Case study · Manufacturing · Report Generation
From A full day to Automatic: Report Generation in Manufacturing
Report Generation was the step everything else waited on. It now runs itself on the same stack — live in half a day, judgement calls still going to a person.
Report Build Time
Build this week's manufacturing report.
Key metric · Manufacturing. Compiled from your connected tools and shared automatically.
SlackGoogle SheetsGmail3 AI agents · 5 tools connected · live in half a day · no code
- Company
- Precision parts manufacturer
- Team size
- 100-500 employees
- Industry
- Manufacturing
- Time to live
- Half a day
- Agents deployed
- 3 AI agents
- Tools connected
- 5 integrations
The context
Why Report Generation is hard in manufacturing.
Nothing about report generation is complicated on a single instance. What makes it expensive in manufacturing is volume arriving through purchase orders, supplier email, shop-floor systems, and quality exceptions, against the production schedule and the promised ship date. Miss the window and the cost is not the minutes — it is a line that stops because a document did not move.
Constraints the build had to hold
ERP is authoritative
Agents hold no state the ERP should hold. Read, act, write back — one schedule for everyone.
Exceptions are the whole job
Designed around short shipments, price variances, and missing certs. Clean transactions were never the bottleneck.
Traceability on quality
Any record touching a quality event keeps its full history, including what the agent changed and why.
The change
Same job. Two chains.
Every handoff in the left-hand chain is somewhere Report Generation used to wait. The right-hand chain has the same steps and none of the waiting.
By hand
- Export from four systemseach counting things differently
- Pasted into a master sheetone renamed column breaks it
- Reconciled by handa full analyst day
- Sent Monday afternoon
already several days old
With agents
- Work arrives on any channelpicked up in seconds
- Collection agenthanded straight on
- Analysis agenthanded straight on
- Narrative agent
logged, and reviewable
When the work can happen
Before and after
What Report Generation cost them, and what replaced it.
The challenge
Nobody at this precision parts manufacturer trusted the reports, and everyone knew why. The same metric came out differently depending on who pulled it, because the definitions lived in individual spreadsheets rather than anywhere shared. Every leadership meeting spent its first fifteen minutes arguing about whether the numbers were right.
The full background
Building them was worse than reading them. Their 100-500 employees team pulled manufacturing data from systems that did not agree on customer identity, date boundaries, or currency handling, and reconciled the differences manually each cycle. Ad-hoc questions — the ones that actually mattered — were quoted at two days because that was how long the pull took. So most of them were never asked, and decisions got made on instinct instead.
What they built
Reporting was rebuilt around definitions rather than exports. The team connected SAP and Google Sheets to DeskFerry and wrote each manufacturing metric down once — what it counts, which source owns it, how the period boundary works.
How it was wired
Everything downstream follows from that. The same numbers come out regardless of who asks, ad-hoc questions are answered in minutes rather than quoted at two days, and every figure in the summary traces to its source in one click. The analyst day that used to go on pulling, pasting, and reconciling now goes on the part of the job that needed a person: deciding what the movement means and what to do about it. Interpretation was deliberately left with the human presenting the report.
The impact
What changed, measured the same way on both sides.
Before and after across the metrics that matter for manufacturing Report Generation.
Report Build Time
Analyst day returned
Data Freshness
Near real-time
Metric Disputes
Definitions settled
Ad-Hoc Question Turnaround
Dramatically faster
Reporting Cost
Major savings
How these were measured
- Baseline
- The "before" column is the team’s own measurement of their manual report generation process, taken over the four weeks before anything was connected.
- Comparison
- The "after" column is the same measurement repeated on the same process once the agents were live, so both sides count the same things in the same way.
- Why no percentages
- These are composite scenarios built from patterns across many deployments, not one audited customer’s books. Directional language is the honest way to report that — your own numbers will depend on your volume, your process, and your starting point.
A day, either side
The same day, before and after.
What Report Generation actually looked like for this manufacturing team — the version they described in the first call, and the version they run now.
Before DeskFerry
Monday 9:00
Export from four systems. Paste into the master sheet.
Monday 11:30
A column moved in one export. Fix the formulas.
Monday 14:00
Numbers do not tie. Start again from the raw pulls.
Monday 17:00
Send the deck. Two figures are already stale.
Tuesday
Someone asks why the manufacturing number differs from last week's.
After DeskFerry
Monday 7:00
The report is compiled and in the channel before anyone opens a laptop.
Monday 9:00
Read the summary. The movements are already called out in words.
Monday 11:30
Schema changes were caught by validation, not by a broken formula.
Monday 14:00
Spend the afternoon on what the numbers mean.
Tuesday
Every figure traces to its source with one click.
The build
The 3 agents that run it.
One job each, with an explicit handoff between them. Splitting Report Generation this way is what makes a failure legible — you can see which step it went wrong at instead of debugging one agent that does everything.
