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Requirements Engineering in Business Analytics for Innovation and Product Lifecycle Management
(2014)
Considering Requirements Engineering (RE) in business analytics, involving market oriented management, computer science and statistics, may be valuable for managing innovation in Product Lifecycle Management (PLM). RE and business analytics can help maximize the value of corporate product information throughout the value chain starting with innovation management. Innovation and PLM must address 1) big data, 2) development of well-defined business goals and principles, 3) cost/benefit analysis, 4) continuous change management, and 5) statistical and report science. This paper is a positioning note that addresses some business case considerations for analytics project involving PLM data, patents, and innovations. We describe a number of research challenges in RE that addresses business analytics when high PLM data should be turned into a successful market oriented innovation management strategy. We provide a draft on how to address these research challenges.
Product development and product manufacturing are entering a new era, namely an era where engineering tasks are executed under collaboration of all involved parties. Engineers and potential customers work together mainly in a virtual world for the design and realization of the product. We address this so called “crowdsourcing” trend in the automotive industry that lowers cost and accelerates production of new car. Current practice and prior studies fail to handle data management and collaboration aspects in sufficient detail. We propose a PLM based crowdsourcing platform that applies best practices to the established approach and adapt it with new methods for handling specific requirements. Our work provides a basis for establishing an improved collaboration platform to support a Gig Economy in the automotive industry.
The development of a new product can be accelerated by using an approach called crowdsourcing. The engineers compete and try their best to provide the related solution based on the given product requirement submitted in the online crowdsourcing platform. The one who has submitted the best solution get a financial reward. This approach is proven to be three time faster than the conventional one. However, the crowdsourcing process is usually not transparent to a new user. The risk for the execution of a new project for developing a new product is not easy to be calculated [1, 2]. We developed a method InnoCrowd to handle this problem and the new user could use during the planning of a new product development project. This system uses AI concepts to generate a knowledgebase representing histories of successful product development projects. The system uses the knowledge to determine qualitative and quantitative risks of a new project. This paper describes the new method, the InnoCrowd design, and results of a validation experiment based on data from a current crowdsourcing platform. Finally, we compare InnoCrowd to related methods and systems in terms of design and benefits.