Companies that want to improve processes and reduce costs need clean and clear master data. These are essential for efficient work. Manufacturing companies today also need to make data-driven decisions to remain competitive. Trustworthy data is of great importance in this regard. Ultimately, high data quality is critical to gaining a competitive edge.
How do you tackle poor data quality once you've identified it? What is its impact on spare parts management processes? We have compiled answers to these and other questions below.
The Challenge: Poor data quality in the material master
Ensuring efficient, reliable and collaborative processes is not always easy for maintenance managers. One of the biggest challenges they face is poor data quality in the material master. This can manifest in different ways, including:
- Incomplete information
Knowing the origin of poor data quality is crucial for comprehensive and continuous data cleansing. These causes must be eliminated sustainably through standardized processes and with the help of software solutions in order to ensure correct and complete master data.
Causes of poor data quality
Once poor data quality has been identified in the material master, the next step is to understand where the problem lies. Only then can it be addressed.
Below are some of the main causes of each of the above types of poor data:
- Duplicates: Often arise from multiple and international production sites, as well as human error.
- Discontinuations: Occur due to discontinued products remaining in the system or outdated information.
- Incorrect data records: Result from incorrect data input or insufficient validation processes.
- Incomplete information: Filling in all the information fields can be time-consuming, as maintenance staff often do not have all the necessary information at hand when creating products in the ERP system.
Other causes of poor quality in the material master are:
Outdated legacy systems
Many organizations have been using these systems for years and over time they become more difficult to maintain and update. As a result, data can become isolated, redundant and inaccurate.
Lack of integration between different systems
This can lead to data inconsistencies, especially when data is entered manually or transferred between systems, resulting in a significant amount of time that must ultimately be spent on data cleansing and consolidation. This reduces efficiency and increases the likelihood of errors.
Lack of data management and data accountability
These factors can also contribute to poor data quality in the material master. Without clear guidelines and responsibilities, it can be difficult to ensure consistency and accuracy. This can lead to data being entered inconsistently or in different formats, resulting in duplicate or inaccurate records.
Addressing these causes is critical to ensure accurate and reliable data for decision-making purposes.
Consequences of poor data quality
The presence of inaccurate, incomplete and outdated data leads to a lack of transparency and uninformed decision-making in companies. One of the main consequences of poor data in manufacturing companies is excess inventory levels. Inaccurate data can make it difficult for manufacturers to forecast demand and plan procurement. This results in either overstocking of certain items, which ties up capital and increases inventory costs, or understocking of necessary spare parts. The latter leads to costly delays in maintenance and repairs.
In addition, poor data quality can lead to high machine downtime resulting from incorrect maintenance planning and scheduling or a lack of spare parts. This leads to increased production costs and lower productivity. The result is high procurement costs, low ROI and high CO2 emissions.
How can you ensure high data quality in the material master?
The challenges of poor data quality in the material master can be effectively addressed through the use of software solutions. By automating material creation and identification processes, software can reduce the risk of human error and ensure standardization across different systems. They can also perform real-time data quality checks to identify and clean up duplicates, inaccuracies and redundancies. This, in turn, leads to clean, duplicate-free master data that can be relied upon for decision-making.
Data cleansing is only the beginning
Significant resources are invested in data cleansing to ensure accuracy, consistency and completeness of data. However, after initial data cleansing, duplicates and erroneous data can re-enter the system if the new material creation process does not include duplicate and quality checks. Data cleansing is therefore only the first step towards long-term high data quality.
Continuous data lifecycle management is the future
The second step is to keep the data up-to-date and correct throughout its lifecycle (data lifecycle management). Continuous data management can already be a real game changer for companies today. Through real-time monitoring and validation, DLM:
- Live duplicate and end-of-life testing for existing and new materials
- Live visibility of alternative suppliers for each match
- Continuously adding relevant new suppliers and manufacturers to the database
- Continuous updates on product discontinuations from original equipment manufacturers (obsolescence management)
So why settle for the tip of the iceberg when it's better to dive deeper?
Automation through software
Ready to dive in? With automation software, continuous data lifecycle management is already possible today. SPARETECH, the leading data platform for industrial spare parts, offers this solution. With the latest semantic data processing and Big Data technologies, duplicates and discontinuations can be reliably identified in the material master and data can be continuously cleansed. In addition, SPARETECH provides valuable insights based on reconciliation with the global spare parts database, which contains over 12 million products from more than 5,000 suppliers. Furthermore, with the integrated workflow for new material creation, 100% correct and complete original manufacturer data can be transferred into the ERP system. Together with the live duplicate check, this ensures long-term high data quality in the material master.
Manufacturing companies such as ElringKlinger and WEPA have already optimized their spare parts management processes by using SPARETECH software. Learn more about their approach in the following blog articles:
If you are struggling with poor data quality, don't hesitate to contact us. We will be happy to help and answer all your questions.