Industry 4.0 in the product development process: benefits, difficulties and its impact in marketing strategies and operations
Bibliographic record
Abstract
Purpose Product development process (PDP) in the context of Industry 4.0 may present several marketing implications. To understand these implications, the purpose of this study is to identify the benefits and difficulties of Industry 4.0 related to the PDP and its impact in marketing strategies and operations. Design/methodology/approach The methodology used to perform this research was a systematic literature review. For this, five steps were followed, namely, research question formulation; studies location; studies selection and evaluation; analysis and synthesis; and reporting and use research results. Findings The systematic literature review considering PDP in Industry 4.0 context resulted in 28 benefits and 14 difficulties, in a total of 53 articles. From the analysis of these benefits and difficulties, several implications for marketing were identified, namely, better understand customer preferences; greater agility in marketing decision-making; better align marketing, product development and operations processes issues; better understand product/service lifecycle; analyze possibilities of new ways of distribution and communication channels; better define the value of products and services and location requirements. Originality/value The findings presented here can be used both by market professionals, interested in the subject and by researchers for future studies. The better understanding of PDP in the context of Industry 4.0 can enhance marketing strategies for market professionals and provide insights for researchers. No similar studies were found in the literature.
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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 | 0.043 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".