Customer Revenue Research Articles

The Impact of Customs Revenues on The General Budget Deficit in A Middle East Country

This research aims to identify the concept of customs revenues and their impact on the state’s general budget deficit. The problem of the study stems from the following question: What is the impact of customs revenues on the general budget deficit in Iraq? To answer this question, this research uses the deductive approach and the estimation approach by analyzing and measuring the impact of customs revenues in supporting the state’s general budget. Among the most important results of the study are: There are many reasons that led to the emergence of a deficit in the state’s general budget throughout the study period, the most important of which is the decrease in Customs revenues as a result of the weak role of tax revenues It recommended the need to address the imbalance in the structure of public revenues by activating the role of taxes and fees and reducing dependence on the oil resource.

Tax and Customs Revenues During the Kingdom of Aceh Darussalam

The Kingdom of Aceh Darussalam (ruled 1514 to 1903) was a famous Islamic kingdom in the history of the archipelago. The peak of its glory was reached during the time of Sultan Iskandar Muda (reigned 1607-1636). The glory and fame of this kingdom was created because of Sultan Iskandar Muda's ability to regulate political-economic policies, especially the tax and customs sectors as the main pillars of the kingdom's wealth income. This article discusses the income of royal wealth from the tax and customs sector, which includes tax and excise objects, the management of their collection and the sultan's policies in reviving the tax sector. Sultan Iskandar Muda has built the port of Bandar Aceh into a large port and concentrated the trade in pepper and commodities which have a high selling value on the world market only in this city. The impact is that Bandar Aceh is visited by merchant ships from the archipelago and abroad. Traders, foreign and local, are charged taxes and excise, such as adat stamp or adat lapik cap (the sultan's permission to land), adat wase kuala (fees for guarding parking and guarding ships entering and leaving the port), stall tax on merchandise/ trading place. Taxes are also collected from local traders who sell at the market. Apart from that, land tax is also levied (wase tanoh), farmers who receive irrigation are subject to the customary but umong tax, farmers who seek forest products are subject to wase gle tax), people who litigate in court are subject to the customary tuha tax, people who die but do not leaving his heirs his wealth to be put into the royal treasury, as well as foreign traders. Income from various types of taxes and excise is intended to build various facilities and infrastructure to strengthen and advance the kingdom

Service in the Periphery of the Romanov Empire: Exile or Springboard for Customs Officials of the Administrative Authorities of Czarist Russia in the 19th and Early 20th Centuries?

Peter I, ruler of Russia, is regarded as the father of the modern administrative apparatus created in order to effectively administer the empire. The then introduced system of personnel selection and promotions became an inherent part of the practice applied during the reign of successive Romanovs until the end of the functioning of absolute rule in Russia. On the basis of preserved source materials of government origin, the fact of the service of customs officials on the borders of the empire was analyzed, as was its influence on appointments to positions within the central administrative authorities that managed the customs revenues of the Romanov Empire in the 19th and early 20th centuries. For some officials, the necessity of serving on the borders of the empire was only a stage in their civil service career, which ultimately ended in the offices operating in the capital city of St. Petersburg. However, many failed to find employment in the capital, pursuing their careers on the borders of the vast Romanov Empire.

THE IMPACT OF CLIMATE CHANGE ON CUSTOMS REVENUES IN THE WAEMU ZONE

In recent years, the planet has been marked by major events: immigration, terrorism, armed conflicts and above all global warming which is attracting the attention of countries around the world and those of WAEMU in particular. Global warming leads to disruptions in the functioning of production cycles. Indeed, these countries which are essentially agricultural are facing several phenomena. The impact of climate change on customs revenues in the West African Economic and Monetary Union (WAEMU) area refers to the effects that climate variations, such as increased temperatures, extreme weather events, rising of sea level and changes in precipitation, can have on the revenue generated by customs activities in this region. 
 Agricultural production is falling sharply. This inevitably leads to a reduction in customs revenue. Customs revenues constitute the main means of development for these countries. This question is of capital importance insofar as it obliges States to adopt new policies. They should diversify areas of production and think about transforming products in order to increase added value. Furthermore, WAEMU States would benefit from taking an increasing interest in industrialization policies in order to minimize imports which weigh heavily on the trade balance because the effects of global warming will continue to grow.

