Maximising Impact: How Charities Can Use Data and Technology to Tackle Food Insecurity

In light of the recent UK budget announced by Chancellor Rachel Reeves, which underscores the urgent need to support low-income families amid rising living costs, charities across the UK are re-evaluating how they can maximise their impact with limited resources. For organisations tackling food insecurity, one of the most pressing logistical challenges is the efficient and equitable allocation of food packages and essential resources to reach those most in need.

Many charities, from large organisations like FareShare, and The Trussell Trust to community-focused groups such as the Mayors Fund for London, FoodCycle and The Felix Project, are working tirelessly to combat food insecurity. However, they all face a similar allocation problem: how to distribute resources effectively across regions to ensure that support reaches the most deprived areas. With demand for food banks and community kitchens surging, it’s crucial to allocate every package in a way that maximises impact and alleviates poverty.

The Allocation Problem: What’s at Stake?

The allocation problem is about balancing supply and demand under tight constraints. Charities receive a limited number of food packages from donations, government support, and partnerships with businesses. However, demand often exceeds supply, and distribution needs to consider various factors such as population size, poverty rates, and historical demand patterns across different regions.

Take London’s 33 boroughs, for example. Each borough has unique needs and characteristics that influence demand. Some areas, like Tower Hamlets, have high poverty rates and thus a significant need for food support, while boroughs like Westminster may have smaller populations but equally high demand. Each borough’s needs vary, shaped by factors such as population size, child poverty rates, and historical demand trends for food packages. How should a charity decide where to send the next shipment of food boxes? Allocating based on population alone would overlook poverty levels, while focusing solely on historical demand might ignore emerging areas of need. These complexities make it challenging for charities to meet demand equitably and efficiently.

Data-Driven Allocation: A Solution Grounded in Fairness and Efficiency

Here’s where data-driven allocation can make a real difference. Charities can now leverage data and advanced algorithms to solve this problem more strategically. By integrating factors such as poverty rates, population size, and previous demand, charities can prioritise high-need areas and distribute resources in a way that has the greatest impact.

One effective approach is to use Linear Programming (LP) models, which are mathematical tools that help allocate resources optimally based on specific constraints. By incorporating data on poverty levels, predicted demand, and available food supply, LP models can generate an allocation plan that ensures food reaches those who need it most. Imagine a system where every borough’s food needs are recalculated in real-time, ensuring no area is overlooked and support goes where it can do the most good.

Leveraging Machine Learning and CRM Integration for Smarter Allocation

For charities, integrating machine learning (ML) with a CRM tool like Salesforce offers a powerful approach to make allocation smarter, faster, and more responsive to changing needs. By embedding ML models into Salesforce, charities can predict demand more accurately, prioritise areas with high needs, and automate allocations with precision. Here’s how:

• Predicting Demand with ML Models: Using historical data on food distribution, poverty levels, population growth, and seasonal demand, ML models can forecast future needs for each borough. For instance, a time-series forecasting model can predict borough demand by analysing trends from previous months or years. This information can then be integrated directly into Salesforce as predictive fields. With tools like Salesforce Einstein, charities can incorporate predictions directly into borough records, offering real-time insights to help teams make data-backed allocation decisions.
• Prioritising Based on Needs: ML models can prioritise boroughs based on a combination of factors—such as poverty rates, historical demand, and recent socioeconomic data—by assigning “need scores” to each area. For instance, a regression model could generate prioritisation scores by weighing these factors, helping charities focus on boroughs where resources will have the most significant impact. This data can feed into Salesforce dashboards, showing team members the boroughs that should receive priority allocations.
• Automation through Salesforce Flow and API Integration: For more complex allocation models, like Linear Programming (LP), charities can run optimisation models externally (e.g., on AWS or Google Cloud) and connect them to Salesforce via an API integration. An ML-powered LP model can evaluate borough needs in real-time, providing updated allocation recommendations based on fluctuating demand and supply. The recommendations can then be pushed back into Salesforce’s Allocation records through automated Apex REST API callouts, updating allocation data for boroughs instantly.

Imagine a scenario where supply levels change - an ML model integrated with Salesforce could automatically trigger a Salesforce Flow to notify coordinators in high-need boroughs, reallocating boxes if needed. This level of integration means that teams can focus on action rather than calculations, ensuring resources get to the right places quickly.

Real-Time Insights with Salesforce Dashboards

Salesforce’s Reports and Dashboards or Tableau CRM (formerly Einstein Analytics) provide a visual overview of ML-powered predictions and allocations. For instance, a Demand Forecasting Dashboard can display ML-based predictions for borough demand, while a Poverty-Weighted Allocation Dashboard can highlight which boroughs receive priority based on needs scores. This integration makes it easier to make transparent, evidence-based decisions and provides instant visibility to team members, stakeholders, and donors alike.

Why This Matters: From Prediction to Precision

Data-driven allocation models are not just about distributing food boxes; they represent a fundamental shift in how charities operate. By embracing technology and automation, charities can amplify their impact, using insights to tackle the root causes of food insecurity rather than simply addressing symptoms. Moreover, this approach can build trust with donors and partners, who can see that resources are being used as efficiently and effectively as possible.

The Road Ahead: Adopting These Solutions

As Chancellor Reeves’s budget underlines the need for sustainable, community-driven solutions, charities have a golden opportunity to adopt data-driven approaches. With tools like LP models, CRM integration, and machine learning for demand prediction, charities of all sizes can transform their operations, ensuring that resources go further and reach those who need them most.

I don’t profess to be an expert in food distribution logistics, but I see how machine learning can be leveraged to provide an efficient, impactful solution to this problem. For those working in or supporting the charity sector, now is the time to explore these technologies. By adopting smarter allocation strategies, charities can rise to the challenge of food insecurity, delivering not just aid but hope to communities across the country.

If you're involved in charity operations, I’d love to connect and discuss how data-driven approaches could enhance your impact. Let’s explore how technology can ensure that every box counts.