External corrosion (EC) is still the leading cause of oil and gas transmission pipeline incidences. The primary source for understanding external corrosion risk is one of the interrelated parameters: EC rate, remaining wall thickness, or percentage wall loss. They may be obtained from five (5) sources: laboratory data (CLab), field operating conditions (CField), field above ground surveys (Csurveys), inline inspection (CILI), and field below ground inspection (CFBGM). • Laboratory data (CLab) and field operating data (CField) are “leading indicators” of localized pitting corrosion rate. • Field above ground surveys (Csurveys) are “current indicators”. • ILI (CILI) and field below ground inspection (CFBGM) are “past indicators”. The paper compares leading indicators CLab and CField with present condition indicator (CAbove) and trailing indicators (CILI, and CFBGM) of two (2) Saudi Aramco pipelines. This paper describes the success of the approach, lessons learned, and future improvements. How this approach is superior to other methods of prediction such as machine learning and simulation has been explained and how using the 5-M methodology as the based can accelerate the development of other approaches (artificial intelligence (AI) and simulation) has been described.