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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.

This study primarily aims to develop and solve an enhanced optimisation model for an assembly product multi-period inbound inventory routing problem (IRP). The many-to-one (inbound) model considers the bill of materials (BOM), supply failure risks (SFR) and customer demand uncertainty. The secondary objective is to study the influence of potential time-dependent model variables on the overall supply network costs based on a full factorial design of experiments (DOE).

A five-step solution approach is proposed to derive the optimal inventory levels, best sourcing strategy and vehicle route plans for a multi-period discrete manufacturing product assembly IRP. The proposed approach considers an optimal risk mitigation strategy by considering less risk-prone suppliers to deliver the required components in a specific period. A mixed-integer linear programming formulation was solved to derive the optimal supply network costs.

The simulation results indicate that lower demand variation, lower component price and higher supply capacity can provide superior cost performance for an inbound supply network. The results also demonstrate that increasing supply capacity does not necessarily decrease product shortages. However, when demand variation is high, product shortages are reduced at the expense of the supply network cost.

A two-echelon supply network for a single assembled discrete product with homogeneous vehicle fleet availability was considered in this study.

The proposed multi-period inbound IRP model considers realistic SFR, customer demand uncertainties and product assembly requirements based on a specific BOM. The mathematical model includes various practical aspects, such as supply capacity constraints, supplier management costs and target service-level requirements. A sensitivity analysis based on a full factorial DOE provides new insights that can aid practitioners in real-life decision-making.

In the context of sequential multistage utilitarian service processes, the purpose of this study is to develop and validate propositions to study the impact of service quality (SQ) perceptions developed in intermediate stages, along with the impact of service gestalt characteristics, such as peak and end experiences, on quality perception at each stage and on overall service quality (OSQ) perception. The cascade phenomenon (interdependency between process stages) is considered in the evaluation of OSQ perception of customer, who experiences service through a series of planned, distinct and partitioned sequential stages.

In this paper, a conceptual framework is used to evolve the propositions. Subsequently, propositions are tested in three different utilitarian service contexts wherein customer survey was conducted for feedback on attributes at each stage, summary perception evaluations of each stage and OSQ evaluation of multistage process. Peak experiences, considered for OSQ evaluation, were defined by a suitable statistical technique. Ordinal logistic regression with nested models is the technique used for analyzing the data.

This work reveals significant cascade effect of summary evaluation of intermediate stages on the subsequent stage. Peak customer experience (negative or positive) is observed to be marginally significant on intermediate stage and OSQ evaluation. In addition, OSQ is observed to be influenced by summary perception evaluations of intermediate stages, which leads to better model adequacy. Finally, among all the stages, end stage performance is observed to have a significant impact on the overall multistage SQ.

The findings suggest that in view of the cascade effect of intermediate stages, managers need to allocate resources to ensure that all stages are performing at an adequate level instead of only focusing on improving peaks and end effects of customer experiences. The proposed approach is easy to implement and suitable for evaluating SQ and OSQ in varied multistage sequential utilitarian service environment.

An integrated approach for evaluation of SQ in sequential multistage utilitarian service processes is proposed from the perspective of cascade effect of intermediate stages and peak and end effects on OSQ perception.

This paper attempts to address three key objectives. The primary aim is to enhance sourcing strategy for a centralized and coordinated multitier multiple suppliers networks with uncertain demand and supplier failure risks. The second objective is to enumerate all possible practical supplier(s) failure scenarios and quantify expected loss of demand cost. Finally, the work illustrates statistical experimentation to identify “influential” variables that can significantly impact the expected supply network and loss costs.

A seven-step solution framework is proposed to derive an optimal sourcing strategy for the specific network configuration with varied supplier failure scenarios. Five distinct models are formulated to address all possible scenarios of supplier failure events. Mixed-integer nonlinear programming technique is used to derive expected supply network cost and loss cost. The solution framework is verified using a real-life case.

A cross-case analysis indicates that an increase in suppliers' failure risk (SFR) probabilities or customer demand rate increases the expected loss of demand costs for a multitier supply network. Besides, an increase in unit component prices increases the expected supply network cost.

A two-tier automotive supply network for a single product is considered for all case studies.

The enhanced strategy can facilitate practitioners enumerate different supply network failure scenarios and implement the best solution.

