The highly networked and real-time data analysis features of smart manufacturing systems (SMS) require different information infrastructure, data analytics technology, and performance assurance methodologies. The main purpose of this paper is to (i) explore the complete product-process performance assurance space to identify the key performance indicators that help evaluate and quantify system performance at different abstraction levels, (ii) discuss models and methodologies for data analytics, and (iii) suggest a digital factory-based simulation technique to evaluate those key indicators for performance prediction. The paper presents a systematic and rigorous approach towards establishing these performance assurance methodologies applicable to complex value chains of smart manufacturing systems by extensively exploring all possible product and process related performance issues. A hypercube information model is proposed for the purpose of formal representation of the highly dimensional and correlated information among different actors in a smart manufacturing system, thus providing a rigorous foundation for the performance assurance space. The relevant taxonomy and an ontology-based framework are then developed for formal representation of the entities, activities and knowledge involved in the performance assurance domain. It provides a detailed insight into the PA space and defines appropriate measures which can be applied to predict and improve system performance assurances.