Hasil untuk "Management. Industrial management"

A. Hopwood, P. Miller

Qingbin Song, Jinhui Li, Xianlai Zeng

J. Payne, P. Mckeown, Matthew D. Jones

Abstract Growing environmental concerns associated with the accumulation of plastic waste in the natural environment has incentivised considerable research into renewable alternatives, and more recently, alternative waste management strategies. This review aims to introduce poly (lactic acid) (PLA), a bioplastic, and recent research within the field. Attention will then shift to research surrounding plastic waste management. Various end-of-life (EOL) options available to plastics will be discussed more broadly, before presenting existing technologies, challenges and future opportunities exclusively for PLA. Herein, all waste management strategies presented for PLA are discussed within the context of industrial feasibility.

D. M. Davenport, Akshay Deshmukh, Jay R. Werber et al.

A. Dolgui, D. Ivanov, S. Sethi et al.

This paper presents a survey on the applications of optimal control to scheduling in production, supply chain and Industry 4.0 systems with a focus on the deterministic maximum principle. The first objective is to derive major contributions, application areas, limitations, as well as research and application recommendations for the future research. The second objective is to explain control engineering models in terms of industrial engineering and production management. To achieve these objectives, optimal control models, qualitative methods of performance analysis and computational methods for optimal control are considered. We provide a brief historic overview and clarify major mathematical fundamentals whereby the control engineering terms are brought into correspondence with industrial engineering and management. The survey allows the grouping of models with only terminal constraints with application to master production scheduling, models with hybrid terminal–logical constraints with applications to short term job and flow shop scheduling, and hybrid structural–terminal–logical constraints with applications to customised assembly systems such as Industry 4.0. Computational algorithms in state, control and adjoint variable spaces are discussed.

L. Ardito, A. Petruzzelli, Umberto Panniello et al.

Purpose The purpose of this paper is to present a comprehensive picture of the innovative efforts undertaken over time to develop the digital technologies for managing the interface between supply chain management and marketing processes and the role they play in sustaining supply chain management-marketing (SCM-M) integration from an information processing point of view. Design/methodology/approach Patent analysis and actual examples are used to carry out this study. In detail, first, the authors identify the subset of enabling technologies pertaining to the fourth industrial revolution (Industry 4.0) that can be considered the most relevant for effective SCM-M integration (i.e. Industrial Internet of Things, Cloud computing, Big Data analytics and customer profiling, Cyber security). Second, the authors carry out a patent analysis aimed at providing a comprehensive overview of the patenting activity trends characterizing the set of digital technologies under investigation, hence highlighting their innovation dynamics and applications. Findings This research provides insightful information about which digital technologies may enable the SCM-M integration. Specifically, the authors highlight the role those solutions play in terms of information acquisition, storage and elaboration for SCM-M integration by relying on illustrative actual examples. Moreover, the authors present the organisations more involved in the development of digital technologies for SCM-M integration over time and offer an examination of their technological impact in terms of influence on subsequent technological developments. Originality/value So far, much has been said about why marketing and supply chain management functions should be integrated. However, a clear picture of the digital technologies that might be adopted to achieve this objective has yet to be revealed. Thus, the paper contributes to the literature on SCM-M integration and Industry 4.0 by highlighting the enabling technologies for the Industry 4.0 that may particularly serve for managing the SCM-M interface from an information processing perspective.

V. Gude

Agnieszka Gębczyńska-Janowicz, Elżbieta Marczak, Anna Zapiec et al.

The global phenomenon of an aging population calls for the modernization of public spaces to better accommodate the needs of older individuals. Beaches in coastal cities are popular destinations for seniors; therefore, this article will examine effective design practices for public spaces in coastal areas. The study focused on eleven pedestrian routes that connect public transport stops to the main beach entrances in the coastal zone of Gdańsk, Poland. Field observations assessed the pedestrian and cycling infrastructure, route lengths, surface quality, availability of small urban elements (such as benches, restrooms, and water fountains), and safety conditions (including lighting, signage, barriers, and general cleanliness). The analysis of these spaces led to the development of guidelines for the architectural and urban design of senior-friendly coastal urban zones.

Xianglong Liu, Hongzhi Wang, Yingze Li et al.

