Um estudo exploratório sobre tecnologias emergentes aplicadas à <i>logística 4.0</i>

Corrêa, Jobel Santos; Sampaio, Mauro; Barros, Rodrigo de Castro

Brasil

Español English

THEMATIC SECTION: DIGITAL TRANSFORMATION, INTELLIGENT MANUFACTURING AND SUPPLY CHAIN MANAGEMENT 4.0 • Gest. Prod. 27 (3) • 2020 • https://doi.org/10.1590/0104-530X5468-20 copiar

Um estudo exploratório sobre tecnologias emergentes aplicadas à logística 4.0

Autoria SCIMAGO INSTITUTIONS RANKINGS

Resumo

O conceito de Logística 4.0 é análogo ao da Indústria 4.0. Enquanto a Indústria 4.0 propõe uma mudança disruptiva na manufatura, a Logística 4.0 defende uma transformação na forma como as organizações compram, fabricam, vendem e entregam produtos. O objetivo deste artigo é identificar, nas empresas brasileiras, o grau de interesse no investimento em seis tecnologias emergentes aplicáveis à logística, de acordo com a literatura científica, bem como identificar a percepção atual da qualidade dos dados dessas empresas. Para atingir esses objetivos, foi realizada uma pesquisa online. A pesquisa mostrou que as tecnologias que mais interessam às empresas brasileiras são Internet das Coisas (IoT) e computação em nuvem, ambas com 82% de intenção de investimento. As duas tecnologias que menos interessam às empresas são crowdsourcing e impressão 3D, ambas com desinteresse de 68% pelos investimentos dos entrevistados.

Palavras-chave:
Logística 4.0; Indústria 4.0; Investimento; Tecnologias emergentes

