Determining the impact of 5G-technology on manufacturing performance using a modified TOPSIS method

Figures & data

Table 1. Summary of the application area and the involved requirements of 5 G-technology

Application area	Requirements of 5 G-technology	Reference
Virtual and/or augmented reality for product design, processing, assembly, and maintenance.	High data rate and low latency.	Cheng et al. 2018
The massive amount of manufacturing data, such as from equipment, from work-in-progress, from operators, and disturbance information from different devices.	Large density of devices to be connected.	Cheng et al. 2018
Automated guided vehicles (AGVs) need to communicate with machines (e.g. for loading/unloading material) and with other AGVs to avoid collisions.	High requirements on high reliability and low latency, to make sure that the stop signal to the AGV will be fast enough.	Cheng et al. 2018
Autonomous shop floor.	Requires low latency communication. If any critical changes in conditions need action (i.e. time-critical controls), low latency will ensure the response without damages due to delays.	Fettweis 2014
Real-time monitoring of a manufacturing process can prevent deviation in product quality.	Sensors and actuators included in the process must have low latency and high-reliability communication in these time-critical control loops, i.e. to stop or change the process before it damages the product.	Rao and Prasad 2018
Wide network coverage is not time-critical but could have a major contribution in a harsh environment.	If the network is reliable and has a wide coverage, remote control of equipment or real-time monitoring of processes can be used for equipment in a harsh environment. Thus, it is possible to avoid or limit the time spent by people in a harsh environment.	Rao and Prasad 2018

Figure 1. The figure gives an overview of the methodology, including data collection and data analysis of quantitative and qualitative data.

Figure 2. The figure describes the data collection procedure. Firstly, data from the literature was used to create an evaluation matrix. Secondly, the evaluation matrix was then used during an interview setting to collect quantitative data. Qualitative data were also collected during the interview by posting open questions.

Table 2. Information about the interviewees including branch, their working title/role, and years of experience

Interviewee	Branch	Role/Title	Experience (y)
1	Manufacture of motor vehicles, trailers and semi-trailers	Senior research and technology development engineer.	8
2	Manufacture of motor vehicles, trailers and semi-trailers	Senior research engineer in flexible manufacturing.	25
3	Manufacture of machinery and equipment n.e.c.	Manufacturing reliability champion.	28
4	Manufacture of machinery and equipment n.e.c.	Global account manager and sales manager.	25

Figure 3. An overview of the modified TOPSIS method that was used for the quantitative analysis.

