PurposeTo permit the system safety and reliability analysts to evaluate the criticality or risk associated with item failure modes.Design/methodology/approachThe factors considered in traditional failure mode and effect analysis (FMEA) for risk assessment are frequency of occurrence (Sf), severity (S) and detectability (Sd) of an item failure mode. Because of the subjective and qualitative nature of the information and to make the analysis more consistent and logical, an approach using fuzzy logic is proposed. In the proposed approach, these parameters are represented as members of a fuzzy set fuzzified by using appropriate membership functions and are evaluated in fuzzy inference engine, which makes use of well‐defined rule base and fuzzy logic operations to determine the criticality/riskiness level of the failure. The fuzzy conclusion is then defuzzified to get risk priority number. The higher the value of RPN, the greater will be the risk and lower the value of RPN, and the lesser will be the risk. The fuzzy linguistic assessment model was developed using toolbox platform of MATLAB 6.5 R.13.FindingsThe applicability of the proposed approach is investigated with the help of an illustrative case study from the paper industry. Fuzzy risk assessment is carried out for prioritizing failure causes of the hydraulic system, a primary element of the feeding system. The results provide an alternate ranking to that obtained by the traditional method. It is concluded from the study that the fuzzy logic‐based approach not only resolves the limitations associated with traditional methodology for RPN evaluation but also permits the experts to combine probability of occurrence (Sf), severity (S) and detectability (Sd) of failure modes in a more flexible and realistic manner by using their judgement, experience and expertise.Originality/valueThe paper integrates the use of fuzzy logic and expert database with FMEA and may prove helpful to system safety and reliability analysts while conducting failure mode and effect analysis to prioritize failures for taking corrective or remedial actions.

PurposeThe purpose of this paper is to develop an integrated model, in order to identify and classify, key criteria, and to study their role in the selection process of third party logistics (3PLs) services providers for shippers' logistics need.Design/methodology/approachIn this paper, an integrated model using interpretive structural modeling (ISM) and FMICMAC analysis has been developed to identify and classify the key selection criteria of 3PL services providers, typically identified by many researchers and practiced by the shippers for effective supply chain management. The key criteria are also modeled to find their role and mutual influence in the selection of 3PL services providers.FindingsThe key finding of this modeling helps to identify and classify the criteria, which may be further used, to identify the potential 3PL services provider. Integrated model reveals, criteria such as information technology capability; size and quality of fixed assets and quality of management as independent criteria, whereas criteria such as compatibility, long‐term relation and reputation as dependent criteria. Criterion namely flexibility in operation and delivery is found to be an autonomous criterion. The important criteria like quality of service, information sharing and trust, geographical spread and range of services, delivery performance, operational performance, financial stability, optimum cost, and surge capacity are found as the linkage criteria. Integrated model also establishes the direct and indirect relationship among various criteria, which plays a significant role in the selection process.Originality/valueThe research provide an integrated model using ISM and FMICMAC to identify and classify various key criteria required for the selection of 3PL services providers. The various key criteria have been grouped under four broad classification, namely, dependent criteria, independent criteria, autonomous criteria and linkage criteria based on their driving and dependence power, deduced from fuzzy reachability value. The model helps in the identification, classification and selection of key criteria along with their behavior, thus this research will help logistics managers to select right criteria for the selection of potential 3PL services providers for their logistics need.

No abstract

No abstract

Despite notable scientific and medical advances, broader political, socioeconomic and behavioural factors continue to undercut the response to the COVID-19 pandemic1,2. Here we convened, as part of this Delphi study, a diverse, multidisciplinary panel of 386 academic, health, non-governmental organization, government and other experts in COVID-19 response from 112 countries and territories to recommend specific actions to end this persistent global threat to public health. The panel developed a set of 41 consensus statements and 57 recommendations to governments, health systems, industry and other key stakeholders across six domains: communication; health systems; vaccination; prevention; treatment and care; and inequities. In the wake of nearly three years of fragmented global and national responses, it is instructive to note that three of the highest-ranked recommendations call for the adoption of whole-of-society and whole-of-government approaches1, while maintaining proven prevention measures using a vaccines-plus approach2 that employs a range of public health and financial support measures to complement vaccination. Other recommendations with at least 99% combined agreement advise governments and other stakeholders to improve communication, rebuild public trust and engage communities3 in the management of pandemic responses. The findings of the study, which have been further endorsed by 184 organizations globally, include points of unanimous agreement, as well as six recommendations with >5% disagreement, that provide health and social policy actions to address inadequacies in the pandemic response and help to bring this public health threat to an end.

