The Evolution of Spend Analysis and the Rise of Big Data

The Evolution of Spend Analysis and the Rise of Big DataSpend 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.

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

forward—looking companies are focusing their effort on improving their 'spend

management'

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.

Spend Analysis

Evolution of Procurement

Spend Analysis

Spend analysis encompasses

all the possible activities where

savings could be realized. Each

activity is examined to see:

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).

Procurement spend analysis is

not a very old concept. Up until

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.

Process

VisibilityAnalysis

Core Areas of Spend

Analysis

Fig. 1: Core areas of 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.

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

Big Data

Before 1980

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Focus on lowest quotationsSpend analysis not practiced

1980

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Automotive manufacturers took initiativesGM &Ford rationalized supplier baseSpend analysis in key commoditiesContinue with or remove present supplier

1990

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Integrated modules for spend analysis by consultants & vendorFocus on indirect spend analysisConcept of Customer Relationship Management (CRM)Spend analysis from Requisition-To-Receipt (R2R)

2000

n Spend analysis extended to direct and MRO items

2002 - 05

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Consolidation of suppliersSpecialist vendors offered optimization of spendingsUse of analytical tools for spend analysisSubcontracting of spend analysis services

Currently

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Strategic sourcingProduct consolidationItem price varianceUnrealized rebatesVolume discount

2001 - 02

n Automated tools for spend analysis by vendors

Fig. 2: Evolution of procurement spend analysis

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

Leveraging Spend Analysis for Big Data

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.

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.

I. 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.

Dedicated executive team with a strong mandate from management

Participation of the commodity managers is key, especially while defining

classification rules and taxonomy

Well-defined objectives and key result areas to be defined at the beginning

of the project

Timely Management Review Meetings and Steering Group Meetings

Dedicated business intelligence team / team which understands data

structure within a business warehouse / ERP system

Selection of correct taxonomy based on sourcing hierarchy, current

taxonomy, if available, and commodities which are procured

Process of data extraction to be defined and well documented to reduce

rework

Selection of correct parameters, based on the reporting needs of the

company

Participation of key stakeholders from all the major business units,

especially during perception check meetings

Selection of correct user groups, based on the profile and reporting needs,

to facilitate adoption

Ensuring that a pre-training questionnaire is filled in by all the

participants. This will ensure that focused group training sessions are held

– one of the key factors of successful adoption.

Best Practices

At Project Level

At Data Level

Basic Overall Strategy

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Key Benefits of Spend Data Analysis

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.

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

Conclusions

Areas Performance

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 More effectively

Effective Inventory Management Reduction in inventory cost 5 - 50%

Process cycle reduction30% - 50% reduction by refocusing on sourcing and strategic tasking.

Better management of product.20% reduction in unnecessary parts. Increased reuse of parts.

About Zycus

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

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.

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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