A hybrid machine learning method for procurement risk assessment of non-ferrous metals for manufacturing firms

ABSTRACT

With the growing complexity of manufacturing systems nowadays, the effective assessment of important risk factors inherent in the manufacturing process is crucial for the stability and reliability of such complex systems. Thus, this article proposes a data-driven approach using the state-of-art machine learning techniques to assess and forecast the procurement risks of non-ferrous metals associated with complex manufacturing systems. A variety of state-of-art machine learning models including ANN, LSTM, BLSTM, GARCH, as well as their combinations which compose the proposed hybrid models, are deployed and analyzed. The testing results show that the proposed hybrid machine learning method can forecast the price uncertainty in procurement and effectively evaluate the procurement risk in a precautious manner. Moreover, it is shown that the hybrid model that combines GARCH, ANN, and LSTM significantly improves the forecasting results. Besides, the optimal choice of the network configurations in the hybrid model is also analyzed via a series of sensitivity analyses. This research can serve as a useful reference for the effective assessment and control of procurement risk for manufacturing firms.

KEYWORDS:

• Material procurement
• risk assessment
• machine learning
• GARCH
• LSTM

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [71601159]; National Social Science Fund of China [19ZDA074]; ITF project [PRP/068/20LI].