Analytics-based self-service portal for reporting
20–30% less for planning and reporting
Analytics-based self-service portal for reporting
Key use cases in finance, HR, and IT
A medium-size steelmaker successfully automated four internal processes with robotic process automation in only one month
Case example 2 / 7
Many companies have fragmented and manual internal production management processes that drive high administrative expenses. A medium-size steel maker decided to automate repetitive tasks to improve efficiency and reduce expenses. The company automated the creation of weekly production reports and the integration of master data for products into its enterprise resource planning (ERP) system. Using a third-party robotic process automation (RPA) software platform, the company quickly automated four reporting processes, starting with the shipment and production overview.
Besides freeing up employee working time, the system reduced errors and cycle times by automating these repetitive tasks.
A major car maker reduced its planning and reporting efforts using an analytics-based self-service portal for reporting
Case example 1 / 7
Multiple, complex, and inconsistent data sources stored in siloed systems often lead to high reporting efforts in areas such as product development or production planning. A major car maker addressed these problems by creating a digital reporting tower that enables automated cross-divisional reporting based on a single source of truth. The system collects and stores all required data (e.g., products and parts, project and milestone planning) in a single, consistent, and uncontested data pool. Employees can easily obtain customized data visualization, conduct live monitoring, and automatically create analytics-based reports.
The solution cut planning and reporting efforts 20–30% via its single source of truth, intelligent cross-divisional input management, automated report creation and good decision making.
20–30%
reduction of planning and reporting time
RPA for report creation
RPA for report creation
1 month to fully automate 4 processes
Managing accounts receivable through machine learning
Managing accounts receivable through machine learning
15% savings for cost of collection
RPA and ML for data transfer between tools
RPA and ML for data transfer between tools
90% of data transfers automated
Analytics-based resume screening
Analytics-based resume screening
90% reduction of manual screening
Automation of IT service desk
Automation of IT service desk
250 fewer working hours a month
Intelligent process mining for process optimization
Intelligent process mining for process optimization
10-15% savings for optimized processes
Optimized sourcing of package delivery services
Optimized sourcing of package delivery services
20% savings in package delivery
Cloud migration for standardized IT infrastructure
Cloud migration for standardized IT infrastructure
15–20% less infrastructure spend
A global technology company reduced its accounts receivable balance and cost of collection by using machine learning in its collections process
Case example 3 / 7
An analysis by a global technology company revealed it could reduce its cost of accounts receivable balance and collection process by speeding up collections and understanding which outstanding receivables were likely the most problematic. The technology company applied machine learning algorithms to create a better collections process. Instead of its former strategy of collecting by segment and country, it shifted to a bottom-up invoice-level strategy.
By managing accounts receivable and collections processes using machine learning, the company decreased its costs of collection by about 15% and its accounts receivable balance by roughly 7%.
An automotive company fully automated about 90% of its data transfers between tools within one year using robotics process automation (RPA) and machine learning (ML)
Case example 4 / 7
Manual data transfers, like manual copying from one tool into another, leads to a high risk of errors and lower quality in reporting and data analyses. An automotive company invested in automation technology to deal with small repetitive tasks. The technology it selected included robotics process automation as well as machine learning components. The selected technology had low costs since it did not require new IT infrastructure.
After only one year, the company had automated 90% of its data transfers, amortized the investments and significantly boosted transfer reliability.
~90%
of data transfers between tools automated
An automotive supplier reduced its HR department’s manual workload and its hiring biases by implementing machine learning-based resume screening
Case example 5 / 7
Hiring processes done by HR departments include the screening of applicants’ resumes. Manual screening processes are time-consuming and run the risk of allowing hiring biases. An automotive supplier therefore decided to automate its review process of incoming resumes using machine learning algorithms. The chosen tool screens resumes by identifying keywords that correlate with skills, characteristics, and experiences as laid out in the job description.
The resume screening automation reduced the company’s manual screening workload by over 90% and cut hiring biases more than 10%.
A consulting firm implemented service desk automation to improve the operation of its IT help desk
Case example 6 / 7
In times of increasing digitalization, a company’s internal demand for IT service desk can grow significantly. A global consulting firm reacted to this increasing demand by automating parts of its IT service desk. The solution used segmentation to route calls addressing specific people, known technical issues or the proactive remediation of issues, based on pattern recognition. It also introduced the implementation of self-service for software.
The service desk automation enabled the company to save about 250 working hours a month by automating troubleshooting steps. Additionally, it proactively deflected roughly 5,000 calls a month by running back-end scripts to fix computer performance. The system now enables caller self-service on about 97% of software requests and approximately 85% of room equipment orders.
~250
less working hours a month
A North America logistics company analyzed its outbound logistics spend to save 20% on package delivery costs
Case example 8 / 9
The company spends about 2.5 Million USD in outbound package delivery. To identify savings, it used an optimization algorithm to search for potential savings in various areas. It sought the lowest price option for the same service-zone-weight combination and compared different transport options like air versus ground shipments, while respecting boundaries such as criticality and on-time delivery.
The analyses enabled the company to capture a total of 20% in savings by, for example, reducing spend on surcharges for non-critical packages or bundling packages within a week to ship fewer heavier packages. The company launched the algorithm in about 3 weeks.
20%
savings in package delivery
A North American insurer reduced its infrastructure spend by 15–20% by migrating to a standardized cloud environment
Case example 7 / 7
Infrastructure expenses are often a large part of a company’s IT spend. To reduce these expenses, a North American insurer migrated its infrastructure to a highly standardized and automated public cloud. This allowed the insurer to centralize its infrastructure activities in areas such as maintenance, the implementation of updates and the continuous improvement of security standards.
The company-wide standardization in the cloud environment enabled it to reduce its infrastructure spend by 15–20%. Additionally, the systems became more secure through increased and centralized security standards.
15–20%
reduction of infrastructure spend
1 month
to fully automate 4 reporting processes
~15%
savings for cost of collection
90%
reduction of manual screening workload
A distributor of industrial goods reduced its costs and cycle times via an automated analysis of internal process variances
Case example 9 / 9
The company found the manual analysis of internal processes (e.g., the order-to-invoice process) for exceptions difficult due to large and rapidly increasing transaction volumes and data volumes (e.g., sales orders, orders across sales offices/channels, or customer data). The distributor of industrial goods and supplies therefore implemented an automated process mining approach to analyze transaction events to understand the root causes of process variations. This approach included the automated creation of process monitoring dashboards and an updated list of insight-based priority processes.
The implementation of intelligent process mining enabled the distributor to lower its process costs by 10-15% and reduce cycle times by 20-30% via optimized processes.
10-15%
cost savings for optimized processes
20-30%
reduction of cycle time
~7%
reduction of accounts receivable balance
10%
reduction of hiring biases