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Energy-efficiency leads to productivity gains as it can lower operating and maintenance costs, increase production yields per unit of manufacturing input and improve staff accountability. Implementation of energy-efficient technologies amongst industries, the factors influencing them and the barriers to their adoption have been the subject of several studies during the past three to four decades. Though energy-use behaviours of individuals or households are sufficiently explored, industrial energy conservation behaviour is scarcely studied. This study identifies the relationship between the different behavioural elements to open up a door for behaviourally informed intervention research.

Total interpretive structural modelling technique was used to determine the relationship between different elements of the behaviour of energy managers. Expert responses were collected to understand the relationship between the behavioural elements, through telephone interviews.

The study identified the relationship between the behavioural elements and found imperfect evaluation as the key element with the highest driving power to influence other elements.

The authors postulate that a behaviourally informed intervention strategy that looks into the elements with high driving power such as imperfect evaluation, lack of focus on energy-saving measures and the lack of sharing energy-saving objectives can lead to: an increase in the adoption of energy efficiency measures and thereby a reduction in the energy efficiency gap; greater productivity gains and reduced greenhouse gas (GHG) emissions; Preparation of M&V protocol that incorporates behavioural, organisational and informational barriers.

Various policy level interventions and regulatory measures in the energy field which did not address the behavioural barriers are found unsuccessful in narrowing the energy-efficiency gap, reducing the GHG gas emissions and global warming. Understanding the key driving factor of behaviour can help to design an effective intervention strategy to address the barriers to energy efficiency improvement.

Understanding the key driving factor of behaviour can help to design an effective intervention strategy to address the barriers to energy efficiency improvement. This study argues that through the systematic analysis of the imperfect evaluation of energy audit recommendations, it is possible to increase the adoption of energy efficiency measures that can lead to greater productivity gains and reduced GHG emissions.

Technological development has not only helped in effective integration of renewable sources but also made it possible for consumers to participate in system operation. Different market players are coming up in the electricity market, microgrid being one of them. Thus, this paper aims to investigate consumers’ role in the dispatch of a microgrid system that has a hybrid market structure under varied system conditions.

The mathematical model developed has been solved by the CONOPT solver in the GAMS optimization tool. GAMS-MATLAB interfacing is done to obtain solutions.

The problem formulated shows the effect of consumers in dispatch and overall operational cost. Consumers’ participation has been proposed through a quadratic cost function. The system operation under pool and bilateral contracts has been investigated. It shows that proper incentives to the consumers can help in reduction and effective management of the demand, carbon emission and overall system operational cost.

This paper considers the hybrid market structure to find the load dispatch in a microgrid system. The participation of consumers in the microgrid system has been implemented considering variations in wind power, solar power and load. The power exchange between the grid and microgrid system has been modeled showing the contribution of the consumers in system operation.

This paper aims to obtain an accurate and robust estimation method of the rotor flux and speed for the sensorless induction motor (IM) drive.

The reduced order observer has been used as an online tuned rotor flux model in the model reference adaptive system (MRAS) concept applied for the IM speed estimation. The output of this observer was used also as a feedback signal required in the direct field‐oriented control (DFOC) structure of the IM.

It is shown that a new rotor flux and speed estimator are more robust to motor parameter changes in comparison with the classical MRAS estimator and can work stably in the DFOC structure, in the wide speed range, even for relatively high (50 per cent) identification errors of equivalent circuit parameters of the IM.

The investigation looked mainly at the estimation accuracy performance and whole system stability while economic issues will still need to be addressed.

The proposed new improved MRAS speed estimator can be easily realised using modern digital signal processors. The implementation was tested in an experimental set‐up with floating point DSP used as the system controller. The fixed‐point realisation needs to be developed to obtain the practical application in the industrial drive systems.

The application of the reduced order flux observer as a tuned flux model in the MRAS type speed estimator instead of the simple, but very sensitive to motor parameter uncertainties, current flux model, enables much better accuracy and stability of the rotor speed estimation in the complex DFOC structure than in the case of classical MRAS estimator.

This paper demonstrates a multi-criteria analytic hierarchy process (AHP) framework for evaluating and benchmarking maintenance performance in the select agro-based industry.

Initially, 20 maintenance practices (criteria) have been identified after a detailed literature review and discussion with the agro-based industry (sugar, textile and dairy industry) executives. These are then grouped into six maintenance management areas referred to as group criteria. The multi-criteria methodology consists of three steps: criteria identification, hierarchical modeling and data collection and maintenance performance evaluation, and benchmarking. The multi-criteria methodology proposed in this work facilitates two ways of carrying out benchmarking: (1) within the agro-based industry and (2) between the agro-based industry. The methodology has been explained by taking a case example of 45 agro-based industries (18 dairy, 13 sugar and 14 textile) from the western region of India. The sensitivity analysis of the model has been performed to ascertain the robustness of the results.

