Product Configuration Management in ICT Companies: The Practitioners’ Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article studies practical challenges experienced by ICT (Information and Communication Technology) companies when managing product configuration under the circumstances of various customer requirements, different product portfolios, and extensive product complexity. The analysis from interview results concentrates on the prioritised issues and how to ensure effective product configuration from practitioners’ perspective. The results of this study indicate that typical challenges for product configuration formalisation include fuzzy product offering, lack of configuration strategy, mechanisms, and general product structure. This research highlights the need for industrial managers to adapt a top-down approach starting from business and strategy, instead of focusing on the challenges of single products when formalising product configuration. Companies need systematic configuration logic over their entire product portfolio and not to focus only single product variant options. Consequently, they need to define a generic product structure to support product configurations that covers all product types such as hardware, software and services. This study also highlights the need for better formalization of service products since they have become an integral part of ICT products. These findings are derived from actual business circumstances and their current difficulties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it