A Roadmap of ERP Modernization for Thai SMEs – (Part 3)

4. The Innovation Layer: Architecting AI-Driven Optimization

The underappreciated value of modernizing SAP B1 on AWS now is in the system’s ability to layer Artificial Intelligence (AI) and Machine Learning (ML) on top of existing transactional data. This capacity moves the ERP from a “System of Record” to a system of intelligence, advice, and planning capacity.

4.1 Predictive Analytics with Amazon Forecast

As mentioned earlier, inventory management is a primary cost driver for most SMEs, and a helpful solution can be found in Amazon Forecast.

The typical architecture of Amazon Forecast is:

1. Data Extraction: A scheduled job (using AWS Lambda or Syntax CxLink) pulls out historical sales data (ItemCode, Quantity, Date, Location) from the SAP HANA database via a Service Layer API. ¹
2. Data Ingestion: The data is added to an Amazon S3 bucket.
3. Training: Amazon Forecast uses historical data to train a proprietary ML model. Importantly, it allows for the inclusion of “Related Time Series” data. For a Thai SME, this could include weather, Google Trends, and tourism arrivals.
4. Inference: The model builds a demand forecast (P10, P50, P90 confidence intervals) for the coming 12 weeks.
5. Action: The resulting values are pushed back into SAP B1’s MRP Recommendation table, where a purchasing manager can see a recommended order quantity that already takes into account a predicted tourism slump or weather event. ²

This workflow then allows management to close the gap between global macro data and local micro decisions, effectively outsourcing the role of a data scientist to the AWS cloud.

4.2 Generative AI and the RAG Framework

Another challenge comes from unstructured data and complex queries. “What is the status of the order for Client X, and did we resolve their last complaint?” This requires looking at Sales Orders (Structured) and Service Calls/Emails (Unstructured).

The Solution: Retrieval-Augmented Generation (RAG) using Amazon Bedrock.

Architecture:

1. Knowledge Base: SAP data (invoices, emails, product PDFs) is “embedded” (converted to vectors) and stored in a vector database (e.g., Amazon OpenSearch Serverless or SAP HANA Vector Engine). ³
2. The Agent: An Amazon Bedrock Agent is configured with “Action Groups.” These are specific permissions to call SAP B1 APIs (e.g., GetBusinessPartner, GetStockStatus).
3. The Interaction: A user asks a question in natural language (Thai or English).
4. Retrieval: The system searches the vector database for relevant context (for example, the client’s last email).
5. Generation: The Agent combines the user’s question, the retrieved email context, and real-time data that it has pulled from the SAP API to generate an answer. ⁴

4.3 Prompt Engineering: Chain-of-Thought

To make sure that an AI is reliable enough for business, we use a technique called Prompt Chaining. Instead of asking an AI to “analyze” information, we break the task down:

• Chain 1 (Identify): “Identify all customers with open invoices > 60 days.”
• Chain 2 (Contextualize): “For each customer, check their credit limit and last payment date.”
• Chain 3 (Draft): “Draft a polite reminder email in Thai for each customer, referencing their specific invoice number and offering a payment plan if their credit history is good.”

This “Chain-of-Thought” processing allows the AI to safely follow business logic, can reduce AI inference costs, and helps to reduce LLM hallucinations. ⁵

5. Implementation Roadmap

For Aware Corporation’s clients, the path to a more efficient, AI-enabled future is well designed and risk-managed. Here is an example of how we recently set up the workflow for a customer:

Phase 1: Foundation (Months 1-3)

• Migration: Lift and shift on-premise SAP B1 SQL/HANA to AWS EC2.
• Stabilization: Set up backups (AWS Backup) and DR (Elastic Disaster Recovery).
• Security: Configure and implement AWS WAF (Web Application Firewall) to protect the web client.

Phase 2: Integration (Months 3-6)

• API Gateway: Expose SAP Service Layer securely via Amazon API Gateway.
• Data Lake: Set up AWS Glue jobs to move historical data to S3.
• Visualization: Connect Amazon QuickSight to S3 for dashboard visibility (replacing the existing Crystal Reports). ⁶

Phase 3: Intelligence (Months 6-12)

• Predictive Pilot: Choose a product line that can be used as a test case for Amazon Forecast integration.
• Agent Deployment: Build a pilot “Sales Assistant” bot using Bedrock Agents to manage stock inquiries.
• Optimization: Review and refine models based on accuracy.

6. Conclusion and Future Outlook

The coming together of SAP Business One, AWS, and Agentic AI is not a flash-in-the-pan; it’s a new standard for efficiency. For Thai SMEs, the opportunity is in having the ability to escape the structural traps of today’s economy. By relying on Aware to support a transition from the status quo to a new architecture, businesses give themselves the ability to upgrade both their internal systems, but also improve their capacity to compete.

References

1. AWS APN Blog — Syntax CxLink + Amazon Forecast for SAP
2. APPSeCONNECT — Master Amazon Integration: Methods & Challenges
3. AWS Docs — Artificial Intelligence (SAP Guides: Vector/RAG)
4. AWS for SAP Blog — Agentic AI Assistant for SAP with AWS GenAI
5. Digital Adoption — What is Prompt Chaining?
6. AWS for SAP Blog — Analytics (QuickSight + SAP)