There is a difference in the maintenance performance across the agro-based industries due to different maintenance practices perceived differently.

The outcome of the model is mainly given by the judgments of the agro-based industry executives. It is also sensitive to any change in the relative importance to the evaluation criteria or the perception about the maintenance performance.

The study contributes in identifying the weakness, if any, by comparing the agro-based industry under investigation with the benchmark factory at three levels, namely, overall performance (factory level), group criteria (maintenance management area level) and criteria (maintenance practice level) allowing further improvement.

The methodology assists in better decision-making and in improving maintenance performance.

This paper aims to address the various issues of board‐level (off‐chip) interconnects testing. A new algorithm based on the boundary scan architecture is developed to test off‐chip interconnect faults. The proposed algorithm can easily diagnose which two interconnects are shorted.

The problems in board‐level interconnects testing are not simple. A new algorithm is developed to rectify some of the problems in existing algorithms. The proposed algorithm to test board‐level interconnect faults is implemented using Verilog on Modelsim software. The output response of each shorting between different wires of different nodes is different, which is the basis of fault detection by the proposed algorithm. The test vectors are generated by the test pattern generator and these test vectors are different for different nodes. This work implements built in self test using boundary scan technique.

The dominant‐1 (wired‐OR, denoted as WOR), dominant‐0 (wired‐AND, denoted as WAND) and stuck‐at faults are tested using the proposed algorithm. The proposed algorithm is also compared with the several algorithms in the literature, i.e. modified counting, walking one's algorithm and others. This paper's results are found to be better than the existing algorithms.

The limitation of the proposed algorithm is that, at any time, the faults on any seven nodes can be tested to avoid aliasing. So, the groups are formed out of total nodes, in a multiple of seven to carry out the testing of faults.

The proposed algorithm is free from the problems of syndromes and utilizes a smaller number of test vectors.

Various existing algorithms namely modified counting, walking one's algorithm and others are discussed. A new algorithm is developed which can easily detect board‐level dominant‐1 (WOR), dominant‐0 (WAND) and stuck‐at faults. The proposed algorithm is completely free from aliasing and confounding syndromes.

This study aims to identify the most profitable segment of construction firms amongst real estate, industrial construction and infrastructure. This paper also examines the determinants of profitability of real estate, industrial construction and infrastructure firms.

The data of 67 firms (20 real estate, 21 industrial construction and 26 infrastructure) is collected for a 15-year period (2003–2017). Two models are created using total return on assets (ROA) and return on invested capital (ROIC) as dependent variables.. Leverage, liquidity, age, growth, size and efficiency of the firm are identified as firm-specific independent variables. Two economic variables, i.e. growth in GDP and inflation, are also used as independent variables. Initially, the models are tested for stationarity, multicollinearity and heteroscedasticity, and finally, the coefficients are estimated using Arellano–Bond dynamic panel data estimation to account for heteroscedasticity and endogeneity.

The results suggest that industrial construction is the most profitable segment of construction, followed by real estate and infrastructure. Their profitability is positively driven by liquidity, efficiency and leverage. The real estate firms are somewhat less profitable compared to industrial construction firms, and their profitability is positively driven by liquidity. The infrastructure firms have low ROA and ROIC.

The real estate, infrastructure and industrial construction drastically differ from each other. The challenges involved in real estate, infrastructure and industrial construction are altogether different. Therefore, authors present a comparative analysis of the profitability of real estate, infrastructure and industrial construction segments of the construction and compare their determinants of profitability. The results provided in the study are robust and reliable because of the use of a superior econometric model, i.e. Arellano–Bond dynamic panel data estimation with robust estimates, which accounts for heteroscedasticity and endogeneity in the model.

Chemical processes, employed for manufacturing basic chemicals are highly energy intensive. Due to escalating costs of fossil fuels and capacity addition, the electricity cost has been increasing for the last few decades. Electricity intensive industries find it very difficult to cope up with higher electricity charges particularly with time-of-use (TOU) tariffs implemented by the utilities with the objective of flattening the load curve. Load management programs focusing on reduced electricity use at the time of utility's peak demand, by strategic load shifting, is a viable option for industries to reduce their electricity cost. This paper presents an optimization model and formulation for load management of continuous process industries. The case study of a chemical industry is taken for conducting load management study. The formulation utilizes nonlinear programming technique for minimizing the electricity cost and reducing the peak demand, by rescheduling the loads, satisfying the industry constraints. Development of a generalized graphical user interface (GUI) in Visual Basic for implementing load management actions in industries and testing the GUI with the case study are done. Real time implementation of load management actions using Programmable Logic Controller (PLC) is also investigated.

Supply chain management.

