Analysis and management of risks associated with outsourcing in China
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyse the risks associated with outsourcing production to emerging countries with lower labour costs, namely China, and study actions and plans used to reduce the influence of factors/drivers that induce these risks. Design/methodology/approach This research uses a multiple case-study methodology, involving seven Canadian manufacturing firms that have chosen an outsourcing strategy in China. It is based on a particular approach of classifying factors/drivers that may generate risks related to this strategy and on interviews with two managers per firm to reduce personal bias. Findings In each of the seven cases studied, outsourcing was chosen to take advantage of lower labour costs in China, but in reality, costs were higher than expected due to unforeseen factors inherent to the risks involved. This study reveals that risks generated by factors/drivers such as lack of experience, reduced control over foreign operations and cultural differences are of major concern for managers outsourcing part of their production to China. However, according to some executives that were interviewed, certain actions can be taken by firms to overcome the negative influence of these factors/drivers. Furthermore, some risks may have multiple causes or be induced by other risks. Research limitations/implications The sample of this study was composed of firms from different industrial sectors, and the authors were therefore unable to analyse sector-specific risks. As the industrial sector has an impact on the technical complexity of the products and their components, it would be appropriate to reconduct our research using samples drawn from similar sectors. Practical implications These findings can help guide the decisions of managers wishing to outsource some of their activities to China and other emerging countries. They will contribute to the success of outsourcing strategies to these countries, as they reveal the risks associated with these strategies and the ways to deal with factors/drivers that can induce them. For example, building long-term relationships with Chinese partners based on collaboration, trust and mutual benefit as well as conducting a rigorous prospecting phase and taking time to select the right subcontractor can have a major impact on reducing risks. Originality/value The main contribution of this work is the analysis of risks associated with outsourcing to China, based on a categorisation of factors/drivers that can generate these risks, and the study of how firms manage these factors/drivers and control their negative effects. The nature of the practices and actions used to manage important risks depends on the characteristics of the companies, their size, resources and the products they outsource.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".