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1 – 10 of over 203000R.H. Khatibi, R. Lincoln, D. Jackson, S. Surendran, C. Whitlow and J. Schellekens
With the diversification of modelling activities encouraged by versatile modelling tools, handling their datasets has become a formidable problem. A further impetus stems from the…
Abstract
With the diversification of modelling activities encouraged by versatile modelling tools, handling their datasets has become a formidable problem. A further impetus stems from the emergence of the real‐time forecasting culture, transforming data embedded in computer programs of one‐off modelling activities of the 1970s‐1980s into dataset assets, an important feature of modelling since the 1990s, where modelling has emerged as a practice with a pivotal role to data transactions. The scope for data is now vast but in legacy data management practices datasets are fragmented, not transparent outside their native software systems, and normally “monolithic”. Emerging initiatives on published interfaces will make datasets transparent outside their native systems but will not solve the fragmentation and monolithic problems. These problems signify a lack of science base in data management and as such it is necessary to unravel inherent generic structures in data. This paper outlines root causes for these problems and presents a tentative solution referred to as “systemic data management”, which is capable of solving the above problems through the assemblage of packaged data. Categorisation is presented as a packaging methodology and the various sources contributing to the generic structure of data are outlined, e.g. modelling techniques, modelling problems, application areas and application problems. The opportunities offered by systemic data management include: promoting transparency among datasets of different software systems; exploiting inherent synergies within data; and treating data as assets with a long‐term view on reuse of these assets in an integrated capability.
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This paper reports the results of a three-year-long research on business relationships, relying on qualitative data gathered through multiple-case study research of four focal…
Abstract
This paper reports the results of a three-year-long research on business relationships, relying on qualitative data gathered through multiple-case study research of four focal companies operating in Australia. The industry settings are as follows: steel construction, vegetable oils trading, aluminum and steel can manufacture, and imaging solutions. The research analyzes two main aspects of relationships: structure and process. This paper deals with structure describing it by the most desired features of intercompany relationships for each focal company. The primary research data have been coded drawing on extant research into business relationships. The main outcome of this part of the research is a five construct model composed by trust, commitment, bonds, distance, and information sharing that accounts for all informants’ utterances about relationship structure.
Nahathai Boontae and Mongkol Ussavadilokrit
Effective facility management (FM) can reduce environmental effects on buildings throughout their life cycle. This study aims to investigate the challenges in implementing…
Abstract
Purpose
Effective facility management (FM) can reduce environmental effects on buildings throughout their life cycle. This study aims to investigate the challenges in implementing building information modelling (BIM) for FM in government buildings in Thailand.
Design/methodology/approach
Eight government-building facility experts were interviewed using an in-depth interview method to identify FM challenges. The collected qualitative data were analysed via thematic analysis to ensure data saturation. The final questionnaire was designed with 45 FM problems, classified into management, technical and human resource problems, to collect quantitative data from 54 government FM officers. The data were used to prioritise the severity and frequency of the FM problems using the severity index (SI) and relative importance index (RII).
Findings
Management problems have the highest impact, with an average SI of 0.285, followed by human resource (average SI = 0.266) and technical (average SI = 0.264) problems.
Originality/value
This study identifies the government-building FM problems in Thailand that are critical to the development of a BIM execution plan (BEP) guideline. The findings can facilitate strategy development for government-building operations and management in line with the public procurement and supply administration of Thailand. These findings can serve as a guideline to inform the development of a BIM Roadmap for integration into the national digital roadmap and the Thailand 4.0 policy to mitigate construction-related environmental and climate issues.
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Ruihan Zhao, Liang Luo, Pengzhong Li and Jinguang Wang
Quality management systems are commonly applied to meet the increasingly stringent requirements for product quality in discrete manufacturing industries. However, traditional…
Abstract
Purpose
Quality management systems are commonly applied to meet the increasingly stringent requirements for product quality in discrete manufacturing industries. However, traditional experience-driven quality management methods are incapable of handling heterogeneous data from multiple sources, leading to information islands. This study aims to present a quality management key performance indicator visualization (QM-KPIVIS) system to enable integrated quality control and ultimately ensure product quality.