Research on Smart Power Sales Strategy Considering Load Forecasting and Optimal Allocation of Energy Storage System in China

With the deepening reform of the power system, power sales companies need to adopt new power sales strategies to provide customers with better economic marketing solutions. Customer-side configuration of an energy storage system (ESS) can participate in power-related policies to reduce the comprehensive cost of electricity for commercial and industrial customers and improve customer revenue. For power sales companies, this can also attract new customers, expand sales and quickly capture the market. However, most of the ESS evaluation models studied so far are based on historical data configuration of typical daily storage capacity and charging and discharging scheduling instructions. In addition, most models do not adequately consider the performance characteristics of the ESS and cannot accurately assess the economics of the energy storage model. This study proposes an intelligent power sales strategy based on load forecasting with the participation of optimal allocation of ESS. Based on long short-term memory (LSTM) artificial neural network for predictive analysis of customer load, we evaluate the economics of adding energy storage to customers. Based on the premise of the two-part tariff, the ESS evaluation model is constructed with the objective of minimizing the annual comprehensive cost to the user by considering the energy tariff and the savings benefits of the basic tariff, assessing the annualized cost of ESS over its entire life cycle, and the impact of battery capacity decay on economics. The particle swarm optimization (PSO) algorithm is introduced to solve the model. By simulating the arithmetic example for real customers, their integrated electricity costs are significantly reduced. Moreover, this smart power sales strategy can provide different sales strategies according to the expected payback period of customers. This smart sales strategy can output more accurate declared maximum demand values than other traditional sales strategies, providing a more economical solution for customers.

Impact of the AfCFTA Agreement on Customs Revenue: case of Togo

The problem of loss of budgetary revenues with the implementation of the African Continental Free Trade Area (AfCFTA) agreement remains a concern, especially for the least developed countries. The losses resulting from the removal of tariff barriers deserve to be assessed in order to allow public authorities to establish mitigating measures. Drawing from this perspective, this paper aims to quantitatively assess the impact that the AfCFTA agreement has on Togo’s customs revenue. Using the differential method of current and future tariff regimes, the results of the study estimate the total loss in Togolese customs revenue during the first three years of the implementation of the Agreement at 30.9 billion CFA. This loss represents up to 0.25 per cent of the GDP and 22.67 per cent of the customs revenue collected, which could further widen the country’s budget deficit, and consequently compromise the financing of its development needs. Faced with such a situation, the Togolese state should carry out major internal tax reforms and public policy, consisting among others of broadening its national tax base, promoting the facilitation of intracontinental trade, and supporting the private sector through financial mechanisms in order to contribute to their competitiveness in light of the competition from other foreign companies.

Replacing Customs Revenue with Taxes on Income and Domestic Consumption: The South African Experience

Replacing Customs Revenue with Taxes on Income and Domestic Consumption: The South African Experience

Time Series Analysis for Customs Revenue Prediction using Arima Model in Python

The Directorate General of Customs and Excise (DJBC) serves as a revenue collector in the field of customs and excise. This revenue plays an essential role in supporting infrastructure development. Predictions are needed to plan a good State Revenue and Expenditure Budget (APBN). Predictions serve as a tool for revenue optimization and control. However, forecasting is problematic because unpredictable external factors also influence these receipts. A logical and accountable approach is needed to predict acceptance to overcome this problem. The prediction method used is Autoregressive Integrated Moving Average (ARIMA). According to the computations, the Root Mean Square Percentage Error (RMSPE) value is less than 10%, indicating that the ARIMA model estimation is excellent

