Product Configuration Management in ICT Companies: The Practitioners’ Perspective
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.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".