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Automating the Order to Cash Process – Why Cloud Technology is So Critical

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This article was originally published by CRF as: The Next Frontier in O2C Automation Reinventing Core Systems with Cloud and Cognitive Technology; by: Vishal Awasthi.

Traditionally, on-premise solutions are being used for order-to-cash (O2C) operations in many companies. Even if there is no need to abandon well operating systems, the question remains if these traditional ERP solutions help businesses to stay ahead and be competitive in the long run. Or will they, at some point, simply vanish? The rapid rise in Cloud and cognitive technology, including machine learning and artificial intelligence, indicates that successful businesses are at a technology tipping point. They will need to invest in the next frontier of O2C automation solutions soon to transform their core systems and lay a solid foundation for their digital future.

“Rather than trying to reinvent the wheel, build on to that which is already excellent”, Oscar Auliq-Ice said. With this quote, the founder of the Icetratt Foundation for Social Investments perfectly captures the dilemma companies face at the beginning of their digital transformation journey. How can you implement new technologies while still running existing IT systems? What does the transformation mean for core business operations such as O2C? And how can we incorporate cognitive and Cloud technology to add value to the inbound payment ecosystem?

The journey has just begun

The “Future of Finance” survey, published by a leading B2B-fintech, concludes that automation is a top priority across all finance areas. Ninety-eight percent (98%) of all interviewees believe increasing automation is the most important priority in finance, coming well before payments control (71%), streamlining disputes (33%), and improving audit response times (29%). “In operations, the automation of order-to-cash processes is seen as the most important topic,  ahead of procure-to-pay, payment management, cash visibility, and record-to-report. These results correspond with the challenge of adopting new technologies, which is seen as an important matter within the foreseeable future, next to rapid business changes and centralizing processes”.

Currently, Cloud seems to be the most accepted technological innovation, as a survey by IDG[1] indicates. It points out that nine in ten companies are already in the Cloud or plan to be by 2019. And according to Forbes, 45% of organizations plan to migrate BI, data warehouse, and analytic applications to the Cloud in the next one to three years.

While amazing opportunities for digital transformation exist, many finance departments are still in the early stages of technology adoption and change is happening slowly. One reason for this resistance to change is because many legacy systems are still running well. As the adoption rate of Cloud and cognitive technology increases, however, more organizations will realize that there is no time to wait.

The future of finance automation

 Increasingly, we have seen Cloud evolve from a promising innovation and novel approach to a fully-fledged technology, expanding across industries and becoming an integral part of the corporate strategy in most organizations. Yet, despite its promise, the full potential of Cloud technology remains largely untapped.

A Cloud environment offers several benefits compared to traditional ERP systems. Cloud solutions can be easily scaled up or down, depending on the business demand and size, which offers businesses a lot of flexibility. Accessing, editing and sharing documents anytime, anywhere, is also a lot easier with Cloud solutions. Teams can share files and updates in real-time, improving company-wide visibility into cash flows. Cloud providers also automatically update applications, which frees companies from maintaining the systems themselves. These benefits provide significant advantages compared to traditional on-premise ERP systems. Cloud solutions also have a lower total cost of ownership (TCO) compared to on-premise systems. Subscription-based pricing models cut out the high investments into hardware required for traditional ERP systems, and the automated backup and recovery available with Cloud systems saves time and effort.

Cloud is also essential if companies want to be able to leverage the many innovations available with cognitive applications. Only highly scalable Cloud solutions have the necessary processing power and access to large data sets required to take advantage of advanced cognitive features such machine learning (ML), robotic process automation (RPA), natural language processing, neural nets, and artificial intelligence (AI). These technologies have the potential to uplift finance operations to another level because they can process and transform vast amounts of data faster and more intelligently than it is possible with humans. Self-learning algorithms explore data connections, identify patterns, and discover relationships delivering greater insights, while bots automate repetitive, rule-based activities. Just as Kenneth D. Moelis, founder of the investment banking firm Moelis & Company said: “Digitization has created opportunities for everybody to accumulate information in a way that they were never able to and analyze it with a speed that just wasn’t there.”

 Imagine cognitive order-to-cash management

For O2C processes, Cloud and cognitive technology has the potential to automate manual tasks and transform the end-to-end process to deliver greater value to the organization. It brings together information, automation and personal interaction in the exact right balance to ensure an optimal allocation of resources. Technology makes it possible to automate repetitive tasks and free knowledgeable staff to work on complex cases that require good judgement and nuanced decision-making. The following examples illustrate how Cloud and cognitive technologies can help companies improve their O2C processes.

Sales and Orders

 Proper order management requires accuracy. The customer’s master data must be up-to-date and correct so orders can be processed quickly and sales can be concluded. Many ERP systems fulfill these requirements easily, automating order processing using RPA, alerting users only when manual intervention is required. Cloud and cognitive technology extends the value of this automation by using the information gathered to analyze and predict customer behavior. Intelligent algorithms learn from historic sales data, anticipating customer decisions and recognizing customer needs ahead of time. Digital online platforms and customer portals, along with mobile or IoT devices, increase customer engagement with chat bots or targeted human interactions. AI increases this engagement by intelligently suggesting personalized offers, such as trade promotions or discounts, throughout the customer journey, creating a more customer-oriented digital experience.

