Quantified benefit of implementing enterprise resource planning through process simulation
Bibliographic record
Abstract
The enterprise resource planning (ERP) system can integrate the major business management functions of the enterprise with a single common database to allow sharing of all information and achieve efficient communications between management functions. Based on the needs of running a construction enterprise, ERP shows potential applicability to the construction industry. This paper sought to quantify the benefits of ERP systems when applied to construction materials procurement. Specifically, this paper briefly described the business processes involved in construction materials procurement and illustrated how ERP systems could be implemented and the efficiency of the construction materials management system consequently enhanced. The transformation from a non-ERP system into an ERP system through application integration, internal integration, external integration, and automation were simulated. Results show that the individual task improvements of models can increase the productivity of the materials management cycle by up to 5.2%, 18.2%, 27.8%, 13.5%, and 79.2% through internal integration, external integration, application integration, automation, and ERP system, respectively, by automating most of the repeated transactions and reducing manpower required to perform the tasks.Key words: enterprise resource planning, materials management system, productivity, simulation.
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.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".