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Maintenance actions are initiated by the annual maintenance budget (AMB) allocated for a plant system. It ensures that the assets are kept in a healthy and reliable state. The purpose of this paper is to scientifically evaluate the AMB for an air conditioning (AC) plant system, which forms an integral part of facility management.

This paper estimates the AMB of an AC system for specific contextual conditions using incidence matrix of the budget influencing variables. The diagonal elements of the matrix represent the values of the budget variables, whereas the off-diagonal elements represent the degree of influence among these. The maintenance budget function is derived from this matrix and is used to evaluate the AMB for the AC system as a percentage of its asset replacement value (ARV).

The AMB for the AC plant system considered in the work is evaluated as 11.1 per cent of ARV for the conditions unique to its context. The actual maintenance expenditure is lower by 7.6 per cent than the estimated value of the budget.

The results of the methodology are a good guide for practicing facility managers. The shortcoming of the methodology is that it relies on fixed weights of the inter-relations among the budget variables, which may not be necessarily true.

This approach is expected to aid the maintenance managers in envisaging the AMB for their plant systems which operate under contextual conditions specific to their plant.

There is no evidence to indicate existence of maintenance budget evaluation methodology for an AC plant system based on its contextual conditions. This paper attempts to fill this gap.

Considerable amount of purchases in business-to-business (B2B) markets make through the tendering process. As technology keeps driving B2B procurement, both the supplier/contractor and buyer firms have settled down in their respective roles in the electronic-tendering environment. Researchers have ignored the supplier-side e-tender-driven marketing process that might lead to substantively successful financial performance. The purpose of this study is to improve the performance of an e-tender-driven marketing process of an original equipment manufacturer (OEM) incorporating the stakeholder's inputs.

Discrete event simulation modelling (DESM) has been used as a methodology to model, analyse and improve the process with the involvement of stakeholders at every stage of the study. Different scenarios are analysed to identify the near-optimal scenario based on agreed-upon key performance indicators.

Scenario that incorporated man-power sharing and eliminating avoidable activities gives the near-optimal solution for implementation.

This study highlights that better insights can be gained by adopting the process-oriented view of the marketing–operations interface. Embracing a stakeholder-based consultative approach gives research a more practical outlook and reduces the gap between theory and practice. Suggestions for further research are provided.

B2B organizations, where lines between marketing and operations are blurred, can improve their marketing processes by implementing operations research tools.

This study provides an attempt to improve the performance of a supplier-side e-tender-driven marketing process of an OEM using the DESM methodology incorporating stakeholder's inputs.

Material handling (MH) is an important facility in any manufacturing system. It facilitates the transport of in-process material from one workstation (WS) to another. MH devices do imply incurring capital costs and, therefore, minimizing their deployment without compromising on smooth material flow will ensure savings in addition to the optimal use of productive shop floor space and, avoid space cluttering. The purpose of this paper is to evaluate the minimal network that connects all the WSs, therefore ensuring economic and safe manufacturing operations.

Graph theoretical approach and Prim’s algorithm for minimal spanning tree is used to evaluate the minimal span of the MH devices. The algorithm is initialized by translating the graph of WSs into a distance matrix to evaluate the minimal MH network.

The minimal length of the MH devices is evaluated for a typical case study.

The step-by step methodology explained in the manuscript acts as a good guide for practicing operational managers. The shortcoming of the methodology is that, it presumes the use of modular MH devices that will need to be reconfigured based on dynamic changes to the manufacturing system.

The methodology is explained in detail to enable the practicing managers to use it for designing their MH networks in any manufacturing system.

There is no evidence to indicate the use of minimal spanning tree algorithm for design of MH networks in a manufacturing system. This paper attempts to fill this gap.

In the past four decades, substantial air traffic growth has triggered enthusiasm in the aviation sector. At the same time, this growth has posed challenges to its financial and environmental sustainability commitments. A buzz has been centered on introducing and supporting aviation sustainability initiatives. These challenges have led to acknowledging the need to reduce aviation fuel consumption, a function of multiple factors. The different stakeholders having a diverse type of interplay govern the effective implementation of the factors at different decision levels (strategic, tactical and operational). Thus, the present study aims to critically examine various decision levels involved to understand opportunities and requirements related to aviation sustainability.

In this study, the best–worst method is used to quantify different decision levels’ role on various factors affecting aviation fuel consumption.

The results of this study signify that tactical-level decisions are most influential in reducing aviation fuel consumption with the highest impact (0.41) followed by operational-level decisions (0.30) and strategic-level decisions (0.29), respectively.

The results point toward the critical role of middle-level hierarchy, i.e. aircraft manufacturers, airlines and others in the aviation industry’s sustainable growth. Thus, middle-level stakeholders must be inspired and empowered to act, being at the center they link the other two levels.

