Data science is game changer for F2B supply driven model
Why supply driven model ?
You must have read a lot of news about Farm2Business startups and fundings. Most of the startups procure based on the demand which end up in transactional business rather than building ecosystem. Farmers have to go mandi if you are buying based on your demand and this is not a solution for them as they need to juggle b/w company and mandis.
AGROWAVE is building a supply driven model where the farmers can sell all of their produce on daily basis at the Mobile Pickup Stations using AGROWAVE farmer mobile application. In this case they don't have to juggle b/w Mandi and companies hence more connivence for them. It's our technology which will optimize the cost and build better supply-demand mapping.
MPS(s) database; Dynamic route mapping
We are working in different pockets in different states, currently we have 55 mobile pickup station and we are planning to expand to 1000 MPS(s) by December 2021 and 25,000 MPS(s) by end of 2022. We are building the dynamic route mapping based on multiple factors like commodity, volume, quality, real time inventory, supply-demand mapping, price fluctuation and the distance using data science. The dynamic route mapping is to reduce the transportation cost and improve overall efficiency for better unit economics.
Farmer Database
We are working with around 4000+ farmers across UP, RJ, HR and MP. AGROWAVE Farmer mobile application enables small and marginal farmers to sell their fresh produce at these MPS(s). The system collects data points such as commodity, volume, pickup location, seasonality, harvest time and farming land size. This farmer database will consist of millions of records which will improve supply-demand mapping. Since now we'll be having millions of farmers working with us so it'll open more cross selling revenue stream for us and more convenience for farmers.
Price Prediction Engine
This is most important system which we are building. Price prediction engines uses various data points such as historical commodity sales and purchase prices from private and public source, weather data, fuel trends, commodity data ( volume, quality, packing type, variety) locations ( sourcing and selling). When a farmer places a request to sell the produce, the system predicts price against each request and reflects in farmer mobile application. This helps us to scale the MPS(s) model with millions of farmers as the system brings transparency and reduce human intervention.
Data driven supply-demand mapping
'Supply demand mapping' is widely used concept across agriculture industry. We are using real time commodity level information from our business orders, request from the farmers and inventory. We are also trying to build this system with more depth of quality mapping with clients. This helps us to move the commodity fast, reduce wastage, increase margins and better price for farmers, and improver overall operational efficiency.
In my personal opinion the current agriculture supply chain lacks the use of technology, hence efficiency issues. The technology can be used in 3 major areas logistics, pricing, and supply-demand mapping. By technology here, I don't mean mobile applications, I am talking about data science which can change the overall direction efficiently by reducing wastage, better crop planning, price prediction for transparency, optimising logistics cost and community building.
Entrepreneur l FMCG | Mars Chocolate |Colgate-Palmolive l Dragon fruit farm owner
3 年Well it's a very good Article which is need of the hour. When we talk about LEAN Methodology in manufacturing sector it speaks volumes about the points mentioned here, you got it right Anu Meena ????
Nobody Really!
4 年public data is garbage. am not sure what inference you are hoping to get out of. been looking at agmarket data from the govt for a while, and live near an APMC mandi... so speaking from experience. weather data is a bigger stretch. and most of them cant get hyper local weather right, its worse actually during monsoons. i dont know what data science or AI people are claiming will put out anything of value if its based on public historical (or current) datasets.
Managing Director @SEG | Learner
4 年Anu Meena I think a potential collaboration with digital newspaper archives can serve as a big and diverse source of training dataset. And can help to come up with a high accuracy predictive models.
Generative AI Pioneer I CEO GoMicro
4 年This is a good reverse approach Anu Meena, because it is based on helping the farmer to sell, instead of helping the buyers to buy. You are right in that this approach creates an eco system that benefits farmers.
Data & Analytics Lead @ MakeMyTrip | Adobe Analytics | Marketing Strategy & Analytics| Conversion Rate optimization (CRO)
4 年Very well written Anu Meena . The number of mouths to feed continue to rise, yet the amount arable land on which to grow the food remains the same. But crop yields are becoming unpredictable due to climate change and over farming. Even a small reduction in projected yield can destabilize the whole food market, knocking supply and demand out of sync. Data science is definitely a game changer which will help you to build a model that will learn from its mistakes and improves forecasting accuracy over time.