Gestão do conhecimento e Indústria 4.0: uma análise crítica da literatura e agenda futura

Ribeiro, Vagner Batista; Nakano, Davi; Muniz Jr., Jorge; Oliveira, Rafaela Brino de

doi:10.1590/1806-9649-2022v29e5222

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ORIGINAL ARTICLE • Gest. Prod. 29 • 2022 • https://doi.org/10.1590/1806-9649-2022v29e5222 linkcopiar

Gestão do conhecimento e Indústria 4.0: uma análise crítica da literatura e agenda futura

Autoria SCIMAGO INSTITUTIONS RANKINGS

Este artigo tem como objetivo discutir como a Gestão do Conhecimento (GC) influencia a implementação da Indústria 4.0 (I4.0). O artigo analisa a literatura relevante e explora oportunidades de pesquisa relacionadas, que podem fornecer insights e auxiliar pesquisadores em estudos futuros. As tecnologias I4.0 podem influenciar na flexibilidade e autonomia do trabalho, no desempenho e na inovação, mas a aquisição e disseminação do conhecimento, principalmente no chão de fábrica, permanecem dependentes dos funcionários, indicando preocupações humanas que podem ser suportadas pela GC. Realizamos uma revisão de literatura sobre KM e I4.0 em 41 artigos selecionados da Clarivate Web of Science Core Collection, publicados entre 2010 e 2021. Foram produzidos resumos estruturados, que conduzem aos temas mais amplos. Os resultados indicam três temas que relacionam GC e I4.0: Tecnologia, que explora as demandas de infraestrutura para implementação e sua influência no processo de criação do conhecimento; GC e aprendizagem, que reforçam a importância das hard e soft skills e indicam a necessidade de investigar os fatores facilitadores para a criação e compartilhamento do conhecimento; e Engajamento do trabalhador, que considera aspectos comunicacionais, culturais e de confiança para o desenvolvimento do trabalhador. Este artigo explora a implementação da I4.0 e aponta preocupações envolvendo os trabalhadores e a adoção de tecnologias, que podem fornecer insights e auxiliar pesquisadores em futuras práticas de Gestão de Operações e pesquisas relacionadas.

Palavras-chave:
Gestão do conhecimento; Compartilhamento do conhecimento; Industria 4.0

Technologies	Definitions	Author
Additive manufacturing, fast prototyping or 3D impression	Versatile machines for flexible manufacturing systems (FMS), able to transform digital 3D models into physical products using additive manufacturing	Pagliosa et al. (2019); Dalenogare et al. (2018)
Artificial Intelligence	Computer-based algorithms using analytical and statistical methods to support data analysis and automated decision-making	Olsen & Tomlin (2020)
Big Data Analytics	Computer-based predictive analytics, data mining and statistical analysis to treat large and unstructured data sets, generated by sensors	Chehbi-Gamoura et al. (2020); Dalenogare et al. (2018)
Computer-Aided Design and Manufacturing	Computer-based systems for product design, manufacturing planning and management	Dalenogare et al. (2018); Feng et al. (2017)
Cloud computing	Storage and processing of large data volumes in remote computers	Pagliosa et al. (2019); Dalenogare et al. (2018)
Cyber Physical System	Production system combining several technologies, able to interact with other systems, communication networks and operators.	Klingenberg et al. (2019)
Sensor-based digital automation	Automated systems with embedded sensor technology	Dalenogare et al. (2018)
Digital Product-Service Systems	Digital services embedded in products, using IoT, sensors, processors and software enabled capabilities	Dalenogare et al. (2018); Pirola et al. (2020)
Flexible manufacturing lines	Digital automation of manufacturing processes using sensors that allow Reconfigurable Manufacturing Systems (RMS) able to change production.	Dalenogare et al. (2018); Del Río Castro et al. (2021)
Integrated engineering system	Integration of IT support systems for information exchange in product development and manufacturing	Dalenogare et al. (2018)
IoT	High-speed internet-based sensors that allow to remotely control equipments	Manavalan & Jayakrishna (2019); Del Río Castro et al. (2021)
Manufacturing Execution Systems (MES) and Supervisory control and data acquisition (SCADA)	Real time, remote shop floor monitoring that allow dynamic scheduling	Buer et al. (2018); Dalenogare et al. (2018)
Robotics	Application of programmable, autonomous manufacturing machines	Bai et al. (2020)
Simulations/analysis of virtual models	Application of analytical methods in engineering projects and systems simulate their properties and outcomes	Dalenogare et al. (2018)
Virtual Factory	Integrated factory simulation model to support decision making capability	Jain et al. (2001)

