Abstract
Emerging technologies like Artificial Intelligence (AI) show the potential to contribute significantly to the digitalization of supply chains. Nonetheless, the question which approaches from the field of AI are applied within supply chains as well as which supply chain problems or tasks are addressed with AI approaches has not been answered by scientific literature yet. Based on a structured literature review this paper aims at providing an answer to these questions. A special focus is given to the application areas for recognition approaches in supply chain execution, for which this paper provides an overview of those areas research is currently focusing upon.
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References
Aengchuan, P., & Phruksaphanrat, B. (2018). Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS+ ANN) and FIS with adaptive neuro-fuzzy inference system (FIS+ ANFIS) for Inventory Control. Journal of Intelligent Manufacturing, 29(4), 905–923.
Aqlan, F., & Saha, C. (2015). Defect analytics in a high-end server manufacturing environment. In 2015 Industrial and Systems Engineering Research Conference.
BVL. (2017a). Chancen der digitalen transformation. Trends und Strategien in Logistik und Supply Chain Management.
BVL. (2017b). Logistics as a science. Central research questions in the era of the fourth industrial revolution.
Cavallo, D. Pietro, Cefola, M., Pace, B., Logrieco, A. F., & Attolico, G. (2018). Non-destructive automatic quality evaluation of fresh-cut iceberg lettuce through packaging material. Journal of Food Engineering, 223, 46–52.
Correll, N., Bekris, K. E., Berenson, Dd., Brock, O., Causo, A., & Hauser, K. (2018). Analysis and observations from the first amazon picking challenge. IEEE Transactions on Automation Science and Engineering, 15(1).
Cui, R., Gallino, S., Moreno, A., & Zhang, D. J. (2017). The operational value of social media information. Production and Operations Management, 18(1), 141.
DHL. (2016). Robotics in logistics. A DPDHL perspective on implications and use cases for the logistics industry. DHL Customer Solutions & Innovation.
Emenike, C. C., Eyk, N. P. V., & Hoffman, A. J. (2016). Improving cold chain logistics through RFID temperature sensing and predictive modelling. In IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. https://doi.org/10.1109/ITSC.2016.7795932.
Fleischmann, B., Meyr, H., & Wagner, M. (2005). Supply chain management and advanced planning. In Concepts, models, software and case studies. New York: Springer.
Frazier, P. D., Gilmore, E. T., Collins, I. J., & Chouika, M. F. (2016). Novel cCunterfeit detection of integrated circuits via infrared analysis: A case study based on the intel cyclone II FPGAS. In Proceedings—International Conference on Machine Learning and Cybernetics.
Griffis, S. E., Bell, J. E., & Closs, D. J. (2012). Metaheuristics in logistics and supply chain management. Journal of Business Logistics, 33(2), 90–106.
Guo, F., & Lu, Q. (2013). Partner selection optimization model of agricultural enterprises in supply chain. Advance Journal of Food Science and Technology, 5(10), 1285–1291.
Holz, D., Nieuwenhuisen, M., Droeschel, D., Stückler, J., Berner, A., & Li, J. (2014). Active recognition and manipulation for mobile robot bin picking (Gearing up). Springer International Publishing (Springer Tracts in Advanced Robotics).
Hosseini, S., & Khaled, A. Al. (2016). A hybrid ensemble and AHP approach for resilient supplier selection. Journal of Intelligent Manufacturing, 42(4), 679.
Huang, L., Li, W., Chen, C., Zhang, F., & Lang, H. (2017). Multiple features learning for ship classification in optical imagery. Multimedia Tools and Applications, 11(2), 196.
Khaldi, R., El Afia, A., Chiheb, R., & Faizi, R. (2017). Artificial neural network based approach for blood demand forecasting. In Proceedings of the 2nd International Conference on Big Data, Cloud and Applications—BDCA’17 (pp. 1–6).
Laskey, M., Lee, J., Chuck, C., Gealy, D., Hsieh, W., & Pokorny, F. T. (2016). Using a hierarchy of supervisors for learning from demonstrators. In IEEE International Conference on Automation Science and Engineering (CASE).
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436–444.
Longo, F., Nicoletti, L., & Padovano, A. (2016). Multi-disciplinary and multi-objective simulation framework to support intelligent and smart manufacturing. In XXI Summer School “Francesco Turco”, Industrial Systems Engineering.
Ma, H., Wang, Y., & Wang, K. (2018). Automatic detection of false positive RFID readings using machine learning algorithms. Expert Systems with Applications, 91, 442–451.
