Analytics of Corporate Spend, Suppliers, and Payment Terms

Introduction

Spend, supplier and payment terms Analytics is the discipline of collecting, processing, and analyzing procurement data. The analysis of corporate spend, their supplier and payment terms uncovers patterns with buying organization through an in-depth review of data points within procurement data. 

Businesses use these insights to better manage their suppliers, to track working capital metrics, to negotiate better payment terms in order to improve working capital and/or to decrease purchasing costs.

Why should you implement Supplier and Spend Analytics?

The analysis of suppliers can generate clear benefits for an organization looking to achieve working capital improvements based on the various spend carried out by a company. The visibility obtained from the analysis helps curtail maverick payment terms and spending, which in turn can also reduce the risk of supply chain disruptions and the costs of purchasing. There are variety of reasons that justify spend and working capital analysis:

• It precisely benchmarks a company’s current working capital performance and accurately measures the organization’s Days Payable Outstanding (DPO) against competitors and other players in the same country or industry.
• It provides deep insight into DPO and cash flow opportunities and generates superior term optimization results.
• The in-depth analysis of suppliers across all spend categories gives superior visibility and insight for decision-making. With more visibility, procurement can become a better term negotiator and a stronger contributor to the company's overall financial performance. 
• Shared financial goals, supplier metrics, and visibility into payment performance enable the procurement team to transform itself from a cost-saving function into a profit-enhancing and cash flow-generating function.
• The analysis enables enterprise-wide visibility, aggregating spend among suppliers with intelligent classification as well as scoring each individual supplier. It matches each supplier with an attribute base and analyses them in terms of opportunities to extend payment terms or generate more cash discounts.
• It identifies which suppliers may be the most receptive to extending payment terms. For example, a supplier with higher cost of goods sold to the buyer but that offers extended payment terms may be a better option than one requiring early payment when the cost of acquiring extra working capital is factored in.
• It provides answers and insights to critical questions such as:
  - How much can I optimize my payment terms?
  - What terms yield the most early cash discount?
  - What is the cash trade-off between DPO versus cost reduction from supplier discounts? In other words, calculating potential savings that can be realized from early payment discounts and comparing these against the real cost of tying up or acquiring more working capital.

Optimizing working capital requires a deep understanding of the nuances of the C2C (Cash to Cash Cycle) across thousands of vendors based in different industries, and countries with individual, financial metrics.  At the core of each analysis needs to be a database that tracks insights about each commodity class, country, competitors up to the individual supplier level. It can also provide the means to analytically model and assess the likelihood of individual suppliers accepting optimized terms and joining a Supply Chain Finance program. 

The sheer number of business units, suppliers, payment terms, spend, etc. creates a volume of data that is immense. Handling this data without an organized spend and working capital analytics system can lead to unsatisfactory or even bad results. Even though corporations understand the value of spend and working capital analytics tools, it is shocking to see how many organizations still resort to manual processes or half-baked Excel sheets trying to identify cash flow opportunities within their spend.

The other challenge faced by companies is not just to collate the data, but to convert it into information that can contribute added value – generating cash flow gains. It can be a nightmare, trying to make sense of disjointed spreadsheets that are merely cobbled together spend data from across the organization. Identifying the right suppliers and optimizing payment terms is more effective if there are technology solutions in place that enrich reliable supplier data and calculate the benefits for the buyer and its suppliers.

Other specific benefits of using automated spend and working capital analytics tools are a reduction of spend analysis project cycles by 30% to 50% and capturing over 95% of spend compared to 30% by using manual solutions. A spend analysis tool must have a minimal set of capabilities. The below outlines the core capabilities required by spend analysis solutions that an organization might want to look for. It is not meant to be a complete list, but a starting point for a technical evaluation of the spend and supplier analysis system.

• Data Feed – The solution should allow the client to easily submit spend and supplier data either via secured data upload or automatically integrate with the buyer's ERP (Enterprise Resource Planning) system. It should also be able to integrate and receive automatic data feeds from external information providers offering financial and market information. At the same time, the solution should allow the user to export the analyzed data or reports into Excel or CSV (Comma-Separated Values) file format.
• ‍Rules Engine – Most buying organizations store their data in various systems, based on different formats, designed for various uses. Most of these systems use different identifiers, supplier and commodity codes. Thus, in order to correctly classify, synchronize, and amalgamate an organization’s data into a single system in an efficient and repeatable manner - the analytics system should have a rules engine allowing for efficient data mapping.
  
• Pattern Detection - The first enhanced capability that an organization might want in its spend and supplier analysis solution is the ability to automatically detect spending patterns across commodities, categories, divisions, suppliers and determine which patterns are inconsistent. Inconsistent patterns often identify potential sources for improvement. For example, analytics solutions can calculate optimal payment terms based on supplier characteristics and automatically detect whether or not spend, discounts and payment terms are optimized, and furthermore, detect savings opportunities for the next round of negotiations.
  
• Updated Data and Artificial Intelligence – The analytics solutions learn from inputs from the procurement people in the field, negotiating terms with suppliers. This creates a feedback loop that helps the system to learn and improve its calculated and optimized payment terms using artificial intelligence. 
  

The third article in this series will focus on - the why and how to implement such analytics solutions for spend, suppliers, and payment terms.