Item	Description	Total
Data base	Web of Science core collection
Period	2010 to 2021
Search criteria	By “Topic”
	Articles (empirical papers) and Reviews
	Papers in English
	Classified by Most Cited
Search string	“Industry 4.0” OR “manufacturing of the future” OR “future manufacturing” OR “advanced manufacturing technology” OR “smart factory” OR “digitalization” OR “smart manufacturing” AND “knowledge management” OR “knowledge sharing”	114
Categories (1st. exclusion criteria)	Engineering Industrial, Engineering Manufacturing, Multidisciplinary Sciences, Business, Psychology Applied, Industrial Relations Labor, Management, Social Issues, Social Sciences Interdisciplinary, Social Work	71
Categories (1st. exclusion criteria)		(57 empirical papers and 14 reviews)
Title and Abstract analysis (2nd. exclusion criteria)	Exclusion of papers without adherence to the topics Knowledge Management in Industry 4.0	41
Title and Abstract analysis (2nd. exclusion criteria)		(27 empirical papers and 14 reviews)

Method	Number of papers	Authors
Literature reviews	14	Del Río Castro et al. (2021); Manesh et al. (2021); Patriarca et al. (2021); Sartori et al. (2022); Núñez-Merino et al. (2020); Schniederjans et al. (2020); Barbosa & Saisse (2019); Santos-Neto & Costa (2019); Cassia et al. (2020); Oztemel & Gursev (2020); Manavalan & Jayakrishna (2019); Chehbi-Gamoura et al. (2020); Chong et al. (2018); Ilvonen et al. (2018)
Structured questionnaires and multivariate analysis	8	Song et al. (2021); Crupi et al. (2020); Drašković et al. (2020); Hsieh et al. (2020); Stachová et al. (2020); Zangiacomi et al. (2020); Birasnav & Bienstock (2019); Tortorella et al. (2020)
Semi-structured questionnaires and qualitative analysis	3	Malik et al. (2021); Ngereja & Hussein (2021); Li et al. (2019)
Simulations and Experiments	4	Cotrino et al. (2021); Wang & Wan (2021); Yang et al. (2021); Dornhöfer et al. (2020)
Case studies	12	Jankowska et al. (2021); Wang et al. (2021); Barbosa et al. (2020); Bruno et al. (2020); Kaasinen et al. (2020); Kolyasnikov & Kelchevskaya (2020); Pinzone et al. (2020); Stentoft et al. (2020); Wilkesmann & Wilkesmann (2018); Sievert & Scholz (2017); Feng et al. (2017); Abubakar et al. (2019)

Themes	Description	Author
Technology	Contribution of digital technologies to achieve the Sustainable Development Goals	Del Río Castro et al. (2021)
	Knowledge sharing between manufacturing resources	Wang & Wan (2021)
	Analysis of KM practices and robotization expertise on automation implementation on the shop floor	Barbosa et al. (2020)
	Manufacturing and knowledge-based solutions interactions, and multi-agent-systems implementation	Dornhöfer et al. (2020)
	Big Data in Supply Chain Management (SCOR)	Chehbi-Gamoura et al. (2020)
	Digital Technologies application in Lean Supply Chain Management	Núñez-Merino et al. (2020)
	Knowledge and experience dissemination for Additive Manufacturing	Stentoft et al. (2020)
	I4.0 technologies (ERP, IoT) and sustainable supply chain	Manavalan & Jayakrishna (2019)
	Digital Manufacturing initiatives at leading universities in major industrial countries	Chong et al. (2018)
	Principles (interoperability, virtualization, local, real-time talent, service orientation and modularity) for I4.0 solutions (cloud computing, ensemble learning, big data, open-source software and Internet of Things)	Oztemel & Gursev (2020)
	Digitalization of work routines, workers’ perception and impact on processes	Wilkesmann & Wilkesmann (2018)
KM and Learning	KM and I4.0 relationship (Smart Manufacturing)	Manesh et al. (2021); Kolyasnikov & Kelchevskaya (2020); Feng et al. (2017)
	Managerial implications of digital transformation	Zangiacomi et al. (2020)
	Knowledge-based design system for decision support in complex engineered systems	Wang et al. (2021)
	Framework to support KM enabling factors and organizational performance	Abubakar et al. (2019)
	Linkages between strategic leadership and supply chain integration	Birasnav & Bienstock (2019)
	Business maturity model level assessment	Santos-Neto & Costa (2019)
	Collaborative, web-based Knowledge Transfer Platform for Industry 4.0	Cotrino et al. (2021)
	KM (knowledge sharing) and Supply Chain digitization relationship	Sartori et al. (2022); Song et al. (2021); Yang et al. (2021); Schniederjans et al. (2020)
	Knowledge creation and conversion within cyber-socio-technical systems	Patriarca et al. (2021)
	Framework for knowledge storage and reuse on the shop floor	Bruno et al. (2020)
KM and Learning (cont.)	Framework for knowledge sharing, IT infrastructure, and innovative capability	Cassia et al. (2020)
	Organizational culture in traditional and knowledge economies	Drašković et al. (2020)
	Knowledge management maturity model	Hsieh et al. (2020)
	Digital innovation process as a knowledge broker	Jankowska et al. (2021); Crupi et al. (2020)
	Organizational knowledge value, knowledge sharing and knowledge base use	Stachová et al. (2020)
	Conceptual framework for new product development, integrating knowledge management, hybrid project management and sociotechnical values	Barbosa & Saisse (2019)
	Relationship between Organizational Learning (OL) capabilities, and operational performance	Ngereja & Hussein (2021); Tortorella et al. (2020)
	Knowledge protection and security requirements for digital transformation	Ilvonen et al. (2018)
Worker engagement	Employee technostress due to artificial intelligence (AI) adoption	Malik et al. (2021)
	Empowerment and engagement of workers for Industry 4.0	Kaasinen et al. (2020)
	Employee engagement (KM and leadership) by internal social media (communication)	Sievert & Scholz (2017)
	Cyber-Physical Production Systems implementation impact on workers´ health, learning and operative performance	Pinzone et al. (2020)
	Knowledge sharing in a human-focused Industry 4.0 context	Li et al. (2019)

