A big data approach for logistics trajectory discovery from RFID-enabled production data

Zhong, Ray Y.; Huang, George Q.; Lan, Shulin; Dai, Qingyun; Xu, Chen; Zhang, T.

doi:10.1016/j.ijpe.2015.02.014

Cited by 400 publications

(174 citation statements)

References 34 publications

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“…Big data technologies can enable a highly variable production process and, in the form of predictive maintenance, reduce errors and down times. Furthermore, deploying big data analytics in logistic processes is discussed as a means to reduce inefficiencies, such as delayed shipments or inconsistent supplies (Wang et al, 2016;Zhong et al, 2015). To capture firms' datadriven activities in the production process of goods and services firms were asked whether they use automated data recording, processing and transmission to make internal processes more efficient (p_efficiency), provide employees with digital assistance systems (p_assistance), exchange information with suppliers and customers (p_edi), customize products or services (p_customize), or adapt internal processes flexibly (p_adapt).…”

Section: Big Data In the Context Of Firms' Digital Transformationmentioning

confidence: 99%

BIG Data BIG Gains? Empirical Evidence on the Link Between Big Data Analytics and Innovation

Niebel

Rasel

Viete³

2017

SSRN Journal

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Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW. BIG Data -BIG Gains? Empirical Evidence on the Link Between Big Data Analytics and InnovationThis paper analyzes the relationship between firms' use of big data analytics and their innovative performance in terms of product innovations. Since big data technologies provide new data information practices, they create novel decision-making possibilities, which are widely believed to support firms' innovation process. Applying German firm-level data within a knowledge production function framework we find suggestive evidence that big data analytics is a relevant determinant for the likelihood of a firm becoming a product innovator as well as for the market success of product innovations. These results hold for the manufacturing as well as for the service sector but are contingent on firms' investment in IT-specific skills. Subsequent analyses suggest that firms in the manufacturing and service sector rely on different data sources and data-related firm practices in order to reap the benefits of big data. Overall, the results support the view that big data analytics have the potential to enable innovation.

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Section: Big Data In the Context Of Firms' Digital Transformationmentioning

confidence: 99%

BIG Data BIG Gains? Empirical Evidence on the Link Between Big Data Analytics and Innovation

Niebel

Rasel

Viete³

2017

SSRN Journal

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“…The implementation of the proposed model carries important cost reduction prospects. Z h o n g et al [6] proposed a holistic Big Data approach to excavate frequent trajectory from massive RFID-enabled shop floor logistics data, which are able to guide end-users to carry out associated decisions. T i n g et al [7] proposed a supply chain Quality Sustainability Decision Support System (QSDSS), adopting association rule mining and Dempster's rule of combination techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Transportation Service Quality Improvement through Closed Sequential Pattern Mining Approach

Huang

Yao

Tsai

2016

Cybernetics and Information Technologies

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“…2477 orthogonal experimental [7] and historical data sources [11,12]. A selected portion of the modeling data is presented in Table 1.…”

Section: W Zhao Et Al / Fuzzy Optimization Control For Nox Emissionmentioning

confidence: 99%

Fuzzy optimization control for NOx emissions from power plant boilers based on nonlinear optimization1

Zhao

Meng

et al. 2015

IFS

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Abstract. Combustion optimization adjustment can effectively suppress NOx emissions from power plant boilers. Current combustion optimization adjustment methods involve nonlinear optimization based on the boiler combustion model, such as optimization by a genetic algorithm or particle swarm algorithm. The computational complexity of these methods results in poor real-time performance, which limits their practical applications. To solve this problem, a fuzzy optimization control method with better real-time performance is proposed. First, the space of the disturbance variables (DV), which are the input variables that combustion systems cannot adjust, is divided into a certain number of sub-spaces. Each sub-space center is then obtained using the corresponding optimal combustion mode by offline nonlinear optimization, thereby forming a complete expert rule base. The corresponding optimal manipulated variables (MV), which are the input variables that combustion systems can adjust, are then quickly obtained online by means of fuzzy inference for each inputted DV. The fuzzy optimization control of boiler combustion adjustment is then determined. Simulation has shown that both the fuzzy optimization control method and the nonlinear optimization method can achieve a consistent control effect. However, the fuzzy optimization control method has a better real-time performance.

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A big data approach for logistics trajectory discovery from RFID-enabled production data

Cited by 400 publications

References 34 publications

BIG Data BIG Gains? Empirical Evidence on the Link Between Big Data Analytics and Innovation

BIG Data BIG Gains? Empirical Evidence on the Link Between Big Data Analytics and Innovation

Transportation Service Quality Improvement through Closed Sequential Pattern Mining Approach

Fuzzy optimization control for NOx emissions from power plant boilers based on nonlinear optimization1

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