UAB Ekonovus is a utilities company that has been operating for over 20 years. The company has 10 branches and over 400 employees.

Situation

The company, with branches all over Lithuania, has implemented a system of digitalisation and KPI indicators.

Problems

1. Each unit produced its own reports, but there was no single, company-wide analysis. Heads of departments used to spend a lot of time every week preparing reports for the management. And of course, countless Excel documents with reports were piling up.
2. The company used at least 4 different information systems to process and combine data from each of them was very complex. The reports of the applications used did not always match the company’s needs, took too long to modify (when quick solutions were needed) and were expensive because external developers did not always understand the company’s needs accurately. On the other hand, it is only by combining data from several systems that certain performance indicators can be calculated.
3. Waste system administrators require real-time servicing and weighing data and photographs for each waste container, with heavy fines applied for non-compliance.
4. The company was in the process of implementing a logistics system and an automated billing application. Managing this process required periodic measurement of the KPIs of the individual units’ performance in implementing the software and real-time identification of potential errors, but the sheer volume of data made this very difficult and time consuming.
5. The company could not respond quickly to changes in the situation – i.e. decisions could only be taken after the next month’s reports.

Solution

The introduction of an analytics culture in the company has been gradual:

1. The first phase involved a reorganisation of the company’s management structure, with precise responsibilities and the KPIs defining them. KPIs started to be analysed at weekly and monthly departmental meetings.
2. Analysing the status of customer service and identifying risks. Analysis was carried out on a daily basis and the necessary decisions were taken in real time. A control system was also put in place to monitor risk areas and recurring errors to detect and eliminate systemic errors – duplications. This made it possible to minimise the fines imposed by the administrator for service and data errors.
3. It has also moved on to analysing financial indicators, controlling new and lost customers, and analysing the profitability of individual activities and projects.
4. By combining data from logistics, billing, financial reporting and budgeting systems, the company has moved towards proactive management – i.e. it has started to see during the month what financial results will be achieved at the end of the month, which customers and performance indicators are driving this. This has enabled to control the causes of financial performance.
5. The analytics system has enabled to quickly process data, measure and manage the logistics and billing implementation process – measure and manage the logistics and billing system implementation process – to measure the progress of specific employees, to identify possible risks and errors.
6. After conducting tests of the analytics system in one department, we proceeded to enterprise-wide reporting. Different departments were able to monitor their own unit’s results, based on the rights granted to them.

Result

The introduction of a data analytics culture in the company has led to greater efficiency, as time is spent on decision-making rather than on report writing. The management of the company can base its decisions not only on financial indicators, but also on indicators of business processes. Analysing data across different dimensions has opened up a wide range of possibilities for data analytics. Visual interactive reporting has made data exclusions easier to discern, and seeing unified reports throughout the company’s mind has made it easier to make decisions.

UAB TRIMB is a wholesale pharmaceutical company with sales in all Baltic countries.

Situation

The accounting system used generated only static reports. The reports were prepared in Excel, collecting data from different data sources, and had to be re-generated when the period changed. Increased data traffic has made this difficult and inefficient. A more user-friendly way of processing information was sought.

Solution

The company uses the Rivilė ERP, so we were able to offer a quick-to-implement standard solution for Rivilė Gama. Several modifications have been made due to the specificities of the company. After the implementation of the analytics system, the customer was able to easily track sales, detail turnover by country, control the cost and profitability of goods, see comparative analysis of sales-purchases by periods, monitor the balance of income – rescues, monitor the distribution and dynamics of wages and salaries of the working people.

A KPI indicator system was implemented. By combining actual sales results with projected results, it was possible to monitor the progress of sales – to what extent sales are meeting targets – and to respond accordingly through marketing actions.

Result

With the introduction of the Power BI analytics system, the Customer was able to conveniently monitor the latest results of the company every day. Generating reports no longer required additional time. The data is presented in a structured and visual way, with the possibility to analyse at different cross-sections and go down to the detailed rows.

The adoption of Power BI has significantly increased the productivity of the company, allowed for faster response to problems, and the tracking of indicators and trends gave a competitive advantage over others.

UAB Ovoko is a company that develops the used car parts platform RRR.lt. This platform unites over 500 sellers from Lithuania, Latvia, Poland, Finland and other European countries.

RRR.lt offers used car parts sellers a continuously upgraded software package that allows them to manage their warehouse and sell already tested and reusable parts. The customer sees one big online shop, but virtually visits warehouses all over Lithuania.

Situation

The RRR.lt platform is growing steadily, and this is what makes the company’s growth rates so high. New sellers are joining the platform, not only from Lithuania but also from other European countries. Shipments are not limited to Europe, but go all over the world. The number of parts uploaded to the RRR.lt sales platform are counted in millions. The dramatic increase in data flows has also created the necessary need to monitor and analyse them. The reports were prepared manually by the sales managers, collecting information from several data sources and updated only once a week.

Solution

• With the introduction of Power BI analytics, weekly reports were eliminated and replaced by daily automated reports. The relevant reports have been prepared for use by managers and sales managers.
• It is now possible to monitor indicators in near real time and see daily changes. The quantities of parts sold, turnover, return percentage, quantity of cancelled orders and other indicators relevant to the company’s activities have become visible.
• In order to improve customer service and speed up the delivery of parcels, a parcel tracking report was created, which allowed to monitor the activity of scrap yards related to consignments.
• One of the competitive advantages of UAB Ovoko is that it has built up a very wide circle of scrap dealers. The analysis reports allow for a detailed analysis of the performance indicators of each scrap yard: when they joined the platform, when they placed the first order and how long it took until the first order. Whether the customers connected to the platform are active or passive. What are the sales during the selected period and what sales are made during the entire lifespan.

Result