In times of high demand variations and uncertainty, analytics has a fundamental role on improving demand anticipation and transparency throughout the network, reducing shortages and the impacts of bullwhip effect.
Because of the extensive supply chains, sophisticated processes for manufacturing and planning and the operational sync required by so many kaizen management systems, the automotive industry can benefit a lot from adopting analytics and leveraging the use of data.
Additionally, the growing pressure for innovation, quality improvements and recent commercial models transformation introduce new dynamics and agility requirements that haven’t been experienced by this industry sector in a long time.
The automotive industry is becoming a mobility industry. This will require more ways to understand customers’ new behaviors and expectations and will expand the supply chain, incorporating rentals, for example.
DHAUZ can help
With our profound industry knowledge and advanced analytics skills, DHAUZ can help automotive clients in various ways:
Manufacturing and production Lines
The sync requirements and supply chain complexity can both be better handled with manufacturing optimization models
Visibility tools enables learning the suppliers cadence and timings
Predictive analytics can evaluate equipment sensors signals, such as and vibrations, to increase OEE
Video analytics enables automated visual quality checks and indicate deviations in format, painting and finishes Sales and Distribution
Sales and Distribution
Segmentation and pattern identification to learn about dealers and customers, creating sales insights regarding products, volumes and special commercial conditions
Segmentation for clients and market, as well as credit risk and insurance, to improve rental service offers
Support for dealers on how to improve their sales outcomes
After-Sales and CRM
Client experience journey based on data and triggers
Inventory planning and distribution network optimization
AI to identify and mitigate fraud, waste and abuse
Product life cycle and client requirements mapping Product Development
Product Development
AI algorithms connected to digital twin models to improve testing speed and issue corrections during development
Root cause analyses for product defects and imperfections to drive innovation