How Reliable & Repeatable Is Your Spend Analytics?

Spend Analytics

Spend analytics is the foundation of all procurement initiatives of most organizations and should be in alignment with critical supply chain strategy. Spend analytics provides comprehensive insights into spend patterns, identifies opportunities for cost saving and process improvements. Many organizations depend on spend analytics to control and reduce costs related to procurement.

But are you leveraging all the benefits of your current spend analytics? If not, then here are some of the benefits you may be missing out on:

  • Comprehensive view of the metrics that drive cost savings
  • Real time accurate visibility of organization’s spending
  • Support to optimise your inventory and improve supply chain efficiency
  • Rationalize supplier base
  • Data for accurate Spend Forecasting
  • Supports to manage risks and optimize the organization’s buying power
  • Effective supplier relationship management
  • Reduced maverick purchases

If your spend analytics is not able to track even at least one of the benefits given above, then it is time to question your data source.

  • How accurate is your data source?
  • Are you following industry specific taxonomy?
  • Is your inventory categorized by adopting an internationally accepted standard?
  • Is your inventory data accurate and complete?

According to various studies, more than 80% of organizations do not have a comprehensive, accurate and repeatable spend analysis capability.

How to Get Accurate Spend Analytics?

Robust data management practice and governance is the key to reliable spend analytics. The success of analytics ultimately depends upon the quality and consistency of inventory and supplier data across all ERP/CMMS/EAM systems within the organization. Here are the key steps to follow in order to have an accurate inventory data:

Create an Inventory list

The first step is to create an inventory list from all source systems where your material and procurement data reside. The goal is to ensure that the entire data is analysed. It can be from multiple systems, from multiple business units.

Taxonomy development

Do you follow a common taxonomy? If not, an industry standard taxonomy is a must to ensure uniformity of the data across various systems. With the implementation of different systems and involvement of multiple stakeholders, the accumulated data will result in varied nomenclatures for similar fields. A standard taxonomy will speed up the process to gather data from diverse systems and enable accurate spend analytics.

Data Classification

Proper categorization of material data helps identify and classify the procurement of materials into different spend buckets. Item-level classification will effectively provide a precise view of spending with each supplier and for each commodity. Data standardization is the key to drive reliable spend analytics, correlation of data and to deliver actionable insights.

Here are the two common Industry standards for materials and services data classification:

  • UNSPSC: The United Nations Standard Products and Services Code® (UNSPSC®), is an open, global, multi-sector standard for efficient and accurate classification of products and services. This is one of the most popular classification systems globally which is available in multiple languages.
  • eCl@ss: A worldwide ISO/IEC-compliant data standard for goods and services. eCl@ss contains tens of thousands of product classes and unique properties. This lets you standardize procurement, storage, production, and distribution activities in and between companies – across sectors, countries and languages. Realize sales potentials and new synergies, reduce costs, and increase the efficiency of your merchandise and data management.

Other data classification standards available are NAICS (North American Industry Classification schema), CPV (Common Procurement Vocabulary) used for vendor and procurement data classification. Granular level classification/categorization is a must for parts like MRO and direct material, whereas for office supplies or general consumables, data classification needs to be at commodity level.

Data Cleansing & Enrichment

Cleansing ensures accuracy by removing invalid records and redundancies from the data. This includes identifying and eliminating errors and discrepancies in descriptions and transactions or even typographical errors. Data enrichment ensures missing key parameters of materials that are captured with up-to-date information.

Data Governance

Spend analysis if done right, is not a one-time activity; reliable data ensures repeatable and consistent results in the long run. Adhering to an established taxonomy and structuring the inventory data in the ERP / EAM / CMMS systems, ensure conformity to predefined business rules and definitions.

Accurate Spend analysis will result in higher savings, better visibility into inventory spend and efficient vendor engagement. It is a great step towards transforming procurement into a strategic, value-adding function. Taxonomy development, Data classification, Cleansing & Enrichment and Data governance are critical aspects of data management for spend analytics to deliver measurable ROI. Organizations with clear spend visibility into their procurement activities can utilize their reports and insights to drive performance, and to make informed business decisions.

Learn more about accurate spend analytics and effective vendor management, speak to one of our Supply Chain experts today.

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