#Python Program

def find_minimum_machine_time(machine_data):

    # Initialize a dictionary to store minimum machine times for each item

    minimum_machine_time = {}

    # Iterate over each item and its corresponding machine data

    for item, machines in machine_data.items():

        min_time = float('inf')  # Set initial minimum time to infinity

        selected_machine = None   # Initialize selected machine to None

        # Iterate over machines for the current item

        for machine in machines:

            # Check if the machine is free

            if machine['Machine Free'] == 'Yes' and machine['Machine Time'] < min_time:

                min_time = machine['Machine Time']

                selected_machine = machine['M/C']

        # If a free machine is found, store the minimum time and the selected machine

        if selected_machine:

            minimum_machine_time[item] = {'M/C': selected_machine, 'Machine Time': min_time}

    return minimum_machine_time

def main():

    # Define the machine data

    machine_data = {

        'a1': [

            {'M/C': 'M/C-1', 'Machine Time': 1.50, 'Machine Free': 'Yes'},

            {'M/C': 'M/C-2', 'Machine Time': 1.00, 'Machine Free': 'Yes'},

            {'M/C': 'M/C-3', 'Machine Time': 2.00, 'Machine Free': 'Yes'},

            {'M/C': 'M/C-4', 'Machine Time': 0.75, 'Machine Free': 'No'}

        ],

        'a2': [

            {'M/C': 'M/C-9', 'Machine Time': 2.00, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 5', 'Machine Time': 0.50, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 6', 'Machine Time': 0.50, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 7', 'Machine Time': 0.50, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 8', 'Machine Time': 0.50, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 9', 'Machine Time': 0.50, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 10', 'Machine Time': 0.40, 'Machine Free': 'Yes'}

        ]

    }

    # Find the minimum machine time for each item

    minimum_machine_time = find_minimum_machine_time(machine_data)

    # Print the result

    for item, data in minimum_machine_time.items():

        print(f"Item: {item}, M/C: {data['M/C']}, Machine Time: {data['Machine Time']}")

if __name__ == "__main__":

    main()

#Result:

Item: a1, M/C: M/C-2, Machine Time: 1.0

Item: a2, M/C: NC M/C 10, Machine Time: 0.4

#Python Program

class AssemblyItem:

    def __init__(self, assembly, level_1_item, unit_1, quantity_1, level_2_item, quantity_2, unit_2):

        self.assembly = assembly

        self.level_1_item = level_1_item

        self.unit_1 = unit_1

        self.quantity_1 = quantity_1

        self.level_2_item = level_2_item

        self.quantity_2 = quantity_2

        self.unit_2 = unit_2

    def __str__(self):

        return f"{self.assembly}\t{self.level_1_item}\t{self.unit_1}\t{self.quantity_1:.2f}\t{self.level_2_item}\t{self.quantity_2:.2f}\t{self.unit_2}"

def generate_assembly_structure(assembly, quantity):

    assembly_items = [

        AssemblyItem('A', 'a1', 'Number', 18.00, 'aa1', 5.00, 'Kg'),

        AssemblyItem('A', 'a1', 'Number', 0.00, 'bb1', 6.00, 'Number'),

        AssemblyItem('A', 'a1', 'Number', 0.00, 'cc1', 8.00, 'Kg'),

        AssemblyItem('A', 'a2', 'Number', 19.00, 'aa2', 18.00, 'Number'),

        AssemblyItem('A', 'a2', 'Number', 0.00, 'bb2', 5.00, 'Kg')

    ]

    print("Assembly\tLevel 1 Item\tUnit of Measurement 1\tQuantity Required for one Assembly\tLevel 2 Item\tQuantity Required for one Sub Assembly\tUnit of Measurement 2")

    for item in assembly_items:

        if item.assembly == assembly:

            item.quantity_1 *= quantity

            item.quantity_2 *= quantity

            print(item)

generate_assembly_structure('A', 50)

Result:

