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The chapter examines the sources of total factor productivity growth (TFPG) of the 2-digit manufacturing industries as well as total manufacturing industry of Gujarat during the period from 1981–82 to 2010–11, using a stochastic frontier approach. The empirical finding clearly states that although factor accumulations as well as resource allocations in most of the 2-digit manufacturing industries of the state have been improved during the postreform period, technological progress (TP) and technical efficiency change (TEC) of the same have deteriorated in most industries of the state during that period. As a result TFPG in the major manufacturing industries as well as total manufacturing industry of the state have declined because the combined effect of their improvement in scale effect (SC) and allocation efficiency effect (AEC) could not offset the declining effect of both the TP and TEC of the same during that period. In this context, the government should take some policy initiatives to improve productive efficiency of the organized manufacturing industries in Gujarat. Once efficiency increases, it enhances competitiveness, thereby increasing productivity growth and its different sources of organized manufacturing industries of the state.

The study estimates total factor productivity growth (TFPG) and its components of the 4-digit manufacturing industries of chemical and chemical products in India from 1998–1999 to 2017–2018, pre-economic crises period (from 1998–1999 to 2007–2008) and post-economic crises period (from 2008–2009 to 2017–2018) using frontier approaches, that is, data envelope analysis DEA and stochastic frontier approach (SFA). The components of TFPG are technological progress (TP), technical efficiency change (TEC) and economic scale change (SC). It is found that the growth rates of total factor productivity (TFP) in most of the 4-digit industries of chemical and chemical products in India increased during the post-economic crises period (from 2008–2009 to 2017–2018) and the increase in TFPG of them during that period is mainly accounted for by the increase in TP of the same during that period. The TEC of almost all the industries remains the same, however, declined during the post-economic crises (from 2008–2009 to 2017–2018) and SC of them remains very low or even negative during the aforementioned study periods.

The purpose of this paper is to identify the tools, methods and models that UK manufacturing companies adopt and apply in order to achieve resiliency and economic sustainability. The results of this work can assist in developing the foundations for defining a new joint resiliency/sustainability paradigm to assist industry.

Through a detailed, triangulated secondary data analysis and industry survey, the authors initially identify and then analyse the key resiliency and sustainability characteristics surrounding manufacturing operations. This paper initially reviews key literatures around resiliency and sustainability models and frameworks and subsequently draws out their key features and weaknesses. The work then details the research survey undertaken in to manufacturing companies aimed at identifying the resiliency/sustainability approaches that are adopted in companies. A sample of 72 manufacturing companies are used in the survey and from which the results are based.

Through analysing the fundamental business data of sales and manufacturing costs for 72 manufacturing companies, the authors cluster the companies in to four key manufacturing profiles. The work then shows through a more detailed analysis of the profiles that companies which are sustainable and more resilient in nature are, better engaged and connected to the development and application of resiliency and sustainability models. It was found that companies who seem to struggle in achieving economic sustainability or lack the ability to bounce back from various set-backs either do not employ such models or at best apply tools and techniques in an ad hoc manner.

The paper provides key insights in to the adoption of tools, techniques and models surrounding the achievement of resiliency and sustainability in manufacturing companies. In so doing, the paper offers a new view on these issues and with the profiling exercise undertaken, companies will be able to identify their position in relation to the survey companies. This can be of benefit to the wider industrial and academic community. The development of a qualitative assessment around a relatively small sample size has its obvious limitations and it is crucial that further work with a range of companies in the area of manufacturing sustainability is key to developing (and also validating) a comprehensive set of resiliency and sustainability characteristics.

The paper highlights the issues surrounding existing academic resiliency/sustainability models and through the industry survey, it provides further information on where UK manufacturing companies are on adopting specific resiliency/sustainability models. The work suggests that the resiliency/sustainability landscape of UK manufacturing companies is much more complex and that a single strategic approach towards achieving improved manufacturing performance is somewhat dated and ineffective.

