A Logistics Service Provider’s billing department helps in maintaining continuous cash flow into the organization. Accuracy and promptness in billing are in fact directly proportional to the revenues realized.
Unfortunately, billing in logistics is not so simple; there are multiple complexity in terms of different charge bases, advance payments, penalty, exceptions, contract clauses and more. When these parameters are combined with a high volume of business, billing involves multiple rounds of evaluation to ensure that an accurate bill is generated for an end customer. An excess or delay in billing would lead to customer dissatisfaction, and a lower billing would burn a hole into the company’s revenue pocket. We mentioned some of these challenges in our last blog.
On a typical day, a mid-sized LSP would need to generate 5000 to 25000 bills. While the billing team would spend ample time in preparing it, the same bills would then be required to be validated for accuracy. Since most of the validation is manual, with the enormous volume and strict service level agreements, errors begin to creep in.
If we could save that evaluation time alone, it would directly result in reduced manual labour, faster billing and quicker payment cycles. Ramco Logistics platform provides an AI&ML based anomaly detection which learns from the historical data of draft bills and helps the billing team validate the bills on exception alone.
Logistics Service Providers can configure the basic rule definitions, variance deviations to enable anomaly detection. Definition of these rules are based on a combination of data from customer/vendors contracts, Tariff IDs, and historic billing information. These rules can be set to auto-approve bills based on certain parameters and manually approve bills in case of a deviation. Once this rule is activated, all the draft bills generated based on specific customer’s contract is auto approved if it meets condition (GREEN in the image below) and bills that need manual intervention are marked separately (AMBER & RED in the image).
While validating draft bills, the billing team just need to validate all the RED and AMBER ones which fall outside the recommended parameters.
With the AI&ML engine managing this huge chunk of output, the billing team just needs to focus on the outliers which will reduce more than 80% of evaluation time. In logistics organizations where the volumes and complexities are high, this anomaly detection can help them improve their billing accuracy quicker, thereby instantly influencing the incoming cash flow.