Spend Analysis The Window into Strategic Sourcing

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Format: Hardcover
Pub. Date: 2008-02-01
Publisher(s): J. Ross Publishing
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Summary

A growing number of senior executives are coming to realize that strategic supply management can be a significant driver of improved corporate performance and better risk management. Spend Analysis is the most critical component, and the starting point, of strategic supply management and it essential to achieving world- class performance. If you can't analyze your spend data, many opportunities for improvement beyond the low hanging fruit are without doubt being missed. One of the foundation blocks for transforming an organization's practices to world-class supply management is the business process called strategic sourcing. And one of the requirements for enabling world-class strategic sourcing results is rigorous spend analysis.

Author Biography

Kirit Pandit is Vice President of Content Strategy and Head of Product Management for the Spend Analysis and Compliance Solution of Emptoris, Inc. He has over a dozen years experience working to design and elicit value from procurement processes to enable better spend visibility and savings opportunity analysis. Kirit joined Emptoris in early 2005 when Emptoris acquired Intigma where he was CEO and co-founder. Kirit founded Intigma in 2000 with a single vision ù to automate the conversion of toxic ERP content sources into valuable repositories of structured information that enable all decision support systems including Supplier Relationship Management (SRM). In 2004, Intigma was providing Spend Data Management services to 4 of the top 6 Spend Analysis providers. In addition to holding the CEO office, Kirit played important roles in product management, key customer and channel relationships, and governance and financing efforts. Prior to starting Intigma, Kirit spent four years in Deloitte Consulting as a Manager, where he consulted with F-500 companies and implemented Supply Chain Optimization and eProcurement solutions. Prior to Deloitte, Kirit spend 6 years in National Instruments as a Dr. Design Engineer, where he developed cutting edge products for the Test and Measurement industry. He is credited with designing the smallest data acquisition PC card for the T&M industry in 1995. Kirit has published numerous papers in scientific and business journals and is a sought-after speaker at investor and industry forums. In 2006, he was given the 'Pro to Know' award by the Supply and Demand Chain Magazine. Kirit holds a B.S. in Electrical Engineering from the Indian Institute of Technology, Bombay, an M.S in Electrical Engineering, as well as M.B.A. from the University of Texas, Austin. Dr. Haralambos Marmanis is Director of Research, and chief architect of Emptoris' Spend Analysis and Compliance Applications. He has worked on the design and implementation of: network protocols; cardiovascular flow simulations; Micro-Electro Mechanical Systems (MEMS); trading applications; electronic marketplaces; supply management, and spend analysis. For the last 7 years, Dr. Marmanis' work is related to the application of machine learning and data mining techniques in the analysis of transactions for the purpose of identifying spending risks, savings opportunities, vendor consolidation, state and federal compliance, and contract optimization. Emptoris was, in 2004, the recipient of the INFORMS Franz Edelman award; Dr. Marmanis received also the Sigma Xi Outstanding Research Award, and his work was on the cover page of ôVisualization '99ö and the Centennial volume of IEEE. His work has been supported by the National Science Foundation. He holds a Ph.D. degree in Applied Mathematics from Brown University, a M.Sc. degree in Theoretical and Applied Mechanics from the University of Illinois at Urbana-Champaign, a B.Sc. and a M.Sc. degree in Civil Engineering from the Aristotle University of Thessalonica. Dr. Marmanis is the author of numerous publications in peer reviewed international scientific journals, conferences, as well as technical periodicals.

