Many tools, strategies, and methodologies have been developed for improving business processes. Most of these aim to achieve continuous, incremental improvement of existing processes. They are suitable for stable businesses or business units that perform relatively stable processes (like accounts payable) that need continuous improvement. Using these approaches, companies can benefit from other businesses’ experiences and leverage what has been proven to work. The latest developments in technology, especially in AI and machine learning, can boost process improvement efforts.
Therefore, this article explains what are the top 4 process improvement technologies and how AI and ML have been augmenting these technologies.
Process mining is the latest solution in this area, which shows “as-is” processes. This feature enables businesses to identify discrepancies from the desired processes and understand the actual performance of their position. Process mining tools connect process steps in a cause-and-effect relationship using event logs and data-driven performance metrics. These metrics also allow companies to identify bottlenecks and help them to find their root causes for eliminating them.
By introducing process mining, companies can eliminate unnecessary costs, create value-adding steps, and reduce deviations from their desired processes. It is a critical solution that supports many process improvement techniques listed above.
For example, in a process mining case study, Metsä Board identified the constraints in supply chain processes and mined their operations to improve their performance. They have identified differences between their actual process and target process flow. It is claimed that these insights enabled them to improve the process, leading to an improved customer experience. For more, you can examine process mining case studies.
To learn how process mining tools work, you can read our detailed process mining guide.
Check out comprehensive and constantly updated list of process mining case studies to find out more on process mining real-life examples.
Robotic Process Automation (RPA)
Robotic Process Automation (RPA) is a popular technology that involves bots that mimic human actions to complete repetitive tasks. RPA enables bots to handle repetitive and tiring tasks instead of humans and focuses on higher-value activities. As a result, they reduce wasted resources and focus more on customer value. This solution ensures businesses accelerate their processes while reducing their costs.
To learn more about this emerging technology, you can read our ultimate RPA guide.
Business Process Management (BPM) and Business Process Automation (BPA)
Business Process Management (BPM) tools include some aspects of process mining and process modelling tools allowing businesses to identify, model, automate, and report on their processes. As a result, companies can optimize their operations to achieve their goals. BPM software is used for process analysis and tracking the performance of any changes made in processes.
BPM tools also include modules for process automation. When used as separate modules, these tools, also called Business process automation (BPA) software, can be used for process improvement to execute repetitive activities to replace human force. This solution allows companies to reduce their costs and increase process performance by benefiting from automation. It consists of integrating applications, restructuring labour resources, and using software applications throughout the organization. Under this technology, robotic process automation (RPA) is a popular solution that relies on bots that use UI intended for human use to automate repetitive activities.
You can read more about BPA and find an extended list of BPA vendors on our website.
Data extraction technologies play two roles in process improvement.
Machine learning enables companies to improve data extraction capabilities and automate their processes involving data extraction. For example, data from documents can be captured automatically with high accuracy using deep learning, a subfield of machine learning. Automated data extraction creates visibility into how individual processes function and enables companies to audit and continuously improve results.
Data extraction software can also collect data from a wide range of sources such as customer reviews and convert unstructured data to structured data. That allows companies to evaluate their position with higher-quality data and ensures more accurate insights for better process improvement.
You can read more about data extraction in our related article.
You can learn more on process improvement by reading our relevant articles:
- 55 Process Improvement Case Studies & Project Results
- Process Improvement: In-depth guide for businesses
If you still have questions about process improvement technologies, we would like to help:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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