Design Science Research na prática: revisão sobre as aplicações na Engenharia de Produção

Goecks, Lucas Schmidt; Souza, Michele de; Librelato, Tatiane Pereira; Trento, Luiz Reni

Lucas Schmidt Goecks

Universidade do Vale do Rio dos Sinos, Engenharia de Produção e Sistemas, São Leopoldo, Brasil. Email: mol.leandro@gmail.com; paulasenra2012@hotmail.com; felipeCMT-1314@hotmail.com

http://orcid.org/0000-0003-2215-6404

Michele de Souza

Universidade do Vale do Rio dos Sinos, Engenharia de Produção e Sistemas, São Leopoldo, Brasil. Email: mol.leandro@gmail.com; paulasenra2012@hotmail.com; felipeCMT-1314@hotmail.com

http://orcid.org/0000-0003-3817-1553

Tatiane Pereira Librelato

Universidade do Vale do Rio dos Sinos, Engenharia de Produção e Sistemas, São Leopoldo, Brasil. Email: mol.leandro@gmail.com; paulasenra2012@hotmail.com; felipeCMT-1314@hotmail.com

http://orcid.org/0000-0003-3129-0218

Luiz Reni Trento

Universidade do Vale do Rio dos Sinos, Engenharia de Produção e Sistemas, São Leopoldo, Brasil. Email: mol.leandro@gmail.com; paulasenra2012@hotmail.com; felipeCMT-1314@hotmail.com

http://orcid.org/0000-0003-3119-3047

Problem classes
Strategic Alignment	Project management	Process modeling and improvement
Problem analysis and decision support	Knowledge management	Motivation
Process automation and standardization	Governance	Production Organization
Supply Chain	Implementation of systems	Planning and production control
Skills	Performance indicators and measures	Relationship with customers and services
Organizational culture	Innovation	Relationship and process integration
Costs and Investments	Process Mapping	Risks
Information flow and management	Best practices	Information systems
Change management	Cost Measurement	Outsourcing
Processes management	BPM Methodologies