Table of Contents

Forewordp. xiii
Prefacep. xv
Acknowledgmentsp. xvii
About the Authorsp. xix
Web Added Valuep. xxi
Fundamentals of Spend Analysisp. 3
What Is Spend Analysis?p. 5
Basicsp. 5
History of Spend Analysisp. 10
Specialist Vendorsp. 11
Consolidationp. 12
Similarities/Differences between Spend Analysis and Data Warehousingp. 12
Characteristics of an Ideal Spend Analysis Applicationp. 15
Data Definition and Loading (DDL)p. 15
Data Cleansing, Structure, and Enrichment (DE)p. 17
Spend Data Analytics (SA)p. 19
Knowledgebase Management (KB)p. 20
Summaryp. 22
Referencesp. 22
Business Imperative and Return on Investmentp. 23
Spend Leakage and Compliance Multiplierp. 23
Importance of Spend Analysis within Supply Managementp. 25
Benefits to Businessesp. 26
Estimation of Spend Analysis Return on Investmentp. 27
Savings Opportunityp. 30
Highly Decentralized Purchasingp. 30
Overview and Objectivesp. 30
Methodologyp. 31
Global Procurement Challengesp. 31
Methodologyp. 34
Resultsp. 35
Merger Planning and Integrationp. 39
Overviewp. 39
Situationp. 40
Methodologyp. 40
Conclusionp. 43
Summaryp. 44
Referencesp. 45
How to Implement a Successful Spend Analysis Programp. 47
Set Objectivesp. 48
Lead from the Topp. 48
Establish a Focused Center of Excellencep. 49
Carefully Evaluate Choicesp. 50
Commodity Classificationp. 50
Data Enrichmentp. 50
Take a Limited-Scope, Phased-Rollout Approachp. 50
Requirements Scopingp. 50
Multicube Approachp. 51
Data Scopingp. 52
Refresh Requirementp. 52
Know Your Datap. 53
Technology Should Support Business, Not Drive Businessp. 53
Align the Team to Support the Organizationp. 54
Increase Organizational Visibilityp. 54
Measure Constantly, Report Frequentlyp. 54
Engage with Your Application Providerp. 55
Sample Project Planp. 55
Procurement Transformation for an $8 Billion Agrochemical Businessp. 55
Overviewp. 55
Backgroundp. 57
Implementation and Key Learningp. 57
Spend Analysis Program Successp. 58
Sourcing Program Successp. 58
The Future-Managing Supplier Relationshipsp. 59
A Fortune 500 Provider in the Healthcare Industryp. 59
Overviewp. 59
Implementationp. 60
Challengesp. 60
Results after One Yearp. 61
A Company with Global Procurement Locationsp. 61
Overviewp. 61
Implementationp. 62
User Adoptionp. 62
Next Stepsp. 62
Summaryp. 62
Referencesp. 63
Opportunity Identificationp. 65
Spend-Level Opportunitiesp. 67
Supplier Rationalizationp. 67
Demand Aggregation (or Disaggregation)p. 67
Bypass of the Preferred Purchasing Processp. 69
Non-PO Spendp. 69
Off-Contract Spendp. 69
Diversity Spend Compliancep. 70
Supplier Performancep. 70
Spend with Poorly Performing Suppliersp. 71
Approved versus Nonapproved Spendp. 71
Spend by Payment Typep. 72
Transaction-Level Opportunitiesp. 72
Contractual Term Opportunitiesp. 72
Unrealized Discounts and Rebatesp. 73
Quantity Violationsp. 74
Delivery Date Violationsp. 76
Payment Term Opportunitiesp. 76
Invoice Processing Opportunitiesp. 77
Consolidated Paymentsp. 77
Frequent Charges and Creditsp. 77
PO Approval Limitsp. 78
Prioritizing Opportunitiesp. 79
Conduct Buyer Interviewsp. 79
Create a Segmentation Frameworkp. 80
Segment the Categoriesp. 80
Assign Category to Implementation Wavesp. 80
Summaryp. 81
The Anatomy of Spend Transactionsp. 85
Types of Spendp. 85
Directp. 85
Indirectp. 86
MROp. 87
Procurement Processesp. 88
Procure-to-Pay (P2P)p. 88
Purchasing Cards (P-Cards)p. 88
Travel and Entertainment (T&E)p. 88
Information and Approval Flow in P2P Processesp. 89
Data Requirements for P-Cardsp. 93
Data Requirements for T&Ep. 93
Source Systems and Data Extractsp. 94
Accounts Payablep. 94
Purchasing/e-Procurementp. 94
Receivingp. 95
Materials Managementp. 95
Freight Transactionsp. 95
Corporate P-Cardsp. 96
Travel and Entertainmentp. 96
Contract Managementp. 96
Material Requirements Planning or Manufacturing Resource Planning Systemsp. 96
Contract Manufacturer Datap. 96
Other (External Sources)p. 96
Quality of Datap. 97
Item Descriptionp. 98
