Credit checks at order entry and automatic payment matching cut DSO by about 20%

The company is a wholesale trader supplying restaurant businesses, retailers and regional distributors, with a large base of long-standing customers.

Some customers are allowed to pay on credit terms, and prices and credit conditions vary by customer size, order frequency and trading agreement.

As sales grew, it became difficult for salespeople to see a customer's credit limit, outstanding balance and available stock in real time when taking an order.

Finance could produce regular accounts receivable reports, but with some overdue customers the risk only came to light after a new order had already shipped.

The challenge

Orders and credit management were not connected

Salespeople focused on customer needs and deal value, while finance handled the accounts.

Without a common credit check, new orders could keep increasing a customer's outstanding exposure.

Customer pricing was complex

Different customers had different contract prices, discount policies and promotional terms.

Salespeople confirmed prices from old orders or personal experience, which easily led to inconsistent pricing.

Cash application was inefficient

A customer might pay several invoices at once, or pay a single invoice in instalments.

Bank receipts were not reliably linked to invoices, so finance staff had to reconcile them by hand.

SHEPHARD's digital solution

Step 1: Unify customer data and commercial terms

Customer records, payment terms, credit limits, currencies, contract prices and tax information are now managed in one place.

Each customer has the appropriate pricing rules and approval authorities.

Step 2: Validate orders at creation

At order confirmation, the system checks available stock, pricing rules, credit limit and overdue receivables.

Orders exceeding credit limits automatically trigger an approval, rather than letting salespeople bypass the rules and ship anyway.

Step 3: Connect orders, delivery and invoicing

We established an Order-to-Cash (O2C) process.

Sales orders drive stock reservation and shipping, and actual deliveries map clearly to the invoices generated.

Standard handling was defined for partial deliveries, partial invoicing and returns.

Step 4: Introduce bank receipt matching

The system suggests matches based on customer, amount, payment reference and open invoice balance.

Bank transactions that cannot be matched unambiguously are passed to finance for manual review, preventing incorrect allocation.

Step 5: Build a receivables risk dashboard

Receivables are analysed by customer, salesperson, ageing bucket and outstanding amount.

Alerts are raised for customers who are persistently overdue, whose balances are growing abnormally or who are approaching their credit limit.

Implementation approach

We started by cleaning up customer master data and historical receivable balances, then standardised pricing and credit approvals.

Orders, shipping, invoicing and bank receipts were connected next.

Partial shipments, returns, credit notes, prepayments and single payments covering multiple invoices were all validated against real business scenarios.

Business value

The company can now identify credit risk when an order is taken, track receivables after delivery and apply cash accurately when payment arrives.

Sales growth and financial risk management are no longer two separate concerns.

Results (approximate): Days sales outstanding approx. −20%; automatic payment matching approx. 80%; share of overdue amounts approx. −35%.

Metrics tracked on an ongoing basis: Days sales outstanding (DSO), share of overdue amounts, automatic payment matching rate, number of orders with credit exceptions, order-to-invoice cycle time.

What this case teaches us

Revenue is not the same as cash flow.

A truly mature digital sales operation treats closing the deal, fulfilment, invoicing and cash collection as one continuous business process.

*To protect client confidentiality, company details in this case study have been anonymised; screenshots are illustrative and results are rounded approximations.*