Strategic justification of integrated resource planning tools in organizations
Bibliographic record
Abstract
Purpose This paper presents a novel multi-objective optimization model aimed at enhancing the success rate of resource planning (RP) implementation. The model optimization is developed based on the organizational structure types, fit-gap contingency analysis reports, uncertainty optimization problems on implementation schedule time and relative time and budget constraints. Design/methodology/approach Two pivotal strategies are employed: RP tools redesign through customization and organizational redesign. The synergistic integration of these strategies is essential, recognizing that RP tools implementation success hinges not only on technical aspects but also on aligning the system with organizational structure, culture and practices. In the analysis phase, a committee of experts identifies the initial gaps, which are evaluated through three conflicting objective functions: cost, time and penalty and running by the e-constraint method. In case of uncertainty nature time of RP tools implementation, the Activity-on-Arrow (A-O-A) method has been utilized. Findings The e-constraint method is utilized to derive the Pareto-optimal front, representing solutions effectively addressing identified gaps. A compromised solution is then proposed using the LP-metric method to strike a balance between conflicting objectives, ultimately improving RP tool implementation by reducing misfits. Originality/value To demonstrate and validate the model, a controlled case study is initially presented, illustrating its effectiveness. Subsequently, a real industry case study is provided, further validating the model’s applicability and practical relevance. This comprehensive approach offers valuable insights to optimize RP tool implementation outcomes, a critical concern for organizations undergoing technological transitions.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".