A Web-based DSS: Information System for Sustainable Fisheries Supply Chain in Coastal Communities of Small Islands Indonesia

Wellem Anselmus Teniwut, Cawalinya Livsanthi Hasyim, Dawamul Arifin

Abstract


Recent advances in the development of information technology and the rapid use of decision support systems play significant roles in various fields, including the fisheries sector. Fishery-based activity for small island regions created more complicated problems that limited marine resources and high production costs. Thus, the need to develop efficient and effective tools for interconnecting supply and production becomes more crucial than ever to help local coastal communities. This study aimed to designs a web-based DSS for a sustainable supply chain of sectors in Southeast Maluku Regency, Indonesia (SIRIPIKAN). Firstly, we crafted the DSS framework to identify the fishing, supplier, and seller locations. Secondly, we measured the level of sustainability of marine resources. Thirdly, the web-based DSS can help local coastal communities increase managers' capability and ability in the fisheries-related business activity carried out. SIRIPIKAN aims to increase the profitability of fisheries business activities in coastal communities and preserve marine resources. This research combined the data mining activities with spatial analysis to obtain the cluster support map and MSY to measure sustainability and feasibility study as an approach for the development of the system. The model provides an integrated sustainable production with users' input used to optimize the decision-making process of profitability and sustainability in existing marine resources.

Keywords


DSS; sustainable; SIRIPIKAN; supply chain; fisheries.

Full Text:

PDF

References


V. Mohagheghi, S. M. Mousavi, M. Mojtahedi, and S. Newton, “Evaluating large, high-technology project portfolios using a novel interval-valued Pythagorean fuzzy set framework: An automated crane project case study,†Expert Syst. Appl., 2020, doi: 10.1016/j.eswa.2019.113007.

S. Ma, Y. Zhang, J. Lv, Y. Ge, H. Yang, and L. Li, “Big data driven predictive production planning for energy-intensive manufacturing industries,†Energy, 2020, doi: 10.1016/j.energy.2020.118320.

M. K. C. S. Wijewickrama, N. Chileshe, R. Rameezdeen, and J. J. Ochoa, “Information sharing in reverse logistics supply chain of demolition waste: A systematic literature review,†Journal of Cleaner Production. 2021, doi: 10.1016/j.jclepro.2020.124359.

J. Wei, Y. Wang, and J. Lu, “Information sharing and sales patterns choice in a supply chain with product’s greening improvement,†J. Clean. Prod., 2021, doi: 10.1016/j.jclepro.2020.123704.

P. Howson, “Building trust and equity in marine conservation and fisheries supply chain management with blockchain,†Mar. Policy, 2020, doi: 10.1016/j.marpol.2020.103873.

W. A. Teniwut, K. D. Betaubun, Marimin, and T. Djatna, “A conceptual mitigation model for asymmetric information of supply chain in seaweed cultivation,†in IOP Conference Series: Earth and Environmental Science, 2017, vol. 89, no. 1, doi: 10.1088/1755-1315/89/1/012022.

W. A. Teniwut, M. Marimin, and T. Djatna, “GIS-Based multi-criteria decision-making model for site selection of seaweed farming information centre: A lesson from small islands, Indonesia,†Decis. Sci. Lett., vol. 8, pp. 137–150, 2019, doi: 10.5267/j.dsl.2018.8.001.

W. A. Teniwut and R. M. K. Teniwut, “Minimizing the instability of seaweed cultivation productivity on rural coastal area : a case study from Indonesia,†vol. 11, no. 1, pp. 259–271, 2018.

W. A. Teniwut, Y. K. Teniwut, R. M. K. Teniwut, and C. L. Hasyim, “Family vs Village-Based: Intangible View on the Sustainable of Seaweed Farming,†IOP Conf. Ser. Earth Environ. Sci., vol. 89, no. 1, p. 012021, Oct. 2017, doi: 10.1088/1755-1315/89/1/012021.

W. A. Teniwut, “For sustainable revenue of fisheries sector in small islands: Evidence of Maluku, Indonesia,†AACL Bioflux, 2016, doi: 10.5281/zenodo.245507.

Y. Yun, D. Ma, and M. Yang, “Human–computer interaction-based Decision Support System with Applications in Data Mining,†Futur. Gener. Comput. Syst., 2021, doi: 10.1016/j.future.2020.07.048.

W. Deng, L. Feng, X. Zhao, and Y. Lou, “Effects of supply chain competition on firms’ product sustainability strategy,†J. Clean. Prod., 2020, doi: 10.1016/j.jclepro.2020.124061.

J. Wang and W. Zhuo, “Strategic information sharing in a supply chain under potential supplier encroachment,†Comput. Ind. Eng., 2020, doi: 10.1016/j.cie.2020.106880.

Y. Wang and S. H. Zhang, “Optimal production and inventory rationing policies with selective-information sharing and two demand classes,†Eur. J. Oper. Res., 2021, doi: 10.1016/j.ejor.2020.05.051.

J. Mar-Ortiz, M. D. Gracia, and N. Castillo-García, “Challenges in the design of decision support systems for port and maritime supply chains,†in Studies in Computational Intelligence, 2018.

