Initial attempts to integrate Process Mining methods in the domestic market were undertaken around 2009-2010. Software AG experts used this technology when implementing projects to implement ARIS Process Performance Manager.
Process Mining Solutions in Russia
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However, this toolkit was primarily focused on assessing italy business mailing list business operations through the Process Intelligence functionality. As a result, the concept of process extraction was not widely adopted, remaining an auxiliary analytical method within a broader approach to process optimization. Around the same time, a group of enthusiasts promoted the Fluxicon Disco system, but with the same result.
The PM market received a new impetus in 2016-2017 with the advent of the Process Mining Celonis system. Marketing support for this product is provided by SAP Corporation, which in itself gives a significant advantage in the market.
As a result, it is this solution that is currently undergoing pilot testing in several large corporations, among which are such financial giants as VTB and UBRR. Other systems are also available on the market, such as QPR ProcessAnalyzer, but their use is more the exception than the rule.
The growing popularity of business process analysis methodology, coupled with the course towards replacing imported technologies, has stimulated the creation of Russia’s own software solution called Proceset.
Considering the significant financial costs of this development, an alternative option is worth considering - using the publicly available ProM (TU/e) toolkit. It has no access restrictions and can become a cost-effective solution for domestic companies.
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Examples of using Process Mining technology by foreign and Russian companies
PM is widely used in various industries: manufacturing, high technology, energy, telecommunications, trade, pharmaceuticals, insurance, fintech, aerospace and defense.
Examples of using Process Mining technology by foreign and Russian companies
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Such market giants as Siemens, Dell, Royal Dutch Shell, Unilever, Adidas AG, LʼOreal Paris, The Coca-Cola Company, AstraZeneca, Сitigroup Inc. use data Process Mining to optimize processes. The most common areas of application: customer service, credit conveyor, call centers, logistics, technical support, delivery, maintenance and repair.
Let's analyze a number of illustrative cases:
Ruukki (metallurgy)
Customer service analysis using data from SAP and Salesforce revealed shortcomings in maintaining customer information in CRM, which slowed down order processing. Correcting these shortcomings with QPR Process Analyzer accelerated service and improved its quality.
Caverion (engineering services)
QPR Process Analyser helped measure the efficiency of the company's core processes. Analysis of data from the ERP system revealed the cause of delays: slow preparation of accounting documents. As a result, the efficiency of the processes was increased, and the company decided to use Process Mining regularly.
Vaisala (manufacturing of metallurgical equipment)
Using QPR Process Analyser, the processes of technical support, sales, delivery, repair and marketing were analyzed. Data was collected from Salesforce and Oracle BI. The analysis allowed to optimize processes and reduce operational costs.
Construction Trading House "Petrovich"
The PM class solution (Promease) helped to study more than 150 thousand applications, which led to the possibility of reducing the order processing time by 7-10%. The implementation manager noted the great potential of Process Mining for the company's operational activities.
PJSC MTS
Russian Process Mining (Proceset) class programs helped MTS identify opportunities to reduce labor costs by more than 16,000 man-hours for 105 HR specialists, which is 11% of working time. Specific recommendations were given for improving processes. And it was decided to scale up the use of Process Mining in combination with Task Mining to other areas.
PJSC Alrosa
The Ramaks Group of Companies conducted an audit of the corporate business process and reconstructed its actual flow. Proposals for optimizing budget expenditures were formulated. According to a representative of Alrosa, the main goal of the project was to convert a qualitative assessment into a quantitative one to determine the impact of the main steps of the algorithm on overall efficiency.