Robotic process automation (RPA) has emerged as a powerful technology for automating repetitive, rules-based tasks. When combined with artificial intelligence (AI) capabilities like machine learning, natural language processing, and more, RPA becomes even more powerful and flexible. This is known as intelligent RPA.
For organizations running SAP, intelligent RPA unlocks new possibilities for workflow automation across the enterprise. According to SAP, over 80% of SAP customers are either implementing or planning to implement intelligent RPA.
In this comprehensive guide, we will explore the top 14 use cases for SAP intelligent RPA in 2024 and beyond. For each use case, we will explain the benefits and potential business impact.
Use Cases in Accounting & Finance
Accounting and finance departments handle high transaction volumes daily. Intelligent RPA can help automate repetitive processes to boost efficiency and free up staff for more strategic work.
1. Financial Closing
The financial closing process involves numerous sub-processes like reconciling accounts, consolidating financial statements, and more. This makes it time-consuming and prone to errors when done manually.
With SAP intelligent RPA, companies can automate various closing activities:
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General ledger entries – Bots can gather data from various sources, validate it, and automatically post journal entries to the general ledger. This eliminates manual data entry.
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Intercompany reconciliation – Bots can quickly identify and reconcile intercompany transactions by matching data across subsidiaries. This ensures transactions are eliminated from the consolidated financial statements.
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Payment reconciliation – By automatically matching incoming payments to invoices and orders, bots help identify exceptions that require review. This automates payment matching and reconciliation.
According to KPMG, financial close automation can reduce closing cycles by 2-4 days on average. It also boosts staff productivity by 20-50%.
2. Financial Planning & Analysis
Financial planning and analysis requires pulling data from different systems and sources. This makes it time-consuming and susceptible to copy-paste errors.
With SAP intelligent RPA, bots can automatically extract data from source systems on a schedule. This data can populate financial models and dashboards in tools like SAP Analytics Cloud. Key metrics are calculated automatically without manual intervention.
This gives finance leaders a real-time, holistic view of financial performance to aid planning and decision making. It also saves significant time compared to manual data collection and analysis.
According to McKinsey, real-time reporting and analysis is a top priority for finance executives amidst business volatility. Intelligent RPA enables this by automating data flows.
3. Procure-to-Pay
Procure-to-pay (P2P) automation ensures suppliers are paid accurately and on time. Slow payments can impact supplier relationships and disrupt supply chains.
Key steps like invoice processing and payment reconciliation are prime targets for intelligent RPA. For example:
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Invoice processing – Bots can capture invoices as they arrive via email or EDI. After data extraction and validation, invoices are posted to the system for approval. This eliminates manual data entry.
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Payment reconciliation – By cross-referencing bank statements, accounts payable sub-ledger, and invoices, bots can match payments to their corresponding invoices. Exceptions are flagged for review.
According to Paystream, P2P automation can reduce process costs by 60-80% and cut approval cycle times by over 50%. It also improves compliance.
4. Accounts Receivable
Running accounts receivable manually leaves room for error tracking outstanding payments. SAP intelligent RPA can reconcile AR data automatically:
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Match incoming customer payments to invoices. Flag any discrepancies between amounts paid and owed for review.
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Identify overdue invoices and trigger automated dunning and follow-up based on set rules and thresholds.
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Update customer account statuses in real-time based on reconciled payment data.
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Generate AR reports and key AR metrics like days sales outstanding automatically for insights.
This tightens the revenue cycle and cash flow. According to an IBM study, automated order-to-cash can accelerate processes by 60-80% and improve cash flow by 20%.
Use Cases in Retail
Retailers deal with huge data volumes and customer interactions daily. Intelligent automation helps scale operations efficiently.
5. Dynamic Pricing
Monitoring competitors‘ pricing across channels and geographies is impossible manually. With intelligent RPA, bots can scrape pricing data and feed it into automated pricing engines.
The engine automatically adjusts prices based on pre-set competitive rules. For larger adjustments falling outside those rules, the system alerts managers.
This "set and forget" approach to pricing frees up managers while ensuring price competitiveness. It also enables rapid, data-driven adjustments to maximize profits.
6. Product Information Management
Customer queries about product specs can overwhelm agents. Chatbots integrated with intelligent RPA can instead field these simple but frequent questions.
Bots scrape product pages to find attributes like size, weight, materials, etc. Natural language processing understands and answers customer questions based on scraped data.
This automates high-volume, repetitive customer interactions so agents can focus on complex issues and building relationships. 30-50% of customer queries can potentially be handled by AI as per IBM.
7. Customer Service
SAP intelligent RPA can power AI-driven self-service options:
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Chatbots – Answer common order, account, and returns questions 24/7. Integrate bots with order/inventory systems for data access.