ЗМІНИ В ОПОДАТКУВАННІ ТА ВВЕДЕНЯ ПОДАТКОВИХ ПІЛЬГ В УКРАЇНІ В УМОВАХ ВІЙСЬКОВОГО СТАНУ

In the article, the authors shed light on the problem of implementing tax changes and introducing tax benefits in Ukraine under martial law, and also the impact of these processes on tax revenues to the State and local budgets of Ukraine from taxes to which these benefits were applied. In particular, the peculiarities of tax benefits introduction to VAT, single tax, land fee, and excise tax were revealed. Using a dynamic analysis of the incomes of these taxes to the State and local budgets of Ukraine, the main causes and consequences of these revenues growth rate were determined. We have identified the main reasons for the cancellation of certain tax benefits, in particular the VAT benefit for imported goods, which caused significant losses in the revenues of the state budget, as this tax accounts for more than 40% of the revenues to the State Budget of Ukraine, it is considered a budget-forming tax. The decrease in customs revenues is caused not only by the introduction of tax relief on imported goods, but also by a physical decrease in the volume of foreign trade transactions. It will be possible to achieve positive growth only if the pre-war volumes are restored. It was concluded that the introduction of tax benefits on single tax and land fee affected the preservation of the activities by small and medium-sized business entities and balanced revenues from the single tax with the revenues of local budgets of Ukraine in the second half of the current year. The results of the study reflect the cause-and-effect relationships between introduced tax benefits, tax changes, and tax revenues from these taxes and also prove the importance of implementing these changes under martial law conditions.

Logistic approach to the segmentation of the company's customers as a basis for the formation of logistics services

The article defines the essence of the logistics customer service concept, as a component of the service process, which makes it possible to ensure the necessary level of the customers’ needs satisfaction, while providing the minimum necessary level of costs and maximizing profit for the company. It was determined that the basis of the formation of logistics customer service is their segmentation or differentiation. A classic logistic approach to the differentiation of customers can be considered their division using ABC-XYZ analysis. At the same time, ABC analysis is usually carried out based on the income brought by clients for a certain period of time, XYZ analysis is carried out according to the stability of relations with the client, that is, according to the number of orders placed by the client during the same period of time. We proposed our own view on the direct process of dividing customers into groups A, B and C, which is based on the calculation of the "jump in share" of customer revenues. The advantage of the proposed approach is that customers with relatively equal importance to the company cannot fall into different categories, and customers with very different values of importance to the company cannot fall into the same category. Another approach to customer differentiation based on customer profitability was also proposed and recommendations for servicing each customer group were made. Thus, the correct segmentation of customers and assessment of the potential of each segment will allow building several different service strategies aimed at increasing the company's profits.

Retail System Scenario Modeling Using Fuzzy Cognitive Maps

A retail business is a network of similar-format grocery stores with a sole proprietor and a well-established logistical infrastructure. The retail business is a stable market, with low growth, limited customer revenues, and intense competition. On the system level, the retail industry is a dynamic system that is challenging to represent due to uncertainty, nonlinearity, and imprecision. Due to the heterogeneous character of retail systems, direct scenario modeling is arduous. In this article, we propose a framework for retail system scenario planning that allows managers to analyze the effect of different quantitative and qualitative factors using fuzzy cognitive maps. Previously published fuzzy retail models were extended by adding external factors and combining expert knowledge with domain research results. We determined the most suitable composition of fuzzy operators for the retail system, highlighted the system’s most influential concepts, and how the system responds to changes in external factors. The proposed framework aims to support senior management in conducting flexible long-term planning of a company’s strategic development, and reach its desired business goals.

An Approach towards Deployable Hybrid Product Recommendation Systems for E-Commerce

As India is moving fast towards digital economy, E-commerce industry has been on rise. Many platforms provide their customers with a shopping experience better than actual physical stores. Several E-commerce websites use different methods to improve the customer engagement and revenue. One such technique is the use of personalized recommendation systems, which uses customer’s data like interests, purchase history, ratings to suggest new products, which they may like. Recom-mendation systems are used by E-commerce websites to suggest new products to their users. The products can be suggested based on the top merchants on the website, based on the interests of the user or based the past purchase pattern of the cus-tomer. Recommender systems are machine learning based systems that help users discover new products. Due to the recent pandemic situation of 2020 and 2021, many of the local retail stores have been trying to shift their business to online plat-forms such as dedicated websites or social media. The proposed methodology based on Machine Learning aims to enable local online retail business owners to enhance their customer engagement and revenue by providing users with personalized recommendations using past data using methods such as Collaborative Filtering, Popularity-based and Content-Based Filtering.

