Summer 2021. The engineering department has finalized its concept design for a critical component in the next product generation. The procurement team enters the specifications into an analytics-based sourcing system, which interprets the document and—drawing on its database of suppliers and the outcomes of prior sourcing efforts—identifies three current suppliers, plus two newly added companies able to produce the part.
Next, the system automatically draws up an electronic request for quotation and sends it to the potential suppliers. When their quotes come back, it conducts an automated review, basing its analysis on internal clean-sheet cost models for the parts, together with data on each supplier’s capabilities and structural costs. The report it generates highlights the top negotiating points for the procurement team—and the system continues to revise its models and guidance as the negotiations proceed.
Once supply commences, the sourcing system continually monitors the winning suppliers, covering not only their quality and delivery performance but also their progress on the ongoing cost reduction they agreed to during the sourcing process. Exceptions trigger a series of automated mitigating actions, with procurement staff alerted only if the actions fail to get supply back on track.
This scenario is closer to reality than you might expect—perhaps well before the summer of 2021. Much of consumer purchasing has already been digitized, and some is even automated: sensor-laden, Internet-connected printers can detect when ink is low and order replacement cartridges, with no intervention from the user.
Large enterprises have not been so successful. Despite significant interest and investment, traditional approaches to automating source-to-pay (see sidebar, “Defining source-to-pay”) have yet to deliver on the promise of a fully digital process requiring minimal human involvement.
That may be about to change. Several emerging technologies, including robotic process automation (RPA), machine learning, and advanced artificial-intelligence programs or “cognitive agents,” have the potential to overcome hitherto stubborn barriers to automation in the enterprise environment. By applying a new form of analysis to the hundreds of individual tasks involved in the source-to-pay process, we have found that almost 60 percent of them have the potential to be fully or largely automated using currently available technologies. Crucially, we found significant automation potential not only in transactional activities, such as order and invoice processing, but also in sourcing’s strategic elements, such as vendor selection and management.
The payback on a robust, automated end-to-end source-to-pay process could be high. Industry benchmarks suggest that most organizations waste 3 to 4 percent of their overall external spend on excessive transaction costs, inefficiency, and noncompliance (Exhibit 1). For an organization with an annual spend of $2 billion, eliminating that leakage could send $70 million a year straight to the bottom line.
