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This paper aims to examine the relationship between environmental factors, risk perception and decision-making in risk management. Specifically, using attribution theory, the authors study the influence of macro-level logistical capabilities of a host country on a firm’s actual and perceived supply chain risk, and examine if this country-level factor and the firm level perception of risk affect a firm’s decision-making in risk management.

This study uses a combination of primary data from 932 manufacturing firms from 22 countries and secondary data from the logistics performance index (LPI), and empirically tests the conceptual framework using partial least squares structural equation modeling.

Key results reveal that a country’s logistical capabilities, measured using LPI, have a significant impact on managers’ risk perception. Firms located in countries with high LPI perceive lower risk in their supply chain both in the upstream and downstream, and therefore do not invest much in external integration, compared to firms in low LPI countries, and hence are exposed to high risk.

This is one of the first empirical studies linking a country’s logistical capabilities with supply chain risk perceptions, objective supply chain risk and supply chain risk management efforts of a firm using the International Manufacturing Strategy Survey database.

The great need for an optimum preventive maintenance strategy coupled with the fast‐developing condition‐monitoring techniques has given rise to the invention of relevant condition predictor (RCP)‐based maintenance approach. The main purpose of this approach is to prevent the failures due to gradual deterioration of mechanical items in order to improve system reliability and availability. This is done by monitoring relevant condition predictors of constituent maintenance significant items of the system, taking into account the availability and cost‐effectiveness of the monitoring techniques. A comprehensive review of all constituent items is carried out and a systematic approach is used to decide an optimum maintenance policy for each corresponding group of items. An optimum time to the examination of relevant condition predictors is derived mathematically with required reliability as the optimisation criterion in order to implement the RCP‐based maintenance activities.

Opportunistic maintenance gives the maintenance crew an opportunity to replace or repair those items, which are found to be defective or needs replacement in the immediate future, during the maintenance of a sub‐system or a module. This paper tries to address the questions of how to decide whether a particular item needs opportunistic maintenance, and if so how cost effective the opportunistic maintenance is in comparison to a later grounding. These questions play an important role, especially in case of complex systems containing expensive items with hard lives and condition monitoring maintenance strategies. A systematic analysis of selection of components that require opportunistic maintenance is carried out, after which genetic algorithms are used to decide whether opportunistic maintenance is cost effective or not. A hypothetical example is used to describe the methodology for genetic algorithms.

The evolution of six sigma has morphed from a method or set of techniques to a movement focused on business‐process improvement. Business processes are transformed through the successful selection and implementation of competing six sigma projects. However, the efforts to implement a six sigma process improvement initiative alone do not guarantee success. To meet aggressive schedules and tight budget constraints, a successful six sigma project needs to follow the proven define, measure, analyze, improve, and control methodology. Any slip in schedule or cost overrun is likely to offset the potential benefits achieved by implementing six sigma projects. The purpose of this paper is to focus on six sigma projects targeted at improving the overall customer satisfaction called Big Q projects. The aim is to develop a mathematical model to select one or more six sigma projects that result in the maximum benefit to the organization.

This research provides the identification of important inputs and outputs for six sigma projects that are then analyzed using data envelopment analysis (DEA) to identify projects, which result in maximum benefit. Maximum benefit here provides a Pareto optimal solution based on inputs and outputs directly related to the efficiency of the six sigma projects under study. A sensitivity analysis of efficiency measurement is also carried out to study the impact of variation in projects' inputs and outputs on project performance and to identify the critical inputs and outputs.

DEA, often used for relative efficiency analysis and productivity analysis, is now successfully constructed for six sigma project selection.

Provides a practical approach to guide the selection of six sigma projects for implementation, especially for companies with limited resources. The sensitivity analysis discussed in the paper helps to understand the uncertainties in project inputs and outputs.

This paper introduces DEA as a tool for six sigma project selection.