Eliminating System Complexity Is Celonis’ Number One Priority
On October 14th, 2020, Co-CEO and Co-Founder, Alexander Rinke, kicked off its Ecosystem Summit and showcased its new Execution Management System (EMS). The goal is to improve an enterprise’s execution capacity by simplifying complex processes. Celonis defines execution capacity as the ability to get things done over the time + cost + effort (see Figure 1).
Figure 1. Celonis Defines Execution CapacitySource: Celonis
Celonis uses its process mining core to deliver the Celonis EMS Platform. The Bavarian software startup is headquartered in Munich, Germany and New York City, USA with 15 offices worldwide.
The Celonis Execution Management System
The Celonis Execution Management System (EMS) brings together a number of applications, instruments, development studios, tools and platform approaches to unlock execution capacity. Sitting on top of business processes and systems, the heart of the solution is the process mining core that identifies and measures capacity barriers. Capacity is unlocked by a methodic approach to applying best practices, actions, and automation. The new additions and updates from the fall launch of EMS include:
- Celonis Execution Applications. These operational applications include popular apps such as Celonis Accounts Payable, Accounts Receivable, and Opportunity Management.
- Celonis Execution Instruments. What was formerly known as process mining analytics applications, can be used to measure current execution capacity and identify execution gaps. Over 170 instruments have been developed to date.
- Celonis Studio. Ecosystem partners and customers use the studio to build new instruments and applications.
- Celonis EMS Store. New solutions can be ecosystem partners can be found in the store
Figure 2. Inside The Celonis EMS Platform
Proof Is In The Pudding
At the Ecosystem Summit, Celonis showcased a number of use cases where EMS improved operations. For example, AVNET reduced throughput times order management by 50% from order to ship. Dell was able to move to a leaner supply chain with 8 days of inventory versus an industry average of 40 days. Ascend improved on time delivery by 27% while simultaneously increasing automation by 43% in just four months. L’Oreal was able to grow on-time touchless orders by 800%, resulting in greater revenue and capacity to ship more products using existing infrastructure. Comcast improved asset utilization and captured $85 million in improvements.
Overall, this ability to improve execution capacity has created more capacity with existing infrastructure. In supply chains, the average on-time delivery rate is 43%, yet Celonis customers using EMS can hit a 95% target. Celonis clams that the average touchless invoice rate is only 27% inside companies while Celonis best-in-class customers are exceeding 90% adoption for finance and administration use cases. In customer experience, most companies average a 32 net promoter score (NPS), yet Celonis best-in-class customers appear to double scores at an average of 70 NPS.
Celonis customers include venerable brands such as ABB, AstraZeneca, Bosch, Coca Cola, Citibank, Dell, GSK, John Deere, L’Oreal, Siemens, Uber, Vodafone and Whirlpool (see Figure 3)
Figure 3. Celonis Customers Include Global Leaders In Every Industry
Bottom Line: The Race To Business Graphs Is Here, Automation and AI are The Facilitators To Decision Velocity.
The convergence of workflow, process mining, robotic process automation, integration services, microservices, and low-code/no-code platforms drive the future of software. This next battle in enterprise software will be the creation of business graphs. Like social graphs which use social networks to provide signal intelligence and digital feedback loops to accumulate massive amounts of data that is mined by AI, business graphs will accomplish the same for enterprises.
In the case of the enterprise, the relationships among users, documents, business processes, and contextual data will power the signal intelligence and digital feedback loops. As the majority of data is collected by digital feedback loops via automated and ambient collection, these systems can improve their precision decision capabilities. Automation and AI are the tools that bring scale to creating decision velocity.