Um novo indicador de medição de desempenho tecnológico: uma abordagem de fabricação inteligente

Tumbajoy Cardona, Luisa Maria; Muñoz-Añasco, Mariela

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Sumário

ORIGINAL ARTICLE • Gest. Prod. 30 • 2023 • https://doi.org/10.1590/1806-9649-2023v30e9622 copiar

Um novo indicador de medição de desempenho tecnológico: uma abordagem de fabricação inteligente

Autoria SCIMAGO INSTITUTIONS RANKINGS

Resumo

A implementação de tecnologias de fabricação digital (DMTs) representa o início da transformação de um sistema de fabricação em um sistema de fabricação inteligente (SMS). Avaliar o desempenho dos DMTs implementados é essencial para cumprir os objetivos de um SMS e permite identificar a sua utilidade. No entanto, estimar esse desempenho é uma tarefa desafiadora devido às características heterogêneas dos DMTs, por exemplo, origem da informação, capacidade, conectividade etc. Embora algumas métricas de medição de desempenho de SMS sejam conhecidas, nenhuma é específica para identificar o desempenho dos DMTs. Este artigo segue uma metodologia para a construção de indicadores de desempenho tecnológico e propõe um novo indicador baseado nas características individuais dos DMTs e no conceito de interoperabilidade de fábrica inteligente. O indicador proposto permite abordar o comportamento de um ou vários DMTs implementados simultaneamente e apresenta uma medição quantificável que pode ser aplicada a qualquer processo industrial. É imortante destacar que tal indicador não está presente na literatura e pode ser de grande interesse para as empresas que estáo atualmente implementam DMTs relacionadas ao SMS. A aplicabilidade do indicador considerando vários DMTs é validada através de um caso de teste ilustrativo.

Palavras-chave:
Tecnologias de fabricação digital; Fabricação inteligente; Indicador; Interoperabilidade, Medição.

Big Data Dimensions	Definition	Measure
Volume	Amount of data collected	Number of mobile device applications downloaded
Variety	Assortment of data collected per observation	Number of types of data collected per application
Veracity	Reliability and insightfulness of data	Percentage of employees devoted to big data analysis

Indicator	Measure
Maximal potential interoperability	$v_{1 \to 2} = \frac{\|R_{c}^{2}\|}{\|R_{c e x p e c t e d}^{2}\|}$ , where $R_{c}^{2}$ is the semantic relationships and $R_{c e x p e c t e d}^{2}$ is the total number of the expected semantic relationships to fully interoperate.
Minimal effective interoperability	$v_{1 \to 2}^{e} = \frac{\|R_{c}^{2 e}\|}{\|R_{c e x p e c t e d}^{2}\|}$ , where $R_{c}^{2 e}$ is the returned effective semantic relationships and $R_{c e x p e c t e d}^{2}$ is the total number of the expected semantic relationships to fully interoperate.

i	DMT	Definition	Characteristic
1	Industrial robots	Intelligent machines capable of performing tasks in the world by themselves, without explicit human control	Autonomy
			Perception
			Deliberation
			Productivity
2	Simulation	Technologies that reproducing the physical world in virtual models and allowing operators to test and optimize the settings to obtain materials, productive processes (discrete elements), and products (finished or distinct elements)	Representation level
3	IoT	This corresponds to a set of devices and intelligent sensors that facilitate communication between people, products, and machines	Latency
3	IoT		Unlimited throughput
4	Cyber security	Security measures designed to protect the flow of information over interconnected corporate systems	Identification and detection
5	Cloud computing	Technologies facilitate the archiving and processing of large quantities of data with high performance in terms of speed, flexibility, and efficiency	Store capacity
			Secure
			Reliable
6	Additive manufacturing	This additive production process allows for complex products by creating layers of materials, including such different types of materials as plastics, ceramics, metals, and resins, thus eliminating the need to assemble the material	Higher speed in prototype
6	Additive manufacturing		Productivity
7	Virtual reality	This involves a series of devices that enriches (or lessens) human sensory perception through the access to virtual environments	Perception of reality
			Latency
			Positioning accuracy
8	Big data and Analytics	Technologies that capture, archive, analyze, and disseminate large quantities of data derived from the products, processes, machines, and people interconnected in a company	Volume
			Velocity
			Variety
9	Integration systems	Integration in two dimensions: internal versus external. The first (horizontal integration) concerns the integration and exchange of information among the different areas in the company. The second (vertical integration) concerns the company's relationships with its suppliers and customers	Reference Architecture

