Project topics and materials
Get Complete Project Material File(s) Now! »
Applications of ATO systems
The ATO strategy has frequently been used in today’s marketplace due to its advantages. The applications of ATO production not only allow companies to offer a large number of products with different appearances and performances, but also let them reduce their operational costs. We describe the following three fields where ATO systems are widely applied.
1) Traditional manufacturing
ATO is an appealing strategy for firms in such industries as high-tech, automotive and white goods manufacturing. One well-known ATO system (also refer to as Configure-To-Order (CTO)) is the operation of Dell Computer. Known for its direct sale, Dell lets its customers configure their computers from sets of processors, memories, monitors, hard drives etc., and build a customized personal computer (R. Kapuscinski, 2004). This strategy has become so successful that other personal computer producers are adopting similar strategies, such a Apple and H&P. In the automotive industry, the ATO system is also referred to as Build-To-Order (BTO) system. For example, BMW allows customers to make changes to their vehicle within 6 days of final assembly. This allows the company to build up to 550,000 permutations of the Z3 vehicle in applying such a BTO system (A. Gunasekaran, 2004).
2) Spare part management
Likewise, the ATO system can be used in the spare part control operation. Due to the uncertain arrival of maintenance jobs that require different spare parts with different units, any shortage of these leads to delayed maintenance jobs. In order to provide short repair turnaround time, the maintenance department needs to keep a high stock level of spare parts. Hence, the manager needs to balance the system cost and service level. In this setting, the spare parts refer to components, while the end product is the maintenance component. There are many practical applications of ATO systems in spare part management. For example, van Jaarsveld (2015) analyzed the maintenance system of Fokker Service, which is a Netherlands-based aircraft repair shop. He provided managerial insights that aim at balancing the operational cost and service quality. Other examples can be found in ASML (Vilegen, I.M.H 2009), Airbus (A. Regattieri et al. 2015) etc.
Inventory continuous review models
In this section, we study ATO systems in continuous review setting. Unlike periodic-review, continuous review lets manufacturers continuously monitor their system and make decisions immediately. The development of Enterprise Resource Planning (ERP) software allows this to be accomplished easily. We divide this section into the following three parts depending on the characterisation of optimal policies, the use of heuristics policies, performance metrics, and approximation methods.
Optimal policies under several ATO configurations
In general, it maybe hopeless to find the optimal policy for multiple-product multiple-component ATO systems due to the high dimensionality of the system in one hand and the lack of special properties of the cost function on the other hand. However, under some assumptions on the demand and the supply processes and for special configurations of ATO systems, one can characterize, fully or partially, the structure of an optimal policy. In the ATO literature, such special configurations have been classified based on the number of components, number of products, and the degree of commonality of the components. Lu et al. (2010) identify four special configurations: (1) The N-configuration is a 2-component, 2-product system where one product is made only from one of the components and the other product is assembled using both components. (2) The M-configuration is a 2-component, 3-product system where one product is assembled using both components while the other two products use different components. (3) The W-configuration is a 3-component, 2-product system where each product is assembled using a common component and a product-specific component. (4) The Nested-configuration is a multiple-component, multiple product system where the “smallest” product uses one component only, the “largest” product uses all components, and the other products use one component less than the next “larger” product. These systems are named after the shape of each configuration, which is shown in Figure 2.1. Depending on components’ supply mode, we divide the literature on continuous review setting into endogenous and load-dependent systems and exogenous and load-independent systems.
Table of contents :
Acknowledgements
Lists of Figures
List of Tables
Abstract
Résumé ét
endue en Francais
Chapter 1. Introduction
1.1 The ATO system and its applications
1.1.1 What is an ATO system?
1.1.2 Applications of ATO systems
1.2 Outline of the thesis
1.3 Conclusion
Chapter 2. Literature Review
2.1 Inventory per iodic review models
2.1.1. One period models
2.1.2. Multi period models
2.2 Inventory continuous review models
2.2.1 Optimal policies under several ATO configurations
2.2.2Heur istic policies and performance metrics
2.2.3Approximation methods
2.3Conclusion
Chapter 3. Problem formulation and numerical example of continuoustime W configuration ATOsystems
3.1Introduction
3.2Lost sale case
3.2.1Model formulation of expected total discounted cost criterion
3.2.2Model f ormulation of average cost criterion
3.2.3 Numerical exampleNumerical example
3.3 ConclusionConclusion
Chapter 4. Structure of optimal policy in WW–configuration ATO systems with lost sales and its extensionsconfiguration ATO systems with lost sales and its extensions
4.1 Structure of optimal policy
4.1.1 Structure of optimal policy under average cost rate criterion
4.2 Extension to batch production and non-unitary compound Poisson demand processunitary compound Poisson demand process
4.3 Extension to KExtension to K–Erlang production processErlang production process
4.4 ConclusionConclusion
Chapter 5. Heuristic methods
5.1 Introduction
5.2 method Decomposition method
5.3 ((SS, , RR) method and Exhaustive search method)
5.3.1 (S, R) method (Heuristic I)(S, R) method (Heuristic I)
5.3.2 Exhaustive searExhaustive search method (Heuristic II)ch method (Heuristic II)
5.4 Numerical experiments
5.5 Decomposition method for larger systems
5.5.1 Decomposition method for system 1
5.5.2 Decomposition method for system 2
5.5.3 Decomposition method for system 3
5.6 ConclusionConclusion
Chapter 6. Conclusion and Future research perspectives
References