In the realm of big data, cloud-edge-device collaboration is prevalent in industrial scenarios. However, a systematic exploration of the theory and methodologies related to data management in this field is lacking. This paper delves into the sub-problem of data storage and scheduling within cloud-edge-device collaborative environments. Following extensive research and analysis of the characteristics and requirements of data management in cloud-edge collaboration, it is evident that existing studies on hierarchical data management primarily focus on the migration of hot and cold data. Additionally, these studies encounter challenges such as elevated operational and maintenance costs, difficulties in locating data within tiered storage, and intricate metadata management attributable to excessively fine-grained management granularity. These challenges impede the fulfillment of the storage needs in cloud-edge-device collaboration. To overcome these challenges, we propose a \underline{B}lock-based hie\underline{R}archical d\underline{A}ta \underline{M}anagement fram\underline{E}work, \textbf{Brame}, which advocates for a workload-aware three-tier storage architecture and suggests a shift from using tuples to employing $Blocks$ as the fundamental unit for data management. \textbf{Brame} owns an offline block generation method designed to facilitate efficient block generation and expeditious query routing. Extensive experiments substantiate the superior performance of \textbf{Brame}.

Tobias Løvebakke Nielsen, Daniela Guericke, Alessio Trivella et al.

The concept Hubs for Circularity (H4C) represents integrated systems that combine efficient use of clean energy and circular economy to enhance resource efficiency within a region. H4C benefit from the geographical proximity of different industries within industrial zones and the surrounding urban and rural areas, allowing them to share resources, technology, and infrastructure. They reduce the use of virgin resources through Industrial Symbiosis (IS), where one company uses waste of another company as resource. Energy management is crucial in H4C, as these systems often integrate renewable energy sources, involve energy-intensive industries, include numerous energy consumers, and may rely on energy-based industrial symbiosis exchanges. This study presents a modelling framework for energy management in H4C, developed through a systematic literature review of related systems including energy-based IS. The framework provides a guideline for researchers and practitioners on which modelling aspects to consider when optimising the energy flows within a hub. We argue that effective energy management in H4C requires combining conventional modelling aspects - such as objective functions, uncertainty, operational flexibility, and market participation - with IS-specific factors like the type of symbiosis, the degree of information sharing, and collaboration structures. In H4C with extensive IS, decentralised resource and energy exchanges often lead to similarly decentralised information flows and decision-making. Yet, our review shows a persistent reliance on centralised model structures, suggesting a path dependency rooted in traditional energy optimisation approaches. This highlights the need for models that better align with the distributed and collaborative nature of H4C systems.

S.A. Hosseini

This paper introduces a direct quadrature method for the numerical solution of Volterra integral equations of the first kind, utilizing a composite quadrature scheme based on the Floater–Hormann family of linear barycentric rational interpolants. The convergence of the proposed method is rigorously proved, and the order of convergence is explicitly derived in terms of the parameters of the method, thereby providing a clear theoretical framework for its performance. Several numerical experiments are provided to demonstrate both the efficiency and accuracy of the method, as well as to verify the excellent agreement between the implementation results and the theoretically predicted convergence rates.

Francisco-Jose Alvarado-Alcon, Rafael Asorey-Cacheda, Antonio-Javier Garcia-Sanchez et al.

The Internet of Things (IoT) is gaining significant attention for its ability to digitally transform various sectors by enabling seamless connectivity and data exchange. However, deploying these networks is challenging due to the need to tailor configurations to diverse application requirements. To date, there has been limited focus on examining and enhancing the carbon footprint (CF) associated with these network deployments. In this study, we present an optimization framework leveraging machine learning techniques to minimize the CF associated with IoT multi-hop network deployments by varying the placement of the required gateways. Additionally, we establish a direct comparison between our proposed machine learning method and the integer linear program (ILP) approach. Our findings reveal that placing gateways using neural networks can achieve a 14% reduction in the CF for simple networks compared to those not using optimization for gateway placement. The ILP method could reduce the CF by 16.6% for identical networks, although it incurs a computational cost more than 250 times higher, which has its own environmental impact. Furthermore, we highlight the superior scalability of machine learning techniques, particularly advantageous for larger networks, as discussed in our concluding remarks.

