Jul 24, 2024
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Service categories
Service Lines
Big Data
Domain focus
Banking & Financial Services
Healthcare
Retail and Restaurants
Programming language
R
Ruby
Frameworks
Hadoop
Subcategories
Big Data
Data Analytics
Data Mining
Data Quality
Data Visualization
Data Warehousing
Challenge
A Detroit based Fortune 500 Automotive components
manufacturing company had recently migrated from a
legacy AS400 ERP system to a cloud based ERP
system. With more than 1600 product lines catering to
over 100 customers, the business development team
struggled to muddle through the transition, leaving
millions of data sitting in the dark.
To build a robust check and balance mechanism to
shield the client from any impetuous contracts or
agreements, by taking into account the
aforementioned constraints, apart from volatility in the
steel and scrap market.
A Detroit based Fortune 500 Automotive components
manufacturing company had recently migrated from a
legacy AS400 ERP system to a cloud based ERP
system. With more than 1600 product lines catering to
over 100 customers, the business development team
struggled to muddle through the transition, leaving
millions of data sitting in the dark.
To build a robust check and balance mechanism to
shield the client from any impetuous contracts or
agreements, by taking into account the
aforementioned constraints, apart from volatility in the
steel and scrap market.
Solution
Drilling through the database
Vuelitics waded through data of numerous raw
materials type and purchased components supplied
from multiple sources.
Despite the cosmic nature of the scope involved,
Vuelitics incorporated a fair number of “what if”
scenarios and “where used” methodologies in building
the model to optimise the efficiency in the supply chain
mechanism.
Drilling through the database
Vuelitics waded through data of numerous raw
materials type and purchased components supplied
from multiple sources.
Despite the cosmic nature of the scope involved,
Vuelitics incorporated a fair number of “what if”
scenarios and “where used” methodologies in building
the model to optimise the efficiency in the supply chain
mechanism.
Results
Vuelitics’ BI Solution inferred that more
than one-third of the client’s product lines
were under quoted and one-fourth had
profit margin better than estimated. The
analytics identified that purchasing
agreements and customer contracts
were not robust enough for the client to
sustain steel market fluctuations. Besides
which, the poor surcharge mechanism
was also eating up the profit margin
quoted in the product cost.
Vuelitics’ BI Solution inferred that more
than one-third of the client’s product lines
were under quoted and one-fourth had
profit margin better than estimated. The
analytics identified that purchasing
agreements and customer contracts
were not robust enough for the client to
sustain steel market fluctuations. Besides
which, the poor surcharge mechanism
was also eating up the profit margin
quoted in the product cost.