DEV Community: Samir Saci

Why Supply Chain is the Best Domain for Data Scientists in 2026 (And How to Learn It)

Samir Saci — Mon, 09 Feb 2026 07:11:00 +0000

My take after 10 years in Supply Chain on why this can be an excellent playground for data scientists who want to see their skills valued.
As we step into 2026, my LinkedIn inbox is full of data scientists reaching out.

Same questions. Same concerns. Is supply chain data science the right move?

After 10 years in supply chain data science, including five years writing on this blog, I have developed strong views on this question.

My first articles published in this blog more than 5 years ago - (Image by Samir Saci)

Supply chain is an exceptional playground for data scientists.

Four types of Supply Chain Analytics - (Image by Samir Saci)

Rich problems, beautiful mathematics and tangible impacts.

But I'm not here to tell you what's best for your career.

In this article, I want to give an honest view of the opportunities that excite me and the challenges that frustrate me.

More importantly, I will show how to explore this domain yourself using the tutorials and case studies shared across this blog.

You can then test yourself to see whether supply chain analytics resonates with you.

Why do we need Supply Chain Analytics?

What is a Supply Chain?

A Supply Chain is usually defined as several parties exchanging flows of material, information or monetary resources with the ultimate goal of fulfilling a customer request.

Representation of a value chain with factories, warehouses and stores - (Image by Samir Saci)

Factories, warehouses and planning teams use systems to communicate and exchange information.

Systems exchanging data across the value chain - (Image by Samir Saci)

These systems store a large amount of transactional data in databases that reflects the activity of your entire supply chain.

Warehouse Management Systems (WMS) store all the inbound (flows coming to the warehouse) and outbound (flows leaving the warehouse) transactions.
Enterprise Resource Planning (ERP) systems can store all the purchase orders (to suppliers) and invoices (to customers)
Transportation Management Systems (TMS) will record all the shipments leaving the warehouse and notify you when they are delivered.

As it is impossible to track each shipment across the supply chain, these data remain the only way to monitor your operations.

Example of transactional data - (Image by Samir Saci)

Therefore, Supply Chain Analytics has emerged as a methodology that organisations use to gain insights from data associated with all processes within the value chain.

We need to exploit this data, but for what?

Descriptive Analytics: Bring Visibility to Operational Teams

Yes, this isn't machine learning or advanced optimisation yet.

But it's where most supply chain analytics journeys begin, and where you can deliver immediate value!

I discovered in my first years as a Supply Chain Solution Designer that operational teams were drowning in data but unable to see patterns.

They know something went wrong, but they can't explain why.

Operations manager: we don't manage to increase our capacity of orders prepared per day and we don't why!

In a warehouse storing products for a cosmetics retailer, I was asked to support the reengineering of operations to increase e-commerce capacity.

Illustration of the warehouse with receiving, storage and outbound area - (Image by Samir Saci)

Around November 11th, you have a huge e-commerce festival in China during which volumes are multiplied by ten.

Operations manager: we recruit more operators but it does not impact the capacity.

To find the root cause, I decided to go on-site and observe a shift during peak hours.

And I quickly observed that many operators were packed in some alleys of the warehouse waiting for their turn to pick products.

I assumed that was probably the root cause, but I needed a way to prove that and communicate it to the operations manager.

Warehouse Heatmap - (Image by Samir Saci)

With this heatmap, showing the percentage of orders per storage location, we unlocked the situations by quickly understanding the root cause.

They knew that some areas contained high rotation SKUs, but not to this extent.

Operations Manager: we must spread the high rotations across the warehouse to avoid congestion.

This simple visualisation, which I learned from EDAs in Kaggle, was the beginning of a more complex optimisation study that I documented in this series of articles.

Series of three articles showing how to use Python to optimise Picking Processes (with source code) - (Image by Samir Saci)

Even if this isn't deep learning or complex optimisation, never underestimate the value of the proper visualisation solving the right operational problem.

The reengineering study that started from this simple visual helped us to renew the contract with this customer and earn several million euros.

What if you want more technical challenge?

Diagnostic Analytics: Data supporting Root Cause Analysis

We can now step up the technical complexity.

Let me introduce a methodology that became my favourite playground for supply chain data science: Lean Six Sigma.

Three articles in my blog with case studies using Lean Six Sigma - (Image by Samir Saci)

Lean Six Sigma is a step-by-step approach to process improvement that uses statistical tools to validate assumptions.

Why Lean Six Sigma? Because it forces rigour through statistically backed assumptions.

Operations teams make assumptions daily that are most often based on their experience, but rarely on data, as they're too deep in the day-to-day management.

