Rejuvenate: AI-enabled, Last-Mile Distribution of Medicines & Vaccines

'Guddi Bajis (GBs)' are last-mile-retailers-cum-frontline-health-workers who digitally connect rural villagers to essential health, nutrition and wellness services and products via telemedicine and tele-pharmacy. With multiple impact partners, Naya Jeevan is building a digitalised, last-mile distribution system across Pakistan that will integrate 6000 villages (30 Million lives) into MNC supply chains.  

Dr. Asher Hasan (Founder): Asher is a TED fellow, 2017 Mckinsey Geneva Forum Health awardee, 2011 Schwab Foundation/WEF Asian Social Entreprenuer of the Year & Co-Founder of MIT SOLVER (doctHERs)

Over 60 Million rural villagers across Pakistan (and over 750 Million globally) remain formally disconnected from access to essential, authentic, health, hygiene, nutrition and wellness products and services. These excluded populations are especially vulnerable to environmental shocks such as pandemics (e.g. COVID-19). This is not only because of the potentially rapid spread of contagion, fuelled by a lack of awareness around adequate preventive measures (e.g. myths around vaccinations) but also because the target population lacks access to these products/services. In addition, the sustainability of rural livelihoods depends on their access to informal, supply chains which are the first to get disrupted by pandemics. 

Since 2012, Naya Jeevan has been working to provide health & wellness services to workers (and their families) in corporate value chains (smallholder farmers, distributors, retailers, etc) which extend from factory to field. Pharmaceutical, health & hygiene supply chains in Pakistan typically terminate in large district towns from where informal 'middle men' have onward distributed to last-mile villages - extracting unreasonable profits from last-mile retailers while imposing a poverty penalty on already-marginalised populations. In addition these informal supply chains are often heavily contaminated with counterfeit medicines and vaccines, which further exacerbates poor health and long-term, socioeconomic outcomes. 

Our target population are the rural 'excluded', i.e. 60 Million lives across rural Pakistan who remain formally disconnected from mainstream supply chains of Fast-Moving Consumer Goods (FMCG) companies and lack access to essential health, nutrition and wellness (HNW) products. 

Our target addressable market (TAM) are 30 million low-income lives residing in rural villages across 78 districts of Pakistan. Median household incomes are ~$3 per day and mean individual income is closer to $1/day, which is below the World Bank poverty line. 

Applying the best principles of human-centred design thinking, we have conducted immersive studies in rural villages to understand the unmet (and in many cases unarticulated but observable) needs of our target population. This includes the end-user/customer/payor, the last-mile retailer (Guddi Baji) and the last-mile distributor (Gudda Bhai). 

Having a more nuanced understanding of the actual needs of these communities (some of which were quite different from the needs we had anticipated going into this exercise) has enabled us to deliver a model that was co-created by the target population. They take great pride in this and feel a sense of ownership which helps drive total community participation and performance driven by passion and purpose (and not just profit).  

We intend to conduct an academically rigorous Randomised Controlled Trial RCT (RCT) that evaluates the impact of our 'shared economy' digital last-mile distribution model on the health and socioeconomic outcomes of rural villagers in an emerging market context (Pakistan).  This RCT will be conducted in collaboration with a world-class, academic research institute and will be accompanied by the submission of several, peer-reviewed publications which will be publicly available. 

Our Theory of Change is the following: 

Inputs (digital supply chain integration, essential Health, Nutrition and Wellness (HNW) products, Gudda Bhai LMDs) are combined to perform Activities (e.g. last-mile distribution of essential Health, Nutrition and Wellness products to a large village-based network of women last-mile retailers). These activities generate Outputs (e.g. tech-enabled sales of essential HNW products and services in rural villages) that yield tangible Outcomes (e.g. a % decrease in the incidence rate of malnutrition in children under 5) that achieve the ultimate Impact we are seeking (i.e. to increase the resilience and systemic capacity of rural villagers and their ability to rapidly bounce back from environmental shocks such as COVID-19 pandemics.

Proof of Concept of this theory of change was achieved between 2019 and 2021 in collaboration with doctHERs and Unilever and with the financial support of $800,000 from Unilever-UKAID and the Transform program. In this model, the 'just-in-time' delivery of health & wellness services by 109 tech-enabled Guddi Bajis to 600,000 villagers across 8 districts of Pakistan, generated a 32% increase in the individual incomes of these Last-Mile Retailers. In addition, there was a 63% increase in the use of COVID-19 PPE by end-users.

Scale at 1 Year: Over the next 12 months, Naya Jeevan plans to train, equip and deploy 24 'Gudda Bhai' Last-Mile Distributors (LMDs who are digitally connected to the supply chains of at least 5 different MNCs. These 'Gudda Bhai' LMDs will distribute essential medicines including health, nutrition and wellness products (HNW) to 240 senior 'Guddi Bajis' as well as 2160 junior Guddi Bajis (a total 2400 Last-Mile Retailers), reaching a target addressable market (TAM) of 12 Million lives.