There is no evidence of earlier research to derive optimal sourcing strategy for a centralized, coordinated multitier multiple supplier's network, considering demand uncertainties and SFR.

The purpose of this study is to propose an effective unsupervised one-class-classifier (OCC) support vector machine (SVM)-based single multivariate control chart (OCC-SVM) to simultaneously monitor “location” and “scale” shifts of a manufacturing process.

The step-by-step approach to developing, implementing and fine-tuning the intrinsic parameters of the OCC-SVM chart is demonstrated based on simulation and two real-life case examples.

A comparative study, considering varied known and unknown response distributions, indicates that the OCC-SVM is highly effective in detecting process shifts of samples with individual observations. OCC-SVM chart also shows promising results for samples with a rational subgroup of observations. In addition, the results also indicate that the performance of OCC-SVM is unaffected by the small reference sample size.

The sample responses are considered identically distributed with no significant multivariate autocorrelation between sample observations.

The proposed easy-to-implement chart shows satisfactory performance to detect an out-of-control signal with known or unknown response distributions.

Various multivariate (e.g. parametric or nonparametric) control chart(s) are recommended to monitor the mean (e.g. location) and variance (e.g. scale) of multiple correlated responses in a manufacturing process. However, real-life implementation of a parametric control chart may be complex due to its restrictive response distribution assumptions. There is no evidence of work in the open literature that demonstrates the suitability of an unsupervised OCC-SVM chart to simultaneously monitor “location” and “scale” shifts of multivariate responses. Thus, a new efficient OCC-SVM single chart approach is proposed to address this gap to monitor a multivariate manufacturing process with unknown response distributions.

The purpose of this paper is to develop a tool, “The Excellence Grid,” to categorize attributes on the basis of their ability to impact customer perception of “excellence” in service compared to perception of “good” service. In addition, provide a three dimensional (3D) model for excellence-performance analysis, which can aid managers in formalizing the strategies for building perceptions of excellence about the service.

The positive zone of performance is analyzed through a two-function modeling technique of ordinal logistic regression (OLR) with the non-proportional odds to categorize attributes on grid. Tool is applied to two case studies to validate and establish the asymmetric impact of attributes on perceptions of “good service” and “excellent service.”

Similar to the Kano model for impact of attributes on positive and negative performances, findings from cases confirm the asymmetric impact of attributes on the positive zone of performance and establish “Excellence Grid” as a means to categorize attributes as drivers of excellence.

The “Excellence Grid” tool is expected to empower managers to focus on strategies directed toward the goal of “service excellence” and recommends that managers should not only strive for process improvement, but also sharpen the external communication of service excellence.

The “Excellence Grid” and the “3D Excellence-Performance model,” proposed in this research, are expected to enrich the body of knowledge on operational tools to achieve service excellence. Using parameter estimates of the two-function model of OLR for service quality has not yet been reported in open literature.

The purpose of this paper is to develop a new easy-to-implement distribution-free integrated multivariate statistical process control (MSPC) approach with an ability to recognize out-of-control points, identify the key influential variable for the out-of-control state, and determine necessary changes to achieve the state of statistical control.

The proposed approach integrates the control chart technique, the Mahalanobis-Taguchi System concept, the Andrews function plot, and nonlinear optimization for multivariate process control. Mahalanobis distance, Taguchi’s orthogonal array, and the main effect plot concept are used to identify the key influential variable responsible for the out-of-control situation. The Andrews function plot and nonlinear optimization help to identify direction and necessary correction to regain the state of statistical control. Finally, two different real life case studies illustrate the suitability of the approach.

The case studies illustrate the potential of the proposed integrated multivariate process control approach for easy implementation in varied manufacturing and process industries. In addition, the case studies also reveal that the multivariate out-of-control state is primarily contributed by a single influential variable.

The approach is limited to the situation in which a single influential variable contributes to out-of-control situation. The number and type of cases used are also limited and thus generalization may not be debated. Further research is necessary with varied case situations to refine the approach and prove its extensive applicability.

The proposed approach does not require multivariate normality assumption and thus provides greater flexibility for the industry practitioners. The approach is also easy to implement and requires minimal programming effort. A simple application Microsoft Excel is suitable for online implementation of this approach.