We can support them with statistics using the Lean Six Sigma approach.

I first used this approach for a project supporting the transportation team of a factory in North America.

Illustration of the case study - (Image by Samir Saci)

The Inbound Transportation Manager received products from two different routes.

Route 1: with difficult road conditions and heavy traffic
Route 2: with no traffic and a beautiful modern road

An external service provider allocates the shipment to a pool of drivers (D1, D2, D3).

What's the problem?

When an order is allocated to the northern regional hub, the lead time for the request to be accepted is 35% higher than at the southern hub.

Transportation Manager: We assume that drivers avoid as much as possible to be allocated to the north route.

Before jumping to conclusions (and starting a conflict with drivers), we decided to analyse the past shipment confirmations.

Analysis of the past shipments - (Image by Samir Saci)

We used cross-validation and the Chi-Squared test to show that there is no significant proof that the driver’s allocation is linked to the Hub.

This helped the team to investigate other potential root causes to solve the problem.

For more details about the approach (and examples with source code), you can have a look at these videos:

With these descriptive and diagnostic tools at hand, you can find the root cause of most operational issues in warehouses, factories, and planning teams.

Why do we have a drop in productivity with this shift of operators?
Why are there +20% picking errors in this area?
Why do we have an additional day of lead time in this specific freight road?

Answering these questions with statistically backed methodologies can help teams implement action plans.

Can we support these action plans with data-backed prescriptions?

If we summarise what we've seen so far, in the continuous improvement journey, you can

Help to identify the problem with a carefully chosen visualisation
Use statistical methods to find the root cause

It is time now to provide solution-oriented insights to support operational improvements.

Prescriptive Analytics: Optimisation for Decision Making

The idea here is to solve an optimisation problem.

We want to maximise or minimise a specific objective function(e.g., procurement cost, number of operators hired, space used, production costs)**** while respecting particular constraints.

Examples in my Towards Data Science blog - (Image by Samir Saci)

In this blog, you can find dozens of examples using linear and non-linear programming to optimise a specific process.

I will use the Supply Chain Network Design problem to illustrate the challenges you may face when conducting this kind of study.

Supply Chain Network Design Problem - (Image by Samir Saci)

A multinational company with markets and factories in different countries would like to redesign its supply chain network to reduce costs and environmental footprint.

Data you have on hand - (Image by Samir Saci)

Their supply chain director expects you to tell them where to open factories to minimise the overall production cost.

These are the decision variables in your optimisation model that you can build using the Python library PuLP.

I faced many issues collecting input data and fixing the objective functions.

What are the challenges?

Indeed, most of the time, the problem is never stated fully and comprehensively.

For instance, in a project for a fashion retailer, it took us weeks to finalise the objective function.

Initial Scenario - Cost of Goods Sold by Market - (Image by Samir Saci)

Indeed, after presenting the initial results, we noticed that the objective of reducing the overall production cost (across all countries combined) can affect markets through the Cost of Goods Sold (COGS).

Scenario where COGS are the highest in the low purchase power markets - (Image by Samir Saci)

You cannot have scenarios in which it's more expensive to produce in India than in the USA.

This is where I had the opportunity to support the customer in adjusting their business and operational assumptions, drawing on my understanding of the model.

My presentation of the consensus effort needed across the teams - (Image by Samir Saci)

These kinds of strategic projects can help you shine with your skills as you directly deal with decision-makers on projects that impact profitability.

Our value-added here is not lines of code, but bridging the gap between business aspects and optimisation levers.

For more details, I explain in this video how I use AI with Claude connected to an MCP server to solve this issue:

To achieve this, you need a basic understanding of the operations coupled with your data science skills.

I have some content for that.

What do you need to start?

The biggest issue I saw when managing analysts and data scientists on analytics projects was their limited understanding of operations.

This creates a trust deficit.

Operations teams may see data scientists as people who've never set foot in a warehouse.

You need operational knowledge to succeed in supply chain analytics.

Not to become an expert. But enough understanding to communicate effectively, frame problems accurately, and design solutions that actually work in practice.

Example of basic supply chain terms explained in short on Supply Science - (Image by Samir Saci)

This means learning the basics: how warehouses and factories operate, how transportation networks function, how inventory flows through a supply chain.

Here's where to start.

Learn Supply Chain Processes with 5-min Explainer Videos.

In this playlist of 40+ short explainer videos, I share a condensed version of my 10 years of experience as a solution design manager and as a logistics performance manager.

Example of playlist to learn the basics of Supply Chain & Logistics - (Image by Samir Saci)

It starts with the basics of warehousing and transportation operations.

