Are you looking to add Process Mining to your arsenal of tools? Maybe for your business or personal use only, whatever it is – it’s always a good idea to know more about the most important Process Mining statistics of 2024.
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On this page, you’ll learn about the following:
Best Process Mining Statistics
☰ Use “CTRL+F” to quickly find statistics. There are total 88 Process Mining Statistics on this page 🙂Process Mining Usage Statistics
- 1.This study was approved by the Research Ethical Committee of Canton de Vaud and includes only patients who did not oppose usage of their data, and was conducted according to the Swiss Federal Act on Research involving Human Beings. [0]
Process Mining Market Statistics
- In 2020, Gartner’s estimate for new product license and maintenance revenue in the process mining market was ~$550 million, which indicates ~70% growth in the market size in a year. [1]
- In 2024, Gartner expects that the process mining market will pass ~$1 billion, by growing 40% to 50%. [1]
- The global process analytics market size is expected to grow from $185 million in 2018 to $1.42 billion by 2024, at a Compound Annual Growth Rate of 50% during the forecast period. [1]
- According to ’s report, Celonis led the market with more than 1000 clients, and 200 partners as of 2021. [1]
- In a blog post from 2020, the company claimed to hold over 60% share of the process mining market with almost 400% yearover. [1]
- Gartner estimated that the market for dedicated process mining tools grew from $110 million in 2018 to $320 million in 2019. [2]
- Based on our analysis, the market will exhibit a growth of 48.6% in 2021. [3]
- According to Fortune Business Insights, the global size market is projected to reach USD 10,383.0 million by 2028. [3]
- The market is projected to grow at a CAGR of 49.3% during the forecast period. [3]
Process Mining Adoption Statistics
- 83% of business decision makers plan to increase the adoption of process optimization in customer journey mapping and 57% of them are planning to increase it significantly. [1]
Process Mining Latest Statistics
- According to the survey in 2020, the top use cases of process mining are business and process improvement (32%), auditing and compliance (23%), process automation (21%) and digital transformation (17%). [1]
- process discovery (38%), enhancement (28%) andconformance(34%). [1]
- It is estimated that process enhancement applications will reach to 42% and exceed process discovery in 2024. [1]
- 93% of all questionnaire respondents stated they wanted to apply process mining within their organizations, 79% indicated never having used this technique. [1]
- Manual routing and process gaps further complicate the picture and 37% of business and technology decision makers report that their organizations experience these problems. [1]
- 61% of respondents state that provision of factual process data which can be used for further diagnosis is the most prominent benefit of process mining. [1]
- By using process mining during RPA implementation, businesses can increase the business value by 40% while reducing RPA implementation time by 50% and RPA project risk by 60%. [1]
- 78% of people who automate say process mining is key to enabling their RPA efforts. [1]
- Procurement process optimization (22%) and audit and control related activities (19%). [1]
- Average process conformance level is around 40. [1]
- For example, in Figure 5 you can see that the most frequent 40 variants cover almost 90% of the cases, and that Variant 40 in particular is followed by 2 cases. [4]
- However, how many of your variants are covering 80% of your process?. [4]
- If the case utilization is 1.0 (100%). [4]
- For example, in the case utilization chart in Figure 8 the highest case utilization that was achieved in the process was 41.2%.The case utilization can only be calculated if your data set has start and completion timestamps for each activity. [4]
- If the case utilization is 1.0 (100%). [4]
- For example, in the case utilization chart in Figure 8 the highest case utilization that was achieved in the process was 41.2%. [4]
- a yellow line is displayed that shows the cumulative, relative sum (how much out of 100%). [4]
- 94% of the activities occurred in the 1st level support and ca. [4]
- 6% were performed in the 2nd level support. [4]
- In our example scenario, we choose the data setProcess map 100% detail, which contains the full purchasing process without theEndpoints FilterinTrimmode applied .Figure 45. [4]
- Get 100% objective, real time view of your processes based on the data in your IT systems, to find and fix inefficiencies without argument. [5]
- The technology is great for processes that are 100% automated and run off the server, but that’s not the reality. [2]
- Given an action–response–effect log according to Definition 1, we shall use the shorthand notation in the remainder of this paper to refer to a trace that consists of events with an identical case identifier. [6]
- To account for this, we select 80% of the data when sorted on duration and take the mean of this subset of data. [6]
- With regard to the fifth assumption , we implement a heuristic selection criterion the value of the expected cells in each table should be 5 or greater in at least 80% of the cells [11]. [6]
- Based on a confidence level (usually 95%). [6]
- Thus, we apply the Bonferroni correction [13] on a confidence level of 95% . [6]
- Given that our Bonferroni correction gave us an alpha of 0.01 , we need to test on the 99% confidence level. [6]
- We can see that it represents a discrete representation of the Bell curve where constellations with an of between 0.20 and 0.25 are most likely. [6]
- The frequencies show that the response Seclusion is almost 1.7 times as likely to result in effect Physical aggression against people than statistically expected. [6]
- For the sake of readability, the filtering settings are set to 5% and 1% respectively, i.e., only the 5% and 1% most frequent action–response–effect patterns are included. [6]
- This shows the process filtered on 5% of the possible activities and paths for the initial action PO and 1% of the possible activities and paths for the initial action PP. [6]
