Abstract

The problem of effectively managing Configuration Lifecycle Management (CLM) processes for customizable products in the manufacturing industry presents challenges in striking the right balance between product customization and standardization, particularly for complex products like wind turbines. This paper addresses this issue through a comprehensive analysis and enhancement of CLM processes in a case company that manufactures, install, and service energy solutions focusing on wind turbines. This paper addresses the challenges in CLM processes through a comprehensive analysis and enhancement within a case company specializing in the manufacturing, installation, and servicing of energy solutions, with a specific emphasis on wind turbines. Specifically, within the CLM framework, the study delves into the Input Signal Management (ISM) process, addressing challenges associated with handling specific requirements, features, or functionalities that customers or stakeholders want in the product. The study maps the current state of the processes, identifies weaknesses, and proposes two suggestions to enhance the so-called Input Signal Management (ISM) process. The ISMs are the specific requirements, features, or functionalities that customers or stakeholders want to be included in the product. The challenge in the ISM process lies in handling a large number of requests, ensuring consistency, avoiding conflicts, and making timely decisions. Hence, the paper proposes two key enhancements to the process: the development of guidelines for data creation and categorization of ISM requests, and the prototyping of a Natural Language Processing (NLP)-based system for expedited request assessment. The paper emphasizes the significance of optimizing CLM processes for effective configuration management. Incorporating data-driven methods, the ISM process can become agile, efficient, and customer-centric, leading to better management of customization, reduced response times, and increased customer satisfaction.

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