Design/methodology/approach
Based on multiple heterogeneous data, an integrated approach is proposed to quantify explicitly the relationship between Internet of Things data and product quality. Specifically, this study identifies the tracing path of quality problems based on multiple heterogeneous quality information tree. In addition, a hierarchical analysis approach is adopted to calculate the key performance indicators of quality influencing factors in the quality control process.
Findings
Proposed QM-KPIVIS system consists of data visualization, quality problem processing, quality optimization and user rights management modules, which perform in a well-coordinated manner. An empirical study was also conducted to validate the effectiveness of proposed system.
Originality/value
To the best of the authors’ knowledge, this study is the first attempt to use industrial Internet of Things and multisource heterogeneous data for integrated product quality management. Proposed approach is more user-friendly and intuitive compared to traditional empirically driven quality management methods and has been initially applied in the manufacturing industry.
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David Charles Robinson, David Adrian Sanders and Ebrahim Mazharsolook
This paper aims to describe the creation of innovative and intelligent systems to optimise energy efficiency in manufacturing. The systems monitor energy consumption using ambient…
Abstract
Purpose
This paper aims to describe the creation of innovative and intelligent systems to optimise energy efficiency in manufacturing. The systems monitor energy consumption using ambient intelligence (AmI) and knowledge management (KM) technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems.
Design/methodology/approach
Energy consumption data (ECD) are processed within a service-oriented architecture-based platform. The platform provides condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase and continuous improvement/optimisation of energy efficiency. The systems monitor energy consumption using AmI and KM technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems.
Findings
The systems produce an improvement in energy efficiency in manufacturing small- and medium-sized enterprises (SMEs). The systems provide more comprehensive information about energy use and some knowledge-based support.
Research limitations/implications
Prototype systems were trialled in a manufacturing company that produces mooring chains for the offshore oil and gas industry, an energy intensive manufacturing operation. The paper describes a case study involving energy-intensive processes that addressed different manufacturing concepts and involved the manufacture of mooring chains for offshore platforms. The system was developed to support online detection of energy efficiency problems.
Practical implications
Energy efficiency can be optimised in assembly and manufacturing processes. The systems produce an improvement in energy efficiency in manufacturing SMEs. The systems provide more comprehensive information about energy use and some knowledge-based support.
Social implications
This research addresses two of the most critical problems in energy management in industrial production technologies: how to efficiently and promptly acquire and provide information online for optimising energy consumption and how to effectively use such knowledge to support decision making.
Originality/value
This research was inspired by the need for industry to have effective tools for energy efficiency, and that opportunities for industry to take up energy efficiency measures are mostly not carried out. The research combined AmI and KM technologies and involved new uses of sensors, including wireless intelligent sensor networks, to measure environment parameters and conditions as well as to process performance and behaviour aspects, such as material flow using smart tags in highly flexible manufacturing or temperature distribution over machines. The information obtained could be correlated with standard ECD to monitor energy efficiency and identify problems. The new approach can provide effective ways to collect more information to give a new insight into energy consumption within a manufacturing system.
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The purpose of this paper is to complement a previous article on using the Cynefin framework to make sense of the electronic records management challenge. Its focus is on how to…
Abstract
Purpose
The purpose of this paper is to complement a previous article on using the Cynefin framework to make sense of the electronic records management challenge. Its focus is on how to use Cynefin, and the ERM framework developed using it, as an approach to addressing this wicked problem. The aim is to provide examples of how they could be used in practice in different organisational contexts.
Design/methodology/approach
Four examples are provided. Empirical research data are used to underpin three of the examples and a thought experiment using published literature informs the fourth.
Findings
The examples illustrate the potential value and power of the Cynefin framework as both a practical and conceptual tool in the ERM context. It can be used to address the ERM challenge in different ways: as a strategic approach taking a holistic view and/or as a tactical approach at a more specific granular level. It can be used to inform practice by helping practitioners choose the most appropriate approach dependent on the level of complexity of the issue they are addressing, whether that is for a specific issue, a project or initiative, for planning or for exploratory, sense-making purposes.
