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The paper aims to explore unavailability of dormant systems that are under both preventive and corrective maintenance. Preventive maintenance is considered as a failure based maintenance model, where full renew is realized at the occurrence of every nth failure. It proposes the imperfect corrective maintenance model, where each restoration process deteriorates the system lifetime, probability distribution of which is gradually changed via increasing failure rate.

Basic reliability mathematics necessary for unavailability quantification of a system which undergoes a real aging process with maintenance has been derived proceeding from renewal theory. New renewal cycle was defined to cover the real aging process and the expectation of its length was determined. All events resulting in the failure of studied system were explored to determine their probabilities. An integral equation where the unavailability function characterizing studied system is its solution was derived.

Preventive maintenance is closely connected with the occurrence of the nth failure, which starts its renew. The number n can be considered as a parameter which significantly influences the unavailability course. The paper shows that the real aging process characterized by imperfect repairs can significantly increase the unavailability courses in contrast with theoretical aging. This is true for both monitored and dormant systems.

Although mathematical methods used in this article were inspired and influenced by the work of reference (van der Weide and Pandey, 2015), derivation of final formulas for unavailability quantification considering the new renewal cycle is original. Idea of the real aging process is new as well. This paper fulfils an identified need to manage the maintenance of realistically aging systems.

This paper aims to investigate whether the cyclicality of local real estate prices affects the systematic risk of local firms using a geography-based measure of land availability as a quasi-exogenous proxy for real estate price cyclicality.

This paper uses the geography-based land availability measure as a proxy for the procyclicality of real estate prices and the location of a firm’s headquarters as a proxy for the location of its real estate assets. Four-factor asset pricing model (market, size, value and momentum factors) is used to examine whether firms headquartered in more land-constrained metropolitan statistical areas have higher systematic risks.

The results show that real estate prices are more procyclical in areas with lower land availability and firms headquartered in these areas have higher systematic risk. This effect is more pronounced for firms with higher real estate holdings as a ratio of their tangible assets. Moreover, there are no abnormal returns to trading strategies based on land availability, consistent with stock market betas reflecting this local real estate factor.

This paper contributes to the literature on local asset pricing factors, the collateral role of firms’ real estate holdings and the co-movement of security prices of geographically close firms.

This paper has important managerial implications by showing that, when firms decide on the location of their buildings (e.g. headquarters building, manufacturing plant and retail outlet), the location’s influence on systematic risk should be part of the decision-making process.

This paper is among the first to use a geography-based measure of land availability to study whether the procyclicality of local real estate prices influences firm risk independent of the procyclicality of the local economy. Thus, both the portfolio formed and firm-level analyses provide a more direct evidence of the positive relation between the procyclicality of local real estate prices and firm risk.

The operating costs and production losses due to equipment failure of offshore gas and oil production systems can exceed initial investment cost. The article explores the probability distribution of downtime associated with random equipment failure and applies availability assessment to design optimisation.

This paper aims to examine the connection between information system (IS) availability and operational risk losses and the capital requirements. As most businesses today become increasingly dependent on information technology (IT) services for continuous operations, IS availability is becoming more important for most industries. However, the banking sector has particular sector-specific concerns that go beyond the direct and indirect losses resulting from unavailability. According to the first pillar of the Basel II accord, IT outages in the banking sector lead to increased capital requirements and thus create an additional regulatory cost, over and above the direct and indirect costs of an outage.

A Bayesian belief network (BBN) with nodes representing causal factors has been used for identification of the factors with the greatest influence on IS availability, thus helping in investment decisions.

Using the BBN model for making IS availability-related decisions action (e.g. bringing a causal factor up to the best practice level), organization, according to the presented mapping table, would have less operational risk events related to IS availability. This would have direct impact by decreasing losses, related to those events, as well as to decrease the capital requirements, prescribed by the Basel II accord, for covering operational risk losses.

An institution using the proposed framework can use the mapping table to see which measures for improving IS availability will have a direct impact on operational risk events, thus improving operational risk management.

The authors mapped the factors causing unavailability of IS system to the rudimentary IT risk management framework implied by the Basel II regulations and, thus, established an otherwise absent link from the IT availability management to operational risk management according to the Basel II framework.

The purpose of this paper is to validate the conceptual model that presents the determinants of Tier 2 consumer’s online shopping attitude and the interrelationships among the constructs across the three Tier 2 cities in India.

The paper uses survey-based data from three Tier 2 cities of North India – Kota, Agra and Jalandhar and uses a combination of statistical techniques to assess psychometric properties of the scales and conduct the measurement and structural invariance.

The findings of the paper reveals that technology readiness, consumer innovativeness, fondness for branded products and perceived brand unavailability act as determinants of online shopping attitude and there is a positive relationship between online shopping attitude and online purchase intention among Tier 2 consumers in India while perceived offline hedonic value do not have any significant impact.

Future researchers can use this model with additional confidence given its cross-segment robustness.

