Automated invoice creation with RPA: VT Project case study

30 October, 2026

Automated invoice creation with RPA: VT Project case study

AFusion built an automated invoice creation system with RPA for a leading industrial supplies company in Singapore. The robot validates purchase order (PO) data against the Tungsten system, then generates electronic invoices automatically — replacing a daily manual process of data entry, reconciliation, and invoice generation that was slow and error-prone. The project was delivered by a team of 2 engineers in 2 months.

The situation: growth outran the invoicing process

The customer, a leading enterprise in the industrial supplies industry, grew quickly on the strength of its products and service. But its invoice generation process did not grow with it. Every day, accounting staff entered order data by hand, reconciled figures, and generated invoices. In this industry an incorrect invoice is never just an accounting problem: it delays delivery, triggers disputes with customers, and shows up in financial reports.

Two problems sat at the center:

Time-consuming, repetitive tasks. Creating daily invoices was a standing burden on the accounting department — data entry, data reconciliation, and calculation of figures took up most of the day. Repeating the same operations daily is tiring, and tired staff make mistakes, which drags productivity down further.
Real risk of errors. Manual invoicing carries many failure points. A small slip in entry or calculation can produce an inaccurate invoice, a dispute with a customer, or damage to the company’s reputation. Checking invoices by hand is itself slow and still misses things — and inaccurate invoices carry legal exposure, not just rework.

What the customer wanted

The customer came to AFusion with a clear target: automate the order-to-invoice process. Concretely, that meant validating PO information automatically so every invoice starts from correct data, and generating electronic invoices quickly and professionally. The business outcome they wanted: shorter order processing time, fewer errors, and higher satisfaction for their own customers.

How AFusion built the automated invoicing system

A team of 2 engineers delivered the RPA solution in 2 months. The robot runs four steps every day:

1. Access Dropbox and get the PO list

The robot connects to the customer’s Dropbox account and downloads the purchase order list as an Excel or CSV file. Working from the latest file each run means the invoice process always starts from current data.

2. Look up each PO on the Tungsten system

For every PO on the list, the robot retrieves the full details from the Tungsten system via API. It pulls the fields needed for invoicing — customer name, products, quantity, unit price — directly from the source of truth.

3. Validate PO information before invoicing

The robot compares the PO data from Dropbox against the Tungsten records on PO number, customer code, and total order value. A PO is confirmed valid only when the two sources agree. This validation step is what stops wrong data from ever becoming a wrong invoice.

4. Generate the invoice automatically

For verified POs, the robot creates the invoice from the standard template: title, invoice date, customer information, product list, total value, plus any additional fields the business requires. Invoices are stored as PDF files or exported in other formats.

The technology stack for this invoice automation project: C#, VB.NET, and RPA.

Results after automation

Work efficiency improved. Invoice generation is fully automated, cutting processing time against the manual baseline and giving accounting staff time for higher-value tasks. The robot runs around the clock, so large volumes clear without overtime or extra headcount.
Accuracy improved. Removing the human element from entry and calculation cuts the common error types, and every invoice follows the same standard format — consistent and professional by construction.
Transparency increased. Every robot action is logged, so the business can trace how each invoice was produced and control the generation process. Less room for fraud or loss.
Costs saved. Less hiring needed for repetitive entry work, and less paper, ink, and office supply consumption.
Customer satisfaction improved. Faster, accurate invoices mean fewer disputes and smoother delivery — trust built at the most visible touchpoint of the business.
Room to grow. The logic is extensible: the same robots can take on adjacent tasks such as debt reconciliation or inventory management when the business needs them.

Is invoice generation slowing your order-to-cash cycle?

Companies in distribution and industrial supplies usually discover the problem the same way: the sales team keeps growing, orders keep growing, and one day the invoicing team is the bottleneck — orders wait for invoices, deliveries wait for orders, and cash waits for all of it. If your accounting team is entering the same data twice a day from two systems, that is the pattern worth mapping. AFusion surveys the order-to-invoice flow with your team, shows exactly where automation pays back first, and scopes what a small team can deliver in weeks rather than quarters.

Frequently asked questions

What is automated invoice creation with RPA?

It is a software robot that builds invoices without manual entry: it collects order data from your file sources, validates the data against your business systems, and generates invoices from your template. Humans review exceptions, not routine invoices.

How does the system make sure an invoice is correct before it is issued?

Through two-source validation. In this project, the robot compares each PO in the customer’s Dropbox list against the Tungsten system on PO number, customer code, and total value. Only POs that match on all criteria proceed to invoice creation.

How long does an invoice automation project take?

This project was delivered by 2 engineers in 2 months, covering requirements, build, and deployment. Timelines depend on how many systems the robot must read from and how complex the invoice rules are.

Can the same robot do more than create invoices?

Yes. The validated data pipeline can extend to adjacent finance tasks — debt reconciliation, inventory checks, and other document processing built on the same PO data.

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