Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups in the logistics industry. As there is a large number of startups working on a wide variety of solutions, we decided to share our insights with you. This time, we are taking a look at 6 promising data analytics startups.
Heat Map: 6 Top Data Analytics Startups
For our 6 picks of data analytics startups, we used a data-driven startup scouting approach to identify relevant solutions globally. The Global Startup Heat Map below highlights 6 interesting examples out of 181 relevant solutions. Depending on your specific needs, your top picks might look entirely different.
Surgere – Analytics-Driven Automotive Packaging
When an automotive OEM or tier supplier introduces a new car model, this results in more complex supply chain logistics due to a significant increase in the variation of parts required. With the rise of the Internet of Things (IoT) sensors and their adoption, real-time data analytics plays a vital role in creating a better packaging solution. This application of packaging analytics has been utilized by OEMs to save millions of dollars as they transition to an analytics-driven packaging system.
US-based startup Surgere impacts the automotive packaging supply chain industry with their technological solution by using analytics from various sensors on package containers and transactional information from OEMs, suppliers and logistics providers. This level of detailed analyses and tracking allows manufacturers to reduce losses and inefficiencies that arise from traditional packaging tracking.
Alloy.ai – Demand Forecasting
Demand forecasting is a key component for manufacturers as even small mistakes in forecasting result in huge losses. Due to efficient advanced data analytics techniques, it is now possible to better identify prevailing trends during demand forecasting, and such techniques have become a key component of after-sales service success.
Canadian startup Alloy.ai designs a machine learning platform to continuously monitor the incoming data from Point-of-Sales and other sources including promotions in order to deliver forecasts on future demand. Their system is built on a single platform that provides multiple forecast models – this ensures greater accuracy of their forecasts.
Simfoni – Procurement & Sourcing Analytics
Procurement is an important part of any business supply chain and a vital part of it is ensuring the purchases are all conforming to the required standards. This is very difficult to manage when the number of suppliers involved starts to increase. Managing various price negotiations, inventory levels, purchase orders, etc. easily places a massive strain on standard procurement systems. Data analytics is already playing a key role in this field by applying machine learning to identify trends and patterns while also automating some aspects of the procurement process. A supplier’s performance is not always guaranteed to remain the same from the day the contract was signed and for this reason, manufacturers need to be able to track their suppliers’ performance to ensure a smooth production process.
British startup Simfoni creates a machine learning analytics platform for procurement specialists by procurement specialists. Their technology allows manufacturers to analyze and identify potential problems during the procurement process.
EPG – Warehouse 4.0
Warehousing has a number of challenges like omnichannel sales, seasonal demand fluctuations, backorders, order returns, etc. All these factors create pressure in operations management. By applying big data and predictive analytics in warehouse management, models and algorithms help with demand prediction, inventory optimization, material flow efficiency and more.
German startup EPG takes connected smart logistics to the next level with its warehouse material flow control and analysis technology. Their system is designed to provide a smooth and seamless flow of materials from the warehouse to the production floor by integrating the system into automated warehouse robots and equipment.
Carto – Data-Driven Route Optimization
Businesses understand or rather gain insights into their sales by monitoring how fast the flow of inventory from their warehouse is but by taking the data from their point of sale and analyzing it they can identify the trends and market demand in up to half the time it normally would take.
The US-based startup Carto develops a mobility planning solution designed to optimize the supply chain network. Their system uses a huge amount of location data and intelligence available in order to analyze and optimize the distribution networks, which saves time and resources to the manufacturer. The importance of data analytics can perhaps be most noticed in this area.
Krunchbox – Point-Of-Sale Analytics
The importance of data analytics can perhaps be noticed the most in this area. Analyzing Point-of-Sale data has many advantages including retail employee scheduling, shelf space optimization, making sure inventory is always available depending on the analytical forecast and more.
Australian startup Krunchbox helps suppliers analyze electronic point-of-sale (EPOS) data to optimize inventory, increase collaboration and drive incremental sales. Their powerful analytics software is designed to work in collaboration with suppliers-retailers to provide better predictive models. Analyzing point of sale data has many advantages which include retail employee scheduling, shelf space optimization, making sure inventory is always available depending on the analytical forecast and more. Using data from the moment a product is sold allows for the best possible real-time analysis of a product or business performance, this allows for reactive measures to be taken in minutes to hours rather than days.
What About The Other 175 Data Analytics Solutions?
While we believe data is key to creating insights it can be easy to be overwhelmed by it. Our ambition is to create a comprehensive overview and provide actionable innovation intelligence for your Proof of Concept (PoC), partnership, or investment targets. The 6 data analytics startups showcased above are promising examples out of 181 we analyzed for this article. To identify the most relevant solutions based on your specific criteria and collaboration strategy, get in touch.