Assemble To Order – ATO
Assemble to order (ATO) is a production model used by manufacturers which entails that goods are produced from scratch to the finishing lone when an order is made by a customer. When this production model is in place, the parts needed to produce a good are already manufactured but yet to be assembled, it is a customer’s order that triggers the assemblage of the parts and the customization of the good to suit the needs of the customer.
An assemble to order is also called “make to order” and “build to order,” in this methods, the components of a product are assembled to match the specific order placed by a customer.
A Little More on What is Assemble To Order – ATO
The assemble to order (ATO) is opposed to the make-to-stock strategy where products are already assembled and made to fill up a particular stock. ATO is a make-to-order technique whereby an order by a customer facilitates the assemblage of the components of a product to form a whole item. When a company adopts this policy, products are only assembled from their components when an order for their purchase has been made.
However, ATO combines the strategies of make-to-stock and make-to-order production techniques with the aim of delivering products to customers without wasting too much time. The presence of technology and a good inventory management system has made ATO an effective production model in the present day.
Reference for “Assemble To Order – ATO”
Academic research on “Assemble To Order – ATO”
Supply chain operations: Assemble–to–order systems, 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 a distribution system has 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 the allocationof 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 an assemble–to–order system 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 in assemble–to–order manufacturing, 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 of assemble–to–order systems 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 of M/G/∞ queues with a common arrival stream. We show that unlike the standard M/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 in assemble–to–order systems, 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 simple lineartrade-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-off—in 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 in assemble–to–order systems, 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 in assemble‐to‐order systems: Models and properties, Gerchak, Y., & Henig, M. (1989). Component commonality in assemble‐to‐order systems: Models and properties. Naval Research Logistics (NRL), 36(1), 61-68. This article presents a general multiperiod model of an assemble‐to‐order 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 product‐specific components can be lower in an assemble‐to‐order system than in a corresponding make‐to‐stock one, a behavior that is impossible in an unconstrained model.