than convincing somebody of a fact they can not see in their very own information.
Knowledge science and sustainability consultants face the identical downside: our ideas could also be too summary and theoretical, making them troublesome for decision-makers to narrate to.
I discovered this the laborious method whereas developping my startup!
After I revealed a case study on Green Inventory Management on TDS in 2024, I assumed the logic was stable and convincing, however the influence was restricted.
The article defined the mathematical concept behind it and used an precise case examine to show the sustainability advantages.
But it didn’t convert sceptics.
Buyer: “I’m positive it gained’t work with our operations!”
Why? As a result of it wasn’t related to their information and constraints.
So I made a decision to alter the method.
I packaged the simulation instrument in a FastAPI microservice and gave my clients the power to check the mannequin themselves utilizing an MCP Server related to Claude Desktop.
The target was to have them ask the LLM to run their very own eventualities, modify their parameters, and see how CO₂ emissions dropped in response to totally different stock insurance policies.
On this article, I’ll share the method I used for this experiment and the suggestions I obtained from a prospect, the Provide Chain Director of a retail group based mostly within the Asia Pacific area.
What’s Inexperienced Stock Administration?
On this part, I wish to briefly clarify the idea of Inexperienced Stock Administration so you’ve the context to grasp the instrument’s added worth.
Context: Stock Administration for a Retail Firm
Allow us to put ourselves in our Provide Chain Director’s footwear.
His groups (stock groups, warehouse and transportation operations) are answerable for replenishing shops from a central distribution centre.
Once they want particular merchandise, shops mechanically ship replenishment orders by way of their ERP to the Warehouse Administration System.
These automated orders comply with guidelines applied by the stock workforce, often called the periodic assessment “Order-Up-To-Stage (R, S)” coverage.
- The ERP is reviewing shops’ stock ranges, additionally known as stock available (IOH), each R days
- The delta between the goal stock S and the stock degree is calculated: Δ = S— IOH
- A Replenishment Order is created and transmitted to the warehouse with the amount: Q = S — IOH
After transmission, the order is ready on the warehouse and delivered to your retailer inside a particular lead time (LD) in days.
To be extra concrete, I share the instance above:
- R = 25 days: we assessment the stock each 25 days as you may see within the blue scatter plot
- S = 1,995 models: we ordered to achieve this degree, as proven within the newest graph.
The stock groups within the methods normally set these parameters, and the replenishment orders are mechanically triggered.
What if we optimise these parameters?
Impacts on Logistics Operations
Primarily based on my expertise, these parameters are, more often than not, not set optimally..
The issue is that they considerably influence the effectivity of your warehouse and transportation operations.
This will increase carton and plastic consumption and reduces productiveness.
Within the instance above, objects are saved in cartons containing models that may be picked individually.
If the order amount is 5, the operator will:
- Open a field of 20 models and take 5 models ;
- Take a brand new field and put this stuff in it ;
- Palletise the containers utilizing plastic movie ;
The opposite influence is on truck filling fee and CO2 emissions.
With a excessive supply frequency, you scale back the amount per cargo.
This results in using smaller vans that will not be full.
What can we do?
Targets of Inexperienced Stock Administration
We will take a look at a number of eventualities, with totally different operational parameters, to seek out the optimum setup.
For that, I’ve loaded buyer information into the simulation mannequin
to check the instrument with actual eventualities.
Customers can modify a few of these parameters to simulate totally different eventualities.
class LaunchParamsGrinv(BaseModel):
n_day: int = 30 # Variety of days within the simulation
n_ref: int = 20 # Variety of SKUs within the simulation
pcs_carton: int = 15 # Variety of items per full carton
cartons_pal: int = 25 # Variety of cartons per pallet
pallet_truck: int = 10 # Variety of pallets per truck
okay: float = 3 # Security issue for security inventory
CSL: float = 0.95 # Cycle service degree goal
LD: float = 1 # Lead time for supply (days)
R: float = 2 # Evaluation interval (days)
carton_weight: float = 0.3 # Carton materials weight (kg)
plastic_weight: float = 0.173 # Plastic movie weight per pallet (kg)These parameters embody:
n_dayandn_ref: outline the scope of simulationpcs_carton,cartons_pal,LDandpallet_truck: parameters linked to warehousing and transportation operationscarton_weight,plastic_weight: sustainability parametersR,okayandCSL: parameters set by the stock workforce
I would like our Provide Chain Director to sit down together with his groups (stock, warehouse, transportation and sustainability) to problem the established order.
