This Collaborative Grant Opportunity for Academic Liaison with Industry (GOALI) project will study strategic capacity planning decisions and their impact on the supply chain in a make-to-order environment. The project will construct analytical models and develop analytical, numerical, and simulation methods that will be tested in General Motors' environment to guide the research direction and ensure their practicality and ease of implementation. To provide their customers with customized products within reasonable lead-times at competitive prices, firms need to shift to make-to-order production where adequate capacity decisions are even more important than in the past. For a make-to-order strategy to be successful, supply and demand need to be reasonably balanced. This can be achieved through: (1) manufacturing flexibility, so that capacity can be shared among different products, (2) price flexibility, so that demand can be managed, and (3) delivery-time flexibility, so that demand coming from time-sensitive customers can be shifted in exchange for a price break. The project will study the effect of each of these levers on supply chain performance and on capacity requirements to offer guidance to companies on the design and management of their make-to-order production systems. In addition, the plan is to integrate the capacity investment decisions for products and additional features or options (e.g., vehicles and leather seats). Several issues need to be considered in the capacity analysis for these options: (1) The option and vehicle model capacity decisions are interdependent. (2) Demand for the various options might be correlated. (3) Customers have a different attitude towards different options. For example, they might not buy a car without an automatic transmission, but may accept other missing options, such as a vanity mirror. This leads to very different risks of over- and under-capacitizing for each particular option. Thus, the project will develop models that address capacity-pricing decisions considering customers' preferences.
The research will provide: (1) Analytical results and insights for better capacity planning in a make-to-order environment, and (2) Tools for vehicle and option capacity planning that consider uncertainty, demand management techniques, and the impact on operational supply chain costs. Also, the projected outcome is to develop general models that can be used in a wide range of industries and demonstrate their practical impact by implementing/testing them at General Motors.