Assembly Itemlevel 1 UOM 1 Quantity level 1 Quantity level2 UOM level 2

A   a1  Number 900.00 aa1   250.00 Kg

A   a1  Number 0.00    bb1  300.00  Number

A   a1  Number 0.00    cc1  400.00 Kg

A   a2  Number 950.00 aa2   900.00  Number

A   a2  Number 0.00    bb2  250.00 Kg

 ======================================================================

#Python Program

data = [

    ["A", "a1", 900, "S1", 2900, 11],

    ["A", "a1", 900, "S2", 3100, 14],

    ["A", "a1", 900, "S3", 2700, 10],

    ["A", "a1", 900, "S4", 3100, 11],

    ["A", "a1", 900, "S5", 2800, 12],

    ["A", "a2", 950, "S1", 1800, 10],

    ["A", "a2", 950, "S3", 1900, 11],

    ["A", "a2", 950, "S4", 1700, 12],

    ["A", "a2", 950, "S5", 1400, 15],

    ["A", "a2", 950, "S6", 1200, 18]

]

# Dictionary to store total costs for each item level 1 and vendor

total_costs = {}

# Calculate total cost for each combination of item level 1 and vendor

for entry in data:

    item = entry[1]

    vendor = entry[3]

    rate_per_unit = entry[4]

    quantity = entry[2]

    total_cost = rate_per_unit * quantity

    if (item, vendor) in total_costs:

        total_costs[(item, vendor)] += total_cost

    else:

        total_costs[(item, vendor)] = total_cost

# Find the minimum total cost for each item level 1

min_total_costs = {}

for key, value in total_costs.items():

    item = key[0]

    cost = value

    if item in min_total_costs:

        if cost < min_total_costs[item][0]:

            min_total_costs[item] = (cost, key[1])

    else:

        min_total_costs[item] = (cost, key[1])

# Print the selected vendor for each item level 1

for item, (cost, vendor) in min_total_costs.items():

    print(f"For Item Level 1 '{item}', select Vendor '{vendor}' with total cost {cost}.")

#Result

For Item Level 1 'a1', select Vendor 'S3' with total cost 2430000.

For Item Level 1 'a2', select Vendor 'S6' with total cost 1140000.

=========================================

#Python Program

from datetime import datetime, timedelta

# Define the data

bom_data = [

    ("A", "a1", 900, "S1", 2900, 11),

    ("A", "a1", 900, "S2", 3100, 14),

    ("A", "a1", 900, "S3", 2700, 10),

    ("A", "a1", 900, "S4", 3100, 11),

    ("A", "a1", 900, "S5", 2800, 12),

    ("A", "a2", 950, "S1", 1800, 10),

    ("A", "a2", 950, "S3", 1900, 11),

    ("A", "a2", 950, "S4", 1700, 12),

    ("A", "a2", 950, "S5", 1400, 15),

    ("A", "a2", 950, "S6", 1200, 18)

]

# Function to calculate delivery date

def calculate_delivery_date(lead_time):

    today = datetime.now()

    delivery_date = today + timedelta(weeks=lead_time)

    return delivery_date.strftime("%Y-%m-%d")

# Selecting vendor for each item

def select_vendor_item_wise(item):

    selected_vendor = None

    min_lead_time = float('inf')

    for assembly, item_code, quantity, vendor, rate, lead_time in bom_data:

        if item_code == item and lead_time < min_lead_time:

            min_lead_time = lead_time

            selected_vendor = (assembly, item_code, quantity, vendor, rate, lead_time)

    return selected_vendor

# Main function

def main():

    items = set(item[1] for item in bom_data)

    for item in items:

        vendor = select_vendor_item_wise(item)

        if vendor:

            print(f"Item: {vendor[1]}")

            print("Selected Vendor: ", vendor[3])

            print("Delivery Date: ", calculate_delivery_date(vendor[5]))

            print("Lead Time: ", vendor[5], " weeks")

            print()

if __name__ == "__main__":

    main()

 #Result

Item: a2

Selected Vendor:  S1

Delivery Date:  2024-06-14

Lead Time:  10  weeks

Item: a1

Selected Vendor:  S3

Delivery Date:  2024-06-14

Lead Time:  10  weeks

======================================================================

#Python Program

def generate_bom_email(bom_data, target_delivery_date, confirmation_date):

  """