P. Shrivastava

M. Dodgson, R. Rothwell

P. Gooderham, Odd Nordhaug, K. Ringdal

Alonel A. Hugo, Gerard Nathaniel C. Ngo

The developed system aims to incorporate a private blockchain technology in the procurement process for the supply office. The procurement process includes the canvassing, purchasing, delivery and inspection of items, inventory, and disposal. The blockchain-based system includes a distributed ledger technology, peer-to-peer network, Proof-of-Authority consensus mechanism, and SHA3-512 cryptographic hash function algorithm. This will ensure trust and proper accountability to the custodian of the property while safeguarding sensitive information in the procurement records. The extreme prototyping model will be used as software development life cycle. It is mostly used for web-based applications and has an increased user involvement. The prototype version of the system allows the users get a better understanding of the system being developed. It also reduces the time and cost, has quicker user feedback, missing and difficult functions can be recognized, and confusing processes can be addressed on an early stage. The implementation of a private blockchain technology has an increased privacy, enhanced security, improved efficiency, and reduced complexity over traditional blockchain network. The use of SHA3-512 as cryptographic hash function algorithm is much faster than its predecessors when cryptography is handled by hardware components. Furthermore, it is not vulnerable to length extension attacks making it reliable in terms of security of data. The study recommends the use of private blockchain-based technology with the procurement and asset management system in the supply office. The procurement records will be protected against tampering using this technology. This will promote trust and confidence of the stakeholders. The implementation of blockchain technology in developing a system served as advancement and innovation in terms of securing data.

Rodrigo Laigner, Zhexiang Zhang, Yijian Liu et al.

Microservice architectures have become a popular approach for designing scalable distributed applications. Despite their extensive use in industrial settings for over a decade, there is limited understanding of the data management challenges that arise in these applications. Consequently, it has been difficult to advance data system technologies that effectively support microservice applications. To fill this gap, we present Online Marketplace, a microservice benchmark that highlights core data management challenges that existing benchmarks fail to address. These challenges include transaction processing, query processing, event processing, constraint enforcement, and data replication. We have defined criteria for various data management issues to enable proper comparison across data systems and platforms. Through case studies with state-of-the-art data platforms, we discuss the issues encountered while implementing and meeting Online Marketplace's criteria. By capturing the overhead of meeting the key data management requirements that are overlooked by existing benchmarks, we gain actionable insights into the experimental platforms. This highlights the significance of Online Marketplace in advancing future data systems to meet the needs of microservice practitioners.

Hang Gao, Shuohua Yang, Xinli Liu

Weather parametric insurance relies on weather indices rather than actual loss assessments, enhancing claims efficiency, reducing moral hazard, and improving fairness. In the context of increasing climate change risks, despite growing interest and demand,, weather parametric insurance's market share remains limited due to inherent basis risk, which is the mismatch between actual loss and payout, leading to loss without payout or payout without loss. This paper proposes a novel empirical research using Monte Carlo simulations to test whether basis risk can be managed through diversification and hedged like other risks. Key findings include: Firstly, portfolio basis risk and volatility decrease as the number of contracts increases. Secondly, spatial relationships significantly impact basis risk, with risk levels correlating with the ratio between insured location, weather station, and disaster footprint radius, and thirdly, event severity does not significantly impact basis risk, suggesting that catastrophic disaster severity should not hinder parametric insurance development.

Laurie Williams, Sammy Migues, Jamie Boote et al.

The Proactive Software Supply Chain Risk Management Framework (P SSCRM) described in this document is designed to help you understand and plan a secure software supply chain risk management initiative. P SSCRM was created through a process of understanding and analyzing real world data from nine industry leading software supply chain risk management initiatives as well as through the analysis and unification of ten government and industry documents, frameworks, and standards. Although individual methodologies and standards differ, many initiatives and standards share common ground. P SSCRM describes this common ground and presents a model for understanding, quantifying, and developing a secure software supply chain risk management program and determining where your organization's existing efforts stand when contrasted with other real world software supply chain risk management initiatives.

Vasily KASHKIN, Yuriy ANDROSIK

Social media analytics is the backbone of successful marketing. It allows you to accurately find and understand the target audience of the company. However, this requires analyzing a huge amount of publicly available data. This is where software tools come to the rescue, allowing such analysis to be carried out in real time. Nevertheless, there are a large number of software products on the market, each of which has a different set of functions - from complex products to highly specialized ones. A wide or vice versa narrow range of functions makes it difficult to choose a specific software solution for a specific task. For this reason, the main purpose of the article is to make a comparative analysis of the functions of the most popular social media analytics software tools designed to identify and analyze the target audience.