Assemble to Order - Explanation
What is Assemble to Order?
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Table of ContentsWhat is Assemble To Order?Academic Research on Assemble To Order
What is Assemble To Order?
Assemble to order (ATO), also known as make to order or assemble to order, is a production model used by manufacturers where goods are produced from scratch when an order is made by a customer. The parts needed to produce a good are already manufactured but yet to be assembled. The customer order triggers assemblye of the good to suit the needs of the customer.
Back to: OPERATIONS, LOGISTICS, & SUPPLY CHAIN MANAGEMENT
Academic Research on Assemble To Order
- Supply chain operations:Assemble-to-ordersystems, Song, J. S., & Zipkin, P. (2003). Supply chain operations: Assemble-to-order systems.Handbooks in operations research and management science,11, 561-596. An assemble-to-order (or ATO) system includes several components and several products. The time to acquire or produce a component is substantial. A product is assembled only in response to demand. This chapter reviews the research on ATO systems. It discusses the modeling issues and analytical methods, and summarizes the managerial insights gained from the research. An assembly system has just one product, and adistribution systemhas just one component. The key issue in an assembly system is the coordination of the components, while the key issue in a distribution system is theallocationof the component among the products. An ATO system combines the elements of assembly and distribution, and resolves both coordination and allocation issues. This makes the ATO systems difficult to analyze, design, and manage. The chapter also discusses one-period models, multi-period models, discrete-time models, and continuous-time models.
- Order-fulfillment performance measures in anassemble-to-ordersystem with stochastic leadtimes, Song, J. S., Xu, S. H., & Liu, B. (1999). Order-fulfillment performance measures in an assemble-to-order system with stochastic leadtimes.Operations Research,47(1), 131-149. We study a multicomponent, multiproduct production and inventory system in which individual components are made to stock but final products are assembled to customer orders. Each component is produced by an independent production facility with finite capacity, and the component inventory is controlled by an independent base-stock policy. For any given base-stock policy, we derive the key performance measures, including the probability of fulfilling a customer order within any specified time window. Computational procedures and numerical examples are also presented. A similar approach applies to the generic multi-item make-to-stock inventory systems in which a typical customer order consists of a kit of items.
- A revenue management approach to demand management and order booking inassemble-to-ordermanufacturing, Pinder, J. P. (1995). A revenue management approach to demand management and order booking in assemble-to-order manufacturing.Journal of operations management,13(4), 299-309. Revenue management is an order acceptance and refusal process that employs differential pricing strategies and stop sales tactics to reallocate capacity, enhance delivery reliability and speed, and realize revenue from change order responsiveness in order to maximize the revenue from pre-existing capacity. While previously considered primarily as a tool of service operations, revenue management has considerable potential for assemble to order (ATO) manufacturing environments. Increasing demand for customer responsiveness has created service-oriented manufacturing environments suitable for the application of revenue management methods. This paper applies revenue management concepts and techniques to ATO manufacturing environments and presents models for optimal pricing and capacity decisions.
- Performance analysis and optimization ofassemble-to-ordersystems with random lead times, Song, J. S., & Yao, D. D. (2002). Performance analysis and optimization of assemble-to-order systems with random lead times.Operations Research,50(5), 889-903. We study a single-product assembly system in which the final product is assembled to order whereas the components (subassemblies) are built to stock. Customer demand follows a Poisson process, and replenishment lead times for each component are independent and identically distributed random variables. For any given base-stock policy, the exact performance analysis reduces to the evaluation of a set ofM/G/ queues with a common arrival stream. We show that unlike the standardM/G/ queueing system, lead time (service time) variability degrades performance in this assembly system. We also show that it is desirable to keep higher base-stock levels for components with longer mean lead times (and lower unit costs). We derive easy-to-compute performance bounds and use them as surrogates for the performance measures in several optimization problems that seek the best trade-off between inventory and customer service. Greedy-type algorithms are developed to solve the surrogate problems. Numerical examples indicate that these algorithms provide efficient solutions and valuable insights to the optimal inventory/service trade-off in the original problems.
- Leadtime-inventory trade-offs inassemble-to-ordersystems, Glasserman, P., & Wang, Y. (1998). Leadtime-inventory trade-offs in assemble-to-order systems.Operations Research,46(6), 858-871. This paper studies the trade-off between inventory levels and the delivery leadtime offered to customers in achieving a target level of service. It addresses the question of how much a delivery leadtime can be reduced, per unit increase in inventory, at a fixed fill rate. We show that for a class of assemble-to-order models with stochastic demands and production intervals there is a simplelineartrade-off between inventory and delivery leadtime, in a limiting sense, at high fill rates. The limiting slope is easy to calculate and can be interpreted as the approximate marginal rate for trading off inventory against leadtime at a constant level of service. We also investigate how various model features affect the trade-offin particular, the impact of orders for multiple units of a single item and of orders for multiple units of different items.
- Order-based cost optimization inassemble-to-ordersystems, Lu, Y., & Song, J. S. (2005). Order-based cost optimization in assemble-to-order systems.Operations Research,53(1), 151-169. We study a multi-item stochastic inventory system in which customers may order different but possibly overlapping subsets of items, such as a multiproduct assemble-to-order system. The goal is to determine the right base-stock level for each item and to identify the key driving factors. We formulate a cost-minimization model with order-based backorder costs and compare it with the standard single-item, newsvendor-type model with item-based backorder cost. We show that the solution of the former can be bounded by that of the latter with appropriately imputed parameters. Starting with this upper bound, the optimal base-stock levels of the order-based problem can be obtained in a greedy fashion. We also show that the optimal base-stock levels increase in replenishment lead times but may increase or decrease in lead-time variability and demand correlation. Finally, we devise closed-form approximations of the optimal base-stock levels to see more clearly their dependence on the system parameters.
- Component commonality inassembletoordersystems: Models and properties, Gerchak, Y., & Henig, M. (1989). Component commonality in assembletoorder systems: Models and properties.Naval Research Logistics (NRL),36(1), 61-68. This article presents a general multiperiod model of an assembletoorder system with component commonality and proves that its solution is myopic. The model is then endowed with a capacity or storage constraint, and the resulting behavior of optimal policies is investigated. Interestingly, with such constraint, the optimal stocks of productspecific components can be lower in an assembletoorder system than in a corresponding maketostock one, a behavior that is impossible in an unconstrained model.