  Generates an email body with BOM information

  Args:

      bom_data (list): List of tuples containing BOM data (assembly, item_code, quantity, vendor, rate, lead_time)

      target_delivery_date (str): Target delivery date for materials (YYYY-MM-DD)

      confirmation_date (str): Date by which delivery date confirmation is needed (YYYY-MM-DD)

  Returns:

      str: Email body containing BOM information

  """

  email_body = """Dear [Raw Material and Component Store Manager name],

This letter serves as an official request for materials and components needed for early production. To ensure a timely launch, we require the following items as soon as possible:

**Bill of Materials (BOM) Information:**

"""

  # Build BOM table within email body

  email_body += "**Item | Quantity | Vendor | Lead Time (weeks)**\n"

  email_body += "-" * 50 + "\n"

  for assembly, item_code, quantity, vendor, rate, lead_time in bom_data:

    email_body += f"{item_code} | {quantity} | {vendor} | {lead_time}\n"

  email_body += """

**Focus on Shortest Lead Times:**

We have prioritized vendors with the shortest lead times to expedite production.

**Delivery Timeline:**

To meet our production schedule, we require all materials and components to be delivered by {target_delivery_date}.

**Production Planning:**

Knowing the confirmed delivery dates for these items will be crucial for our production planning team. This will allow them to schedule production activities efficiently and avoid any delays.

**Sales and Inventory Planning:**

Confirmation of delivery dates will also be used to update our sales and inventory planning systems. This ensures we can accurately communicate availability to customers and avoid overselling products.

**Action Required:**

* Please review the BOM information above and prioritize processing orders for the listed items with the shortest lead times.

* Kindly provide a confirmation email with estimated delivery dates for each item by {confirmation_date}.

**Next Steps:**

Upon receiving confirmation of delivery dates, we will finalize the production schedule and provide further updates.

We appreciate  prompt attention to this urgent matter. Please don't hesitate to contact me at [ Phone Number] or [ Email Address] if you have any questions or require further clarification.

Thank you for  cooperation in ensuring a successful early production launch.

Sincerely,

[ Name]

"""

  return email_body

# Example usage (replace with  actual BOM data)

bom_data = [

  ("A", "a1", 900, "S1", 2900, 11),

  ("A", "a2", 950, "S3", 1900, 10),

]

target_delivery_date = "2024-05-31"

confirmation_date = "2024-04-10"

email_content = generate_bom_email(bom_data, target_delivery_date, confirmation_date)

# Use the generated email_content in  email sending process (not included here)

print(email_content)

#Result

Dear [Raw Material and Component Store Manager name],

This letter serves as an official request for materials and components needed for early production. To ensure a timely launch, we require the following items as soon as possible:

**Bill of Materials (BOM) Information:**

**Item | Quantity | Vendor | Lead Time (weeks)**

--------------------------------------------------

a1 | 900 | S1 | 11

a2 | 950 | S3 | 10

**Focus on Shortest Lead Times:**

We have prioritized vendors with the shortest lead times to expedite production.

**Delivery Timeline:**

To meet our production schedule, we require all materials and components to be delivered by {target_delivery_date}.

**Production Planning:**

Knowing the confirmed delivery dates for these items will be crucial for our production planning team. This will allow them to schedule production activities efficiently and avoid any delays.

**Sales and Inventory Planning:**

Confirmation of delivery dates will also be used to update our sales and inventory planning systems. This ensures we can accurately communicate availability to customers and avoid overselling products.

**Action Required:**

* Please review the BOM information above and prioritize processing orders for the listed items with the shortest lead times.

* Kindly provide a confirmation email with estimated delivery dates for each item by {confirmation_date}.

**Next Steps:**

Upon receiving confirmation of delivery dates, we will finalize the production schedule and provide further updates.

We appreciate  prompt attention to this urgent matter. Please don't hesitate to contact me at [ Phone Number] or [ Email Address] if you have any questions or require further clarification.

Thank you for  cooperation in ensuring a successful early production launch.