Scale at 3 Years: Over the next 36 months, Naya Jeevan plans to train, equip and deploy 78 'Gudda Bhai' Last-Mile Distributors (LMDs who are digitally connected to the supply chains of at least 12 different MNCs. These 'Gudda Bhai' LMDs will distribute essential medicines including health, nutrition and wellness products (HNW) to 600 senior Guddi Bajis (GBs) and 5400 junior GBs (a total 6000 GB Last-Mile Retailers), reaching a target addressable market (TAM) of 30 Million lives.

We will monitor and evaluate our impact through the monthly tracking of the following Key Performance Indicators (KPIs):

• % change in access to maternal, neonatal and child healthcare (MNCH) by end-users
• % of pregnant women with access to antenatal care via qualified GPs and OB-GYNs
• % change in children under age 5 with malnutrition
• % change in malnutrition in pregnant & lactating women (PLWs)
• % change in women of child-bearing age with anaemia
• % Relative Change in maternal and neonatal morbidity & mortality 

Proof of Concept of the Naya Jeevan last-mile (distribution + retail) model was achieved between 2019 and 2021 in collaboration with doctHERs (lead partner) and Unilever and the financial support of $800,000 from Unilever via the UKAID co-fundedTransform program.  In this model, the 'just-in-time' delivery of health & wellness products and services by 109, tech-enabled Guddi Bajis to 600,000 villagers across 8 districts of Pakistan, generated a 32% increase in the individual incomes of these Last-Mile Retailers. In addition, there was a 63% increase in the use of COVID-19 PPE (e.g. use of face masks and hand sanitiser/soap) by end-users.

1 Year Barriers: 

• Financial: Providing the initial capital expenditure to finance the inputs of the Gudda Bhai last-mile distributor (e.g. delivery van, hardware/software) may be a potential barrier

• Connectivity: As the predictive demand forecasting component of our digital distribution model is based on 4G connectivity, we do anticipate some initial challenges in specific areas where broadband service is patchy. One way to mitigate this risk is to use 4G wifi devices from multiple telecom carriers.

• Market: Initially, as data is being collected (to form the dataset that will be utilised to build the AI-enabled predictive analytics model), the 'supply-demand' matching between last-mile distributors and last-mile retailers may not be as accurate - leading to some market inefficiencies. One way to mitigate this to to ensure that the 'Guddi Bhais' are in constant contact with the mega-distributors as well as the 300 Guddi Bajis they are supplying goods to. 

3 Year Barriers: 

• Policy: we anticipate policies will be introduced to curtail the use of consumer purchase data; to mitigate this risk, we are going to proactively engage the government and help co-create these policy guidelines which enable anonymised data to be used with informed consumer consent.

The Naya Jeevan & doctHERs' team is a fellow/member or awardee/grantee of the following: 

Fortune Impact 20, TED, World Economic Forum, Schwab Foundation, Rockefeller Foundation, Synergos, Ashoka, Mckinsey Geneva Health Forum, G20 Financial Inclusion, GSMA Mobile For Humanitarian Innovation Fund, UKAID, USAID Development Innovation Ventures, Google (Tides),  Inclusive Fintech 50, 

In order for our 'shared economy' digital last-mile distribution to scale effectively and overcome any financial barriers, it is important for us to integrate large-scale manufacturers such as GSK and RB into this model who can share the costs of this last-mile rural market access with other large manufacturers (e.g. Friesland Campina, etc).

In addition, in order to generate robust evidence of the impact of our model that will inform federal policymaking, it will be important to collaborate with academic researchers (e.g. behavioural economists from ICL, LSE and Univ. of Cambridge) and conduct RCTs that explore different aspects of our model. 

The Trinity Challenge can enable us to overcome both of these barriers to success. 

As mentioned above, we would like to integrate the pharmaceutical, medical consumer healthcare and fast-moving consumer products of both GSK, RB and BD into our model. 

In addition, we would like to partner with Google, Microsoft and the Patrick J. McGovern Foundation to help build our AI model and use Google and Microsoft's cloud-based services to design and build the full stack for our digital platform (telemedicine + telepharmacy + televeterinary care + inventory management + electronic medical records + insurance claim utilisation).

We would also like to explore with Swiss Re how we might be able to adapt our AI model to predict  healthcare utilisation, that will drive individualised health insurance pricing and potentially transform the insurance/re-insurance industry as we know it.

Finally, in order to generate robust evidence of the impact of our model that will inform federal policymaking, it will be important to collaborate with academic researchers (e.g. behavioural economists from ICL, LSE and Univ. of Cambridge) and conduct RCTs that explore different aspects of our model.