AI-Powered Warehouse Temperature Automation Using IoT and SAP

 

Overview

In this project, we present a smart, scalable solution that leverages AI, IoT, and SAP technologies to automate and optimize warehouse temperature management across a nationwide network. The system ensures ideal storage conditions for temperature-sensitive goods such as chocolates, food items, and medicines, thereby reducing spoilage, improving compliance, and enhancing operational efficiency.

 

Business Case

Warehouses storing temperature-sensitive goods face significant challenges in maintaining optimal conditions, often relying on manual monitoring and reactive maintenance, which can lead to spoilage, energy inefficiency, and operational delays. The proposed solution integrates Arduino-based IoT devices with SAP Cloud and SAP Extended Warehouse Management (EWM) to automate temperature monitoring and control. These devices send real-time temperature data to SAP, which uses AI to analyze stock requirements and environmental conditions, then remotely adjusts air conditioning systems via infrared signals. The implementation plan includes nationwide deployment of IoT sensors, RESTful API integration with SAP, AI model development for predictive maintenance and anomaly detection, and workflow automation for alerts and emergency responses. This system ensures centralized control, reduces waste, improves energy efficiency, and enhances safety and compliance across all warehouses.

 

Benefits and Value Addition

This idea is innovative because it seamlessly combines IoT, SAP, and AI to create a fully automated, intelligent warehouse temperature management system that goes beyond simple monitoring to enable real-time control, predictive maintenance, and emergency response. By leveraging AI models to analyze temperature trends and stock sensitivity, the system proactively adjusts environmental conditions and initiates workflows, reducing spoilage, energy waste, and downtime. Its modular architecture allows for easy scaling across multiple warehouses, and integration with SAP ensures enterprise-grade reliability and data visibility. Over time, this solution can generate sustainable value by improving operational efficiency, enhancing product quality, and supporting ESG goals through smarter energy use and compliance automation.

 

Problem Statement

Warehouses storing sensitive stock often face challenges in maintaining optimal temperature conditions. Manual monitoring is inefficient and reactive, leading to product degradation, energy waste, and delayed maintenance. There is a need for a centralized, intelligent system that can monitor, analyze, and control temperature conditions in real-time.

 

 

 

 

 

 

 

 

 

System Components

1. IoT Device (Arduino)
• Modules:
• Temperature sensor
• Smoke detector (optional)
• Infrared transmitter (for AC control)
• Connectivity:
• Internet-enabled (Wi-Fi/GSM)
• Sends data via RESTful API every 5 minutes
2. SAP Cloud Server
• Receives temperature data
• Stores and visualizes warehouse conditions
• Integrates with SAP EWM to identify stock types and their required temperature ranges
3. SAP EWM
• Maintains inventory details
• Provides recommended temperature ranges for each stock item
4. Air Conditioning System
• Controlled via infrared signals from Arduino
• Adjusted based on SAP's response

 

Automation Process

The automation workflow begins with Arduino-based IoT devices installed in warehouses, which continuously monitor temperature and send readings to the SAP Cloud every five minutes via RESTful API. SAP Cloud receives this data and integrates it with SAP Extended Warehouse Management (EWM) to identify the types of stock stored and their optimal temperature ranges. Using AI, the system analyzes current conditions and calculates the ideal temperature, which is then sent back to the IoT device. The Arduino reads this response and adjusts the air conditioning system via infrared signals. If the temperature fails to stabilize, the system triggers a maintenance workflow, and in critical cases, alerts emergency services. This closed-loop automation ensures real-time control, proactive maintenance, and safety compliance.

 

The Solution can be broadly classified in 3 sections:

·         IoT Deployment:

o   Arduino-based devices installed in each warehouse.

o   Equipped with temperature sensors, infrared modules (for AC control), and optional smoke detectors.

o   Devices send temperature data every 5 minutes to SAP Cloud via RESTful API.

·         SAP Integration:

o   SAP Cloud receives and stores temperature data.

o   SAP Extended Warehouse Management (EWM) identifies stock types and their recommended temperature ranges.

o   SAP system calculates optimal temperature and sends it back to the Arduino device.

·         Automated Control:

o   Arduino reads the response and adjusts the AC via infrared signals.

o   If temperature remains abnormal, alerts are triggered, and maintenance workflows are initiated.

o   In critical cases, alerts are sent to emergency services.

 

Automation Workflow

 

Data Collection

·         Arduino reads temperature

·         Sends data to SAP Cloud via REST API

Data Analysis & Decision Making

·         SAP Cloud checks current temperature against recommended range from SAP EWM

·         Calculates optimal temperature

·         Sends response back to Arduino

Temperature Adjustment

·         Arduino reads response

·         Sends infrared signal to AC to adjust temperature

Exception Handling

·         If temperature remains abnormal:

·         Alert sent to central system

·         Maintenance workflow initiated

·         If temperature reaches critical levels:

·         Alert sent to fire department

·         Smoke detector can trigger additional alerts

 

AI-driven possibilities from Thermal Data sent by IoT Device

 