Short explainer videos covering basics of logistics operations - (Image by Samir Saci)

These videos will give the basics to understand:

Warehouse processes : receiving goods (inbound), storage of goods (inventory) and shipping of goods (outbound)
Transportation Management : Full Truck Load vs. Less Than a Truck Load, performance indicators and cost structures

The focus is operational and financial, as this remains a key concern in logistics operations and is my primary expertise as a Supply Chain Solution Manager.

These videos will give you the basic knowledge to understand most of the logistics-related case studies presented in this blog.

Purely Warehousing-Related Analytics Case Studies that you can find in here - (Image by Samir Saci)

In each of these articles, you can find the solution's source code linked to a GitHub repository and an explainer video summarising the case study.

What can you do with that?

I usually advise playing with the input data, parameters, and scenarios to adapt the solution to your company's problem.

You can be imaginative or ask LLM to generate new scenarios and play around with them.

Do not forget that the objective is to develop your technical skills as well as your operational understanding.

Productise your solutions for better adoption.

In this blog, we want to build solutions that impact operations.

We need to ensure their adoption with a user-friendly deployment.

Therefore, I dedicated multiple tutorials and articles to the productisation of algorithms and visualisations.

Example of web application that you can deploy following this tutorial - (Image by Samir Saci)

In my last article, I showed you step by step how to deploy an inventory simulation application using Python's Streamlit library.

This approach can be used for any of the 50+ analytics solutions shared in this blog.

I hope that you are now convinced that you have everything on hand to become a supply chain data scientist whose skills are valued for their impact on operations.

As someone who recently started my own company providing analytics products, I can confirm there is a need for these skills.

And we can have fun working on these kinds of projects!

About Me

Let’s connect on LinkedIn and Twitter. I am a Supply Chain Engineer who uses data analytics to improve logistics operations and reduce costs.

For consulting or advice on analytics and sustainable supply chain transformation, feel free to contact me via Logigreen Consulting.

If you are interested in Data Analytics and Supply Chain, look at my website.

Samir Saci | Data Science & Productivity

The Hidden Opportunity in AI Workflow Automation with n8n for Low-Tech Companies

Samir Saci — Mon, 02 Feb 2026 13:09:32 +0000

Every day on professional social media, someone claims their “AI agent” will run your entire business while you sleep.

It is as if they can deploy AGI across factories, finance teams, and customer service using their "secret" n8n template.

n8n is a low-code automation platform that lets you connect APIs and AI models using visual workflows - (Image by Samir Saci)

My reality check is that many companies are still struggling to collect and harmonise data to follow basic performance metrics.

Logistics Director: “I don’t even know how many orders have been delivered late, what do you think your AI agent can do?”

And these advertised AI workflows, which are often not ready for production, can unfortunately do nothing to help with that.

Therefore, I adopt a more pragmatic approach for our supply chain projects.

Instead of promising an AGI that will run your entire logistics operations, let us start with local issues hurting a specific process.

Logistics Director: "I want our operators to get rid of papers and pens for order preparation and inventory cycle count."

Most of the time, it involves data extraction, repetitive data entry, and heavy admin work using manual processes that are inefficient and lack traceability.

For example, a customer was using paper-based processes to organise inventory cycle counts in its warehouse.

Example of a Warehouse with hundreds of picking locations to manage - (Image by Samir Saci)

Imagine an inventory controller who prints an Excel file listing the locations to check.

Then he walks through the alleys and manually records the number of boxes at each location on a form like the one below.

Actual Example of the Inventory Cycle Count List used by the operators - (Image by Samir Saci)

At each location, the operator must pause to record the actual quantity and confirm that the area has been checked.

We can (and must) digitalize this process easily!

This is what we did with a Telegram Bot using n8n, connected to a GPT-powered agent, enabling voice commands.

Example of "digitalisation" of inventory cycle count using a Telegram bot - (Image by Samir Saci)

Our operator now only needs to follow the bot's instructions and use audio messages to report the number of boxes counted at each location.

This local digitalisation becomes the first concrete step in the digital transformation of this low-data-maturity company.

We even added logging to improve the traceability of the process and report productivities.

In this article, I will use two real-world operational examples to show how n8n can support SMEs' supply chain digital transformations.

The idea is to use this automation platform to implement simple AI workflows that have a real impact on operations.

For each example, I will provide a link to a complete tutorial (with a GitHub repository containing a template) that explains in detail how to deploy the solution on your instance.

Vocalisation of Processes

In logistics and supply chain operations, it is always about productivity and efficiency.

Example of two packing stations in a fashion retail warehouse - (Image by Samir Saci)

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.

