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Modern Manufacturing

Digital Transformation is Incomplete without Optimizing Your Data

John Klinke
Director, Oracle Industry Strategy Group

Guest Author: Vijay Natarajan, Specialist Leader, Deloitte Consulting LLP.

Manufacturing organizations often struggle with item and parts proliferation, product data quality, and data completeness. This leads to myriad business and operation pain points around limited visibility into spend data, ineffective supplier negotiations, subpar sourcing strategy and the inability to use enterprise data to make real-time product decisions. Organizations often end up inheriting data issues from legacy systems even after implementing digital transformation projects, resulting in the inability to achieve the maximum benefits and savings of embracing digital technology and moving to the cloud. As part of any transformation project, organizations should also focus on data enrichment in order to realize the sustained savings that will result from improved data quality and completeness.

Some of the complex questions organizations face during transformation projects tie directly to improving data quality and completeness: How do I fund new supply chain transformation avenues? How do I avoid inheriting legacy data issues resulting in business and operations inefficiencies during transformation projects? How can I monitor benefits, ensure sustained savings and enable on-going parts reuse and parts master governance?

In evaluating the best approach to improving data quality, organizations should look for solutions that have the following capabilities:

  1. Data Enrichment and Advanced Classification: Automated tools that use Optical Character Recognition (OCR), Natural Language Processing (NLP) and Machine Learning to enrich part attribute data from engineering drawings, PDF data sheets, and other unstructured data sources and automate classification using pre-defined libraries (e.g. UNSPSC).
  2. Cognitive Analytics to Reduce Part Complexity: Though machine learning models, use enriched part attributes merged with supplier, price, and volume data to identify parts and supplier rationalization opportunities.
  3. Expedited Data Transformation: Automate configuration and migration using predefined classifications and enriched data models during transformation in order to uncover part rationalization opportunities and promote long term reuse.

By leveraging the above capabilities as part of a transformation project, organizations can realize significant short-term cost reductions as well as long-term efficiencies around improved product data search and re-use, enriched data quality, easier industry standard classification, and better decision making around preferred parts and suppliers.

 

About Deloitte Consulting
By looking more deeply into your business, Deloitte Consulting LLP helps bring bold strategies to life in unexpected ways. Through disruption and innovation, our clients are able to transform from market followers to market leaders. For more information on how Deloitte can help you with improving data quality with product data analytics, visit Deloitte DesignSource.