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Activity title

Advanced Analytics and Artificial Intelligence for Defence Enterprise Resource Planning

Activity Reference

SAS-150

Panel

System Analysis and Studies

Security Classification

NATO UNCLASSIFIED

Status

Active

Activity type

ST

Start date

2019

End date

2020

Keywords

Analytics, Artificial Intelligence, Data Mining, Data Science, Decision Support, Enterprise Resource Management, Enterprise Resource Planning, Machine Learning, Operational Research, Optimization, Predictive Analytics, Prescriptive Analytics, Statistical Modelling

Background

NATO nations are increasingly relying on Enterprise Resource Planning (ERP) systems to integrate and manage core business processes associated with finance, human resources (HR), materiel, infrastructure, maintenance, procurement, sustainment, performance reporting, and other key business processes. These ERP systems rely on shared databases that typically contain huge amounts of real-time data and historical transaction data. Although ERP systems often include business intelligence (BI) tools used by staff officers in support of day-to-day business activities, the output of BI is generally static and often leaves much data underexploited. Advanced analytics and artificial intelligence (AI) can be leveraged to exploit this data much more effectively. They can provide much more value to planners and decision makers than BI by helping them to forecast what will happen (predictive analytics) and recommend courses of actions (prescriptive analytics). Furthermore, advanced analytics models can be deployed within ERP systems to be used on a recurring basis by planners and decision makers. For all these reasons, many defense departments across NATO are starting to develop new data science capabilities under the auspices of their Chief Information Officer (CIO) or Chief Data Officer (CDO), in partnership with their S&T branches. Because these new capabilities are still in the early stages of delivering analytics, they must carefully select use cases that can generate most value quickly, in order to minimize the risk of low returns on investments, slow results, and loss of support from senior leaders. There are multiple factors to consider that can influence the feasibility and cost of solutions (e.g., data complexity, analytical complexity, political complexity, regulations, implementation risks) and the amount of data science resources available within defense organizations often remains limited. Therefore, prioritizing and characterizing potential use cases and opportunities is critical, but there is currently no standard approach to do it.

Objectives

This Specialist Team will first create a method for enabling defense organizations to easily prioritize and characterize use cases based on their feasibility and potential benefits. This framework will be tested and used to prioritize a set of use cases identified by the Specialist Team in a workshop setting. For the most promising use case(s), this activity will establish during a second workshop a way ahead for developing a solution (such as applicable techniques or even specific algorithms), taking into account the advantages of advanced analytics and AI over other techniques, the benefits & risks of using advanced analytics and AI, and various considerations for model development and deployment.

Topics

• How can defense organizations rapidly identify the most promising applications of advanced analytics and AI to ERP problems? • What are the benefits and risks involved in applying advanced analytics and AI to some ERP use cases? • What ERP use cases should defense organizations launching into advanced analytics and AI tackle first? • How can solutions to these use cases be developed and deployed for routine decision support?

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