Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives

Zhong, Ray Y.; Newman, Stephen T.; Huang, George Q.; Lan, Shulin

doi:10.1016/j.cie.2016.07.013

Cited by 488 publications

(308 citation statements)

References 51 publications

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“…Firstly, more characteristics from the input images could be selected for the experiments. The characteristics may include color information and veins features [16]. Secondly, the study will be extended by using the SPCNN model to deal with more complex images like GIS images.…”

Section: Resultsmentioning

confidence: 99%

Automatic Spiking Pulse-Coupled Neural Network for Image Segmentation

Xi¹

2017

IJFGCN

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Section: Resultsmentioning

confidence: 99%

Automatic Spiking Pulse-Coupled Neural Network for Image Segmentation

Xi¹

2017

IJFGCN

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“…Lower costs will speed up the evolution of Big Data development. Zhong et al [11] note that the manufacturing sectors such as the social internet network are facing a data tsunami; that is, data volumes are increasing immensely every second. Big Data amounts into the range of exabytes, and Big Data is not only about the data, but also about a complete conceptual and technological stack including raw and processed data, storage, ways of managing data, processing, and analytics.…”

Section: Big Datamentioning

confidence: 99%

“…Zhong et al [11] have listed steps to utilize and roll out Big Data-based solutions in the industry. They have classified the challenges, opportunities, and future perspectives in SM-SCM with the following terms: (1) data collection methods; (2) data transmission; (3) data storage; (4) processing technologies for Big Data; (5) Big Data-enabled decision-making models; and (6) Big Data interpretation and applications.…”

Section: Refining and Filtering Of Big Datamentioning

confidence: 99%

How to Generate Economic and Sustainability Reports from Big Data? Qualifications of Process Industry

Hämäläinen

Inkinen

2017

Processes

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Big Data may introduce new opportunities, and for this reason it has become a mantra among most industries. This paper focuses on examining how to develop cost and sustainable reporting by utilizing Big Data that covers economic values, production volumes, and emission information. We assume strongly that this use supports cleaner production, while at the same time offers more information for revenue and profitability development. We argue that Big Data brings company-wide business benefits if data queries and interfaces are built to be interactive, intuitive, and user-friendly. The amount of information related to operations, costs, emissions, and the supply chain would increase enormously if Big Data was used in various manufacturing industries. It is essential to expose the relevant correlations between different attributes and data fields. Proper algorithm design and programming are key to making the most of Big Data. This paper introduces ideas on how to refine raw data into valuable information, which can serve many types of end users, decision makers, and even external auditors. Concrete examples are given through an industrial paper mill case, which covers environmental aspects, cost-efficiency management, and process design.

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“…As the cutting-edge technologies and concepts used widely in our daily life, advanced decision-makings are achieved in recent years such as the Internet of Things (IoT) enabled manufacturing and logistics [4], Cloud-based healthcare and planning & scheduling [5], as well as Big Data Analytics for services and supply chain management [6]. However, the computer vision field is lagged compared to the other research areas like manufacturing and aerospace [7,8].…”

Section: Introductionmentioning

confidence: 99%