Product support improvement by considering system operating environment
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
Purpose The ongoing availability of existing industrial systems/machines depends to a great extent on the form and level of product support. Product support, or the after sale service of a product, is important because it assures the expected function of the product in its operational phase. Product support is affected by a number of factors, including system reliability and maintainability characteristics and the operating environment. The purpose of this paper is to analyze the influence of time independent external factors of industrial systems on product support requirements and spare parts need. Design/methodology/approach This paper, after discussing the factors influencing product support, describes a method to estimate spare part requirements based on estimation of the actual reliability of a product under the influence of the product‐operating environment using a proportional hazard model. A spare parts estimation software, Spare Management Software (SMS), is used to check the results. Then a case study addresses the management of the spare parts inventory based on the geographical location and required performance of the product. Findings The lack of good support and critical spare parts can cause the untimely stoppage of a machine/system. The forecasting of product support and spare parts requirements based on the reliability and maintainability characteristics of systems/components, along with influencing environmental factors, is one of the most effective strategies for preventing unplanned stoppages. The operating environment of a system/machine has a considerable influence on the performance of the system and its technical characteristics, such as its reliability, maintainability, and, consequently, availability. Therefore, the system operating environment should be considered when the required support and spare parts estimation is under review. Research limitations/implications In this research, the focus is on the estimation of the number of spare parts required. Only non‐repairable components/parts in repairable systems are studied. In other words, the paper considers one‐component systems or a single component within a larger system. The operation and maintenance phases are dealt with in the study, along with the external operating environment and time independent influencing factors. Practical implications The introduced method for spare parts estimation will enable management to improve system availability and production line efficiency while minimizing total production costs. Consequently, the plant life cycle cost will be minimized by releasing the tied‐up costs incurred when stocking extra parts for a long time. Originality/value The paper provides a new outlook on product support and spare parts forecasting by taking the actual system operating environment into consideration. It helps managers and engineers to be realistic and act pragmatically while running and analyzing technical/industrial systems.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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