A novel technological performance measurement indicator: a smart manufacturing approach

Tumbajoy Cardona, Luisa Maria; Muñoz-Añasco, Mariela

doi:10.1590/1806-9649-2023v30e9622

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

A novel technological performance measurement indicator: a smart manufacturing approach

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

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

The implementation of digital manufacturing technologies (DMTs) represents the beginning of transforming a manufacturing system towards a smart manufacturing system (SMS). Assessing the performance of the DMTs implemented is essential to meet the objectives in a SMS and allows identifying their usefulness. However, estimating this performance is a challenging task due to the heterogeneous characteristics of the DMTs, such as the origin of information, capacity, connectivity, etc. Although some SMS performance measurement metrics are known, none are intended to identify the performance of DMTs. This article follows a methodology for the construction of technological performance indicators and proposes a novel indicator based on the individual characteristics of the DMTs and the smart factory concept of interoperability. The proposed indicator allows approaching the behavior of one or multiple DMTs implemented simultaneously and introduces a quantifiable measurement that can be applied to any industrial process. It is noteworthy, that such an indicator is not present in the literature and may be of great interest to enterprises currently implementing DMTs related to SMS. The applicability of the indicator considering multiple DMTs is validated through an illustrative test case.

Keywords:
Digital manufacturing technologies; Smart manufacturing; Indicator; Interoperability, Measurement

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

Universidade Federal de São Carlos Departamento de Engenharia de Produção , Caixa Postal 676 , 13.565-905 São Carlos SP Brazil, Tel.: +55 16 3351 8471 - São Carlos - SP - Brazil
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.