G. Dellino, T. Laudadio, R. Mari, N. Mastronardi, and C. Meloni, “A reliable decision support system for fresh food supply chain management,†Int. J. Prod. Res., 2018, doi: 10.1080/00207543.2017.1367106.

F. Geri, S. Sacchelli, I. Bernetti, and M. Ciolli, Urban-rural bioenergy planning as a strategy for the sustainable development of inner areas: A GIS-based method to chance the forest chain, no. 9783319757. 2018.

M. Dev, P. Kaur, and K. K. Sarma, “Fuzzy Approach to Decision Support System Design for Inventory Control and Management,†J. Intell. Syst., 2017, doi: 10.1515/jisys-2017-0143.

M. Chica and W. Rand, “Building agent-based decision support systems for word-of-mouth programs: A freemium application,†J. Mark. Res., 2017, doi: 10.1509/jmr.15.0443.

Marimin, W. Adhi, and M. A. Darmawan, “Decision support system for natural rubber supply chain management performance measurement: A sustainable balanced scorecard approach,†Int. J. Supply Chain Manag., 2017.

G. Dutta, N. Gupta, J. Mandal, and M. K. Tiwari, “New decision support system for strategic planning in process industries: Computational results,†Comput. Ind. Eng., 2018, doi: 10.1016/j.cie.2018.07.016.

P. K. Tarei, J. J. Thakkar, and B. Nag, “Development of a decision support system for assessing the supply chain risk mitigation strategies: an application in Indian petroleum supply chain,†J. Manuf. Technol. Manag., 2020, doi: 10.1108/JMTM-02-2020-0035.

M. Alkahtani, A. Choudhary, A. De, and J. A. Harding, “A decision support system based on ontology and data mining to improve design using warranty data,†Comput. Ind. Eng., 2019, doi: 10.1016/j.cie.2018.04.033.

H. Talebian, O. E. Herrera, and W. Mérida, “Spatial and temporal optimization of hydrogen fuel supply chain for light duty passenger vehicles in British Columbia,†Int. J. Hydrogen Energy, 2019, doi: 10.1016/j.ijhydene.2019.07.218.

E. P. Sarabi and S. A. Darestani, “Developing a decision support system for logistics service provider selection employing fuzzy MULTIMOORA & BWM in mining equipment manufacturing,†Appl. Soft Comput., 2020, doi: 10.1016/j.asoc.2020.106849.

B. Imène and T. Noria, “Towards a new supporting platform for collaboration in industrial diagnosis within an agent-based WEB DSS,†Int. J. Comput. Aided Eng. Technol., 2018, doi: 10.1504/IJCAET.2018.092865.

H. Allaoui, Y. Guo, and J. Sarkis, “Decision support for collaboration planning in sustainable supply chains,†J. Clean. Prod., 2019, doi: 10.1016/j.jclepro.2019.04.367.

M. Yazdani, P. Zarate, A. Coulibaly, and E. K. Zavadskas, “A group decision making support system in logistics and supply chain management,†Expert Syst. Appl., vol. 88, pp. 376–392, 2017, doi: 10.1016/j.eswa.2017.07.014.

G. Im and A. Rai, “IT-enabled coordination for ambidextrous interorganizational relationships,†Inf. Syst. Res., 2014, doi: 10.1287/isre.2013.0496.

J. Xin, “A supply chain optimization DSS Web-services-based for e-retail industry,†2011, doi: 10.1109/PEAM.2011.6135052.

A. Bonfante et al., “LCIS DSS—An irrigation supporting system for water use efficiency improvement in precision agriculture: A maize case study,†Agric. Syst., 2019, doi: 10.1016/j.agsy.2019.102646.

K. L. Carder, F. R. Chen, Z. Lee, S. K. Hawes, and J. P. Cannizzaro, MODIS Ocean Science Team Algorithm Theoretical Basis Document ATBD 19: Case 2 Chlorophyll a. 2003.

O. B. Brown and P. J. Minnett, “MODIS infrared sea surface temperature algorithm - Algorithm Theoretical Basis Document Version 2.0,†1999.

N. Zendrato, H. W. Dhany, N. A. Siagian, and F. Izhari, “Bigdata Clustering using X-means method with Euclidean Distance,†2020, doi: 10.1088/1742-6596/1566/1/012103.

O. Stavrakidis-Zachou, N. Papandroulakis, A. Sturm, P. Anastasiadis, F. Wätzold, and K. Lika, “Towards a computer-based decision support system for aquaculture stakeholders in Greece in the context of climate change,†2018, doi: 10.1504/IJSAMI.2018.099235.

Ã. Cobo, I. Llorente, L. Luna, and M. Luna, “A decision support system for fish farming using particle swarm optimization,†Comput. Electron. Agric., 2019, doi: 10.1016/j.compag.2018.03.036.




DOI: http://dx.doi.org/10.18517/ijaseit.11.3.12462

Refbacks

  • There are currently no refbacks.



Published by INSIGHT - Indonesian Society for Knowledge and Human Development