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Decision support system for distributed manufacturing based on input-output analysis and economic complexity
Decision support system for distributed manufacturing based on input-output analysis and economic complexity
The disruption of supplies during the Covid-19 crisis has led to shortages but has also shown the adaptability of some companies, which have succeeded in adapting their production chains quickly to produce goods experiencing shortages: hydroalcoholic gel, masks, and medical gowns. These productive jumps from product A to product B are feasible because of the know-how proximity between the two classes of products. The proximities were computed from the analysis of co-exports and resulted in the construction of the product space. Based on the product space, as well as the customer-supplier relationships resulting from the input-output matrices, we propose a recommender system for companies. The goal is to promote distributed manufacturing by recommending a list of local suppliers to each company. As there is not always a local supplier for a desired product class, we consider the proximity between products to identify, in the absence of a supplier, a substitute supplier able to adapt its production tools to provide the required product. Our experiments are based on French data, from which we build a graph of synergies illustrating the potential productive links between companies. Finally, we show that our approach offers new perspectives to determine the level of territories' industrial resilience considering potential productive jumps.
- Clermont Université France
- University of Clermont Auvergne France
Computational Engineering, Finance, and Science (cs.CE), FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Information Retrieval (cs.IR)
Computational Engineering, Finance, and Science (cs.CE), FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Information Retrieval (cs.IR)
4115
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Albino, V., Petruzzelli, A. M., Okogbaa, G., & Yazan, D. (2011). Logistics flows and enterprise input-output models: Aggregate and disaggregate analysis. International Journal of Operations and Quantitative Management, 17, 123-146.
Alshamsi, A., Pinheiro, F. L., & Hidalgo, C. A. (2018). Optimal diversification strategies in the networks of related products and of related research areas. Nature Communications, 9(1), 1328. https://doi.org/10.1038/s41467-018-03740-9 [OpenAIRE]
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Balsalobre, S. J. P., & Verduras, C. L. (2017). Measuring the economic complexity at the sub-national level using international and interregional trade. In Nineteenth Annual Conference of European Trade Study Group (ETSG 2017) , Florence, Italy.
Balsalobre, S. P., Verduras, C. L., & Diaz-Lanchas, J. (2019). Measuring subnational economic complexity: An application with Spanish data (JRC Working Papers on Territorial Modelling and Analysis No. 2019‑05). Seville: Joint Research Centre. https://ideas.repec.org/p/ipt/termod/201905.html
Ben Saâd, M., & Assoumou-Ella, G. (2019). Economic complexity and gender inequality in education: An empirical study. SSRN Electronic Journal, 39(1), 321- 334. https://doi.org/10.2139/ssrn.3340913
Boschma, R., & Capone, G. (2015). Institutions and diversification: Related versus unrelated diversification in a varieties of capitalism framework. Research Policy, 44(10), 1902-1914. https://doi.org/10.1016/j.respol.2015.06.013 [OpenAIRE]
Craighead, C. W., Blackhurst, J., Rungtusanatham, M. J., & Handfield, R. B. (2007). The severity of supply chain disruptions: Design characteristics and mitigation capabilities. Decision Sciences, 38(1), 131-156. https://doi.org/10.1111/j.1540-5915.2007.00151.x [OpenAIRE]
Delgado, M., Porter, M. E., & Stern, S. (2014). Clusters, convergence, and economic performance. Research Policy, 43(10), 1785-1799. https://doi.org/10.1016/j.respol.2014.05.007
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!- Clermont Université France
- University of Clermont Auvergne France
The disruption of supplies during the Covid-19 crisis has led to shortages but has also shown the adaptability of some companies, which have succeeded in adapting their production chains quickly to produce goods experiencing shortages: hydroalcoholic gel, masks, and medical gowns. These productive jumps from product A to product B are feasible because of the know-how proximity between the two classes of products. The proximities were computed from the analysis of co-exports and resulted in the construction of the product space. Based on the product space, as well as the customer-supplier relationships resulting from the input-output matrices, we propose a recommender system for companies. The goal is to promote distributed manufacturing by recommending a list of local suppliers to each company. As there is not always a local supplier for a desired product class, we consider the proximity between products to identify, in the absence of a supplier, a substitute supplier able to adapt its production tools to provide the required product. Our experiments are based on French data, from which we build a graph of synergies illustrating the potential productive links between companies. Finally, we show that our approach offers new perspectives to determine the level of territories' industrial resilience considering potential productive jumps.