Date Published 27 Sep 19
Richard Hogg - Managing Director at JAGGAER
A closed-loop machine learning system
JAGGAER has itself been moving towards closed-loop feedback systems that rely on machine learning for continuous improvement. Here we describe an example: the JAGGAER Digital Assistant.
JAGGAER, ERP and third-party data is fed into a central data layer. The information is used in traditional analytics and reporting, but what is new is that algorithms are now providing real-time support for decisions, recommendations and actions. Typically, there might be several recommendations and the end-user takes a decision based on which of these makes most sense. These decisions drive machine learning in an artificial intelligence application, which returns information on these decisions to the JAGGAER application set, enabling continuous improvement.
Digital Assistant Functionality
For example, a decision could result in a change in the status of a supplier, the supplier’s performance rating, or it could mean the creation or updating of an RFQ etc. The point is that the updates are triggered automatically by an API, without manual intervention.
There are three building blocks to the logical architecture behind the JAGGAER Digital Assistant: the Recommendation Engine (which interrogates and checks the base data); the Workflow Engine (which enables the user to execute an action); and the Artificial Intelligence Engine (by means of which the system learns and feeds back the new knowledge into applications). This means that the application could recognize an action on which a decision has been based in the past, so it can make a recommendation for a similar action.
Such a digital assistant has both strategic and operational benefits. It simplifies strategy development and execution within the procurement function. Available data sources are analyzed on an ongoing basis, recommendations and actions are proposed automatically, and these proposals are based on the expertise and best-practice know-how of specialists. Specialist knowledge is thus shared and taken advantage of more broadly across the organization, e.g. cascaded from HQ to the regions.
The system learns independently and evolves, and the benefits are accessible and transparent to everyone involved across the procurement value chain – a clear and effective basis for collaborative work.
Operationally, the digital assistant greatly simplifies interaction with procurement software. Some manual tasks can be completely automated through RPA. Elsewhere, a single human input command can initialize an entire process chain. Recurring tasks are performed by the system, with human beings only needing to intervene to take care of exceptions.
There are no outstanding technological obstacles to such developments. However, there are a few criteria that must be met. Most notably, all the data must be of high quality and easily accessible by intelligent systems. Secondly, all of the expertise that is in people’s heads must be made available in digital format. And thirdly, processes must be capable of being automated. However, it does not require the implementation of a complete suite of software. The JAGGAER Digital Assistant can work with individual modules in the JAGGAER ONE suite and integrate with solutions from our partners, including EdgeVerve.
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