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creactives success stories

Automotive Industry
Main Figures:
  • Over 4,6 B€/year
  • Over 800.000 PO
  • Over 200K active items master
  • 15 different languages
  • 6 Different ERP
Issues:
  • Different Coding criteria
  • Categories too generic
  • Languages barriers
  • Too many Free text orders
  • Lack of corporate policy compliance
  • Inventory proliferation
Goals:
  • Frame Contract Management
  • Global Categories identification
  • Free text orders reduction
TSV - Spend Analysis
  • RECURRENT SAVING >0,83 M€/year
  • Elapsed: 5 months
  • 10 days of effort from the client side
TAM - MMDM
  • 2,6M€ potential inventory revamping
  • Elapsed: 6 months
  • 30 FTE effort client side
  • Next step: smart material creation implementation & enrichment
Material Group Prompt
  • 30% quality improvement in Material group categorization
  • Elapsed: less than 1 month
  • Few days of integration effort client side (webservice)
Chemical Industry
Main Figures:
  • Approx 0,7 B€/year
  • Approx 300.000 invoice lines
  • Approx. 70K active items master
  • 6 different languages (including Chinese)
Issues:
  • Material groups in use not granular and with bad assignation quality
  • Different Coding criteria
  • Languages barriers
  • Lack of corporate policy compliance
  • Inventory proliferation
Goals:
  • Optimize procurement process
  • Identify savings
  • Ensure greater transparency
  • Avoid maverick-buying
  • Find double items in item master
  • Implement a new corporate taxonomy
TSV - Spend Analysis
  • Definition and implementation of a new global taxonomy within 3 months
  • ROI after only one tender
  • Few days of effort for Lenzing domain experts
TAM - MMDM
  • 10% Material Master data reduction
  • Elapsed less than 1 year
  • Effort Lenzing side approx 40 FTE
Material Group Prompt
  • 95% of certified correct suggestions
  • Few days of integration effort Lenzing side (webservice)
  • 2 weeks elapsed
Oil And Gas Industry
Main Figures:
  • Operations in 14 Countries
  • 1,5 b€ Indirect Spending
  • Over 100K item master
  • Over 6.000 Requisitioners
Issues:
  • No homogeneous procurement process
  • Lack in blanket contracts implementation
  • Lot of Maverick buying
Goals:
  • Implement a global P2P platform
  • Implement an user friendly e-procurement for inexpert requisitioners
  • Improve contract compliance
  • Put under control 100% of the procurement process
3SP– e Procurement
  • Over 60.000 orders created in the first year with less than 0,1% of process issues
  • Elapsed: 1 year
TAM - MMDM
  • Project for massive material master data cleansing starts in Q4 2016
Transport Industry
Main Figures:
  • Over 300Mio€ yearly indirect spend
  • 1 Mio€ Inventory value
  • Over 200K item master
  • 298 inhomogeneous commodity groups
Issues:
  • Categories too generic
  • Lack in train reference
  • Data quality issue
Goals:
  • Define a corporate taxonomy
  • Implement UNI-EN15380-2
  • Exploit existing information
  • Detect missing information
  • Find duplicates
  • Avoid new duplicates
  • Support Search (reuse)
TSV – Spend Analysis
  • RFQ time reduction over 70%
TAM - MMDM
  • Double items found in the first semester: 1 M€ of inventory value
Public Healthcare Service
Main Figures:
  • Third more populated region in Italy (5.7 million residents)
  • 365K item master
  • >100Billions€/year
Issues:
  • Every hospital has a separated ERP
  • Materials are codified differently
  • Materials doesnt match with Master data Medical device/drug codes
  • Data quality issue
Goals:
  • Part number reduction
  • Re-use of equivalent items
  • Supplier codes reduction
  • Cost Reduction (economies of scale)
  • Special contracts for high rotation products
TSV – Spend Analysis
  • Automated continuous reporting on medical devices and drugs detecting internal best practices
TAM - MMDM
  • Semantic match between:
    • Master data and Ministry of health Medical device code
    • Master data and Ministry of health Drug code
Financial Industry
Main Figures:
  • Italy’s largest bank
  • 11 Countries
  • >6 B€/year
  • >65K suppliers
Issues:
  • Segregated data
  • Duplicated suppliers
  • Lack of transparency
  • Classification not granular
Goals:
  • Centralized spend analysis of UBIS, UCB (Italia), BACA (Austria), HVB (Germany), CEE
  • Supplier normalization
  • Procurement performance tracking
TSV – Spend Analysis
  • Definition and implementation of a new global taxonomy within 1 year (3 waves: Italy – Germany/Austria – CEE)
  • Few days of effort for Unicredit domain experts
  • Effective Supplier Normalization (6% supplier reduction) with DUNS enrichment
Web Cockpit
  • Demand planning
  • Centralized forecast, ongoing e planned analysis
Procurement Information Tool
  • Data Quality
  • Cost avoidance
  • KPI tracking
  • Grey negotiations tracking