Delivering Locally Sourced Nutritious Food to Indian Households

WHO reports that in the South Asian region, the number of undernourished has hardly decreased in the last decade. This situation calls for a concerted effort to combat malnutrition the world over. The effort must be grounded in nutrition and executed through a robust distribution mechanism to reach all segments of the society. In this thesis, we take a step in that direction by combining expertise from the supply chain and nutrition areas to address protein-energy malnutrition among poor households in India. While the country is the largest producer of pulses, milk, and other dairy products, and many food grains, Indian diets are traditionally low in protein intake, especially among the poor. Within our scope of the problem, we target the poorest households in India, which currently hold the Antyodaya Anna Yojana ration cards from the Government of India. We develop a framework to improve their diet diversity nutritionally. We propose matching the demand of food (as recommended by Indian Council of Medical Research for a balanced diet) with locally available, culturally preferred supply by designing ‘customized food baskets’ for different consumer clusters. We suggest distributing the proposed food baskets at scale to all target households via the government Public Distribution System mechanism operational in India. We use PCA and K-means clustering to segment the customers, create a food basket model inspired by the knapsack problem, and use a Mixed Integer Linear optimization program to solve the distribution problem. The key contribution of this thesis is a framework of basket assortment and distribution. The approach is generalizable and can be used on many different customer types and (public or private) distribution channels to match demand with supply of nutritious assortments and enable delivery at scale. We can serve 65 to 75% of recommended daily quantity of cereals and pulses to our target households via the proposed framework.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Supply Chain Management Program

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Das, Sanchita

Advisors dc:contributor.advisor

• Argueta, Chris Mejia
• Collese, Tatiana Sadalla

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139392

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139392