social media, somebody claims their “AI agent” will run your whole enterprise whilst you sleep.
It’s as if they’ll deploy AGI throughout factories, finance groups, and customer support utilizing their “secret” n8n template.
My actuality test is that many corporations are nonetheless struggling to gather and harmonise knowledge to comply with fundamental efficiency metrics.
Logistics Director: “I don’t even know what number of orders have been delivered late, what do you suppose your AI agent can do?”
And these marketed AI workflows, which are sometimes not prepared for manufacturing, can sadly do nothing to assist with that.
Subsequently, I undertake a extra pragmatic strategy for our provide chain tasks.
As a substitute of promising an AGI that may run your whole logistics operations, allow us to begin with native points hurting a selected course of.
Logistics Director: “I need our operators to eliminate papers and pens for order preparation and stock cycle depend.”
More often than not, it entails knowledge extraction, repetitive knowledge entry, and heavy admin work utilizing guide processes which are inefficient and lack traceability.
For instance, a buyer was utilizing paper-based processes to organise stock cycle counts in its warehouse.
Think about a list controller who prints an Excel file itemizing the areas to test.
Then he walks by the alleys and manually information the variety of containers at every location on a kind just like the one beneath.
At every location, the operator should pause to document the precise amount and ensure that the realm has been checked.
We will (and should) digitalize this course of simply!
That is what we did with a Telegram Bot utilizing n8n, related to a GPT-powered agent, enabling voice instructions.
Our operator now solely must comply with the bot’s directions and use audio messages to report the variety of containers counted at every location.
This native digitalisation turns into the primary concrete step within the digital transformation of this low-data-maturity firm.
We even added logging to enhance the traceability of the method and report productivities.
On this article, I’ll use two real-world operational examples to point out how n8n can assist SMEs’ provide chain digital transformations.
The concept is to make use of this automation platform to implement easy AI workflows which have an actual impression on operations.
For every instance, I’ll present a hyperlink to an entire tutorial (with a GitHub repository containing a template) that explains intimately tips on how to deploy the answer in your occasion.
Vocalisation of Processes
In logistics and provide chain operations, it’s at all times about productiveness and effectivity.
Provide Chain Resolution Designers analyse processes to estimate the optimum productiveness by analysing every step of a process.
A breakthrough was the implementation of voice-picking, additionally referred to as vocalisation.
The concept is to have the operators talk with the system by voice to obtain directions and supply suggestions with interactions like this one:
- Voice Selecting: “Please go to location A, choose 5 containers.”
- Operator: “Location A, 5 containers picked.”
- Voice Selecting: “Please go to location D, choose six containers.”
- Operator: “Location D, six containers picked.”
This boosts operators’ productiveness, as they now want solely concentrate on choosing the proper portions on the correct areas.
However these options, sometimes offered by Warehouse Administration System distributors, could also be too costly for small operations.
That is the place we are able to use n8n to construct a light-weight resolution powered by multimodal generative AI.
Vocalisation of Stock Cycle Rely
I need to come again to the preliminary instance to point out you ways I used Textual content-To-Speech (TTS) to digitalise a paper-based course of.
We assist the inventory administration group at a medium-sized trend retail warehouse.
Frequently, they conduct what we name stock cycle counts:
- They randomly choose storage areas within the warehouse
- They extract from the system the stock degree in containers
- They test on the location the precise amount
For that, they use a spreadsheet like this one.
Their present course of is extremely inefficient as a result of the inventory counter should manually enter the precise amount.
We will exchange printed sheets with smartphones utilizing Telegram bots orchestrated by n8n.
The operator begins by connecting to the bot and initiating the method with the /begin command.
Our bot will take the primary unchecked location and instruct the operator to go there.
The operator arrives on the location, counts the variety of containers, and points a vocal command to report the amount.
The amount is recorded, and the situation is marked as checked.
The bot will then robotically ask the operator to maneuver to the subsequent unchecked location.
If the operator’s vocal suggestions comprises an error, the bot asks for a correction.
The method continues till the ultimate location is reached.
