Nowadays electronic commerce plays an important role in many business activities, operations, and transaction processing. The recent advances on e-businesses have created tremendous opportunities to increase profitability. This paper presents a multi-objective marketing planning model which simultaneously determines efficient marketing expenditure, service cost and product & apos; s selling price in two competitive markets. To solve the proposed model, we discuss a multi-objective geometric programming (GP) approach based on compromise programming method. Since our proposed model is a signomial GP and global optimality is not guaranteed for the problem, we transform the model to posynomial form. Finally, the solution procedure is illustrated via a numerical example and a sensitivity analysis is presented.

This paper presents a new mathematical model to select an optimal combination of productivity improvement techniques. The proposed model of this paper considers four-stage cycle productivity and the productivity is assumed to be a linear function of fifty four improvement techniques. The proposed model of this paper is implemented for a real-world case study of manufacturing plant. The resulted problem is formulated as a mixed integer programming which can be solved for optimality using traditional methods. The preliminary results of the implementation of the proposed model of this paper indicate that the productivity can be improved through a change on equipments and it can be easily applied for both manufacturing and service industries.

One of the most important issues on educational systems is to measure the relative efficiency of similar units based on non-financial factors. Data envelopment analysis (DEA) has become popular among many who wish to rank educational systems using different factors such as the rate of educational success or the number of employees, etc. However, one of the main concerns on implementing DEA methods is the uncertainty involved in input/output parameters. In this paper, a robust data envelopment analysis (RDEA) is developed to measure the efficiency of high schools considering uncertainty on output parameters. We present an empirical study on a set of high schools located in Tehran, which is the capital city of Iran. The study uses uncertain data for input/output information and the results are compared with an existing parametric stochastic frontier analysis (SFA). The preliminary results indicate that the robust DEA approach is relatively a reliable method for efficiency estimating.

Turning is the utmost elementary process among several CNC machine tool operations which is a versatile, efficient and widely used in industries including mechanical, aerospace and automotive sectors. Most frequently the manufacturing industries face the problem of cutting down the production cost without any oblation in the quality of products. Selection of process parameter is essential for assuring a quality product. Convenient choice of the cutting conditions, parameters and tools for the maximum Material removal rate (MRR) and surface finish needs a sophisticated approach adopting mathematical and statistical models as well as experimental method. Taguchi’s method is a very compelling tool to optimize the process variables. The intention of the present study is to determine surface roughness (Ra) and MRR which are key responses to justify the quality of turning operation. Four process parameters have been selected viz. Nose radius (NR), depth of cut (DOC), feed rate (FR) and spindle speed or cutting speed (CS) that influences these responses. During machining operations, the influence of the process parameters and their interac-tion have been analyzed using a statistical tool ANOVA. Aluminum alloy is widely acceptable by industries owing to its good strength to weight ratio, high resistance to corrosion, malleability and excellent resistance during elevated temperatures.

This research gives an overview of an investigation into existing non-destructive testing (NDT) methods used to analyse composite materials. From the investigation and subsequent experimentation a new technique of failure identification in composite materials was developed. The new technique is a form of thermography whereby a temperature rise in a composite material during failure is detected via a thermal camera and a thermal image captured. By observing the thermal images captured of the event the location and the severity of the failure could be gained by only using the thermal images combined with a visual inspection to validate the results. By taking a thermal image and analysing the constituent red, green and blue colours that make up the image, the location of the defect could be pinpointed. This analysis method was developed using MATLAB® in order for the location of a defect to be found using only a thermal image of the composite during failure. The analysis showed that the information that yields the most accurate location of failure was the red part of the images.

The purpose of the current study was to investigate the determinants of small and medium manu-facturing enterprises’ export performance empirically in the context of Pakistan. The study adopted survey method to collect forty-five (45) responses from small and medium manufacturing firms through E-mail survey. The study produced four determinants of SME export performance, such as entrepreneurial orientation (EO), total quality management (TQM), business network (BN) and export market orientation (EMO). Thus, internal consistency reliability and discriminant validity were examined of the study instruments through panel expert’s opinion and the small sample size was investigated using statistical software SPSS-21 and smartPLS-3. The results of the study con-firmed that scale reliability and validity and moreover, current study found a positive relationship between TQM and SME export performance. On the other hand, the study also found insignificant relationship between EO, BN, EMO and SME export performance. Furthermore, the study recommended future study should validate the current study research framework on large sample size.

