social media, someone claims their “AI agent” will run your entire business when you sleep.
It’s as in the event that they can deploy AGI across factories, finance teams, and customer support using their “secret” n8n template.
My reality check is that many firms are still struggling to gather and harmonise data to follow basic performance metrics.
Logistics Director: “I don’t even know the way many orders have been delivered late, what do you’re thinking that your AI agent can do?”
And these advertised AI workflows, which are sometimes not ready for production, can unfortunately do nothing to assist with that.
Subsequently, I adopt a more pragmatic approach for our supply chain projects.
As a substitute of promising an AGI that may run your entire logistics operations, allow us to start with local issues hurting a particular process.
Logistics Director: “I need our operators to eliminate papers and pens for order preparation and inventory cycle count.”
More often than not, it involves data extraction, repetitive data entry, and heavy admin work using manual processes which might be inefficient and lack traceability.
For instance, a customer was using paper-based processes to organise inventory cycle counts in its warehouse.
Imagine a listing controller who prints an Excel file listing the locations to ascertain.
Then he walks through the alleys and manually records the variety of boxes at each location on a form just like the one below.
At each location, the operator must pause to record the actual quantity and ensure that the realm has been checked.
We are able to (and must) digitalize this process easily!
That is what we did with a Telegram Bot using n8n, connected to a GPT-powered agent, enabling voice commands.
Our operator now only must follow the bot’s instructions and use audio messages to report the variety of boxes counted at each location.
This local digitalisation becomes the primary concrete step within the digital transformation of this low-data-maturity company.
We even added logging to enhance the traceability of the method and report productivities.
In this text, I’ll use two real-world operational examples to point out how n8n can support SMEs’ supply chain digital transformations.
The concept is to make use of this automation platform to implement easy AI workflows which have an actual impact on operations.
For every example, I’ll provide a link to an entire tutorial (with a GitHub repository containing a template) that explains intimately how you can deploy the answer in your instance.
Vocalisation of Processes
In logistics and provide chain operations, it’s at all times about productivity and efficiency.
Supply Chain Solution Designers analyse processes to estimate the optimal productivity by analysing each step of a task.
A breakthrough was the implementation of voice-picking, also called vocalisation.
The concept is to have the operators communicate with the system by voice to receive instructions and supply feedback with interactions like this one:
- Voice Picking: “Please go to location A, pick five boxes.”
- Operator: “Location A, five boxes picked.”
- Voice Picking: “Please go to location D, pick six boxes.”
- Operator: “Location D, six boxes picked.”
This boosts operators’ productivity, as they now need only give attention to picking the proper quantities at the right locations.
But these solutions, typically provided by Warehouse Management System vendors, could also be too expensive for small operations.
That is where we will use n8n to construct a light-weight solution powered by multimodal generative AI.
Vocalisation of Inventory Cycle Count
I need to return back to the initial example to point out you ways I used Text-To-Speech (TTS) to digitalise a paper-based process.
We support the stock management team at a medium-sized fashion retail warehouse.
Repeatedly, they conduct what we call inventory cycle counts:
- They randomly select storage locations within the warehouse
- They extract from the system the inventory level in boxes
- They check at the placement the actual quantity
For that, they use a spreadsheet like this one.
Their current process is extremely inefficient since the stock counter must manually enter the actual quantity.
We are able to replace printed sheets with smartphones using Telegram bots orchestrated by n8n.
The operator starts by connecting to the bot and initiating the method with the /start command.
Our bot will take the primary unchecked location and instruct the operator to go there.
The operator arrives at the placement, counts the variety of boxes, and issues a vocal command to report the amount.
The amount is recorded, and the placement is marked as checked.
The bot will then routinely ask the operator to maneuver to the following unchecked location.
If the operator’s vocal feedback comprises an error, the bot asks for a correction.
The method continues until the ultimate location is reached.
The cycle count is accomplished without using any paper!
This lightweight solution has been implemented for 10 operators with cycle counts orchestrated using a straightforward spreadsheet.
How did we achieve that?
Allow us to have a have a look at the workflow intimately.
Vocalise Logistics Processes with n8n
A majority of the nodes are used for the orchestration of the various steps of the cycle count.
First, we have now the nodes to generate the instructions:
- (1) is triggering the workflow when an operator sends a message or an audio
- (6) guides the operator if he asks for help or uses the mistaken command
- (7) and (8) are the spreadsheet to seek out the following location to ascertain
For that, we don’t have to store state variables because the logic is handled by the spreadsheet with “X” and “V” within the checked column.
The important thing part on this workflow is within the green sticker
The vocalisation is handled here as we collect the audio file within the Collect Audio node.
This file is distributed to OpenAI’s Audio Transcription Node in n8n, which provides a written transcription of our operator’s vocal command.
As we cannot guarantee that each one operators will follow the message format, we use this OpenAI Agent Node to extract the placement and quantity from the transcription.
[SYSTEM PROMPT]
Extract the storage location code and the counted quantity from
this short warehouse transcript (EN/FR).
Return ONLY this JSON:
{"location_id": "...", "quantity": "0"}
- location_id: string or null (location code, e.g. "A-01-03", "B2")
- quantity: string or null (convert words to numbers, e.g. "twenty seven" → 27)
If a price is missing or unclear, set it to null.
No extra text, no explanations.[
{
"output": {
"location_id": "A14",
"quantity": "10"
}
}
]Due to the Structured Output Parser, we get a legitimate JSON with the required information.
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 informed by the operator if locations and quantities are valid
Now we have now covered all potential scenarios with a sturdy AI-powered solution.