Online marketers can use the antecedents identified in this study to develop and encourage positive online shopping attitude in small town India.

This research paper is the first one that investigated online shopping attitudes of Indian Tier 2 consumers. Importantly, it validated the determinants of online shopping attitude among Tier 2 consumers. National and international e-tailers aiming to develop and expand their operations to India now have the critical empirical verification concerned with the determinants of online shopping attitude and behaviour in India which would be meaningful to develop a sound marketing strategy.

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.

This paper aims to investigate the relationship between residential real estate prices and unemployment rates at the Metropolitan Statistical Area (MSA) level.

This paper uses a long time-series of MSA-level quarterly data from 1990 to 2018. It uses an instrumental variable approach to estimate the effects of residential real estate prices on unemployment rates using the geography-based land constraints measure of Saiz (2010) as the instrument.

The results show that changes in residential real estate prices do not have a causal effect on unemployment rates in the same quarter. However, it takes 9-12 months for an increase (decrease) in real estate prices to decrease (increase) unemployment rates. This effect is significant during both pre- and post-financial crisis periods and robust to control for the economic characteristics of MSAs.

This paper contributes to the emerging literature that studies the real effects of real estate. Particularly, the methodology and the findings can be used to investigate causal relationships between housing prices and small business development or economic growth. The findings are also of interest to policymakers and practitioners as they illustrate how and when real estate price shocks propagate to the real economy through unemployment rates.

This study’s findings have important implications for academics, policymakers and investors as they provide evidence of a snowball effect associated with shocks to real estate prices: increasing (decreasing) unemployment rates following a decrease (increase) in real estate prices exacerbates the real estate price movements and their economic consequences.

This paper analyzes a significantly longer period, from 1990 to 2018, than the existing literature. Additionally, it uses the MSA-level land unavailability measure of Saiz (2010) as an instrument to explore the effects of residential real estate prices on unemployment rates and when those effects are observed in the real economy.

The purpose of this paper is to demonstrate the potential of simulation approach for performance evaluation in a complex environment with a case of application from Indian Nuclear Power Plant.

In this work, stochastic simulation approach is applied to availability evaluation of AC Power supply system of Indian Nuclear Power Plant (INPP). In the presently followed test, maintenance policies on diesel generators and circuit breakers are considered to exactly model the practical scenario. System success logic incorporates the functional dependencies and dynamics in the sequence of operations and maintenance policies. In each iteration (random experiment), from simulated random behaviour of the system, uptime and down time are calculated based on system success logic. After sufficient number of iterations, unavailability and other required reliability measures are estimated from the results.

The subsystems of AC Power Supply System of NPP are having multi‐states due to surveillance tests and scheduled maintenance activities. In addition, the operation of DG involves starting and running (till its mission time) which is a sequential (or conditional) event. Furthermore, the redundancies and dependencies are adding to the complexity.

This paper emphasizes the importance of realistic reliability modelling in complex operational scenario with Monte‐Carlo simulation approach. Simulation procedure for evaluating the availability/reliability of repairable complex engineering systems having stand‐by tested components is presented. The same simulation model finds application in importance measures calculation, technical specification optimization and uncertainty quantification.

The purpose of this paper is to describe an integer linear programming model to schedule the maintenance crew and the maintenance tasks in a bus operating company.

The proposed methodology relies on an integer linear programming model that finds feasible maintenance schedules. It minimizes the costs associated with maintenance crew and the costs associated with unavailability. The model is applied in a real-world case study of a Portuguese bus operating company. A constructive heuristic approach is put forward, based on solving the maintenance scheduling problem for each bus separately.

The heuristic finds better solutions than the exact methods (based on branch-and-bound techniques) in a much lower computational time.

The results suggest the relevance of such heuristic approaches for maintenance scheduling in practice.

This proposed model is an effective decision-making support method that provides feasible maintenance schedules for the maintenance technicians and for the maintenance tasks in a fleet of buses. It also complies with several operational, technical and labour constraints.

Evidence suggests that unorganized manufacturing units are extremely credit-starved. The purpose of this paper is to study the effect of such credit unavailability for small firms, and to see if it has altered the erstwhile production organization in a way which has led to the withdrawal of the small firms from both input and output market, leading to increased production outsourcing between the formal and informal firms.

Based on data collected from two textile clusters in Maharashtra, India, the study shows that credit unavailability has led small firms to increasingly work for bigger firms as outsourced units. The paper uses a measure of technology and productivity to undertake logistic regression and sub-sample regressions to confirm production reorganization resulting from credit unavailability. This would provide additional insights for standard measures of intra-country intra-industry trade

The exclusion of the unorganized production units from the formal lending process has, over the years, led to a reorganization of the existing production structure, whereby the small firms are forced to work for bigger formal firms on piece-rate basis. To circumvent their credit issues, the small unorganized sector firms have increasingly started to work for bigger firms as outsourced units.

This is an original research work. The paper fulfils the identified need of addressing how the plaguing social issue of financial exclusion of unorganized firms has led to production reorganization.