Odoo Artificial Intelligence

ERP Is No Longer Just Software

For decades, ERP system built to record what happened. Sales orders were logged, Reports are generated,

Tickets are raised, Quotations were created so far . But today’s businesses not enough on history and information,

They want answer ,they want predictions . They want system that can help, assist, recommend, suggest, and execute in the operations and fully act according to its requirement.

This is where Odoo artificial intelligence getting in the picture.

AI is not replacing the ERP, it actually assisting and fully evolving the ERP to make the operations easy, and instantly.

So, we can say “When AI meets Odoo, Odoo is transformed from a system record into a system intelligence”

The Real Business Problem with Traditional ERP

Lets discuss a real problem which occurred usually in business software’s, there are a huge software’s are available in the market who assist in B2B and ERP businesses. There are endless problems happen with operations So mainly three types of problems can be there,

1) They are completely limited in terms of assistance,
2) They Cannot fulfil the complete requirement
3) They Cannot execute the operations

Due to above three main reasons including the other undiscussed causes below problems usually happen in almost every business:

User don’t know what to do next
Managers get report too late
Decision is completely depended on human interpretation
Data exist but insights don’t
Tool won’t interact with customer in the absence of coordinator
Complex and heavy operations
Problem is get cached after occurring and damaging is done
Follow-ups depend on memory not systems
Forecasting is guesswork and human effort not intelligence expectation
Team work categorizes without centralization

Now Imagine against of these complexities:

Your ERP tells you what you have to do for next, what likely to happen next
Warn and remind you before problem occurred
Suggest the best work automatically
Bring the data and customer history on a single command
Interact with the customer without taking assistance from coordinator
Learn from past behavior and predict upcoming challenges and solutions
Assists the user instead of waiting commands

That’s not futuristic, that’s happening right now

That’s Odoo artificial intelligence,

Bringing an intelligence power to fulfil the all-problems solution in intelligence standard.

What Is Odoo Artificial Intelligence?

Odoo artificial intelligence is a integration of AI capabilities into Odoo eco system which enables the below activities:

Predictive analysis
Context-aware automation and interaction
Expected and analyzed outcome
Conversational ERP assistance
Intelligence decision support
Continuous learning from past data and behavior

Unlike the role-based ERP system, Odoo AI enhance the operations and make the easier.

A Real-World Scenario: BlueWave Fisheries

To make and feel this practically exist let follow on example in the entire article
Company Profile
BlueWave Fisheries

Industry: Fishing and seafood Export

Operations:

Fishing boat
Cold storage warehouse
Processing units
Global B2B export

Modules used in Odoo:

Sales
Inventory
Purchase
Accounting
HR
Logistics

BlueWave struggle with:

When demand is huge than get uncertainty
Fewer times stock get spoil
Usually, Late shipments happen
Manual Planning
Unexpected and reactive decisions

Imagine this scenario than keep on eye on the solution which will be provided by Odoo Artificial Intelligence, which will change everything from beginning to end and top to bottom.

Core Technologies Powering Odoo Artificial Intelligence

1. Machine Learning (ML)

ML analyze the historical and previous data to catch the issues and detect a complete pattern. It actually revolutionizing fishing industry by transforming vast data from satellite imagery to underwater activities of fishes, like sensor reading, giving actionable insights and many more.

ML applications in the fishing are divided into two main areas:
A. Aquaculture (Fish Farming)
B. Wild Capture Fisheries

ML in Aquaculture (Fish Farming) For BlueWave:

Once ML involve means prediction will be there So, it predicts seasonal fish demand: like festival / regional / export demand, and help farmers too.
Optimized fishing schedule: like fish growth, water temperature etc.
Forecast export count: Total farm production, survival rate, size and quality etc.
Intelligence feeding system: ML analyze the system and process through the video to fee the fished according to their hunger to protect them, and automatically adjust feed rate according to hunger and reducing the waste.
Health and diseases prediction: Machine analyze the activity through the sensor, analyzes their skin, color, size, to detect the disease and outbreaks early.

ML in Wild Capture Fisheries

ML assist in the wild capture fisheries to prevent the illegals activities, manage the wild resources, improve the catch efficiency.

Illegal fishing Detection: ML analyze the satellite data and read the patterns to identify and to protect the harmful, illegal, unreported and unregulated activities and unlawful pursuits.
Catch Monitoring and Species Identification: Cameras get installed on boards to catch the different fish species to identify the count, and help in maintain accurate records, while ensuring compliance with regulations.
Stock Assessment: ML modal analyzes the data from logbooks, surveys, historical data, environmental catch-up to provide the more accurate assessment of fish population, sizes, and sustainable fish limits.

2. Natural Language Processing (NLP):

NLP allows users to talk Odoo using simple, clean, and natural language instead of complex menus, navigations and filters.