Table 3. Notations used to construct the modified TOPSIS method

Notations	Descriptions
D	Number of demos.
K	Number of interviewees.
d	Demo index, i.e. $d=1,\dots ,D$.
k	Interviewee index, i.e. $k=1,\dots ,K$.
m	Number of 5G-characteristics.
n	Number of PIs.
i	5G-characteristic index, i.e. $i=1,\dots ,m$.
j	PI index, i.e. $j=1,\dots ,n$.
$5G_i$	Set of 5G-characteristics, i.e. $5G=\left\{5G_i|i\in N,andi=1,\dots ,m\right\}$.
$P{I}_j$	Set of PIs, i.e. $PI=\left\{P{I}_j|j\in N,andj=1,\dots ,n\right\}$.
$P$	Set of Productivity PIs, i.e. $P=\left\{P{I}_j|j\in N,and1\le j\le 3\right\}$.
$Q$	Set of Quality PIs, i.e. $P=\left\{P{I}_j|j\in N,and4\le j\le 6\right\}$.
$M$	Set of Maintenance performance PIs, i.e. $P=\left\{P{I}_j|j\in N,and7\le j\le 9\right\}$.
$F$	Set of Flexibility PIs, i.e. $P=\left\{P{I}_j|j\in N,and10\le j\le 12\right\}$.
$S$	Set of Sustainability PIs, i.e. $P=\left\{P{I}_j|j\in N,and13\le j\le n\right\}$.
$D_d^k$	Evaluation matrix by interviewee k for demo d
$x_{dij}^k$	The impact value of $5G_i$with respect to $P{I}_j$ evaluated by interviewee k for demo d.
$D_d^{agg}$	Aggregated matrix for demo d.
$x_{dij}^{agg}$	Aggregated impact value of $5G_i$ with respect to $P{I}_j$ for demo d.
$LI{V}_d$	The ideal lowest impact value for demo d.
$HI{V}_d$	The ideal highest impact value for demo d.
$DLI{V}_{i(individual)}^d$	Euclidian distance between $x_{dij}^{agg}$ and $LI{V}_d$ for sets $P$, $Q$, $M$, $F$, $S$ for demo d.
$DHI{V}_{i(individual)}^d$	Euclidian distance between $x_{dij}^{agg}$ and $HI{V}_d$ for sets $P$, $Q$, $M$, $F$, $S$ for demo d.
$I_{i(individual)}^d$	Individual impact index of $5G_i$ for sets $P$, $Q$, $M$, $F$, $S$ for demo d.
The following notations are used to construct the aggregated analysis of all demos
$x_{ij}^{agg}$	Aggregated impact value of $5G_i$ with respect to $P{I}_j$ across all demos.
$LIV$	The ideal lowest impact value across all demos.
$HIV$	The ideal highest impact value across all demos.
$DLI{V}_{i(global)}$	Euclidian distance between $x_{ij}^{agg}$ and $LIV$for sets$P$, $Q$, $M$, $F$, $S$ across all demos.
$DLI{V}_{i(overall)}$	Euclidian distance between $x_{ij}^{agg}$ and $LIV$across all$P{I}_j$ and all demos.
$DHI{V}_{i(global)}$	Euclidian distance between $x_{ij}^{agg}$ and $HIV$for sets $P$, $Q$, $M$, $F$, $S$ across all demos.
$DHI{V}_{i(overall)}$	Euclidian distance between $x_{ij}^{agg}$ and $HIV$ across all$P{I}_j$ and all demos.
$I_{i(global)}$	Global impact index of $5G_i$ for sets $P$, $Q$, $M$, $F$, $S$ across all demos.
$I_{i(overall)}$	Overall impact index of $5G_i$ across all$P{I}_j$ and all demos.
$I_{PI(overall)}$	Overall impact index of sets $P$, $Q$, $M$, $F$, $S$ across all demos

Figure 4. The figure describes the individual impact index of additional data sources, secured spectrum and, wide network coverage on productivity, quality, maintenance performance, flexibility, and sustainability respectively in Demo 1.

Figure 5. The figure describes the individual impact index of additional data sources, data traffic policies – prioritized devices, and centrally controlled device connectivity on productivity, quality, maintenance performance, flexibility, and sustainability respectively in Demo 2.

Figure 6. The figure describes the individual impact index of low latency, deterministic latency, and additional data sources on productivity, quality, maintenance performance, flexibility, and sustainability respectively in Demo 3.

Figure 7. The figure describes the individual impact index of wide network coverage, additional data sources, and scalability on productivity, quality, maintenance performance, flexibility, and sustainability respectively in Demo 4.

Figure 8. The figure describes the individual impact index of low latency, high bandwidth, and high data rate on productivity, quality, maintenance performance, flexibility, and sustainability respectively in Demo 5.

Figure 9. The figure describes the individual impact index of additional data sources, low latency, and deterministic latency on productivity, quality, maintenance performance, flexibility, and sustainability respectively in Demo 6.

Figure 10. The figure describes the individual impact index of low latency, additional data sources, and high bandwidth on productivity, quality, maintenance performance, flexibility, and sustainability respectively in Demo 7.

Figure 11. The figure describes the individual impact index of wide network coverage, low latency, and scalability on productivity, quality, maintenance performance, flexibility, and sustainability respectively in Demo 8.

