Machine learning technology, which is a form of artificial intelligence, has now made its way into the area of procurement process support.
A simplified explanation of this technology is that algorithms actually “learn” from data and information patterns to make subsequent predictions based on such patterns. Supply Chain Matters has previously pointed out how machine-learning has been applied to manufacturing and supply chain planning focused processes. The technology when leveraged to support B2B direct procurement support can bring added scale and improved value for direct spend supplier collaboration. The added benefit for this type of technology is the ability to leverage item-level business intelligence as well as to provide more timely and robust support in the area of master data management.
In a previous commentary, Supply Chain Matters raised awareness to the critical importance of seamless interoperability for B2B business networks. Teams are well aware of the pain associated with maintaining connectivity and end-to-end visibility throughout their constantly changing B2B network. Managing today’s complex supply chain networks therefore demands not only end-to-end transactional messaging management but key planning, replenishment, and supply-chain decision support.
Teams that are dealing with existing ERP backbone systems such as SAP, require and expect seamless integration. They often have the added challenge in assessing and evaluating SAP’s changing product strategies and application roadmaps related in support of supplier networks. In the specific case of SAP’s Ariba cloud-based network, there are elongated roadmap timetables concerning full direct materials processes support as well as migration to the HANA platform.
One of the major benefits of working with best-of-breed technology vendors is their ability to innovate at a quicker pace than larger ERP providers. Because such vendors often support customers with existing ERP backbones, best-of-breed vendors understand that they must integrate information as seamless as possible. In the specific area of procurement and B2B business networks, ERP vendors have often accelerated their own path to innovation by acquiring emerging cloud-based vendors.
Nipendo, a cloud-based global provider of B2B network based supply chain technology recently announced that it was awarded a U.S. patent related to: “automated reconciliation of cross-enterprise transactions and digital documents.” Nipendo Supplier Cloud leverages this machine learning technology in the automated reconciliation of Procure-to-Pay processes throughout the entire supplier base, including direct goods suppliers.
What makes Nipendo’s technology different among existing vendor approaches is its leveraging of best-practice process templates (business rules) that govern interactions among suppliers and trading partners. Rather than custom programming and field-to-field information mapping that is often required in EDI grounded processes, machine learning techniques are applied to automate the majority of processes. The business rule platform enables teams to more quickly exchange real-time information with suppliers, orchestrate supply chain processes, and reconcile transactions to existing ERP systems. The advantage to teams is noted as simplicity, speed, and scale in supplier collaboration, and we tend to agree.
In our observations of the procurement technology landscape, this was our first awareness to such leveraged use of machine learning techniques, and we were impressed.
For further information, readers can explore Nipendo’s B2B Integration Solutions web page.
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Disclosure: Nipendo is a current client of the Ferrari Consulting and Research Group LLC