Vendor Namep. 99
Cost Center and GL Codep. 99
Summaryp. 100
Spend Analysis Componentsp. 101
Introductionp. 101
Data Definition and Loading (DDL)p. 103
Data Cleansingp. 105
DDL Considerationsp. 107
Data Assessmentp. 108
Data Enrichment (DE)p. 110
Overviewp. 110
Classificationp. 115
Clusteringp. 116
Business Rules and Manual Editingp. 116
Dimensional Enrichmentp. 116
Classificationp. 119
Clusteringp. 120
Transactional Enrichmentp. 120
Business Rulesp. 120
Transactional Classificationp. 123
Knowledge Acquisition and Managementp. 125
Conceptsp. 126
Attributesp. 127
Instancesp. 127
Knowledgebase Essential Functionalityp. 127
Browsingp. 127
Editingp. 128
Knowledge Assimilationp. 128
Spend Reporting and Analyticsp. 129
Business Intelligence Key Capabilitiesp. 131
Reportingp. 131
Analyticsp. 131
Scorecardingp. 132
Dashboardsp. 132
Business Event Managementp. 132
Basic OLAP Reportsp. 133
Pivot Tablesp. 133
Cross-tabular Reportsp. 133
Graphical Reportsp. 135
Specialized Chartsp. 141
Waterfall Chartp. 141
Pareto Chartp. 142
Treemapp. 142
Multidimensional Reportp. 144
Map Reportp. 144
Ad Hoc Analyticsp. 144
What-If Analysisp. 147
Dashboardsp. 148
Summaryp. 148
Referencesp. 150
Taxonomy Considerationsp. 151
Ontologies and Taxonomiesp. 151
Popular Industry Standard Commodity Taxonomiesp. 153
UNSPSCp. 154
Designp. 154
Advantages of UNSPSCp. 155
Disadvantages of UNSPSCp. 156
eOTDp. 157
Designp. 157
Advantages of eOTDp. 165
Disadvantages of eOTDp. 166
eCl@ssp. 166
Designp. 166
Advantages of eClassp. 168
Disadvantages of eClassp. 169
RUSp. 170
Designp. 171
Advantages of RUSp. 172
Disadvantages of RUSp. 173
Quantitative Comparison of Taxonomiesp. 173
Size and Growthp. 174
Hierarchical Order and Balance of Contentp. 175
Quality of Property Librariesp. 175
Conclusion and Summaryp. 177
Referencesp. 178
Technology Considerationsp. 179
Technical Considerations in the SA Modulep. 179
Online Analytic Processing (OLAP)p. 179
What Is Your OLAP Schema?p. 180
What Do Your Dimensions Look Like?p. 181
Precooked Data or On-the-Fly Calculations?p. 182
Continental or International Deployment?p. 182
Pivoting or Multidimensional Navigation?p. 183
Technical Considerations in the DDL Modulep. 184
Representative Samplingp. 185
Missing Valuesp. 185
Balanced Dimensional Hierarchiesp. 187
Detection of Systematic Errorsp. 189
Technical Considerations in the DE Modulep. 190
String Matchingp. 190
Classificationp. 191
Statistical Algorithmsp. 193
Distance-Based Algorithmsp. 194
Decision Tree-Based Algorithmsp. 194
Neural Networks-Based Algorithmsp. 195
Rule-Based Algorithmsp. 196
Clusteringp. 196
Classifier Evaluationp. 198
ROC Graph Detailsp. 200
Association Rulesp. 201
Summaryp. 203
Referencesp. 203
Tracking and Monitoringp. 209
Introductionp. 209
Spend Under Managementp. 212
Integrated Supply Managementp. 212
External Integration Using Web Servicesp. 214
Using Spend Analysis for Measuring Benefitsp. 214
Summaryp. 217
Spend Analysis and Compliancep. 219
Introductionp. 219
Control Violations in the Procurement Processp. 220
Supplier Assessmentp. 220
Supplier Selectionp. 221
Contractp. 221
Procurement or Requisitionp. 222
Fulfillmentp. 222
Invoicingp. 222
Paymentp. 222
Implementing Controls in Procurementp. 222
Commonly Implemented Controlsp. 222
Payment Controls via 3-Way Matchp. 224
Authorizationsp. 224
P-Card Controlp. 224
Travel and Entertainmentp. 224
Spend Leakagep. 225
Contract Compliancep. 225
Vendor Compliancep. 226
Fraudp. 226
Case Studyp. 227
The Future of Procurement Compliancep. 228
Measurementp. 228
Preventionp. 228
Summaryp. 229
Referencesp. 229
The Future of Spend Analysisp. 231
Supplier Discoveryp. 232
Spend Forecastingp. 232
What-If Spend Analysisp. 232
Microanalysisp. 233
New Product Target Cost Analysisp. 233
Real-Time Classificationp. 233
Relevance Searchp. 234
Price Benchmarkingp. 235
Summaryp. 235
Indexp. 237
Table of Contents provided by Ingram. All Rights Reserved.

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