McCorduck, P. (2004). Machines who think: A personal inquiry into the history and prospects of artificial intelligence. London: Transatlantic Publishers.
Medini, K., & Rabénasolo, B. (2014). Analysis of the performance of supply chains configurations using multi-agent systems. International Journal of Logistics Research and Applications, 17(6), 441–458.
Mirkouei, A., & Haapala, K. R. (2014). Integration of machine learning and mathematical programming methods into the biomass feedstock supplier selection process. Flexible Automation and Intelligent Manufacturing.
Mo, J., & Lorchirachoonkul, W. (2016). Gesture interpreter for randomised supply chain operations using 3D cameras array. In 10th International Conference on Software, Knowledge, Information Management & Applications.
Mortazavi, A., Arshadi Khamseh, A., & Azimi, P. (2015). Designing of an intelligent self-adaptive model for supply chain ordering management system. Engineering Applications of Artificial Intelligence, 37, 207–220.
Nieuwenhuisen, M., et al. (2013). Mobile bin picking with an anthropomorphic service robot. In Proceedings—IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/ICRA.2013.6630892.
Ponte, B., Pino, R., & La Fuente, D. (2014). Multiagent methodology to reduce the bullwhip effect in a supply chai. In Transactions on computational collective intelligence XVII. Heidelberg: Springer.
Poole, D. L., & Mackworth, A. K. (2017). Artificial intelligence: Foundations of computational agents (2nd ed.). Cambridge, New York, NY, Port Melbourne, Daryaganij, Delhi, Singapore: Cambridge University Press.
Russell, S. J., & Norvig, P. (2010). Artificial intelligence. A modern approach. Upper Saddle River, NJ: Prentice-Hall.
Schlüter, M., Niebuhr, C., Lehr, J., & Krüger, J. (2018). Vision-based identification service for remanufacturing sorting. Procedia Manufacturing, 21, 384–391.
Sharma, S., Ratti, R., Arora, I., Solanki, A., & Bhatt, G. (2018). Automated parsing of geographical addresses: A multilayer feedforward neural network based approach. In 12th International Conference on Semantic Computing (pp. 123–130).
Silva, N., Ferreira, L., Silva, C., Magalhães, V., & Neto, P. (2017). Improving supply chain visibility with artificial neural networks. Procedia Manufacturing, 11, 2083–2090.
Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., & Hager, G. (2016). Artificial intelligence in life 2030. One hundred years study on artificial intelligence. Report of the 2015 Study Panel—Stanford University.
Stoyanov, T., Vaskevicius, N., Mueller, C., Fromm, T., Krug, R., & Tincani, V. (2016). No more heavy lifting: Robotic solutions to the container unloading problem. IEEEE Robotics & Automation Maganzine, 23(4), 94–106.
Thamer, H., Börold, A., Benggolo, A. Y., & Freitag, M. (2018). Artificial intelligence in warehouse automation for flexible material handling. In 9th International Scientific Symposium on Logistics.
Thomé, A. M. T., Scavarda, L. F., & Scavarda, A. J. (2016). Conducting systematic literature review in operations management. Production Planning & Controll, 27(5), 408–420.
Tuszynski, J., Briggs, J. T., & Kaufhold, J. (2013). A method for automatic manifest verification of container cargo using radiography images. Journal of Transportation Security, 6(4), 339–356.
Uriarte, C., Thamer, H., Freitag, M., & Thoben, K.-D. (2016). Flexible Automatisierung logistischer prozesse durch modulare roboter. Logistics Journal.
Watanabe, T., Muroi, H., Naruke, M., Yono, K., Kobayashi, G., & Yamasaki, M. (2016). Prediction of regional goods demand incorporating the effect of weather. IEEE International Conference on Big Data.
Ye, S., Xiao, Z., & Zhu, G. (2015). Identification of supply chain disruptions with economic performance of firms using multi-category support vector machines. International Journal of Production Research, 53(10), 3086–3103.
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Hellingrath, B., Lechtenberg, S. (2019). Applications of Artificial Intelligence in Supply Chain Management and Logistics: Focusing Onto Recognition for Supply Chain Execution. In: Bergener, K., Räckers, M., Stein, A. (eds) The Art of Structuring. Springer, Cham. https://doi.org/10.1007/978-3-030-06234-7_27
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DOI: https://doi.org/10.1007/978-3-030-06234-7_27
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