Xeeva has developed spend analysis that not only takes disparate data and normalizes, categorizes and standardizes it – but also enriches that data at a commodity, supplier and item level allowing the organization to develop a multi-dimensional view of all indirect spend data regardless of where it sits in the business. The end result is a complete 360 degree view of all spend activity to help increase spend under management, identify new sourcing opportunities, better control maverick spend, rationalize your supply base and exert greater control over areas such as diversity objectives and contract compliance.

We start with extracting data from multiple source systems which is then merged into a master data file. This master file is now the basis from which the normalization process begins. Supplier names which usually exist in multiple forms and formats, e.g., IBM, International Business Machines, IBM Inc., are matched, exceptions can be reviewed and approved manually and then the consolidated data is enriched with additional 3rd party content including for example, SIC codes, credit information and D&B detail to name a few.

Proprietary algorithms drive category classification that results in an extensive category map which along with our pre-populated supplier universe is used to assign primary and secondary categories to the suppliers. Leveraging this combination of supplier and commodity data leads to a rich sourcing taxonomy – grounded in UNSPSC classifications but still with the flexibility to support customized schema as required.

With clean data in hand, spend analysis is simplified and any dimension of the data can be aggregated, drilled down into and reported in combination with other data types. There are multiple pre-packaged and role based reports along with complete configurability to drive your own customized views. But beyond just multiple views of the data, the spend analytics tool allows for deeper intelligence around suppliers such as diversity spending, contract compliance and supplier risk.

Spend analysis also allows for item level analytics, including pricing vs. benchmarks, price variance by UNSPC and performance price indices.

xeeva spend analytics

Rapidly normalize, classify and operate around your organization’s disparate and fragmented indirect spend data.

www.xeeva.com

IdentifySpend Data

Normalize Spend Data Standardize

Spend Data

CategorizeSpend Data

Indirect spend data often lies in disparate systems across the enterprise whether it’s multiple ERPs accumulated through various acquisitions or discrete transactional systems supporting a single plant or geography. Combine that with a lack of data standardization, ad hoc commodity taxonomies and a decentralized procurement organization and the task of trying to understand the granular details of your company’s indirect spend is daunting if not impossible.

Xeeva has developed spend analysis that not only takes disparate data and normalizes, categorizes and standardizes it – but also enriches that data at a commodity, supplier and item level allowing the organization to develop a multi-dimensional view of all indirect spend data regardless of where it sits in the business. The end result is a complete 360 degree view of all spend activity that allows you to increase spend under management, ID new sourcing opportunities, better control maverick spend, rationalize your supply base and have greater control over areas such as diversity objectives and contract compliance.

We start with extracting data from multiple source systems which is then merged into a master data file. This master file is now the basis from which the normalization process begins. Suppliers names which usually exist in multiple forms and formats, e.g., IBM, International Business Machines, IBM Inc., are matched, exceptions can be reviewed and approved manually and then the consolidated data is enriched with additional 3rd party content including for example, SIC codes, credit information and D&B detail to name a few.

Proprietary algorithms drive category classification that results in an extensive category map used to assign primary and secondary categories to the suppliers. Leveraging this combination of supplier and commodity data leads to a rich sourcing taxonomy – grounded in UNSPSC classifications but still with the flexibility to support customized schema as required.

With clean data in hand, spend analysis is simplified and any dimension of the data can be aggregated, drilled down into and reported in combination with other data types. There are multiple pre-packaged and role based reports along with complete configurability to drive your own customized views. But beyond just multiple views of the data, the spend analytics tool allows for deeper intelligence around suppliers such as diversity spending, contract compliance and supplier risk.

Spend analysis also allows for item level analytics, including pricing vs. benchmarks price variance by UNSPC and performance price indices.

xeeva spend analytics

Rapidly normalize, classify and operate around your organization’s disparate and fragmented indirect spend data.

www.xeeva.com

IdentifySpend Data

Normalize Spend Data Standardize

Spend Data

CategorizeSpend Data

Indirect spend data often lies in disparate systems across the enterprise whether it’s multiple ERPs accumulated through various acquisitions or discrete transactional systems supporting a single plant, or geography. Combine that with a lack of data standardization, ad hoc commodity taxonomies and a decentralized procurement organization and the task of trying to understand the granular details of your company’s indirect spend is daunting if not impossible.

Imperfect Data Management

While you can’t create “perfect data,” certainly you can manage it more effectively. Xeeva’s intelligent [imperfect] data technology is built to address big data in several ways. For example, the algorithms we deploy to look across catalogs of various commodity types, vendors and attributes (metadata) allows us to automate the categorization of multiple catalogs. We then apply an intelligent part numbering schema that assigns new item numbers to each commodity while still maintaining a reference to the old number for search-ability and consistency. This approach speeds the creation of better, cleaner global and local catalogs versus simply taking all your imperfect data and transferring it from one system to another, thus perpetuating the problem. The result – your data is cleaner and clearer from the start. Once your new catalog is deployed, requests for any new part or commodity are constrained by an intelligent request engine, rigorous in its enforcement of the business rules you create but transparent to the end user submitting the request. Buyers can then validate if the commodity is unique and needs to be added to the catalog.

This same approach applies to adding new vendors, launching RFPs and providing analytics on the back-end of the system – analytics that help drive additional results by identifying new areas for cost savings, strategic sourcing efforts and vendor consolidation.

xeeva spend analytics

www.xeeva.com

Results

360 degree view of spend. Full view of spend by category, supplier, region, plant – any dimension.

Standardized commodity classi�cations. Whether UNSPSC or your own customer taxonomy, data is consistent and standardized.

Rapid opportunity identi�cation. Immediate opportunities are identi�ed through the normalization and identi�cation of like or same purchases.

Reduction of procurement operational costs. Analysis leads to multiple productivity improvements, e.g., tail consolidation, reduction in number of POs and reduce time spent on manual analytics.

Strengthen compliance. Ensure a greater level of control and compliance over existing vendor contracts and purchasing arrangements.

Imperfect data management technology

Self-learning supplier enrichment

Intelligent, configurable role-based views

Embedded best practice methods and templates

Price comparison to benchmark for a market basket by category

Embedded category and sourcing market intelligence

Opportunity identification engine

Embedded Intelligence

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