The cycle depend is accomplished with out utilizing any paper!
This light-weight resolution has been carried out for 10 operators with cycle counts orchestrated utilizing a easy spreadsheet.
How did we obtain that?
Allow us to take a look on the workflow intimately.
Vocalise Logistics Processes with n8n
A majority of the nodes are used for the orchestration of the completely different steps of the cycle depend.
First, we have now the nodes to generate the directions:
- (1) is triggering the workflow when an operator sends a message or an audio
- (6) guides the operator if he asks for assist or makes use of the unsuitable command
- (7) and (8) are trying on the spreadsheet to seek out the subsequent location to test
For that, we don’t have to retailer state variables because the logic is dealt with by the spreadsheet with “X” and “V” within the checked column.
The important thing half on this workflow is within the inexperienced sticker
The vocalisation is dealt with right here as we gather the audio file within the Accumulate Audio node.
This file is distributed to OpenAI’s Audio Transcription Node in n8n, which supplies a written transcription of our operator’s vocal command.
As we can not assure that each one operators will comply with the message format, we use this OpenAI Agent Node to extract the situation and amount from the transcription.
[SYSTEM PROMPT]
Extract the storage location code and the counted amount from
this quick warehouse transcript (EN/FR).
Return ONLY this JSON:
{"location_id": "...", "amount": "0"}
- location_id: string or null (location code, e.g. "A-01-03", "B2")
- amount: string or null (convert phrases to numbers, e.g. "twenty seven" → 27)
If a price is lacking or unclear, set it to null.
No further textual content, no explanations.[
{
"output": {
"location_id": "A14",
"quantity": "10"
}
}
]Because of the Structured Output Parser, we get a sound JSON with the required info.
This output is then utilized by the blocks (4) and 5)
- (4) will ask the operator to repeat if there may be an error within the transcription
- (5) is updating the spreadsheet with the amount knowledgeable by the operator if areas and portions are legitimate
We’ve got now coated all potential eventualities with a sturdy AI-powered resolution.
Vocalisation of processes utilizing TTS
With this straightforward workflow, we improved inventory counters’ productiveness, lowered errors, and added logging capabilities.
We’re not promoting AGI with this resolution.
We clear up a easy downside with an strategy that leverages the Textual content-To-Speech capabilities of generative AI fashions.
For extra particulars about this resolution (and how one can implement it), you possibly can take a look at this tutorial (+ workflow)
What about picture processing?
Within the following instance, we’ll discover tips on how to use LLMs’ image-processing capabilities to assist receiving processes.
Automate Warehouse Harm Reporting
In a warehouse, receiving broken items can rapidly grow to be a nightmare.
As a result of receiving can grow to be a bottleneck in your distribution group, inbound operations groups are underneath vital stress.
They should obtain as many containers as potential so the stock is up to date within the system and shops can place orders.
Once they obtain broken items, the entire machine has to cease to comply with a selected course of:
- Damages need to be reported with detailed info
- Operators want to connect footage of the broken items
For operators which have excessive productiveness targets (containers acquired per hour), this administrative cost can rapidly grow to be unmanageable.
Hopefully, we are able to use the pc imaginative and prescient capabilities of generative AI fashions to facilitate the method.
Inbound Harm Report Course of
Allow us to think about you’re an operator on the inbound group on the similar trend retail firm.
You acquired this broken pallet.
You’re supposed to arrange a report that you just ship by electronic mail, with:
- Harm Abstract: a one-sentence abstract of the problems to report
- Noticed Harm: particulars of the harm with location and outline
- Severity (Superficial, Average, Extreme)
- Really helpful actions: return the products or fast fixes
- Pallet Info: SKU or Bar Code quantity
Luckily, your group gave you entry to a newly deployed Telegram Bot.
You provoke the dialog with a /begin command.
You comply with the directions and begin by importing the image of the broken pallet.
The bot then asks you to add the barcode.
Just a few seconds later, you obtain this notification.
Now you can switch the pallet to the staging space.
What occurred?
The automated workflow generated this electronic mail that was despatched to you and the standard group.
