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Anthrobyte

Cross Referencing

How one network stopped a single point of failure from shaping every branch's answer

Outcome

Branches answering the same question

600+

Manufacturer catalogs, built up over decades

50+

To a trusted substitution, from 15 minutes

2sec

Manual lookup time reclaimed network-wide, every week

20+ hrs

01

The Problem

The same physical part could have different names and numbers across the distributor and its vendors, with no reliable way to identify interchangeable products. That made sourcing alternatives, avoiding duplicate inventory, and comparing vendor prices dependent on the experience of long-tenured employees.

02

What We Understood First

01

One Product, Many Names

The same product could appear under different vendor names and item numbers, making simple name-based matching unreliable.

02

Vendor Items Didn't Map Cleanly

Vendor item numbers had no consistent link to the distributor's own item numbers, so finding an equivalent product often meant manual lookup.

03

Exact Matches Weren't Enough

A product could be the right substitute without sharing the same identifier, description, or naming convention. Matching needed to go beyond exact text.

04

The Catalog Was Hiding Duplicates

Different names could point to the same physical product, creating duplicate entries and making the true inventory picture harder to see.

05

The Goal Was Confidence, Not Just a Match

A useful product had to identify the right substitute with enough confidence for teams to pick it without forcing them to manually validate every result.

03

Solution

01

Standardized product specifications

Converted 24 key specs into consistent formats so equivalent parts could be recognized reliably.

02

Improved numeric and semantic matching

Handled measurements, quantities, and product meaning to improve match accuracy beyond exact wording.

03

Matched specifications before descriptions

Prioritized technical specifications first, then used product wording to distinguish true matches from near-misses.

04

Built for reliable substitution

Combined standardized specs, semantic matching, and human validation to make product substitutions dependable at scale.

04

What We Didn’t Automate

The judgment call stays human, on purpose.

A wrong substitution can mean a compatibility problem, a safety problem, a customer who doesn't come back. So the system shows confidence, not certainty. Lower-confidence matches go to a person.

05

What Changed

85%

Fewer double stock

7%

Average price difference between equivalent products

“$11.6M in savings, earned by trusting people with better information, not less of it.”

Nationwide distribution network · Hundreds of local companies

06

The Impact at Scale

Substitution-driven savings

Substitutions Before

10%

Savings

$1.4M in savings

Substitutions After

65%

Savings

$11.6M in savings

What this translated to

34%

Fewer stockout losses

55%

More substitution revenue

2×

Higher conversion rate

07

If you are facing this too

The Principle

2sec vs 15min decides whether a customer waits, or calls someone else.

If the knowledge lives in one head, today looks fine. The real test comes next month, when one of them is gone.

Talk to us
08

What’s Next?

PHASE 01IN FLIGHT

More manufacturer lines

Expanded manufacturer coverage, giving branches access to more viable product alternatives.

PHASE 02NEXT

Tighter quoting and BOM links

Connected cross-referencing more closely to BOMs and quoting, making substitutions easier to use across the network.

PHASE 03PLANNED

Expertise without the tenure

One system now carries the product knowledge that once required a specialist helping new reps operate with the confidence.

If you're navigating layered challenges and want a thinking partner, let's think together.

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