This study has added to the body of knowledge by exploring the decision-making competencies needed by different aviation sector stakeholders. It also presents the possible options available in the sector and the role of stakeholders at different levels in exploiting and implementing the sustainable aviation sector changes.

Based on social science theories of customer's action such as theory of planned behaviour, theory of reasoned action, and technology acceptance model, this paper adopts belief–attitude–intention model to study impact of perceived process-belief of different stages of e-service delivery system process (e-SDSP), i.e., searching process belief (SPB), agreement process belief (APB), fulfilment process belief (FPB) and after-sales service process belief (ASPB) on customer attitude and intention towards service providers. The study also focuses on the mediating effect of customer attitude on the relationship between process-beliefs of different stages of e-SDSP and their behavioural intention.

A quantitative method has been employed using data collected from 414 Indian e-retail customers. Structural equation modelling with bootstrap estimation is used to find the mediating effect of attitude.

The findings suggest that SPB and ASPB directly impact the customer attitude while APB and FPB directly impact the customer behavioural intention. The study also finds that customer attitude towards e-retailers fully mediates the effect of SPB and ASPB on the behavioural intention but there are no mediating effects for APB and FPB.

The sample used may not be generalizable for India, given its huge diversity and population. As the sample considers only Indian e-retail customers, this study may lack generalizability across countries.

In our knowledge, this study is the first step to conceptualize the process-oriented customer's perceived belief of different stages of e-SDSP and how these beliefs impact the customers' attitude and intention towards the e-retailers. The findings offer insight to managers on how they can create and cultivate customer happiness and positive behavioural intention by enhanced customer journey throughout the e-SDSP.

E-government quality (e-GovQual) and e-government user value (e-GUV) are multidimensional concepts. While previous studies have identified apparent factors influencing e-government satisfaction (e-GovSat) and e-government adoption intention (e-GovAI), such as e-GovQual and e-GUV, but they have neglected to explain the influence of the dimensions of these two concepts. The purpose of this research is to study e-government service value chain (e-GSVC) one-GovQual dimensions, e-GUV dimensions, e-GovSat and e-GovAI.

The study employs a quantitative method to test the hypotheses and validate the proposed model. Data are collected from 378 e-government users across different parts of India comprising of different demographic characteristics. The model is analyzed using structural equation modeling.

The findings highlight the impact of the dimensions of e-GovQual (efficiency, trust, reliability and citizen support) on the dimensions of e-GUV (functional, economic, social and emotional value) as e-GUV dimensions affect e-GovSat, which in turn influences e-GovAI. The results validate the e-GSVC and also stress the partial mediating role of the dimensions of e-GUV on the relationship between the dimensions of e-GovQual and e-GovSat.

The sample size of 378 may not be a proper representation of a country like India, which has huge diversity within its vast population.

The study offers practitioners a clear picture and a useful guide to better understand the drivers of value, satisfaction and adoption in the case of e-government users.

This study is probably the first attempt toward demonstrating the process influencing e-GovSat via e-GUV dimensions originating from excellent e-GovQual dimensions to ultimately trigger e-GovAI.

The purpose of this paper is to propose a methodology to evaluate the annual maintenance budget (AMB) for a plant system as a percentage of its asset replacement value (ARV).

Variables influencing the budget for the plant system are identified and modeled in terms of a plant maintenance Budget digraph. The nodes in the digraph represent the function of budget influencing variables and edges represent the degree of influence among these. The plant maintenance budget function is derived from the equivalent matrix of the plant maintenance budget digraph and is used to evaluate the AMB for the plant system as a percentage of its ARV. The presented model is illustrated with an example.

The proposed methodology enables the maintenance managers and practicing engineers to evaluate the AMB for maintenance tasks based on plant specific variables that can vary from one plant to another, without resorting to general rules-of-thumb method of budgeting or to expert judgment.

The methodology suggested does not claim to cover all aspects of budgeting and is confined to direct maintenance costs.

A fair estimation of AMB for a plant system gives an appropriate direction to maintenance actions and ensures that the assets will be kept in healthy and reliable state. This is desirable because it will ensure that maintenance resources are appropriately benchmarked and the resources are utilized judiciously. This ensures maintenance effectiveness.

The paper presents a structural approach to budgeting for maintenance tasks. This approach is valuable for plant managers, with ease in implementation.

This is a thought-leadership interview with digital-era experts Thomas Davenport and Nitin Mittal on the strategic desirability and implications for legacy companies on being fully committed to becoming AI-driven businesses.

An interview with thought-leaders in the area of digital strategy.

The interview explains what it means to be an AI-driven company and how to manage the kind of transformation that legacy will need to go through to become AI-fueled leaders in their sector.

The main value is the direct dialog with digital-era experts on their latest research insights.