If they should attain a particular goal, our director can:
- Problem his stock groups to seek out higher assessment durations (R), or cycle service degree (CSL) targets
- Ask the sustainability workforce to seek out lighter carton supplies
- Redesign his warehouse operations to scale back the lead time (LD)
For that, we have to present them with a instrument to simulate the influence of particular adjustments.
That is what we’re going to do with the assist of an MCP Server related to Claude AI.
Demo of the Inexperienced Stock Administration AI Assistant
Now that we all know how this simulation instrument can add worth to my clients, let me present you examples of analyses they’ve carried out.
These exams have been carried out utilizing buyer information over a simulation horizon of as much as 90 days.
I’ve replicated the questions and interactions utilizing anonymised dummy information to keep away from sharing confidential info right here.
Onboarding of customers
I’ve related the MCP server to the Claude setting utilized by the Provide Chain managers to have them “play with the instrument”.
The bulk didn’t take the time to assessment the preliminary case examine and immediately requested Claude in regards to the instrument.
Hopefully, I’ve documented the MCP instruments to offer context to the agent, like within the toot launch_greeninv shared beneath.
@mcp.instrument()
def launch_greeninv(params: LaunchParamsGrinv):
"""
Launch an entire Inexperienced Stock Administration simulation.
This instrument sends the enter parameters to the FastAPI microservice
(by way of POST /grinv/launch_grinv) and returns detailed sustainability
and operational KPIs for the chosen replenishment rule (Evaluation Interval R).
-------------------------------------------------------------------------
🌱 WHAT THIS TOOL DOES
-------------------------------------------------------------------------
It runs the complete simulation described within the "Inexperienced Stock Administration"
case examine, reproducing the conduct of an actual retail replenishment system
utilizing a (R, S) Periodic Evaluation Coverage.
The simulation estimates:
- Replenishment portions and order frequency
- Inventory ranges and stockouts
- Variety of full and combined cartons
- Variety of pallets and truck deliveries
- CO₂ emissions for every retailer and globally
- Carton materials and plastic utilization
- Operator productiveness (orderlines and items per line)
[REMAINDER OF DOC-STRING OMITTED FOR CONCISION]
"""
logging.data(f"[GreenInv] Working simulation with params: {params.dict()}")
attempt:
with httpx.Shopper(timeout=120) as shopper:
response = shopper.publish(LAUNCH, json=params.dict())
response.raise_for_status()
outcome = response.json()
last_run = outcome
return {
"standing": "success",
"message": "Simulation accomplished",
"outcomes": outcome
}
besides Exception as e:
logging.error(f"[GreenInv] Error throughout API name: {e}")
return {
"standing": "error",
"message": str(e)
}I used to be fairly glad with Claude’s introduction to the instrument.
It begins with the introduction of the core capabilities of the instruments from an operational standpoint.
Rapidly, our director began to ship me lengthy emails with questions on the right way to use the instrument:
- Easy methods to arrange the parameters?
- Who ought to I contain on this train?
My preliminary reflex was to reply: “Why don’t you ask Claude?”.
That is what they did, and the outcomes are glorious. Claude proposed a framework of study.
This framework is sort of excellent; I might simply have put the lead time (LD) additionally within the scope of the Warehouse Supervisor.
Nonetheless, I have to admit that I might by no means have been capable of generate such a concise and well-formatted framework by myself.
Then, Claude proposed a plan for this examine with a number of phases.
Let me take you thru the totally different phases from the person’s perspective.