	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 organisational perspectives. Technological Rorecasting 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), 80-81. http://dx.doi.org/10.1111/jbl.12130. http://dx.doi.org/10.1111/jbl.12130...	Zhong et al. (2016)Zhong, R. Y., Newman, S. T., Huang, G. Q., & Lan, S. (2016). Big data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives. Computers & Industrial Engineering, 101, 572-591. http://dx.doi.org/10.1016/j.cie.2016.07.013. http://dx.doi.org/10.1016/j.cie.2016.07....	Rossmann et al. (2018)Rossmann, B., Canzaniello, A., Von der Gracht, H., & Hartmann, E. (2018). 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 (2016)Wang, K. (2016) Logistics 4.0 solution: new challenges and opportunities. In Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation. The Netherlands: Atlantis Press. http://dx.doi.org/10.2991/iwama-16.2016.13. http://dx.doi.org/10.2991/iwama-16.2016....	Richey et al. (2016)Richey, R. G., Jr., 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...	Tiwari et al. (2018)Tiwari, S., Wee, H. M., & Daryanto, Y. (2018). Big Data Analytics in Supply Chain Management Between 2010 and 2016: insights to Industries. Computers & Industrial Engineering, 115, 319-330. http://dx.doi.org/10.1016/j.cie.2017.11.017. http://dx.doi.org/10.1016/j.cie.2017.11....	Dai et al. (2015)Dai, H., Ge, L., & Zhou, W. (2015). A design method for supply chain traceability systems with aligned interests. International Journal of Production Economics, 170, 14-24. http://dx.doi.org/10.1016/j.ijpe.2015.08.010. http://dx.doi.org/10.1016/j.ijpe.2015.08...	Lindner et al. (2010)Lindner, M., Galán, F., Chapman, C., Clayman, S., Henriksson, D., & Elmroth, E. (2010). The Cloud Supply Chain: a framework for information, monitoring, accounting and billing. In 2nd International ICST Conference on Cloud Computing. Budapest: Budapest Science Portals.	Bocek et al. (2017)Bocek, T., Rodrigues, B., Strasser, T., & Stiller, B. (2017). Blockchains everywhere: a use-case of blockchains in the pharma supply-chain. In 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). New York: IEEE.	Vazquez-Martinez et al. (2018)Vazquez-Martinez, G. A., Gonzalez-Compean, J. L., Sosa-Sosa, V. J., Morales-Sandoval, M., & Perez, J. C. (2018). CloudChain: a novel distribution model for digital products based on supply chain principles. International Journal of Information Management, 39, 90-103. http://dx.doi.org/10.1016/j.ijinfomgt.2017.12.006. http://dx.doi.org/10.1016/j.ijinfomgt.20...	Durach et al. (2017)Durach, C. F., Kurpjuweit, S., & Wagner, S. M. (2017). The impact of additive manufacturing on supply chains. International Journal of Physical Distribution & Logistics Management, 47(10), 954-971. http://dx.doi.org/10.1108/IJPDLM-11-2016-0332. http://dx.doi.org/10.1108/IJPDLM-11-2016...	Sasson & Johnson (2016)Sasson, A., & Johnson, J. C. (2016). The 3D printing order: variability, supercenters and supply chain reconfigurations. International Journal of Physical Distribution & Logistics Management, 46(1), 82-94. http://dx.doi.org/10.1108/IJPDLM-10-2015-0257. http://dx.doi.org/10.1108/IJPDLM-10-2015...	Rogers et al. (2016)Rogers, H., Baricz, N., & Pawar, K. S. (2016). 3D printing services: classification, supply chain implications and research agenda. International Journal of Physical Distribution & Logistics Management, 46(10), 886-907. http://dx.doi.org/10.1108/IJPDLM-07-2016-0210. http://dx.doi.org/10.1108/IJPDLM-07-2016...	Waller & Fawcett (2014)Waller, M. A., & Fawcett, S. E. (2014). Print a maker movement supply chain: how invention and entrepreneurship will disrupt supply chain design. Journal of Business Logistics, 35(2), 99. http://dx.doi.org/10.1111/jbl.12045. http://dx.doi.org/10.1111/jbl.12045...	Carbone et al. (2017)Carbone, V., Rouquet, A., & Roussat, C. (2017). The rise of crowd logistics: a new way to co‐create logistics value. Journal of Business Logistics, 38(4), 238-252. http://dx.doi.org/10.1111/jbl.12164. http://dx.doi.org/10.1111/jbl.12164...	Castillo et al. (2018)Castillo, V. E., Bell, J. E., Rose, W. J., & Rodrigues, A. M. (2018). Crowdsourcing last mile delivery: strategic implications and future research directions. Journal of Business Logistics, 39(1), 7-25. http://dx.doi.org/10.1111/jbl.12173. http://dx.doi.org/10.1111/jbl.12173...	Carbone et al. (2018)Carbone, V., Rouquet, A., & Roussat, C. (2018). A typology of logistics at work in collaborative consumption. International Journal of Physical Distribution & Logistics Management, 48(6), 570-585. http://dx.doi.org/10.1108/IJPDLM-11-2017-0355. http://dx.doi.org/10.1108/IJPDLM-11-2017...
IoT	■	■	■	■
BDA					■	■	■	■	■
cloud										■	■
blockchain												■	■
3D printing														■	■	■	■
crowdsourcing																		■	■	■

	Traditional Supply Chain	Cloud Supply Chain
Main objective	To meet the demand for the lowest cost level	Meet demand at the lowest cost level and respond quickly to demand.
Product design strategy	Maximize performance at minimum product cost	Create modularity to allow individual configuration while maximizing service performance.
Price strategy	Lower margins because price is the main factor for the customer.	Lower margins, offering high competition to comparable products.
Manufacture strategy	Lower costs with high utilization.	High utilization while flexibly reacting to demand.
Inventory strategy	Minimize inventory to reduce costs	Optimize buffer to meet unpredictable demand.
Lead-time strategy	Reduce, but not at the expense of costs.	Strong service level agreement (SLA)
Supplier strategy	Select suppliers based on cost and quality.	Select suppliers based on the optimal complex of velocity, cost and flexibility.
Transport strategy	Greater reliability in low-cost modes.	Implement highly responsive and low-cost modes.