- According to [37], we can distinguish two counteractions to this explainable by design or explainable post. [6]
- The activities can be clinical and non clinical and may represent different behaviours according to the specific organization [12]. [0]
- Table 1 reports, for each treatment category, its absolute and relative frequency of occurrence, and its duration in terms of median and inter. [0]
- The inter quartile ranges are computed at 25% and 75%. [0]
- Table 3 reports the frequency of occurrence of each pattern, the median OS time , and the percentage of patients alive at 1.5 and 3 years (CI at 95%). [0]
- Earn a Degree Breakthrough pricing on 100% online degrees designed to fit into your life. [7]
- Breakthrough pricing on 100% online degrees designed to fit into your life. [7]
- Furthermore, over 60 percent of respondents saw the technology’s potential in improving the customer journey map and IT processes. [8]
- Available to download in PNG, PDF, XLS format 33% off until Jun 30th. [8]
- Filtered log object log = pm4py.filter_variants_percentage. [9]
- percentage=0.8) Filter a log on a specified set of variants. [9]
- The percentage of variants to keep must be specified in the percentage parameter as a number between 0 and 1. [9]
- from pm4py.algo.filtering.log.variants import variants_filter filtered_log = variants_filter.filter_log_variants_percentage. [9]
- A number N is chosen such that if we take all the variants with at least N occurrences, we include a percentage of cases that is at least P, while if we choose N+1 we would include a percentage of cases that is below P. [9]
- with percentage=1, all the 20 cases would have been kept. [9]
- If we choose percentage=0.1, and N=1, then we include all the cases, while choosing N=2 we include only the cases of the first variant (that are the 5% of the log, hence N=2 is not valid according to the above principle). [9]
- If we choose percentage=0.05, and N=2, then we include exactly 5% of the cases of the log, that is the minimum requirement. [9]
- cost contains the cost of the alignment according to the provided cost function fitness is equal to 1 if the trace is perfectly fitting To use a different classifier, we refer to the Classifier section. [9]
- If that value exceeds a threshold , then the couple of activities is signaled. [9]
- Overall, 55% of inpatients were discharged within 4 days. [10]
- Secondly, LOS analysis according to diagnosis using Z scores [12] to analyze differences in LOS by diagnosis. [10]
- Also, as far as the distribution of the LOS was concerned, approximately 55% of hospitalized patients were discharged within 4 days, and out of these patients, approximately 20% were left the hospital on the second day of hospitalization. [10]
- Fig 3 shows the results of the analysis according to the average and IQR of LOS per department. [10]
- Fig 4 shows the distribution of diagnostic standard scores according to each department. [10]
- Patient number (percentage, %). [10]
- Transferred patients (%) 0.75 12.35 41.97 Patient on antibiotics treatment (%). [10]
- (percentage, %). [10]
- In this study, the ratio of restricted antibiotics administered to 1,000 randomly selected patients (12.79%) was higher than that in group A (0.7%) or group B (2.99%). [10]
- According to the analysis of hospital days based on transfer pattern, it was found that out of all patients, 5.25% those who have been transferred on average spent 17 more days stay the hospital than those who were not transferred. [10]
- were more likely to have longer LOS than those who were not . [10]
- patients on general wards were more likely to remain in hospital longer than patients on upper grade wards . [10]
- Also, we partitioned data into the training and test dataset to measure the accuracy of the model; 80% and 20% of data became the training and test data, respectively. [10]
- As far as this analysis was concerned, random forest was employed to build a model, and data partitioning with 80% for the training and 20% for the test dataset was performed to measure the accuracy. [10]
- Also, the relative importance of each feature, the transfer frequency was the was the highest at 41.40%, while surgery frequency, diagnosis frequency, severity, and insurance type were 28.44%, 24.13%, 4.77%, and 1.26%, respectively. [10]
- [24] Long term hospitalization exceeding 30 days was associated with a higher percentage of surgical operations and transfer rates, as well as restricted antibiotic use compared to other patients. [10]
- For example, at 1 percent with an event log that has cases with 3 or more tasks, the number of tasks will be 3 if possible. [11]
- On the contrary, at 100 percent with an event log with a total of 8 tasks the number of tasks would be 8 if possible. [11]
- For example, at 1 percent with an event log that has cases with 5 or more arcs, the number of arcs will be 5 if possible. [11]
- On the contrary, at 100 percent with an event log with a total of 11 arcs the number of arcs would be 11 if possible. [11]
- It is projected to grow from USD 627.0 million in 2021 to USD 10,383.0 million in 2028 at a CAGR of 49.3% in the 2021. [3]
- As per the Accenture Process Reimagined report of 2018, 88% of companies that implemented machine learning in the business process witnessed 200% improvement. [3]
- Key Findings The majority of the respondents, namely63%, have already started to implement process mining. [12]
- But there is still room for more, as currently87% of non adopters are planning to conduct pilot projectsor are willing to try it out with a proof of concept. [12]
- 83% of companiesalready using process mining on a global scaleplan to expand their initiatives, as do 77% of respondents that currently have island solutions and 63% of respondents that have recently launched a Proof of Concept. [12]
- 84%of the respondentsbelieve that process mining delivers value.31. [12]
I know you want to use Process Mining Software, thus we made this list of best Process Mining Software. We also wrote about how to learn Process Mining Software and how to install Process Mining Software. Recently we wrote how to uninstall Process Mining Software for newbie users. Don’t forgot to check latest Process Mining statistics of 2024.
