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VENDOR MANAGED CONSIGNMENT INVENTORY (VMCI) MODEL FOR SINGLE VENDOR MULTI RETAILERS UNDER PROBABILISTIC Erly Ekayanti Rosyida; I Nyoman Pujawan; Nani Kurniati
Jurnal PASTI (Penelitian dan Aplikasi Sistem dan Teknik Industri) Vol 9, No 2 (2015): Jurnal PASTI
Publisher : Universitas Mercu Buana

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Abstract

Vendor managed consignment inventory (VMCI) adalah suatu strategi kolaborasi yang terjalin diantara pihak-pihak yang terkait dalam supply chain, dimana vendor sebagai supplier mempunyai wewenang untuk memutuskan order quantity yang harus dikirimkan ke buyernya serta menjadi pemilik barang tersebut sampai barang tersebut terjual atau dipakai. Integrasi pada sistem supply chain ini terjadi karena adanya information sharing dan business process reengineering. Pada penelitian ini akan dilakukan analisa keuntungan dari strategi vendor managed consignment inventory (VMCI) pada vendor tunggal multi retail jika permintaan konsumen bervariasi ditinjau dari minimasi total biaya yang dihasilkan. Pada penelitian ini dikembangkan algoritma untuk menyelesaikan model matematis yang telah dibuat. Selain itu, pada penelitian ini juga dilakukan analisa sensitivitas untuk mengetahui parameter yang meliputi biaya pesan vendor, biaya simpan vendor, biaya storage retail, biaya opportunity retail yang ditanggung vendor, biaya pesan retail, service level retail dan standar deviasi permintaan retail. Pada penelitian ini juga membandingkan model persediaan consignment dengan model Vendor Managed Consignment Inventory (VMCI). Percobaan numerik dari model ini menyatakan bahwa model VMCI lebih menguntungkan dibandingkan dengan model persediaan consignment.Kata Kunci: Vendor Managed Inventory, Consignment, Permintaan Probabilistik
Analysis and Mitigation of Strategic Risk Business Process by Considering Relationship Between Risk Case Study in Electricity Generation Companies Lalu Bramantias Gutama; I Nyoman Pujawan
JURNAL SOSIAL HUMANIORA (JSH) Special Edition 2019
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (661.597 KB) | DOI: 10.12962/j24433527.v0i01.5777

Abstract

In this study an analysis of the linkages between the causes of strategic risks of business processes which refers to the balance scorecard perspective in company X one of electricity generation company. Where at the initial stage prioritization of 87 risk causes identified at the outset using the House of Risk 1 method and the Pareto principle so that 17 dominant risk causes were obtained, which were then analyzed using the ISM method and then weighted using the analytic network process (ANP) method to obtain the new ARP value causes risk that has accommodated the relationship between the causes of risk.To make it easier for companies to prioritize the handling of the 17 risk-causing agents, a mitigation analysis is then carried out using the House of Risk 2 by considering the ranking of existing Effectiveness to Difficulty so that 8 strategies for handling agents that cause risk are considered effective was chosen.
Resource-constrained project scheduling with ant colony optimization algorithm Niken Anggraini Savitri; I Nyoman Pujawan; Budi Santosa
Journal of Civil Engineering Vol 35, No 2 (2020)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20861206.v35i2.8115

Abstract

Resource allocation commonly becomes one of the critical problems in project scheduling. This issue usually occurs because project managers estimate the schedule of activities and network time without considering resource availability. Resource-Constrained Project Scheduling Problem (RCPSP) links to the allocation of resource or set of resources into certain activities in order to accomplish particular objectives. Various approaches have been performed to overcome RCPSP, including the heuristic approach. In this research, we used the Ant Colony Algorithm in solving RCPSP. We used 11 examples of projects with dissimilarity in-network and several activities. The implementation of the Ant Colony Algorithm resulted in the percentage of a near-optimal solution of 63.64%. Besides, the duration obtained from the algorithm above the manual scheduling (assumed optimal) was only 4.29%. Sensitivity analysis was performed to understand how substantial the changes of ACO parameters influenced the result obtained from the algorithm. Based on the result, we could conclude that the parameters of ACO have no significant effect to project duration.