	Frequency (total 108)	%
Company size
Large size ¹ 1 Over 100 employees for commerce and services and over 500 employees for Industry. 2From 50 to 99 employees for commerce and services and from 100 to 499 employees for industry. 310 to 49 employees for commerce and services and 20 to 99 employees for industry. 4Up to 9 employees for commerce and services and up to 19 employees for industry. 5Includes storage, transportation, fleet management, risk management, etc. 6Includes consulting, information technology (IT), software development, etc. Source: Authors.	37	34.3
Medium size 2	21	19.4
Small size 3	27	25.0
Micro-enterprise 4	23	21.3
Sector in which the company operates
e-Commerce	8	7.4
Industry	25	23.1
Retail	7	6.5
Logistics Services 5	35	32.4
Other Services 6	27	25.0
Others	6	5.6
Region of Brazil where company's headquarters are located
Northern	0	0.0
Northeastern	6	5.6
Midwestern	3	2.8
Southeastern	72	66.7
Southern	27	25.0

Variable	Measure	Scale	Source
Gain	Expected gain for investing in IoT	5-point Likert: [1] No gain up to [5] High gain.	Rogers et al. (2017)Rogers, Z., Gooley, T., Harrington, L., Rogers, D., Sharpe, R., & Kitajima, T. (2017). Big data analytics in supply chain: tackling the tidal wave (pp. 1-6). CSCMP’s Supply Chain Quarterly.
	Expected gain for investing in BDA
	Expected gain for investing in cloud
	Expected gain for investing in blockchain
	Expected gain for investing in 3D printing
	Expected gain for investing in crowdsourcing
Time	Expected time to realize gains with IoT	5-point Likert: [1] No gain up to [5] High gain	Rogers et al. (2017)Rogers, Z., Gooley, T., Harrington, L., Rogers, D., Sharpe, R., & Kitajima, T. (2017). Big data analytics in supply chain: tackling the tidal wave (pp. 1-6). CSCMP’s Supply Chain Quarterly.
	Expected time to realize gains with BDA
	Expected time to realize gains with cloud
	Expected time to realize gains with blockchain
	Expected time to realize gains with 3D printing
	Expected time to realize gains with crowdsourcing
Data quality	company's data is stored in appropriate (reliable) formats	5-point Likert: [1] I totally disagree Up to [5] I 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, 19(4), 471-483. http://dx.doi.org/10.1016/S0272-6963(00)00066-8. http://dx.doi.org/10.1016/S0272-6963(00)...
	I can easily access my company's data (accessible)
	All organizational functions have the same data (unique)
	My company data can be exchanged with my supply chain (available)
	My company already exchanges data and information with suppliers and / or customers (collaborative)

	Frequency (total 108)	%
Respondent's position 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
Respondent's age group
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
Over 60 years	12	11.1

	IoT		BDA		Cloud		Blockchain		3D Printing		Crowdsourcing
	f	%	f	%	f	%	f	%	f	%	f	%
Does your company intend to invest?
Yes	89	82.4	60	55.6	89	82.4	57	52.8	35	32.4	34	31.5
No	19	17.6	48	44.4	19	17.6	51	47.2	73	67.6	74	68.5
When does your company intend to invest?
We have already invested	70	64.8	35	32.4	70	64.8	23	21.3	15	13.9	11	10.2
We will invest in the short term (up to 1 year)	13	12.0	19	17.6	13	12.0	17	15.7	2	1.9	12	11.1
We will invest in the medium term(between 2 and 5 years)	3	2.8	5	4.6	3	2.8	13	12.0	12	11.1	6	5.6
We will invest in the long term(over 5 years)	3	2.8	1	0.9	3	2.8	4	3.7	6	5.6	5	4.6
There are no investment plans	19	17.6	48	44.4	19	17.6	51	47.2	73	67.6	74	68.5
What is the main reason for the investment?
Remain competitive	54	50.0	39	36.1	54	50.0	20	18.5	10	9.3	16	14.8
Anticipate competition	22	20.4	19	17.6	22	20.4	27	25.0	18	16.7	11	10.2
Customers' requirements	11	10.2	2	1.9	11	10.2	7	6.5	1	0.9	1	0.9
Suppliers' requirement	2	1.9	0	0.0	2	1.9	3	2.8	6	5.6	6	5.6
There are no investment plans	19	17.6	48	44.4	19	17.6	51	47.2	73	67.6	74	68.5