Table 4. Top three 5G-characteristics and global impact index (all demos) per PI category

Productivity
Top three 5 G-characteristics	Additional data sources	High data volume	Low latency
Global impact index	0.64	0.63	0.63
Quality
Top three 5 G-characteristics	Low latency	Additional data sources	Deterministic latency
Global impact index	0.54	0.53	0.52
Maintenance performance
Top three 5 G-characteristics	Low latency	Additional data sources	High data volume
Global impact index	0.69	0.67	0.64
Flexibility
Top three 5 G-characteristics	Additional data sources	Scalability	Wide network coverage
Global impact index	0.76	0.66	0.64
Sustainability
Top three 5 G-characteristics	Low latency	Scalability	Additional data sources
Global impact index	0.41	0.39	0.38

Table 5. The top three 5 G-characteristics, based on all demos

All demos: Top three 5G-characteristics and overall impact index
Additional data sources	Low latency	Scalability
0.58	0.57	0.53

Table 6. Overall impact index of 5 G on each PI category, based on all demos

All demos: 5G’s impact on PI category and overall impact index
Productivity	Quality	Maintenance performance	Flexibility	Sustainability
0.59	0.47	0.60	0.58	0.35

Table

		Sets of PIs
		$P$			$Q$			$M$			$F$			$S$
	$5G_i$	$P{I}_1$	$P{I}_2$	$P{I}_3$	$P{I}_4$	$P{I}_5$	$P{I}_6$	$P{I}_7$	$P{I}_8$	$P{I}_9$	$P{I}_{10}$	$P{I}_{11}$	$P{I}_{12}$	$P{I}_{13}$	$P{I}_{14}$	$P{I}_{15}$
$D_1^{agg}=$	$5G_1$	3.56	2.38	3.94	3.16	2.99	2.63	3.56	3.76	2.99	3.46	3.22	3.87	2.78	2.11	3.31
	$5G_2$	3.72	3.22	4.47	3.56	3.36	3.36	3.94	4.16	3.46	3.72	3.46	4.16	2.78	2.00	3.56
	$5G_3$	3.72	4.16	4.47	2.83	3.13	2.91	3.31	3.31	3.13	3.36	3.94	4.73	2.51	1.86	3.94
	$5G_4$	4.23	3.22	4.47	3.13	2.91	2.91	3.94	3.72	3.13	3.94	3.36	4.23	2.21	1.86	3.08
	$5G_5$	4.47	3.72	4.73	2.91	2.91	2.62	4.47	4.23	3.36	3.66	3.13	3.94	2.34	1.86	2.78
	$5G_6$	4.00	3.46	3.94	3.13	2.91	3.13	3.94	3.72	3.46	3.31	3.36	3.56	2.21	1.68	2.78
	$5G_7$	4.23	3.94	4.00	4.23	3.72	4.00	4.47	3.94	4.00	4.16	4.16	4.47	2.63	1.86	4.23
	$5G_8$	4.73	3.94	4.23	3.46	3.46	3.72	4.47	4.23	4.00	4.47	3.94	3.94	2.21	2.00	3.31
	$5G_9$	4.47	4.16	4.23	4.23	3.94	3.91	4.16	4.23	3.72	3.76	4.16	3.87	2.45	1.86	3.08
	$5G_{10}$	3.66	3.41	3.94	3.22	3.46	3.22	3.72	3.94	3.94	4.47	4.23	3.94	2.51	2.38	4.23
	$5G_{11}$	3.94	3.31	4.16	3.94	4.00	3.63	3.94	3.94	3.72	4.00	3.72	4.00	2.38	2.00	3.94

