Cumulative Downtime Research Articles

Proposed Machine Maintenance Management To Reduce Breakdown In Kacang Atom Production Process PT. XYZ With Preventive Maintenance Method

PT. XYZ is engaged in the production of snacks. This company makes products from raw materials to finished products. In the packaging section using a machine called CingFong, the problem that often occurs in the packaging section is that there is often a buildup of production materials due to waiting for machines that have not finished maintenance so that it can reduce the productivity of this factory. Overall the method used is Preventive Maintenance by looking for Mean Time To Failure (MTTF) to identify critical components in Cingfong engine parts. From the data processing that has been done, it is known that the Clutch, Blade and Emergency Stop are components that fall into the category of critical components with a total Cumulative Downtime of 70.3%.

Radiothérapie et lutte contre les cancers : défis de maintenance de l’unique accélérateur linéaire à l’Hôpital du Mali

Cancer incidence and mortality rates are increasing in West Africa. Cancer is a recent discipline in Mali and the means available to treat patients are insufficient. Mali has only one radiotherapy machine for the country and its malfunctions are regularly reported in the media. In order to understand the recurrent dissatisfactions linked to access to radiotherapy in Mali, we retraced the history of this machine and described its functioning. Based on semi-directive interviews with patients’ associations and health professionals involved in cancer care in Bamako, we describe how radiotherapy in Mali reveals global health issues through the intervention of numerous international cooperations. In addition, based on data collection from medical registers and institutional reports, we report that the average time to get a radiotherapy appointment is 3 to 6 months in Mali, but also that the radiotherapy machine has experienced 198 breakdowns between April 3, 2014 and September 24, 2021, which represents more than 54 weeks of cumulative downtime. Radiotherapy is a crucial element in the treatment of cancer and the lack of access to this treatment worsens the vital diagnosis of patients. While the Malian government is committed to universal health coverage reforms, strengthening cancer treatment facilities should also be considered a public health priority for Mali.

Mission Availability for Bounded-Cumulative-Downtime System

In this research, a mathematics model is proposed to describe the mission availability for bounded-cumulative-downtime system. In the proposed model, the cumulative downtime and cumulative uptime are considered as constraints simultaneously. The mission availability can be defined as the probability that all repairs do not exceed the bounded cumulative downtime constraint of such system before the cumulative uptime has accrued. There are two mutually exclusive cases associated with the probability. One case is the system has not failed, where the probability can be described by system reliability. The other case is the system has failed and the cumulative downtime does not exceed the constraint before the cumulative uptime has accrued. The mathematic description of the probability under the second case is very complex. And the cumulative downtime in a mission can be set as a random variable, whose cumulative distribution means the probability that the failure system can be restored to the operating state. Giving the dependence in the scheduled mission, a mission availability model with closed form expression under this assumption is proposed. Numerical simulations are presented to illustrate the effectiveness of the proposed model. The results indicate that the relative errors are acceptable and the proposed model is effective. Furthermore, three important applications of the proposed mission availability model are discussed.

Contracting for Infrequent Restoration and Recovery of Mission-Critical Systems

Firms that rely on functioning mission-critical equipment for their businesses cannot afford significant operational downtime due to system disruptions. To minimize the impact of disruptions, a proper incentive mechanism has to be in place so that the suppliers provide prompt restoration and recovery services to the customer. A widely adopted incentive mechanism is performance-based contracting (PBC), in which suppliers receive compensation based on realized system uptime. A key obstacle is that disruptions occur infrequently, making it very expensive for a supplier to commit the necessary resources for recovery because they will be idle most of the time. In this paper, we show that designing a successful PBC creates nontrivial challenges that are unique to this environment. Namely, because of the infrequent and random nature of disruptions, a seemingly innocuous choice of performance measures used in contracts may create unexpected incentives, resulting in counterintuitive optimal behavior. We compare the efficiencies of two widely used contracts, one based on sample-average downtime and the other based on cumulative downtime, and identify the supplier's ability to influence the frequency of disruptions as an important factor in determining which contract performs better. We also show that implementing PBC may create high agency cost when equipment is very reliable. This counterintuitive situation arises because the realized downtimes from which the customer might intuit about the supplier's capacity investment are highly uncertain when there are not many samples of downtimes, i.e., when disruptions occur rarely.