	Frequency (total 108)	%
Did not respond	68	63.0
Blockchain for tracking goods	10	9.3
3D printing for printing small components on site	6	5.6
BDA to lower operating costs	5	4.6
Blockchain for tracking goods and smart contracts	4	3.7
Increase the security of IT infrastructure using cloud	4	3.7
IoT for cargo tracking	3	2.8
BDA to analyze consumer behavior	2	1.9
BDA for forecasting	2	1.9
Using cloud computing for the storage of documents	2	1.9
Cloud computing to reduce capex value	1	0.9
Crowdsourcing to reduce idle space on trucks	1	0.9

	IoT		BDA		Cloud		Blockchain		3D Printing		Crowdsourcing		Total
	f	%	f	%	f	%	f	%	f	%	f	%	f	%
Lack of adoption strategy	15	11.2	17	14.0	12	15.0	17	12.0	15	11.3	16	13.9	92	29.5
High investment in software and hardware	33	24.6	23	19.0	20	25.0	24	16.9	22	16.5	9	7.8	131	42.0
High investment in human resources	17	12.7	22	18.2	18	22.5	26	18.3	12	9.0	7	6.1	102	32.7
Difficulty understanding how technology adds value to the business	16	11.9	9	7.4	7	8.8	20	14.1	7	5.3	6	5.2	65	20.8
Difficulty obtaining support or approval from higher levels management	6	4.5	2	1.7	4	5.0	4	2.8	4	3.0	3	2.6	23	7.4
There are no investment plans	47	35.1	48	39.7	19	23.8	51	35.9	73	54.9	74	64.3	312	100.0

Are there any other barriers for the adoption of emerging technologies?	Frequency (total 108)	%
There are no other barriers	72	67.3
Lack of human resources	11	10.3
Lack of financial resources	7	6.5
Lack of specific training in blockchain	4	3.7
Approval from company headquarters	3	2.8
Lack of professionals qualified in emerging technologies	3	2.8
Lack of ready BDA 3 packages in the market	3	2.8
Internet infrastructure not consistent with emerging technologies	2	1.9
Lack of a centralized portal for crowdsourcing	1	0.9
Lack of financial and human resources	1	0.9
Customer culture	1	0.9

	Frequency (total 108)	%
Low implementation	46	42.6
Medium implementation	38	35.2
High implementation	24	22.2

Company Size	Intention to Implement Emerging Technologies (IIET)
	Low Implementation		Medium Implementation		High Implementation		Total
	Frequency	Expected Frequency	Frequency	Expected Frequency	Frequency	Expected Frequency	Total
Large Size	10	15.8	16	13.0	11	8.2	37
Medium Size	11	8.9	7	7.4	3	4.7	21
Small Size	16	11.5	7	9.5	4	6.0	27
Micro-enterprise	9	9.8	8	8.1	6	5.1	23
Total	46	46.0	38	38.0	24	24.0	108
Significance Level	0.05
p-value	0.229
Degrees of Freedom	6
χ²-value	8.12

Sector in which company operates	Intention to Implement Emerging Technologies (IIET)
	Low Implementation		Medium Implementation		High Implementation		Total
	Frequency	Expected Frequency	Frequency	Expected Frequency	Frequency	Expected Frequency	Total
e-Commerce	2	14.9	6	12.3	0	7.8	8
Industry	11	10.6	7	8.8	7	5.6	25
Retail	4	3.0	2	2.5	1	1.6	7
Logistics Services	18	14.9	9	12.3	8	7.8	35
Other Services	8	11.5	12	9.5	7	6.0	27
Others	3	2.6	2	2.1	1	1.3	6
Total	46	57.5	38	47.5	24	30.1	108
Significance Level	0.05
p-value	0.362
Degrees of Freedom	10
χ2-value	10,947

Region of Brazil where the company's headquarters is located	Intention to Implement Emerging Technologies (IIET)
	Low Implementation		Medium Implementation		High Implementation		Total
	Frequency	Expected Frequency	Frequency	Expected Frequency	Frequency	Expected Frequency	Total
Northern	0	0.00	0	0.00	0	0.00	0
Northeastern	2	2.60	2	2.10	2	1.30	6
Midwestern	1	1.30	2	1.10	0	0.70	3
Southeastern	31	30.70	27	25.30	14	16.00	72
Southern	12	11.50	7	9.50	8	6.00	27
Total	46	46.10	38	38.00	24	24.00	108
Significance Level	0.05
p-value	0.712
Degrees of Freedom	6
χ2-value	3.742

