The article proposes a method for predicting the daily energy consumption level for every day of a whole year, taking into account the season-al factor, based on only twelve actual power consumption data by the months of the year. Then a mathematical model is developed for moni-toring and controlling the level of electricity consumption on a daily basis, taking into account the seasonal factor. The model is consistent with a common model for the length of daylight (in hours). In addition, on the basis of this model, a method of monitoring and diagnostics of electricity consumption is presented, which will allow users to monitor the level of power consumption and be timely notified of any deviations from the theoretical level. Finally, this method gives rise to the operational principle for a proposed device, a smart energy meter, for detecting suspicious deviations from the theoretical level. The device will help timely detect over-consumption (or under-consumption) of electricity in order to take preventive measures. The proposed method consists of the following steps: (1) choice of a function to model the level of electricity consumption (theoretical calculated level), (2) choice of a tubular control neighborhood of the graph of the model function, (3) choice of a criterion on when the smart energy meter should notify the user of an unexpected deviation from the theoretical level in the case of exit from the tubular control neighborhood.
The article presents a statistical approach to the service management. This approach is based on the use of the exponential probability distribution in service models and is demonstrated on the examples of multifunctional service centers. The authors offer a method for setting the maximum limit for the waiting time in a queue to be served, which is interpreted in statistical terms as the failsafe (by level) quantile of waiting time. Given the average waiting time, a formula for specifying the maximum limit for the waiting time considering an allowable percentage of customers, who will have to wait longer than the maximum waiting time, is given. The formula reads as follows: the maximum limit for the waiting time is equal to the average waiting time multiplied by the modulus of natural logarithms of the failure level F, where F is equal to the anticipated share of customers who will have to wait longer than the time set by the manager as the maximum waiting time or, in other words, F⋅⋅ 100% is the percentage of failures. For advertising the efficiency, the manager is interested in setting the minimum allowable maximum waiting time; this time corresponds to a maximum allowable failure level F. Software for computing the maximum limit for the waiting time is offered. As a byproduct, an interesting result is obtained: In any queue, 37% of customers wait longer than the average waiting time to be served while 39% of customers wait shorter than half of the average waiting time. In summary, the main time-related quality indicator of service is the average waiting time in a queue. This indicator is equal to the ratio of two characteristics: the maximum limit for the waiting time / the absolute value of the natural logarithm of the share of failures in the total number of customers, that is, the proportion of customers who will have to wait longer than the time declared as the maximum waiting time.
The purpose of this article is to reduce the gap between queuing and quality control theories on the one hand and lagging practical successes on the other hand. In this paper statistical approaches in management and service are developed and demonstrated. They are based on the use of the normal and exponential probability distributions in modeling service systems. Those approaches are demonstrated on the following examples: (I) fast-food restaurants and (II) mul- tifunctional service centers in Moscow (Russia). The related Pascal software is given; its usage is illustrated on concrete examples. In particular, a method is suggested for setting the maximum limit for the waiting time in a queue to be served, which is interpreted in statistical terms as the failsafe (by level) quantile of waiting time. Given the average waiting time, a formula is obtained for specifying the maximum limit for the waiting time considering an allowable percentage of customers who will have to wait longer than the maximum waiting time set. The formula reads as follows: the maximum limit for the waiting time is equal to the average waiting time multiplied by the absolute value of the natural logarithm of the quantity F, where F is the failure level which is equal to the anticipated share of customers who will have to wait longer than the time set by the manager as the maximum waiting time or, in other words, 100 F% is the percentage of failures. For the sake of advertising efficiency, the manager is interested in setting the minimum allowable maximum limit for the waiting time; this time corresponds to a maximum allowable F. Software is provided for computing the maximum limit for the waiting time. As a byproduct, a curious result is obtained: In any queue, 37% of customers wait longer than the average waiting time to be served while 39% of customers wait shorter than half of the average waiting time. In summary, the main time-related quality characteristic of service is the average waiting time in a queue. This characteristic is equal to the ratio of two characteristics: the maximum limit for the waiting time / the absolute value of the natural logarithm of the share of failures in the total number of customers, that is, the proportion of customers who will have to wait longer than the time declared as the maximum waiting time.
The article presents an analysis of the factoring services market at the national and global levels, and identifies the main factoring market development trends. Based on the material of transport service company, limited liability company "Fairway", the authors substantiate the economic efficiency of factoring as a tool for replenishment of circulating assets of transport companies in the current economic conditions. Accumulation by factoring a number of functions is an important advantage over other forms of funding. This is especially important for small and medium-sized businesses in sphere of transport service that do not have sufficient volume of human and financial resources. Factoring allows to avoid a situation where the liability to tax on income appears before inflow of funds from the sale. Greater incentive for the factoring development for tax optimization is applying of a similar approach to the determination of the date of VAT payment. Funding within the factoring will allow Fairway to solve the problem of shortage of working capital without growth of accounts payable. Factoring has a positive effect on the financial indicators of an enterprise, such as liquidity and solvency. In general, the impact of factoring on the financial indicators of the transport service company "Fairway" is carried out by means of reduction of accounts receivable turnover period (shortening of the financial cycle), increasing the receivables turnover (number of turnovers), the reduction of the value of accounts receivable (release), more efficient use of working capital, increasing the coefficient of instant liquidity, reducing the tax base for income tax.
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