How Machine Learning Transforms BPMhttps://www.comidor.com/wp-content/uploads/2019/07/ml-and-bpm-KB.png789443Comidor BPM PlatformComidor BPM Platform//www.comidor.com/wp-content/uploads/2021/07/Comidor-logo-new-444x112-2.png
If you already use a BPM software in your business you already see the benefits: proper risk management, transparency, employee satisfaction, measurability, and sustainability of business processes are some of them. Now, imagine using all the data from the BPM so as to automatically optimize processes and improve decision making. With machine learning and BPM combined, you can accomplish just this.
What is Machine Learning?
Machine learning, according to Wikipedia, “ is the scientific study of algorithms and statistical models that computers systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence”. In the 21st century, many enterprises have realized that machine learning will bring the revolution in every business sector, making the study of machine learning of vital importance. . Information technology could not be an exception. Machine learning is transforming the future of IT while it makes business software smarter and recovers valuable hours during the day of an employee.
How Machine Learning and BPM can be combined?
Plenty of data are stored in a BPM software. Combining Artificial Intelligent, in particular machine learning algorithms, with these data,makes decision making easier by identifying patterns as the process advances through the workflow. Existing machine learning tools already includelibraries ready tobe implemented in combination with BPM solutionsso processesare improvedimmediately.
Below, we share 6 applications of Machine Learning and BPM that help you understand the power of this combination:
1. Process Scheduling
Machine learning could be applied during process execution. For example, a BPM Software could trigger a new process or reroute running processes according to predictions. After machine learning’s predictions, the BPM reacts immediately (task reallocation, form’s update) or in the long term (redesign the process or the user interface (UI), analyze UI performance).
2. Decision’s Recommendation
Managers have to make decisions, like “approve/reject a partnership proposal”, “authorize/decline a new project”, and so on. The final decisions for cases like these can be analyzed through sophisticated machine learning algorithms (such as the decision trees and neural networks) in order to find the best decisions for other similar ones.
A combination of machine learning techniques can be used also by the human-resources department. Through natural language processing (NLP) techniques, recruiters can analyze the CVs and create summaries of them. Also, they can predict how well a candidate will fit into the company based on additional interviews or other data.
4. Project Management
Going beyond the above benefits, intelligent BPM with machine learning functionalities allow businesses to automate project management. So, the BPM can assign tasks to the most suitable team member, correct task estimates and recommend corrective actions to meet deadlines.
Businesses target the campaigns right and organize a successful marketing strategy by takingadvantage ofthe power of machine learning. For example, sales and marketing teams often spend hours to figureout which leads are the highest-value targets for sales promotion email campaigns. By using supervised machine learning, a BPM tool is “taught ” to navigate lead dashboards and find the most valuable targets.
6. Process Mining and Machine Learning
Regarding Wikipedia, “ process mining is a family of techniques in the field of process management thatsupports the analysis of business based on event logs ”. This new trend aims to improve process efficiency and comprehension ofthem. The data that arise from process mining can be used as input to create future predictions. For example, process performance can be monitored by evaluating how ongoing cases flow through the process until they are completed.
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