Research limitations/implications
The examples draw on one qualitative, empirical set of research data and one published use. Further experimentation and practical use are required; others are encouraged to use Cynefin to test the propositions and provide further examples.
Practical implications
The examples provided can be adopted and/or adapted by records professionals, both practitioners and/or academics, at strategic and tactical levels in different records contexts.
Originality/value
This paper provides examples of adopting a different approach to tackling the wicked problem of managing electronic records using the Cynefin framework as a new lens.
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Elizaveta Gavrikova, Irina Volkova and Yegor Burda
The purpose of this paper is to design a framework for asset data management in power companies. The authors consider asset data management from a strategic perspective, linking…
Abstract
Purpose
The purpose of this paper is to design a framework for asset data management in power companies. The authors consider asset data management from a strategic perspective, linking operational-level data with corporate strategy and taking into account the organizational context and stakeholder expectations.
Design/methodology/approach
The authors conducted a multiple case study based on a literature review and three series of in-depth interviews with experts from three Russian electric power companies.
Findings
The main challenge in asset data management for electric power companies is the increasing amount and complexity of asset data, which is frequently incomplete or inaccurately collected, hard to translate to managerial language, focused primarily on the operational level. Such fragmented approach negatively affects strategic decision-making. The proposed framework introduces a holistic approach, provides context and accountability for decision-making and attributes data flows, roles and responsibilities to different management levels.
Research limitations/implications
The limitations of our study lie in the exploratory nature of case study research and limited generalization of the observed cases. However, the authors used multiple sources of evidence to ensure validity and generalization of the results. This article is a first step toward further understanding of the issues of transformation in power companies and other asset intensive businesses.
Originality/value
The novelty of the framework lies in the scope, focus and detailed treatment of asset data management in electric power companies.
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Dumitru Roman, Neal Reeves, Esteban Gonzalez, Irene Celino, Shady Abd El Kader, Philip Turk, Ahmet Soylu, Oscar Corcho, Raquel Cedazo, Gloria Re Calegari, Damiano Scandolari and Elena Simperl
Citizen Science – public participation in scientific projects – is becoming a global practice engaging volunteer participants, often non-scientists, with scientific research…
Abstract
Purpose
Citizen Science – public participation in scientific projects – is becoming a global practice engaging volunteer participants, often non-scientists, with scientific research. Citizen Science is facing major challenges, such as quality and consistency, to reap open the full potential of its outputs and outcomes, including data, software and results. In this context, the principles put forth by Data Science and Open Science domains are essential for alleviating these challenges, which have been addressed at length in these domains. The purpose of this study is to explore the extent to which Citizen Science initiatives capitalise on Data Science and Open Science principles.
Design/methodology/approach
The authors analysed 48 Citizen Science projects related to pollution and its effects. They compared each project against a set of Data Science and Open Science indicators, exploring how each project defines, collects, analyses and exploits data to present results and contribute to knowledge.
Findings
The results indicate several shortcomings with respect to commonly accepted Data Science principles, including lack of a clear definition of research problems and limited description of data management and analysis processes, and Open Science principles, including lack of the necessary contextual information for reusing project outcomes.
Originality/value
In the light of this analysis, the authors provide a set of guidelines and recommendations for better adoption of Data Science and Open Science principles in Citizen Science projects, and introduce a software tool to support this adoption, with a focus on preparation of data management plans in Citizen Science projects.
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David Robinson, David Adrian Sanders and Ebrahim Mazharsolook
– This paper aims to describe research work to create an innovative, and intelligent solution for energy efficiency optimisation.
Abstract
Purpose
This paper aims to describe research work to create an innovative, and intelligent solution for energy efficiency optimisation.
