Building Parallel Supply Chains: How the Manufacturing Location Decision Influences Supply Chain Ambidexterity
Bibliographic record
Abstract
Abstract The purpose of this paper is to examine how managers can develop ‘parallel’ supply chains to overcome the efficiency/flexibility trade‐offs of offshored versus reshored/nearshored production. Primary evidence is gathered from 22 field interviews with eight companies from multiple countries, all operating in the textile and apparel industry. The interview data is triangulated using a cross‐industry focus group with 28 participants and secondary sources including company annual reports and website information. The study contributes to organizational ambidexterity theory by identifying how companies embed structural ambidexterity in their supply chains, and in so doing create ‘parallel supply chains’. Our findings show that companies partition their production in terms of width (meaning that specific product lines were relocated) and depth (meaning that specific production activities were relocated). Companies then use a mix of offshored production facilities to manufacture low‐margin, long‐lead‐time products as well as reshored/nearshored production facilities to make high‐margin, quick‐response items. The ability to swap production volumes between parallel supply chains enables supply chain ambidexterity, which in turn allows companies to exploit efficiency and flexibility benefits simultaneously. Managers are provided with an empirically informed, step‐by‐step framework for developing structural ambidexterity and building parallel supply chains.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".