A state-space model of a three tier APVIOBPCS supply chain

White, Anthony S.; Censlive, Michael

doi:10.1108/jm2-10-2012-0036

Cited by 5 publications

(6 citation statements)

References 44 publications

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“…Furthermore, Disney et al A discrete time system has also been studied based on a state space model. This model has the advantage of controllability and observability (Lalwani et al 2006) as well as the possibility of representing a multi-echelon supply chain system (White and Censlive 2015c). By transforming the z-transfer function into a control canonical form, Franklin et al (1998) did not capture various production delays (only one-time unit delay is investigated).…”

Section: The Optimisation Of Decision Parameters In the Iobpcs Familymentioning

confidence: 99%

The extension and exploitation of the inventory and order based production control system archetype from 1982 to 2015

Lin

Naim

Purvis

2017

International Journal of Production Economics

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In 1994, through classic control theory, John, Naim and Towill developed the 'Automatic Pipeline, Inventory and Order-based Production Control System' (APIOBPCS) which extended the original IOBPCS archetype developed by Towill in 1982 ─ well-recognized as a base framework for a production planning and control system. Due to the prevalence of the two original models in the last three decades in the academic and industrial communities, this paper aims to systematically review how the IOBPCS archetypes have been adopted, exploited and adapted to study the dynamics of individual production planning and control systems and whole supply chains. Using various databases such as Scopus, Web of Science, Google Scholar (111 papers), we found that the IOBPCS archetypes have been studied regarding the a) modification of four inherent policies related to forecasting, inventory, lead-time and pipeline to create a 'family' of models, b) adoption of the IOBPCS 'family' to reduce supply chain dynamics, and in particular bullwhip, c) extension of the IOBPCS family to represent different supply chain scenarios such as order-book based production control and closed-loop processes. Simulation is the most popular method adopted by researchers and the number of works based on discrete time based methods is greater than those utilizing continuous time approaches. Most studies are conceptual with limited practical applications described. Future research needs to focus on cost, flexibility and sustainability in the context of supply chain dynamics and, although there are a few existing studies, more analytical approaches are required to gain robust insights into the influence of nonlinear elements on supply chain behaviour. Also, empirical exploitation of the existing models is recommended.

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Section: The Optimisation Of Decision Parameters In the Iobpcs Familymentioning

confidence: 99%

The extension and exploitation of the inventory and order based production control system archetype from 1982 to 2015

Lin

Naim

Purvis

2017

International Journal of Production Economics

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“…The lead-time in the production-inventory control system is modelled as a first-order lag (Towill, 1982;Edghill and Towill, 1990;John et al, 1994;Disney et al, 1997). A first-order lag responds to a sudden change in the demand gradually with an exponential path (White and Censlive, 2015). However, Riddalls and Bennett (2002a) studied the production-inventory using a pure production delay, where nothing happens until a particular time is reached, instead of using production lag.…”

Section: Block Diagrammentioning

confidence: 99%

“…Supply chain management today influences more than ever large numbers of human and economic activities (Sadraoui and Mchirgui, 2014). A substantial part of the value of car companies exists in their supply chain (White and Censlive, 2015). Therefore, the efficiency of supply chain performance has been considered as a competitive strategy for many companies to save money (White and Censlive, 2013b).…”

Section: Introductionmentioning

confidence: 99%

“…An important contributory factor to the bullwhip effect is the replenishment rule implemented by supply chain members (Dejonckheere et al, 2003). Therefore, it is a significant issue for any business to know how to decide the ordering policies that should be adopted in any given circumstance (White and Censlive, 2015). In the literature, many authors have investigated how various smoothing parameters of the Automatic Pipeline Inventory and Order-Based Production Control System (APIOBPCS), a well-established production and inventory control model presented by John et al (1994) and a member of the family of Inventory and Order-Based Production Control System (IOBPCS) introduced by Towill (1982), impact on the bullwhip dampening efficacy.…”

Section: Introductionmentioning

confidence: 99%

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Dynamics analysis of a production-inventory control system with two pipelines feedback

Al-Khazraji

Cole

Guo

2017

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Purpose The purpose of this study is to propose a new dynamic model of a production-inventory control system. The objective of the new model is to maximise the flexibility of the system so that it can be used by decision makers to design inventory systems that adopt various strategies that provide a balance between reducing the bullwhip effect and improving the responsiveness of inventory performance. Design/methodology/approach The proposed production-inventory control system is modelled and analysed via control theory and simulations. The production-inventory feedback control system is modelled through continuous time differential equations. The simulation experiments design is conducted by using the state-space model of the system. The Automatic Pipeline Inventory and Order-Based Production Control System (APIOBPCS) model is used as a benchmark production-inventory control system. Findings The results showed that the Two Automatic Pipelines, Inventory and Order-Based Production Control System (2APIOBPCS) model outperforms APIOBPCS in terms of reducing the bullwhip effect. However, the 2APIOBPCS model has a negative impact on Customer Service Level. Therefore, with careful parameter setting, it is possible to design control decisions to be suitably responsive while generating smooth order patterns and obtain the best trade-off of the two objectives. Research limitations/implications This research is limited to the dynamics of single-echelon production-inventory control systems with zero desired inventory level. Originality/value This present model is an extension and improvement to Towill’s (1982) and John et al.’s (1994) work, since it presents a new dynamic model of a production-inventory control system which utilises an additional flow of information to improve the efficiency of order rate decisions.

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“…There are several methods available in control literature which have been applied to the supply chain system: LQR control (Abad, 1985), state feedback and internal modal control (Mak et al, 1976), stochastic control (Bensoussan et al, 1975), stochastic optimal control (White and Censlive, 2015), model predictive control (MPC) (Qin and Badgwell, 2003) and robust control (Palhares et al, 2005). Models developed using control systems theory can be efficiently used to study the effect of lead times on the system and to develop appropriate ordering policies.…”

Section: Introductionmentioning

confidence: 99%

Control Theoretic Modeling and Simulation of Supply Chain Management System

Rehman¹,

Ryan²

2017

INCOSE International Symp

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Control theoretic modelling approaches are very effective in developing a dynamical model of a system. A supply chain system is a dynamical system which contains a number of lead times among supply nodes and is susceptible to demand variation. Control theoretic modelling approaches are proved to be very effective in developing ordering policies for such systems. In this work, we developed a linear discrete time model of a supply chain with multiple supply nodes and pure delays. The model presented here can be used to develop an optimal ordering and distribution policy so that the entire system remains stable. In addition to the mathematical model of the supply chain, a simulation model is also developed using Simulink® toolbox SimEvents®. The simulation model can be used to test various ordering policies to determine system stability. In this work the simulation model is used to test system stability and can be further used to find optimum ordering and optimum routing policies under uncertainty by running a Monte Carlo type simulation using the developed simulation model.

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A state-space model of a three tier APVIOBPCS supply chain

Cited by 5 publications

References 44 publications

The extension and exploitation of the inventory and order based production control system archetype from 1982 to 2015

The extension and exploitation of the inventory and order based production control system archetype from 1982 to 2015

Dynamics analysis of a production-inventory control system with two pipelines feedback

Control Theoretic Modeling and Simulation of Supply Chain Management System

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