Earned Value Management (EVM) has been a well-known methodology used since the 1960s when the US department of defense proposed a standard method to measure project performance. This system relies on a set of often straightforward metrics to measure and evaluate the general health of a project. These metrics serve as early warning signals to timely detect project problems, or to exploit project opportunities. A key aspect of EVM is to estimate the completion cost of a project by considering both cost and schedule performance indices. However, good performance of cost and schedule performance indices does not necessarily guarantee cost effectiveness of the project regardless of the overhead costs. The reason is because, in most project-based organizations, overhead costs constitute a significant proportion of the total costs. However, EVM indices are usually calculated in the absence of the so-called overhead costs. This paper, first, seeks to remedy this problem by proposing a practical procedure of allocating overhead costs in project-based organizations. Then the traditional EVM indices are revised by consider-ing the allocated overhead costs. Finally, a case study demonstrates the applicability of the proposed method for a real-life project.

Understanding mechanical properties of healthy and unhealthy cerebral vessels is a key element in the development of their science and the relevant clinical diagnosis, prevention and treatment. Thirteen healthy samples were obtained from 23 middle cerebral arteries. The changes of force and deformation until the vessel rupture were recorded using a biaxial device. Thereafter, the stress-strain curve was plotted and fitted with a hyperelastic five-parameter Mooney-Rivlin model and the model parameters (C1, C2, C3, C4, and C5) were determined according to the best fit. For statistical comparison, the samples were divided into three age and two gender groups and subjected to non-parametric statistical analyses. Comparison of obtained results for different age groups showed that there is a significant difference between the "old" group and the other two groups (middle-aged and young). There was no significant difference between male and female groups. Therefore, the results demonstrate the changes of blood vessel wall properties with aging. The results also depicted that the arterial wall is stiffer in the circumferential direction than the axial direction. Anisotropy of cerebral vessels was confirmed by all of the tests. Therefore, the significance of the biaxial tests is in the spot light in the derived data. Moreover, good fitting of data illuminated that the use of multiple-parameter constitutive models is useful for mathematical demonstration of cerebral vessel tissue behavior. In conclusion, good fitting of data illuminated that the use of multiple-parameter constitutive models is useful for mathematical demonstration of cerebral vessel tissue behavior.

In financial industry, the accurate forecasting of the stock market is a major challenge to optimize and update portfolios and also to evaluate several financial derivatives. Artificial neural networks and technical analysis are becoming widely used by industry experts to predict stock market moves. In this paper, different technical analysis measures and resilient back-propagation neural networks are used to predict the price level of five major developed international stock markets, namely the US S & P500, Japanese Nikkei, UK FTSE100, German DAX, and the French CAC40. Four categories of technical analysis measures are compared. They are indicators, oscillators, stochastics, and indexes. The out-of-sample simulation results show a strong evidence of the effectiveness of the indicators category over the oscillators, stochastics, and indexes. In addition, it is found that combining all these measures lead to an increase of the prediction error. In sum, technical analysis indicators provide valuable information to predict the S & P500, Nikkei, FTSE100, DAX, and the CAC40 price level.

Cash is the life blood of the organizations and cash management is the important aspect of any organization. Corporate cash management boosts the companies from small to giant in the competitive environment. In this study we highlight three factors of a good cash management practices. Such factors are Cash Conversion Cycle (CCC), cash holding and credit score. Influence of one factor to other helps organizations manage their corporate cash in better way. In this study, we collect data from Capitaline corporate database of Mumbai, India over the period 2002-2011. Using some regression techniques, we observe that due to higher credit score companies forced to minimize their CCC and it helped to maintain lower level of working capital.