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The paper performs a literature review on existing models and methodologies to analyse and identify the cost of quality in manufacturing systems. The review shows that existing accounting models are insufficient to identify quality cost in detail in production processes. Existing models also do not cover the entire production activities and therefore need to be extended over the entire product life cycle. The authors therefore develop a new approach by refining and extending the method of activity-based costing to make it applicable for quality cost identification. The proposed methodology can serve as a building block for a later integration into superior supply chain management systems which allow to trigger continuous quality improvements of entire production networks.

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Dynamic and volatile markets, triggered by increasing competition, rising product variety and many more factors, lead to a series of reactions: An increasing product complexity (e.g. a higher number of software components) shorter life cycles, faster development cycles and the call for a prompt market readiness. This has immense repercussions on the value chain and on the manufacturing sites. Additionally, it leads to interactions between product features and the production environment, which includes development, production, quality management, supply chain management and all other related disciplines.As an example, the average development time decreased within the last 20 years up to 20 % (impact on research and development), while the product complexity steadily increased (impact on the product features). Until recently, activities based on information and communication technology (ICT) to address these profound challenges have been referred to as “Digitalization”. However, to counteract the afore mentioned challenges in a more target-oriented manner, the German government introduced the strategic initiative ‘Industry 4.0’. It is crucial for companies to primarily perceive the options and content of Industry 4.0 for potential transformation from machine dominant production to digital manufacturing.