Initiatives to improve processes for how companies handle customer sales orders are usually driven by issues related to inefficiency and scalability. Days sales outstanding (DSO) are typically a key driving metric, along with the time/cost of personnel to process orders. But, once companies implement solutions to streamline sales order processes, the real, strategic benefits emerge. More than just an efficiency play, sales order automation can deliver greater insights into buyer behavior and purchasing trends that can help to manage cash flow, grow revenues and maintain inventory.
For most companies today, however, sales order processing remains an inefficient, mostly manual operation, burdened by document handling and data entry bottlenecks. Often, this inefficiency translates into a missed opportunity to gather and analyze data about customer purchases and buying trends, along with the more obvious disadvantage of slowing down the fulfillment of customer orders and delaying receivables.
Many times, particularly for companies with complex sales orders that include pages of line items, sales order data entry processes may fail to gather a complete set of data in a timely enough manner to help the organization better understand and anticipate customer demands.
This goes beyond understanding which products customers prefer, or whether there are unforeseen seasonal/annual buying trends. Without good data, companies may fail to identify a customer segment that isn’t paying as quickly, or where there are constantly delayed payments. These are the kinds of insights that could potentially impact the ability to optimize cash flow.
The challenge with getting to the data isn’t just a function of companies relying on paper orders/invoices. Even firms that digitize their process may lack the tools necessary to handle documents and process orders efficiently. The real key is having the ability to sort through a steady stream of orders, capture all of the details from digital documents and aggregate that data for analysis.
Business transformation platforms like docAlpha from Artsyl focus on automating the identification and handling of sales orders, along with the automatic extraction of data, including both header-level information and line item details. By relying on machine learning and intelligent process automation, platforms like docAlpha, supported by process-specific applications like OrderAction, companies can quickly automate their sales order process without any custom coding or heavy lifting from IT. By integrating easily with existing ERP/CRM/ECM systems, these platforms can cross-check the data they extract from sales orders, so they can identify errors or flag exceptions like a duplicate order, or a situation like a credit hold.
From an efficiency standpoint, sales order automation can slash days sales outstanding, radically reducing cycle times while relieving a major burden from staff members for document handling and data entry. Instead, they have time to dive into the data and look for opportunities to improve the customer experience and anticipate demands.
It’s the efficiency play that generally gets sales order automation in the door. But it’s the data and the capacity to leverage it and achieve more strategic, higher-level goals where the REAL ROI is.