Sincerely,

[ Name]

 =========================================================================

#Result from the Above Python Program

def generate_bom_email(bom_data, target_delivery_date, confirmation_date):

  """

  Generates an email body with BOM information

  Args:

      bom_data (list): List of tuples containing BOM data (assembly, item_code, quantity, vendor, rate, lead_time)

      target_delivery_date (str): Target delivery date for materials (YYYY-MM-DD)

      confirmation_date (str): Date by which delivery date confirmation is needed (YYYY-MM-DD)

  Returns:

      str: Email body containing BOM information

  """

  email_body = """Dear [Raw Material and Component Store Manager name],

This letter serves as an official request for materials and components needed for early production. To ensure a timely launch, we require the following items as soon as possible:

**Bill of Materials (BOM) Information:**

"""

  # Build BOM table within email body

  email_body += "**Item | Quantity | Vendor | Lead Time (weeks)**\n"

  email_body += "-" * 50 + "\n"

  for assembly, item_code, quantity, vendor, rate, lead_time in bom_data:

    email_body += f"{item_code} | {quantity} | {vendor} | {lead_time}\n"

  email_body += """

**Focus on Shortest Lead Times:**

We have prioritized vendors with the shortest lead times to expedite production.

**Delivery Timeline:**

To meet our production schedule, we require all materials and components to be delivered by {target_delivery_date}.

**Production Planning:**

Knowing the confirmed delivery dates for these items will be crucial for our production planning team. This will allow them to schedule production activities efficiently and avoid any delays.

**Sales and Inventory Planning:**

Confirmation of delivery dates will also be used to update our sales and inventory planning systems. This ensures we can accurately communicate availability to customers and avoid overselling products.

**Action Required:**

* Please review the BOM information above and prioritize processing orders for the listed items with the shortest lead times.

* Kindly provide a confirmation email with estimated delivery dates for each item by {confirmation_date}.

**Next Steps:**

Upon receiving confirmation of delivery dates, we will finalize the production schedule and provide further updates.

We appreciate  prompt attention to this urgent matter. Please don't hesitate to contact me at [ Phone Number] or [ Email Address] if you have any questions or require further clarification.

Thank you for  cooperation in ensuring a successful early production launch.

Sincerely,

[ Name]

"""

  return email_body

# Example usage (replace with  actual BOM data)

bom_data = [

  ("A", "a1", 900, "S1", 2900, 11),

  ("A", "a2", 950, "S3", 1900, 10),

]

target_delivery_date = "2024-05-31"

confirmation_date = "2024-04-10"

email_content = generate_bom_email(bom_data, target_delivery_date, confirmation_date)

# Use the generated email_content in  email sending process (not included here)

print(email_content)

 ===============================================================

#Python Progran

from datetime import datetime, timedelta

# Define the data

payment_data = [

    ("A", "a1", 900, "S3", 2700.00, 10, 2430000.00, "06-06-2024", 30),

    ("A", "a2", 950, "S1", 1800.00, 10, 1710000.00, "06-06-2024", 40)

]

# Function to calculate payment date

def calculate_payment_date(supply_date, credit_period):

    supply_date = datetime.strptime(supply_date, "%d-%m-%Y")

    payment_date = supply_date + timedelta(days=credit_period)

    return payment_date.strftime("%d-%m-%Y")

# Main function

def main():

    print("Vendor Payment Details:")

    for assembly, item, quantity, vendor, rate, lead_time, order_amount, supply_date, credit_period in payment_data:

        payment_date = calculate_payment_date(supply_date, credit_period)

        print(f"Assembly: {assembly}, Item: {item}")

        print(f"Quantity: {quantity}, Vendor/Supplier: {vendor}")

        print(f"Rate Per Unit: {rate}, Lead time weeks: {lead_time}")

        print(f"Order Amount: {order_amount}")

        print("Supply Date:", supply_date)

        print("Credit Period (Days):", credit_period)

        print("Payment Date:", payment_date)

        print()

if __name__ == "__main__":

    main()

#Result

Vendor Payment Details:

Assembly: A, Item: a1

Quantity: 900, Vendor/Supplier: S3

Rate Per Unit: 2700.0, Lead time weeks: 10

Order Amount: 2430000.0

Supply Date: 06-06-2024

Credit Period (Days): 30

Payment Date: 06-07-2024

Assembly: A, Item: a2

Quantity: 950, Vendor/Supplier: S1

Rate Per Unit: 1800.0, Lead time weeks: 10

Order Amount: 1710000.0

Supply Date: 06-06-2024

Credit Period (Days): 40

Payment Date: 16-07-2024

============================================================================================