1. Predictive Maintenance: AI models analyze temperature trends and equipment behavior to forecast potential failures, enabling proactive maintenance before breakdowns occur.
2. Stock Quality Prediction: By correlating temperature data with stock shelf-life, AI can predict spoilage risks and alert warehouse managers to take timely action.
3. Intelligent Stock Placement: AI recommends optimal placement of temperature-sensitive items within the warehouse based on historical and real-time temperature data.
4. Dynamic Energy Optimization: AI adjusts air conditioning usage by analyzing temperature patterns and external weather data, reducing energy consumption while maintaining ideal conditions.
5. Anomaly Detection: AI detects sudden or unusual temperature changes that may indicate equipment malfunction, fire risk, or unauthorized access, triggering immediate alerts.
6. Compliance & Audit Automation: AI automatically generates reports based on temperature logs and regulatory standards, simplifying audits and ensuring compliance.
7. Environmental Impact Analysis: AI evaluates the relationship between temperature control and energy usage to support sustainability goals and ESG reporting.
8. Demand Forecasting Integration: AI links temperature data with sales trends to forecast demand for temperature-sensitive products, improving inventory planning and reducing waste.

 

AI Model Architecture: Predictive Maintenance for Warehouse Equipment

 

The architecture begins with IoT devices collecting temperature and equipment usage data from warehouses, which is ingested into the SAP Cloud via RESTful APIs. This data is stored and processed in a centralized data lake or SAP HANA Cloud, where it undergoes cleaning and feature engineering to extract meaningful patterns such as temperature deviations, AC activity frequency, and maintenance history. A hybrid AI model is then applied, combining LSTM for time-series forecasting and XGBoost for classification to predict equipment failure probabilities and estimate remaining useful life. The model is trained on historical labeled data and continuously retrained to adapt to new patterns. Predictions are integrated into SAP systems to trigger automated maintenance workflows, generate alerts, and visualize equipment health in dashboards, ensuring proactive and intelligent asset management.

1. Data Sources

•      IoT Devices (Arduino):

•      Temperature readings (every 5 minutes)

•      Smoke detector alerts

•      AC response logs

•      SAP EWM:

•      Equipment metadata (type, age, maintenance history)

•      Warehouse layout and stock sensitivity

•      External Data (optional):

•      Weather data

•      Power supply fluctuations

2. Data Ingestion Layer

•      RESTful API Gateway (SAP BTP or SAP Integration Suite)

•      Data Lake or SAP HANA Cloud for storage

•      Real-time stream processing (e.g., Apache Kafka or SAP Event Mesh)

3. Data Preprocessing

•      Cleaning: Remove outliers, fill missing values

•      Feature Engineering:

•      Temperature deviation trends

•      Frequency of AC adjustments

•      Time since last maintenance

•      Equipment usage patterns

4. AI/ML Model Layer

•      Model Type: Time Series Forecasting + Classification

•      LSTM (Long Short-Term Memory) for temporal patterns

•      Random Forest or XGBoost for failure classification

•      Output:

•      Failure probability score

•      Remaining Useful Life (RUL)

•      Maintenance urgency level

5. Model Training & Evaluation

•      Historical labeled data (failures, maintenance logs)

•      Cross-validation and hyperparameter tuning

•      Model retraining pipeline (weekly/monthly)

6. Integration with SAP

•      Embed model in SAP AI Core or SAP AI Foundation

•      Trigger workflows in SAP EWM or SAP Maintenance Management

•      Alert dashboards in SAP Fiori or SAP Analytics Cloud

7. Action Layer

•      Automated Alerts: Email, SMS, SAP notifications

•      Maintenance Scheduling: Auto-create work orders

•      Dashboard Visualization: Real-time health status of equipment

 

POC for IOT Integration with SAP AI.

 

Configuring the IOT Device

We have a Raspberry Pi (IOT Device) that reads temperature from a DHT22 thermal sensor every 3 minutes and sends it to a SAP server via REST API. It also analyzes the response to determine whether to turn the AC on/off or adjust its temperature.

Given below is the source code in Python that is programmed into the Raspberry Pi device.

The following Python Library files are required:

sudo apt-get update

sudo apt-get install python3-pip

pip3 install Adafruit_DHT requests

 

Given below code will read temperature and send the information to SAP server.

import time

import requests

import Adafruit_DHT

import json

# Sensor configuration

DHT_SENSOR = Adafruit_DHT.DHT22  # Use DHT11 if applicable

DHT_PIN = 4  # GPIO pin connected to the sensor

# SAP API configuration

API_URL = "https://sap-server.example.com/api/temperature"  # Replace with actual SAP endpoint

HEADERS = {

    "Content-Type": "application/json",

    "Authorization": "Bearer YOUR_API_TOKEN"  # Replace with actual token if required

}

def read_temperature():

    humidity, temperature = Adafruit_DHT.read(DHT_SENSOR, DHT_PIN)

    if humidity is not None and temperature is not None:

        return round(temperature, 2), round(humidity, 2)

    else:

        print("Failed to retrieve data from sensor")

        return None, None

def send_data_to_sap(temperature, humidity):

    payload = {

        "device_id": "warehouse_pi_001",

        "temperature": temperature,

        "humidity": humidity,

        "timestamp": time.strftime("%Y-%m-%d %H:%M:%S")

