Narrow Results

We study static repair priorities in a system consisting of one repair shop and one stockpoint, where spare parts of multiple, critical repairables are kept on stock to serve an installed base of technical systems. Demands for ready-for-use parts occur according to Poisson processes, and are accompanied by returns of failed parts. The demands are met from stock if possible, and otherwise they are backordered and fulfilled as soon as a ready-for-use part becomes available. Returned failed parts are immediately sent into repair. The repairables are assigned to static priority classes. The repair shop is modeled as a single-server queue, where the failed parts are served according to these priority classes. We show that under a given assignment of repairables to priority classes, optimal spare parts stock levels follow from Newsvendor equations. Next, we develop fast and effective heuristics for the assignment of repairables to priority classes. Subsequently, we compare the performance of the system under these static priorities to the case with a First-Come First-Served (FCFS) service discipline. We show that in many cases static priorities reduce total inventory holding and backordering costs by more than 40%. Finally, we analyse the effect of the number of priority classes. We show that 2 priority classes suffice to obtain 90% of the maximal savings via static priorities.

Telecommunication service providers, also called operators or carriers, must maintain 99.999% of their telecom network availability. To avoid or mitigate the effects of an outage in the network, carriers perform different activities to restore service. Spare parts management plays an important role in meeting the required service. Unfortunately, the closed-loop supply chain that supports the availability of spare parts experiences variability in its processes as a result of endogenous and exogenous variables that make matching the recovery process with the demand process difficult. Understanding the effect of the variables on the variability outcome and the emergence of the variability can help to guide the development of future mathematical models for the management of spare parts. In this article, a complex system approach to analyze the variability behavior is presented.

Industrial operation cost analysis shows that, in general, maintenance represents a significant proportion of the overall operating costs. Therefore, the improvement of maintenance follows the final goal of any company, namely, to maximize profit. This paper studies spare parts availability, an issue of the maintenance process, which is an important way to improve production through increased availability of functional machinery and subsequent minimization of the total production cost. Spare parts estimation based on machine reliability characteristics and operating environment is performed. The study uses an improved statistical-reliability (S-R) approach which incorporates the system/machine operating environment information in systems reliability analysis. For this purpose, two methods of Poisson process and renewal process are introduced and discussed. The renewal process model uses a multiple regression type of analysis based on Cox's proportional hazards modeling (PHM). The parametric approaches with baseline Weibull hazard functions and time-independent covariates are considered, and the influence of operating environment factors on this model is analyzed. The outputs represent a significant difference in the required spare parts estimation when considering or ignoring the influence of the relevant system operating environment. The difference is significant in the sense of spare parts forecasting and inventory management which can enhance the parts and consequently machine availability, leading to economical operation and savings.

The principle aim in complex defects in hand is to restore a functioning tripod pinch. Among the various options, the use of locally available spare parts offers to improve both functions and cosmetics. 10 patients underwent surgeries to restore tripod pinch using this concept of spare parts. Four of them were children with congenital differences and the rest were adults with post traumatic defects. Eight of them underwent on-top plasty out of which four underwent an island pedicled transfer of phalanges and the rest involved distraction lengthening and transfer of metacarpals. One patient underwent a vascularized tenocutaneous joint transfer and another, a non-vascularized metacarpal transfer. At last follow up, eight of them were using hand with tripod pinch and one, using a lateral pinch. A carefully planned use of local tissues as spare part results in satisfactory outcome without the need for additional graft material in hands with absent or poor function.

Reconstruction of extensive traumatic bone and soft tissue deficits in the hand often presents a significant challenge. We present a case of a gunshot wound managed with a resourceful “vascularized spare parts” reconstruction in which a single compromised digit provided two separate vascularized tissue transfers. A rarely reported pedicled phalanx restored osseous stability, a digital fillet flap achieved soft tissue coverage, and the flexor tendons reanimated the hand. An excellent functional and cosmetic result was obtained and the patient was able to return to manual labor within six months of injury.

Predicting the sequential patterns of maintenance activities (replacement or repair) with the needed spare parts for faulty products becomes the main challenge to maintenance engineers. This research, therefore, develops a data mining framework for predicting the integrated sequential patterns of maintenance activities and identifying the classification of the frequent components’ spare parts for faulty products under warranty. In this framework, a large data set was mined for products under warranty including product attributes, maintenance activities, and spare parts. Then, data mining techniques were performed to determine the frequent sequence pattern of maintenance activities, involving: the selection of monthly maintenance activities, generalized sequential patterns (GSP), generation of association rules, and rule-based classification with/without considering product attributes. The frequent sequential patterns were validated using testing data. Further, the GSP was applied to determine the frequent sequential patterns of spare parts. Finally, integrated sequential patterns were generated for maintenance activities and spare parts. A case study of water-cooled chiller products was deployed to illustrate the developed framework. The effectiveness of the framework was illustrated with the warranty data mining for a water-cooled chiller. In conclusion, the proposed framework allows maintenance engineers to extract hidden knowledge regarding sequential patterns of maintenance planning and provides valuable information for maintenance prediction.

Accurate forecast of spare parts demand is of great significance for modern enterprises to provide accurate support and improve market competitiveness. In most studies, mathematical laws are used to forecast, without enough consideration of the actual operation of equipment and the fact that the accuracy of spare parts demand forecasting is not high, which cannot adapt to the new characteristics of complex equipment use environment and fierce market competition in modern enterprises. The digital twin model can be used to forecast the demand for spare parts more accurately and guide modern enterprises to carry out accurate support. By analyzing the current spare parts demand of modern enterprises, the paper puts forward the forecasting ideas of spare parts demand based on the digital twin model by using the digital twin model of equipment maintenance management in modern enterprises. In the digital twin model, the theoretical demand forecasting model of spare parts based on life distribution of replaceable units is introduced, and the sensitivity coefficient system of spare parts demand of replaceable units to operation and environment is constructed. The digital twin model is used to feedback train the sensitivity coefficient to obtain the reliable spare parts demand rules. Based on the theoretical demand and sensitivity coefficient of spare parts, the forecasting method of spare parts demand is given, and the spare parts demand forecasting model is established. Through case analysis, the feasibility and accuracy of the forecasting model are verified.