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Following the internet revolution and the rapid development of artificial intelligence, various nations, such as Great Britain (Hall & Pesent, 2017), the USA (OWH, 2016), Germany (Industry 4.0), Japan (Government of Japan, 2015), China (State Council of the PRC, 2015), and France (Marc et al., 2018), from around the world have been busy with their industrial transformations from the current state of automated manufacturing to an imagined future state of intelligent manufacturing. Based on an important lesson learned from the past successes of several rounds of industrial revolutions (Wen, 2016), these nations have been actively orchestrating their desired transformations to take place in various business endeavors in the manufacturing sector.
From these leading nations’ efforts, the following natural question arises: What is the important role of manufacturing sector in a nation’s effort to develop its self-sustained moment of economic growth?
This question is critical to both developed and developing nations. Because developed nations attempt to maintain their leading positions in the increasingly globalized world economy, they need to consider whether or not their current policies and government supports are sufficient in their efforts. And, with their miserable failures of modernization in the recent past (Wen, 2016), developing nations face the possibility of falling further behind the developed nations economically, socially, and politically. Therefore, they need to consider (Forrest et al. 2018) how to materially modernize their societies and to provide their citizens with their longed prosperity.
This academically and practically important question and related issues have been considered in the literature from different angles with varied emphases via either data- or anecdotes-based approaches. However, visible constraints of these approaches have led relevant studies to inconclusive or inconsistent results (Wen, 2016). Due to this reason, Rostow (1960) openly called for the introduction of new methods and different thinking logic for studies of such question and issues. On the backdrop of this call and the need to clear the theoretical logic underneath various nations’ attempts to maintain their leading positions in the world economy, this paper employs systems thinking, methods, and the logical reasoning that has helped to win successes one after another in mathematics and natural science to break through the constraints of conventional data- and anecdotes-based approaches to develop sharp theoretically important and practically meaningful insights. In particular, these visible constraints include, but not limited to, the following: the quality of data generally cannot be assured; crucial data might be missing; when an economic success is recognized, it is often too late to collect the relevant data. And, no matter which statistics- or calculus-based method is employed, the results tend to be the discovery of some potential facts. Their general validity is always conditional. Conclusions, produced by analyzing data or anecdotes, tend to be suggestions instead of recommendations. For a more in-depth discussion on this issue, see Chapter 1 in (Forrest et al., 2019).
Before the conclusion of this introduction section, the methodology – logical reasoning and systems thinking – employed in this work is briefly discussed here. First, the logical reasoning used throughout this paper parallels that successfully employed in Euclidean geometry and most parts of mathematics (Kline, 1972). It starts with a basis that is obviously true and an intuition that helps facilitate advanced thinking in the development of generally true propositions. Such logical reasoning has been universally utilized for knowledge generation and won successes for such well-established disciplines as physics, chemistry, etc., where derived conclusions are not constrained by specific data and anecdotes (Kuhn, 1962). Speaking differently, from the theoretical conclusions established in the following sections one can derive general and reliable policy recommendations instead of suggestions of limited validity from data- and/or anecdote-based theories. That is, one can articulate the main difference between conclusions established in this paper and the related ones developed empirically in the literature as follows: The empirical literature represents pioneering works that reveal potential facts, and offers empirical evidence for the truthfulness of such theoretical conclusions as those established in this paper.