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A Nash bargaining game data envelopment analysis (NBG-DEA) model is proposed to measure the efficiency of dynamic multi-period network structures. This paper aims to propose NBG-DEA model to measure the performance of decision-making units with complicated network structures.

As the proposed NBG-DEA model is a non-linear mathematical programming, finding its global optimum solution is hard. Therefore, meta-heuristic algorithms are used to solve non-linear optimization problems. Fortunately, the NBG-DEA model optimizes the well-formed problem, so that it can be solved by different non-linear methods including meta-heuristic algorithms. Hence, a meta-heuristic algorithm, called particle swarm optimization (PSO) is proposed to solve the NBG-DEA model in this paper. The case study is Industrial Management Institute (IMI), which is a leading organization in providing consulting management, publication and educational services in Iran. The sub-processes of IMI are considered as players where their pay-off is defined as the efficiency of sub-processes. The network structure of IMI is studied during multiple periods.

The proposed NBG-DEA model is applied to measure the efficiency scores in the IMI case study. The solution found by the PSO algorithm, which is implemented in MATLAB software, is compared with that generated by a classic non-linear method called gradient descent implemented in LINGO software.

The experiments proved that suitable and feasible solutions could be found by solving the NBG-DEA model and shows that PSO algorithm solves this model in reasonable central process unit time.

The purpose of this study is to investigate the system of factors influencing the efficiency of internal evaluation in Polish secondary schools.

Data collected in interviews with evaluation experts and teachers on the barriers to the efficiency of internal evaluation and their causes were subjected to a qualitative cause-and-effect analysis.

Five barriers to the evaluation efficiency (6B model) and five actions of the school head stopping their impact (6A model) were identified. The latter include selecting the key evaluation function (3KEF model) and ensuring the conditions for efficiency in the improvement loop.

Although the research was carried out in Polish schools, the conclusions indicate regularities affecting organizations throughout the world.

Students’ educational needs may exceed the schools’ ability to meet them at class time mostly due to resource constraints. The implementation of the principle of equal opportunities in education requires continuous improvement of the efficiency of schools’ processes. Evaluation can help qualify tasks for improvement.

The level of engagement (reactive/active) of the school principal and teachers in evaluation and improvement is a crucial factor in overcoming the barriers to the efficiency of the school processes.

The ability to respond to the efficiency gaps of the school processes depends on the choice and efficiency of the KEF. The rationale for selecting the function and the schemes for its implementation have paradigmatic grounds.

This paper aims to analyze the influence of business decision on process efficiency and production technology in the communication equipment and consumer electronics sectors of Indian hardware electronics industry after liberalisation.

The study is based on 26 sample firms, belonging to the two sectors of Indian hardware electronics industry with continuous operation from 1993 to 2004. Estimation of the contribution of production technology and process efficiency to output growth is based on stochastic production function. Influence of business decisions on production technology and process efficiency is based on indigenously developed business decision model.

Sectors, in general, fail to achieve improvement in process efficiency in comparison to progress in production technology. The business decisions factors like improve capacity utilization and operational efficiency together with decisions to adopt vertical integration to help sample firms to achieve both progress in technology and improvement in process efficiency in the sectors. However, R&D investments and technology imports are costly for both, indicating inadequate and inappropriate investments made by the sample firms. Disinclination of firms towards improving or developing their own technology is evident from their high dependence on import of technology.

This study only analyses the role and importance of business decision factors in the past. What lies ahead is to meet the challenges and improve performance to withstand the stiff competition. This study suggests for further research about how to improve on the effectiveness of business decision factors that can help Indian hardware electronics industry to survive competition.

The paper develops a business decision model which analyses the impact of business decisions on production technology and process efficiency for a sample of firms in Indian hardware electronics industry.

This paper discusses logistics service providers' (LSPs’) energy efficiency initiatives for sustainable development, both from an evolutionary perspective and based on a framework consisting of actions, processes (i.e. at the operations interface) and services (i.e. at the customer interface).

Following a qualitative research design, semi-structured interviews were conducted with sustainability managers at LSPs and the data were analysed via inductive coding. Based on the results and the literature, the authors developed a maturity model for LSPs' transitions to environmental sustainability.

LSPs' sustainable development occurs via operational processes, services at the customer interface, and actions that support those processes and services. Energy efficiency efforts are characterised by process depth that helps LSPs to align with their customers' energy efficiency improvement processes. While services related to energy efficiency connect LSPs and their customers, actions in support vary depending on the logistics activities in which LSPs participate.

Further research is needed to test and verify the maturity model and to clarify the interdependency of its three dimensions.

By categorising energy efficiency initiatives and proposing a maturity model for LSPs' sustainable development via energy efficiency, the authors have developed a tool for logistics actors to assess their progress towards improved sustainability.

The paper contributes to the literature by providing a three-pillar framework to understand the sustainability transitions of LSPs through energy efficiency. Developing a maturity model using this framework also contributes to the literature with an approach to assess sustainability advancement in the logistics industry.

