Способы закупок в России и их характеристики: эмпирический анализ на микроданных
One of the main goal of any industrial company is making profit by producing high quality and competitive products. Mostly, the production of meat industry enterprises are not a complete cycle, companies are divided into farms which are specialized in cultivation of livestock and poultry, slaughters and meet processing plants. The latters are the final link in the chain of supply of animal origin raw materials. For these companies it is important to establish a procurement process so that to have a sufficient number of fresh high-quality resources for production and to minimize losses releated with forced sales, often with a discount, the damage of excessive amount of purchased raw materials and also avoid unnecessary costs associated with their storage. This problem will be discussed in this article.
In this article we describe a system allowing companies to organize an efficient inventory management with 40 suppliers of different products. The system consists of four modules, each of which can be improved: demand planning, inventory management, procurement planning and KPI reporting. Described system was implemented in a real company, specializing on perishable products totaling over 600 SKUs. The system helped the company to increase its turnover by 7% while keeping the same level of services.
We address the external effects on public sector efficiency measures acquired using Data Envelopment Analysis. We use the health care system in Russian regions in 2011 to evaluate modern approaches to accounting for external effects. We propose a promising method of correcting DEA efficiency measures. Despite the multiple advantages DEA offers, the usage of this approach carries with it a number of methodological difficulties. Accounting for multiple factors of efficiency calls for more complex methods, among which the most promising are DMU clustering and calculating local production possibility frontiers. Using regression models for estimate correction requires further study due to possible systematic errors during estimation. A mixture of data correction and DMU clustering together with multi-stage DEA seems most promising at the moment. Analyzing several stages of transforming society’s resources into social welfare will allow for picking out the weak points in a state agency’s work.