IoT and BDA in the Brazilian future logistics 4.0 scenario

Correa, Jobel Santos; Sampaio, Mauro; Barros, Rodrigo de Casto; Hilsdorf, Wilson de Castro

doi:10.1590/0103-6513.20190102

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Sumário

Thematic Section - Present and Future of Production Engineering • Prod. 30 • 2020 • https://doi.org/10.1590/0103-6513.20190102 copiar

IoT and BDA in the Brazilian future logistics 4.0 scenario

Autoria SCIMAGO INSTITUTIONS RANKINGS

Abstract

Paper aims

This paper aims to identify the degree of interest and expected return time on investment in Internet of Things (IoT) and Big Data Analytics (BDA) technologies by Brazilian logistics companies.

Originality

A logistics system that meets the requirements imposed by Industry 4.0 is known as Logistics 4.0. According to the current scientific literature, IoT and BDA technologies are the most promising for the Logistics 4.0 applications. Studies on the adoption of these technologies in Brazil are still embryonic and this paper collaborates to narrow this knowledge gap.

Research method

Exploratory research was conducted using the quantitative approach.

Main findings

The evidence of the interest in investment in IoT and BDA by Brazilian logistics companies, confirming the current literature.

Implications for theory and practice

The identification of the intended practical applications for Iot and BDA, as well as the expected difficulties in the implementation of these Technologies.

Keywords:
Logistics; Supply chain; Internet of things; Big data analytics; Industry 4.0

	Kim & Kim (2016)Kim, S., & Kim, S. (2016). A multi-criteria approach toward discovering killer IoT application in Korea. Technological Forecasting and Social Change, 102, 143-155. http://dx.doi.org/10.1016/j.techfore.2015.05.007. http://dx.doi.org/10.1016/j.techfore.201...	Lu et al. (2018)Lu, Y., Papagiannidis, S., & Alamanos, E. (2018). Internet of things: a systematic review of the business literature from the user and organizational perspectives. Technological Forecasting and Social Change, 136, 285-297. http://dx.doi.org/10.1016/j.techfore.2018.01.022. http://dx.doi.org/10.1016/j.techfore.201...	Gubbi et al. (2013)Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): a vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660. http://dx.doi.org/10.1016/j.future.2013.01.010. http://dx.doi.org/10.1016/j.future.2013....	Goldsby & Zinn (2016)Goldsby, T. J., & Zinn, W. (2016). Technology Innovation and New Business Models: can logistics and supply chain research accelerate the evolution? Journal of Business Logistics, 37(2), 80-86. http://dx.doi.org/10.1111/jbl.12130. http://dx.doi.org/10.1111/jbl.12130...	Zhu et al. (2018)Zhu, S., Song, J., Hazen, B. T., Lee, K., & Cegielski, C. (2018). How supply chain analytics enables operational supply chain transparency: an organizational information processing theory perspective. International. Journal of Physical Distribution & Logistics Management, 48(1), 47-68. http://dx.doi.org/10.1108/IJPDLM-11-2017-0341. http://dx.doi.org/10.1108/IJPDLM-11-2017...	Rossmann et al. (2017)Rossmann, B., Canzaniello, A., von der Gracht, H., & Hartmann, E. (2017). The future and social impact of Big Data analytics in supply chain management: results from a Delphi study. Technological Forecasting and Social Change, 130, 135-149. http://dx.doi.org/10.1016/j.techfore.2017.10.005. http://dx.doi.org/10.1016/j.techfore.201...	Wang et al. (2016)Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: certain investigations for research and applications. International Journal of Production Economics, 176, 98-110. http://dx.doi.org/10.1016/j.ijpe.2016.03.014. http://dx.doi.org/10.1016/j.ijpe.2016.03...	Richey Junior et al. (2016)Richey Junior, R. G., Morgan, T. R., Lindsey-Hall, K., & Adams, F. G. (2016). A global exploration of Big Data in the supply chain. International Journal of Physical Distribution & Logistics Management, 46(8), 710-739. http://dx.doi.org/10.1108/IJPDLM-05-2016-0134. http://dx.doi.org/10.1108/IJPDLM-05-2016...	Waller & Fawcett (2013)Waller, M. A., & Fawcett, S. E. (2013). Data scientist: big data, predictive analytics, and theory development in the era of a maker movement supply chain. Journal of Business Logistics, 34(4), 249-252. http://dx.doi.org/10.1111/jbl.12024. http://dx.doi.org/10.1111/jbl.12024...
IoT	●	●	●	●
BDA					●	●	●	●	●