Example of an operator receiving instructions via vocalisation - (Image generated with Gemini by Samir Saci)

The idea is to have the operators communicate with the system by voice to receive instructions and provide 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 focus on picking the correct quantities at the proper locations.

But these solutions, typically provided by Warehouse Management System vendors, may be too expensive for small operations.

This is where we can use n8n to build a lightweight solution powered by multimodal generative AI.

Vocalisation of Inventory Cycle Count

I want to come back to the initial example to show you how 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.

Regularly, they conduct what we call inventory cycle counts:

They randomly select storage locations in the warehouse
They extract from the system the inventory level in boxes
They check at the location the actual quantity

For that, they use a spreadsheet like this one.

Inventory Cycle Count Spreadsheet - (Image by Samir Saci)

Their current process is highly inefficient because the stock counter must manually enter the actual quantity.

We can replace printed sheets with smartphones using Telegram bots orchestrated by n8n.

Step 1: Initialisation of the process - (Image by Samir Saci)

The operator starts by connecting to the bot and initiating the process with the /start command.

Our bot will take the first unchecked location and instruct the operator to go there.

Step 2: The operator uses the vocal command to inform the number of units - (Image by Samir Saci)

The operator arrives at the location, counts the number of boxes, and issues a vocal command to report the quantity.

Step 3: Results Recorded - (Image by Samir Saci)

The quantity is recorded, and the location is marked as checked.

Step 4: Next Location - (Image by Samir Saci)

The bot will then automatically ask the operator to move to the next unchecked location.

If the operator's vocal feedback contains an error, the bot asks for a correction.

Our bot asks for a correction - (Image by Samir Saci)

The process continues until the final location is reached.

Final Location - (Image by Samir Saci)

The cycle count is completed without using any paper!

Cycle Count Completed - (Image by Author)

This lightweight solution has been implemented for 10 operators with cycle counts orchestrated using a simple spreadsheet.

How did we achieve that?

A light version of the workflow is available on my GitHub - (Image by Samir Saci)

Let us have a look at the workflow in detail.

Vocalise Logistics Processes with n8n

A majority of the nodes are used for the orchestration of the different steps of the cycle count.

All the nodes in red are only for the orchestration - (Image by Samir Saci)

First, we have 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 wrong command
(7) and (8) are looking at the spreadsheet to find the next location to check

For that, we don't need to store state variables as the logic is handled by the spreadsheet with "X" and "V" in the checked column.

The key part in this workflow is in the green sticker

Section using Generative AI - (Image by Samir Saci)

The vocalisation is handled here as we collect the audio file in the Collect Audio node.

The audio file is sent to OpenAI Audio Transcription API using this node - (Image by Samir Saci)

This file is sent to OpenAI's Audio Transcription Node in n8n, which provides a written transcription of our operator's vocal command.

The transcription here is "Location A14, 10 Boxes" - (Image by Samir Saci)

As we cannot guarantee that all operators will follow the message format, we use this OpenAI Agent Node to extract the location 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 value is missing or unclear, set it to null. 
No extra text, no explanations.


[
  {
    "output": {
      "location_id": "A14",
      "quantity": "10"
    }
  }
]

Thanks to the Structured Output Parser , we get a valid JSON with the required information.

This output is then used by the blocks (4) and 5)

n8n workflow section for output processing - (Image by Samir Saci)

(4) will ask the operator to repeat if there is an error in the transcription
(5) is updating the spreadsheet with the quantity informed by the operator if locations and quantities are valid

We have now covered all potential scenarios with a robust AI-powered solution.

Vocalisation of processes using TTS

With this simple workflow, we improved stock counters' productivity, reduced errors, and added logging capabilities.

We are not selling AGI with this solution.

We solve a simple problem with an approach that leverages the Text-To-Speech capabilities of generative AI models.

For more details about this solution (and how you can implement it), you can have a look at this tutorial (+ workflow)

https://youtu.be/_EOJ3M7APsQ

What about image processing?

In the following example, we will explore how to use LLMs' image-processing capabilities to support receiving processes.

Automate Warehouse Damage Reporting

In a warehouse, receiving damaged goods can quickly become a nightmare.

Example of a receiving area with a workstation for quality check - (Image by Samir Saci)

Because receiving can become a bottleneck for your distribution team, inbound operations teams are under significant pressure.

They need to receive as many boxes as possible so the inventory is updated in the system and stores can place orders.

When they receive damaged goods, the whole machine has to stop to follow a specific process:

Damages have to be reported with detailed information
Operators need to attach pictures of the damaged goods

For operators that have high productivity targets (boxes received per hour), this administrative charge can quickly become unmanageable.

