The Evolution of Spend Analysis and the Rise of Big Data
The Evolution of Spend Analysis and the Rise of Big Data Spend analysis has always had the attention of an organization's top management since it has such a direct impact on the productivity of the organization, the cost of finished product, the quality of products/services, and the organization's competitiveness.
This whitepaper deals with the evolution of spend analysis, decision making based on the analysis of big data and automatic spend analysis. It further examines the constraints and best practices in the implementation of spend analysis and its advantages to corporate enterprises. Today's business model has changed to a global model. Although the globalization of business has opened up new opportunities for increased sales and profits, it has also created many challenges not experienced earlier. Therefore, the optimization of operations has become a necessity. Individual managerial excellence, though, is still one of the key factors in successful operations, but professionally designed systems are in evidence everywhere to assist management. New management tools are the hallmark of remaining competitive. In the global competitive scenario, most of the traditional channels for earning better profit margins have already been exhausted. The options for cost reduction and improvement of profit margins are basically limited to internal operations. The area yet to be tapped, with the highest potential for increasing profits and remaining competitive, is spend analysis. Multi-national companies operate with big data, but it is not easy to handle big data without some user- friendly management tools. Handling of big data and leveraging it for the benefit of the business is a challenge. That's why forwardâ€”looking companies are focusing their effort on improving their 'spend management'
Spend analysis encompasses all the possible activities where savings could be realized. Each activity is examined to see:
Core Areas of Spend Analysis
Where are the savings possibilities? And what should be done to improve savings? Spend analysis comprise three core areas: process, analysis and visibility (Fig. 1).
Evolution of Procurement Spend Analysis Procurement spend analysis is not a very old concept. Up until
Fig. 1: Core areas of spend analysis
1980, the concept was unknown in the business world. Procurement decision making was based on the practice of favoring the supplier with the lowest quotation. In many cases, initially it was felt that the company had negotiated an excellent deal, but soon it was realized that there were many areas where adequate visibility was missing which led to monetary resources being drained through various hidden costs not previously considered in the contract. The results were not encouraging and beneficial. The shift away from this operational model came in 1980 with the initiatives taken by the automotive industry in the United States, especially by General Motors (GM) and Ford, by applying spend analysis to key commodities. The approach adopted was to rationalize the supplier base in key commodities. Since 1980, the approach of spend analysis has become increasingly popular and is enjoying ever wider acceptance daily. Fig. 2 presents the evolution of procurement spend analysis from 1980 to the present date.
Currently n Strategic
2002 - 05
sourcing n Product
consolidation n Consolidation
2001 - 02 n Automated
2000 n Spend
1990 n Integrated
1980 n Automotive
Before 1980 n Focus on
lowest quotations n Spend analysis not practiced
manufacturer s took initiatives n GM &Ford rationalized supplier base n Spend analysis in key commodities n Continue with or remove present supplier
modules for spend analysis by consultants & vendor n Focus on indirect spend analysis n Concept of Customer Relationship Management (CRM) n Spend analysis from RequisitionTo-Receipt (R2R)
analysis extended to direct and MRO items
tools for spend analysis by vendors
n Item price
of suppliers n Specialist
vendors offered optimization of spendings n Use of analytical tools for spend analysis n Subcontractin g of spend analysis services
variance n Unrealized
rebates n Volume
Fig. 2: Evolution of procurement spend analysis
Various improvements were obtained through new strategies adopted by companies. A more prominent role of spend analysis was observed in managing effectively big data, especially in case of multi-national and global companies.