Area	Definition
Infrastructure	Define a single standard for networks and communication protocols between the interoperable systems facilitating the communication between different suppliers for the same solution
Architecture standard	An architecture that meets the international standards, with the application of open protocols and ease of access to decrease the complexity and barriers in the integration of this architecture
Software platform	Flexible software platforms with easy remote access and availability of access by Web Services
Technology update	Define the potential of future integration with other systems. Software updates facilitated and hardware components exchange occurs in a modular way.

i	DMT	Characteristic	Measure
1	Industrial robots	Productivity	Short cycle time
2	Simulation	Representation level	Product and processes
			Production lines, workstations, internal logistics
			Enterprise, operational environment
3	IoT	Latency	Time transferring data (Cappa et al., 2021Cappa, F., Oriani, R., Peruffo, E., & McCarthy, I. (2021). Big data for creating and capturing value in the digitalized environment: unpacking the effects of volume, variety, and veracity on firm performance. Journal of Product Innovation Management, 38(1), 49-67. http://dx.doi.org/10.1111/jpim.12545. http://dx.doi.org/10.1111/jpim.12545... )
4	Cyber security	Detection	Physical
4	Cyber security	Detection	Virtual (Rubio et al., 2018Rubio, J. E., Roman, R., & Lopez, J. (2018). Analysis of cybersecurity threats in industry 4.0: the case of intrusion detection. In Critical Information Infrastructures Security: 12th International Conference, CRITIS 2017 (pp. 119-130). Lucca, Italy: Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-99843-5_11. http://dx.doi.org/10.1007/978-3-319-9984... )
5	Cloud computing	Store capacity	Substantial quantities of storage (Rajan, 2013Rajan, A. P. (2013). Evolution of cloud storage as cloud computing infrastructure service. Retrieved in 2023, April 5, from https://arxiv.org/abs/1308.1303 https://arxiv.org/abs/1308.1303... )
6	Additive manufacturing	Higher speed in prototype	Time prototyping (Wong & Hernandez, 2012Wong, K. V., & Hernandez, A. (2012). A review of additive manufacturing. International Scholarly Research Notices, 2012, 208760.)
7	Virtual reality	Perception of reality	Mean Opinion Score (MOS) (Hu et al., 2021Hu, M., Luo, X., Chen, J., Lee, Y. C., Zhou, Y., & Wu, D. (2021). Virtual reality: a survey of enabling technologies and its applications in IoT. Journal of Network and Computer Applications, 178, 102970. http://dx.doi.org/10.1016/j.jnca.2020.102970. http://dx.doi.org/10.1016/j.jnca.2020.10... )
8	Big data and Analytics	Volume	Amount of data collected (Ferrari et al., 2017Ferrari, P., Sisinni, E., Brandão, D., & Rocha, M. (2017, September). Evaluation of communication latency in industrial IoT applications. In 2017 IEEE International Workshop on Measurement and Networking (M&N) (pp. 1-6). Naples, Italy: IEEE. http://dx.doi.org/10.1109/IWMN.2017.8078359. http://dx.doi.org/10.1109/IWMN.2017.8078... )
9	Integration systems	Reference Architecture	Architecture application level

$r_{e i j}$	$r_{p i j}$
$r_{e 11} = (3)$	1
$r_{e 12} = r_{e 21} = (3,2, 3,1)$	0.75
$r_{e 22} = (1)$	0.33

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E-mail: gp@dep.ufscar.br

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[1] Authors contribution

Luisa Maria Tumbajoy Cardona collaborated on the conceptualization and theoretical-methodological approach of the study, conducted the theoretical review, took charge of coordinating the data collection, participated in the data analysis, and contributed to the writing and final revision of the manuscript. Mariela Muñoz-Añasco collaborated on the conceptualization and theoretical-methodological approach of the study and contributed to the writing and final revision of the manuscript.