Is Industry 4.0 a Good Fit for High Performance Work Systems? Trade Unions and Workplace Change in the Southern Ontario Automotive Assembly Sector
Bibliographic record
Abstract
The automotive industry has long been a leader in the introduction of new forms of work organization and technology—including mass production and high performance work systems (HPWS). It has also been a focal point for how trade unions negotiate such systems. Recently, much attention has focused on Industry 4.0 (I 4.0)—a manufacturing system featuring advanced robotics, digitalization and artificial intelligence. However, in the automotive industry, I 4.0 is confronted with considerable technical and social challenges, and I 4.0 paradigms have been criticized for marginalizing the continuing importance of employees in shaping, if not ‘hybridizing,’ such new production processes. Based on a study of UNIFOR union locals in Canadian automotive assembly plants, we argue that I 4.0 has to be analyzed in terms of the ways unions have influenced the almost universal adoption of HPWS in that sector. We thus investigate the ways unions have impacted HPWS and its implications for their roles in workplace integration of I 4.0. As such, we first argue that, while overlapping, HPWS and I 4.0 represent different managerial strategies. Second, we develop an exploratory analytical framework for use in examining union roles in negotiating HPWS and technology adoption. Based on this framework, we then analyze 18 interviews we conducted in 2017-2018 with plant managers and key UNIFOR representatives at five southern Ontario assembly plants. The interviews illustrate not only commonalities in adoption of HPWS, but also differing ways in which the union influences the ‘hybridization’ of HPWS. Union practices differ significantly from one plant to another as a function of three variables: 1- firm-plant competitive positions; 2- the union’s overall monopoly face; and 3- internal union local solidarity and narratives around HPWS. Keeping these commonalities and differences in mind, we then consider the challenges that unions are likely to confront as they begin negotiating I 4.0.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".