Building an application to streamline the due diligence process through a combination of automated web searches and web scraping

Challenge

• Client offers due diligence as a service
• Due diligence process is multi-tenant; users from multiple separate parties work together
• Process slowed down by back-and-forth communication and repetitive tasks
• Goal was to automate due diligence process

Solution

• Built a multi-tenant application to streamline the due diligence process end-to-end
• Implemented automated web search, website scraping and screening of entities linked to the target company

Business impact

• Project still in deployment, no impact available at this time

Developing internal causal inference library consisting of 10+ algorithms and applied library to 2 use cases

Challenge

• Build causal inference library to be used by other data scientists in the future for various projects
• Enable activities such as churn analysis and A/B testing to be conducted

Solution

• Developed library in collaboration with internal stakeholders, did unite testing/documentation/user guide
• Worked on two related use cases needed for the analysis
• Unit8 implemented 60% of the algorithms for the project

Business impact

• Library enables data scientists to easily use multiple algorithms for causal inference through a unified API design
• 10+ algorithms implemented

Developing tools on Foundry and applying causal models to identify optimal steps in glass bottle production process to increase yield of production line

Challenge

• Want to improve yield on production line form 86% to 90%
• Already identified step in process where gain could be realised
• But no way to automatically prioritise sensor data which should be further analysed
• Goal is to apply ML models to identify most promising captors to improve yield

Solution

• Initially built E2E model of yield on production line, but hit data quality roadblock
• Shifted towards:

1. developing tool on Foundry to surface “golden production parameters” when production quality was high
2. using causal models to understand optimal actions to stabilise first step of production process

Business impact

• Highlighted data quality issues and suggested next best steps for improvements
• Demonstrate Foundry’s ability to replace existing system
• Trained key internal stakeholders on Foundry platform
• Due to lack of high quality sensor data → no direct impact on yield realised during the PoC

Building a forecasting model for a medical equipment manufacturer resulting in significant forecasting accuracy improvements allowing better procurement planning and contract management

Challenge

• Forecasting challenges when it comes to their supply chain
• Demand Planners working very manually with back and forth between planners, sales, and manufacturing
• Goal is to improve this with a more intelligent tool

Solution

• Built forecasting model with features included in Darts in combination with hierarchical reconciliation
• Solution would stream data directly from SAP

Impact

• Forecasting improvements of 10-60% compared with historical forecasting methods, depending on the time outlook in the model
• Helps procurement negotiate better contracts with suppliers and plan manufacturing accordingly

Demonstrated how a cloud platform can ingest different data sources and made dashboards and applications that could drive strategic decision-making

Challenge

• Client wanted to have a view of the current raw material inventory in the warehouse
• Need to forecast the inventory on customer side in order to optimize production and logistic cost and to increase customer satisfaction

Solution

• Data ingestion from different data sources including SAP, MeteoSwiss, and IoT devices
• Using Supply Chain offerings to define ontology
• Clean data
• Apply forecasting models through our Darts library

Business impact

• Showcase different forecasting models alternatives
• Optimized warehouse logistics
• Reduced excess inventory stock

Developed a wildfire prediction system in collaboration with WWF to anticipate wildfire in Bolivian forests through a new fuel model based on geo data

Challenge

• Bolivian forests are at a high risk of wildfire
• Wildfire prediction system needed to anticipate how a fire might start and where it will spread

Solution

• Used publicly available data such as weather, climate, past fires, and geo data to model the wildfire spread starting at a certain point in time
• Created a new fuel model for Bolivia by transferring knowledge from available US data

Business impact

• A complete fuel model for the Bolivian territory is now available
• Applications created enable visualisation of past fires
• Discovered main pain points of ML with geo data and established a clear plan on how to tackle them and speed up future use cases