Exploring the interaction of inventory policies across the supply chain: An agent-based approach
| Autor: | Enrique Sierra, David de la Fuente, Borja Ponte, Jesús Simal Lozano |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Upstream (petroleum industry)
0209 industrial biotechnology 021103 operations research Supply chain management General Computer Science Operations research Computer science Supply chain 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Demand chain 020901 industrial engineering & automation Safety stock Modeling and Simulation Bullwhip effect Bullwhip Lead time Simulation |
| Zdroj: | Computers & Operations Research. 78:335-348 |
| ISSN: | 0305-0548 |
| DOI: | 10.1016/j.cor.2016.09.020 |
| Popis: | The Bullwhip Effect, which refers to the increasing variability of orders traveling upstream the supply chain, has shown to be a severe problem for many industries. The inventory policy of the various nodes is an important contributory factor to this phenomenon, and hence it significantly impacts on their financial performance. This fact has led to a large amount of research on replenishment and forecasting methods aimed at exploring their suitability depending on a range of environmental factors, e.g. the demand pattern and the lead time. This research work approaches this issue by seeing the whole picture of the supply chain. We study the interaction between four widely used inventory models in five different contexts depending on the customer demand variability and the safety stock. We show that the concurrence of distinct inventory models in the supply chain, which is a common situation in practice, may alleviate the generation of inefficiencies derived from the Bullwhip Effect. In this sense, we demonstrate that the performance of each policy depends not only upon the external environment but also upon the position within the system and upon the decisions of the other nodes. The experiments have been carried out via an agent-based system whose agents simulate the behavior of the different supply chain actors. This technique proves to offer a powerful and risk-free approach for business exploration and transformation. We analyze different smoothing replenishment rules in the Beer Game scenario.KAOS methodology is used to devise the agent-based simulation model.The concurrence of distinct inventory models may mitigate the Bullwhip Effect.Forecasting is a more robust solution than adding a proportional controller.ABMS is a powerful approach for exploring and transforming the supply chain. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect