Bibliographic record
Abstract
Abstract Background Axis Communications is a Lund based company which specializes in network cameras. In addition to the cameras, the company has a wide range of accessories that ensure their customers receive complete solutions. While the company has had significant successes by investing heavily in R&D, increased competition from low-cost competitors is now placing higher demands on cost-efficiency. Problem The high introduction rate of new cameras and solutions have resulted in an ever-expanding portfolio of accessories. As the number has grown they are starting to cause problems in the company’s operations. For example, the increased complexity is making it more difficult to balance supply and demand. Further, it is a challenge due to the administrative complexities it introduces. By making more informed decisions early in the development process, it is believed the inflow of new accessories can be more controlled. Purpose To design a practically viable decision support model that enables more informed decisions with regards to accessory development at Axis Communications. Method The study has used a constructive research approach. The construct is a decision support model for Axis to use when developing accessories. The study started with a literature review to gain insight into the current body of knowledge. A multiple case study was used to contribute with empirical data on how companies work with the selection of projects in practice. Interviews and workshops at Axis were then conducted to see what construct would fit the company and product context. The final model was constructed and tested with key stakeholders at the company to ensure its validity. Theory In the literature review we have looked at areas which are deemed important for the problem at hand. Product portfolio management is a broad field and a literature review by Jugend & da Silva (2014) was used for the initial structure. To further structure the selection and development process, Michael Cooper’s work around the Stage-Gate process has been used. By combining the stage-gate perspective with portfolio management an input-output framework was developed. This framework has then been the basis to which we have formulated 26 theoretical propositions. Empirical research Axis’ camera development was used as a test case, this enabled fine tuning of the case study protocol ahead of the main cases. The companies which participated were Sony Mobile, Thule Group, Husqvarna Group and Alfa Laval. Companies were chosen to ensure a wide perspective on the contextual aspects of life cycle length, company size, profitability and technology sophistication. The study contributed with ways of practically working with selection of projects and methods of maintaining data integrity. Analysis Many of the theoretical propositions were supported by the case study findings. Several empirical propositions were also developed and evaluated for generalizability. Theory and empirics were then combined to form a generic decision support model which companies can use to improve development decisions. It starts with choosing criteria that are aligned with company strategy and goals of the product portfolio. These criteria then dictate the input deliverables and decision makers needed to make the decision. After the decision, a formal decision and goal setting for the ensuing work should be performed. Finally, a post-launch review should be conducted to follow-up on deliverables and decisions made at the decision point. This to ensure a learning process. The construct It was decided that the focus of the model would be accessories developed within camera projects. Criteria and methods were developed in order to reach the strategic goals of increasing structure, priority and data integrity of accessories. Nine suggestions were proposed to Axis. They were fitted into four different points of the current camera development structure. In short, they serve to increase early awareness of accessories within camera projects. This through requesting that certain deliverables are presented before an accessory is released for development. We also suggest the decision to order tooling receives more focus as it results in the most significant investment for an accessory. Finally, a post launch review is suggested in order to follow-up on assumptions made at the decision point. This way the process will receive continuous feedback and learning. As a result, Axis will be able to make more informed decisions regarding accessories and thus increase control over their product portfolio.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".