We continue today with Part 2A of our case study of how Enel, Europe’s second largest utility company with annual revenue exceeding 64 billion €, made its case for an investment in Spend Analysis. Part One is found here. In June, we laid the foundation for this case study by introducing you to Mario Mosca, Enel’s Head of Procurement, Processes and Systems based in Rome (and a procurement executive who is rising in 2010). Those two articles discussed Enel’s approach to process automation and the technology infrastructure that supports its procurement operations and can be found here and here. Once again, we thank Mario (“amico del sito”) for his time, effort, and interest in allowing us to share Enel’s experiences in such a first-hand way.
As we closed part one of this Spend Analysis case study, Mario had successfully made the case. Now, with the support of his IT organization, Mario’s team developed a project strategy with specific goals and developed a series of system or solution requirements.
Project Goals
The project team had expressed the need to adopt a “Spend Analysis” solution in order to gain visibility and a new depth of detail for all items purchased and to improve the understanding of its supply base. The team also laid out several specific objectives:
- Increase savings by identifying categories for cost reduction projects;
- Support strategic sourcing initiatives focused on supplier rationalization
- Improving overall data quality
- process optimization rationalization of master data (analysis codes and identifies duplicate master);
- Empower and accelerate the plan to centralize procurement operations
Project (Solution) Requirements
The project team defined a series of solution requirements to help it identify the best solution in the market for its specific needs. Selecting a fully-automated solution that could analyze Enel’s spend with minimal assistance from Enel procurement or IT resources was a key focus for the team. Enel’s key requirements included (the list below is not exhaustive):
- Initial spend classification should be at least 85%
- Able to automatically classify of “free text” items
- Able to enrich master data by identifying their attributes
- Will identify and propose new commodity classification structures and/or the standardization of those currently in use
- No impact to the ERP systems that held the raw spend data
- Available as Software as a Service (SaaS) or “On-demand”
- Able to work with multiple languages;
- Has strong analysis and reporting capabilities
Solution Selection
Once the project team was in place and solution requirements finalized, Mario and his team canvassed the marketplace to identify the top providers of spend analysis solutions and conducted a Proof of Concept with several of those providers.
Ultimately they selected the Italian provider and relative upstart Creactive Consulting which is a regional management consultancy that has grown at a steady clip this past decade and is now, a small player in the Spend Analysis space with its new “Total Spending Visibility” tool.
According to Mario, “Their performance in the Proof of Concept which initially classified 86% of the sample spend and ended with 96% classified was impressive. The higher percentage initially classified means that there would be less effort [needed by Enel staff] during the early phases of setting up the system. We also felt that Creactive’s classification engine was the right design for our specific. We also felt that Creactive’s classification engine which takes a semantic approach to classification was very important for several of our large business units whose major parts [or supplies] are not part of the material master data but coded using free text spending descriptions.”
Thanks Mario!
Postscript I: Back to our normal schedule next week including a “very special” announcement and Part 2B of this case study!
Postscript II: Happy Birthday B!
