13,418 research outputs found
Supply Chain Resource Planning Systems: A Scenario of Future Enterprise Systems
To envisage possible future enterprise systems, we describe four scenarios that all respond to the increasing need for better supply chain-wide coordination of resource allocation decisions. We use two drivers to derive these scenarios; namely “normal form of providing corporate computing resources” and “stance of regulators towards explicit forms of industry-wide coordination”, the latter of which includes cooperation among competitors. While three of our scenarios are familiar to contemporary readers, the fourth, supply chain resource planning (SCRP) systems, marks a radical break with current practice. We describe the operating principle of SCRP systems and discuss possible governance structures for organizations supporting SCRP systems. We hope to encourage discussion about the future of enterprise systems that moves beyond extrapolating past and current trends. The paper concludes by outlining four areas for promising future research
Archival Study of Blockchain Applications in the Construction Industry From Literature Published in 2019 and 2020
Purpose: This paper aims to investigate proposed blockchain applications in the construction industry from contemporary
literature.
Methodology: Archival studies will be used to obtain academic content from secondary sources. An explorative strategy
will be adopted with no preconception or biases on the preferred route of execution. Blockchain is a fast-evolving technology
with a high rate of yearly progression; therefore, this paper refines the search to recently published material in 2019 and
2020. Data is collected in two stages, firstly, categories of research are extrapolated from secondary literature and recorded
into a table, and afterwards, the corresponding proposed application of blockchain is documented and reviewed.
Findings: An adequate breadth and variety of categories are substantiated from archival literature, which effectively
contributes to the extraction of proposed blockchain applications for construction. The data collection extracts 19 categories
from the explorative study, in which 19 proposed solutions (one per category) is presented. All of the advisory content for
the proposed solutions were obtained from a deliberated selection of 21 academic study papers.
Limitations: The study is limited to one proposed application per category, totalling 19 proposed solutions; however,
assessing various approaches per category could not be researched comparatively due to voluminous information. Thus,
recommendations incorporate a holistic case study of one subject category which incorporates a multitude of various
proposed applications.
Originality: This paper contributes to new knowledge through extrapolating proposed blockchain applications from
academic literature in 2019 and 2020
A review of Smart Contract Blockchain Based on Multi-Criteria Analysis: Challenges and Motivations
A smart contract is a digital program of transaction protocol (rules of
contract) based on the consensus architecture of blockchain. Smart contracts
with Blockchain are modern technologies that have gained enormous attention in
scientific and practical applications. A smart contract is the central aspect
of a blockchain that facilitates blockchain as a platform outside the
cryptocurrency spectrum. The development of blockchain technology, with a focus
on smart contracts, has advanced significantly in recent years. However
research on the smart contract idea has weaknesses in the implementation
sectors based on a decentralized network that shares an identical state. This
paper extensively reviews smart contracts based on multi criteria analysis
challenges and motivations. Therefore, implementing blockchain in
multi-criteria research is required to increase the efficiency of interaction
between users via supporting information exchange with high trust. Implementing
blockchain in the multi-criteria analysis is necessary to increase the
efficiency of interaction between users via supporting information exchange and
with high confidence, detecting malfunctioning, helping users with performance
issues, reaching a consensus, deploying distributed solutions and allocating
plans, tasks and joint missions. The smart contract with decision-making
performance, planning and execution improves the implementation based on
efficiency, sustainability and management.
Furthermore the uncertainty and supply chain performance lead to improved
users confidence in offering new solutions in exchange for problems in smart
contacts. Evaluation includes code analysis and performance while development
performance can be under development.Comment: Revie
The Footprint of Things: A hybrid approach towards the collection, storage and distribution of life cycle inventory data
Life cycle assessment is a well-established methodology for assessing the environmental impacts of products and services. Unfortunately, an essential part of this life cycle assessment method, collecting inventory data, is extremely time consuming. The quality of manually conducted LCA studies is often limited by uncertainty in the inventory data or narrow scope. Past attempts to overcome these challenges through automation of data collection utilizing the Internet of Things have relied on fully centralized architectures. The drawback of a central repository is the complex coordination between all involved actors in supply chains of products and services. This paper proposes an alternative hybrid approach combining a primary distributed system supplemented with a central repository reducing the need for coordination. This hybrid approach is named "the Footprint of Things". We present a system design that embeds the automatic reporting of life cycle inventory data, such as energy and material flows, into all product components involved in a service delivery. The major strength of our novel system design, among others, is its capacity for real-time and more precise impact calculation of ICT services
A QoS aware services mashup model for cloud computing applications
Purpose: With the popularity of cloud computing, cloud services have become to be application programming platform where users can create new applications mashup(composing) the functionality offered byothers.By composing of distributed, cloud services dynamicallyto provide more complex tasks, services mashup provides an attractive way for building large-scale Internet applications.One of the challenging issues of cloud services mashup is how to find service paths to route the service instances provider through whilemeeting the applications’ resource requirements so that the QoS constraints are satisfied. However, QoS aware service routing problem istypically NP-hard.The purpose of this paper is to propose a QoS Aware Services Mashup(QASM) model to solve this problem more effectively.
