According to Wikipedia, spend analysis is, “the process of collecting, cleansing, classifying and analyzing expenditure data with the purpose of reducing procurement costs, improving efficiency and monitoring compliance.”

Quoting The Aberdeen Group, savings are typically achieved by:
  • Identifying opportunities to aggregate spend and negotiate superior contracts
  • Identifying and reducing non-compliant or “maverick” spend and
  • Improving procurement operations and supplier performance.
There are 3 key activities associated with creating an effective spend analysis:
  • Gathering the data,
  • Cleansing the data,
  • Re-classifying the data.
Gathering the data is not always as easy as it sounds, data can reside in many different places. Many companies (including my last 2 employers), had data in multiple data bases. Data gathering therefore can often mean pulling detail manually by running reports in “hard copy” and inputting the information into Access or Excel. The other primary source of data can normally be found in Accounts Payable. This is most often the case in areas of indirect spend. Compiling data manually can be extremely time consuming and is often used as a reason to avoid undertaking the exercise. It is however a necessary activity and one that should ultimately yield significant savings and repay the investment in time several times over.

Once the data has been entered into a centralized database, it needs to be “cleansed.” This involves identifying duplicate entries, a common problem where multiple databases exist. Also, the database will always have items which cannot be clearly identified. In most cases, the amount of time dedicated to researching these items will normally be determined by the dollars spent on the item(s) in question.

Once the data has been cleansed, it is necessary to put it into “buckets.” This involves either creating commodity classifications within the organization, or utilizing an existing structure, such as UNSPSC. Classifications can be broken down into various “levels.” For example, in the food ingredient business, a classification might be “fruit.” This in turn might be segregated into different types of fruit, e.g. cherries, peaches, blueberries, apples etc. Another category might be “nuts and seeds.” A sub-category might be “nuts”, almonds, walnuts, pecans. Seeds would then be separated; poppy, sesame, caraway.

Monitoring compliance is the final “piece in the jigsaw.” Periodic updates must be conducted to ensure the integrity of the data. This is especially important in distribution, where supply and demand can fluctuate significantly depending on market conditions.

Utilizing this data to create effective competition is the next step; this will be addressed in a separate blog.
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