Strategies for Effective Product Roadmap Development and Execution in Data Analytics Platforms

Authors

  • Ranjit Kumar Gupta Independent Researcher, USA.
  • Sagar Shukla Independent Researcher, USA.
  • Anaswara Thekkan Rajan Independent Researcher, USA.
  • Sneha Aravind Independent Researcher, USA.

Keywords:

Internet-of-Things (IoT), Business Process, Manufacturing Industry, Industry 4.0, Roadmaps, Big Data Analytics, IT Industry, Transformational Potential

Abstract

Manufacturing is only one of several industries going through a digital transformation in this era of digital disruption. Manufacturing businesses are racing to adopt IoT-based solutions in order to innovate, increase productivity, lower costs, and gain greater market share because of the enormous transformational potential offered by Internet-of-Things (IoT) & Big Data. It is being used by industrial companies all over the world to increase flexibility while achieving cost savings, reduced inefficiencies, and better performance. It is no longer a trend for the future. But putting Industry 4.0 technology support into practice is a tough endeavour that gets increasingly harder in the absence of a common method. These are produced during the course of business operations and are kept in databases, email communication, transaction logs, free form texts on (business) social media, and other places. Businesses want to integrate data analytics methods into their decision-making processes by utilisation of these data. The field of Big Data analyses has seen tremendous progress in the IT sector in recent years. It appears that in order to deal with the extremely dynamic situations of today, new methods for product route mapping are required. This article provides an overview of the literature in science on product road mapping in order to shed light on the current state of the art & pinpoint research gaps.  In order to demonstrate the influence of Industry 4.0 technological innovations on the manufacturing sector, this study provides an organised and content-focused evaluation of the literature. This paper offers recommendations for converting a legacy manufacturing facility into an Industry 4.0-compliant smart plant. Research gaps encompass topics including how to include objectives or results in product roadmaps, how to match a roadmap with the item's vision, and how to include activities related to product discovery in product roadmaps.

References

Gorecky, D.; Schmitt, M.; Loskyll, M.; Zühlke, D. Human-machine-interaction in the industry 4.0 era. In Proceedings of the 2014 12th IEEE international conference on industrial informatics (INDIN), Porto Alegre, RS, Brazil, 27–30 July 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 289–294. DOI: https://doi.org/10.1109/INDIN.2014.6945523

Kiel, D.; Müller, J.M.; Arnold, C.; Voigt, K.I. Sustainable industrial value creation: Benefits and challenges of industry 4.0. Int. J. Innov. Manag. 2017, 21, 1740015. DOI: https://doi.org/10.1142/S1363919617400151

Urciuoli, L.; Hintsa, J.; Ahokas, J. Drivers and barriers affecting usage of e-Customs—A global survey with customs administrations using multivariate analysis techniques. Gov. Inform. Quart. 2013, 30, 473–485. DOI: https://doi.org/10.1016/j.giq.2013.06.001

Müller, J.M.; Däschle, S. Business model innovation of industry 4.0 solution providers towards customer process innovation. Processes 2018, 6, 260. DOI: https://doi.org/10.3390/pr6120260

Zhu, Q.; Geng, Y. Drivers and barriers of extended supply chain practices for energy saving and emission reduction among Chinese manufacturers. J. Clean. Prod. 2013, 40, 6–12. DOI: https://doi.org/10.1016/j.jclepro.2010.09.017

M. Komssi, M. Kauppinen, H. Töhönen, L. Lehtola, A. M. Davis, Integrating analysis of customer´s process into roadmapping: The value-creation perspective,” pp. 57-66, September 2011 19th IEEE International Requirements Engineering Conference. DOI: https://doi.org/10.1109/RE.2011.6051662

U. Pora, N. Thawesaengskulthai, N. Gerdsri, S. Triukose, “Data-driven roadmapping turning challenges into opportunities,” pp.1-11, January 2018, Portland international conference on management of engineering and technology. DOI: https://doi.org/10.23919/PICMET.2018.8481975

A. Maglyas, U. Nikula, K. Smolander, “Software product management in the Russian companies,” pp. 1-9, November 2011 7th central nd eastern european software engineering conference. DOI: https://doi.org/10.1109/CEE-SECR.2011.6188469

A. Kerr, R. Phaal, “Visualizing roadmaps: A design-driven approach,” in Research-technology management, vol. 58, 2015, pp.45-54. DOI: https://doi.org/10.5437/08956308X5804253

J. Vähäniitty, C. Lassenius, R. Rautiainen, P. Pekkanen, „Long-term planning of development efforts by roadmapping – A model and expereicne from small software companies,”pp. 300-305, August 2009, 35th Euromicro Conference on Software Engineering and Advanced Applications. DOI: https://doi.org/10.1109/SEAA.2009.29

L. Simonse, E. J. Hultink, J. A. Bujis, “Innovation roadmapping: Building concepts from practitioner´s insights,” in Journal of product innovation management, vol. 32, issue 6, 2015, pp. 904-924. DOI: https://doi.org/10.1111/jpim.12208

