THE ASSESSMENT OF RECEIVABLES MANAGEMENT AS THE BASIC ELEMENT OF THE INTERNAL CONTROL SYSTEM OF AN ENTERPRISE OF LOGISTICS CLUSTER (BY THE EXAMPLE OF JSC TC “RAIL CONTINENT”)
The paper considers the problem of receivables management in the internal control system in the context of the crisis of insolvency of logistics enterprises of the Far East. The relevance of this study is determined by the interrelation of the financial state of enterprises of the logistics cluster with the efficiency of credit policy carried out by it and daily work with debtors.
The authors developed the structural model of the formation of the receivables of the selected segment of enterprises. Based on the current techniques of monitoring of the receivables’ level, the authors proposed the scheme of the integrated technique of the receivables analysis for the enterprises of transport sector, identified the main ways of improvement of the efficiency of internal control over the receivables taking into account the size of a company and its financial ability.
The results of the practical part of the study based on the implementation of author’s technique of analysis of the receivables of the logistics enterprises proved the existence of problems and bottlenecks of the companies of the selected territorial cluster which are, in particular, the lack of appropriate control over the due dates, the receivables amount and its correlation with the level of credit debt, the expediency of debt settlement; low level of record keeping allowing carrying out the control over receivables; poor organization of work with debtors (the preliminary check of their solvency, identification of the level of non-payment risk). The authors specified three sectors of the main ways for improvement of the receivables state: bookkeeping and auditing, analysis and monitoring, control automation.
The paper shows the efficiency of the proposals through the reassessment of the receivables management system in the case of their implementation within the enterprises of the logistics cluster in aggregate.
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