The case is suitable for post graduates in management, and for those managing small sector supply and manufacturing systems.

ACPL is an organisation which moved from trading to manufacturing a technology product instrument transformers (ITs) for power utility companies for 11 years, competing with the best in industry, reducing internal costs, and modernising the supply chain. ACPL was started as a trading organisation in electrical items in Delhi by Munish Kumar, an engineer by profession in 2001. In 2004 he ventured into manufacturing, which expanded in two locations in Ghaziabad, NCR Delhi. Later his two sons, engineer and management graduate, respectively, joined the organisation. In less than a decade, by 2007, ACPL had grown to be a private limited organisation. ACPL manufactures ITs required by power boards and companies for conversion and usage of high voltage (11 kV/33 kV) transmitted power into 220 V single phase/440 V three phase power. From tender/enquiry through manufacturing to inspection and despatch takes a long supply chain cycle time holding space as well as inventory. An interview with the chairman of ACPL in the case highlights issues affecting its margins and growth. The long process to delivery time may be in vogue in this type of industry but this holds up a huge inventory. The company management has been working to resolve this crisis along with an urgent need to grow in a competitive environment. The problem is being addressed.

This case study should help students to understand the concept of the supply chain and supply cycle, in a manufacturing company in particular. It has been found that students understand the supply chain as part of the marketing function dealing with finished stocks, warehousing and delivery to end customers as per agreements, and arranging payments from customers. The supply chain also deals with in bound materials management. Raw materials planning, purchasing, inventory management are crucial for effective business operations management in any organisation.

Teaching notes are available; please contact your librarian for access.

Modern service robots are designed to work in a complex indoor environment, in which the robot has to interact with the objects in different ambient light intensities (day light, tube light, halogen light and dark ambiance). The variations in sudden ambient light intensities often cause an error in the sensory information of optical sensors like laser scanner, which reduce the reliability of the sensor in applications such as mapping, path planning and object detection of a mobile robot. Laser scanner is an optical sensor, so sensory information depends upon parameters like surface reflectivity, ambient light condition, texture of the targets, etc. The purposes of this research are to investigate and remove the effect of variation in ambient light conditions on the laser scanner to achieve robust autonomous mobile robot navigation.

The objective of this study is to analyze the effect of ambient light condition (dark ambiance, tube light and halogen bulb) on the accuracy of the laser scanner for the robust autonomous navigation of mobile robot in diverse illumination environments. A proposed AIFA (Adaptive Intensity Filter Algorithm) approach is designed in robot operating system (ROS) and implemented on a mobile robot fitted with laser scanner to reduce the effect of high-intensity ambiance illumination of the environment.

It has been experimentally found that the variation in the measured distance in dark is more consistent and accurate as compared to the sensory information taken in high-intensity tube light/halogen bulbs and in sunlight. The proposed AIFA approach is implement on a laser scanner fitted on a mobile robot which navigates in the high-intensity ambiance-illuminating complex environment. During autonomous navigation of mobile robot, while implementing the AIFA filter, the proportion of cession with the obstacles is reduce to 23 per cent lesser as compared to conventional approaches.

The proposed AIFA approach reduced the effect of the varying ambient light conditions in the sensory information of laser scanner for the applications such as autonomous navigation, path planning, mapping, etc. in diverse ambiance environment.

The purpose of this paper is to investigate the determinants of financing the outward foreign direct investment (OFDI) by building a three-level framework residing on host country market imperfections, ownership advantages of parent firm investing abroad and the industry to which it belongs.

The paper used random effects probit model.

Parent debt financing of OFDI by Indian parent firms is driven by the credit market development of the host country, the uniqueness of the industry to which parent firm belongs and systematic risk. Debt-oriented firms are found to invest more via parent debt.

The limitations of this study are as follows: –first, time period before 2008 could not be considered due to unavailability of data in the public domain. Second, the characteristics of foreign affiliates that spread across diverse host countries have not been factored in. Third, in the case of parent’s industry-level determinants, financial sector has not been included because the financing and risk-taking strategy of this sector are quite different from other sectors. Finally, the present study assumes financing decision to be centralized in the multinational system at the parent firm.

The practical implications of this study are as follows: first, industry innovativeness must be taken as a guide by the Indian MNEs to finance their OFDI and they must provide equity. Second, the study suggests that Indian MNEs rely on their existing capital structure while financing their OFDI. Third, parent firms are found to follow the industry norms. Fourth, parent firms must finance their OFDI by considering the development of credit market in the host country. Fifth, host government must focus on improving the credit market development of their economy and not just reducing tax rates to attract FDI into their economy.

Empirically examining internal flows in a multinational system has limited the research in the area of financing the OFDI. The paper is one of the first attempts to formally develop a model of factors that shape financing of OFDI in case of one such emerging market – India.