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The purpose of this paper is to highlight various aspects of business-to-business brand equity (B2BBE) and explain relative impact of marketing/advertising, research and development (R&D), human resource and distribution network to build compelling business brands that display better firm performance.

A total of 51 in-depth semi-structured interviews with distributors and industrial buyers revealed different facets of B2BBE. Generalized method of moments (GMM) was applied on a large-scale panel data set of industrial firms to estimate the effects of firms’ R&D, advertising/marketing, distribution and staff training (proxy to sources of B2BBE) on sales.

First, varying levels of product application criticality and end-customer brand stature reflect four distinct organizational purchase requirements, namely, assured performance, prestige, brand leaders and commodity. Second, a taxonomy of five sources of B2BBE (prominence, solutions, accessibility, relationships and network strength) manifests buyers’ interactive experience during the purchase cycle. Third, it illustrates the positive short-term effect of all explanatory variables coupled with the positive long-term impact of R&D on sales.

Features like B2C brand image, clear and precise product information, credit/flexible payment terms, distributor image, add-on services to the core product and upstream–downstream referrals characterize strong brands. GMM model results help managers, in budget allocation.

The originality of this paper lies in proposing a comprehensive B2BBE framework based on triangulation; deployment of a common structure to simultaneously investigate distributors and industrial buyers, to discover whether their philosophies reinforce/undermine industrial branding strategies; and suggesting the use of GMM model to arrive at actionable insights.

The primary objective of this study is to propose a robust multiobjective solution search approach for a mean-variance multiple correlated quality characteristics optimisation problem, so-called “multiple response optimisation (MRO) problem”. The solution approach needs to consider response surface (RS) model parameter uncertainties, response uncertainties, process setting sensitivity and response correlation strength to derive the robust solutions iteratively.

This study adopts a new multiobjective solution search approach to determine robust solutions for a typical mean-variance MRO formulation. A fine-tuned, non-dominated sorting genetic algorithm-II (NSGA-II) is used to derive efficient multiobjective solutions for varied mean-variance MRO problems. The iterative search considers RS model uncertainties, process setting uncertainties and response correlation structure to derive efficient fronts. The final solutions are ranked based on two different multi-criteria decision-making (MCDM) techniques.

Five different mean-variance MRO cases are selected from the literature to verify the efficacy of the proposed solution approach. Results derived from the proposed solution approach are compared and contrasted with the best solution(s) derived from other approaches suggested in the literature. Comparative results indicate significant superiorities of the top-ranked predicted robust solutions in nondominated frequency, closeness-to-target and response variabilities.

The solution approach depends on RS modelling and considers continuous search space.

In this study, promising robust solutions are expected to be more suitable for implementation than point estimate-based MOO solutions for a real-life MRO problem.

No evidence of earlier research demonstrates the superiority of a MOO-based iterative solution search approach for mean-variance MRO problems by simultaneously considering model uncertainties, response correlation and process setting sensitivity.

The purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).

We estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.

We state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.

This study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.