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Value driven maintenance (VDM) is a fairly new maintenance management methodology based on four maintenance value drivers and the formula of net present value (NPV) to calculate the value of different maintenance strategies. However, the dependability of the engineering assets needs to be assessed in order to make an estimation of the NPV. Therefore, the purpose of this paper is to critically analyse standardised indicators to find the most essential indicators for the four value drivers and for estimation of the NPV. Terminology containing performance drivers and killers are common in the field of asset management, but not many publications can be found for their detailed descriptions. One section in this paper is therefore dedicated to review these terms. A comprehensive description and classification of performance killers and drivers, and of indicators for VDM are presented in this paper.

Review of literature for technical terminology and review of standards for identification of indicators for maintenance performance measurement and NPV of maintenance.

Common description of technical terminology, as used by researchers, and identification of the most important indicators for maintenance performance measurement and the NPV of maintenance. Indicators classified under economic, technical, organizational and health, safety and environment (HSE) perspectives from EN 15341 standards are discussed and identified.

Description of emerging terminology in maintenance performance measurement adds to the consistency in communication of researchers and business stakeholders. Also, the identified maintenance performance indicators can facilitate performance measurement of organisations new to the process of measuring and analysing their performance.

The purpose of this study is to improve supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development. During the COVID-19 pandemic, the organizations have struggled a lot to maintain the supplier performance and strategic sourcing decisions in the organizational benefit. However, in this context, the organization’s agile new product development (ANPD) process must be aligned with this requirement by maintaining the inventory and jobshop scheduling. As a result, identifying ANPD indicators, performance metrics and developing a structural framework to guide practitioners at various stages for smooth adoption is essential to improve the overall performance.

A comprehensive literature review is conducted to identify jobshop scheduling, inventory management and ANPD indicators along with the performance metrics, and the hierarchical structure is developed with the help of expert opinion. The modified stepwise weight assessment ratio analysis (SWARA) and weighted aggregated sum product assurance (WASPAS) techniques, along with expert judgement, are used in this study to calculate the weights of the indicators and the ranking of the performance metrics.

As per the weight computation by SWARA method, the strategy indicators have the highest relative weight, followed by the product design indicators, management indicators, technical indicators, supply chain indicators and organization culture indicators. According to the ranking of performance metrics obtained through WASPAS, the “frequency of new product development is at the top”, followed by “advances in product design and development” and “estimated versus actual time to market”.

It is believed that the framework developed will help industrial practitioners to plan effectively to improve supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison.

The outcomes of the present study will be extremely beneficial for the industry practitioners to improve the supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison.

A unique combination of modified SWARA–WASPAS technique has been used in this study which would be beneficial for organizations willing to adopt the jobshop scheduling and inventory management and ANPD for improving supply chain performance.

This paper discusses the concept, definition and usage of Key Activity Indicators (KAIs) and their integration within a Performance Measurement and Management system (PMM).

The actual definition and application areas of the KAIs are determined through a systematic literature review. Successively, a thorough definition of Key Activity Indicators is provided, along with a set of criteria for their deployment. Lastly, a case involving a Large Scale Retail Trade (LSRT) company is reported to report an example for guiding KAIs adoption.

This research shows that the scientific background concerning KAIs is still not mature. Moreover, the paper defines the role of KAIs for measuring operational activities and their possible connection with Key Performance Indicators (KPIs).

Although KAIs have been introduced and discussed in the scientific literature; there is no evidence of criteria to deploy these indicators, leaving organizations without any guidance for their operational implementation.

From an academic standpoint, the study provides an overview of the usage of KAIs within the present scientific contributions, showing the advancements of this research field. From an industrial standpoint, the research proposes a set of criteria for the organizational deployment of KAIs.

The study investigates the concept of KAIs that, besides being originally conceived within World Class Manufacturing (WCM), has not received much attention in the scientific literature.

The purpose of this paper is to provide an overview of research and development in the measurement of maintenance performance. It considers the problems of various measuring parameters and comments on the lack of structure in and references for the measurement of maintenance performance. The main focus is to determine how value can be created for organizations by measuring maintenance performance, examining such maintenance strategies as condition‐based maintenance, reliability‐centred maintenance, e‐maintenance, etc. In other words, the objectives are to find frameworks or models that can be used to evaluate different maintenance strategies and determine the value of these frameworks for an organization.

A state‐of‐the‐art literature review has been carried out to answer the following two research questions. First, what approaches and techniques are used for maintenance performance measurement (MPM) and which MPM techniques are optimal for evaluating maintenance strategies? Second, in general, how can MPM create value for organizations and, more specifically, which system of measurement is best for which maintenance strategy?

The body of knowledge on maintenance performance is both quantitatively and qualitatively based. Quantitative approaches include economic and technical ratios, value‐based and balanced scorecards, system audits, composite formulations, and statistical and partial maintenance productivity indices. Qualitative approaches include human factors, amongst other aspects. Qualitatively based approaches are adopted because of the inherent limitations of effectively measuring a complex function such as maintenance through quantitative models. Maintenance decision makers often come to the best conclusion using heuristics, backed up by qualitative assessment, supported by quantitative measures. Both maintenance performance perspectives are included in this overview.

A comprehensive review of maintenance performance metrics is offered, aiming to give, in a condensed form, an extensive introduction to MPM and a presentation of the state of the art in this field.

