Uncertainty reduction in measuring and verification of energy savings by statistical learning in manufacturing environments

Oses, Noelia; Legarretaetxebarria, Aritz; Quartulli, Marco; García, Igor; Serrano, M.

doi:10.1007/s12008-016-0302-y

Cited by 23 publications

(14 citation statements)

References 8 publications

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“…Analysis of BI and related technological aspect reveals 33 out of 42 articles included real time or near real-time data processing. In many of these articles, authors emphasized the technical difficulty of processing machine data in real time, because of database limitations [5], the integration of unstructured data [23], limits to the acceptable visual complexity [46] or the number of variables required to develop a sufficiently precise model [35], [45].…”

Section: Content Analysismentioning

confidence: 99%

“…Strategic value creation measurement is underrepresented in the sample studies, being addressed in only three articles as shown in Table 4. [32] Improved agility by reacting more quickly to change in orders n/a Proof of concept Niño et al [33] Reduced waste, improved return on production process n/a Proof of concept Oneto et al [34] Improved accuracy of data model n/a Proof of concept Oses et al [35] Improved prediction of energy savings n/a Proof of concept Shafiq et al [39] Maintain just-in-time maintenance, improved asset utilization, improved flexibility n/a Proof of concept Table 4 presents the 20 articles out of 42 mentioning operational value, strategic value or both. Operational value creation is measured in 20 articles.…”

Section: Figure 5 Bi Subject Distributionmentioning

confidence: 99%

“…Oses et al [35] Reduction of the range of prediction interval in energy savings measurement Need to include more factors for better prediction…”

Section: Informationmentioning

confidence: 99%

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Business Intelligence in Industry 4.0: State of the art and research opportunities

Bordeleau¹,

Mosconi²,

Santa-Eulalia³

2018

Proceedings of the Annual Hawaii International Conference on System Sciences

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Data collection and analysis have been at the core of business intelligence (BI) for many years, but traditional BI must be adapted for the large volume of data coming from Industry 4.0 (I4.0) technologies. They generate large amounts of data that need to be processed and used in decision-making to generate value for the companies. Value generation of I4.0 through data analysis and integration into strategic and operational activities is still a new research topic. This study uses a systematic literature review with two objectives in mind: understanding value creation through BI in the context of I4.0 and identifying the main research contributions and gaps. Results show most studies focus on real-time applications and integration of voluminous and unstructured data. For business research, more is needed on business model transformation, methodologies to manage the technological implementation, and frameworks to guide human resources training.

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Section: Content Analysismentioning

confidence: 99%

Section: Figure 5 Bi Subject Distributionmentioning

confidence: 99%

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Business Intelligence in Industry 4.0: State of the art and research opportunities

Bordeleau¹,

Mosconi²,

Santa-Eulalia³

2018

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Application-pull is easier to be introduced which has background of fierce market competition and promising return on investment. Energy conservation measures (ECM) is one another concern in smart factory [25]. Using high frequency energy meter, a model to estimates the risks, measure and verifying energy conversation is proposed [25].…”

Section: Smart Factorymentioning

confidence: 99%

“…Energy conservation measures (ECM) is one another concern in smart factory [25]. Using high frequency energy meter, a model to estimates the risks, measure and verifying energy conversation is proposed [25]. Virtual manufacturing which is similar to the simulation in a PLM (product lifecycle management) system is proposed consists of three entities, i.e., virtual engineering objects, virtual engineering processes, and virtual engineering factories.…”

Section: Smart Factorymentioning

confidence: 99%

The SURUGA Gateway: An Advanced CPS Equipping Smart Factory

Jin

Nakatsuka

Matsukawa

2019

iscm

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Cyber-physical system (CPS) is one core concept of Industry 4.0. A cyber space has mirror information of physical space and activities in the physical space are synchronized with activities in cyber space. SURUGA Gateway is a CPS, running in a Japanese robot component producer. Innovation process (design) and operation (manufacturing) process is integrated together on the SURUGA Gateway, enabling high quality, short leadtime, low cost, and high flexibility. Various decisions were made in the cyber space, then executed in physical space automatically. Materials input to manufacturing process according to production plan which created by modified ERP (enterprise resource planning) system, fabricated according to optimized routing and scheduling. All NC (numerical control) machines and MC (machining center) were connected and integrated on a centralized platform using Ethercat protocol and interface, and serial electronic signal control for old machines. The KAMISHIBAI (VPS: virtual production system) is embedded in the SURUGA Gateway, providing online training to assembly operators. Innovation process drives operation process, while know-how to improve efficiency and quality in operation process is feedback to the innovation process. The integration of innovation process and operation process, specifically the integration of design, material flow, machine flow and human resource is most successful point of the SURUGA Gateway. SURUGA Gateway successfully reduced leadtime by 80% and recognized as best practice in Hannover Messe and G20 business conference.

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