The key steps of the MSPC approach are identifying the out-of-control point, diagnosing the out-of-control point, identifying the “influential” variable responsible for the out-of-control state, and determining the necessary direction and the amount of adjustment required to achieve the state of control. Most of the approaches reported in open literature are focused only until identifying influencing variable, with many restrictive assumptions. This paper addresses all key steps in a single integrated distribution-free approach, which is easy to implement in real time.

The purpose of this paper is to address three key objectives. The first is the proposal of an enhanced multiobjective optimisation (MOO) solution approach for the mean and mean-variance optimisation of multiple “quality characteristics” (or “responses”), considering predictive uncertainties. The second objective is comparing the solution qualities of the proposed approach with those of existing approaches. The third objective is the proposal of a modified non-dominated sorting genetic algorithm-II (NSGA-II), which improves the solution quality for multiple response optimisation (MRO) problems.

The proposed solution approach integrates empirical response surface (RS) models, a simultaneous prediction interval-based MOO iterative search, and the multi-criteria decision-making (MCDM) technique to select the best implementable efficient solutions.

Implementation of the proposed approach in varied MRO problems demonstrates a significant improvement in the solution quality in worst-case scenarios. Moreover, the results indicate that the solution quality of the modified NSGA-II largely outperforms those of two existing MOO solution strategies.

The enhanced MOO solution approach is limited to parametric RS prediction models and continuous search spaces.

The best-ranked solutions according to the proposed approach are derived considering the model predictive uncertainties and MCDM technique. These solutions (or process setting conditions) are expected to be more reliable for satisfying customer specification compared to point estimate-based MOO solutions in real-life implementation.

No evidence exists of earlier research that has demonstrated the suitability and superiority of an MOO solution approach for both mean and mean-variance MRO problems, considering RS uncertainties. Furthermore, this work illustrates the step-by-step implementation results of the proposed approach for the six selected MRO problems.

Multiple stages of procurement for a product in a supply chain (SC) altogether form a “multi-Tier” supply network. The purpose of this paper is to develop and verify a systematic solution approach to ascertain the realistic cost advantage of a coordinated centralized sourcing strategy as compared to an isolated decentralized sourcing strategy for a multi-tier supply network under demand uncertainty.

The proposed systematic solution approach consists of seven steps to compare and contrast the cost advantage of a centralized coordinated sourcing strategy over a decentralized stage-wise sourcing strategy for a multi-tier supply network. A real-life automotive industry case analysis of two distinct products provides sufficient empirical evidence on the expected cost advantage of centralized coordinated sourcing strategy under demand uncertainty.

The case analysis affirms the practicability of the proposed seven-step solution approach to determine the realistic cost advantage of coordinated sourcing.

The scope of this research is restricted to a single product and two-tier supply network analysis. This research work also considers a restrictive assumption of negligible coordination cost.

The suitability of the proposed solution approach is verified using real-life case examples. This research provides theoretical insights and factual evidence to SC practitioners, so as to adopt a centralized sourcing strategy in a varied manufacturing environment.

There is no evidence of a systematic step-by-step solution approach to determine the cost advantage of a coordinated sourcing strategy over an isolated decentralized sourcing strategy for a multi-tier supply network under demand uncertainty.

The purpose of this study is to understand through the qualitative assessment, how the current strategy plans are geared toward reducing urban flood risks and achieving Sustainable Development Goals (SDGs) 11 and 13.

The Bangkok Metropolitan Region (BMR) plays a major role in Thailand’s economic development. Thus, when the 2011 Thailand flood disaster occurred, BMR suffered major economic and social losses, which impacted the rest of the country. This mega disaster prompted policymakers, the academe and other relevant stakeholders to reevaluate and amend the current urban flood risk reduction measures and governance. The present study attempts to evaluate and compare the post-2011 Thailand flood disaster strategy and master plans, policies and reports that directly and indirectly reduce urban flood risks in the provinces of BMR. Basing on SDGs 11 and 13 targets that impact urban flood risk and resilience, a set of criteria was developed to screen, score and asses the selected documents. A screening process of three levels are conducted to limit the documents to be reviewed, and subsequent content analysis for scoring also has been done.

The projected results indicate the need for improved and increased number of localized strategic plans and policies, which are more comprehensive and integrated as risk governance documents.

Furthermore, it is projected that there is need to integrate measures to increase adaptive capacity for BMR.

This study is original, and methodology can be replicated for other urban areas for flood risks and resilience assessment.