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Virtual agents – Guide customers through account management, returns, and other processes via conversational interfaces.
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Payment bots – Scan inboxes for online orders and automatically trigger fulfillment, shipping, and payment confirmations.
This boosts CS capacity. An IBM study showed chatbots can resolve up to 80% of routine inquiries. They also operate 24/7 unlike human agents.
Use Cases in HR
HR teams handle high volumes of employee data and paperwork. Intelligent RPA can eliminate repetitive administrative work.
8. Resume Screening
Manually screening piles of applications is tedious. With SAP Intelligent RPA, bots can automatically check applicant resumes for required skills, experience, and other criteria.
Bots read resumes using OCR, then match candidate credentials against predefined hiring filters. Top candidates are flagged for further review instead of being lost in the pile.
This allows HR to accelerate recruiting by automating the early screening process. An IBM study found that RPA can reduce time spent on talent acquisition by 50-70%.
9. Salary Cap Automation
Sports leagues and other organizations require companies to submit reports showing they are complying with salary spending limits. Gathering payroll data from multiple systems to generate these reports is tedious manual work.
With SAP Intelligent RPA, bots can be configured to automatically:
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Pull employee payroll data from the HR system
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Validate salaries against employee contracts
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Aggregate total salary spending
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Populate regulatory reports for submission
By automatically generating these reports, intelligent RPA saves HR teams significant time and effort. One company cut reporting time from 480 hours to just 7 hours using RPA bots.
Use Cases in Supply Chain & Manufacturing
Supply chain and manufacturing processes generate massive datasets across systems like ERP. Intelligent RPA helps unite data for streamlined operations.
10. Order Processing
Processing orders involves repetitive steps like:
- Validating order data
- Checking inventory
- Creating delivery and invoicing documents
- Entering data across systems
RPA bots can be programmed to carry out these rule-based steps automatically after orders are received. This accelerates order fulfillment and frees up staff for exception handling.
11. Reporting
Generating reports from ERP systems often requires extracting data from multiple modules (inventory, procurement, production, etc.) and manually aggregating it in spreadsheets.
RPA bots can be scheduled to automatically pull and compile needed data into reports and dashboards. Staff then simply review and analyze the reports rather than building them manually.
12. Accounting Close
Each period, manufacturing accounting teams need to close the books and consolidate financial data. This involves reconciling accounts, eliminating intercompany transactions, and validating data across systems.
With SAP intelligent RPA, bots can be programmed to automate closing tasks like:
- Matching invoices to shipments and purchase orders
- Posting accruals, prepayments, deferrals, and other closing journal entries
- Performing intercompany eliminations
- Running depreciation processes
- Reconciling sub-ledger to general ledger transactions
This tightens period close and improves the quality and accuracy of financial data for reporting.
13. Inventory Management
Monitoring inventory levels and costs is critical but labor intensive. Intelligent RPA offers automation possibilities like:
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Automated inventory counts via IoT sensors and drones. Bots transfer the captured data to update inventory records and flag discrepancies.
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Natural language processing of unstructured product specs and costs from supplier invoices, rather than manual data entry, to maintain pricing master data.
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Machine learning algorithms can forecast demand and optimize inventory levels and locations.
This improves inventory accuracy and agility while reducing costs associated with excess stock or stock-outs.
14. Predictive Maintenance
Equipment downtime leads to lost production and urgent maintenance costs. SAP Intelligent RPA enables predictive models for maintenance:
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Sensor data integration – Bots can continuously collect real-time sensor and telemetry data from production equipment and feed it into predictive maintenance models.
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ML algorithms then analyze the data to detect anomalies, predict failures, and prescribe maintenance.
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Alerts to technicians can then automatically be triggered regarding needed interventions to minimize downtime.
This proactive approach prevents unplanned downtime and optimizes maintenance budgets.
The Promise of Intelligent RPA for SAP
As these use cases demonstrate, SAP Intelligent RPA unlocks new levels of productivity and performance across the enterprise:
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80% faster processes – Automating repetitive workflows dramatically boosts process speed. One retailer cut order processing time from 120 hours to 25 hours per month.
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70% cost reduction – Reduced manual labor, errors, and cycle times cut process costs by up to 70% as per McKinsey.
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99% accuracy – Automated processes have far lower error rates compared to manual procedures. Data quality improves.
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24/7 scalability – Intelligent bots extend operational capacity beyond human limits to enable round-the-clock processing.
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Rapid ROI – Companies can see full RPA payback in less than 12 months due to immediate productivity gains.
For CIOs and business leaders, now is the time to leverage intelligent RPA and transform operations for the future. The use cases and benefits highlighted in this guide provide a roadmap of where to start.
To learn more about driving value from RPA and AI in your SAP landscape, see our detailed guide on RPA in SAP.