Variable	Measure	Scale	Source
Gain	Expected gain with IoT investment Expected gain with BDA investment Expected gain with cloud investment Expected gain with blockchain investment Expected gain with 3D printing investment Expected gain with crowdsourcing investment	5-point Likert scale: [1] No gain Up to [5] High gain.	Rogers (2017)Rogers, Z. (2017). Big data analytics in supply chain: tackling the tidal wave (pp. 1-6). CSCMP’s Supply Chain Quarterly. Retrieved in 2019, August 15, from http://bit.do/eHXRa http://bit.do/eHXRa...

Time	Expected time to obtain gains with investment in Expected time to obtain gains with BDA investment Expected time to obtain gains with cloud investment Expected time to obtain gains with blockchain investment Expected time to obtain gains with 3D printing investment Expected time to obtain gains with crowdsourcing investment	5-point Likert scale: [1] No impact Up to [5] Long term.	Rogers (2017)Rogers, Z. (2017). Big data analytics in supply chain: tackling the tidal wave (pp. 1-6). CSCMP’s Supply Chain Quarterly. Retrieved in 2019, August 15, from http://bit.do/eHXRa http://bit.do/eHXRa...

Quality of data	My company’s data are stored in appropriate format (reliable) I can have easy access to my company's data (accessible) All organizational functions have the same data (unique) My company’s data can be exchanged with my supply chain (available) My company already exchanges data and information with suppliers and / or clients (collaborative)	5-point Likert scale: [1] Fully disagree Up to [5] Fully agree	Dong et al. (2001)Dong, Y., Carter, C. R., & Dresner, M. E. (2001). JIT purchasing and performance: an exploratory analysis of buyer and supplier perspectives. Journal of Operations Management, 9(4), 471-483. http://dx.doi.org/10.1016/S0272-6963(00)00066-8. http://dx.doi.org/10.1016/S0272-6963(00)...

Integration of data	My company's computer systems communicate with customers and / or suppliers My company shares data with customers and / or suppliers My company shares cost savings with customers and / or suppliers My company works with suppliers aiming to improve of processes	5-point Likert scale: [1] Fully disagree Up to [5] Fully agree	Dong et al. (2001)Dong, Y., Carter, C. R., & Dresner, M. E. (2001). JIT purchasing and performance: an exploratory analysis of buyer and supplier perspectives. Journal of Operations Management, 9(4), 471-483. http://dx.doi.org/10.1016/S0272-6963(00)00066-8. http://dx.doi.org/10.1016/S0272-6963(00)...

Profile of responding companies	frequency (total 108)	%
Company size
Large size ¹	37	34.3
Medium size ²	21	19.4
Small size ³	27	25.0
Micro-company ⁴	23	21.3
Sector of company operation
e-Commerce	8	7.4
Industry	25	23.1
Retail	7	6.5
Logistic Services ⁵	35	32.4
Other Services ⁶	27	25.0
Others	6	5.6
Region of Brazil where company headquarters is located
Northern	0	0.0
Northeastern	6	5.6
Mid-western	3	2.8
Southeastern	72	66.7
Southern	27	25.0

Profile of respondents	frequency (total 108)	%
Position of respondent in the company
President	14	13.0
Vice-President/ Director	23	21.3
Manager	28	25.9
Engineer	5	4.6
Analyst/ Specialist	18	16.7
Supervisor/ Coordinator	13	12.0
Other	7	6.5
Age group of respondents
21 to 30 years	10	9.3
31 to 40 years	33	30.6
41 to 50 years	45	41.7
51 to 60 years	8	7.4
More than 60 years	12	11.1