Management Insights

Management Insights

Cost and quality of service analysis of production systems based on the cumulative downtime

The paper presents a method for the analysis and design of industrial production systems based on a joint assessment of the cost and the quality of service. The operation of a production system is seen as the accomplishment of a sequence of missions, each one corresponding to the cost-effective production and delivery of a specified quantity of products within a specified time frame. The paper shows that the probability of successfully accomplishing a mission is a non-linear function of the cumulative production downtime and that this time cannot be obtained using conventional Markov based techniques. The paper also introduces an analytical model and a procedure that allows the density function of the downtime to be obtained and shows how, using these tools, the production costs and the quality of service may be assessed and related to the internal design of the shop floor. The method seems to be particularly valuable in the analysis of production systems integrated in just-in-time supply chains, in which the reliability of the deliveries is an outstanding requirement.

Six-Year Trends in Laboratory Computer Availability

Failure of a clinical laboratory computer system can disrupt work flow and charge capture and affect patient care. The first comprehensive survey of computer downtime was conducted in 1995 and demonstrated significant interinstitutional variation in system availability. Despite numerous changes in the laboratory and computer industries since 1995, no follow-up study has been reported. To quantify current laboratory computer availability and compare it with 1995 performance. Ninety-seven laboratories prospectively recorded the frequency and duration of computer downtime during 30 days in 2001. Results were compared with 1995 survey data. For the median facility, the number of downtime episodes decreased from 8 events per 30 days during 1995 to 3 events per 30 days during 2001 (P <.01). The frequency of unscheduled downtime also improved, from a median of 2 to 0.5 events per 30 days (P <.01). Reduced downtime events were paralleled by reduced cumulative downtime (14.3 vs 4.0 hours per 30 days; P <.01). Improvements were not restricted to the median facility; laboratories performing in the bottom quartile in 2001 recorded substantially less downtime than laboratories in the bottom quartile in 1995. When the comparison was restricted to the 37 institutions that participated in both the 1995 and 2001 surveys, a significant reduction in overall downtime and unscheduled downtime events was still evident (P <.01). More recent installation of vendor software patches was associated with a reduced frequency of downtime events in the 2001 data set. Laboratory computer downtime was less frequent in 2001 than in 1995; industry performance appears to be improving.

Refining the delay-time-based PM inspection model with non-negligible system downtime estimates of the expected number of failures

In this paper a refinement to the delay-time-based PM model is presented to account for the downtime incurred at failures when estimating the expected number of failures over a PM inspection period. Extensions to PM models have been made in the context of downtime modelling. Previously, in downtime modelling using delay time, it has been generally assumed that the downtime of failures is small compared with a PM cycle length and, therefore, assuming that defects continue to arise uninterrupted during a downtime period has negligible consequences. However, in some cases, it is possible that the cumulative downtime of failure repairs is not sufficiently small. The paper presents revised models for perfect and non-perfect homogeneous processes, and for a perfect non-homogeneous process. Numerical examples are provided to highlight the differences. The revised downtime model is a more sensitive model with which to determine the actual downtime or cost.

Estimating the cumulative downtime distribution of a highly reliable component

The distribution of the cumulative downtime for a highly reliable component over an interval of time is approximated using a compound Bernoulli process. Given a set of observed cumulative downtimes, the maximum likelihood (ML) and uniformly minimum variance unbiased (UMVU) estimator of the approximate cumulative downtime distribution are derived under the assumption of exponential repair times and are compared to the nonparametric estimator. The ML estimator is more efficient than the UMVU estimator which itself is more efficient than the nonparametric estimator.

Comprehensive Downtime Prediction in Next-Generation Internet

The benchmark for the reliability quality of networks depends mainly on the accuracy and comprehensiveness of the reliability parameters. Downtime prediction of a communication system is crucial for the quality of service (QoS) offered to the end-user. Markov model enables analytical calculation of average single figure cumulative downtime over one year. The single average approach, generally, does not adequately describe the wide range of service performance that is likely to be experienced in communications systems due to the random nature of the failure. Therefore, it would be more appropriate to add downtime distribution obtained from network availability models to predict the expected cumulative downtime and other performance parameters among a large number of system populations. The distribution approach provides more comprehensive information about the behavior of the individual systems. Laplace-Stieltjes transform enables analytical solutions for simple network architectures, i.e. the simplex system and the parallel system. This paper uses simulations to determine reliability parameters for complex architecture such as the Multiprotocol Label Switching (MPLS) backbone planned for next-generation Internet. In addition to the single figure downtime, simulations provide other reliability parameters such as probability of zero downtime. The paper also considers the downtime distribution among a population of equally designed systems.Key Words: Reliability, availability, downtime, Multiprotocol label switching