    }

    try:

        response = requests.post(API_URL, headers=HEADERS, json=payload)

        print(f"Sent data: {payload}")

        print(f"Response status: {response.status_code}")

        if response.status_code == 200:

            analyze_response(response.json())

        else:

            print("Error in response from SAP server.")

    except Exception as e:

        print(f"Exception occurred while sending data: {e}")

def analyze_response(response_data):

    print(f"Received response: {response_data}")

    action = response_data.get("ac_action")

    temperature_setting = response_data.get("ac_temperature")

    if action == "ON":

        print("Turning AC ON")

        # Add GPIO or IR control logic here

    elif action == "OFF":

        print("Turning AC OFF")

        # Add GPIO or IR control logic here

    elif action == "SET_TEMPERATURE" and temperature_setting is not None:

        print(f"Setting AC temperature to {temperature_setting}°C")

        # Add GPIO or IR control logic here

    else:

        print("No actionable command received.")

# Main loop

if __name__ == "__main__":

    while True:

        temp, hum = read_temperature()

        if temp is not None and hum is not None:

            send_data_to_sap(temp, hum)

        time.sleep(180)  # Wait for 3 minutes

 

GUI for Dashboard for Monitoring, Automation, and Actions.

Key Features Implemented:

• Real-time monitoring for 4 different warehouses (Chocolate, Food, Medicine, Cold Storage)
• Temperature gauges with status indicators (normal/warning/critical)
• Interactive alerts panel with maintenance workflows
• AC control system with manual and automated modes
• Analytics charts showing temperature trends and humidity data
• Stock management for temperature-sensitive items
• Action forms for temperature control and maintenance requests

 

Top of Dashboard:

It shows number of warehouses, critical, warnings on temperature based on live data.

 

Normal State:

When there are no issues it provides the details as shown below.

Warning State:

When there are some issues due to temperature then it gets highlighted as warning. Warning message can be seen in the dashboard.

 

Critical State:

When temperature is beyond tolerable limited it is considered critical. In this case we can use AC Control to change the AC temperature or switch on/off the ac remotely.

 

Message Monitor:

The Message Monitor is used to find warnings and critical information. The Message Monitor responses are used as input for Generative AI which can take automated actions like controlling the air conditioner, informing maintenance team or calling emergency helping services.

Control Centre:

The control centre can be used to adjust the temperature of warehouses as well as switch on/off the ait conditioners as and when needed. With Generative AI Integration the temperature can be automatically controlled using the Auto Mode feature.

 

For AC Control the Raspberry Pi was fitted with IR LED connected to a GPIO pin (e.g., GPIO 17).

For IR control, the pigpio library needs to be installed.

sudo apt-get install pigpio python3-pigpio

sudo systemctl enable pigpiod

sudo systemctl start pigpiod

 

Given below are the codes or air conditioner control. This code needs to be customized for various air conditioners being used.

import pigpio

# IR LED GPIO pin

IR_GPIO = 17

pi = pigpio.pi()

# Example IR codes (replace with actual codes for your AC)

AC_ON_CODE = [9000, 4500, 560, 560, 560, 1690, ...]  # Replace with actual pulse sequence

AC_OFF_CODE = [9000, 4500, 560, 1690, 560, 560, ...]

AC_TEMP_CODES = {

    22: [...],  # IR pulse sequence for 22°C

    24: [...],  # IR pulse sequence for 24°C

    # Add more temperature codes as needed

}

def send_ir_signal(code):

    pi.wave_clear()

    wave = []

    for i in range(0, len(code), 2):

        wave.append(pigpio.pulse(1 << IR_GPIO, 0, code[i]))

        wave.append(pigpio.pulse(0, 1 << IR_GPIO, code[i+1]))

    pi.wave_add_generic(wave)

    wave_id = pi.wave_create()

    pi.wave_send_once(wave_id)

    while pi.wave_tx_busy():

        time.sleep(0.1)

    pi.wave_delete(wave_id)

def control_ac(action, temperature=None):

    if action == "ON":

        send_ir_signal(AC_ON_CODE)

    elif action == "OFF":

        send_ir_signal(AC_OFF_CODE)

    elif action == "SET_TEMPERATURE" and temperature in AC_TEMP_CODES:

        send_ir_signal(AC_TEMP_CODES[temperature])

    else:

        print("Unknown AC command or temperature setting.")

 

Conclusion

The AI-powered warehouse temperature automation system presents a transformative approach to managing sensitive inventory across distributed locations. By integrating IoT devices with SAP and leveraging AI for real-time decision-making, the solution ensures optimal storage conditions, reduces spoilage, and enhances operational efficiency. Predictive maintenance, anomaly detection, and energy optimization further contribute to cost savings and reliability. Its scalable architecture allows easy expansion to new warehouses, while its alignment with sustainability and compliance goals ensures long-term value. This intelligent, automated system not only modernizes warehouse operations but also sets a foundation for future innovations in smart supply chain management.