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The purpose of this paper is to identify various risks in the power distribution supply chain and further to prioritize the risk variables and propose a model to the power distribution industry for managing the interruptions in its supply chain. To accomplish this objective, a case of a major power distribution company has been considered.

Failure mode and effects analysis (FMEA) analysis has been done to identify the potential failure modes, their severity, and occurrence and detection scores. Then an interpretive structural model (ISM) has been developed to identify and understand the interrelationships among these enablers followed by MICMAC analysis, to classify the risk variables in four quadrants based on their driving and dependency powers.

The results of this study exhibit that technical failure in the information and technology system, the use of improper equipment, poor maintenance and housekeeping in the internal operations are the major risk drivers. Exposure to live wires and commercial loss in power supply has strong dependence power.

This study is limited to a single power distribution company and not the whole power distribution sector.

This study suggests the managers of the power distribution company develop an initial understanding of the drivers and the dependent powers on the supply chain risks.

Through prioritization, identification of drivers and the dependent risks, the losses in the power distribution supply chain can be minimized.

Various failures in the power distribution have been studied in the past, but they have not investigated the supply chain risks in the power distribution of a power distribution company.

Image segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc. However, an accurate segmentation is a critical task since finding a correct model that fits a different type of image processing application is a persistent problem. This paper develops a novel segmentation model that aims to be a unified model using any kind of image processing application. The proposed precise and parallel segmentation model (PPSM) combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Moreover, a parallel boosting algorithm is proposed to improve the performance of the developed segmentation algorithm and minimize its computational cost. To evaluate the effectiveness of the proposed PPSM, different benchmark data sets for image segmentation are used such as Planet Hunters 2 (PH2), the International Skin Imaging Collaboration (ISIC), Microsoft Research in Cambridge (MSRC), the Berkley Segmentation Benchmark Data set (BSDS) and Common Objects in COntext (COCO). The obtained results indicate the efficacy of the proposed model in achieving high accuracy with significant processing time reduction compared to other segmentation models and using different types and fields of benchmarking data sets.

The proposed PPSM combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions.

On the basis of the achieved results, it can be observed that the proposed PPSM–minimum cross-entropy thresholding (PPSM–MCET)-based segmentation model is a robust, accurate and highly consistent method with high-performance ability.

A novel hybrid segmentation model is constructed exploiting a combination of Gaussian, gamma and lognormal distributions using MCET. Moreover, and to provide an accurate and high-performance thresholding with minimum computational cost, the proposed PPSM uses a parallel processing method to minimize the computational effort in MCET computing. The proposed model might be used as a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc.

Investigating the influence of intensive distribution and sales promotion towards customer-based brand equity, repurchase intention and word-of-mouth (WOM) (study on Suzuki car owners in PT Surya Batara Mahkota Wilayah Nusa Tenggara Timur).

The research was conducted in East Nusa Tenggara (NTT) and the analysis unit was customers of PT. Surya Batara Mahkota NTT (PT SBM NTT) as the owner of the Suzuki car. The population is 1,782 Suzuki car owners who bought their cars from PT SBM NTT, based on data from 2015. The sampling technique is the multi-stage area sampling.

Incentives distribution had significant and positive influence towards brand equity and repurchase intention. Sales promotion had significant and positive influence towards word-of-mouth (WOM), but it did not have influence towards brand equity. Brand equity had significant influence towards repurchase intention and WOM. On the other hand, repurchase intention did not have influence towards WOM.

The originality of this study was that the researchers did not find a previous study that discussed the relationship between intensive distribution and repurchase intention, between sales promotion and WOM and between customer-based brand equity and WOM. Previous studies used different variables as determinants of positive WOM.

Over the last few years, distribution developments in non‐food have been considerable. Centralisation and contract distribution have gained favour, and an increasing number of companies are re‐appraising their choice of distribution channels and systems. Just as in food, distribution systems are having to be changed to meet the demands of the consumer.

One of the basic problems hindering effective implementation of the physical distribution management (PDM) concept in many companies today is organisational rigidity. By this is meant the inability and unwillingness of management to respond to the changing demands of new concepts and new techniques inherent in the successful adoption of PDM in existing corporate organisations. This rigidity manifests itself in two related ways: firstly, in the relationships structure within an organisation, and the place of physical distribution relative to other functional areas in management, such as marketing, production, finance, etc., and secondly, in the characteristics of distribution executives and their perceived role within an organisation.

Physical distribution organisations may be defined as organisational units whose duty is to administer economic activities that impact upon the flow of finished goods between points of production and consumption. Physical distribution components occupy a unique role in the organisation. Their mission has been defined as “getting the right assortment of materials to the right location in an efficient manner timely to marketing and manufacturing requirements”. To accomplish this mission there must be continual interaction between suppliers of materials and receivers of materials. Material suppliers can be thought of as the rest of the organisation of which the physical distribution component is a part, primarily the production or manufacturing component. Receivers of materials are the organisation's customers or distribution points. The physical distribution organisation, by virtue of the activities it performs, must deal with both the internal suppliers and the external receivers.

The physical distribution function of a firm is a complex process. It consists of all the activities involved in the flow of goods from the raw material supplier to the final consumer and incorporates the major activity centres of purchasing, warehousing, transportation, order processing, and inventory control. The goal of a firm's distribution operation is to insure that established customer service levels are achieved at a minimum total cost.

This study aims to estimate the firm size distributions that belong to the service sector and manufacturing sector in Korea.

When estimating the firm size distribution, the author considers the following two major factors. First, the firm size distribution can have a gamma distribution rather than traditional accepted distributions such as Pareto distribution or log-normal distribution. In particular, industry-specific enterprises can have different size distributions of the type of gamma distribution. Second, the firm size distribution that is applied to this study’s data set should reflect a number of factors. For example, estimating mixture gamma distribution for firm size distribution should be required and compared, because the total amount of configuration data is composed of small businesses, medium-sized and large companies.

Using 8,230 number of firm data in 2013, the author estimates mixture gamma distribution for the firm size.

From the comparison, empirical results are found for the following characteristics of core firm size distribution: first, the firm size distribution of the manufacturing sector has a longer tail than firm size distribution of the service sector. Second, the manufacturing firm size distribution dominates the entire country firm size distribution. Third, one factor among the three factors that make up the mixed gamma firm size distribution is described for 99 per cent of the firm size distributions. From the estimated firm size distributions of the service sector and manufacturing sector in Korea, the author simply implies the strategy and policy implications for the start-up firm.

The concept of the evolution of the distribution function is used to derive an energy‐scale distribution that is able to describe transport phenomena, including inter‐valley transfer effect, in the scale as small as the energy relaxation time. The energy‐scale distribution is used to study the evolution of electrons in n‐type GaAs under the influence of rapid change in field. Results indicate that, near the peak of strong velocity overshoot or the bottom of pronounced undershoot in the Γ valley caused by the rapid change in field, the energy‐scale distribution can not respond as fast as the distribution function calculated from the Monte Carlo method. The average velocity resulting from the energy‐scale distribution therefore leads to less pronounced overshoot and undershoot than those obtained from the Monte Carlo method. However, since velocity overshoot and undershoot are not pronounced in the L‐valleys, the L‐valley energy‐scale distribution is in excellent agreement with that determined by the Monte carlo simulation.

The lack of a satisfactory theory of personal income distribution is a problem that economists have pondered for most of the twentieth century. In 1912 Irving Fisher wrote: