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An Artificial Intelligence Based Sourcing Automation Concept for Smaller and Mid-Sized Enterprises in the Metal Industry

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 195)


Smaller and mid-sized enterprises in the metal industry often face problems during their sourcing and procurement processes. These problems are caused by the policies of wholesalers, which optimize their business processes by passing on better, i.e. cheaper prices for bulk procurement or even just selling products only in large quantities. These policies mainly confer benefits to big companies and leading to the elimination of small customers who want to order only small amounts of a product. That is the reason why smaller and mid-sized metal enterprises must invest a lot of time to find the right suppliers and to order the right products. To save time and money, there are already a few known approaches like marketplaces and shops. However, these solutions do neither solve the problem of finding the right wholesaler nor do they automate the whole process from order to delivery. This paper focuses on an approach to simplify the sourcing and procurement processes by automating parts of it. To achieve this goal, an automated bot technology is suggested which allows for an easy search for dealers and potential suppliers. The implemented bots will be linked to a web crawler with a matching algorithm to detect relevant offers in the world wide web. All information gathered by the crawler will then be processed automatically to start a request for quotation (RFQ) process. The artificial intelligence starts the RFQ process by (1) analyzing existing offers found in the world wide web with Natural Language Processing (NLP) and (2) generating automated written requests to dealers. The algorithm will additionally help by finding other prospective buyers, i.e. other small or mid-sized companies which are interested in the same product. This enables a group of customers to perform bulk procurement together. This paper discusses the current possibilities as well as the practical implications of the suggested approach.


  • Natural language processing
  • Natural language generation
  • Text processing
  • Automation
  • Sourcing
  • Procurement
  • Artificial intelligence

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Correspondence to Nicolas Dolle .

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Dolle, N., Wilhelm, C., Wergunow, A., Rössle, M., Fernandes, M., Glißmann, L. (2021). An Artificial Intelligence Based Sourcing Automation Concept for Smaller and Mid-Sized Enterprises in the Metal Industry. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2020. Lecture Notes in Networks and Systems, vol 195. Springer, Cham.

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