ASML 2.0: Drivers at the learning curve at customer service
ASML's customers increasingly demand greater accuracy of lithography machines that enable them to deliver highly advanced integrated circuits (IC) to the market. ASML has the objective to be first to the market to give customers first mover advantages. This requires them to rapidly introduce new machines. Every new product introduction (NPI) therefore has to go through a steep learning curve which results in the reduction of production and service cycle times. This ability to reduce cycle times by mastering the learning curve explains most of the success of ASML in the past years. However, despite of the past success, one of ASML biggest future challenges is the management of the service organization in the introduction of the new machines.
Prior research has shown that in the factory ASML is able to realize a steep cycle time learning curve for the first systems, followed by less steeper one for the rest of the systems. The shape of the learning curve has a major impact on performance indicators such as cost and quality. Prior research also shows that this learning process can be managed by a specific number of drivers. The Customer Service (CS) organization has, as a result of the introduction of new machines, a similar challenge: the CS organization must identify the drivers to realize a steep learning curve in order to achieve the optimal service performance in terms of cost, quality and time.
Although the CS learning curve is fundamentally different from the one in the factory, as the underlying learning principles that apply in the CS organization are different, there are many and perhaps even more opportunities to learn for several reasons. First, the CS organization provides many more opportunities to learn in parallel on the machine level as service knowledge accumulates over time. Second, as the CS learning process is distributed across many offices, so there are opportunities for learning between offices. Third, CS is involved in the complete life cycle of the machines (in contrast to the factory) which provides a broader and deeper knowledge base ready for use for other departments. Fourth, typical field issues, such as upgrades, and system downs might also lead to precious learnings and performance improvements. Finally there is the ability to learn at the system level which creates the opportunity to be more efficient and to learn from mistakes. Realizing the advantages of these multiple opportunities however does not happen spontaneously. This requires a well thought-out learning curve management model for the CS organization. The principal aim of this PhD research is to identify the process indicators and management principles to improve the CS learning curve.
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