A Model Predictive Control Framework for Robust Management of Multi-Product, Multi-Echelon Demand Networks

Braun, Martin; Rivera, Daniel E.; Carlyle, W. Matthew; Kempf, Karl G.

doi:10.3182/20020721-6-es-1901.01394

Cited by 60 publications

(36 citation statements)

References 5 publications

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“…Applications of MPC to multi-echelon production-inventory problems and supply chains have been reported in the literature (Tzafestas et al 1997;Bose and Pekny 2000;Perea et al 2003;Braun et al 2003;Seferlis and Giannelos 2004). The unique characteristics of semiconductor manufacturing supply chains merit the development of a novel MPC algorithm for this purpose (Wang and Rivera 2008).…”

Section: Model Predictive Control Based Tactical Inner Loop Formulationmentioning

confidence: 97%

Inner and outer loop optimization in semiconductor manufacturing supply chain management

2008

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Section: Model Predictive Control Based Tactical Inner Loop Formulationmentioning

confidence: 97%

Inner and outer loop optimization in semiconductor manufacturing supply chain management

2008

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“…Braun et al (2003aBraun et al ( , 2003b showed that an MPC framework could handle demand networks efficiently with robust management. Other significant predictive control studies include Rasku et al (2004) and Dunbar and Desa (2005).…”

Section: Inventory Control Studiesmentioning

confidence: 99%

An RFID-based inventory management framework for emergency relief operations

Özgüven

Özbay

2015

Transportation Research Part C: Emerging Technologies

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“…Riddalls and Bennett (2002) develop a stability criterion for the same, which is further refined by Warburton et al (2004). Braun et al (2003) use model predictive control to manage inventory control and safety stock so as to determine material release plans and inventory targets that match the supply and demand better. They show that the 6166 K. Sourirajan et al amount of safety stock can be reduced considerably and observe that the bullwhip effect is reduced as a by-product.…”

Section: Literature Surveymentioning

confidence: 99%

Application of control theoretic principles to manage inventory replenishment in a supply chain

Sourirajan

Ramachandran

Thi

2008

International Journal of Production Research

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Using uncertain real-time information to update supply chain operational policies creates a need for developing dynamic supply chain management capabilities that increase responsiveness to demand and decrease volatility of the replenishment process (popularly known as the Bullwhip Effect). To this end, we explore the use of control theoretic principles to manage the inventory replenishment process in a supply chain under different forecast situations. We study the use of proportional, proportional-integral and proportional-derivative control schemes to determine the conditions under which specific control actions are beneficial. Analytical models and simulation runs are used to study the trade-off between responsiveness to demand and volatility. Our analysis indicates that using proportional control to manage inventory replenishment is suitable for high forecast error situations. Proportional control along with integral control works well in situations where the forecast bias is relatively higher than the forecast error. Proportional control along with derivative control works best in situations with moderate forecast errors.

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A Model Predictive Control Framework for Robust Management of Multi-Product, Multi-Echelon Demand Networks

Cited by 60 publications

References 5 publications

Inner and outer loop optimization in semiconductor manufacturing supply chain management

Inner and outer loop optimization in semiconductor manufacturing supply chain management

An RFID-based inventory management framework for emergency relief operations

Application of control theoretic principles to manage inventory replenishment in a supply chain

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