Real-Time Surplus Food Redistribution Engine: Leveraging AI to Tackle Infant Malnutrition in Indonesia
By Aishadinda Qaulan Maisura, GRC 2024 Global Essay Competition Top 5
If local news highlights the rural villages of Java Island, the impoverished coastal areas of Sumatra, or the remote regions of Papua, stories of child hunger often saturate the headlines. Grim pictures of scrawny and starveling children, their faces gaunt and eyes hollow, flash between homepages and emerge as an ongoing stereotype. As Indonesia grapples with the alarming reality that more than two million children suffer from a triple burden of malnutrition (Nutrition International 2024), a key solution to eradicating this issue may lie in adopting a circular food economy model. Data from Statista (2024) indicates that the country ranks fourth globally in the baby food marketplace, underscoring an untapped opportunity with significant potential for surplus within the industry. Imagine a world where an abundance of food is prudently redirected to vulnerable communities. In accelerating this vision, the integration of technology, particularly Artificial Intelligence (AI), has the power to reinforce a sustainable framework. However, government intervention in establishing policies, infrastructure, and mechanisms is essential for the implementation of a real-time system. This essay explores the pivotal role of an AI-powered baby food waste engine in addressing the malnutrition crisis across Indonesia’s archipelago.
From one perspective, the conceptualization of circular economy (CE) promotes a regenerative industrial ecosystem in which edible surplus from enterprises provides input for the subsequent, creating a loop that eliminates the notion of “waste”. In the food sector, Indonesia generates 23-48 million tons of food waste annually (Impact Report 2022), far exceeding the approximately 7 million tons of plastic disposed of each year (World Bank 2021). This staggering amount of food waste is equivalent to the weight of about 2,277 Eiffel Towers. The proponents of CE present a sufficiently compelling framework to address the country’s hunger issue, which affects 16.9% of the population, according to the 2024 Global Hunger Index (GHI). The Indonesian government has advocated for this approach, with the Ministry for Development Planning projecting a roadmap to ensure that surplus food generated can be recirculated. However, despite such efforts, 7.2 percent of the population remains undernourished, as derived from the latest findings by Bright Institute. The paradox places Indonesia at a critical juncture, questioning whether a plethora of resources is capable of improving lives. Perhaps, the government should regulate the proper valuation of waste for infant nutrition, particularly young children under five who represent a significant proportion of the GHI populace. Thus, deliberately targeting the formative years to address intergenerational disparities.
To begin with, the redistribution unit in Indonesia compromises third-party organizations, where retailers donate through the system. Three groups were selected as initial examples for further analysis and estimation of the chain scenario. The diagram in Figure 1 illustrates the flow of the framework from a source, such as excess high quality packaged baby food or formula of a retail store or producer to its destination: posyandus, integrated primary health posts that proliferate in every village in Indonesia, and then allocated to end-users. Detail samples from each section are further mentioned in Figure 2, which provides distinct characteristics within its category. Furthermore, the collection of surplus food is divided into two schemes. The use of a digital platform is concentrated in materials taken from urban areas that will be distributed subsequently into rural areas. Independent donors will be based on delivery services in Indonesia such as Gojek, a motorcycle taxi application in Indonesia that functions as a real-time tracking with AI algorithms to streamline routes. Alternatively, surplus donations from large food stores will be distributed using vans from forwarder.ai, an innovative logistic solution that employs data analytics to revolutionize delivery network and strategies.
The key mechanisms involved in the system can be broken down as follows:
a. Predictive Analysis
The performance of the framework is piloted at regional level, due to Indonesian’s demographic composition of islands. Predictive analysis presents a need to identify baby food demands in areas, providing precisions of real-time surplus production and routes suggestions in redirecting food to health posts before expiration. A 2017 study from M McKinsey & Company initiated that AI-driven supply chain improvements indicates potential reduction in forecasting errors by 20-50%. Therefore, cooperation with logistic providers through corporate social responsibility (CSR) is recommended to adopt Artificial Intelligence in surplus redistribution purpose (Ciccullo et al., 2022).
b. IoT Cold Chain Solutions
There is one occurrence that should be preserved regarding the source, and that is the short shelf life of baby foods. The deployment of tools from Binus AI Lab, an AI software developed by Bina Nusantara University, Indonesia, can be integrated into the aforementioned applications through scan features to assess packaging conditions and expiration dates. To minimize the spoilage of baby food, health policies encourage couriers to be equipped with portable cold storage units, monitored with IoT from SmartTrainer. In forecasting, AI is capable of perceiving environmental data and analysing regional preferences to alert expiration aspects. The technologies based on Tetra Pak for instance, demonstrate models that sense shifts in gas composition (i.e. signals of microbial activity and rise in CO₂ level). Image recognition through computer vision detects defects in packaged goods. In addition, these sensors can relay real-time data to central servers for tracking.
c. Real-time Tracking
In the context of perishable goods transfer, prompt delivery is critical. The primary goal of this system in leveraging AI algorithms is to integrate GPS tracking and real-time analysis that streamlines the process. Especially in traversing the road infrastructure in Indonesia, which remains fragmented and has inadequate gaps in certain regions.
Artificial Intelligence holds a wide range in assessing surplus good quality baby food redistribution in Indonesia. This paper outlines a proposed solution to address child hunger by leveraging a circular food economy model that integrates AI into the redistribution process to vulnerable communities. Infant malnutrition rates may continue to exist; however technological advancements thrive to suppress the number.
Bibliography
Impact Report. Tackling Food Waste in Southeast Asia: Annual Report 2022. Singapore: Southeast Asia Food Network, 2022.
Nutrition International. Triple Burden of Malnutrition in Indonesia: A National Overview. Ottawa, Canada: Nutrition International, 2024. https://www.nutritionintl.org.
Statista. "Global Baby Food Market by Country, 2024." Statista. Accessed December 2024. https://www.statista.com.
World Bank. Plastic Waste in Indonesia: Annual Country Analysis 2021. Washington, DC: World Bank, 2021. https://www.worldbank.org.
Ciccullo, Francesco, Marco Fabbri, Nizar Abdelkafi, and Margherita Pero. "Exploring the Potential of Business Models for Sustainability and Big Data for Food Waste Reduction." Journal of Cleaner Production 340 (2022): 130673. https://doi.org/10.1016/j.jclepro.2022.130673.