Taking Intelligent Spend Analysis To A New Level

Pat McCarthy

Going from data to intelligent spend analysis? Traditional tools just don’t cut it anymore. How do we get from data to tangible, valuable ideas?

Supply chain leaders today are being asked to help contribute to business growth and promote innovation while reducing costs. That’s a lot to ask. Managing expenditures across global organizations has become increasingly complex and involves just about everyone, especially procurement, legal, finance, and operations.

Intelligent spend management is about more than classifying and reviewing historical spend. It is also about compliance, governance, and mitigating supplier risk, all of which are needed to make informed decisions about future purchasing strategies.

Traditional spend management

The main objective of spend management is to identify and exploit savings opportunities and ultimately improve the firm’s profitability. It involves collecting, collating, cleansing, maintaining, categorizing, and evaluating spend data from across the enterprise.

Traditional free-standing tools that manage spend analysis, P2P, contracts, and supplier relationships are not set up to provide a consolidated view across all spend categories. Often these data sets reside on systems that don’t talk to each other and provide limited information.

What is intelligent spend management?

A strategic, intelligent approach to spend management allows for a unified view and better management of risks across the supply chain and harmonization of procurement policies and processes. This approach requires all spend data to be centralized so that any analysis efforts are applied to the total organizational spend, thereby providing visibility of, and control over, each spend category. Intelligence means learning from past actions and improving responses over time.

Supply chain leaders can exploit spend opportunities provided by this “big data” to increase their cost savings, decrease operating costs, and reduce risk. To do this, they need an end-to-end platform that uses the best-suited technology and the right tools.  These resources need to be applied in such a way as to deliver reliable real-time information that can be used for decision-making.

Using conventional tools, it is difficult to achieve these outcomes.

Machine learning

Machine learning is an application of artificial intelligence (AI) that provides enterprise-wide systems with the ability to automatically learn and improve from experience. Using these new approaches we can learn much from the historical volumes of spend data without human intervention.

We can answer questions related to customer profiles and their spend history, provide explanations for past actions, and guide users with recommendations about future purchases.

What can intelligent spend management deliver?

Intelligent spend management has the capacity to transform any organization’s data into information that becomes a source of ideas and plans that can be used to grow the business.

1. Historical data that tells a story  

It reveals repeating patterns about users, approvers, and suppliers. It also offers insights into which types of requests should be approved automatically and which others call for human intervention. Intelligent technologies that have exposure to a holistic view of spend-related data can provide learnings to continuously improve decision-making across the source-to-pay spectrum.

2. Improved user experience 

Intelligent processes provide a real benefit to users and approvers alike by ensuring compliance with all corporate policies and regulations while cutting down on waiting times and streamlining document approval processes. Supply chain staff and end-users need easy-to-access, reliable, real-time information on supplier performance to make optimal buying decisions.

3. Better market intelligence

To manage spend efficiently and identify new sources of supply, businesses need to interact with data coming from external sources such as from suppliers, government, and business associations, as well as from internal sources. This includes accumulating industry and economic intelligence automatically from third parties and using it in conjunction with their historical spend data.

Predictive spend analysis in the supply chain is still fairly new, but it is becoming increasingly essential in large organizations that have already been exposed to spend analysis tools, supplier segmentation, and relationship management.

An enterprise-wide system that can provide a unified view of spending that brings spend data together from across all sources and categories allows users to make smarter spending decisions faster.

Achieving this requires investment in the right IT infrastructure and systems that will really put your spend data to work.

As enterprises embrace an increasing number of partners to compete globally, and as value chains grow longer and more complex, they need intelligent platforms not only to extract the fullest value from data but to extend its broadest capabilities.

This article was originally published on Procurious and is republished by permission.

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Pat McCarthy

About Pat McCarthy

Pat McCarthy is senior vice president and general manager of SAP Ariba, the world’s largest business network, linking together buyers and suppliers from 4.1 million companies in 190 countries. He is responsible for the go-to-market strategy, sales performance, and operations of the SAP Ariba field organization globally. On a deeper level, he leads SAP Ariba’s effort to understand customers’ goals and to marshal the power of digital transformation to help meet or exceed them. Since joining SAP in 2005, he has held a broad range of executive roles, including successfully leading the SAP Ariba North American region and various teams responsible for mobile and other emerging technology platforms. Prior to joining the technology world, Pat practically grew up in supply chain and procurement. He spent 11 years at PepsiCo and its subsidiary Frito-Lay, Inc., where he led teams in warehouse and fleet management, distribution operations, manufacturing, and supply chain planning and optimization. As a former practitioner turned technology enthusiast, Pat loves finding new ways to extend a customer’s competitive advantage, from funding its strategic objectives to leveraging speed and efficiency to outpace rivals. A native Chicagoan, Pat graduated from Elmhurst College; sits on the board of Chicago Tech Academy, a charter non-profit for Chicago high school students, and supports global charitable endeavors through Kiva.