PurposeTo help the maintenance managers/decision makers to select a suitable maintenance strategy for the components/parts associated with the system.Design/methodology/approachAn approach based on fuzzy linguistic modeling is used to select the most effective and efficient maintenance strategy. Three input parameters, i.e. historical data (I1), present data (I2) and competence of data (I3) related to failures of a component (gears), were taken to judge the effectiveness of the nature of maintenance strategies. These parameters are represented as members of a fuzzy set, combined by matching them against (if‐then) rules in rule base, evaluated in fuzzy inference system (Mamdani, min‐max type) and then defuzzified to assess the capability or effectiveness of maintenance strategy.FindingsThe results show how the fuzzy logic approach translates vague, ambiguous, qualitative and imprecise information into numerical/quantitative terms, which helps to identify the most informative and efficient maintenance strategy. From the computed performance index values for each maintenance strategy it is observed that proactive (CBM) and aggressive maintenance strategy (TPM) are far better compared with traditional, reactive (BDM) maintenance strategy.Originality/valueThe paper integrates fuzzy logic modeling – a knowledge‐based approach with database obtained through maintenance logs, historical records, equipment manuals and expert judgement, which might prove beneficial for maintenance managers/engineers/practitioners to select a suitable maintenance strategy for each piece of equipment associated with the systems.

Purpose -To examine the need to develop, practice and implement such maintenance practices, which not only reduce sudden sporadic failures in semi-automated cells but also reduce both operation and maintenance (O&M) costs. Design/methodology/approach -A case-based approach in conjunction with standard tools, techniques and practices is used to discuss various issues related with TPM implementation in a semi-automated cell. Findings -The findings indicate that TPM not only leads to increase in efficiency and effectiveness of manufacturing systems, measured in terms of OEE index, by reducing the wastages but also prepares the plant to meet the challenges put forward by globally competing economies to achieve world class manufacturing (WCM) status. Originality/value -The paper presents an interesting investigation of TPM implementation issues which may help the managers/practitioners to prepare their plants/units to meet the challenges of competitive manufacturing in twenty-first century by adopting and implementing TPM.

PurposeThe purpose of this paper is to model the key variables of logistics outsourcing relationship between shippers and logistics service providers (LSPs) and to study their influence on productivity and competitiveness of the shipper company.Design/methodology/approachIn this paper, an interpretive structural modeling (ISM) based approach has been used to model the variables of logistics outsourcing relationship. Various variables, used by researchers and practiced by the shippers for effective management of logistics outsourcing relationship have been identified. These variables have been classified as enablers and outcome variable. Enablers are those variables that boost the “relationship bond” between shippers and LSPs, while outcome variables are the resultant variables arising out of outsourcing relationship between shippers and LSPs.FindingsA key finding of this modeling helps shippers as well as LSPs to take various initiatives, in order to have prosperous, outsourcing relationship between shippers and LSPs. Top management from both shippers as well as LSPs should focus, on improving on the enablers such as trust or commitment, direct assistance, long term contract, evaluation of supplier performance, practices of TQM and JIT to add distinctive values, and top management support.Originality/valueIn this research, an interpretation in terms of driving and dependence powers has been carried out for enablers and outcome variables of logistics outsourcing relationship. Those variables found possessing strong driving power in the ISM model, need to be taken care on a priority basis because of their impact on dependent variables. A variable emerging with high dependence contributes to productivity enhancement and competitiveness in a logistical supply chain.