• Conduct regular data audits to assess the current stock levels of warehouse materials.
• Identify slow moving or obsolete items during these audits.
• Identify Same Materials stocked in different places with different Material code and descriptions.
• Identify Materials stocked and not used for more than 5 years.
• Consider selling or donating items that are no longer needed.
• Immediately Scrap, if any material no longer be used.

• Improve your demand forecasting processes to better predict the actual need for raw materials, consumable materials and spare parts.
• Use historical data, market trends, and production schedules to refine and redefine forecasts.

• Maintain open relationship with suppliers to adjust orders based on actual production requirements.

• Adopt a just-in-time inventory system for materials to minimize excess stock.

• Constitute minimum and maximum inventory levels for critical parts.
• Implement automated systems to trigger reorders when stock levels reach minimum predetermined thresholds and investigate if it reaches maximum.

• Explore the use of Vendor Managed Inventory where suppliers/vendors monitor and manage predefined stock levels on your behalf.
• This will improve efficiency and reduce the risk of overstocking.

• Implement a centralized inventory management system to review, track and control stock levels.
• Utilize technology to automate inventory tracking and ordering processes in the best possible way.

• Establish a clear returns and exchanges policy for all the direct and Indirect material.
• So that excess stock can be managed by returning of unused items.

• Regular check and audit by MRO experts on the overall data is must.
• Regular Meeting to be conducted and feedback from different department involved in the inventory management process should be discussed and changes must be made if needed.

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could result in increased cost, in addition to having a negative impact on efficiency and production quality. In general, data that is inaccurate, non-conforming or missing can give rise to undesirable consequences such as:

• Causing harm to the production
• Unexpected and unwanted machine downtime
• Inability to find the equivalent cross-reference/replacement
• Damaged reputation and decreased revenue

The Why and Wherefore of Data Inaccuracy

While the likelihood of occurrence of error cannot be ruled out, care can be taken to ensure that it does not permeate into the system, causing further damage. The sources of errors can be an ambiguity in measurement, maintenance lapse or simply, an entry being mistyped.

Data inaccuracy can occur due to any or all the following:

• Missing Data: Fields that should contain core data are empty.
• Wrong or inaccurate data: Information has not been entered correctly or maintained.
• Inappropriate data: Data that’s been entered in the wrong field.
• Non-conforming data: Data that hasn’t been normalized as per the guidelines.
• Duplicate data: A single item occupies more than one record in the database.
• Poor data entry: Misspells, typos, transpositions, and variations in spelling, naming or formatting.

Quality over Quantity

Experience tells us that even a minor error in the input can get magnified and lead to huge output errors. involves gathering and merging data from different sources to provide information that is valuable, trusted and relevant. It is not recommended to gather ‘as much information as possible’ unless it can be verified for authenticity and whether it comes from trusted sources. It is important to ensure data quality rather than quantity so that further application of the data will also be flawless.

Data enrichment helps to uncover existing challenges and to offer structured, consistent and accurate data. The key to discovering meaningful results and useful information pertaining to an item is to research the right information. A unified approach to data enrichment can guarantee precision and validity of data. Specific standards and practices for data enrichment ensure the accuracy of the data being enriched.

Data Quality Assurance

Data Quality assurance comprises of identifying data inconsistencies and making the necessary correction. Data quality services enable the customer to maintain the quality of their data and to ensure the data integrity. The efficiency of the product is directly dependent on the quality of data.

Data Quality Management makes data more reliable, accessible and reusable by improving completeness, accuracy, conformity and consistency of data.

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