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Most of the history of systems engineering has been focused on processes for engineering a single complex system. However, most large enterprises design, manufacture, operate, sell, or support not one product but multiple product lines of related but varying systems. They seek to optimize time to market, costs of development and production, leverage of intellectual assets, best use of talented human resources, overall competitiveness, overall profitability and productivity. Optimizing globally across multiple product lines does not follow from treating each system family member as an independently engineered system or product. Traditional systems engineering principles can be generalized to apply to families. This article includes a multi-year case study of the actual use of a generic model-based systems engineering methodology for families, Systematica™, across the embedded electronic systems products of one of the world's largest manufacturers of heavy equipment.

The application of Artificial Intelligence (AI) in the automotive industry can dramatically reshape the industry. In past decades, many Original Equipment Manufacturers (OEMs) applied neural network and pattern recognition technologies to powertrain calibration, emission prediction and virtual sensor development. The AI application is mostly focused on reducing product development and validation cost. AI technologies in these applications demonstrate certain cost-saving benefits, but are far from disruptive. A disruptive impact can be realized when AI applications finally bring cost-saving benefits directly to end users (e.g., automation of a vehicle or machine operation could dramatically improve the efficiency). However, there is still a gap between current technologies and those that can fully give a vehicle or machine intelligence, including reasoning, knowledge, planning and self-learning.