Case Studies 2019-07-10T15:58:19+00:00

See our latest case studies

Discover how we’re changing the scale of automation in sectors ranging from pharma to finance and supply chain to retail with our latest customer stories.

Invoice Processing

Over time, staffing at the out-sourced supplier should be reduced by 80% and the client will benefit from reduced late payment penalties as well as an enhanced ability to offer early payment terms in return for supplier discounts.

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Invoicing Enquiry

Decision Engines logs each instance, enabling analysis of requests to indicate possible invoice processing issues, resulting in improved service levels, removing bottlenecks and reducing the workload and stress on Accounts Payable staff.

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Missing PO Reconciliation

As a result human intervention has been largely removed, with exceptions flagged when insufficient data can be derived from the invoice. The impact has been to accelerate none PO invoice processing, reducing the time and associated cost of handling thousands of invoices every month.

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Statements Audit

A previously labor intensive process costing over $1 million annually has been automated, removing the need for exhaustive staff searches and enabling them to simply process credits that have been identified as ready for application to the
General Ledger.

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Supplier Pricing Updates

The result is significantly faster and more accurate processing of price changes on a 24×7 basis, saving the cost previously incurred through pricing errors and greatly reducing the hours spent by staff on a critical but complex task.

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Bill of Lading

Data entry costs are lower and, with a much shorter lag between product receipt and entry into the company’s inventory system, it is easier to plan workload and staffing around busy or peak delivery times.

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Know Your Customer

The automated process was so fast that staffing requirements should reduce to just 20% for the same number of requests, with each application processed on the day of receipt, accelerating the client’s time from application to live lending. Data extraction and entry is also much more accurate than the standard, manual industry average of 70%.

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