Name
Abstract | ||
|---|---|---|
While simulation has been used extensively to model supply chain processes, the use of a Bayesian approach has been limited. However, Bayesian modeling brings key advantages, especially in cases of uncertainty. In this paper, we develop a data informatics model that could be used to realize a digital synchronized supply chain. To realize this model, we develop a hybrid model that combines Bayesian modeling with discrete- event simulation and apply it to the supply chain process at a Proctor & Gamble (P&G) manufacturing plant. Moreover, we use approximately one year of transactional data, including information on customer orders, production, raw materials, inventory, and shipments. A driving force for creating this model is to better understand Total Shareholder Return expressed in terms of cash, profit, and service. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/WSC.2016.7822406 | Winter Simulation Conference |
DocType | ISSN | ISBN |
Conference | 0891-7736 | 978-1-5090-4484-9 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bianica Pires | 1 | 9 | 1.57 |
| Joshua Goldstein | 2 | 0 | 0.68 |
| David Higdon | 3 | 0 | 0.34 |
| Gizem Korkmaz | 4 | 98 | 11.10 |
| Sallie Keller | 5 | 0 | 0.34 |
| Stephanie Shipp | 6 | 0 | 0.34 |
| Ken Hamall | 7 | 0 | 0.34 |
| Art Koehler | 8 | 0 | 0.34 |