Promising Practices: 6 - Data Management. Brief #6 in the Promising Practices in Supply Chain Management Series
(2014; 17 pages)

Systems for Improved Access to Pharmaceuticals and Services (SIAPS) Program. 2014. Promising Practices: Data Magement. Arlington, VA: Management Sciences for Health.

Abstract

This series of briefs has been developed for use by in-country stakeholders. The briefs provide both proven and promising practices that may be used to address specific supply chain barriers faced by each country.

  • Proven practices are defined as interventions with proven outcomes in improving health commodity supply chains in low- and middle-income countries tested using experimental or quasi-experimental evaluation designs. Examples of proven practices are identified by this symbol throughout these briefs.
  • Promising practices are defined as interventions showing progress toward improving health commodity supply chains in low- and middle-income countries.

To view all the briefs in the Promising Practices in Supply Chain Management Series, visit http://siapsprogram.org/publication/promising-practices-in-supply-chain-management.

Data management is intrinsic to all aspects of running the supply chain. It is essential for managing the ongoing operations of the supply chain, assessing performance over time, and identifying problems and opportunities for improvements.

Data management encompasses identifying, collecting, validating, storing, analyzing, and applying information to make decisions and, most importantly, to take action. Depending on the scope and sophistication of the supply chain operations, useful data may include:

  • Detailed stock information, such as initial stock on hand, quantity received, consumption, remaining stock on hand, wastage/spoilage, transfers, stock-outs, etc.
  • Lead times to replenish individual facilities
  • Seasonal variations in consumption and accessibility of facilities
  • Stock levels at warehouses that, at times, may indicate the need to ration available supplies
  • Demographic data on the target population
  • Disease prevalence, which will affect demand for medications and commodities used to treat the disease
 
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