Vocalisation of processes using TTS
With this easy workflow, we improved stock counters’ productivity, reduced errors, and added logging capabilities.
We will not be selling AGI with this solution.
We solve a straightforward problem with an approach that leverages the Text-To-Speech capabilities of generative AI models.
For more details about this solution (and the way you may implement it), you may have a have a look at this tutorial (+ workflow)
What about image processing?
In the next example, we are going to explore how you can use LLMs’ image-processing capabilities to support receiving processes.
Automate Warehouse Damage Reporting
In a warehouse, receiving damaged goods can quickly change into a nightmare.
Because receiving can change into a bottleneck to your distribution team, inbound operations teams are under significant pressure.
They should receive as many boxes as possible so the inventory is updated within the system and stores can place orders.
After they receive damaged goods, the entire machine has to stop to follow a particular process:
- Damages should be reported with detailed information
- Operators need to connect pictures of the damaged goods
For operators which have high productivity targets (boxes received per hour), this administrative charge can quickly change into unmanageable.
Hopefully, we will use the pc vision capabilities of generative AI models to facilitate the method.
Inbound Damage Report Process
Allow us to imagine you might be an operator on the inbound team at the identical fashion retail company.
You received this damaged pallet.
You might be speculated to prepare a report that you simply send by email, with:
- Damage Summary: a one-sentence summary of the problems to report
- Observed Damage: details of the damage with location and outline
- Severity (Superficial, Moderate, Severe)
- Beneficial actions: return the products or quick fixes
- Pallet Information: SKU or Bar Code number
Fortunately, your team gave you access to a newly deployed Telegram Bot.
You initiate the conversation with a /start command.
You follow the instructions and begin by uploading the image of the damaged pallet.
The bot then asks you to upload the barcode.
A couple of seconds later, you receive this notification.
You may now transfer the pallet to the staging area.
What happened?
The automated workflow generated this email that was sent to you and the standard team.
The report includes:
- Pallet ID
- Damage Summary, Observed damages and severity assessment
- Beneficial actions
This was routinely generated just after you uploaded the photo and the barcode.
How does it work?
Behind this Telegram bot, we even have an n8n workflow.
Damage Evaluation with Computer Vision using n8n
Like within the previous workflow, most nodes (in red sticky notes) are used for orchestration and knowledge collection.
The workflow can also be triggered by messages received from the operator:
- (1) and (2) be sure that we send the instruction message to the operator if the message doesn’t contain a picture
- (3) is using state variables to know if we expect to have an image of damaged goods or a barcode
The output is distributed to AI-powered blocks.
If we expect a barcode, the file is distributed to section (4); otherwise, it is distributed to section (5).
For each, we’re using OpenAI’s Analyze Image nodes of n8n.
The downloaded image is distributed to the image evaluation node with a simple prompt.
Read the barcode, just output the worth, nothing else.Here, I selected to make use of a generative AI model because we cannot guarantee that operators will at all times provide clear bar code images.
For (5), the system prompt is barely more advanced to make sure the report is complete.
You might be an AI assistant specialized in warehouse operations
and damaged-goods reporting.
Analyze the image provided and output a clean, structured damage report.
Stay factual and describe only what you may see.
Your output MUST follow this exact structure:
Damage Summary:
- [1–2 sentence high-level description]
Observed Damage:
- Packaging condition: [...]
- Pallet condition: [...]
- Product condition: [...]
- Stability: [...]
Severity: [Minor / Moderate / Severe]
Beneficial Actions:
- [...]
- [...]
Guidelines:
- Do NOT hallucinate information not visible within the image.
- If something is unclear, write: "Not visible".
- Severity should be one in every of: Minor, Moderate, Severe.This method prompt was written in consultation with the standard team, who shared their expectations for the report.
This report is stored in a state variable that will probably be utilized by (6) and (7) to generate the e-mail.
Generate Report – (Image by Samir Saci)The report includes JavaScript code and an HTML template which might be populated with the report data and the barcode.
The outcome is a concise report able to be sent to our quality team.
If you need to test this workflow in your instance, you may follow the detailed tutorial (+ template shared) on this video.
All these solutions will be directly implemented in your n8n instance.
But what if you have got never used n8n?
Start Learning Automation with n8n
For the beginners, I actually have prepared an entire end-to-end tutorial by which I show you how you can:
- Set your n8n instance
- Arrange the credentials to connect with Google Sheets, Gmail and Telegram
- Perform basic data processing and create your first AI Agent Node
At the tip of this tutorial, you’ll have the ability to run any of those workflows presented above.
A fantastic approach to practice is to adapt them to your personal operations.
Find out how to improve this workflow?
I challenge you to enhance this initial version using the Text-To-Speech capabilities of generative AI models.
We are able to, as an example, ask the operator to offer additional context via audio and have an AI Agent node incorporate it into the report.
Conclusion
This is just not my first project using n8n to automate workflows and create AI-powered automations.
Nevertheless, these workflows were at all times linked to complex analytics products performing optimisation (budget allocation, production planning) or forecasting.
These advanced prescriptive analytics capabilities addressed the challenges faced by large firms.
To support less mature SMEs, I needed to take a more pragmatic approach and give attention to solving “local issues”.
That is what I attempted to display here.
I hope this was convincing enough. Don’t hesitate to try the workflows yourself using my tutorials.
In the following article, we are going to explore using an MCP server to reinforce these workflows.
Let’s connect on LinkedIn and Twitter; I’m a Supply Chain Engineer using data analytics to enhance logistics operations and reduce costs.
For consulting or advice on analytics and sustainable supply chain transformation, be happy to contact me via Logigreen Consulting.