What happened in Odoo for fisheries businesses like in aquaculture business, when a user wants to understand the operations or find any issue like delayed export shipment problem, so the current manual and static process is lengthy and fully time-consuming process:

A user needs to:

Open multiple modules such as Inventory, Sales, Logistics, Accounting, Export etc.
Apply filters such as date range, fish type, size, batch, customer and destination many more.
Analyze report manually such as we need to compare data across the modules:

Catch quantity
Stock availability
Shipments delays
Export performance

So, what happened here information is completely separated in multiple places, scattered, and derived into multiple menus, so the interpretation would be complex, there will be huge manual navigations and multiple pages need to check which takes time, increased the errors chances and delays decision making also.

Where NLP protect from these manual operations and simplifies them on communication assistance.

For knowing the last month delayed shipments just, A manger simply types or tell:
“Why were exports delayed last month?”

Using NLP Odoo understand the question completely:

Time and Period
Process
Problem
Intent means root cause

Then Odoo automatically checks the relevant module such as:

Inventory: How much fishes or fish batches stock is available currently
Logistics / Deliveries: What are the differences between shipment dates and planned dates
Purchase: Packaging material and ice shortage
Quality: How many inspections are failed and how many are rejected in that
Accounting: what and how much payments are hold and what documents are there.

If you closely see the above process there is no manual effort happen yet, Odoo itself analyzing everything as a human and more than better that, from beginning process to the end of that, so no manual filter required more.

Finally, Odoo finds the problem and its root causes by deep analyzing:

Insufficient stock: Fish removal and final collection was delayed due to low maturity
Logistics delay: Cold-chain transport arrived.
Documentation issues: Export certificates approved late.
Packaging shortage: Ice and packing material are not available on time.

After a detail analysis Odoo don’t confuse the users by lengthy and overlong text, won’t let them to diffuse anyway, it reacts simply by saying:

“Export was delayed last month due to 2-3 days delay in cold storage transport, and late approval of export documents for two shipments, this is actually affected exports t UAE and Oman.

Predictive Analytics:

Predictive analysis is the intelligence layer of Odoo that a one-step more and deeper analysis, while reports explain what happened and NLP stands for why happened, than predictive analysis answers:

What is going to happen?
When will it happen?
How much serious will it be?
What actions should take now?

It uses the:

ML modals
Historical data
Trends analysis
Current operational data

To assume futuristic forecast, risks, growth, outcome and downfall possibilities.

Predictive Analytics in Odoo for BlueWave

Export Delay Prediction:

Odoo predicts shipment delay by analyzing past performance, operations flow, logistics availability and reliability and seasonal crowd. Managers can be alerted early with plan of action before customer experience delay.

Inventory Spoilage & Quality Risk Prediction:

Odoo forecast spoilage risk by analyzing the fish batch, duration, time, sales movement.

Cash Flow Risk Forecasting:

Odoo predicts and forecast the cash flow risk by analyzing payable, receivable payments, expenses per unit or batch, entire paym ent behavior and upcoming expenses as well.

Hence finance teams get early message to plan activities specially for avoiding liquidity stress.

Demand vs Supply Forecasting:

Odoo forecast the demand and supply by analyzing export details, seasonal trends, and overall fish growth cycle. Production and pricing strategies set in advance to avoid missing sales and shortages.

Disease & Mortality Risk Forecast (Aquaculture):

Odoo predicts the disease by analyzing sensor data, fish previous incidents, fish behavior. Early alerts give benefits and prevent unrequired actions and to take timely intervention and attention.

Intelligent Automation:

Intelligence automation is states intelligence defined workflows and automation, which avoid the predefined and traditional workflow style to predict and forecast the outcomes properly and to bring the intelligent driven results, that will perform the actions dynamically according to the scenario like real-time conditions, data changes, and business flows and priorities.

In simple terms we can understand like this:

Predefined workflow Says: IF-THEN rules
Intelligent automation workflow says: “UNDERSTAND → DECIDE → ADJUST” logic

How Intelligent automation workflow is different from fixed or traditional workflow:

Traditional Workflow example:

Shipment data is fixed
Transport is pre-assigned
Harvest schedule is static completely

If any thing get change required different approach than this system fails in that, like weather, delay, urgency changes, but in the intelligence that won’t be an issue.

Intelligent Automation in Odoo:

Odoo continuously monitors:

Weather conditions
Vessel and transporter availability
Order priority and urgency
Inventory readiness
Compliance and documentation status

When conditions change, Odoo automatically recalculates and adjusts the plan accordingly.

Intelligent Automation in Action (BlueWave Example):

Scenario:

BlueWave have an urgent export order for fresh fish to UAE

Suddenly:

Whether conditions will be on high alert (if required)
Order assigned to specific boat to be available on time
Export deadlines become highly critical looking the complex situation while preventing lateness in customer order delivery.