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Cross-post: Turning Data into Decisions: Transforming public health supply chains using decision support systems

  • Coby Jones

Decision support systems are computer-based systems or subsystems that enhance the ability to use data to identify where decisions need to be made and assist in making them. They have the potential to reduce the burden on decision-makers and help overcome challenges in using data and analysis in decision-making.

This blog originally appeared on Digital Square’s website and is cross-posted with permission.

How do bananas make it from the tree where they grow to your local store? For that matter, how do they seem to arrive just as they are ripening?

This is the power of an effective and efficient supply chain. Companies around the world use data to ensure that fruit is harvested at the right moment, packaged the right way, and delivered to the right places—giving consumers access to more choices than ever before. These complex systems require the careful coordination of people, technology, processes, and in this case, produce.  

But what if we’re not talking about produce? What if we’re talking about vaccines that must travel from the manufacturer to a rural community in Ethiopia? The processes and data required in medical supply chains are similar to the supply chains used in commerce, however the stakes are much higher. These supply chains have a vital role in ensuring that vaccines, essential medicines, and other medical supplies are available—the commodities needed to improve the health and well-being of communities around the world.

The six “rights”

In supply chains, we talk about the six “rights”: the right goods, in the right quantities, in the right condition, at the right place, at the right time, for the right cost. In order to achieve this, supply chains rely on the effective coordination of organizations, people, technology, activities, and resources.

Each of these “rights” uses a large, diverse set of data to determine what they mean. For example, the right quantity of a vaccine might be based on the population of an area, the anticipated wastage, existing stock on the ground, disease outbreaks that cause an increase in vaccine demand, and more. This can quickly turn into an extensive, multi-layered data set that can be unwieldy for health managers.  

As health data systems become increasingly digitalized and sophisticated, the amount of data that health managers have access to is growing quickly. Companies (like grocery stores) use a tool known as decision support systems (DSS) to help supply chain managers parse this wave of data—turning it into usable, actionable insights that can create new efficiencies and increase profit. Similar tools can have incredible impact on medical supply chains and health outcomes.

Decision support systems are computer-based systems or subsystems that enhance the ability to use data to identify where decisions need to be made and assist in making them. They have the potential to reduce the burden on decision-makers and help overcome challenges in using data and analysis in decision-making.

DSS in the health sector

Accenture Development Partnerships, with support from USAID, the Bill & Melinda Gates Foundation, and Digital Square conducted in-depth research into the role that DSS could have in the transformation of medical supply chains in low-resource contexts. Through the cataloging of more than 150 examples, 45 in-depth interviews, and 160 survey responses, this research reviewed current use of DSS, defined how DSS can improve supply chain outcomes, and made recommendations on promising investment-ready DSS applications.

The results of this research show that there are real opportunities for DSS to strengthen public health supply chains. Some applications of DSS are already being used in low-resource environments, particularly in increasing visibility across supply chains.

The potential impact of a DSS is specific to a given supply chain, in particular to a supply chain’s maturity and what the priority improvements are for the supply chain. However, based on common themes across interviewees, the research outlines some promising applications of DSS for supply chains in low-resource contexts that can be divided into four categories:


  • Integrate machine learning and external factors into the demand forecast.
  • Ensure visibility of the supply chain performance.
  • Optimize interrelated decisions around purchasing and replenishment.


  • Establish a digital model of the supply chain to optimize strategic scenario planning and the supply chain network.


  • Optimize management of contract compliance and supplier performance evaluation.


  • Connect consumers and health supply chain to enable the consumers to make informed decisions.
  • Create or support digital marketplaces that match the buyers and sellers of services and products.

As the information ecosystem evolves, the combined effect on how data is used by decision-makers fundamentally changes how supply chains operate. Improved visibility allows supply chains to become more agile, and automated monitoring allows for exception management rather than scheduled processes. Efficiencies in supply and demand planning reduce the need to carry inventory, hollowing out the number and size of the physical storage required in the supply chain. The empowerment of decision-makers at each extreme of the supply chain leads to a trimming of the decision-making. The combined effect of DSS drives a transformation to more responsive, flatter, and asset-light supply chains.

DSS implementations are best thought of in terms of continuous improvement. While there are great opportunities to combine DSS, data collection, and sharing technologies, it is also fine to start small. Each implementation reinforces using data as the basis of decision-making and creates trust in these types of systems. Each implementation improves the supply chain and gradually drives supply chain transformation.

A new report Opportunities to Transform Public Health Supply Chains in Developing Countries using Decision Support Systems provides addition insight into the research findings, and highlights implementations seeing success though their use.

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