Order reconciliation and smart SAP operations

To win customers and retain them, B2B companies need to deliver frictionless experiences. According to a report by PwC, 1 in 3 loyal customers can leave a brand after just one bad experience. Delivering great customer experience starts with smooth order management. As the first step of the order-to-cash (O2C) process, order management is critical in driving revenue and customer satisfaction. However, inaccurate data and manual processes can lead to delays in order fulfillment and deliveries.

The customer was looking to reduce cost and effort through smart operations and autonomous actions.

To stay competitive, the company had to streamline O2C processes and eliminate the issues that were causing delays in order fulfillment and delivery.

The business wanted to ensure that:

• All the orders received by the CRM gets processed successfully by their legacy order management system (OMS) without delays in contract creation process
• Orders are reconciled accurately and speedily before dispatch
• Orders are transferred to the right manufacturing plant (as per the location of the order placed) quickly and reliably
• Customers get deliveries and invoices on time

This meant that the technology infrastructure needed to be ready to handle the influx of huge volumes of the orders’ transactional data. The operations was tasked to address P1 issues such as a complete or critical business service down with high financial impact and implement proactive measures to improve the system performance and stability.

Ensuring seamless data transfer across varied systems and remote locations

The company received large numbers of daily orders in their CRM. These needed to be processed and transferred to all facilities spanning across 30 countries. The company’s OMS had four main problems. First, due to the high volume of order data, the company’s OMS was not able to process the data appropriately, leading to data breakage. This eventually resulted in delays in the contract creation process and the OMS’s batch job failures. All these issues accumulated with a snowball effect, leading to P1 issues that could cause system failure. This, in turn, resulted in delays in data distribution to the manufacturing plants.

Second, the orders’ data needed to be transferred to the nearest manufacturing plant location, some of which are in remote areas with bad/fluctuating network connectivity, resulting in data consistency issues. Third, after orders were processed by manufacturing plants, all of the data needed to be reconciled in the OMS, sometimes manually in some cases of order cancellation and order fulfillment.

And lastly, there were no checks or alerts if invoice delivery or any other process was delayed. All of this led to many tickets being raised to resolve the issues. The company’s leaders realized they needed a solution to address their operational challenges. Also the resolution time of the tickets was very high due to manual processes. The leaders wanted to reduce resolution time on overall tickets and efficiently process any service/change request. In fact, they wanted to entirely ‘reinvent’ their operations to be smarter and greener to reduce their carbon footprint and deliver more efficient solution.

The customer concluded that ignio AI.ERPOps was the right fit to address the challenges because an intelligent solution can automate processes and provide health checks and alerts if something goes wrong with the system/process. ignio AI.ERPOps could eliminate incidents at the source through proactive health checks and monitoring of applications/interfaces.

Digitate deployed ignio AI.ERPOps to provide the much-needed automation, health checks, and alerts, helping the company’s operations in four ways.

First, to counter a huge influx of data into CRM, ignio AI.ERPOps does automated load balancing and transfers data in batches so that the OMS can process it seamlessly. This ensures that the system doesn’t accumulate a series of errors resulting in P1 issues, leading to OMS system failure. As a result, delays related to contract creation and distribution of orders’ data to manufacturing plants can be avoided. ignio AI.ERPOps also deploys health checks for any delays in order creation process and batch job failure and sends alerts to the concerned team, so the cause of the delay can be quickly rectified at the source of the problem. ignio AI.ERPOps also self-heals issues wherever possible. Introducing ignio AI.ERPOps reduced the effort required to distribute data to the manufacturing plants from weeks to a few mins.

Second, ignio AI.ERPOps re-triggers the data transfer for the orders that have inconsistent data due to connectivity issues at the remote manufacturing plant location by automating the order ID and LDS ID mapping process.

Third, ignio AI.ERPOps automatically reconciles the data received from different manufacturing plants with the data of cancelled orders and manually fulfilled orders to clear data reconciliation process and self-heals data. Finally, ignio AI.ERPOps deploys health checks and proactively send salerts for any delays in the distribution of data or delivery of invoices.

• Time-to-market reduced by 30%
• Freed up 8 FTE to focus on other duties
• 1.3M Euros revenue realization delay/loss prevented per month
• ~100% elimination of P1 issues related to OMS master data
• 50% cycle time reduced on the entire ticket volume
• Streamlined data distribution handling ~0.5 M records per ticket eliminates risks, effort, errors
• ~90% reduction in MTTR
• Seamless adoption of new rollouts for other regions with no effort/modification required