Total Factor Productivity Growth and Output Growth in Indian Electronics Industry in the Liberalization Era: An Empirical Examination

This paper analyses the efficiency and productivity growth of Electronics industry, which is considered one of the vibrant and rapidly growing manufacturing industry sub-sectors of India in the liberalization era since 1991. The main objective of the paper is to examine the extent and growth of Total Factor Productivity (TFP) and its components namely, Technical Efficiency Change (TEC) and Technological Progress (TP) and its contribution to total output growth. In this study, the electronics industry is broadly classified into communication equipments, computer hardware, consumer electronics and other electronics, with the purpose of performing a comparative analysis of productivity growth for each of these sub-sectors for the time period 1993-2004. The paper found that the sub-sectors have improved in terms of economies of scale and contribution of capital.The change in technical efficiency and technological progress moved in reverse directions. Three of the four industry witnessed growth in the output primarily due to TFPG and the contribution of input growth to output growth had been negative/negligible, except for Computer hardware where contribution from both input growth and TFPG to output growth were prominent. The paper explored the possible reasons that addressed the issue of low technical efficiency and technological progress in the industry.

Single-machine scheduling with past-sequence-dependent setup times and learning effects: a parametric analysis

In this article, we consider the single-machine scheduling problem with past-sequence-dependent (p-s-d) setup times and a learning effect. The setup times are proportional to the length of jobs that are already scheduled; i.e. p-s-d setup times. The learning effect reduces the actual processing time of a job because the workers are involved in doing the same job or activity repeatedly. Hence, the processing time of a job depends on its position in the sequence. In this study, we consider the total absolute difference in completion times (TADC) as the objective function. This problem is denoted as 1/LE, (Spsd)/TADC in Kuo and Yang (2007) ('Single Machine Scheduling with Past-sequence-dependent Setup Times and Learning Effects', Information Processing Letters, 102, 22-26). There are two parameters a and b denoting constant learning index and normalising index, respectively. A parametric analysis of b on the 1/LE, (Spsd)/TADC problem for a given value of a is applied in this study. In addition, a computational algorithm is also developed to obtain the number of optimal sequences and the range of b in which each of the sequences is optimal, for a given value of a. We derive two bounds b* for the normalising constant b and a* for the learning index a. We also show that, when a < a* or b > b*, the optimal sequence is obtained by arranging the longest job in the first position and the rest of the jobs in short processing time order.

Support Vector Machine Approach to Downscale Precipitation in Climate Change Scenarios

Concern over changes in global climate has increased in recent years with improvement in understanding of atmospheric dynamics and growth in evidence of climate link to long‐term variability in hydrologic records. Climate impact studies rely on climate change information at fine spatial resolution. Towards this, the past decade has witnessed significant progress in development of downscaling models to cascade the climate information provided by General Circulation Models (GCMs) at coarse spatial resolution to the scale relevant for hydrologic studies. While a plethora of downscaling models have been applied successfully to mid‐latitude regions, a few studies are available on tropical regions where the atmosphere is known to have more complex behavior. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling to interpret climate change signals provided by GCMs over tropical regions of India. Climate variables affecting spatio‐temporal variation of precipitation at each meteorological sub‐division of India are identified. Following this, cluster analysis is applied on climate data to identify the wet and dry seasons in each year. The data pertaining to climate variables and precipitation of each meteorological sub‐division is then used to develop SVM based downscaling model for each season. Subsequently, the SVM based downscaling model is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to assess the impact of climate change on hydrological inputs to the meteorological sub‐divisions. The results obtained from the SVM downscaling model are then analyzed to assess the impact of climate change on precipitation over India.

Optimal maintenance policy of a machine subject to failure

Optimal maintenance policies for a machine with degradation in performance with age and subject to failure are derived using optimal control theory. The optimal policies are shown to be, normally, of bang-coast nature, except in the case when probability of machine failure is a function of maintenance. It is also shown, in the deterministic case that a higher depreciation rate tends to reverse this policy to coast-bang. When the probability of failure is a function of maintenance, considerable computational effort is needed to obtain an optimal policy and the resulting policy is not easily implementable. For this case also, an optimal policy in the class of bang-coast policies is derived, using a semi-Markov decision model. A simple procedure for modifying the probability of machine failure with maintenance is employed. The results obtained extend and unify the recent results for this problem along both theoretical and practical lines. Numerical examples are presented to illustrate the results obtained.