Data-related challenges in automotive and manufacturing arise during IT transformations, product launches, and process excellence initiatives.

In today's rapidly evolving digital age, the automotive and industrial sector faces challenges such as:


Master Data Accuracy

Inaccurate master data can lead to production delays and increased costs, making precise data essential for efficiency.


Discrepancies in Data

Inconsistent data across systems causes errors in operations and decision-making, disrupting workflows.


Quality Maintenance Across Multiple Systems

Ensuring consistent quality standards across different systems is complex but vital for seamless operations.

 

  • Material Master Data

    Smooth Business Operations

    Efficient and smooth business processes rely on clean material master data.

     

    Failure to ensure high-quality material master data can lead to critical process disruptions - such as planning mistakes and material shortages. Proper system support is not possible, and manual intervention becomes necessary.

    Data Quality Navigator's comprehensive repository of validation rules covering a wide range of material-related processes identifies potential process disruptions and enables proactive countermeasures.

    • Efficient business processes Efficient business processes
    • Stabilized processes along the product life cycle Stabilized processes along the product life cycle
    • Supporting reliable planning Supporting reliable planning

  • Demand and Capacity Management

    Balance Production Demand and Capacity

    Synchronize your supplier's delivery capacities and production planning to optimize your inbound supply chain. Benefit from a resilient flow of raw materials.

    Poorly managed demand and capacity planning data results in delayed supplier delivery and causes disruptions in the production schedule.

    Integrating data from different source systems, the Data Quality Navigator supports the validation of the consistency between material demand requirements from program planning and the required supplier capacity.

     

    • Steady material supply Steady material supply
    • Timely order fulfillment Timely order fulfillment
    • Reduced manual intervention Reduced manual intervention

  • Missing Parts Prevention

    Improve the Flow of your Intra Logistics

    Prevent missing parts and optimize assembly stock by improving production line supply control parameters.

    Complex demand patterns require adequate parameter settings for digital line supply processes (e.g., number of KANBAN containers and replenishment strategy). Failure to do so may cause excessive stocks, missing parts, and eventually, line stoppages.

    By combining advanced optimization algorithms with a powerful simulation engine, the Data Quality Navigator can detect critical demand patterns and identify inadequate out parameter settings. Decision-makers can directly derive optimal parameter settings to prevent overstock or line stoppages.

    • Prevent missing parts Prevent missing parts
    • Avoid excessive stock Avoid excessive stock
    • Decision-maker support Decision-maker support

  • Production Launch

    Right the First Time

    Make your product launch successful by taking control over your data and proactively preventing process disruptions.

    During a production launch, data issues often cause unstable logistics or production processes, resulting in material excess or shortages, discrepancies between system and physical inventory, and line supply delays - eventually leading to frictions during the start of production.

    Data Quality Navigator’s comprehensive repository of validation rules identifies inconsistencies and enables their elimination before they can cause problems.

    • Prevention of process disruptions Prevention of process disruptions
    • Effective data quality controlling Effective data quality controlling
    • Successful production launches Successful production launches

Effortless Data Quality Starts Here

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