United Paper Mills (OBF) (iT Pro: BI.Qube)

2021: Production Cost Plan Analysis System

Purposes

• Automation of cost calculation and production margin;
• Obtaining a perspective (plan) for all cost elements;
• Flexible implementation of additional indicators;
• Reduce reporting labor costs to a possible minimum.

Project uniqueness

An automated analytical system for calculating costs and production margins is designed to carry out a plan of actual analysis of production costs. The system is developed on the basis of the universal IT Pro solution "Factor analysis of production costs." The solution provides the ability to automatically calculate 14 indicators by cost and margin and predict costs.

The system is used to calculate production costs in the Company United Paper Mills"" - a group of production enterprises of pulp. paper industries As of to data 2021 BSF , 5 organizations have a total annual revenue of more than 11.5 billion. rubles

The OBF company is one of the top 15 large enterprises of the Central Bank of Russia, ranks fourth in the ranking of domestic manufacturers of container cardboard, and is an expert on the commodity production of waste paper packaging cardboard in the country.

After implementing the system, the time to generate interactive reports on processes and objects is 2 seconds. To create an analytical system, the meta-components BI.Qube were used - Russian software developed by IT PRO based on Microsoft products. The process stack of the project includes the following components:

• MetaStaging - a module for quickly connecting new sources and downloading them to the ACH staging area;
• MetaVault is the core of the system that allows you to easily expand the composition of measures, measurements, attribute composition, and maintain the historicity of data.

Implementation features

The key challenge for IT Pro specialists was the ability to quickly plan the actual analysis of cost and margin data from several disparate sources. Two parameters were important for the customer: as soon as possible to implement the system into the company's processes and the minimum amount of additional costs for adapting the universal IT Pro solution based on the BI.Qube product.

Project Description

As in any dynamically developing group of manufacturing enterprises, the following problems of "growth" of the data landscape were observed in the BSF:

• Heterogeneity of the systems from which the reporting data was received;
• Use of different tools, methods and algorithms for solving the same problems in different divisions of the holding;
• Unreasonably high time costs in data processing;
• The impossibility of creating interactive reports that could be quickly changed, supplemented and modified;
• Inability to access reporting at any time and from any device. Prior to the implementation of the system, data on production costs of the enterprise were collected once a month.

Decision

The problems were solved by developing an automated analytical system for calculating direct costs and production margins. The system includes a single OLAP data cube designed to store, process and organize incoming information, and Power BI dashboards - information screens for viewing, adding and modifying reports.

The solution provides the following capabilities:

• Daily automatic calculation of 14 indicators in the framework of reporting;
• Accounting of planned and actual cost data in a single report;
• Reporting in PBI format;
• Create a perspective for any cost element.

Features of the system operation

Every day, at night, data is read from all sources to load, transform, and merge this data with DWH data. Analytical measurements are automatically posted to the DataCube using the following parameters:

• Nomenclature series;
• Nomenclature;
• Subdivisions (workshops);
• Organizations/legal entities;
• Counterparties;
• Cost items;
• Warehouses.

After the implementation of the analytical system, planning and actual data from different accounting systems do not require long manual processing. With the online addition of new key figures, the system can adapt to changes in the production processes of the BSF. The solution has excellent potential for further development. The next step in automating cost data analytics may be the transition from universal calculation methods to deeply individualized ones.