. Det er dog udfordrende for avlere at håndtere dette dilemma, fordi drivhusklimakontrol er et meget dynamisk og meget koblet komplekst system. Ved at analysere funktionerne i ikke-linearitet og dynamik i drivhusklimaet kan de eksisterende løsninger ikke korrekt opfylde de praktiske krav i gartneriindustrien.
For at tackle disse problemer foreslås en digital tvilling af drivhusklimakontrol (DT-GCC) rammer i denne forskning for at optimere aktuatorens driftsplan til minimering af energiforbrug og produktionsomkostninger uden at gå på kompromis med produktionskvaliteten. Arkitekturen i DT-GCC-rammen og de anvendte metoder er uddybet modulært, herunder fysisk tvilling af drivhusklimakontrol (PT-GCC) systemforståelse, design af DT-GCC-system, sammenkobling af DTGCC og PT-GCC og integration med andre digitale tvillinger (DTS).
DT-GCC omfatter en virtuel drivhus (VGH) og en multi-objektiv optimeringsbaseret klimakontrol (MOOCC) platform. VGH er den digitale repræsentation af det fysiske drivhus gennem modellering af de faktorer, der kan påvirke drivhusklimaet markant og aktuatorens driftsstrategier. MOOCC er ansvarlig for at definere drivhusklimakontrol som et multi-objektivt optimeringsproblem (MOO) og optimere driftsplanen for kunstigt lys (lysplan) og varmesystem (varmeplan). Desuden er en hierarkisk struktur af DT-GCC designet i henhold til funktionerne og ansvaret for individuelle lag, der gavner den praktiske realisering af DT-GCC med en organiseret arkitektur af design og styring.
Funktionaliteterne i DT-GCC er udviklet i en drivhusklimakontrolplatform, der er navngivet af Dynalight, som er kombineret med en genetisk algoritme (GA) ramme kaldet Controleum. Dynalight definerer et MOO -problem til at abstrahere drivhusklimakontrolsystemet med flere objektive funktioner, og omkostningerne beregnes baseret på modelleringsresultaterne fra VGH. Controleum er ansvarlig for implementeringen af GA for at generere en Pareto Frontier (PF) og endelig løsning af løsning til let plan og varmeplan.
Forskellige scenarier og tilsvarende eksperimenter er designet til at evaluere ydelsen af DT-GCC fra individuelle perspektiver, herunder VGH, MOOCC og DT-integration. Eksperimenterne på VGH verificerer forudsigelsesydelsen for kunstigt neuralt netværk (ANN) metoder på indendørs temperatur, opvarmning af forbrug og netto fotosyntese (PN). Hvad angår de to standaloneeksperimenter, garanterer resultaterne DT-GCCs evne til at kortlægge avlernes beslutningstagning om let plan og varmeplan og verificere MOOCC-ydelsen for at opfylde voksende krav og samtidig reducere energiforbruget og omkostningerne. Endelig, i DT-integrationseksperimenterne med Digital Twin of Production Twin (DT-PF) og Digital Twin of Energy System (DT-ES), afslutter DT-GCC det tilsvarende svar på forudsigelser og optimeringsanmodninger.
The real-time provision of high-quality estimates of the ocean wave parameters at appropriate spatial resolutions are essential for the sustainable operations of marine structures. Machine learning affords considerable opportunity for providing additional value from sensor networks, fusing metocean data collected by various platforms. Exploiting the ship-as-a-wave-buoy concept, this article proposes the integration of vessel-based observations into a wave-nowcasting framework. Surrogate models are trained using a high-fidelity physics-based nearshore wave model to learn the spatial correlations between grid points within a computational domain. The performance of these different models are evaluated in a case study to assess how well wave parameters estimated through the spectral analysis of ship motions can perform as inputs to the surrogate system, to replace or complement traditional wave buoy measurements. The benchmark study identifies the advantages and limitations inherent in the methodology incorporating ship-based wave estimates to improve the reliability and availability of regional sea state information.
A practical estimation methodology of the mean added resistance in irregular waves is shown, and the present paper provides statistical analyses of estimates for ships in actual conditions. The study merges telemetry data of more than 200 in-service container vessels with ocean re-analysis data from ERA5. Theoretical estimates relying on spectral calculations of added resistance are made for both long- and short-crested waves and are based on a combination of a parametric expression for the wave spectrum and a semi-empirical formula for the added resistance transfer function. The theoretical estimates are compared to predictions from an indirect calculation of added resistance relying on shaft power measurements and empirical estimates of the remaining resistance components. Overall, the comparison reveals a bias in bow oblique waves and higher sea states of the spectral estimates as well as the large variance of the empirically derived predictions — particularly in beam-to-following waves. One of the study’s main findings, confirming previous studies but based on a much larger dataset than in earlier similar studies, is that added resistance assessment based on in-service data is complex due to significant associated uncertainties.