	IoT		BDA
	f	%	f	%
Does your company intend to invest?
Yes	89	82.4	60	55.6
No	19	17.6	48	44.4
When does your company intend to invest?
We have already invested	70	64.8	35	32.4
We will invest in the short term (up to 1 year)	13	12.0	19	17.6
We will invest in the medium term (2 - 5 years)	3	2.8	5	4.6
We will invest in the long term (over 5 years)	3	2.8	1	0.9
There are no investment plans	19	17.6	48	44.4
What is the main reason for the investment?
Maintaining competitive	54	50.0	39	36.1
Anticipating against competition	22	20.4	19	17.6
Customers’ requirement	11	10.2	2	1.9
Suppliers’ requirement	2	1.9	0	0.0
There are no investment plans	19	17.6	48	44.4

Intended applications for emerging technologies in logistics	frequency (total 108)	%
No response	68	63.0
BDA to lower operation costs	5	4.6
IoT for tracking cargo	3	2.8
BDA to analyze consumer behavior	2	1.9
BDA for forecasting	2	1.9

	IoT			BDA
	f¹	%		f¹	%
An adoption strategy is lacking	15		11.2	17	14.0
High investment in software and hardware	33		24.6	23	19.0
High investment in human resources	17		12.7	22	18.2
Difficulty in understanding how technology adds value to the business	16		11.9	9	7.4
Difficulty obtaining support or approval from top management	6		4.5	2	1.7
There are no investment plans	47		35.1	48	39.7

	frequency (total 108)	%
There are no other constraints	72	67.3
Lack of human resources	11	10.3
Lack of financial resources	7	6.5
Approval from company headquarters	3	2.8
Lack of professionals qualified in emerging technologies	3	2.8
Lack of ready-to-use BDA packages on the market	3	2.8
Internet Infrastructure not compatible with emerging technologies	2	1.9
Customer culture	1	0.9

Internet of things investment	frequency (total 108)	%
Does your company intend to invest in IoT?
Yes	61	56.5
No	47	43.5
When does your company intend to invest in IoT?
We have already invested	37	34.3
We will invest in the short term (up to 1 year)	9	8.3
We will invest in the medium term (2 - 5 years)	12	11.1
We will invest in the long term (over 5 years)	3	2.8
There are no investment plans	47	43.5
What is the main reason for the investment?
Staying competitive	37	34.3
Anticipating against competition	17	15.7
Customers’ requirement	5	4.6
Suppliers’ requirement	2	1.9
There are no investment plans	47	43.5

What is the expectation of gain with IoT investment and at what time do you believe that this gain will occur?			N	Mean	Standard deviation
All respondents	Expected gain ¹		108	2.54	1.997
All respondents	Expected time ²		108	1.49	1.411

Tests of effects among subjects (Scheffe) Dependent variable: What is the expected gain with IoT investment?
Company Size	Origin	Type III Sum of Squares	Degrees of Freedom	Mean Square	f	Sig.
Large size	Corrected model	25.039^d	2	12.520	4.696	0.016
	Intercept	351.050	1	351.050	131.688	0.000
	Error	90.636	34	2.666
	Total	492.000	37
	Total corrected	115.676	36
Medium size	Corrected model	15.662^c	2	7.831	2.860	0.083
	Intercept	70.634	1	70.634	25.794	0.000
	Error	49.29	18	2.738
	Total	120.000	21
	Total corrected	64.952	20
Small size	Corrected model	44.680^b	2	23.340	10.388	0.001
	Intercept	114.580	1	114.500	53.276	0.000
	Error	51.616	24	2.151
	Total	168.000	27
	Total corrected	96.296	26
Micro-company	Corrected model	29.103^a	2	14.552	6.016	0.009
	Intercept	280.280	1	280.280	115.878	0.000
	Error	48.375	20	2.419
	Total	342.000	23
	Total corrected	77.478	22