Big Data Business cannot be done in isolation. It is affected by various factors like economy, technology, political situation, social environment, religion etc. A strategic decision process, especially in very large organizations like multinational and global companies, requires analysis of big data, perhaps in hundreds of gigabytes or terabytes. Big data can be broadly segmented into four major areas: data relating to procurement spending, performance of suppliers, enterprise contracts, and
procurement process analysis. Procurement big data will be used for forecasting demand, with demand data according to each product, suppliers, regions, industry, government departments, and restricted use as per directives of the controlling authorities, data on the methodology adopted in procurement, bid receipt, bid opening, bid analysis, proposal development, and approval of competent authority, negotiations, and placement of orders. Appraisal of vendors is carried out before the request for quotation is sent to the potential vendors. Appraisal of the performance of vendors on criteria of cost, quality, delivery schedule and after-sale service are all very important components of the big data. The selection of supply sources and the development of new sources of supply, including the identification of strategic supply partners, occupies international organizations globally and constitutes a good share of the big data. Likewise, Enterprise Contract Management (ECM) and the related data has assumed great importance in spend analysis. Handling of big data is a challenge for any organization in any part of the world as it involves a large number of activities, like data collection, storage, search, transfer, sharing, analysis, retrieval and data management (Aberdeen Study). Since there are a large number of data sets, processing and managing big data is very complex, but the impact of procurement decisions made on such data is very significant and far reaching when it comes to the overall profitability of the organization. According to a report published by Kinsey & Co. in June 2011, the easy and timely accessibility of big data to relevant stakeholders creates tremendous value.
Benefits of Big Data and its Analysis Big data analytics are changing fast. Interpretation of big data creates knowledge and enables greater transparency and improvement. Another advantage of analysis of big data is that it helps align the supply chain and logistics, along with the technology, to global business needs. This strong linkage helps companies better understand their supply chain parties, e. g., suppliers, manufacturers and customers. Big data analysis extracts the relevant and appropriate action plans and makes real-time changes. It also assists in their implementation when faced with constraints. Real- time data from the Internet, speech and video, and images from satellites can be used effectively by companies to make changes in their supply chain strategies. Big data analysis forms a core platform involving third parties to develop solutions for the improvement of operational efficiency.
Leveraging Spend Analysis for Big Data Management of big data calls for an automated spend data management system. These systems are software applications that obtain spend data from a number of sources, like purchase orders, invoices and many other documents, as well as systems like ERP, etc. The data is classified according to the product category, supplier and data users. Spend analysis can accurately classify about 80% of the spend data records in the first attempt alone. That classification rate can be further improved by experts. It is to be noted that accurate spend data enables the managers to get factual information and direction for sourcing and development of business strategies. This allows the business to uncover savings and performance improvement opportunities available in its products, inventories and supply support system. Advanced analytics permit inferences from granular data. Inferences transform data into knowledge, which results in greater process transparency and improvements. It turns data into actionable intelligence. The refined data helps organizations study the current/past spend trend and also enable them to predict future trends with improved accuracy.
Spend Analysis Implementation – Constraints and Best Practices Constraints Although spend analysis is found to be very useful by most corporations, there are a number of constraints against implementation of the process of spend analysis (Aberdeen Study). Some of these constraints are examined here: a. Spend analysis is a time-consuming process. It involves a lot of man hours to complete the analysis. Usually, several weeks are required to complete the exercise. b. Information required for carrying out the spend analysis is to be collected from various sources, both from within and from outside the enterprise. The sources within the enterprise are Accounts Payable (AP), General Ledger (GL), Purchasing Department and Enterprise Resource Planning (ERP) system. Outside sources of information include Credit-and-Procurement Card (P-card), Banks, Contract Manufacturers, Logistics Service Providers, etc. c. Due to limited financial data from the available internal financial system, only partial information is available for decisions to be made. Therefore, decisions lack quality. d. The data in the ERP system and the company’s financial system is unstructured, with occasional errors or lack of critical data. For example, the name of a vendor, product information and account codes may be incomplete or even wrong. e. Correction of such data errors is not satisfactory due to lack of expertise with the personnel working in the data section. Most of the data processing is done by the staff of the IT department which lacks familiarity with the commodities or with the services under review. f. Classification of the spending information is not done correctly. In addition, the “miscellaneous” category creates difficulties in analyzing the data. g. Mistakes in part numbering or in item descriptions, or of suppliers is usually a hurdle against effective spend analysis. The name of the same item or of the same vendor may be written differently in different records. Under such conditions, the spend analysis cannot be fully relied upon. h. Classification of spending information within the enterprise and outside the enterprise does not match, resulting in difficulties in spend analysis.