To promote university education quality, performance measure indicators (PMIs) must be established. These indicators ensure the maintenance of university operating standards, encourage individual universities to work on inadequacies, and promote university competitiveness. The purpose of this paper is to address this issue.

A literature analysis was conducted and European, American and Taiwanese education evaluation indicators were used to develop the PMIs. Data were collected from the following sources: US‐MBNAQ; US News & World Report; two renowned US education journals; UK University Committee Reports; UK‐CVCP/UGC; three renowned UK education evaluation newspapers, opinions from the Ministry of Education and submissions from scholars in Australia and Taiwan. A total of 13 scholars were invited to participate in the literature review and analysis, which employed Delphi methods.

These indicators included 18 measurement dimensions, and 78 PMIs were developed. Applying these PMIs resulted in the creation of key performance indicators and a balanced scorecard (BSC) of performance measures. In addition, BSC represented a self‐evaluation performance indicator that universities could use to achieve the objective of performance management.

The PMI by means of their integration into the BSC, allow management by objectives, a method to build the performance of each department and organization.

Selecting the most meaningful performance indicators, i.e. key performance indicators (KPIs), represents one of the major challenges that companies have to face for developing an effective performance measurement system (PMS). Selecting KPIs can be interpreted as a multiple criteria decision‐making (MCDM) problem, involving a number of factors and related interdependencies. The purpose of this paper is to propose a model, based on the analytic network process (ANP), for driving managers in the selection of KIPs. The model draws upon the consideration that KPIs can be evaluated and selected on the basis of a set of criteria, theoretically founded, and the feedback dependencies between the criteria and performance indicators as well as among indicators.

Based on a review of management literature regarding the information quality required by performance measures, the paper identifies a set of criteria for selecting KIPs. The criteria form the building blocks of the proposed ANP model. The feasibility of the model is proved through its application to a real case.

The paper proposes and illustrates the practical application of an ANP‐based model for selecting KPIs. The use of the ANP makes it possible to extracts weights for setting the priorities among indicators, by taking account of mutual dependencies among indicators and criteria. This enhances the quality of the selection process.

Often managers choose KPIs without an accurate approach. The paper offers a novel model for driving managers towards the choice of KPIs through a rigorous approach, based on the ANP method. The model draws on a solid theoretical foundation and has been proven in practice.

The paper aims to review existing performance indicators in technology business incubators (TBIs) and propose some new indicators with a focus on incubation activities in higher educational institutes (HEIs) of India.

Performance indicators of various types of incubators were identified from research papers followed by interview, consultation and suggestion from experts of the subject. Nature of interrelationship between the identified indicators has been established with the help of Interpretive Structural Modelling methodology and Matrice d’impacts croisés multiplication appliquée á un classment analysis.

Number of ideas came for screening and number of ideas converted to start-ups, survival rate of incubatees is the indicators which have the highest driving power followed by time taken in screening an idea and number of failed or rejected ideas returned back into incubation. Few indicators (driving indicators) are affecting performance of other indicators as well.

Some performance indicators are proposed which can be used for measuring performance of technology incubators in India. The actual implications will be known when these findings are used to assess performance of some technology incubator. This also is the limitation of the study that some cases can be included to validate the findings of this research.

A total of 15 performance indicators for measuring performance of TBIs in Indian HEIs have been proposed. The proposed indicators will help incubator management to prioritize the efforts and resource allocation.

TBIs are looked upon as mechanism for promoting entrepreneurial culture in Indian HEIs. Their success is well linked to growth of society. This research will help technology incubators to identify the most important factors in incubation process. Performance improvement will directly affect society in whole. Culture of IEE (Innovation, Entrepreneurship and Employment ) can be achieved through technology incubators

Identification of new indicators for performance measurement of incubators in Indian HEIs is the novelty of this research. This has a lot of value due to multilevel hierarchy model.

The development of performance indicators specifically within and for polytechnic libraries is reviewed. A framework of library activities is derived on which performance measurement might be based and six indicators are identified. The practical applicability of the framework is considered in relation to polytechnic libraries and to both institutional and library aims and objectives.

The literature on warehouse performance assessments is mainly focussed on the efficiency and effectiveness of an action or activity due to customer demand and tailored fulfilment, with less attention being given to the performance measurement of each function of the warehouse and its overall productivity. Therefore, this study was aimed at revising the key warehouse performance metrics to a set of productivity measurement indicators that can be adopted internationally for benchmarking productivity performance.

A literature review and semi-structured survey questionnaire were used for this study. The importance of warehouse productivity performance was reviewed to revamp the measurement indicators. Through the use of a directed content analysis and descriptive analysis, an extensive study was carried out to analyze existing warehouse productivity indicators.

The findings of this study provide comprehensive references for practitioners and academicians for improving the classification of productivity measurements from existing key performance metrics for warehousing. Also, this paper highlights the warehouse resources related to the respective warehouse operation activities.

The study was limited to productivity performance indicators adapted from Staudt et al. (2015). Furthermore, the samples for this study comprised Malaysian academicians and practitioners in the related field. The findings can be adapted on a global scale as this study implemented general warehouse operation processes.

Consequently, the contributions of this study are that it provides relevant benchmarks for key productivity performance indicators in the warehousing sector that has worldwide applicability and the developed model provides a conceptual platform from which further theoretical and empirical developments can be carried out.