{'fraction_non_alphanumeric': 0.03051181102362205, 'fraction_numerical': 0.001968503937007874, 'mean_word_length': 5.690789473684211, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Image and reality: ‘digital twins’ in smart factory automotive process innovation – critical issues

ABSTRACT This paper is interested in the application of ‘digital innovation’ modes in an extended geographical economy setting, namely automotive industry process innovation. It is clear that challenging, complex and combinatory or ‘crossover’ knowledge issues arise periodically as competing system processes rise and fall in popularity with rivalrous automakers. This involves achieving the image if not the reality as ‘digital twins’ of prescience in marketing, monitoring and maintenance. These are all central elements of ‘surveillance capitalism’ facilitated by big data analytics, artificial intelligence and machine learning. Such combinations are envisioned and to varying degrees implemented in what Industry 4.0 advocates call the ‘smart factory’, but its critics call the ‘lights out’ or ‘dark factory’. This is because of its potentially full automation and absence of humans. Brief conclusions follow.