Credit Management

Just as customer needs can be predicted with machine learning, so can credit risk. Cognitive technology is able to process massive amounts of structured and unstructured data, so that credit managers can develop new scoring models and methodologies. By combining the power of cognitive technology with knowledgeable credit managers, organizations can make faster and more accurate credit decisions than ever before. New data sources – machine logs, images, videos, biometric information, and social media feeds – can also be used to improve credit decisions. The key to leveraging this type of information lies with solutions that can process such vast amounts of data and make sense of them. The solutions can evaluate which data is relevant to credit decisions, to predict payment behavior and determine the appropriate credit, compliance or fraud risks.

Billing

Efficient, automated billing begins with automating the related upstream O2C processes: sales orders, credit assessments and invoice creation. By connecting and automating the different strands of the upstream O2C process, all of the data in the process can be used during the billing phase. For example, discounts can be automatically applied based on historic customer data, such as past payment behavior or contract terms. This data can also be used to set or adjust terms of credit and payment due dates.

Cash Application

Highly automated cash application is reality today with RPA and machine learning technologies, capturing different payment formats and matching them against open items. Cognitive technology recognizes incoming unstructured data in documents. However, as businesses face increasing complexity and growing data volumes, intelligent processing capabilities will become more important in the future. For example, more advanced cognitive solutions can leverage the knowledge earned during the cash application process, such as a customer’s preferred payment method, to improve upstream order and billing, thus increasing the overall efficiency of the O2C process.

Collections and Disputes

Bots with embedded machine learning can very easily use the data produced along the O2C process to automate the collections process. These bots initiate collections based on previously effective collections measures for the specific customer: telephone calls, emails, or mailed dunning letters. Advanced cognitive applications can improve this automation by gathering data about the individual case and suggesting appropriate escalation strategies from the initial collection approach all the way to litigation. The intelligence gathered throughout the collection process automatically adjusts credit limits and payment terms for the specific customer. Automating the dispute management process would be similar, with dispute escalations powered by cognitive technologies, providing the possibility for human intervention when it makes sense.

Re-thinking the core and going hybrid

The enormous potential of Cloud and cognitive technologies to improve O2C processes was shown in the provided examples. And yet, the question remains: how do you get there? Any pursuit of digital transformation must start from the technological status quo. Organizations must consider how to harness these innovative technologies while maintaining operational integrity. This process begins with determining whether the existing ERP system serves as a basis for innovation. In most cases, companies can use a hybrid Cloud approach to extend and enhance existing ERP systems with newer Cloud and cognitive technologies. This approach enables companies to benefit from the new technology while extracting as much value as possible from their legacy systems.

Depending on the Cloud and cognitive capabilities available, companies must define how to improve or replace their existing systems with the capabilities available in a hybrid Cloud environment. For inefficient or even obsolete processes, such as paper-based work, look out for ways to replace them with newer Cloud-based capabilities. For other parts of the process, look for ways to leverage them with new technology to move the organization forward into a digital future. There are several ways to do this:

  • Upgrading legacy systems: Staying with the on-premise ERP environment and updating and/or upgrading it might be less costly, but it is important to consider the long-term implications carefully. Staying on legacy systems could prove detrimental to your company by preventing you from being competitive in the future.
  • Move to the Cloud: Re-platforming the entire system and replacing it altogether with Cloud-based technology is also an option. Cloud will most certainly dominate the future; however, an abrupt shift could unnecessarily disrupt business operations and overstrain employees. Consider a phased approach to implement Cloud solutions for the best results.
  • Building a hybrid Cloud environment: A hybrid Cloud environment which incorporates both on-premise and Cloud solutions is a pragmatic approach. It enables organizations to combine the best elements of legacy core systems and advanced Cloud and cognitive applications to improve processing without disrupting operations.
  • Managed services: Moving certain O2C functions such as order capture or collections to external service providers can be an excellent business and IT strategy. This approach is ideal to quickly unburden the back-office, as well as providing flexibility through scalability.

The path toward modernization depends on your organization’s corporate strategy and objectives, existing legacy infrastructure, and available resources. At the end of the day, digital transformation for the only reason of transformation is not the goal. Technology only supports the company as it responds to changing market conditions, evolving customer needs and line of business requirements. When reinventing core systems, companies should consider how technology will help them meet their business goals and be conscious of how the solution’s reliability, scalability, compliance, and security will affect their approach to digitization. Bringing these different aspects together defines how core modernization should be orchestrated to meet the company’s needs.

Vishal Awasthi is the SVP of Technology and Products at Serrala. He helps to set the technological direction of the Serrala Group and leads the management of several key product areas and the product development in North America. Vishal Awasthi specializes in the full lifecycle of software product development for both on-premise and Cloud-based enterprise business and content management solutions. He has held various leadership positions in the business application software industry for more than 20 years.

Prior to his current position, he was the CTO at Dolphin, a company acquired by Serrala in 2017. In that role he carried full P&L responsibilities, helped build and manage product management teams and successful technology partnerships. Vishal Awasthi holds a BS in Electrical Engineering and a MS/MBA in Industrial Engineering and Management.

[1] https://www.idg.com/tools-for-marketers/2018-Cloud-computing-summary/

Additional Resources on this Topic:

Understanding Credit Scoring Techniques and Best Practices (CRF On-Demand Course)

Impacts and Influences of RPA, ML and AI (Q2 2019 CRF News Article)

The Risk Management Organization: A Functional Comparative & Benchmarking (CRF Benchmarking Resource)