Table

	Sets of PIs
$5G_i$	$P$		$Q$		$M$		$F$		$S$
	$DLI{V}_{i(individual)}^1$	$DHI{V}_{i(individual)}^1$	$DLI{V}_{i(individual)}^1$	$DHI{V}_{i(individual)}^1$	$DLI{V}_{i(individual)}^1$	$DHI{V}_{i(individual)}^1$	$DLI{V}_{i(individual)}^1$	$DHI{V}_{i(individual)}^1$	$DLI{V}_{i(individual)}^1$	$DHI{V}_{i(individual)}^1$
$5G_1$	4.13	3.18	3.36	3.61	4.26	2.77	4.39	2.60	3.12	4.01
$5G_2$	4.94	2.25	4.21	2.73	4.97	2.05	4.85	2.17	3.27	4.00
$5G_3$	5.43	1.62	3.40	3.55	3.90	3.03	5.30	1.97	3.41	4.14
$5G_4$	5.24	2.01	3.44	3.49	4.54	2.50	4.96	2.10	2.56	4.62
$5G_5$	5.78	1.41	3.15	3.79	5.30	1.88	4.50	2.53	2.39	4.67
$5G_6$	4.87	2.12	3.57	3.37	4.70	2.26	4.18	2.76	2.26	4.87
$5G_7$	5.30	1.65	5.18	1.80	5.45	1.55	5.66	1.30	3.72	4.01
$5G_8$	5.74	1.34	4.42	2.52	5.61	1.37	5.41	1.59	2.79	4.43
$5G_9$	5.70	1.25	5.25	1.70	5.28	1.71	5.09	1.87	2.68	4.48
$5G_{10}$	4.64	2.34	4.00	2.94	4.97	1.97	5.58	1.42	3.82	3.69
$5G_{11}$	4.89	2.17	4.96	2.00	4.97	1.97	5.04	1.91	3.39	4.12

Table

	Sets of PIs
$5G_i$	$P$	$Q$	$M$	$F$	$S$
	$I_{i(individual)}^1$	$I_{i(individual)}^1$	$I_{i(individual)}^1$	$I_{i(individual)}^1$	$I_{i(individual)}^1$
$5G_1$	0.57	0.48	0.61	0.63	0.44
$5G_2$	0.69	0.61	0.71	0.69	0.45
$5G_3$	0.77	0.49	0.56	0.73	0.45
$5G_4$	0.72	0.50	0.64	0.70	0.36
$5G_5$	0.80	0.45	0.74	0.64	0.34
$5G_6$	0.70	0.51	0.68	0.60	0.32
$5G_7$	0.76	0.74	0.78	0.81	0.48
$5G_8$	0.81	0.64	0.80	0.77	0.39
$5G_9$	0.82	0.75	0.76	0.73	0.37
$5G_{10}$	0.67	0.58	0.72	0.80	0.51
$5G_{11}$	0.69	0.71	0.72	0.73	0.45

Table

		Sets of PIs
		$P$	$Q$	$M$	$F$	$S$
$5G_i$	5 G-characteristics	Ranking	Ranking	Ranking	Ranking	Ranking
$5G_1$	Low latency	11	10	10	10	6
$5G_2$	Deterministic latency	9	5	7	8	5
$5G_3$	Scalable	4	9	11	5	3
$5G_4$	High bandwidth	6	8	9	7	9
$5G_5$	High data volume	3	11	4	9	10
$5G_6$	High data rate	7	7	8	11	11
$5G_7$	Additional data sources	5	2	2	1	2
$5G_8$	Wide network coverage	2	4	1	3	7
$5G_9$	Secured spectrum	1	1	3	4	8
$5G_{10}$	Centrally controlled device connectivity	10	6	5	2	1
$5G_{11}$	Data traffic policies – prioritized devices	8	3	6	6	4

Cheng, J., W. Chen, F. Tao, and C. L. Lin. 2018. “Industrial IoT in 5G Environment Towards Smart Manufacturing.” Journal of Industrial Information Integration 10: 10–19. doi:https://doi.org/10.1016/j.jii.2018.04.001.

 Web of Science ®Google Scholar

Fettweis, G. P. 2014. “The Tactile Internet: Applications and Challenges.” IEEE Vehicular Technology Magazine 9 (1): 64–70. doi:https://doi.org/10.1109/MVT.2013.2295069.

 Web of Science ®Google Scholar

Rao, S. K., and R. Prasad. 2018. “Impact of 5G Technologies on Industry 4.0.” Wireless Personal Communications 100 (1): 145–159. doi:https://doi.org/10.1007/s11277-018-5615-7.

 Web of Science ®Google Scholar