Many enterprises are still using old systems with manual operations. This makes the spending analysis more difficult. At times, the results obtained from such systems are inconsistent.
Best Practices At Project Level n Dedicated executive team with a strong mandate from management n Participation of the commodity managers is key, especially while defining
classification rules and taxonomy n Well-defined objectives and key result areas to be defined at the beginning
of the project n Timely Management Review Meetings and Steering Group Meetings
At Data Level n Dedicated business intelligence team / team which understands data
structure within a business warehouse / ERP system n Selection of correct taxonomy based on sourcing hierarchy, current
taxonomy, if available, and commodities which are procured n Process of data extraction to be defined and well documented to reduce
rework n Selection of correct parameters, based on the reporting needs of the
Basic Overall Strategy n Participation of key stakeholders from all the major business units,
especially during perception check meetings n Selection of correct user groups, based on the profile and reporting needs,
to facilitate adoption n Ensuring that a pre-training questionnaire is filled in by all the
participants. This will ensure that focused group training sessions are held â€“ one of the key factors of successful adoption.
Key Benefits of Spend Data Analysis There are number of advantages to spend data analysis in the areas of costs of materials and services, compliance with contractual conditions, vendor management, meeting the regulatory requirements, effective inventory management, reduction of the process cycle, and better management of product. The findings of a study carried out by Aberdeen Group in 2004 are shown in Table 1. Table 1: Advantages of spend data analysis
Reduction in cost of material / services
2-12% through strategic sourcing
Compliance with contractual conditions
Improvement in compliance by 55%
Effective vendor management
Eliminates non-performing vendors
Meeting the regulatory requirements
Effective Inventory Management
Reduction in inventory cost 5 - 50%
Process cycle reduction
30% - 50% reduction by refocusing on sourcing and strategic tasking.
Better management of product.
20% reduction in unnecessary parts. Increased reuse of parts.
Conclusions Spend analysis was used for the first time in 1980 by automotive manufacturers with very encouraging results in optimizing the costs and saving money. The change in the business approach from local operations to global operations forced the companies to deal with big data in various activities and areas of procurement, both inside the enterprise as well as outside the enterprise. Business applications of big data analytics will continue to expand from demand-related sales, marketing, customer service and manufacturing into more supply side areas, such as procurement, inventory management and supply risk management. Implementation and the impact on the supply chains will be slow, yet steady this year. Without collection of big data, systematic analysis and evolving appropriate strategies as well as correct decision making, enterprises find it difficult to meet competitive challenges, especially in international and global markets. Some companies have embraced the big data analysis systems to their great advantage. Yet there are many more companies who are continuing with earlier manual operations. Automated spend analysis has become the order of the day, and companies should adopt such systems at the earliest to manage their money optimally. Today, spend analysis has become a necessity to remain competitive and grow the business.
Zycus is dedicated to positioning procurement at the heart of business performance. With our spirit of innovation and a passion to help procurement create even greater business advantages, we have evolved our portfolio to a complete Source-to-Pay suite of procurement performance solutions which includes - Spend Analysis, eSourcing, Contract Management, Supplier Management, Financial Savings Management, and Procure-to-Pay. Behind every Zycus solution stands an organization that possesses deep, detailed procurement expertise and a
FINANCIAL SAVINGS MANAGEMENT
sharp focus on being responsive to customers. We are a large — 600+ and growing — company with a physical presence in virtually every major region of the globe. We see each customer as a partner in innovation and no client is too
small to deserve our attention.
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With more than 200 solution deployments among Global 1000 clients, we search the world continually for procurement practices proven to drive competitive business performance. We incorporate these practices
into easy-to-use solutions that give procurement teams the power to get moving quickly — from any point of departure — and to continue innovating and pushing business and
procurement performance to new heights.
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Published on Mar 26, 2014
This whitepaper deals with the evolution of spend analysis, decision making based on the analysis of big data and automatic spend analysis....