Data Transformation Intelligence & Impact

inSupply, through the Data Transformation, Intelligence & Impact (DTII) domain, strengthens decision-making by turning data into actionable insights, while using practical tools, and learning systems. We co-create evidence with end users, design and operationalize Monitoring, Evaluation & Research frameworks, build digital health and analytics solutions, and cultivate a data-in-action culture. Through the utilization of adaptive learning and other participatory approaches, our work ensures that programs generate the right evidence, use it to learn and adapt, and ultimately achieve measurable, sustainable impact.

“If AI technology will be well implemented it will bring huge positive impact in not only health sector but other sectors as well.”

Dr. Ntuli Kapologwe

Director of Health Services , President's Office, Regional Administration and Local Government Tanzania

Data: Capture, Management, Analysis, and Visualization

inSupply specializes in harnessing both quantitative metrics and rich qualitative insights from the health supply chain, utilizing agile, user-centered methodologies for real-time data capture and implementing robust data governance to ensure quality, security, and interoperability. We are at the forefront of data analysis, leveraging advanced technologies including Artificial Intelligence (AI), Machine Learning (ML), and Predictive Analytics to anticipate supply chain bottlenecks, forecast demand, and recommend optimal operational strategies. Crucially, we enhance this capability by building custom data applications and visualization tools which foster a strong data-use culture through the development of free/affordable, accessible, and user-friendly digital dashboards that translate complex information into action-oriented reports for immediate, evidence-based decision-making.

Data: Capture, Management, Analysis, and Visualization Use Cases

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Using Artificial Intelligence for the Quantification of Essential Medicines in Kenya and Tanzania

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Using AI to Enhance Forecasting in Kenya & Tanzania Brief

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Using AI to improve quantification

Assessments and Routine Monitoring, Learning, and Evaluation

We design contextually fit research and evaluation approaches for all of our projects. We have extensive experience measuring and documenting progress toward outcomes. We use data to report to our partners and funders, support monitoring and continuous learning, and to maximize health and supply chain outcomes.

Assessments and Routine Monitoring, Learning, and Evaluation Use Cases

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Wave 2 Report

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TOC/TOA for data Use

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Wave 3 Report

Utilization focused Monitoring, Learning and Evaluation (MLE)

Use-focused MLE recognizes that traditional monitoring and evaluation approaches do not always capture the complexities associated with system changes and uncertain environments. inSupply creates adaptable learning systems to support data use and application to programming. Examples:

Development evaluation.
Adaptive learning.
Complexity awareness monitoring techniques (e.g., ripple effects mapping, outcome harvesting, and most significant change).

These techniques help us identify unanticipated outcomes and effects of our programs and understand what is working and why, and what needs to be adjusted and how.

Use-focused Monitoring, Learning, and Evaluation Use Cases

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Developmental Evaluation of Community Health Models in ASAL Counties in Kenya (Developmental Evaluation Wave 1)

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Measuring the Complicated and Complex: Incorporating Complexity-Aware Methods in Monitoring, Evaluation & Learning

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Adaptive Learning webinar we did for the global tech exchange

Artificial Intelligence – Machine Learning & Automation

inSupply empowers health supply chain leaders across East Africa and resource-constrained settings to strategically understand, adopt, and integrate advanced technologies like Artificial Intelligence (AI), Machine Learning (ML), and Automation. Our core objective is to leverage these innovations to significantly improve supply chain efficiency and effectiveness, specifically targeting historical bottlenecks in key upstream and downstream functions that compromise product availability for public health programs, including Health Commodity Demand Forecasting, Supply Planning & Resource Allocation, and Ordering and Resupply Planning. inSupply ensures the adoption of these systems is context-specific, user-centric, and sustainable. We offer comprehensive services including needs assessment, building step-by-step adoption processes, analyzing technical and resource gaps, mobilizing required resources, and building strategic partnerships, all underpinned by robust stakeholder engagement.