Factory Data Hygiene: Definition, Importance, and Best Practice
What is factory data hygiene?
In the context of manufacturing industries, factory data hygiene refers to the practices and processes involved in ensuring the cleanliness, accuracy, and quality of factory operational data collected and used within ERP systems. It involves managing data throughout its lifecycle, including data collection, storage, analysis, and utilization.
There is so much operational data in the manufacturing process it is often overlooked. When we think of manufacturing, we often think about the tangible production of a product as the primary goal. While this is certainly true, many steps of the production process generate valuable data as a byproduct. Areas within manufacturing like procurement, operations, supply chain management and engineering all generate an abundance of consumable data waiting to be utilized. Implementing data science and analytics techniques with data hygiene can help companies in areas like quality, efficiency and cost reduction.
Most data science and analytics implementations follow the same road map, starting with identifying a problem that needs to be solved. For example, do you have issues with failing equipment on your line, and do you want to predict the failure before it happens? Is your process producing more scrap than you want? Do you need a real-time dashboard to track overall delivery status?
However, in this fast-paced ramp-up context, factory teams often operated outside of IT tools. Teams are working in silos in the parallel universe of Excel sheets to manage the complexity and volatility of their daily operations. As a result, ERPs were not updated according to the latest changes leading to poor data hygiene.
For example, inaccurate data in ERP included:
- Purchase Orders reception dates in the past
- Work Orders start dates in the past
- Fulfilled Work Orders with open sub Work Orders
- Lost components tied to a Work Order
- Wrong number of equipments in WIP
- Open work orders since 2+ years
Why is factory data hygiene important?
Factory data hygiene is an integral part of data management, and it is becoming increasingly important as manufacturing companies collect, store, and use more data than ever before. The main goal of data hygiene is to ensure that all relevant datasets are accurate and consistent across all systems and all teams. Without proper data hygiene processes in place, factory decision makers run the risk of making decisions based on inaccurate or outdated information, which can be costly and damaging to the production, delivery and customer satisfaction. A good data hygiene plan should enable factory teams to identify, analyze, and resolve any issues with their data quickly and effectively.
On the other hand, poor data hygiene caused the following challenges at factory-level:
Inaccurate operational decisions
Relying on inaccurate operational data can lead to flawed operational decision-making. Decisions made based on incorrect information can result in suboptimal outcomes, inefficiencies, and additional challenges that could have been avoided with accurate and reliable data. For example, this can lead to delays in product or service deliveries, negatively impacting customer satisfaction and business performance.
Downgraded performance & Loss of revenue
Poor data hygiene makes it challenging to provide accurate information on the status of orders and commitments to customers. Without a clear view of the factory's status, it becomes difficult to provide timely updates and meet customer expectations, leading to loss of revenue.
Furthermore, inaccurate data not only affects decision-making processes but also degrades the performance of other digital tools within the factory. When these tools are fed with unreliable data, their effectiveness and efficiency are compromised, limiting their potential benefits.
Loss of team productivity & tough cross-functional collaboration
When data hygiene is lacking, factory and supply chain teams often spend a significant amount of time collecting and cleaning data instead of focusing on problem-solving and collaborative efforts. This misalignment and inefficiency result in reduced productivity and hindered cross-functional collaboration.
What is the best practice for factory data hygiene?
The best practice to maintain a good factory data hygiene involves three stages: scope & set-up, data cleaning, and data hygiene maintenance.
Scope & set-up
The initial stage involves defining the scope of data to be cleaned and establishing a robust framework for data classification. Key steps in this stage include:
1. Identification of Data to be Cleaned
Assess the specific usage scenarios and identify the data elements that require cleaning. This may include purchase orders (POs), work orders (WOs), customer orders (COs), or other relevant data points.
2. Classification of Unclean Data
Define rules and criteria to classify unclean data into distinct categories or buckets. For example:
- POs: Identify delivery dates recorded in the past.
- COs: Identify missing dates or incomplete information.
- WOs: Identify fulfilled or closed WOs with open sub WOs (linked to parts) still pending.
3. Implementation of Data Hygiene Indicators
Establish data hygiene indicators or metrics to track the progress of data cleaning efforts. These indicators can provide insights into the cleanliness status of different data sets and help monitor overall improvement.
Data cleaning involves systematically addressing the identified unclean data. It can be divided into two phases:
Phase 1 - Cleaning 80% of the Data (Data Impacting Current Production):
- Assign a specific number of lines to clean per user (e.g., 200 lines).
- Involve multiple users in the cleaning process (e.g., 2 users).
- Allocate a defined amount of time for cleaning per user (e.g., 5 hours).
Phase 2 - Cleaning the Remaining 20%:
- Conduct weekly data cleaning sessions with users facilitated by a designated platform, such as Pelico.
- Utilize pre-set routines or standardized procedures to address the remaining unclean data efficiently.
Data Hygiene Maintenance
Once the initial data cleaning is complete, it is crucial to establish practices for ongoing data hygiene maintenance. This involves continuous monitoring, proactive recommendations, and timely alerts to ensure sustained data accuracy. The key aspects of data hygiene maintenance include:
1. Monitoring Data Accuracy Progress
Leverage analytics and visualizations to track the progress of data cleaning efforts. Establish clear metrics and indicators to monitor the accuracy and cleanliness of data over time.
2. Proactive Recommendations
Provide users with proactive recommendations for data cleaning. Leverage insights gained from data analysis to identify potential areas of improvement and guide users in addressing specific data issues.
3. Automatic Alerts
Implement automatic alert mechanisms that notify managers or responsible individuals when certain cleanliness indicators reach predefined limits. This ensures timely awareness of data quality issues and enables prompt corrective actions.
By following these best practices for factory data hygiene, organizations can establish a solid foundation for data-driven decision-making processes, enhance cross-functional collaboration, improve operational efficiency, and ensure accurate reporting. Regularly reviewing and refining data hygiene practices will contribute to sustained data quality and integrity within manufacturing environments.