Author	Journal	Problem class	Application Subarea
Dresch et al. (2019)Dresch, A., Veit, D. R., Lima, P. N., Lacerda, D. P., & Collatto, D. C. (2019). Inducing brazilian manufacturing SMEs productivity with lean tools. International Journal of Productivity and Performance Management, 68(1), 69-87. http://dx.doi.org/10.1108/IJPPM-10-2017-0248. http://dx.doi.org/10.1108/IJPPM-10-2017-...	International Journal of Productivity and Performance Management	Process Improvement	1.1. Production Systems and Operations Management
Sanches et al. (2015)Sanches, C., Meireles, M., & Silva, O. R. (2015). Framework for the generic process of diagnosis in quality problem solving. Total Quality Management & Business Excellence, 26(11-12), 1173-1187. http://dx.doi.org/10.1080/14783363.2014.918707. http://dx.doi.org/10.1080/14783363.2014....	Total Quality Management & Business Excellence	Process modeling and improvement	4.1. Quality Systems Management
Anke (2019)Anke, J. (2019). Design-integrated financial assessment of smart services. Electronic Markets, 29(1), 19-35. http://dx.doi.org/10.1007/s12525-018-0300-y. http://dx.doi.org/10.1007/s12525-018-030...	Electronic Markets	Process modeling and improvement	5.3. Product Planning and Design
Saraswat et al. (2014)Saraswat, S. P., Anderson, D. M., & Chircu, A. M. (2014). Teaching business process management with simulation in graduate business programs: an integrative approach. Journal of Information Systems Education, 25(3), 221-232.	Journal of Information Systems Education	Process modeling and improvement	10.1. Production Engineer Training Study
Amrollahi & Rowlands (2018)Amrollahi, A., & Rowlands, B. (2018). OSPM: a design methodology for open strategic planning. Information & Management, 55(6), 667-685. http://dx.doi.org/10.1016/j.im.2018.01.006. http://dx.doi.org/10.1016/j.im.2018.01.0...	Information & Management	Processes management	6.1. Strategic and Organizational Management
Mamoghli et al. (2018)Mamoghli, S., Cassivi, L., & Trudel, S. (2018). Supporting business processes through human and IT factors: a maturity model. Business Process Management Journal, 24(4), 985-1006. http://dx.doi.org/10.1108/BPMJ-11-2016-0232. http://dx.doi.org/10.1108/BPMJ-11-2016-0...	Business Process Management Journal	BPM Methodologies	6.1. Strategic and Organizational Management
Krawatzeck et al. (2013)Krawatzeck, R., Hofmann, M., Jacobi, F., & Dinter, B. (2013). Constructing software-intensive methods: A design science research process with early feedback cycles. In: International Conference on Design Science Research in Information Systems (pp. 486-493). Berlin, Heidelberg: Springer. http://dx.doi.org/10.1007/978-3-642-38827-9_41. http://dx.doi.org/10.1007/978-3-642-3882...	Conference: ICDSRIS	Project management	6.2. Project management
Manfio & Lacerda (2016)Manfio, N. M., & Lacerda, D. P. (2016). Definition of scope in new product development projects for the food industry: a proposed method. Gestão & Produção, 23(1), 18-36. http://dx.doi.org/10.1590/0104-530X1009-13. http://dx.doi.org/10.1590/0104-530X1009-...	Gestão & Produção	Project management	6.2. Project management
Lehnert et al. (2016)Lehnert, M., Linhart, A., & Röglinger, M. (2016). Value-based process project portfolio management: integrated planning of BPM capability development and process improvement. Business Research, 9(2), 377-419. http://dx.doi.org/10.1007/s40685-016-0036-5. http://dx.doi.org/10.1007/s40685-016-003...	Business Research	Project management	6.2. Project management
Hao et al. (2015)Hao, J., Yu, Y., Law, R., & Fong, D. K. C. (2015). A genetic algorithm-based learning approach to understand customer satisfaction with OTA websites. Tourism Management, 48, 231-241. http://dx.doi.org/10.1016/j.tourman.2014.11.009. http://dx.doi.org/10.1016/j.tourman.2014...	Tourism Management	Relationship with customers and services	6.3. Organizational Performance Management
Wu (2009)Wu, J. H. (2009). A design methodology for form-based knowledge reuse and representation. Information & Management, 46(7), 365-375. http://dx.doi.org/10.1016/j.im.2009.06.004. http://dx.doi.org/10.1016/j.im.2009.06.0...	Information & Management	Knowledge management	6.4. Information management
Wang et al. (2011)Wang, M., Vogel, D., & Ran, W. (2011). Creating a performance-oriented e-learning environment: a design science approach. Information & Management, 48(7), 260-269. http://dx.doi.org/10.1016/j.im.2011.06.003. http://dx.doi.org/10.1016/j.im.2011.06.0...	Information & Management	Knowledge management	6.7. Technology Management

