Big data analytics architecture design—An application in manufacturing systems

Fahmideh, Mahdi; Beydoun, Ghassan

doi:10.1016/j.cie.2018.08.004

Cited by 44 publications

(27 citation statements)

References 44 publications

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“…The data collected through web analytics can be helpful in better understanding how visitors use the site. Through web analytics, managers can improve their sites, making them better for customers and increasing their site revenue [71,72]. In Google Analytics, for example, the traffic source overview shows where the traffic comes from (e.g., percentage of direct traffic, search engines, referring sites) [70].…”

Section: Big Datamentioning

confidence: 99%

Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations

et al. 2019

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Business model innovation is considered key for organizations to achieve sustainability. However, there are many problems involving the operationalization of business model innovation. We used a design science methodology to develop an artifact to assist business model innovation efforts. The artifact uses performance measurement indicators of the company's business model, which are powered by Big Data analytics to endow customer-driven business model innovation. Then, we applied the artifact in a critical case study. The selected company is a fashion ecommerce that proposes a vegan and sustainable value using recycled plastic bottle yarn as raw material, and ensures that no material with animal origin is used. Our findings show that the artifact successfully assists a proactive and continuous effort towards business model innovation. Although based on technical concepts, the artifact is accessible to the context of small businesses, which helps to democratize the practices of business model innovation and Big Data analytics beyond large organizations. We contribute to the business model innovation literature by connecting it to performance management and Big Data and providing paths for its operationalization. Consequently, in practice, the proposed artifact can assist managers dealing with business model as a dynamic element towards a sustainable company.Sustainability 2020, 12, 277 2 of 29 to frequent unsuccessful results about the underlying business logic and, therefore, perpetuating failures that slow down the process of knowledge creation and, thus, undermining possibilities for growth. In this regard, there is a gap between what has been studied in academia and what can be potentially useful for managers willing to innovate their BM [8,9].Thus, BM innovation, validation, and experimentation processes need to be structured and implemented. This is relevant in several industries as the greater frequency of disruption and dislocation are shortening business model life cycles and the sustainability of any specific business model is unclear [10,11]. In a scenario of constant change and need for a better structured management process for BMI, it is important to consider alternatives for testing and experimenting with new alternatives of BM. However, it is not clear in the BMI literature how to do this in the face of the recognized complexity of this process [12,13].Christensen, Bartman, and van Bever [14] argue that there is the need for a systematic mechanism to evaluate the relationship between opportunities and BM. Besides, it is necessary to consider that innovation in BM cannot be generated randomly through guesswork. Nonetheless, Laudien and Daxböck [15] show that, in many cases, innovations in BM are not deliberate strategic options but rather a result of an emergent process, often unplanned. Strategic options, however, are usually related to a company's performance management which means the measurement of a set of indicators on various perspectives of the company [16,17]. In this sense, it seems natura...

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Section: Big Datamentioning

confidence: 99%

Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations

et al. 2019

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“…By comparing directly with GuideArch in the same dimensions, we can be confident eQual outperforms these techniques as well. Third, since GuideArch's publication, despite improvement attempts [13,30,50,76], GuideArch has remained the state of the art. GuideArch uses fuzzy mathematical methods to automatically select a set of nearoptimal decisions from a large design space [29].…”

Section: Choice Of State-of-the-art Comparisonmentioning

confidence: 99%

“…These very concerns have already been considered by GuideArch (and by a GuideArch predecessor [56]), and ultimately the resulting facilities improve neither GuideArch's applicability nor its scalability. Fahmideh et al [30] applied GuideArch's fuzzy-math approach to find an optimal set of design decisions in another domain Ð designexploration of manufacturing systems Ð but did not improve the underlying GuideArch capabilities. As a result, GuideArch has remained the state-of-the-art approach with respect to a large number of competitors [1,5,13,21,27,30,50,56,58,76,77].…”

Section: Choice Of State-of-the-art Comparisonmentioning

confidence: 99%

eQual: informing early design decisions

Shahbazian

Karthik

Brun

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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When designing a software system, architects make a series of design decisions that directly impact the system's quality. The number of available design alternatives grows rapidly with system size, creating an enormous space of intertwined design concerns that renders manual exploration impractical. We present eQual, a model-driven technique for simulation-based assessment of architectural designs. While it is not possible to guarantee optimal decisions so early in the design process, eQual improves decision quality. eQual is effective in practice because it (1) limits the amount of information the architects have to provide and (2) adapts optimization algorithms to effectively explore massive spaces of design alternatives. We empirically demonstrate that eQual yields designs whose quality is comparable to a set of systems' known optimal designs. A user study shows that, compared to the state-of-the-art, engineers using eQual produce statistically significantly higherquality designs with a large effect size, are statistically significantly more confident in their designs, and find eQual easier to use. CCS CONCEPTS • Software and its engineering → Software design engineering; Software implementation planning.

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“…A generic architecture of CMfg consists of five layers: physical resource layer, virtual resource layer, core service layer, application interface layer and application layer [3]. Considering the goals, uncertainties and stakeholders' preferences to incorporate big data analytics in manufacturing systems, a goal-oriented modelling and fuzzy logic-based approach, was proposed to reason and select suitable big data solution architecture [35]. Even though the cloud has powerful computing and storage capabilities for big data analytics, the cloud architecture is not ready to support the reliable real-time or near real-time response of shop-floor applications (such as the control of machine tools and industrial robots) at the ''edge'' of the manufacturing system, as the data communication between edge things and the cloud as well as big data collection, cleaning, combination, synchronization and processing would be time consuming.…”

Section: B Cloud Based Manufacturing Platformmentioning

confidence: 99%

Big Data Driven Edge-Cloud Collaboration Architecture for Cloud Manufacturing: A Software Defined Perspective

et al. 2020

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In the practice of cloud manufacturing, there still exist some major challenges, including: 1) cloud based big data analytics and decision-making cannot meet the requirements of many latencysensitive applications on shop floors; 2) existing manufacturing systems lack enough reconfigurability, openness and evolvability to deal with shop-floor disturbances and market changes; and 3) big data from shop-floors and the Internet has not been effectively utilized to guide the optimization and upgrade of manufacturing systems. This paper proposes an open evolutionary architecture of the intelligent cloud manufacturing system with collaborative edge and cloud processing. Hierarchical gateways connecting and managing shop-floor things at the ''edge'' side are introduced to support latency-sensitive applications for real-time responses. Big data processed both at the gateways and in the cloud will be used to guide continuous improvement and evolution of edge-cloud systems for better performance. As software tools are becoming dominant as the ''brain'' of manufacturing control and decision-making, this paper also proposes a new mode-''AI-Mfg-Ops'' (AI enabled Manufacturing Operations) with a supporting software defined framework, which can promote fast operation and upgrading of cloud manufacturing systems with smart monitoring-analysis-planning-execution in a closed loop. This research can contribute to the rapid response and efficient operation of cloud manufacturing systems.

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Big data analytics architecture design—An application in manufacturing systems

Cited by 44 publications

References 44 publications

Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations

Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations

eQual: informing early design decisions

Big Data Driven Edge-Cloud Collaboration Architecture for Cloud Manufacturing: A Software Defined Perspective

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