J. Vähäniitty, K. T. Rautiainen, “Towards a conceptual framework and tool support for linking long-term product and business planning with agile software development,” pp. 25-28, May 2008, 1st international workshop on software development governance. DOI: https://doi.org/10.1145/1370720.1370730

M. C. Dissel, R. Phaal, C. J. C. J. Farrukh, D. R. Probert, „Value Roadmapping: A structured approach for early stage technology investment decisions,” pp. 1488-1495, July 2006 Portland international conference on management of engineering and technology. DOI: https://doi.org/10.1109/PICMET.2006.296713

I van de Weerd, S. Brinkkemper, R. Nieuwenhuis, L. Bijlsma, A reference framework for software product management. Utrecht, The Netherlands: Department of Information and Computer Science, 2006. DOI: https://doi.org/10.1109/RE.2006.66

M. G. Oliveira, A. L. Fleury, „A framework for improving the roadmapping performance,” pp. 2255-2263, August 2015, Portland international conference on management of engineering and technology. DOI: https://doi.org/10.1109/PICMET.2015.7273103

J. D. Strauss, M. Radnor, „Roadmapping for dynamic and uncertain environments,” in: Research technology management, vol. 47, issue 2, 2004, pp. 51-58. DOI: https://doi.org/10.1080/08956308.2004.11671620

González, A. J. Calderón, A. J. Barragán, and J. M. Andújar, ‘‘Integration of sensors, controllers and instruments using a novel OPC architecture,’’ Sensors, vol. 17, no. 7, p. 1512, 2017. DOI: https://doi.org/10.3390/s17071512

N. Padhi, ‘‘Setting up a smart factory (industry 4.0)—A practical approach,’’ MESA Int., Tech. Rep., Oct. 2017, pp. 13–17.

‘‘The Cisco connected factory: Holistic security for the factory of tomorrow,’’ Cisco, San Jose, CA, USA, White Paper, 2014, pp. 7–11.

D. Barnes and B. Dauphinais. (Jan. 2017). Smart Factories and the Challenges of the Proximity Network. Industrial Internet Consortium. [Online].

V. Ferme, A. Ivanchikj, and C. Pautasso, ‘‘A framework for benchmarking BPMN 2.0 workflow management systems,’’ in Business Process Management. Cham, Switzerland: Springer, 2015, pp. 251–259. DOI: https://doi.org/10.1007/978-3-319-23063-4_18

S. Harrer, Effective and Efficient Process Engine Evaluation, vol. 25. Bamberg, Germany: Univ. Bamberg Press, 2017.

Mangler and S. Rinderle-Ma, ‘‘CPEE—Cloud process execution engine,’’ in Proc. BPM Demo Sessions Co-Located 12th Int. Conf. Bus. Process Manage. (BPM), Eindhoven, The Netherlands, Sep. 2014, p. 51. CEUR-WS.org, 2014.

P. Soffer et al., ‘‘from event streams to process models and back: Challenges and opportunities,’’ Inf. Syst., to be published.

A. Barnawi, A. Awad, A. Elgammal, R. E. Shawi, A. Almalaise, and A. S. Sakr, ‘‘Runtime self-monitoring approach of business process compliance in cloud environments,’’ Cluster Comput., vol. 18, no. 4, pp. 1503–1526, 2015. DOI: https://doi.org/10.1007/s10586-015-0494-0

Awad, A. Barnawi, A. Elgammal, R. Shawi, A. Almalaise, and A. S. Sakr, ‘‘Runtime detection of business process compliance violations: An approach based on anti-patterns,’’ in Proc. 30th Annu. ACM Symp. Appl. Comput., Salamanca, Spain, Apr. 2015, pp. 1203–1210. DOI: https://doi.org/10.1145/2695664.2699488

Teinemaa, M. Dumas, F. M. Maggi, and C. D. Francescomarino, ‘‘Predictive business process monitoring with structured and unstructured data,’’ in Proc. 14th Int. Conf. Bus. Process Manage. (BPM) in Lecture Notes in Computer Science, vol. 941w850. Rio de Janeiro, Brazil: Springer, Sep. 2016, pp. 401–417. DOI: https://doi.org/10.1007/978-3-319-45348-4_23

M. A. Iman Helal, A. Awad, and A. E. Bastawissi, ‘‘Runtime deduction of case ID for unlabeled business process execution events,’’ in Proc. 12th IEEE/ACS Int. Conf. Comput. Syst. Appl. (AICCSA), Marrakech, Morocco, Nov. 2015, pp. 1–8. DOI: https://doi.org/10.1109/AICCSA.2015.7507132

S. J. van Zelst, A. Bolt, M. Hassani, B. F. van Dongen, and W. M. P. van der Aalst, ‘‘Online conformance checking: Relating event streams to process models using prefix-alignments,’’ Int. J. Data Sci. Analytics. Cham, Switzerland: Springer, Oct. 2017, pp. 1–16. DOI: https://doi.org/10.1007/s41060-017-0078-6

D. Güemes-Castorena, M. A. Toro, “Methodology for the integration of business model canvas and technological road map,” pp. 41-52 August 2015, Portland international conference on management of engineering and technology. DOI: https://doi.org/10.1109/PICMET.2015.7273080

