Smart support system of material procurement for waste reduction based on big data and predictive analytics

ABSTRACT

Nowadays, enterprises are transitioning toward sustainable consumption and production. Hence, to determine an effective strategy, most of these have begun to collect reliable information from the consumption of complex supply networks and conduct data science. This entails an intelligent event-driven feedback control that can be designed to control the process plans. In this research, a decision support system for material usage reduction was developed based on material usage data. Furthermore, using data science and predictive analytics techniques, several scenarios were simulated to enable the procurement manager to make better decisions. This approach is effective in semiconductor manufacturing.

HIGHLIGHTS

• A big challenge for the manufacturers to manage the material resource.

• A smart support procurement (SSP) system is developed based on the BDPA based on Industry 3.5.

• New network and decision tree algorithms are developed in SSP system

• Procurement manager can reduce the material waste based on SCP.

KEYWORDS:

• Smart procurement
• big data analytics
• prediction model
• sustainable consumption and production
• Industry 3.5

Acknowledgement

The authors also want to express the special thanks for the experts’ opinions of Prof. Wei-Jung Shiang and Prof. Potsang B Huang in model development.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was funded by the Ministry of Science Technology of the Republic of China, Taiwan, grant numbers MOST 109-2621-M-011 -002 and MOST 110-2621-M-011-002-MY3; Ministry of Science and Technology, Taiwan.