#Python Program

from datetime import datetime, timedelta

# Define the data

payment_data = [

    ("A", "a1", 900, "S3", 2700.00, 10, 2430000.00, "06-06-2024", 30),

    ("A", "a2", 950, "S1", 1800.00, 10, 1710000.00, "06-06-2024", 40)

]

# Function to calculate payment date

def calculate_payment_date(supply_date, credit_period):

    supply_date = datetime.strptime(supply_date, "%d-%m-%Y")

    payment_date = supply_date + timedelta(days=credit_period)

    return payment_date.strftime("%d-%m-%Y")

# Main function to calculate cash flow for payment dates

def calculate_cash_flow(payment_data):

    cash_flow = {}

    for assembly, item, quantity, vendor, rate, lead_time, order_amount, supply_date, credit_period in payment_data:

        payment_date = calculate_payment_date(supply_date, credit_period)

        if payment_date not in cash_flow:

            cash_flow[payment_date] = []

        cash_flow[payment_date].append((vendor, order_amount))

    return cash_flow

# Main function to print cash flow for payment dates

def main():

    cash_flow = calculate_cash_flow(payment_data)

    print("Cash Flow for Payment Dates:")

    for payment_date, transactions in cash_flow.items():

        print(f"Payment Date: {payment_date}")

        for vendor, amount in transactions:

            print(f"Vendor: {vendor}, Amount: {amount}")

        print()

if __name__ == "__main__":

    main()

#Result

Cash Flow for Payment Dates:

Payment Date: 06-07-2024

Vendor: S3, Amount: 2430000.0

Payment Date: 16-07-2024

Vendor: S1, Amount: 1710000.0

 ===================================================================

#Python Program for Graph

import matplotlib.pyplot as plt

# Cash flow data with formatted amounts

cash_flow_data = [

    {"Payment Date": "06-07-2024", "Amount": 2430000.0},

    {"Payment Date": "16-07-2024", "Amount": 1710000.0},

]

# Extract payment dates and amounts

payment_dates = [item["Payment Date"] for item in cash_flow_data]

amounts = [item["Amount"] for item in cash_flow_data]

# Create a bar graph

plt.figure(figsize=(10, 6))  # Set figure size

plt.bar(payment_dates, amounts, color='red')  # Set bar color to red

# Customize the plot

plt.xlabel("Payment Date")

plt.ylabel("Amount (₹)")

plt.title("Cash Flow for Payment Dates")

plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability

plt.grid(axis='y', linestyle='--', alpha=0.6)  # Add grid lines

# Display the plot

plt.tight_layout()

plt.show()

======================================================================

#Python Program

def find_minimum_machine_time(machine_data):

    # Initialize a dictionary to store minimum machine times for each item

    minimum_machine_time = {}

    # Iterate over each item and its corresponding machine data

    for item, machines in machine_data.items():

        min_time = float('inf')  # Set initial minimum time to infinity

        selected_machine = None   # Initialize selected machine to None

        # Iterate over machines for the current item

        for machine in machines:

            # Check if the machine is free

            if machine['Machine Free'] == 'Yes' and machine['Machine Time'] < min_time:

                min_time = machine['Machine Time']

                selected_machine = machine['M/C']

        # If a free machine is found, store the minimum time and the selected machine

        if selected_machine:

            minimum_machine_time[item] = {'M/C': selected_machine, 'Machine Time': min_time}

    return minimum_machine_time

def main():

    # Define the machine data

    machine_data = {

        'a1': [

            {'M/C': 'M/C-1', 'Machine Time': 1.50, 'Machine Free': 'Yes'},

            {'M/C': 'M/C-2', 'Machine Time': 1.00, 'Machine Free': 'Yes'},

            {'M/C': 'M/C-3', 'Machine Time': 2.00, 'Machine Free': 'Yes'},

            {'M/C': 'M/C-4', 'Machine Time': 0.75, 'Machine Free': 'No'}

        ],

        'a2': [

            {'M/C': 'M/C-9', 'Machine Time': 2.00, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 5', 'Machine Time': 0.50, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 6', 'Machine Time': 0.50, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 7', 'Machine Time': 0.50, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 8', 'Machine Time': 0.50, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 9', 'Machine Time': 0.50, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 10', 'Machine Time': 0.40, 'Machine Free': 'No'}