Design/methodology/approach: In this paper, we focus on the QoS aware services selection problem in cloud services mashup, for example, given the user service composition requirements and their QoS constraint descriptions, how to select the required serviceinstances and route the data flows through these instances so that the QoS requirements are satisfied. We design a heuristic algorithm to find service paths to route the data flows through whilemeeting the applications’ resource requirements and specific QoS constraints.
Findings: This study propose a QoS Aware Services Mashup(QASM) model to solve this problem more effectively. Simulations show that QASM can achieve desired QoS assurances as well as load balancing in cloud services environment.
Originality/value: This paper present a QASM model for providing high performance distributed applications in the cloud computingPeer Reviewe
Scenarios for the development of smart grids in the UK: literature review
Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid.
It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers.
The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.
Smart Manufacturing
This book is a collection of 11 articles that are published in the corresponding Machines Special Issue “Smart Manufacturing”. It represents the quality, breadth and depth of the most updated study in smart manufacturing (SM); in particular, digital technologies are deployed to enhance system smartness by (1) empowering physical resources in production, (2) utilizing virtual and dynamic assets over the Internet to expand system capabilities, (3) supporting data-driven decision-making activities at various domains and levels of businesses, or (4) reconfiguring systems to adapt to changes and uncertainties. System smartness can be evaluated by one or a combination of performance metrics such as degree of automation, cost-effectiveness, leanness, robustness, flexibility, adaptability, sustainability, and resilience. This book features, firstly, the concepts digital triad (DT-II) and Internet of digital triad things (IoDTT), proposed to deal with the complexity, dynamics, and scalability of complex systems simultaneously. This book also features a comprehensive survey of the applications of digital technologies in space instruments; a systematic literature search method is used to investigate the impact of product design and innovation on the development of space instruments. In addition, the survey provides important information and critical considerations for using cutting edge digital technologies in designing and manufacturing space instruments
Sustainable digital marketing under big data: an AI random forest model approach
Digital marketing refers to the process of promoting, selling, and delivering products or services through online platforms and channels using the internet and electronic devices in a digital environment. Its aim is to attract and engage target audiences through various strategies and methods, driving brand promotion and sales growth. The primary objective of this scholarly study is to seamlessly integrate advanced big data analytics and artificial intelligence (AI) technology into the realm of digital marketing, thereby fostering the progression and optimization of sustainable digital marketing practices. First, the characteristics and applications of big data involving vast, diverse, and complex datasets are analyzed. Understanding their attributes and scope of application is essential. Subsequently, a comprehensive investigation into AI-driven learning mechanisms is conducted, culminating in the development of an AI random forest model (RFM) tailored for sustainable digital marketing. Subsequent to this, leveraging a real-world case study involving enterprise X, fundamental customer data is collected and subjected to meticulous analysis. The RFM model, ingeniously crafted in this study, is then deployed to prognosticate the anticipated count of prospective customers for said enterprise. The empirical findings spotlight a pronounced prevalence of university-affiliated individuals across diverse age cohorts. In terms of occupational distribution within the customer base, the categories of workers and educators emerge as dominant, constituting 41% and 31% of the demographic, respectively. Furthermore, the price distribution of patrons exhibits a skewed pattern, whereby the price bracket of 0–150 encompasses 17% of the population, whereas the range of 150–300 captures a notable 52%. These delineated price bands collectively constitute a substantial proportion, whereas the range exceeding 450 embodies a minority, accounting for less than 20%. Notably, the RFM model devised in this scholarly endeavor demonstrates a remarkable proficiency in accurately projecting forthcoming passenger volumes over a seven-day horizon, significantly surpassing the predictive capability of logistic regression. Evidently, the AI-driven RFM model proffered herein excels in the precise anticipation of target customer counts, thereby furnishing a pragmatic foundation for the intelligent evolution of sustainable digital marketing strategies
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