Purpose -The purpose of this paper is to propose a risk-based approach for spare parts demand forecast and spare parts inventory management for effective allocation of limited resources. Design/methodology/approach -To meet the availability target and to reduce downtime, process facilities usually maintain inventory of spare parts. The maintaining of non-optimized spare parts inventory claims more idle investment. Even if it is optimized, lack of attention towards the critical equipment spares could threaten the availability of the plant. This paper deals with the various facets of spare parts inventory management, mainly risk-based spare parts criticality ranking, forecasting, and effective risk reduction through strategic procurement policy to ensure spare parts availability. A risk-based approach is presented that helps managing spare parts requirement effectively considering the criticality of the components. It also helps ensuring the adequacy of spare parts inventory on the basis of equipment criticality and dormant failure without compromising the overall availability of the plant. Findings -The paper proposes a risk-based approach that used conjugate distribution technique with the capability to incorporate historical failure rate as well as expert judgment to estimate the future spare demand through posterior demand distribution. The approach continuously updates the prior distribution with most recent observation to give posterior demand distribution. Hence the approach is unique in its kind. Practical implications -Appropriate spare parts unavailability could have great impact on process operation and result in costly downtime of the plant. Following proposed approach the availability target can be achieved in process industry having limited maintenance resources, by forecasting spare parts demand precisely and maintaining inventory in good condition. Originality/value -Adopting the approach proposed in the paper, risk level can be minimized and plant availability can be maximized within the financial constraint. The resources are allocated to the most critical components and thereby increased availability, and reduce risk.

Failure probability of oil and gas pipelines due to external corrosion defects can be estimated using corrosion growth model and the evaluation of remaining strength. Codes/standards have been developed for the assessment of the remaining strength of corroded pipeline. The remaining strength and the operating pressure were considered to develop the limit state equation and consequently the failure probability of the burst models recommended by codes/standards. In the present paper, comparative analyses of the failure probability estimated by the codes/standards were conducted, using Monte Carlo simulation and first order second moment methods. The analysis revealed that the failure probability of the burst models recommended by codes/standards varies significantly for the same defects size. The study further explored the cause of variability in failure probabilities. The study observed that different defect shape specifications (rectangular, parabolic, etc.) and different stress concentration factor derivations (different contributions of l) for burst pressure estimation are responsible for high variability in the probability of failure. It is important to reduce variability to ensure unified risk-based design approach considering any codes/standards.

Denial of service attack is one of the most devastating and ruinous attacks on the internet. The attack can be performed by flooding the victim's machine with any kind of packets. Throughout all these years many methods have been proposed to reduce the impact, but with machines of higher capabilities coming in, the attack has also become more potent, and these proposals are either less effective or less efficient. A DoS attack exhausts the victim's resources affecting the availability of the resource. This paper will be comparing a few methods that have been proposed and published in various papers along with a newly proposed method. The comparison of the methods is done on a number of parameters including resource utilization, reaction time, worst case scenarios, etc. This paper also checks the viability of these methods over various layers of the network. Concluding with the best aspects of all the papers and the best among these for the current real conditions.