Theme	Research opportunities	Author
Technology	Big data influence on knowledge creation and competitiveness	Manesh et al. (2021); Chehbi-Gamoura et al. (2020)
	Robot implementation in different production environments and products	Barbosa et al. (2020)
	Drivers and barriers for Additive Manufacturing networks, and related knowledge sharing practices	Stentoft et al. (2020)
	Knowledge-based approaches and technological resources for production process simulation	Wang & Wan (2021); Dornhöfer et al. (2020)
	Cyber-Physical Production System technologies implementation	Pinzone et al. (2020)
KM and Learning	Knowledge-based systems efficiency for decision-making support	Wang et al. (2021)
	Managerial perspectives on learning for innovation	Ngereja & Hussein (2021)
	I4.0 implementation challenges for different sectors and countries	Zangiacomi et al. (2020); Tortorella et al. (2020); Wilkesmann & Wilkesmann (2018)
	Decision-making styles influence on I4.0	Núñez-Merino et al. (2020); Abubakar et al. (2019)
	Sustainability aspects for stakeholders during technology implementation	Pinzone et al. (2020)
	Organizational culture influence on I4.0 implementation	Sartori et al. (2022); Drašković et al. (2020); Li et al. (2019)
	Environmental uncertainty, organizational structure and training influence on leadership behaviors for supply chain integration	Birasnav & Bienstock (2019)
	Knowledge Transfer resources for different manufacturing contexts	Cotrino et al. (2021)
	KM for innovation processes	Jankowska et al. (2021); Manesh et al. (2021); Cassia et al. (2020); Núñez-Merino et al. (2020); Schniederjans et al. (2020); Ilvonen et al. (2018)
	Knowledge sharing enablers in different technological contexts	Manesh et al. (2021); Sartori et al. (2022); Cassia et al. (2020); Núñez-Merino et al. (2020); Stachová et al. (2020)
	Digital innovation hubs as knowledge brokers in different industries, technology and partnership contexts	Jankowska et al. (2021); Crupi et al. (2020)
KM and Learning (cont.)	Knowledge creation enablers in different technological contexts	Manesh et al. (2021); Schniederjans et al. (2020); Abubakar et al. (2019); Patriarca et al. (2021)
	Quality indicators for knowledge sharing in supply chains	Yang et al. (2021)
	Explore data analysis techniques and resources for shop floor knowledge management	Bruno et al. (2020)
	KM maturity models in different manufacturing and service contexts	Hsieh et al. (2020)
	KM and supply chain digitalization	Song et al. (2021); Chehbi-Gamoura et al. (2020); Schniederjans et al. (2020)
	I4.0 technology impact on the employment and knowledge loss	Manesh et al. (2021); Sartori et al. (2022); Oztemel & Gursev (2020)
	KM and sociotechnical digital transformation	Barbosa & Saisse (2019)
	Critical knowledge protection the balance between knowledge sharing and protection	Ilvonen et al. (2018)
	I4.0 training strategies	Manesh et al. (2021); Sartori et al. (2022); Oztemel & Gursev (2020); Wilkesmann & Wilkesmann (2018)
	I4.0 technology effects on individual and group learning	Manesh et al. (2021); Oztemel & Gursev (2020)
Worker Engagement	Worker´s competencies and skills for I4.0	Del Río Castro et al. (2021); Malik et al. (2021); Manesh et al. (2021); Sartori et al. (2022); Zangiacomi et al. (2020); Li et al. (2019); Manavalan & Jayakrishna (2019); Oztemel & Gursev (2020); Wilkesmann & Wilkesmann (2018)
	HR management for I4.0 implementation	Song et al. (2021)
	Operators´ interaction with technology resources	Kaasinen et al. (2020); Wilkesmann & Wilkesmann (2018)
	The worker perspective on I4.0 implementation	Song et al. (2021); Wilkesmann & Wilkesmann (2018)
	The worker role in I4.0	Kaasinen et al. (2020); Kolyasnikov & Kelchevskaya (2020); Chong et al. (2018); Wilkesmann & Wilkesmann (2018); Sievert & Scholz (2017)

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E-mail: gp@dep.ufscar.br

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