Part 1: Baseline Evaluation
I suggested the workforce to repeatedly ask Claude for a pleasant dashboard with a concise government abstract.
That’s what they did for Part 1.
As you may see within the screenshot above, Claude used the MCP Server instrument launch_greeninv to run an evaluation with the default parameters outlined within the Pydantic mannequin.
With the outputs, it generated the Govt Abstract for our director.
The abstract is concise and straight to the purpose.
It compares the outputs (key efficiency indicators) to the targets shared within the MCP docstring and the grasp immediate.
What in regards to the managers?
Then it generated team-specific outputs, together with tables and feedback that clearly highlighted probably the most vital points, as proven within the instance beneath.
What’s attention-grabbing right here is that our warehouse supervisor solely talked about the goal items per line in a earlier message.
Meaning we will have the instrument study not solely from the MCP’s instruments docstrings, grasp immediate, and Pydantic fashions, but additionally from person interactions.
Lastly, the instrument demonstrated its potential to have a strategic method, offering mid-term projections and alerting on the important thing indicators.
Nonetheless, nothing is ideal.
When you’ve weak prompting, Claude by no means loses the chance to hallucinate and suggest choices exterior the scope of the examine.
Allow us to proceed the train, following Anthropic’s mannequin, and proceed to Part 2.
Part 2: Situation Planning
After brainstorming with its workforce, our director collected a number of eventualities from every supervisor.
What we will see right here is that every supervisor wished to problem the parameters targeted on their scope of accountability.
This thought course of is then transcribed into actions.
Claude determined to run the six eventualities listed above.
The problem right here is to compile all the outcomes into an artificial, insight-driven abstract.
Within the case examine revealed in 2024, I targeted solely on the primary three eventualities, analyzing every efficiency indicator individually.
What about Claude?
Claude was smarter.
Though we had the identical kind of information available, it produced one thing extra “cross-functional” and decision-driven.
- Now we have business-friendly names for every state of affairs which are comprehensible throughout features.
- Every state of affairs is linked to the workforce that pushed for it.
Lastly, it offered an optimum state of affairs that may be a consensus between the groups.
We’re even supplied with a scorecard that explains to every workforce why the state of affairs is finest for everyone.
For a extra detailed breakdown of the agent’s outputs, be happy to have a look at this tutorial:
Conclusion
A brand new hope for the idea of Inexperienced Stock Administration
After a few weeks of experimentation, the Provide Chain Director is satisfied of the necessity to implement Inexperienced Stock Administration.
The one bottleneck right here is on their aspect now.
With Claude’s assist, our 4 managers concerned within the examine understood the influence of their roles on the distribution chain’s total effectivity.
This helps us at LogiGreen onboard Provide Chain departments for advanced optimisation workouts like this one.
In my view, it’s simpler to conduct a inexperienced transformation when all groups have possession and sponsorship.
And the one technique to get that’s to verify all people understands what we’re doing.
Primarily based on the preliminary results of this modest experiment, I feel we have now discovered a wonderful instrument for that.
Would you like different case research utilizing MCP Server for Provide Chain Optimisation?
AI Agent for Provide Chain Community Optimisation
In one other article revealed on In the direction of Knowledge Science, I share an identical experiment targeted on the Provide Chain Community Design train.
The target right here is extra macro-level.
We wish to decide where items are produced to serve markets on the lowest value in an environmentally pleasant method.
Whereas the algorithm differs, the method stays the identical.
We attempt a number of eventualities with parameters that favour totally different groups (finance, sustainability, logistics, manufacturing) to achieve a consensus.
Like right here, Claude does an excellent job in synthesising the outcomes and offering data-driven suggestions.
For extra particulars, you may watch this video.
About Me
Let’s join on Linkedin and Twitter. I’m a Provide Chain Engineer who makes use of information analytics to enhance logistics operations and scale back prices.
For consulting or recommendation on analytics and sustainable provide chain transformation, be happy to contact me by way of Logigreen Consulting.
In case you are concerned with Knowledge Analytics and Provide Chain, have a look at my web site.