Hopefully, we can use the computer vision capabilities of generative AI models to facilitate the process.

Inbound Damage Report Process

Let us imagine you are an operator on the inbound team at the same fashion retail company.

You received this damaged pallet.

Picture of a damaged pallet generated with Gemini by Samir Saci

You are supposed to prepare a report that you send by email, with:

Damage Summary: a one-sentence summary of the issues to report
Observed Damage: details of the damage with location and description
Severity (Superficial, Moderate, Severe)
Recommended actions: return the goods 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.

Initiate the process with the bot - (Image by Samir Saci)

You follow the instructions and start by uploading the picture of the damaged pallet.

Step 2: uploading the barcode - (Image by Samir Saci)

The bot then asks you to upload the barcode.

Step 3: uploading the bar code - (Image by Samir Saci)

A few seconds later, you receive this notification.

You can now transfer the pallet to the staging area.

What happened?

The automated workflow generated this email that was sent to you and the quality team.

Damage Report Generated by the automated workflow - (Image by Samir Saci)

The report includes:

Pallet ID
Damage Summary, Observed damages and severity assessment
Recommended actions

This was automatically generated just after you uploaded the photo and the barcode.

How does it work?

Behind this Telegram bot, we also have an n8n workflow.

n8n workflow to automate damage reporting - (Image by Samir Saci)

Damage Analysis with Computer Vision using n8n

Like in the previous workflow, most nodes (in red sticky notes) are used for orchestration and information collection.

AI sections are in green - (Image by Samir Saci)

The workflow is also triggered by messages received from the operator:

(1) and (2) ensure that we send the instruction message to the operator if the message does not contain an image
(3) is using state variables to know if we expect to have a picture of damaged goods or a barcode

The output is sent to AI-powered blocks.

If we expect a barcode, the file is sent to section (4); otherwise, it is sent to section (5).

For both, we are using OpenAI's Analyze Image nodes of n8n.

Nodes to extract the bar code - (Image by Samir Saci)

The downloaded image is sent to the image analysis node with a straightforward prompt.

Read the barcode, just output the value, nothing else.

Here, I chose to use a generative AI model because we cannot guarantee that operators will always provide clear bar code images.

(5) Analyse Damage Goods and Generate the report - (Image by Samir Saci)

For (5), the system prompt is slightly more advanced to ensure the report is complete.

You are 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 can 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]

Recommended Actions:
- [...]
- [...]

Guidelines:
- Do NOT hallucinate information not visible in the image.
- If something is unclear, write: "Not visible".
- Severity must be one of: Minor, Moderate, Severe.

This system prompt was written in consultation with the quality team, who shared their expectations for the report.

This report is stored in a state variable that will be used by (6) and (7) to generate the email.

State variables are collected to generate a report using the JS node Generate Report - (Image by Samir Saci)

The report includes JavaScript code and an HTML template that are populated with the report data and the barcode.

Final report - (Image by Samir Saci)

The final result is a concise report ready to be sent to our quality team.

If you want to test this workflow on your instance, you can follow the detailed tutorial (+ template shared) in this video.

All these solutions can be directly implemented in your n8n instance.

But what if you have never used n8n?

Start Learning Automation with n8n

For the beginners, I have prepared a complete end-to-end tutorial in which I show you how to:

Set your n8n instance
Set up the credentials to connect to Google Sheets, Gmail and Telegram
Perform basic data processing and create your first AI Agent Node

At the end of this tutorial, you will be able to run any of these workflows presented above.

A great way to practice is to adapt them to your own operations.

How to improve this workflow?

I challenge you to improve this initial version using the Text-To-Speech capabilities of generative AI models.

We can, for instance, ask the operator to provide additional context via audio and have an AI Agent node incorporate it into the report.

Conclusion

This is not my first project using n8n to automate workflows and create AI-powered automations.

However, these workflows were always linked to complex analytics products performing optimisation like budget allocation, production planning or forecasting.

Other examples of workflow automation using n8n - (Image by Samir Saci)

These advanced prescriptive analytics capabilities addressed the challenges faced by large companies.

To support less mature SMEs, I had to take a more pragmatic approach and focus on solving "local issues".

This is what I tried to demonstrate here.

I hope this was convincing enough. Do not hesitate to try the workflows yourself using my tutorials.

In the next article, we will explore using an MCP server to enhance these workflows.

About Me

Let’s connect on LinkedIn and Twitter; I am a Supply Chain Engineer using data analytics to improve logistics operations and reduce costs.

For consulting or advice on analytics and sustainable supply chain transformation, feel free to contact me via Logigreen Consulting.