Precious Matemba, the data Clerk at Queen Elizabeth Lab, Blantyre uploads data sent to him via SMS from local clinics and community hospitals. © UNICEF/Schermbrucker

Precious Matemba, the data Clerk at Queen Elizabeth Lab, Blantyre, Malawi, uploads data sent to him via SMS from local clinics and community hospitals. © UNICEF/Schermbrucker


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


What is the problem?

Unfortunately, the supply chain often has to settle for imperfect data, or rather, data that are inaccurate, incomplete, delayed and/or not specific to the situation. Even when high quality and timely data are collected, many countries struggle to use data to inform supply chain decision making.

The reasons for these data management challenges are widespread, including the lack of technical capacity of personnel and the lack of suitable data collection and management tools. Human resource capacity is likewise a cross-cutting issue affecting all supply chain functions, including data management.

Training may be used to successfully address capacity for data collection, however, the bigger challenge is to build capacity for data analysis and use in decision making. For additional information on improving human resource capacity for supply chain management outside of data management, please see the Promising Practices: Human Resources brief.  

Promising practices:

The Supply Chain Technical Resource Team (TRT) documented promising practices along the supply chain, finding over 30 practices and 50 examples of these practices in action in low and middle income countries. The table below presents the most common barriers to effective data management and lists the promising practices that address each barrier. The promising practices encompass all three primary activities of effective data management solutions.

Read the full Data Management brief here.

pic data mgt


Indicators to Measure Progress in Data Management

              • ♦ The recommended performance indicator for data management is to review the presence and characteristics of your logistics management information system.
            • ♦ An additional important indicator to measure is health facility reporting rates.

Read the full Supply Chain Performance Indicators Guidance here.



The Supply Chain Technical Resource Team (TRT) supported the development and implementation of a number of tools that can help countries strengthen data management within their supply chain.

OpenLMIS: OpenLMIS is a global initiative to support the development of shareable, interoperable open-source software for electronic logistics information systems.  Download the OpenLMIS Feature Guide or visit for more information and a demo of the system.

DHIS 2: DHIS2 is a flexible, web-based, open source information system with visualization features.

CommTrack:  CommTrack is a mobile logistics management system for low-resource settings.

Inventory of ICT Tools: For descriptions of other ICT tools being used for supply chain management, view the ICT Tools Inventory.