Modelling Customs Revenue in Ghana Using Novel Time Series Methods

Governments across the world rely on their Customs Administration to provide functions that include border security, intellectual property rights protection, environmental protection, and revenue mobilisation amongst others. Analyzing the trends in revenue being collected from Customs is necessary to direct government policies and decisions. Models that can capture the trends being purported from the nominal (nonreal) tax values with respect to the trade volumes (value) over the period are indispensable. Predominant amongst the existing models are the econometric models (the GDP-based model, the monthly receipts model, and the microsimulation model), which are laborious and sometimes unreliable when studying trends in time series data. In this study, we modelled monthly revenue data obtained from the Ghana Revenue Authority-Customs Division (GRA-CD) for the period January 2010 to December 2019 using two traditional time series models, ARIMA model and ARIMA Error Regression Model (ARIMAX), and two machine learning time series models, Bayesian Structural Time Series (BSTS) model and a Neural Network Autoregression model. The Neural Network Autoregression model of the form NNAR (1, 3) provided the best forecasts with the least Mean Squared Error (MSE) of 53.87 and relatively lower Mean Absolute Percentage Error (MAPE) of 0.08. Generally, the machine learning models (NNAR (1, 3) and BSTS) outperformed the traditional time series models (ARIMA and ARIMAX models). The forecast values from the NNAR (1, 3) indicated a potential decline in revenue and this emphasizes the need for relevant authorities to institute measures to improve revenue generation in the immediate future.

الجامعة المستنصرية / كلية الادارة والاقتصاد

The customs tariff represents a very important financial tool after oil in financing the general budget and expanding the scope of public revenues and providing them with financial resources. However, the legislative authority legislated the tariff law and applied it late, and that corruption was one of the most important reasons that made this resource weak contributing to public revenues, and the problem of research lies in the financing role of the customs tariff is very weak, even after the application of the customs tariff law of 2010, and the tariff did not contribute as required to financing the state with customs revenues. The research is on two axes, as the first axis deals with the theoretical framework of the customs tariff and the second axis is the customs revenues in Iraq and their role in financing the public budget after 2003, and that one of the most prominent conclusions reached by the research is the percentage of the contribution of customs revenues to public revenues is still weak, as the average contribution rate reached The customs revenue in the general revenue is (0.71%), which is less than the correct one until After the application of the customs tariff law, the indicator of customs evasion has increased for some selected years, which were chosen since the law was applied, to find that the rates are still high and that this indicates the weakness of the state to curb these practices, and the most prominent recommendations came that the economic authority must activate its control tools in order to preventing customs evasion or using other practices to avoid wasting resources from customs to expand the scope of its contribution to the structure of tax revenues and raise its contribution to the public budget.

Forecasting Customs Revenue Collection in Light of the Spread of the COVID-19 Pandemic using ARIMA Models and the Exponential Smoothing Methods in Libya.

Forecasting future values of economic variables is one of the most critical tasks for governments, especially the values related to customs revenue collection are to be forecasted efficiently as the need for planning is great in this sector, because it is considered one of the sources of funding for the state's public treasury. The main objective of this research is to identify an appropriate statistical model for time series forecasting customs revenue collection during the current COVID-19 pandemic in Libya. The decision throughout this research is mainly concerned with ARIMA model, and Simple, Brown’s linear trend, exponential smoothing methods. The obtained data covers 108 observations, starting from the first week of the 6th month of the year 2019 to the last week of the 8th month of the year 2021.Based on the forecasting results of the current research, it was revealed that ARIMA (0,1,1) model offered more probabilistic information that improves forecasting the volume of customs revenue collection in light of the COVID-19 pandemic. According to this model, the research forecasts the new period in the next eight weeks or two months and finds that it will be increasing. In this research, ARIMA model and exponential smoothing methods are linear models based on the reactions to customs revenue collection due to the spread of the COVID-19 pandemic in the world. Furthermore, the forecasting performance between linear and nonlinear models can be compared in future studies.

