data-cleaning-importance-manufacturing-industry

Data quality, or rather the lack of it, is one of the common challenge that is faced by companies in the business environment of today. Data cleansing, which involves the detection and correction of corrupted/inaccurate data records from an organization’s database, in order to ensure that the data stays clean and actionable, is essential for manufacturing/operational industry.  It is particularly so, as asset management and clean material master data is quite a task in the manufacturing industry. The reason for this challenging feat is the fact that, the companies in this sector, generally have multiple sites, across geographic regions with data entries being made by multiple employees. Coupling it with the issue that there is minimal communication or a lack of presence of standard guidelines, this quite often leads to the generation of unreliable and inconsistent data.

Hence, in this two-part blog, we will take a look at the importance of data cleaning the manufacturing industry, vis-a-vis the issues that arise with the Low-quality master material data, which are as follows:

  1. Lack of Inventory Visibility: ERP systems, warehouse management systems and third-party service (logistics) providers, generally manage parts of finished good inventories. Fragmentation of data in such a system landscape, leads to a lack of inventory visibility, which in turn, leads to over-purchasing, inventory write-offs, stock-outs and disruption in manufacturing operations. Corrupt/bad material data hence, becomes the main cause of cash lock-up (in excess inventory), lack of inventory visibility across plants, decrease in employee productivity etc.
  2. Decrease in Plant/Equipment Availability: Poor descriptions of material items (especially MRO) is often singularly responsible for incorrect and untimely part orders. The inefficient procurement of critical supplies ends up increasing the cost of equipment maintenance, which results in decreased plant and equipment availability frequently.
  3. Duplicate Items: Duplication of data is one of the greatest inefficiencies that any dataset suffers from (it accounts for almost 20% of the item master). The existence of duplicate items leads to larger problems such as wastage of space, updating of multiple occurrences required when a field value changes and the resultant inconsistent data when the requisite changes are not made. Duplicate items, hence, stifles a businesses’ agility as it leads to wasted costs, a decrease in user adoption, inaccurate reportage, poor business processes etc.
  4. False Stock-outs: As a result of low-quality data, instances of false stock-out arise, wherein the product might be available in your warehouse but the system flags it as being unavailable. Apart from the unhappy/lost customers scenario, you also lose potential revenue, driving up cost due to paying for expensive expedited deliveries, overstocking as a remedial measure etc.
  5. Excess Inventory: Due to data redundancy, excess inventory accretion is a very real problem within the manufacturing industry. This leads to multifaceted long-lasting problems, like an increase in costs due to additional warehousing requirements (or operational difficulties), having to sell at discounted rates by destroying or dumping when the error is discovered etc. A clean collection of master data would help you avoid the situation by planning the demand, immediate identification of the surplus and even automated replenishment.

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