Journal article
Information management strategies and supply chain performance under demand disruptions
International journal of production research, Vol.54(1), pp.8-27
01/02/2016
DOI: 10.1080/00207543.2014.991456
Abstract
The operations management literature presents inadequate comprehensive understanding on information management strategies of mitigating supply chain disruption risks. By using control theory modelling and simulation, this study compares the disruption mitigation effects of three information management strategies. From the aspect of stability, the existing stability boundaries are revised by a new method in a two-echelon case. It shows that supply chains (SC) with popular information management strategies are not evidently more stable than traditional ones. From the aspect of disruption recovery time, an innovative two-echelon swiftest response problem under these information management strategies is formulated and solved. Results show that a collaborative planning, forecasting and replenishment (CPFR) SC with complete SC information performs the best. However, in a later operational risk mitigation test, an information sharing (IS) SC with partial information has the smallest bullwhip effect. From the aspect of demand amplification and frequency response, an innovative frequency-response plot of order amplification is proposed in a time-continuous SC with moving average forecasts. It implies the best frequency response for concurrently mitigating both operational and disruption risks coming from a CPFR SC. But for a certain SC structure there is still a balance between mitigating bullwhip effect and quick response. Moreover, it also implies that anti-bullwhip should exist in a certain condition, as realised in our numerical experiments.
Details
- Title: Subtitle
- Information management strategies and supply chain performance under demand disruptions
- Creators
- Tianjian Yang - School of Economics and Management, Beijing University of Posts and TelecommunicationsWeiguo Fan - Center for Business Intelligence and Analytics, Virginia Tech
- Resource Type
- Journal article
- Publication Details
- International journal of production research, Vol.54(1), pp.8-27
- Publisher
- Taylor & Francis
- DOI
- 10.1080/00207543.2014.991456
- ISSN
- 0020-7543
- eISSN
- 1366-588X
- Grant note
- 71001010 / National Natural Science Foundation of China (10.13039/501100001809) Fundamental Research Funds for the Central Universities of China
- Language
- English
- Date published
- 01/02/2016
- Academic Unit
- Business Analytics
- Record Identifier
- 9984083201902771
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