- 01
Collection agent
Trigger
The reporting window opens
Pulls from every connected source — the CRM, Google Sheets, finance — and reconciles them onto one set of definitions.
Agent 1 of 3 in the Manufacturing workflow.
- Passes a validated dataset to analysis.
- 02
Analysis agent
Trigger
A dataset is validated
Computes the manufacturing metrics, compares against prior periods and targets, and identifies which movements are worth a sentence.
Agent 2 of 3 in the Manufacturing workflow.
- Hands findings, not just figures, to the narrative agent.
- 03
Narrative agent
Trigger
Analysis completes
Writes the summary in plain language, links each figure back to its source, and delivers to the channel the audience already reads.
Agent 3 of 3 in the Manufacturing workflow.
How they did it
From nothing to production in half a day.
No code, no IT ticket, no vendor implementation team. These are the steps in the order this team took them.
Step 01
Mapped the current workflow
Every step of the manual report generation process, including exceptions — and which of them a person should keep.
Step 02
Built it in DeskFerry
SAP and Slack as sources, manufacturing decision logic, automated actions and alerts.
Step 03
Ran it in parallel
One week alongside the manual process. Edge cases flagged for review rather than actioned.
The stack
Nothing was replaced. Everything was connected.
The manufacturing team kept the tools they already ran — DeskFerry sits between them.
SAP
ERP system of record; read broadly, written narrowly
NetSuite
ERP system of record for orders, inventory, and financials
Slack
Where the team is told, and where approvals happen in one tap
Google Sheets
The lightweight ledger the team already reads, kept current automatically
Airtable
The working table the team edits when a rule needs changing
Report Generation handled end to end · automatic, every time
What stayed human
The parts they deliberately did not automate.
Automating Report Generation end to end was never the goal. Removing the volume so the judgement calls got proper attention was.
The interpretation
The narrative agent describes what moved. What it means, and what to do about it, is written by the person presenting it.
Anomalies
A figure outside its expected band holds the report and alerts the owner, rather than publishing a number that will have to be walked back.
The supply chain visibility question
Asked first by every manufacturing team. Agents run on the access the staff account already had, every action is logged, and any step can be stopped without unwinding what ran.
Takeaways
What transfers to your team.
The parts of this that are not specific to one company's tooling or volume.
- 01
No technical expertise needed — the people who own the report generation process built it.
- 02
Capacity scaled without headcount, which changed the unit economics.
- 03
Every decision is logged, so the workflow can be audited rather than trusted.
- 04
Leadership got report generation numbers in real time for the first time.
In their words
“Before DeskFerry, our report generation process was the bottleneck that every manufacturing team complained about. Now it's our competitive advantage. We process faster, more accurately, and at a fraction of the cost. Our competitors are still doing this manually.”
Composite — written from what teams running this workflow report, not a single named customer.
FAQ
Questions people ask about this build.
Automating Report Generation in manufacturing — what it takes, and where it stops.
How long does it take to set up report generation automation for a manufacturing business?
This team was live in half a day. Pre-built manufacturing templates cover the wiring, so most of that time goes on your business rules rather than on connecting things. No code.
How many AI agents does report generation automation actually need?
3 here: collection agent, analysis agent, narrative agent. The split matters more than the count — one job and one handoff each means a failure tells you which step broke. One agent doing everything does not.
What results can a manufacturing business expect?
The figures here are directional, not audited — composite scenarios, not one customer's books. What transfers is the shape: routine volume stops needing a person, exceptions surface instead of sinking, and nothing waits for office hours. Your numbers depend on your volume and starting point.
How does DeskFerry handle manufacturing data and access?
Agents run on the same access the staff account already had — throughput widens, permissions do not. Every action is logged with what it read and changed, and any step can be stopped without unwinding what ran. DeskFerry holds no formal manufacturing certification, so scope it as you would any other system in your control environment.
What still needs a person?
More than most automation pages admit. Anything outside the rules stops and goes to a named owner with context attached, rather than being guessed at. The rules themselves are changed by people — agents never widen their own tolerances. The carve-outs this team kept are named above.
What tools does this connect to?
1,500+ integrations. This build used SAP, NetSuite, Slack, Google Sheets and Airtable; most manufacturing stacks are a variation on that. CRM, email, chat, databases, and industry-specific software all connect without code.
Run this in your own stack.
Describe how Report Generation should work at your manufacturing business, in a sentence. DeskFerry builds the agents, wires your tools, and takes the routine volume from there. Start free — no credit card.
Or start from a template — Report Generation agent for Manufacturing.
Keep exploring
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Report Generation in other industries
Composite scenario — built from patterns across many manufacturing Report Generation deployments rather than one customer's books. Figures are directional; your own depend on your volume, process, and starting point.