Order management is a critical process in the supply chain world. With the evolution of customer expectations and behavior, the importance of the order management process has increased. It is therefore not surprising that in the initial responses to our research survey for Process Automation for Supply Chain, order management emerged as one of the top focus areas for process automation, along with procure-to-pay, as shown in figure 1.  In the Process Automation and SAP S/4HANA research that we conducted in April 2022, intelligent automation was one of the key capabilities that SAPinsiders highlighted as the ones they will be focused on over the next couple of years. This article will review the opportunities to leverage intelligent automation in the overall order management process. Figure 1: Focus area processes for process automation in the supply chain Source: SAPinsider research August 2022 You can get a sneak peek of some of the key initial survey insights in this video: https://vimeo.com/744685062

An Overview of The Order Management Process

Figure 2 illustrates the order management process flow at a high level, which starts with an incoming order and generally ends by processing accounts receivable after the goods or services have been delivered. Figure 2: Order management process flow at a high level Note that the representation is a summarized high-level representation. A good example of details under each can be the order validation and processing element. That element itself can be represented in detail, as shown in figure 3. Figure 3: Order processing process flow Even figure 3 is simplified at some level but highlights that each of the elements in the process flow in figure 1 has its sub-steps. Now let us use the example of the above-simplified process flow to understand how intelligent automation, enabled by Artificial Intelligence (AI) and Machine Learning (ML), can transform the order management process.

Intelligent Automation Opportunities in Order Processing: An Example

Order collection and harmonization across channels: Order today may come through various channels and formats. AI can be leveraged to ensure that all the orders are captured effectively and harmonized to get in the order pipeline for review. In essence, algorithms help machine readable orders (like orders coming through EDI) into a format that humans who will interact with these orders can comprehend. Order accuracy validation: AI can play a significant role in automating validation for errors and discrepancies. Deep learning-enabled algorithms can also help analyze manual order forms to check for completeness and errors. AI algorithms can also help validate that all business rules, like discounts etc. are being applied in validating the orders.

Analyst Tip: Integrating this stage of automation with customer intelligence solutions can help develop insights that have the potential to enhance customer experience significantly.

Multiple technologies already exist in the SAP technology ecosystem with intelligent automation features. SAP has its offering like SAP Intelligent RPA and SAP Signavio, but end-to-end vendors also offer third-party tools that leverage AI and ML. Examples are MS Power Automate, UiPath, Blue Prism, Automation Anywhere, and specialized vendors like Esker and Conexiom.

Analyst Tip: While the example explores only one element of the overall order management process depicted in figure 1, opportunities to infuse intelligent automation exists across the entire process. Consider the inventory reconciliation stage. Intelligent automation can take this beyond a simple availability check into a process that can help optimize overall fulfillment costs. Similar opportunities exist throughout the order management process flow. All you have to do to explore these opportunities and build capabilities that no one else has is to take the approach of seeing both "the forest and the trees." This means that when you formulate your AI algorithms strategy, you also think about the enterprise AI view.