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Case Studies

Supply Chain

Area: Supply Chain

Areas in this sector

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Supply Chain

Sensor System for increasing shelf life

Cooking Robots 

Food calories checker 

Safety & hygiene in restaurants

Intelligent Vending

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Digitizing Agriculture

Sector: Food
Area: Supply Chain

Problem: There is a lack of Reliable data for production estimation, production planning, and market creation.

Solution: FOODPRINT™ is a produce aggregation and food traceability solution which aggregates the farm produce and implements food traceability using technology such as BLOCKCHAIN to capture the information of the product at various points in the supply chain such as production, processing, and distribution.

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Blockchain for connecting Agricultural stakeholders 

Sector: Agriculture
Area: Supply Chain

Problem: Farmers today still face challenges related to improper decision-making, unnecessary paperwork and dockets and supply chain inefficiency and risk.

Solution: AgriChain builds a simple-to-use, secure, independent platform that connects and transfers information between supply chain participants. AgriChain is a software platform that brings together all stakeholders in the agricultural supply chain. It is a Blockchain firm dedicated to enabling peer-to-peer agriculture transactions and processing without the use of intermediaries.

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Farm to Fork

Sector: Agriculture
Area: Supply Chain

Problem: Farm to Fork focuses on the supply chain without having any control over produce (of perishable products). With perishables, the supply chain has to be very efficient. There cannot be any hoarding when it comes to perishables, and hence AIBono can help farmers find a market for their products without having them waste the produce.

Solution: AIbono start-up follows produce from the moment the seed is sown which is 60 to 90 days before harvest until it reaches the end consumer. Aibono uses soil sensors IoT devices and imaging drones to collect farm data to be used in predictive analytics. These analytics are then used to help farmers make smart agriculture decisions and these decisions in turn result in higher quality products and a more abundant harvest.

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Data is driving decisions in the supply chain Infrastructure

Sector: Supply chain
Area: Supply Chain

Problem: In food and grocery ecosystem, both supply and demand are highly fragmented. There is very little transparency on demand and pricing which eventually leads to huge inefficiencies. Even when demand is known to some extent, modern and efficient supply chain infrastructure and reliable payment mechanisms are lacking, resulting in delays, wastage and lost opportunities for producers and buyers throughout the ecosystem. 

Solution: Jumbotail generates billions of data points from a plethora of data sources ranging from multiple mobile apps for customers, sellers, supply chain operations, and other partners from the marketplace platforms. The system drives real-time decision-making using machine learning and AI that forms the backbone of the supply chain optimisation systems, recommendation engines, pricing and merchandising systems, credit scoring, and fraud detection.

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AI driven 'farm to market' supply chain

Sector: Agriculture
Area: Supply Chain

Problem: Farmers experience price risk, information asymmetry about demand, distribution inefficiency, and late payment receival. Retailers face problems like higher costs, low quality and unhygienic produce, high price volatility, and the everyday hassle of going to the market.

The traditional Supply Chain is highly inefficient, disorganized, and has a high rate of food waste.

Solution: The application attempts to remove inefficiencies by putting in place AI driven farm to market supply chain.

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Reduction of food wastage from the farm to the shelf.

Sector: Agriculture
Area: Supply Chain

Problem: Large amount of food wastage takes place in the journey from farm to the shelf. There is a need for an application that will work across fresh produce supply chains and reduce food waste by detecting variance from specifications and matching output to needs.

Solution: Machine learning and computer vision are used to digitize the quality assessment of fresh fruits and vegetables. The technology makes quality processes objective, efficient, and less wasteful.

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Farm to fork traceability

Sector: Agriculture
Area: Supply Chain

Problem: To bring farm to fork traceability and ensure quality control.

Solution: The solution digitizes the farm management, while managing the data for the entire ecosystem.

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