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This paper evaluates the working capital management (WCM) efficiency of the Indian manufacturing industries through data envelopment analysis (DEA) and empirically investigates the influence of several exogenous variables on the WCM efficiency.

WCM efficiency was calculated using BCC input-oriented DEA model. Further, the panel data fixed effect model was used on a sample of 1391 Indian manufacturing firms spread across nine industries, covering the period from 2008 to 2019.

Firstly, the WCM efficiency of Indian manufacturing industries has been stable over the analysis period. Secondly, the capacity to generate internal resources, size, age, productivity, gross domestic product and interest rate significantly influence WCM efficiency.

First, the selected study period has observed various economic uncertainties including demonetization and recession, so the scenario might differ in normal conditions or country-wise. Second, the findings might not be generalizable to the developed economies, since the current study sample belongs to a developing economy, which further provides scope for comparative study.

An efficient model for managing the working capital comprising most vital determinants could enhance the firms' valuation and goodwill. Also, this study would be helpful for financial executives, manufacturers, policymakers, investors, researchers and other stakeholders.

This study estimates the industry-wise WCM efficiency of the Indian manufacturing sector and suggests measures to the concerned parties on areas to focus on and provide evidence on the estimated relationships of firm-level and macroeconomic determinants with WCM efficiency.

This study examines the innovation-efficiency linkage for Indian and Chinese manufacturing and service firms.

We applied the stochastic production and cost frontier approach to determine the output and cost efficiency of the firms surveyed in World Bank enterprise surveys. We then used both unconditional and conditional propensity score matching (PSM) estimation techniques to examine the effects of innovation as well as R&D on output and cost efficiency of the firms surveyed.

The study results suggest that innovation-efficiency linkage varies between countries and sectors. Innovations significantly raise output and cost efficiency of Indian manufacturing firms, whereas innovations in Chinese manufacturing firms are cost-oriented and negatively affect output efficiency. For the service firms of both countries, innovations are significantly positively linked with output and cost efficiency. The study also suggests that R&D acts as a crucial moderator for innovation-efficiency linkage for Chinese manufacturing firms but not for Indian firms, and the interaction effects of innovations are not substantially higher in magnitude than their individual effects. Finally, conditional PSM results suggest knowledge spillover for effective innovations of Indian firms, whereas R&D is a must for substantial innovation-efficiency linkage in Chinese firms.

This study offers quite a few crucial policy decisions concerning the relationship between innovation and efficiency as well as the moderation effect of R&D on innovation-efficiency linkage. It concludes that the effects of innovation on firms' efficiency and the role of R&D as a moderator of the innovation-efficiency relationship differ between India and China across the manufacturing and service sectors.

The purpose of this paper is to address the gap between definition and practical aspects of production efficiency in mass customization (MC). The paper summarizes all major issues impacting efficiency in MC. Also, the paper reviews metrics, relationship between various parameters and provides a best practices benchmark toolkit to achieve higher machine efficiencies.

The paper identified and categorized multiple challenges impacting machine efficiency in MC through a literature review spanning over three decades, and also ranked the identified issue-based parameters. Top issues were found varying across different types of industries identified through the review. Metrics pertaining to efficiency and degree of MC are reviewed in the paper. A chronological review of issues is presented, and a chain diagram is built in the paper. Toolkit of best practices created with solution strategies and tools are summarized through the review.

The paper found that MC reasonably impacts machine efficiency which needs to be addressed. Major issues through literature review-based ranking are uncovered, and worldwide research trend and comparison are presented. Active research in this area is observed to be at its peak since 2010. The extensive use of strategies and benchmark toolkit for improving efficiency are summarized.

Ranking of issues has been done through a literature review; hence, there can be skewness depending on the frequency of issues researched by various authors in various areas of industries.

This paper is useful for manufacturing managers and companies willing to increase the size of their product portfolio and choices within their available resources without compromising machine efficiencies and, thereby, the cost. The identified issues help in providing a comprehensive issue list to the academia.

This paper describes what is believed to be the first study that explicitly examines the issues faced in achieving machine efficiency while manufacturing in an MC environment.

This paper aims to describe the creation of innovative and intelligent systems to optimise energy efficiency in manufacturing. The systems monitor energy consumption using ambient intelligence (AmI) and knowledge management (KM) technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems.

Energy consumption data (ECD) are processed within a service-oriented architecture-based platform. The platform provides condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase and continuous improvement/optimisation of energy efficiency. The systems monitor energy consumption using AmI and KM technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems.