A Survey on Hybrid Recommendation Systems

As India is moving fast towards digital economy, E-commerce industry has been on rise. Many platforms such as Amazon and Flipkart provide their customers with a shopping experience better than actual physical stores. Several E-commerce websites use different methods to improve the customer engagement and revenue. One such technique is the use of personalized recommendation systems which uses customer’s data like interests, purchase history, ratings to suggest new products which they may like. Recommendation systems are used by E-commerce websites to suggest new products to their users. The products can be suggested based on the top merchants on the website, based on the interests of the user or based the past purchase pattern of the customer. Recommender systems are machine learning based systems that help users discover new products. Due to the recent pandemic situation of 2020 and 2021, many of the local retail stores have been trying to shift their business to online platforms such as dedicated websites or social media. The proposed methodology based on Machine Learning aims to enable local online retail business owners to enhance their customer engagement and revenue by providing users with personalized recommendations using past data using methods such as Collaborative Filtering and Content-Based Filtering.

Customs Revenues Prediction Using Ensemble Methods (Statistical Modelling vs Machine Learning)

This paper considers the problems associated with prediction of customs revenues by ministries of finance and customs administrations. Accurate predictions of customs revenues result in better liquidity of the central budget, and for that reason, they are extremely important for successful management of public finances. The orthodox approach to forecasting revenues is usually based on forecasting revenues based on tax buoyancy and tax elasticity, with respect of some economic proxy. However, this approach has some shortcomings which can negatively affect accuracy, and for that reason we examine different approaches (machine learning and ensembling). Namely, nowadays in the era of Big Data and digitisation in customs, new approaches based on computer algorithms can give us better results as compared to classic modelling. The paper concludes that using ensemble methods that combine different types of heterogeneous models such as statistical modelling and machine learning can improve forecast accuracy when predicting customs revenues.

Digitization of the Customs Revenue Administration as a Factor of the Enhancement of the Budget Efficiency of the Russian Federation

In this study, we analyzed the main tendencies of the digitization of the tax and customs administration in Russia and worldwide. The main focus of this study is on the determination of the role of customs payments in the enhancement of the budget efficiency of the Russian Federation. For this purpose, we carried out an analysis of the collectability of the customs payments over 18 years according to their different types. Our research showed that further development of the use of digital technologies in customs payments administration will be carried out in the informational exchange systems between the tax and customs authorities. We conducted an expert evaluation of the predictive model parameters to define the basic technological points of the project for the modernization of the digital information exchange system between the customs and tax authorities. The use of the suggested management decisions makes it possible to form a balanced view of the key values to perfect the customs payments collection system in the context of the future development of the digital economy and the improvement of budget efficiency.

Planning and Forecasting Customs Revenues to the State Budget: A Case Study of Ukraine

Planning and Forecasting Customs Revenues to the State Budget: A Case Study of Ukraine

Impact of adopted Quality Management on a business model in globalized economy

Research background: Changes have become an important topic of discussion among enterprises making it a top priority, especially in addressing sustainability concerns. No other issue is as important as sustainability from the point of globalization. Discussion is about to work intensively towards reinventing existing business models and integrating some methods to stay competitive and profitable. It requires an updated management capability not only through the modernization of processes, but also the creation of a new level of value creation within the company. The Quality Management System (QMS) can be used as a tool for reconstruction of the business activities and creation of new innovative ideas in the company, which comply with changing customer preferences, revenue mechanisms, and, finally, processes and value chains. Purpose of the article: This research aims to analyse the transformation of a business model which is affected by implementation of the Quality Management system and to find out how it influences the sustainability of a business model. Methods: In this research the authors used content analysis method with inductive and deductive approach as well as methods of selection and survey of respondents and appropriate quantitative methods to process the obtained data. Findings & Value added: The results drawn from this research provide academics and practitioners with a better understanding of impacts that an adopted QMS have on business model dimensions within an enterprise. Findings of the research show whether these impacts aid them in becoming more sustainable. This knowledge can then be applied to strategies and management skills for the purpose of achieving better modelling results.