|Advanced Analytics for Defence Enterprise Resource Planning |
|System Analysis and Studies|
Analytics, Artificial Intelligence, Cognitive Analytics, Data Mining, Data Science, Decision Support, Enterprise Resource Management, Enterprise Resource Planning, Machine Learning, Operational Research, Optimization, Predictive Analytics, Prescriptive Analytics, Statistical Modelling
NATO nations are increasingly relying on enterprise resource planning (ERP) systems to integrate and manage core business processes associated with finance, human resources, materiel, infrastructure, maintenance, procurement, sustainment, performance reporting, and other key business processes. These ERP systems (from SAP, Oracle, Microsoft, or other vendors) rely on shared databases that typically contain huge amounts of real-time data and historical transaction data. The systems generally include business intelligence (BI) tools that can be used by staff officers and analysts to mine the data and conduct descriptive analytics on it, in support of day-to-day business activities and decision making.
The aim of this Exploratory Team (ET) is to develop a shared understanding of:
- statistical models and advanced analytics products that NATO nations have been developing to work within (or in conjunction with) their respective ERP systems to support planning and decision making related to various business processes (e.g., finance, materiel, infrastructure, maintenance, procurement, sustainment, HR, etc.);
- processes and tools being used by NATO nations to develop, validate, deploy and maintain these models; and
- overlaps, gaps, and opportunities for future collaboration.
Emphasis will be put on advanced analytics models and tools developed in house (possibly with assistance from industry or academia) that could be eventually shared or collaboratively developed, not on commercial software and products.
The following topics will be covered by the ET:
- key ERP systems used by participating nations;
- examples of statistical models and advanced analytics (in particular predictive and prescriptive analytics) that participating nations have been developing and implementing into their respective ERP systems;
- examples of standalone OR models and decision aids that have been migrated/ported to ERP systems;
- unique contributions that advanced analytics can bring to defence ERP;
- data science / scientific computing tools (e.g., R, Python, Spark) used to develop and deploy advanced analytics as part of ERP systems;
- the process (e.g., CRISP-DM, agile processes) being followed to develop, deploy, validate and maintain advanced analytics as part of ERP systems and the role(s) of scientific organisations in that process; and
- analytical capability gaps and opportunities for future collaborations.