Huron_Spend_Analysis_PitfallsPainPromise_vFinal
1 like480 views
Jens Brown discusses the pitfalls, pain, and promise of spend analytics. He highlights how one institution used Spend Radar to analyze spend data from multiple systems, including two ERP systems, expense management, credit cards, and eProcurement. This resulted in insights into spending. Key challenges include data being in different systems and variability in supplier names. Spend analytics tools can help address these issues by standardizing data, classifying spending, and providing reports and dashboards. When implemented properly, spend analytics has the promise of strategic sourcing insights, compliance management, and cost reductions.
Huron_Spend_Analysis_PitfallsPainPromise_vFinal
- 2. Jens Brown Director, Huron Education Spend Analytics – Pitfalls, Pain and Promise
- 3. Spend Analytics Pitfalls, Pain and Promise • Contemplating spend analysis as a next step in your procure to pay journey? • Where does unstructured data from credit card and travel come into play? • Juggling multiple ERP systems or a big financial system upgrade? • All of these real world challenges will be discussed. We will highlight key concepts, data standardization practices, and how one institution found solutions with Spend Radar; combining data from two ERP systems, a travel and expense management tool, credit cards and eProcurement that resulted in actionable insights into their spend.
- 4. Spend Analytics KEY CONCEPTS What is spend analytics? • Process of aggregating, classifying, and leveraging spend data for the purpose of reducing costs, improving operational performance and ensuring compliance. The importance of spend analytics • Spend data insight is critical for identifying savings opportunities and developing sourcing and planning strategies. • Drives purchasing compliance with negotiated contracts and identifies missed opportunities. • Pressures to extend the value of existing sourcing, procurement and ERP technology investments.
- 6. Spend Analytics Pitfalls Getting started… • Where is our data? • What data is important? • Do we have a commodity standard? • How are we getting the data? • What is our process for analysis and standardization? • How are purchasing and sourcing staff going to interact with the data? • What the process to do it again?
- 7. Streamline potential with a spend analytics tool Spend Analytics Pitfalls • Validate spend totals of each data set with client as a gut check to ensure all spend data has been captured Consolidate Available Data Normalize Vendor Names Spend Analysis & Associated Output Categorize Clean Vendor Names • One of the most common obstacles to successful vendor level spend analysis is the redundancy / variability of supplier names • Goal: Categorize 80%-90% of total spend • Focus on high-spend and high-volume vendors • Highlight supplier consolidation potential • Additional analysis on transactional data may be required for select categories Step 1 Two Weeks + Step 2 ≈5-10 hours Step 3 ≈10-20 hours Step 4 ≈10-20 hours Employing a spend analytics tool to normalize and categorize data could potentially reduce the Sourcing Team’s effort on spend analyses by 50%-75%. AP PCardOther
- 8. Spend Analysis Pain Growing pains • Our data is spread across our legacy ERP, new ERP and eProcurement solution • Credit Card for purchases and travel is somewhere else • We care about Employee reimbursements, but its buried in AP voucher data • We have goods under control, but service and travel need attention • How do my purchasing staff start using the tool more broadly?
- 9. Spend Analytics Promise Keep focused • Data is automated and comprehensive • Unified category structure for all spend • Mapping of unstructured data covers card, travel and reimbursements • Spend data, supplier diversity, Key Performance Indicators and contract compliance are available • Non-traditional areas are next – supplier enrichment, savings and benefit reporting, KPI, benchmarking and year-to-year comparisons
- 10. ERP GL, PO/Invoice, T&E, Vendor Master eProcurement POs, Product Catalog, Contracts External Systems P-Cards, DATA SOURCES Analyze / Report Extract spend data from internal & external sources Validate data to ensure accuracy & completeness Cleanse data to eliminate errors & discrepancies Classify data with standard schema Enhance/Enrich data with related business information Analyze spend data with advanced analytics and reporting Validate Cleanse Classify Enhance / Enrich Extract IMPLEMENTATION Strategic Sourcing Spend Volume Aggregation Supplier Rationalization Compliance Management OUTPUTS Spend Analytics Framework
- 11. Implementing Spend Radar Adopt • People • Training • Monitoring Validate • Data • Reports • Dashboard Build • Data specs • Mapping logic • Confirm rules • Review UI • Test files Design • Data Scope • Extract Process • Mapping • Normalize • Logic & Rules • Dashboard Experience Plan • People • Process • Data • KPIs • Define Spend • Taxonomy 1 Month 3 - 4 Months Ongoing
- 12. Mapping & Translation OLD DATA GL Details Invoice Details Spend Radar Extract Process Validate Cleanse Classify Apply Logic & Rules Analyze / Report
- 13. Mapping & Translation NEW DATA GL Details Invoice Details GL Details OLD DATA Invoice Details Spend Radar Extract Process Validate Cleanse Classify Apply Logic & Rules Analyze / Report PO Details Credit Card Details Contracts
- 17. Our Spend Radar experiences in Higher Ed
- 18. Huron provides services to a wide variety of organizations, including leading academic institutions, healthcare organizations, Fortune 500 companies, medium-sized businesses, and law firms. About Huron Consulting Group Huron Consulting Group History • Formed in March, 2002 by experienced financial and operational consultants, including CEO and President Jim Roth • Over 2,800 consultants company- wide • Approximately 50% of resources in Health & Education consulting • Over 11,000 engagements at over 4,100 higher education, government, and commercial clients Value Proposition • Focused and integrated financial and operational consulting • Implementation and service provider • Over 50+ SciQuest implementations • Independence and objectivity
- 19. Questions?