The purpose of this paper is to examine the technical efficiency of wheat and paddy rice processing in China. Understanding the level of technical efficiency of food processing helps to decide whether efforts are warranted to improve this efficiency. Studies on China's technical efficiency of flour and rice processing are scarce. This paper fills this gap.

With a unique set of firm‐level survey data collected by China's State Statistical Bureau, this study adopts a stochastic frontier model to investigate the technical efficiency of flour and rice processing.

The technical efficiency for both flour and rice processing is low in China, being only about 50 per cent. On average, rice processing firms have slightly higher technical efficiency than flour processing firms. It is also found that a significant proportion of firms experienced negative growth of technical efficiency during the time period of investigation.

Each year, some 300 million tonnes of wheat and paddy rice are processed in China. Any small improvement in technical efficiency is translated into huge economic gains. Further, a tiny improvement in flour or rice output rate is equivalent to an enormous increase in food supply, contributing to China's food security. The paper confirms the need and potential to raise technical efficiency in China for wheat and paddy rice processing.

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 study aims to test the success of university-industry (U-I) collaboration in terms of innovation process efficiency. Then, this study explores the moderating role of a set of organizational routines in the U-I relationship, which can help in overcoming the issues undermining the collaboration success.

The study is based on an international Open Innovation (OI) survey. The survey investigated the items to build the main variables of the conceptual framework, measured through seven-point Likert scales. Steps to ensure the reliability and validity of the variables were conducted. Then, hypotheses were tested with an ordinary least squares regression.

Results show that the higher the collaboration intensity (depth) with universities, the higher the innovation process efficiency. Furthermore, organizational routines aimed at improving firms' assimilation absorptive capacity further strengthen the positive effects of intensive collaboration on innovation process efficiency.

Findings indicate that R&D managers should strive to build deep collaborations with universities to enhance process efficiency and invest in the quality of these relationships. Managers should create and maintain an internal environment that further enhances the positive effects of intensive collaboration on innovation process efficiency.

The OI literature has not reached a shared view on the positive contribution of universities toward industrial firms' innovation performance. The study adopts a process-efficiency view, rarely used by other OI studies usually focused on output indicators; this study unpacks, respectively, the role of the intensity of collaboration and the organizational routines, thus disclosing the benefit of U-I collaboration on innovation efficiency.

This study examines the performance of India's food processing sector by estimating its output growth, technical efficiency (TE) and input-driven growth (IDG)

This study used panel data from six food processing manufacturing industries for the period 2000–01 to 2017–18. Technical efficiency and input-driven growth was measured using the parametric half-normal stochastic frontier production function.

The findings of this study showed that the estimated average technical efficiency is 86.6%, which specifies that the Indian food processing sector is technically inefficient. In addition, the output growth rate is 5.5%, driven by high doses of inputs (5.7%), whereas there is no indication of constant returns to scale. However, the food processing sector has experienced more input-driven expansion than either technological or efficiency changes.

This study is limited to India's organized manufacturing food processing sector; the aggregate macro data at a three-digit level based on the national industrial classification (NIC) was used. This study provides robust estimates for industrialists and processors, as well as concrete policy formulations on how overdoses of inputs may lead to high exploitation of resources, whereas outputs can be augmented by implementing upgraded and new technologies.

Previous research has estimated the total factor productivity and technical efficiency only in order to analyze the food sector's performance, but none of the studies have evaluated the share of inputs in growth performance and efficiency. Therefore, this study contributes by measuring growth performance and the share of inputs in the growth performance of India's food processing sector.

Knowledge management (KM) is increasingly becoming critical to public sector organizations as it is to private sector organizations. This is because public sector organizations are increasingly held accountable for their actions and are forced to move away from traditional bureaucratic approaches to more managerial ones. For instance, public sector is facing pressure to improve its operational efficiency such as productivity, lead time and expenditure. Similarly, it is also facing pressure to improve its transparency, trust, reliability and accountability, which are key elements of sustainable development (SD). The purpose of this study therefore is to understand the intricate relationship between KM processes, operational efficiency and SD in the public sector.

A framework consisting of eight constructs (knowledge acquisition, knowledge creation, knowledge capture, knowledge storage and retrieval, knowledge sharing, knowledge utilization, operational efficiency and SD) and their underlying items was developed through an extensive literature review. Using 383 usable responses collected through a structured questionnaire from the UAE public sector, the constructs and framework were first validated and then the current level of implementation/achievement of KM processes, operational efficiency and SD was assessed along with the hypothesized relationships between the constructs.

The findings show that KM processes had a positive and significant direct impact on operational efficiency and SD. Also, operational efficiency was found to strongly mediate the relationship between KM processes and SD.

The findings are expected to help UAE public sector organizations devise strategies and policy interventions to align and improve their KM processes for achieving operational efficiency and SD. Public sectors looking to focus on SD must focus on KM processes and strive to improve stakeholder engagement, which is a vital aspect of SD.

Integration of KM processes, operational efficiency and SD has not been attempted previously and hence constitutes the novelty of this work.