Toward understanding outcomes associated with data quality improvement

Hazen, Benjamin T.; Weigel, Fred K.; Ezell, Jeremy D.; Boehmke, Bradley C.; Bradley, R. S.

doi:10.1016/j.ijpe.2017.08.027

Cited by 19 publications

(10 citation statements)

References 67 publications

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“…However, unlike capital, big data has no value without the tools by which deeper insights can be extracted from it (Chen et al, 2012;Waller and Fawcett, 2013;Gandomi and Haider, 2015;Aydiner et al, 2019). The best-informed managers with the greatest understanding of their data (Hazen et al, 2014;Hazen et al 2017;Verma and Bhattacharyya, 2017;Cao and Duan, 2017;Kache and Seuring, 2017) can use it to create benchmarks for their organisation (Merendino et al, 2018;Mikalef et al, 2019a;Chehbi-Gamoura et al 2019). Big data and predictive analytics helps organisations reduce costs (Choi et al 2018;Aydiner et al, 2019;Dubey et al, 2019), make products faster (Giannakis and Louis, 2016;Dubey et al, 2018), and create new products or services to meet customers' changing needs (George et al, 2014;Opresnik and Taisch, 2015;Choi et al 2018;Ghasemaghaei and Calic, 2019).…”

Section: Introductionmentioning

confidence: 99%

Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations

Dubey

Gunasekaran

Childe

et al. 2020

International Journal of Production Economics

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256

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Section: Introductionmentioning

confidence: 99%

Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations

Dubey

Gunasekaran

Childe

et al. 2020

International Journal of Production Economics

Self Cite

422

256

View full text Add to dashboard Cite

show abstract

“…Hazen et al (2014) proposed an approach based on statistical quality control to measure and control data quality in the big data era. In another study, Hazen et al (2017) discussed that the business decisions are highly dependent on the quality of the data they are based on. Their finding implies the direct relationship between high-quality data and decision quality.…”

Section: Literature Reviewmentioning

confidence: 99%

Incorporating data quality into a multi-product procurement planning under risk

Shabani-Naeeni

Yaghin

2021

JBIM

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Purpose In the data-driven era, the quality of the data exchanged between suppliers and buyer can enhance the buyer’s ability to appropriately cope with the risks and uncertainties associated with raw material purchasing. This paper aims to address the issue of supplier selection and purchasing planning considering the quality of data by benefiting from suppliers’ synergistic effects. Design/methodology/approach An approach is proposed to measure data visibility’s total value using a multi-stage algorithm. A multi-objective mathematical optimization model is then developed to determine the optimal integrated purchasing plan in a multi-product setting under risk. The model contemplates three essential objective functions, i.e. maximizing total data quality and quantity level, minimizing purchasing risks and minimizing total costs. Findings With emerging competitive areas, in the presence of industry 4.0, internet of things and big data, high data quality can improve the process of supply chain decision-making. This paper supports the managers for the procurement planning of modern organizations under risk and thus provides an in-depth understanding for the enterprises having the readiness for industry 4.0 transformation. Originality/value Various data quality attributes are comprehensively subjected to deeper analysis. An applicable procedure is proposed to determine the total value of data quality and quantity required for supplier selection. Besides, a novel multi-objective optimization model is developed to determine the purchasing plan under risk.

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“…It serves as the standard language for the communication between multiple, decentralised, and heterogeneous partners in designing air transport systems, such as aircraft. CPACS has been widely used and further developed for a different design and engineering purposes in the aviation sector (e.g., [14,[46][47][48]). Besides the modelling of data using standard data models, data quality is also considered since it has an essential influence on the quality of engineering decisions.…”

Section: Management Of Multidisciplinary Datamentioning

confidence: 99%

“…In this regard, Li and Ryerson [47] review the diversity, availability, tractability, applicability, and sources (DATAS) of aviation research data. Furthermore, Hazen et al [48] exemplarily explore the outcomes of a data quality improvement process implementation in an operations management environment within an organisation with a large aircraft fleet.…”

Section: Management Of Multidisciplinary Datamentioning

confidence: 99%

Sustainability Assessment and Engineering of Emerging Aircraft Technologies—Challenges, Methods and Tools

et al. 2020

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Driven by concerns regarding the sustainability of aviation and the continued growth of air traffic, increasing interest is given to emerging aircraft technologies. Although new technologies, such as battery-electric propulsion systems, have the potential to minimise in-flight emissions and noise, environmental burdens are possibly shifted to other stages of the aircraft’s life cycle, and new socio-economic challenges may arise. Therefore, a life-cycle-oriented sustainability assessment is required to identify these hotspots and problem shifts and to derive recommendations for action for aircraft development at an early stage. This paper proposes a framework for the modelling and assessment of future aircraft technologies and provides an overview of the challenges and available methods and tools in this field. A structured search and screening process is used to determine which aspects of the proposed framework are already addressed in the scientific literature and in which areas research is still needed. For this purpose, a total of 66 related articles are identified and systematically analysed. Firstly, an overview of statistics of papers dealing with life-cycle-oriented analysis of conventional and emerging aircraft propulsion systems is given, classifying them according to the technologies considered, the sustainability dimensions and indicators investigated, and the assessment methods applied. Secondly, a detailed analysis of the articles is conducted to derive answers to the defined research questions. It illustrates that the assessment of environmental aspects of alternative fuels is a dominating research theme, while novel approaches that integrate socio-economic aspects and broaden the scope to battery-powered, fuel-cell-based, or hybrid-electric aircraft are emerging. It also provides insights by what extent future aviation technologies can contribute to more sustainable and energy-efficient aviation. The findings underline the need to harmonise existing methods into an integrated modelling and assessment approach that considers the specifics of upcoming technological developments in aviation.

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Toward understanding outcomes associated with data quality improvement

Cited by 19 publications

References 67 publications

Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations

Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations

Incorporating data quality into a multi-product procurement planning under risk

Sustainability Assessment and Engineering of Emerging Aircraft Technologies—Challenges, Methods and Tools

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