The systems produce an improvement in energy efficiency in manufacturing small- and medium-sized enterprises (SMEs). The systems provide more comprehensive information about energy use and some knowledge-based support.

Prototype systems were trialled in a manufacturing company that produces mooring chains for the offshore oil and gas industry, an energy intensive manufacturing operation. The paper describes a case study involving energy-intensive processes that addressed different manufacturing concepts and involved the manufacture of mooring chains for offshore platforms. The system was developed to support online detection of energy efficiency problems.

Energy efficiency can be optimised in assembly and manufacturing processes. The systems produce an improvement in energy efficiency in manufacturing SMEs. The systems provide more comprehensive information about energy use and some knowledge-based support.

This research addresses two of the most critical problems in energy management in industrial production technologies: how to efficiently and promptly acquire and provide information online for optimising energy consumption and how to effectively use such knowledge to support decision making.

This research was inspired by the need for industry to have effective tools for energy efficiency, and that opportunities for industry to take up energy efficiency measures are mostly not carried out. The research combined AmI and KM technologies and involved new uses of sensors, including wireless intelligent sensor networks, to measure environment parameters and conditions as well as to process performance and behaviour aspects, such as material flow using smart tags in highly flexible manufacturing or temperature distribution over machines. The information obtained could be correlated with standard ECD to monitor energy efficiency and identify problems. The new approach can provide effective ways to collect more information to give a new insight into energy consumption within a manufacturing system.

The purpose of this paper is to measure profitability and marketability efficiencies as well as examine the efficiencies’ determinants of listed manufacturing firms in Vietnam.

This study employs a bootstrap two-stage data envelopment analysis (DEA) approach to investigate the profitability and marketability efficiencies of 102 listed manufacturing firms on Vietnam stock market from 2007 to 2018. The study also applies fractional regression models (FRM) to identify the determinants of Vietnam manufacturing firms’ efficiencies.

The results reveal that Vietnam manufacturing firms obtain higher average profitability efficiency scores (0.888) than marketability efficiency scores (0.527) from 2007 to 2018. The high-tech firms achieve better profitability and marketability efficiencies than the traditional (resource-intensive and labour-intensive) Vietnam manufacturing firms in recent years (2016–2018). Further, the financial and non-financial factors have heterogeneous impacts on Vietnam manufacturing enterprises’ profit and market valuation efficiencies.

Due to the nature of DEA technique that requires every decision-making unit to have available data of all inputs and outputs, the listed Vietnam manufacturing firms that have incomplete data or go public after 2007 are not included in the data set.

This study provides a reference for Vietnam manufacturing managers to position their firms competitively in the market as well as make wise operating, financing and management decisions.

This is the first study that attempts to combine bootstrap two-stage DEA and FRM, which are considered advantageous methods for DEA scores’ measurements and determinant evaluations in the current literature.

This paper aims to explore true technical efficiency in order to select the most competitive manufacturing industries in China. And the paper intends to discuss how environmental variables measured by energy consumption affect performance in different industrial sectors under the restriction of low-carbon economy.

In order to measure the calculated efficiency of industrial sectors more accurately, Three-stage DEA model is presented in the empirical analysis using data from 2007 to 2010 covering 29 manufacturing industries in China. The advantage of using this method is enabling us to separate the managerial factor from external environmental factors and random errors factors on the technical efficiency.

The results using this Three-stage DEA model show that textile manufacturing sector has the highest technical efficiency, and when environment variables are not considered, efficiencies in machinery and electronics manufacturing industries have a significant increase. Moreover, this empirical model enables us to evaluate the technical performance in various manufacturing sectors more accurately.

This study provides a useful efficiency measurement tool (Three-stage DEA model) to calculate technical efficiency among different industrial sectors. Technical efficiency plays a key role in building the competitiveness of manufacturing industry. Based on the objective efficiency evaluation, the paper can make a better selection of the most competitive industries.

The paper contributes to the existing literature by developing a Three-stage DEA to examine the technical efficiency and competitive power of manufacturing sectors in China. This study has great policy implications for the research of China's manufacturing in both ideas and methodology.

The purpose of this paper is to examine the impact of radio frequency identification (RFID) technology utilization in manufacturing firms on manufacturing efficiency and effectiveness.

Using systems theory as a basis, a RFID utilization and outcome(s) performance model was developed from the literature. Data from a sample of 155 manufacturers were collected and the model was assessed using a structural equation methodology.