The development of a set of resiliency/sustainability profiles including the identification of the specific tools and techniques adopted by industry is aimed at tackling directly the issues of improving company performance and is considered by the authors as one of a kind. The results of the survey provide essential information on the resiliency/sustainability landscape of UK manufacturing companies.

The purpose of this paper is to compare light fastness assessments by exposure of fabric dyes with various dyes in daylight and an artificial xenon arc lamp.

Cotton fabric dyed with 66 reactive, vat, azoic and direct dyes dyed in different depths were exposed to daylight and Xenon arc lamp for assessment of light fastness by standard methods. The light fastness rating and fading hours by the two methods were analysed and compared statistically.

The correlation between the corresponding light fastness rating (LFR) measured in Xenotest and daylight is quite high (0.93). The logarithmic correlation coefficients between fading hour (FH) and LFR in Xenotest and daylight are 0.95 and 0.88, respectively. For Xenotest, the assessed LFRs are same as those predicted from geometric progression up to LFR of 5.5, and thereafter, the former is higher. On the other hand, in the case of daylight, the assessed LFR is lower. Assessments for three successive seasons showed high repeatability in case of Xenotest and moderate repeatability in case of daylight. Assessments for three successive seasons showed high repeatability in case of Xenotest and moderate repeatability in case of daylight.

The exposure conditions in daylight cannot be controlled or standardised, whereas the exposure in Xenon arc lamp in the accelerated fading instrument can be strictly controlled. These differences in exposure control may affect the repeatability of experimental findings.

Inconsistent ratings may be because of little deterioration of samples during storage, as well as seasonal variation of daylight.

There are no direct social implications.

The researches on the comparison of the two light fastness assessment methods have not been reported in any recent publication to the best our knowledge.

Vendor selection is the main activity in a sourcing decision, which is a strategic decision in that it leads enterprises to eliminate costs and improve their performance. However, an inappropriate selection may compromise the financial and operational status of the enterprise. But vendor selection is a complex, multi-criteria decision-making process because different and conflicting criteria have to be considered and assessed in order to find consistent suppliers. Consequently, evaluating and selecting the best vendor is the key to successful business. Traditionally, vendors are normally selected on the basis of traditional criteria (TC), such as costs and quality, neglecting resilience criteria (RC) (e.g. agility and flexibility). Thus, enterprises ultimately realize that a selecting method which involves TC as the only ones is inefficient and needs to be changed. The paper aims to discuss this issue.

This study was set in motion by a problem in practice. It aims to provide a user-friendly decision-making tool for selecting the best vendor from a group which submitted their tenders for implementing a proposed radio frequency identification (RFID)-based passport tracking system (Dukyil et al., 2017). The main traditional and resilience (“trasilience” henceforth) selection criteria were identified in a unified framework in collaboration with experts in the institution. Next, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) algorithm was used to determine the relative importance of each criterion and the weights thus obtained were integrated into the ELimination Et Choix Traduisant la REalité (ELECTRE) algorithm. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm was also applied, to evaluate the performance of vendors and to select the best one. The qualitative evaluation of the criteria and the vendors was based on four decision makers. Finally, the Spearman’s rank correlation coefficient (SRCC) approach was applied to obtain the statistical difference between the ranking orders obtained from the two algorithms.

The efficiency of the proposed decision-making tool was evident from the real-case study of six tenders submitted for implementing a RFID-based passport tracking system. The SRCC also turned out a “very strong” association value between TOPSIS and ELECTRE.

The developed trasilient decision-making tool can easily be used to solve similar vendor or supplier selection problem. Moreover, other criteria can be added to fit other cases. Later, the tool was made available to the institution under study for solving future evaluation problems.

The literature shows that none of the previous papers presented an integrated trasilient approach that considers RC and TC simultaneously. This study presents a new trasilience tool for selecting a vendor.