Design/methodology/approach
A novel approach is taken to energy consumption monitoring by using ambient intelligence (AmI), extended data sets and knowledge management (KM) technologies. These are combined to create a decision support system as an innovative add-on to currently used energy management systems. Standard energy consumption data are complemented by information from AmI systems from both environment-ambient and process ambient sources and processed within a service-oriented-architecture-based platform. The new platform allows for building of different energy efficiency software services using measured and processed data. Four were selected for the system prototypes: condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase, and continuous improvement/optimisation of energy efficiency.
Findings
An innovative and intelligent solution for energy efficiency optimisation is demonstrated in two typical manufacturing companies, within one case study. Energy efficiency is improved and the novel approach using AmI with KM technologies is shown to work well as an add-on to currently used energy management systems.
Research limitations/implications
The decision support systems are only at the prototype stage. These systems improved on existing energy management systems. The system functionalities have only been trialled in two manufacturing companies (the one case study is described).
Practical implications
A decision support system has been created as an innovative add-on to currently used energy management systems and energy efficiency software services are developed as the front end of the system. Energy efficiency is improved.
Originality/value
For the first time, research work has moved into industry to optimise energy efficiency using AmI, extended data sets and KM technologies. An AmI monitoring system for energy consumption is presented that is intended for use in manufacturing companies to provide comprehensive information about energy use, and knowledge-based support for improvements in energy efficiency. The services interactively provide suggestions for appropriate actions for energy problem elimination and energy efficiency increase. The system functionalities were trialled in two typical manufacturing companies, within one case study described in the paper.
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Álvaro Rodríguez-Sanz, Rosa Maria M. Arnaldo Valdes, Javier A. Pérez-Castán, Pablo López Cózar and Victor Fernando Gómez Comendador
Airports are limited in terms of capacity. Particularly, runways can only accommodate a certain number of movements (arrivals and departures) while ensuring safety and determined…
Abstract
Purpose
Airports are limited in terms of capacity. Particularly, runways can only accommodate a certain number of movements (arrivals and departures) while ensuring safety and determined operational requirements. In such a constrained operating environment, any reduction in system capacity results in major delays with significant costs for airlines and passengers. Therefore, the efficient operation of airports is a critical cornerstone for demand and delay management of the whole air transportation system. Runway scheduling deals with the sequencing of arriving and departing aircraft at airports such that a predefined objective is optimized subject to several operational constraints, like the dependency of separation on the leading and trailing aircraft type or the runway occupancy time. This study aims to develop a model that acts as a tactical runway scheduling methodology for reducing delays while managing runway usage.
Design/methodology/approach
By considering real airport performance data with scheduled and actual movements, as well as arrival/departure delays, this study presents a robust model together with an optimization algorithm, which incorporates the knowledge of uncertainty into the tactical operational step. The approach transforms the planning problem into an assignment problem with side constraints. The coupled landing/take-off problem is solved to optimality by exploiting a time-indexed (0, 1) formulation for the problem. The Binary Integer Linear Programming approach allows to include multi-criteria and multi-constraints levels and, even with some major simplifications, provides fewer sequence changes and target time updates, when compared to the usual approach in which the plan is simply updated in case of infeasibility. Thus, the use of robust optimization leads to a protection against tactical uncertainties, reduces delays and achieves more stable operations.
Findings
This model has been validated with real data from a large international European airport in different traffic scenarios. Results are compared to the actual sequencing of flights and show that the algorithm can significantly contribute to the reduction of delay, while adhering as much as possible to the operative procedures and constraints, and to the objectives of the airport stakeholders. Computational experiments performed on the case study illustrate the benefits of this arrival/departure integrated approach: the proposed algorithm significantly reduces weighted aircraft delay and computes efficient runway schedule solutions within a few seconds and with little computational effort. It can be adopted as a decision-making tool in the tactical stage. Furthermore, this study presents operational insights regarding demand and delay management based on the results of this work.
Originality/value
Scheduling arrivals and departures at runways is a complex problem that needs to address diverse and often competing considerations among involved flights. In the context of the Airport Collaborative Decision Making programme, airport operators and air navigation service providers require arrival and departure management tools that improve aircraft flows at airports. Airport runway optimization, as the main element that combines airside and groundside operations, is an ongoing challenge for air traffic management.
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