Conrad Electronic realizes €10M+ value and doubles Order Management automation with Celonis
The problem
Conrad Electronic had achieved isolated process mining wins but struggled to scale them into lasting, company-wide improvements. Process knowledge was fragmented — individual departments suggested optimizations piecemeal — and the company lacked the structural foundation and documentation needed to support future AI use cases.
Workflow diagram · grounded in source
1
Process mining on support tickets
Ai action
Process mining was deployed to analyze, organize, and reduce the tens of thousands of customer support tickets Conrad received each month due to a lack of process visibility.
▾ source quote
“they deployed process mining to analyze, organize, and reduce the tens of thousands of customer support tickets Conrad received each month due to a lack of process visibility”
2
Order cancellation reasons surfaced
Ai action
Conrad surfaced nearly 100 reasons for order cancellations using Celonis, allowing the team to address those within their control.
▾ source quote
“Conrad surfaced nearly 100 reasons for order cancellations using Celonis, allowing the sourcing platform to address those within their control (such as out-of-stock cancellations)”
3
Open order dashboards and alerts
Output
User-friendly dashboards and daily alerts were rolled out to Customer Care agents flagging open prepayment orders at risk of cancellation.
▾ source quote
“rolling out user-friendly dashboards to the Customer Care department and setting up daily alerts informing Customer Care agents of open prepayment orders at risk of being cancelled”
4
AI classifies service inquiries
Ai action
AI solutions automatically classified customer inquiries, reducing workload and throughput time.
▾ source quote
“AI solutions automatically classified customer inquiries, reducing workload and throughput time. Meanwhile, AI analyzed the reasons behind customers' inquiries to uncover opportunities to improve customer service and simultaneously reduce unnecessary customer contact”
5
Product return rate analysis
Ai action
Conrad used Celonis to analyze return rates by product type and cross-referenced the data with customer-reported return reasons to identify root causes.
▾ source quote
“Conrad used Celonis to analyze the rates at which different product types are returned. Cross-referencing and consolidating this data with the reasons customers sent each item back enabled Christoph's team to identify and address what caused product returns”
6
Continuous impact measurement
Feedback loop
Conrad orchestrated and measured the impact of improvement actions within the Celonis Platform.
▾ source quote
“Conrad has been able to orchestrate and measure the impact of improvement actions within the Celonis Platform”
Reported outcome
Conrad realized over €10M in value through Celonis in three years, achieved a 100% increase in Order Management automation, and raised order-block-processing automation from 40% to 90%, while also slashing product return rates and reducing service-ticket workload and throughput time.
Reported metrics
Realized value€10M+
Order Management automation increase100%
Order-block-processing automation ratefrom 40% to 90%
Product return ratesslashed
Show all 6 reported metrics
realized value€10M+
Order Management automation increase100%
order-block-processing automation ratefrom 40% to 90%
product return ratesslashed
service ticket workload and throughput timereducing workload and throughput time
order cancellation reasons identifiednearly 100
Reported stack
CelonisCelonis Process Management (CPM)Open Order Processing AppCelonis AI automation
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Conrad realized over €10M in value through Celonis in three years, achieved a 100% increase in Order Management automation, and raised order-block-processing automation from 40% to 90%, while also slashing product ret…
What tools did this team use?
Celonis, Celonis Process Management (CPM), Open Order Processing App, Celonis AI automation.
What results were reported?
Realized value: €10M+; Order Management automation increase: 100%; Order-block-processing automation rate: from 40% to 90%; Product return rates: slashed (source-reported, not independently verified).
How is this order processing AI workflow structured?
Process mining on support tickets → Order cancellation reasons surfaced → Open order dashboards and alerts → AI classifies service inquiries → Product return rate analysis → Continuous impact measurement.
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