Exploring the Effects of Enterprise Resource Planning Systems on Direct Procurement: An Upstream Asset-intensive Industry Perspective
Bibliographic record
Abstract
The past two decades have experienced an unprecedented rise in enterprise resource planning (ERP) systems implementation among asset-intensive organizations. Typical asset-intensive industries such as oil & gas, energy, and mining, rely heavily on the performance of their asset investments to stay competitive. Recently, several ERP vendors have developed solutions with diverse functionalities to address different business processes within such organizations. However, challenges unique to asset-intensive industries such as multiplex global supply chains, geographically dispersed sites, and sporadic climatic conditions add to existing impediments. This paper explores the effects of ERP systems on direct procurement with a focus on upstream asset-intensive industries. The study examines existing functionalities within ERP to determine benefits and constraints and builds on a framework with which to address potential gaps and opportunities. A quantitative research method was used to address five constructs related to ERP systems functionality to support inventory levels, delivery lead-times, procure-to-pay process, engineering change management, and ERP usability. The findings reveal statistically significant relationships between ERP systems effectiveness and all mentioned constructs, except the procure-to-pay process and ERP usability. The study informs on future improvements and feasible developments in procurement management and extends the scope of ERP systems knowledge in asset intensive industries.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".