Findings indicate that utilization of RFID technology leads to improved manufacturing efficiency and manufacturing effectiveness. Improvements in efficiency lead directly to improved organizational performance, and improvements in effectiveness lead directly to improved supply chain performance.

Data were collected during the growth stage of RFID technology adoption and were only collected from firms in the manufacturing sector. Results should be interpreted with these limitations in mind.

The implementation of RFID technology can result in improved manufacturing efficiency and effectiveness. Practitioners considering adoption of the technology should fully account for these potential efficiency and effectiveness related benefits when determining the justification for adoption of this technology.

The paper describes one of the first empirically‐based studies investigating the impact of RFID technology implementation on supply chain and organizational performance in manufacturing organizations.

This study develops an integrated approach combining data envelopment analysis (DEA) and structural equation modeling (SEM) for estimating the working capital management (WCM) efficiency and evaluating the effects of diverse exogenous variables on the WCM efficiency and firms' performance.

DEA is applied for deriving WCM efficiency for 212 Indian manufacturing firms over a period from 2008 to 2019. Also, the effect of human capital (HC), structural capital (SC), cost of external financing (CEF), interest coverage (IC), leverage (LEV), net fixed asset ratio (NFA), asset turnover ratio (ATR) and productivity (PRD) on the WCM efficiency and firms' performance is examined using SEM.

The average mean efficiency scores ranging from 0.623 to 0.654 highlight the firms operating at around 60% of WCM efficiency only, which is a major concern for Indian manufacturing firms. Further, IC, LEV, NFA, ATR revealed direct effect on the WCM efficiency as well as indirect effect on firms' performance, whereas CEF had only a direct effect on WCM efficiency. HC, SC and PRD had no effects on WCM efficiency and firms' performance.

The findings offer vital insights in guiding policy decisions for Indian manufacturing firms.

This study is the first to identify the endogenous nature of the relationship of HC, SC, CEF, IC altogether with firms' performance, compounded by the WCM efficiency, by applying a comprehensive methodology of DEA and SEM and provides an efficiency performance model for better decision-making.

This paper aims to examine the effect of inventory information sharing on inventory efficiency and its intervening effect of information technology (IT) capability in manufacturing firms.

Stratified random sampling and filter questions selected targeted respondents, and an online survey collected 124 completed questionnaires from Malaysian manufacturing firms. partial least squares structural equation modeling (PLS-SEM) examined the structural model and hypothesis statement. An analysis of importance-performance map analysis (IPMA) test identified the relative importance drivers of inventory efficiency.

The findings showed that enhanced IT capabilities in manufacturing firms mediate a positive relationship between inventory sharing and inventory efficiency.

This study portrays the relationship between inventory level, demand and information sharing. The research was carried out only within Malaysian manufacturing firms.

These findings will enable the management of manufacturing firms to design and visualise their inventory levels and share best practices across supply chain networks to achieve effective and optimised inventory planning.

This study illustrates an intervention model that offers a direct and indirect impact of IT capabilities that allow scholars to close inventories productivity gaps in research.

This paper extends the limited literature on the sharing of inventory information and inventory productivity, notably from a strategic management perspective. The findings help scholars clearly understand the information systems capability and its mediating impact on information sharing and inventory efficiency’s relationship in the manufacturing sector. Moreover, demand information sharing affected the dynamic supply chain.

The purpose of this paper is to empirically investigate the relationship between working capital management (WCM) efficiency and exogenous variables of the Indian manufacturing sector along with its sub-industries that are involved in export activities.

Panel regression (fixed effects) was used on a sample of 563 Indian manufacturing firms involved in export activities, covering a time period from 2008 to 2018.

Industry-wise results showed a significant relation of leverage, net fixed asset ratio, profitability, asset turnover ratio, total asset growth rate and productivity with cash conversion cycle (CCC).

Firstly, having taken a sample from a developing economy, the results of our study may be generalizable only among developing contexts. Secondly, the time period taken in this study (2008–2018) has witnessed several economic fluctuations such as recession and demonetization which might differ for the firms or countries in normal conditions.

An improved working capital model could advance the firms' performance by reducing the CCC of the firm, thereby creating efficiency in WCM. In addition, the results of this study could be helpful for many stakeholders such as working capital managers, debt holders, investors, financial consultants and others for monitoring the firms.

This study contributes to the existing literature in the relation between WCM efficiency and exogenous variables of the Indian manufacturing firms engaged in the export activities. Moreover, this study is one of the few research studies to investigate this relationship among Indian export firms in different industries, thus filling the gap in similar work done in other countries.