Tests of effects among subjects (Scheffe) Dependent variable: When does your company expect to obtain gains with IoT investment?
Company Size	Origin	Type III Sum of Squares	Degrees of Freedom	Mean Square	f	Sig.
Large size	Corrected model	7.673^d	2	3.836	2.240	0.122
	Intercept	126.429	1	126.429	73.834	0.000
	Error	58.219	34	1.712
	Total	206.000	37
	Total corrected	65.892	36
Medium size	Corrected model	10.390^c	2	5.195	3.483	0.053
	Intercept	22.930	1	22.930	15.373	0.000
	Error	26.848	18	1.492
	Total	51.000	21
	Total corrected	37.238	20
Small size	Corrected model	12.772^b	2	6.386	3.576	0.444
	Intercept	57.280	1	57.280	32.077	0.000
	Error	42.587	24	1.786
	Total	101.000	27
	Total corrected	55.630	26
Micro-company	Corrected model	1.770^a	2	0.885	0.525	0.599
	Intercept	55.039	1	55.039	32.656	0.000
	Error	33.708	30	1.685
	Total	95.000	23
	Total corrected	35.478	22

Big data analytics investment	frequency (total 108)	%
Does your company intend to invest in BDA?
Yes	60	55.6
No	48	44.4
When does your company intend to invest in BDA?
We have already invested	35	32.4
We will invest in the short term (up to 1 year)	19	17.6
We will invest in the medium term (2 - 5 years)	5	4.6
We will invest in the long term (over 5 years)	1	0.9
There are no investment plans	48	44.4
What is the main reason for the investment?
Staying competitive	39	36.1
Anticipating against competition	19	17.6
Customers’ requirement	2	1.9
Suppliers’ requirement	0	0.0
There are no investment plans	48	44.4

What is the expected gain and in what time does the company intend to obtain this gain in the adoption of big data analytics?			N	Mean	Standard deviation
All respondents	Expected gain ¹		108	2.67	2.109
All respondents	Expected time ²		108	1.36	1.329

Tests of effects among subjects (Scheffe) Dependent variable: What is the expected gain with BDA investment?
Company Size	Origin	Type III Sum of Squares	Degrees of Freedom	Mean Square	f	Sig.
Large size	Corrected model	27.056^d	2	13.528	4.158	0.024
	Intercept	353.168	1	353.168	108.550	0.000
	Error	110.619	34	3.254
	Total	514.000	37
	Total corrected	137.676	36
Medium size	Corrected model	21.429^c	2	10.714	3.894	0.039
	Intercept	169.682	1	169.682	61.673	0.000
	Error	49.524	18	2.751
	Total	254.000	21
	Total corrected	79.952	20
Small size	Corrected model	75.061^b	2	37.531	17.355	0.000
	Intercept	178.235	1	178.235	82.418	0.000
	Error	51.902	24	2.163
	Total	239.000	27
	Total corrected	126.963	26
Micro-company	Corrected model	42.036^a	2	21.018	5.772	0.011
	Intercept	139.655	1	139.655	38.349	0.000
	Error	72.833	20	3.642
	Total	237.000	23
	Total corrected	114.870	22

Tests of effects among subjects (Scheffe) Dependent variable: When does your company expect to obtain gains with BDA investment?
Company Size	Origin	Type III Sum of Squares	Degrees of Freedom	Mean Square	f	Sig.
Large size	Corrected model	2.760^d	2	1.380	0.830	0.445
	Intercept	103.144	1	103.144	62.028	0.000
	Error	56.538	34	1.663
	Total	170.000	37
	Total corrected	59.297	36
Medium size	Corrected model	14.035^c	2	7.017	4.175	0.032
	Intercept	45.314	1	45.314	26.963	0.000
	Error	30.251	18	1.681
	Total	79.000	21
	Total corrected	44.286	20
Small size	Corrected model	15.138^b	2	7.569	5.914	0.080
	Intercept	45.894	1	45.894	35.861	0.000
	Error	30.714	24	1.280
	Total	77.000	27
	Total corrected	45.852	26
Micro-company	Corrected model	11.971^a	2	5.986	6.192	0.080
	Intercept	33.379	1	33.379	34.530	0.000
	Error	19.333	20	0.967
	Total	63.000	23
	Total corrected	31.304	22

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