Strategic Roadmapping for Technological Change
Bibliographic record
Abstract
The modern-day industry has become increasingly turbulent, requiring firms to create a long-term vision that innovates their internal and external processes. Firms, nowadays, must consider strategic and technological growth as fundamental factors for cultural change and a source for decision-making. Achieving this requires the creation of differentiated strategies involving the development or acquisition of intangible assets, which depend on the innovation and creativity capabilities of the firm. Such assets include patents, engineering designs, and technological improvements to existing products. The intellectual capital of firms offers the most significant value to the economic development of modern society, and as we enter the Industry 4.0 era, firms possessing substantial intellectual capital are considered knowledge-based firms. Roadmapping is a critical tool that allows firms to develop products with short, medium, and long-term objectives, allowing them to engage their stakeholders throughout the entire lifecycle of a product. As product managers, we use technology roadmaps to steer our firms toward their strategies and to establish milestones for product development that will prepare us for meeting market and consumer requirements. This chapter outlines some traditional management strategies (e.g. strategic planning and direction, business models, technological management, and process analysis) to enable the creation of technology roadmaps, including a set of actions. A structured sequence of actions is proposed to develop indicators linked to specific technologies depending on the problem(s) being experienced by firms. Such indicators aim to improve the competitiveness of the firm. The proposed approach applies holistic strategies, which include internal and external analysis; part of this approach involves examining the existing capabilities and skills of the firm and the marketplace competition to better position the firm in the market. Finally, the social impact and cultural change required by a firm for planning a strategic approach are explained, including competitive, holistic, and influential entities in its environment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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; both teacher heads agree on what is shown here.
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".