Assemble-to-Order and Postponement Strategies at ABC Wireless Inc.

ABC Wireless has achieved tremendous success in the wireless telematics industry and in recent years has experienced dramatic growth in both sales and operations. As a result of this expansion, there is increased pressure on the supply chain, specifically on activities related to inventory management. Rising sales volumes, high variability in demand and proliferation of product variety has lead to a surge in supply chain complexity and uncertainty. As ABC’s business has evolved, they have pursued a supply chain strategy focused on reducing manufacturing costs. While this strategy has allowed ABC to benefit from economies of scale, it has hindered their ability to manage or respond quickly to changing demand

Modified (Q, r) Inventory Control Policy for an Assemble-to-Order Environment

The traditional (Q,r) inventory control model assumes that the date at which the order is entered is the same as the date at which it is requested or expected to be delivered. Hence, the penalty cost is incurred when the customer places the order if inventory is unavailable. This is a reasonable assumption for retail systems and most distribution centers (DC), but not for an assemble-to-order (ATO) environment. In this scenario, there is a delivery time which is usually pre-negotiated and in addition to considering the manufacturing process time and in some cases the outbound transportation time, it also has some safety time built-in. This safety time is defined by the manufacturer and represents information related to when the penalty is incurred. The main objective of this research is to develop a modified (Q,r) policy that incorporates the safety time, and to evaluate this policy in terms of expected inventory cost and expected penalty cost / late orders. The problem is addressed following the heuristic approach discussed by Hadley and Whitin (1963). Two main models are developed based on the following assumptions: 1) early shipments are allowed by the customer, and 2) no early shipments are allowed. The behavior of both models is analyzed mathematically and by means of numerical examples. It is shown that from a manufacturer perspective, the first model is preferred over the traditional (Q,r) model. However, it poses a threat for the long term business relationship with the customer because the service level deteriorates, and for the implications that early shipments have on the customer inventory. The behavior of the second model is strictly related to the problem being addressed. Its merits with respect to the traditional and the "early shipment" model are discussed. This discussion is centered on the coefficient of variation of the lead-time demand, the ratio (IC/pi), and the location of the supplier. A final model which is a hybrid of the previous two shipping policies is developed. The models developed in the course of this research are generalizations of the traditional (Q,r) model

System dynamics modelling, analysis and design of assemble-to-order supply chains

Background and purpose: The assemble-to-order supply chains (ATO) is commonly-adopted in personal computer (PC) and semiconductor industries. However, the system dynamics of PC and semiconductor ATO systems, one of the main sources of disruption, is not well-explored. Thereby this thesis aims to 1) develop a nonlinear system dynamics model to represent the real-world PC and semiconductor ATO systems, 2) explore the underlying mechanisms of ATO system dynamics in the nonlinear environment and 3) assess the delivery lead times dynamics, along with bullwhip and inventory variance. Design/methods: Regarding the semiconductor industry, the Intel nonlinear ATO system dynamics model, is used as a base framework to study the underlying causes of system dynamics. The well-established Inventory and Order based Production Control System archetypes, or the IOBPCS family, are used as the benchmark models. Also, the IOBPCS family is used to develop the PC ATO system dynamics model. Control engineering theory, including linear (time and frequency response techniques) and nonlinear control (describing function, small perturbation theory) approaches, are exploited in the dynamic analysis. Furthermore, system dynamics simulation is undertaken for cross-checking results and experimentation. Findings: The ATO system can be modelled as a pull (order driven) and a push (forecasting driven) systems connected by the customer order decoupling point (CODP). A framework for dynamic performance assessment termed as the ‘performance triangle’, including customer order delivery lead times, CODP inventory and bullwhip (capacity variance), is developed. The dynamic analysis shows that, depending on the availability of CODP Abstract iii inventory, the hybrid ATO system state can be switched to the pure push state, creating poor delivery lead times dynamics and stock-out issues. Limitations: This study is limited to the analysis of a closely-coupled two-echelon ATO systems in PC and semiconductor industries. Also, the optimization of control policies is not considered. Practical implications: Maintaining a truly ATO system state is important for both customer service level and low supply chain dynamics cost, although the trade-off control design between CODP inventory and capacity variance should be considered. Demand characteristics, including variance and mean, play an important role in triggering the nonlinearities present in the ATO system, leading to significant change in the average level of inventory and the overall transient performance. Originality / value: This study developed system dynamics models of the ATO system and explored its dynamic performance within the context of PC and semiconductor industries. The main nonlinearities present in the ATO system, including capacity, non-negative order and CODP inventory constraints, are investigated. Furthermore, a methodological contribution has been provided, including the simplification of the high-order nonlinear model and the linearization of nonlinearities present in the ATO system, enhancing the understanding of the system dynamics and actual transient responses. The ‘performance triangle’ analysis is also a significant contribution as past analytical studies have neglected customer order lead time variance as an inclusive metric