DSR Areas	Research Agenda	Class of problems	Artifacts
Organizational	Industry 4.0	Lack of a digital strategy (Cavata et al., 2020Cavata, J. T., Massote, A. A., Maia, R. F., & Lima, F. (2020). Highlighting the benefits of industry 4.0 for production: an agent-based simulation approach. Gestão & Produção, 27(3), e5619. http://dx.doi.org/10.1590/0104-530x5619-20. http://dx.doi.org/10.1590/0104-530x5619-... ; Raj et al., 2020Raj, A., Dwivedi, G., Sharma, A., Jabbour, A. B. L. S., & Rajak, S. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: an inter-country comparative perspective. International Journal of Production Economics, 224, 107546. http://dx.doi.org/10.1016/j.ijpe.2019.107546. http://dx.doi.org/10.1016/j.ijpe.2019.10... ).	Strategic Road Map, Simulations, Artificial Intelligence, Big Data, Machine Learning.
	Industry 4.0	Integration and digitalization of the quality management system (Goecks et al., 2020Goecks, L. S., Santos, A. A., & Korzenowski, A. L. (2020). Decision-making trends in quality management: a literature review about Industry 4.0. Production, 30, 20190086. http://dx.doi.org/10.1590/0103-6513.20190086. http://dx.doi.org/10.1590/0103-6513.2019... ; Korzenowski et al., 2020Korzenowski, A. L., Simões, W. L., Goecks, L. S., Gerhard, M., Fogaça, P., & Noronha, R.S. (2020). Economic sustainability of the X̅ implementation in uncapable processes. International Journal of Qualitative Research, 14(3), 881-894. http://dx.doi.org/10.24874/IJQR14.03-15. http://dx.doi.org/10.24874/IJQR14.03-15... ).	Big Data, Decision-Making Models, Machine Learning, Neural Networks, Cyber-Physical Systems, Automated Process Control.
	Business model	It requires in-depth knowledge of customer needs and the technological and organizational resources that might meet those needs (Sousa & Barros, 2020Sousa, A. M. H., & Barros, J. D. P., No. (2020). Is it possible to implement ERP in the production function of civil construction? Gestão & Produção, 27(3), e4445. http://dx.doi.org/10.1590/0104-530x4445-20. http://dx.doi.org/10.1590/0104-530x4445-... ; Teece, 2018Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40-49. http://dx.doi.org/10.1016/j.lrp.2017.06.007. http://dx.doi.org/10.1016/j.lrp.2017.06.... ).	Customer Relational Management (CRM), Enterprise Resource Planning (ERP), Big Data, Artificial Intelligence, Machine Learning.
	Dynamic Capabilities	How dynamic capabilities contribute to digital transformation (Vial, 2019Vial, G. (2019). Understanding digital transformation: a review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118-144. http://dx.doi.org/10.1016/j.jsis.2019.01.003. http://dx.doi.org/10.1016/j.jsis.2019.01... ).	Sensing: assessment of technological opportunities in relationship to customer needs; Seizing: address needs and opportunities, and to capture value; Transformation: continued renewal.
Informational	Supply Chain Management	Lack of specification can affect coordination (Vosooghidizaji et al., 2020Vosooghidizaji, M., Taghipour, A., & Canel-Depitre, B. (2020). Supply chain coordination under information asymmetry: a review. International Journal of Production Research, 58(6), 1805-1834. http://dx.doi.org/10.1080/00207543.2019.1685702. http://dx.doi.org/10.1080/00207543.2019.... ).	Simulations, Artificial Intelligence, Big Data, Machine Learning, Blockchain, IoT.
	Supply Chain Management	Consideration of supply chain data analytic approaches (Kakhki & Gargeya, 2019Kakhki, M., & Gargeya, V. B. (2019). Information systems for supply chain management: a systematic literature analysis. International Journal of Production Research, 57(15-16), 5318-5339. http://dx.doi.org/10.1080/00207543.2019.1570376. http://dx.doi.org/10.1080/00207543.2019.... ).	Machine learning technique, neural networks and data-driven approaches to integrate the production and distribution planning in the supply chain.
	Industry 4.0	Fashion manufacturing companies and their Industry 4.0 strategies to extract potential success factors (Braglia et al., 2020Braglia, M., Marrazzini, L., Padellini, L., and Rinaldi, R. (2020). Managerial and Industry 4.0 solutions for fashion supply chains. Journal of Fashion Marketing and Management: An International Journal, 25(1), 184-201.).	Artificial Intelligence, Big Data, Machine Learning.
