app.py

import streamlit as st
import numpy as np

# Optimization function
def optimize_process(resource_allocation, machine_efficiency, production_goal, time_frame, waste_tolerance):
    # Calculate current production capacity
    current_capacity = resource_allocation * machine_efficiency * time_frame
    machines_needed = np.ceil(production_goal / (machine_efficiency * time_frame))
    expected_output = min(current_capacity, production_goal)
    waste_output = (expected_output * waste_tolerance) / 100
    
    # Determine realistic efficiency improvement recommendation
    required_efficiency = production_goal / (resource_allocation * time_frame)
    realistic_efficiency = max(75, min(95, required_efficiency * 100))  # Efficiency capped between 75% and 95%
    efficiency_improvement_needed = max(0, realistic_efficiency - machine_efficiency * 100)

    return {
        'Machines Needed': machines_needed,
        'Expected Output': expected_output,
        'Waste Output': waste_output,
        'Efficiency Improvement Needed': efficiency_improvement_needed,
        'Recommendation Efficiency': realistic_efficiency,
        'Optimization Recommendation': f"Machines efficiency should ideally be at least {realistic_efficiency:.1f}% for optimal results based on industry standards."
    }

# Streamlit App Layout
st.set_page_config(page_title="Manufacturing Process Optimization", layout="wide")
st.title("🌟 Welcome to the AI-Powered Manufacturing Process Optimization Tool 🌟")
st.markdown("""
This tool helps you **optimize your manufacturing processes** by adjusting **resource allocation**, **machine efficiency**, and **production goals** to maximize **efficiency**, reduce **waste**, and improve **product quality**.
""")

# Sidebar for user input
with st.sidebar:
    st.header("🔧 Enter Manufacturing Parameters")
    resource_allocation = st.number_input("🔢 Number of machines available", min_value=1, max_value=100, value=10, step=1)
    machine_efficiency = st.slider("⚙️ Machine Efficiency (%)", min_value=50, max_value=95, value=80, step=1)  # Max efficiency capped at 95%
    production_goal = st.number_input("📈 Desired production goal (units)", min_value=1, max_value=1000, value=100, step=1)
    time_frame = st.number_input("⏳ Production time frame (hours)", min_value=1, max_value=24, value=8, step=1)
    waste_tolerance = st.slider("♻️ Maximum waste tolerance (%)", min_value=0, max_value=100, value=5)

# Main content
st.subheader("🔍 Optimization Results")

if st.button("🚀 Optimize Process"):
    # Get optimization results
    optimized_output = optimize_process(
        resource_allocation, 
        machine_efficiency / 100, 
        production_goal, 
        time_frame, 
        waste_tolerance
    )
    
    # Display the optimized configuration
    st.write(f"### 🛠️ Optimized Configuration:")
    st.write(f"**Machines Needed**: {int(optimized_output['Machines Needed'])}")
    st.write(f"**Expected Output**: {optimized_output['Expected Output']} units")
    st.write(f"**Expected Waste**: {optimized_output['Waste Output']:.2f} units")
    st.write(f"**Efficiency Improvement Needed**: {optimized_output['Efficiency Improvement Needed']:.2f}%")
    st.write(f"**Recommendation**: {optimized_output['Optimization Recommendation']}")

    # Generate Optimization Report
    st.subheader("📊 Optimization Report")
    efficiency_message = (
        "The current machine efficiency is adequate to meet your production goals. No further improvement is required."
        if optimized_output['Efficiency Improvement Needed'] == 0
        else "The current machine efficiency may not be sufficient to meet your production goals. Improving machine efficiency could yield better results."
    )
    st.markdown(f"""
    ### Key Insights:
    - **Production Efficiency**: {efficiency_message}
    - **Waste Management**: The waste is currently within the acceptable tolerance, but reducing waste further will improve overall efficiency.
    - **Resource Allocation**: The number of machines available is adequate, but you could potentially increase the machine count to optimize the process.
    
    ### Suggestions for Improvement:
    1. **Improve Machine Efficiency**: A **{optimized_output['Efficiency Improvement Needed']:.2f}%** increase in machine efficiency will help meet the desired production standards.
    2. **Increase Machines**: Allocating more machines could help meet the production goal faster and reduce the time required.
    3. **Reduce Downtime**: Consider adjusting shift lengths or optimizing machine usage to reduce downtime and improve efficiency.
    
    ### Next Steps:
    - Aim to **improve machine efficiency to {optimized_output['Recommendation Efficiency']:.1f}%** for optimal results.
    - **Monitor waste** closely to reduce it further and ensure the production process remains efficient.
    - Review your **production goals** to ensure you are using the most efficient configuration of resources.
    """)

# Footer with contact information
st.markdown("""
---
For support or more information, feel free to contact us at **support@manufacturing-ai.com**.
""")