Diwo
CatalystManufacturingRetail

Workforce Efficiency Optimization

Mid-shift, the floor supervisor watches a surge of inbound pallets pile up at receiving while two pickers stand idle three aisles over.

Book a working demoHow Diwo solves it
Ranked in dollars
·
What-if validation
·
Push to ops
01The Problem

Today’s workflow is the bottleneck.

Mid-shift, the floor supervisor watches a surge of inbound pallets pile up at receiving while two pickers stand idle three aisles over. By the time the WMS dashboard refreshes, the bottleneck has already cost him forty-five minutes of throughput. Labor is one of the top three operating costs in warehousing, logistics, distribution, and manufacturing, but the signal he needs to act is buried across a WMS, a labor system, and a conveyor feed that don't talk to each other. He reallocates on instinct, and hopes the next pallet wave lands softer.
The status quo· typical decision cycle
signal decays
  1. Signal captured
    Models score. Data is fresh.
    Day 1
  2. Dashboard built
    Analyst pulls CSVs, joins sources
    Day 2–3
  3. Review meeting
    Stakeholders ask for context, re-pull
    Day 4
  4. Window has closed
    Signal stale, action wasted
    Day 5+
By the time the action is ready, the window has closed.
5 days between signal and action. The data science team did their job. The operator is still waiting.
02The Approach

How Catalyst handles it.

Catalyst doesn't give the control tower another dashboard to watch. It pushes a fresh recommendation every 15 minutes, drawing on current workload status, processed and incoming resources, and live resource movements on the floor. A combined data and AI/ML pipeline synthesizes outputs from multiple models into a single cohesive move for the supervisor — shift two from pack to receiving, hold the next wave for 10 minutes. Structured systems-of-record and unstructured context feed a Semantic Knowledge Graph, and every reallocation trains the system on what "good" looks like for this site. The supervisor stops reacting and starts steering.
Catalyst · Conversation
A
Which zones are over- or under-staffed against current workload right now?
Catalyst · 320ms · sources: 4
Here’s what I found — three drivers explain most of the signal, and I’ve ranked them by impact.
RECOMMENDATION
Take action on the top-ranked driver first.
Expected lift: +11.7% · this quarter.
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03Ask

Questions you can ask.

Every case ships with a set of high-leverage prompts — the shortlist operators reach for every week. Here are the ones we see working for workforce efficiency optimization.
Anchor question
01 · start here
Which zones are over- or under-staffed against current workload right now?
02
How much work is in progress versus landing in the next hour?
03
Where will we bottleneck in the next 15 minutes if we don't move anyone?
04
What's the optimal labor reallocation for the next refresh window?
05
How is throughput tracking against plan so far today?
06
Which single resource move would unlock the most throughput right now?
Proof Points

What this looks like in production.

Claim 01

Real-time recommendations refreshed every 15 minutes.

Claim 02

No other explicit numeric claims on source page.

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See it on your data

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