		Support the production strategy using data processing aspects (Campos & Alves, 2020Campos, F. C., & Alves, A. G., Fo. (2020). Proposal for a framework for production strategy utilizing Big Data: illustrative case in public service. Gestão & Produção, 27(3), e4651. http://dx.doi.org/10.1590/0104-530x4651-20. http://dx.doi.org/10.1590/0104-530x4651-... ). Intelligent hospitals are optimizing the use of resources and reducing hospital stay (Flórez et al., 2020Flórez, C. A. C., Rosário, J. M., & Hurtado, D. A. (2020). Application of Automation and Manufacture techniques oriented to a service-based business using the Internet of Things (IoT) and Industry 4.0 concepts. Case study: smart Hospital. Gestão & Produção, 27(3), e5416. http://dx.doi.org/10.1590/0104-530x5416-20. http://dx.doi.org/10.1590/0104-530x5416-... ).	Big Data and IoT (Flórez et al., 2020Flórez, C. A. C., Rosário, J. M., & Hurtado, D. A. (2020). Application of Automation and Manufacture techniques oriented to a service-based business using the Internet of Things (IoT) and Industry 4.0 concepts. Case study: smart Hospital. Gestão & Produção, 27(3), e5416. http://dx.doi.org/10.1590/0104-530x5416-20. http://dx.doi.org/10.1590/0104-530x5416-... ; Campos & Alves, 2020Campos, F. C., & Alves, A. G., Fo. (2020). Proposal for a framework for production strategy utilizing Big Data: illustrative case in public service. Gestão & Produção, 27(3), e4651. http://dx.doi.org/10.1590/0104-530x4651-20. http://dx.doi.org/10.1590/0104-530x4651-... )
		Decentralization of the productive system’s control by autonomous and intelligent devices interconnected by a communication system (Guirro et al., 2020Guirro, D. N., Asato, O. L., Santos, G. A., & Nakamoto, F. Y. (2020). Manufacturing operational management modeling using interpreted Petri nets. Gestão & Produção, 27(2), e3920. http://dx.doi.org/10.1590/0104-530x3920-20. http://dx.doi.org/10.1590/0104-530x3920-... ).	Enterprise Resource Planning (ERP); Big Data, Artificial Intelligence, Machine Learning (Guirro et al., 2020Guirro, D. N., Asato, O. L., Santos, G. A., & Nakamoto, F. Y. (2020). Manufacturing operational management modeling using interpreted Petri nets. Gestão & Produção, 27(2), e3920. http://dx.doi.org/10.1590/0104-530x3920-20. http://dx.doi.org/10.1590/0104-530x3920-... ).
Humans	Management	Investigate how Top Managers stimulate internal/external debate without generating conflicts (Pereira et al., 2019Pereira, G., Tzempelikos, N., Trento, L. R., Trento, C. R., Borchardt, M., & Viegas, C. V. (2019). Top managers’ role in key account management. Journal of Business and Industrial Marketing, 34(5), 977-993. http://dx.doi.org/10.1108/JBIM-08-2018-0243. http://dx.doi.org/10.1108/JBIM-08-2018-0... ).	Big data insights for more effective relational selling; Leverage artificial. intelligence for relational selling (Arli et al., 2018Arli, D., Bauer, C., & Palmatier, R. W. (2018). Relational selling: Past, present and future. Industrial Marketing Management, 69(1), 169-184. http://dx.doi.org/10.1016/j.indmarman.2017.07.018. http://dx.doi.org/10.1016/j.indmarman.20... ).
	Industry 4.0	Lack of firms’ internal capabilities in order to overcome the challenges of implementing Industry 4.0 (Raj et al., 2020Raj, A., Dwivedi, G., Sharma, A., Jabbour, A. B. L. S., & Rajak, S. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: an inter-country comparative perspective. International Journal of Production Economics, 224, 107546. http://dx.doi.org/10.1016/j.ijpe.2019.107546. http://dx.doi.org/10.1016/j.ijpe.2019.10... ).	Building roadmaps and planning strategically to invest in suitable resources. Framework to prioritize their allocation of resources.
	Consumers Intentions	Factors such as price and time might influence their actual behavior. Future research might be able to measure subjects actual usage and purchasing behavior (Wu et al., 2015Wu, J., Kang, J. Y. M., Damminga, C., Kim, H. Y., & Johnson, K. K. P. (2015). MC 2.0: testing an apparel co-design experience model. Journal of Fashion Marketing and Management, 19(1), 69-86. http://dx.doi.org/10.1108/JFMM-07-2013-0092. http://dx.doi.org/10.1108/JFMM-07-2013-0... ).	Software’s for decision-making in properties portfolio (Baierle et al., 2020Baierle, I. C., Schaefer, J. L., Sellitto, M. A., Fava, L. P., Furtado, J. C., & Nara, E. O. B. (2020). MOONA software for survey classification and evaluation of criteria to support decision-making for properties portfolio. International Journal of Strategic Property Management, 24(4), 226-236. http://dx.doi.org/10.3846/ijspm.2020.12338. http://dx.doi.org/10.3846/ijspm.2020.123... ). Customer Relational Management (CRM), Big Data, Artificial Intelligence, Machine Learning.

[1] Source: Prepared by the authors of the research.

Algorithm	An approach, method, or process described mostly by a set of formal logical instructions.
Construct	Concept, assertion, or syntax that has been constructed from a set of statements, assertions, or other concepts.
Framework	Meta-model.
Instantiation	The structure and organization of a system’s hardware or system software or part thereof.
Method	Actionable instructions that are conceptual (not algorithmic).
Model	Simplified representation of reality documented using formal notation or language.

Brasil

Brasil

Design Science Research na prática: revisão sobre as aplicações na Engenharia de Produção

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