The Internet is changing the automotive industry as the traditional manufacturer and dealer structure faces increased threats from third party e-tailers. Dynamic pricing together with the Direct-to-Customer business model can be used by manufacturers to respond to these challenges. Indeed, by coordinating production and inventory decisions with dynamic pricing, the automotive industry can increase profits and improve supply chain performance. To illustrate these benefits, we discuss a strategy that incorporates pricing, production scheduling, and inventory control under production capacity limits in a multi-period horizon. We show that under concave revenue curves, a greedy algorithm provides the optimal solution, and we describe extensions to the model such as multiple products sharing production capacity. Using computational analysis, we quantify the profit potential and sales variability due to dynamic pricing, and we suggest that it is possible to achieve significant benefit with few price changes.

Flexible capacity has been shown to be very effective to hedge against forecast errors at the investment stage. In a make-to-order environment, this flexibility can also be used to hedge against variability in customer orders in the short term. For that purpose, production levels must be adjusted each period to match current demands, to give priority to the higher margin product, or to satisfy the closest customer. However, this will result in swings in production, inducing larger order variability at upstream suppliers and significantly higher component inventory levels at the manufacturer. Through a stylized two-plant, two-product capacitated manufacturing setting, we show that the performance of the system depends heavily on the allocation mechanism used to assign products to the available capacity. Although managers would be inclined to give priority to higher-margin products or to satisfy customers from their closest production site, these practices lead to greater swings in production, result in higher operational costs, and may reduce profits.resource flexibility, capacity allocation, supply chain performance, demand uncertainty, operational hedging

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In manufacturing industry, downtimes have been considered as major impact factors of production performance. However, the real impacts of downtime events and relationships between downtimes and system performance and bottlenecks are not as trivial as it appears. To improve the system performance in real-time and to properly allocate limited resources/efforts to different stations, it is necessary to quantify the impact of each station downtime event on the production throughput of the whole transfer line. A complete characterization of the impact requires a careful investigation of the transients of the line dynamics disturbed by the downtime event. We study in this paper the impact of downtime events on the performance of inhomogeneous serial transfer lines. Our mathematical analysis suggests that the impact of any isolated downtime event is only apparent in the relatively long run when the duration exceeds a certain threshold called opportunity window. We also study the bottleneck phenomenon and its relationship with downtimes and opportunity window. The results are applicable to real-time production control, opportunistic maintenance scheduling, personnel staffing, and downtime cost estimation.

Investments in dedicated and flexible capacity have traditionally been based on demand forecasts obtained under the assumption of a predetermined product price. However, the impact on revenue of poor capacity and flexibility decisions can be mitigated by appropriately changing prices. While investment decisions need to be made years before demand is realized, pricing decisions can easily be postponed until product launch, when more accurate demand information is available. We study the effect of this price decision delay on the optimal investments on dedicated and flexible capacity. Computational experiments show that considering price postponement at the planning stage leads to a large reduction in capacity investments, especially in the more expensive flexible capacity, and a significant increase in profits. Its impact depends on demand correlation, elasticity and diversion, ratio of fixed to variable capacity costs, and uncertainty remaining at the times the pricing and production decisions are made.