SPAR put a million transactions in order and finally trusted the numbers.
Kate Hands joined SPAR Group to take charge of indirect spend. Over R3bn of it was misclassified, with no category taxonomy to speak of. Three weeks after handing Mithra the data, she had a clean, fully covered spend cube she could act on.
Group Procurement · Indirect spend
South Africa
"Time, quality, and cost, rarely all three. With Mithra, you can."
Kate Hands, Group Procurement Executive at SPAR, on inheriting a million-transaction indirect-spend problem and what changed once the data was clean.
Kate HandsGroup Procurement Executive, SPAR Group
Kate HandsSPAR Group
A million transactions, and no way to trust them.
Cleaning up indirect spend was first on Kate's list at SPAR. The scale made it impossible by hand: a million-plus transactions, reviewed line by line, would have taken two analysts a full year.
- ~1M indirect transactions 2 analyst-years to review by hand
- Categorization tied to article and material codes
- R3bn+ of spend incorrectly classified or categorized
- No formal category taxonomy in place
- Limited visibility and no trust in the numbers
"If you take a million transactions and try to look at each line item, that would take two full-time employees an entire year. We had well over three billion rand that was incorrectly classified, drawing actionable insights from that was impossible."
Three weeks, and the data told a different story.
SPAR handed over its spend data and let Mithra's AI, refined over years on exactly this problem, do the reconciliation, build the taxonomy, and harmonize the supplier base.
Build the taxonomy
With no formal category structure to start from, Mithra established a procurement-native taxonomy SPAR's spend could finally map to.
Classify & harmonize
Spend that was uncategorized or incorrectly categorized was unified, cleansed, harmonized, and correctly classified under the new taxonomy.
See the real supplier base
A true view of unique suppliers across the landscape is the basis for trimming the long tail and focusing on strategic partnerships.
From "I didn't trust the data" to 100% covered.
In three weeks, SPAR went from very limited visibility to 100% of spend covered and correctly categorized, at roughly 97% accuracy and climbing toward 100% over time.
See more customer stories- 100% of spend covered and correctly categorized
- ~97% first-pass accuracy, improving toward 100%
- A formal taxonomy, with insights aligned to it
- A true unique-supplier view to trim the long tail
- Months of manual reconciliation removed entirely
A roadmap that listens.
Always close
A shared Teams channel with the Mithra team that comes back quickly and is genuinely curious about new use cases.
Feedback that ships
Requests for new insights and efficiencies are taken seriously and built into the product roadmap.
Features come to life
Even in a short engagement, Kate has watched her feedback turn into shipping product capabilities.
"My advice to any procurement leader: do it with Mithra, and don't delay. The insights you get from an accurate spend cube are extremely valuable."
Kate HandsGroup Procurement Executive, SPAR Group
See what Mithra could find in your spend data.
Bring a representative sample, and we'll show you your classified spend, normalized suppliers, and a prioritized savings analysis on your own data in weeks.