AI-Driven Customer Relationship Management in PK Salon Management System. (2019). International Journal of Open Publication and Exploration, ISSN: 3006-2853, 7(2), 28-35. https://ijope.com/index.php/home/article/view/128

Narukulla, Narendra, Joel Lopes, Venudhar Rao Hajari, Nitin Prasad, and Hemanth Swamy. "Real-Time Data Processing and Predictive Analytics Using Cloud-Based Machine Learning." Tuijin Jishu/Journal of Propulsion Technology 42, no. 4 (2021): 91-102. DOI: https://doi.org/10.52783/tjjpt.v42.i4.6757

Big Data Analytics using Machine Learning Techniques on Cloud Platforms. (2019). International Journal of Business Management and Visuals, ISSN: 3006-2705, 2(2), 54-58. https://ijbmv.com/index.php/home/article/view/76

Shah, J., Prasad, N., Narukulla, N., Hajari, V. R., & Paripati, L. (2019). Big Data Analytics using Machine Learning Techniques on Cloud Platforms. International Journal of Business Management and Visuals, 2(2), 54-58. https://ijbmv.com/index.php/home/article/view/76

Fadnavis, N. S., Patil, G. B., Padyana, U. K., Rai, H. P., & Ogeti, P. (2021). Optimizing scalability and performance in cloud services: Strategies and solutions. International Journal on Recent and Innovation Trends in Computing and Communication, 9(2), 14-23. Retrieved from http://www.ijritcc.org

Challa, S. S. S., Tilala, M., Chawda, A. D., & Benke, A. P. (2021). Navigating regulatory requirements for complex dosage forms: Insights from topical, parenteral, and ophthalmic products. NeuroQuantology, 19(12), 971-994. https://doi.org/10.48047/nq.2021.19.12.NQ21307

Fadnavis, N. S., Patil, G. B., Padyana, U. K., Rai, H. P., & Ogeti, P. (2020). Machine learning applications in climate modeling and weather forecasting. NeuroQuantology, 18(6), 135-145. https://doi.org/10.48047/nq.2020.18.6.NQ20194.

Tilala, M., & Chawda, A. D. (2020). Evaluation of compliance requirements for annual reports in pharmaceutical industries. NeuroQuantology, 18(11), 27.

Challa, S. S. S., Tilala, M., Chawda, A. D., & Benke, A. P. (2019). Investigating the use of natural language processing (NLP) techniques in automating the extraction of regulatory requirements from unstructured data sources. Annals of Pharma Research, 7(5),

Challa, S. S. S., Tilala, M., Chawda, A. D., & Benke, A. P. (2021). Navigating regulatory requirements for complex dosage forms: Insights from topical, parenteral, and ophthalmic products. NeuroQuantology, 19(12), 15.

Shah, J., Prasad, N., Narukulla, N., Hajari, V. R., & Paripati, L. (2020). AI-driven data governance framework for cloud-based data analytics. Webology: International Peer-Reviewed Journal, 17(2), 1551-1561.

Venudhar Rao Hajari et al, International Journal of Computer Science and Mobile Computing, Vol.9 Issue.11, November- 2020, pg. 118-131 DOI: https://doi.org/10.47760/ijcsmc.2020.v09i11.011

Shah, J., Prasad, N., Narukulla, N., Hajari, V. R., & Paripati, L. (2020). AI-driven data governance framework for cloud-based data analytics. Webology: International Peer-Reviewed Journal, 17(2), 1551-1561.

Shah, J., Narukulla, N., Hajari, V. R., Paripati, L., & Prasad, N. (2021). Scalable machine learning infrastructure on cloud for large-scale data processing. Tuijin Jishu/Journal of Propulsion Technology, 42(2), 45-53. DOI: https://doi.org/10.52783/tjjpt.v42.i2.7166

Narukulla, N., Hajari, V. R., Paripati, L., Prasad, N., & Shah, J. (2021). Blockchain-enabled data analytics for ensuring data integrity and trust in AI systems. International Journal of Computer Science and Engineering (IJCSE), 10(2), 27-37.

Santhosh Palavesh. (2019). The Role of Open Innovation and Crowdsourcing in Generating New Business Ideas and Concepts. International Journal for Research Publication and Seminar, 10(4), 137–147. https://doi.org/10.36676/jrps.v10.i4.1456 DOI: https://doi.org/10.36676/jrps.v10.i4.1456

Santosh Palavesh. (2021). Developing Business Concepts for Underserved Markets: Identifying and Addressing Unmet Needs in Niche or Emerging Markets. Innovative Research Thoughts, 7(3), 76–89. https://doi.org/10.36676/irt.v7.i3.1437 DOI: https://doi.org/10.36676/irt.v7.i3.1437

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Published

18-03-2022

How to Cite

Ranjit Kumar Gupta, Sagar Shukla, Anaswara Thekkan Rajan, & Sneha Aravind. (2022). Strategies for Effective Product Roadmap Development and Execution in Data Analytics Platforms. International Journal for Research Publication and Seminar, 13(1), 328–342. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/1515