TY - GEN
T1 - E-procurement Performance Model for Construction Tendering
T2 - 2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
AU - Hendrata, Ferial
AU - Vanany, Iwan
AU - Suwignjo, Patdono
AU - Siswanto, Nurhadi
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - E-procurement is a popular enterprise information systems (EISs) that were implementing by many companies and governments in digital transformation era. E-procurement for the tendering process increases transparency and organizational performance. The objective of this paper is to utilize the web mining to gain insight from data patterns in e-procurement systems regarding procurement performance. Construction tender data from two central provinces in Indonesia: East Java and DKI Jakarta, are case studies to be absorbed from tender applications via web mining. The case studies data are processed using the multiple linear regression method to produce a predictive model for one of the effectiveness performance indicators: bidder appointment time. Four independent variables: contract price, number of participants, number of bidders, and number of revisions were proven to predict bidder appointment time significantly. The number of revisions has the most influence on bidder appointment time in terms of its coefficient value. The model can be used for tender scheduling, setting procurement targets, to resource planning.
AB - E-procurement is a popular enterprise information systems (EISs) that were implementing by many companies and governments in digital transformation era. E-procurement for the tendering process increases transparency and organizational performance. The objective of this paper is to utilize the web mining to gain insight from data patterns in e-procurement systems regarding procurement performance. Construction tender data from two central provinces in Indonesia: East Java and DKI Jakarta, are case studies to be absorbed from tender applications via web mining. The case studies data are processed using the multiple linear regression method to produce a predictive model for one of the effectiveness performance indicators: bidder appointment time. Four independent variables: contract price, number of participants, number of bidders, and number of revisions were proven to predict bidder appointment time significantly. The number of revisions has the most influence on bidder appointment time in terms of its coefficient value. The model can be used for tender scheduling, setting procurement targets, to resource planning.
KW - e-procurement
KW - performance indicator
KW - web mining
UR - http://www.scopus.com/inward/record.url?scp=85146352994&partnerID=8YFLogxK
U2 - 10.1109/IEEM55944.2022.9989962
DO - 10.1109/IEEM55944.2022.9989962
M3 - Conference contribution
AN - SCOPUS:85146352994
T3 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 924
EP - 928
BT - IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
PB - IEEE Computer Society
Y2 - 7 December 2022 through 10 December 2022
ER -