The report consists of:
- Pallet ID
- Harm Abstract, Noticed damages and severity evaluation
- Really helpful actions
This was robotically generated simply after you uploaded the picture and the barcode.
How does it work?
Behind this Telegram bot, we even have an n8n workflow.
Harm Evaluation with Laptop Imaginative and prescient utilizing n8n
Like within the earlier workflow, most nodes (in pink sticky notes) are used for orchestration and knowledge assortment.
The workflow can also be triggered by messages acquired from the operator:
- (1) and (2) be sure that we ship the instruction message to the operator if the message doesn’t include a picture
- (3) is utilizing state variables to know if we anticipate to have an image of broken items or a barcode
The output is distributed to AI-powered blocks.
If we anticipate a barcode, the file is distributed to part (4); in any other case, it’s despatched to part (5).
For each, we’re utilizing OpenAI’s Analyze Picture nodes of n8n.
The downloaded picture is distributed to the picture evaluation node with an easy immediate.
Learn the barcode, simply output the worth, nothing else.Right here, I selected to make use of a generative AI mannequin as a result of we can not assure that operators will at all times present clear bar code photographs.
For (5), the system immediate is barely extra superior to make sure the report is full.
You're an AI assistant specialised in warehouse operations
and damaged-goods reporting.
Analyze the picture offered and output a clear, structured harm report.
Keep factual and describe solely what you possibly can see.
Your output MUST comply with this actual construction:
Harm Abstract:
- [1–2 sentence high-level description]
Noticed Harm:
- Packaging situation: [...]
- Pallet situation: [...]
- Product situation: [...]
- Stability: [...]
Severity: [Minor / Moderate / Severe]
Really helpful Actions:
- [...]
- [...]
Tips:
- Do NOT hallucinate info not seen within the picture.
- If one thing is unclear, write: "Not seen".
- Severity should be considered one of: Minor, Average, Extreme.This method immediate was written in session with the standard group, who shared their expectations for the report.
This report is saved in a state variable that will probably be utilized by (6) and (7) to generate the e-mail.
Generate Report – (Picture by Samir Saci)The report consists of JavaScript code and an HTML template which are populated with the report knowledge and the barcode.
The ultimate result’s a concise report able to be despatched to our high quality group.
If you wish to take a look at this workflow in your occasion, you possibly can comply with the detailed tutorial (+ template shared) on this video.
All these options might be straight carried out in your n8n occasion.
However what if in case you have by no means used n8n?
Begin Studying Automation with n8n
For the inexperienced persons, I’ve ready an entire end-to-end tutorial during which I present you tips on how to:
- Set your n8n occasion
- Arrange the credentials to hook up with Google Sheets, Gmail and Telegram
- Carry out fundamental knowledge processing and create your first AI Agent Node
On the finish of this tutorial, it is possible for you to to run any of those workflows offered above.
A good way to apply is to adapt them to your individual operations.
How you can enhance this workflow?
I problem you to enhance this preliminary model utilizing the Textual content-To-Speech capabilities of generative AI fashions.
We will, for example, ask the operator to supply extra context through audio and have an AI Agent node incorporate it into the report.
Conclusion
This isn’t my first venture utilizing n8n to automate workflows and create AI-powered automations.
Nonetheless, these workflows had been at all times linked to advanced analytics merchandise performing optimisation (budget allocation, production planning) or forecasting.
These superior prescriptive analytics capabilities addressed the challenges confronted by massive corporations.
To assist much less mature SMEs, I needed to take a extra pragmatic strategy and concentrate on fixing “native points”.
That is what I attempted to show right here.
I hope this was convincing sufficient. Don’t hesitate to attempt the workflows your self utilizing my tutorials.
Within the subsequent article, we’ll discover utilizing an MCP server to boost these workflows.
Let’s join on LinkedIn and Twitter; I’m a Provide Chain Engineer utilizing knowledge analytics to enhance logistics operations and cut back prices.
For consulting or recommendation on analytics and sustainable provide chain transformation, be happy to contact me through Logigreen Consulting.