				IoT		BDA		Cloud		Blockchain		3D Printing			Crowdsourcing
			N	ṁ	s²	ṁ	s²	ṁ	s²	ṁ	s²	ṁ	s²		ṁ	s²
All	Expected gain 1		108	2.5	2.0	2.7	2.1	3.1	1.8	2.4	2.0	1.6		1.9	1.6		1.9
All	Expected time 2		108	1.5	1.4	1.4	1.3	1.7	1.2	1.3	1.4	0.8		1.3	0.6		1.1
IITE	Expected gain ¹	Low implementation	46	1.3	1.6	1.3	1.7	2.3	1.8	1.3	1.7	1.2		1.7	0.8		1.3
		Medium implementation	38	3.0	1.9	3.4	2.0	3.5	1.6	3.0	1.9	1.6		1.9	1.6		1.9
		High implementation	24	4.2	1.0	4.2	1.2	4.0	1.3	3.4	1.7	2.2		2.0	3.3		2.0
	Expected time ²	Low implementation	46	0.9	1.4	0.7	1.2	1.6	1.5	0.7	1.3	0.6		1.3	0.3		0.8
		Medium implementation	38	1.8	1.4	1.8	1.2	1.7	1.0	1.7	1.3	0.7		1.2	0.6		1.0
		High implementation	24	2.0	1.1	2.0	1.1	1.8	1.2	1.9	1.4	1.3	1.4		1.4	1.2

Company Size	IIET	Quality of data
		A₁				A₂				A₃				A₄				A₅
		N	ṁ	s²	S	N	ṁ	s²	S	N	ṁ	s²	S	N	ṁ	s²	S	N	ṁ	s²	S
Large size	Low implementation	10	3.6	1.4	0.4	10	3.8	1.3	0.1	10	2.9	1.5	0.1	10	3.4	1.5	0.2	10	3.4	1.6	0.7
	Medium implementation	16	3.5	1.5	0.4	16	3.1	1.3	0.1	16	2.6	1.4	0.1	16	2.7	1.6	0.2	16	3.0	1.6	0.7
	High implementation	11	4.2	1.2	0.4	11	4.1	1.2	0.1	11	3.8	1.3	0.1	11	3.7	1.3	0.2	11	3.5	1.4	0.7
Medium size	Low implementation	11	3.5	1.8	0.9	11	4.2	1.2	0.8	11	2.6	1.4	0.3	11	3.5	1.8	0.5	11	2.9	1.6	0.6
	Medium implementation	7	3.6	1.8	0.9	7	4.4	0.8	0.8	7	3.6	1.3	0.3	7	4.3	1.1	0.5	7	3.7	2.3	0.6
	High implementation	3	4.0	1.0	0.9	3	4.0	1.0	0.8	3	3.0	0.0	0.3	3	3.3	2.1	0.5	3	2.3	1.9	0.6
Small size	Low implementation	16	4.2	1.4	0.1	16	4.3	1.2	0.7	16	3.4	1.6	0.4	16	3.1	1.8	0.3	16	3.3	2.1	0.8
	Medium implementation	7	2.9	1.7	0.1	7	3.9	1.2	0.7	7	2.6	1.8	0.4	7	4.3	1.1	0.3	7	3.9	1.6	0.8
	High implementation	4	2.8	1.7	0.1	4	4.0	1.2	0.7	4	4.0	1.4	0.4	4	4.0	1.4	0.3	4	3.8	1.9	0.8
Micro-enterprise	Low implementation	9	2.9	1.8	0.3	9	3.2	2.0	0.2	9	2.2	1.9	0.1	9	2.3	1.8	0.5	9	1.9	1.7	0.1
	Medium implementation	8	2.5	1.2	0.3	8	4.1	1.4	0.2	8	2.8	1.2	0.1	8	3.1	1.5	0.5	8	3.0	1.1	0.1
	High implementation	6	3.8	1.6	0.3	6	4.7	0.8	0.2	6	4.0	1.1	0.1	6	3.3	1.5	0.5	6	4.2	1.0	0.1

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

Acompanhe os números deste periódico no seu leitor de RSS

[1] Source: Authors.