Product support improvement by considering system operating environment
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".