Reference
- springer – https://link.springer.com/chapter/10.1007/978-3-030-72693-5_22.
- aimultiple – https://research.aimultiple.com/process-mining-stats/.
- venturebeat – https://venturebeat.com/2021/02/04/why-process-mining-is-seeing-triple-digit-growth/.
- fortunebusinessinsights – https://www.fortunebusinessinsights.com/process-mining-software-market-104792.
- fluxicon – https://fluxicon.com/book/read/statisticsview/.
- celonis – https://www.celonis.com/process-mining/what-is-process-mining/.
- sciencedirect – https://www.sciencedirect.com/science/article/pii/S0306437922000357.
- coursera – https://www.coursera.org/courses?query=process%20mining.
- statista – https://www.statista.com/statistics/1289110/process-mining-application-areas-russia/.
- fraunhofer – https://pm4py.fit.fraunhofer.de/documentation.
- nih – https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5898738/.
- bizagi – https://help.bizagi.com/process-modeler/en/process_discovery.htm.
- deloitte – https://www2.deloitte.com/de/de/pages/finance/articles/global-process-mining-survey-2021.html.
How Useful is Process Mining
Process mining involves the use of specialized software to examine event logs generated by IT systems. By mapping out the sequence of events and interactions within a process, organizations can identify inefficiencies, bottlenecks, and areas for improvement. This data-driven approach provides valuable insights into how processes are actually being executed, rather than relying on assumptions or subjective assessments.
One of the key benefits of process mining is its ability to uncover hidden patterns and deviations that may not be apparent through traditional methods. By visualizing the end-to-end process flow, organizations can pinpoint instances of rework, delays, or non-compliance with established protocols. This not only helps in streamlining operations but also enables proactive decision-making based on real-time data.
Furthermore, process mining can enhance transparency and accountability within an organization. By tracking and analyzing every step of a process, companies can hold individuals and teams accountable for their actions. This can lead to greater compliance with policies and regulations, as well as a culture of continuous improvement and innovation.
Another advantage of process mining is its ability to support digital transformation initiatives. As organizations embark on digitalization efforts, it becomes crucial to understand how technology is impacting their operations. Process mining can provide valuable insights into the interaction between human activities and digital systems, helping organizations to optimize technology usage and drive efficiency.
Moreover, process mining can help in identifying opportunities for automation and cost reduction. By identifying repetitive manual tasks or inefficient processes, organizations can streamline their operations and free up resources for more strategic initiatives. This can result in significant cost savings and improved competitiveness in the market.
While process mining offers numerous benefits, it is important to acknowledge that its effectiveness depends on the quality and accuracy of data. Organizations must ensure that they have access to reliable event logs and that data integrity is maintained throughout the analysis process. Additionally, interpreting the results of process mining requires a certain level of expertise and domain knowledge to extract meaningful insights and ensure actionable outcomes.
In conclusion, process mining is a valuable tool for organizations looking to optimize their processes, increase efficiency, and drive digital transformation. By leveraging data-driven insights, companies can uncover hidden inefficiencies, enhance transparency, and identify opportunities for automation and cost savings. While it is not a one-size-fits-all solution, process mining can complement traditional process improvement methods and provide a deeper understanding of how operations are being executed.Embracing process mining can help organizations stay ahead of the competition and achieve sustainable growth in an increasingly complex business landscape.
In Conclusion
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