        ]

    }

    # Find the minimum machine time for each item

    minimum_machine_time = find_minimum_machine_time(machine_data)

    # Print the result

    for item, data in minimum_machine_time.items():

        print(f"Item: {item}, M/C: {data['M/C']}, Machine Time: {data['Machine Time']}")

if __name__ == "__main__":

    main()

Result

Item: a1, M/C: M/C-2, Machine Time: 1.0

Item: a2, M/C: NC M/C 5, Machine Time: 0.5

 ============================================================================

#Python Program

def find_minimum_machine_time(machine_data):

    # Initialize a dictionary to store minimum machine times for each item

    minimum_machine_time = {}

    # Iterate over each item and its corresponding machine data

    for item, machines in machine_data.items():

        min_time = float('inf')  # Set initial minimum time to infinity

        selected_machine = None   # Initialize selected machine to None

        # Iterate over machines for the current item

        for machine in machines:

            # Check if the machine is free

            if machine['Machine Free'] == 'Yes' and machine['Machine Time'] < min_time:

                min_time = machine['Machine Time']

                selected_machine = machine['M/C']

        # If a free machine is found, store the minimum time and the selected machine

        if selected_machine:

            minimum_machine_time[item] = {'M/C': selected_machine, 'Machine Time': min_time}

    return minimum_machine_time

def main():

    # Define the machine data

    machine_data = {

        'a1': [

            {'M/C': 'M/C-1', 'Machine Time': 1.50, 'Machine Free': 'Yes'},

            {'M/C': 'M/C-2', 'Machine Time': 1.00, 'Machine Free': 'Yes'},

            {'M/C': 'M/C-3', 'Machine Time': 2.00, 'Machine Free': 'Yes'},

            {'M/C': 'M/C-4', 'Machine Time': 0.75, 'Machine Free': 'No'}

        ],

        'a2': [

            {'M/C': 'M/C-9', 'Machine Time': 2.00, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 5', 'Machine Time': 0.50, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 6', 'Machine Time': 0.50, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 7', 'Machine Time': 0.50, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 8', 'Machine Time': 0.50, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 9', 'Machine Time': 0.50, 'Machine Free': 'Yes'},

            {'M/C': 'NC M/C 10', 'Machine Time': 0.40, 'Machine Free': 'Yes'}

        ]

    }

    # Find the minimum machine time for each item

    minimum_machine_time = find_minimum_machine_time(machine_data)

    # Print the result

    for item, data in minimum_machine_time.items():

        print(f"Item: {item}, M/C: {data['M/C']}, Machine Time: {data['Machine Time']}")

if __name__ == "__main__":

    main()

#Result:

Item: a1, M/C: M/C-2, Machine Time: 1.0

Item: a2, M/C: NC M/C 10, Machine Time: 0.4

========================================================================

#Python Program

# Define a list to store balance data

balances = []

# Input data

data = [

    (310101, "Product A", "A00012", "ABC Co.", "Delhi", 2022, 450098.00, 356789.00),

    (310101, "Product A", "A00014", "XYZ Co.", "Delhi", 2022, 750098.00, 556789.00),

    (310101, "Product A", "A00015", "PQR Co.", "Delhi", 2022, 750098.00, 656789.00),

    (310101, "Product A", "A00012", "ABC Co.", "Calcutta", 2022, 350098.00, 296789.00),

    (310101, "Product A", "A00014", "XYZ Co.", "Calcutta", 2022, 750098.00, 656789.00),

    (310101, "Product A", "A00015", "PQR Co.", "Calcutta", 2022, 750098.00, 750098.00),

    (310101, "Product A", "A00012", "ABC Co.", "Delhi", 2023, 750098.00, 556789.00),

    (310101, "Product A", "A00014", "XYZ Co.", "Delhi", 2023, 650098.00, 456789.00),

    (310101, "Product A", "A00015", "PQR Co.", "Delhi", 2023, 650098.00, 556789.00),

    (310101, "Product A", "A00012", "ABC Co.", "Calcutta", 2023, 450098.00, 426789.00),

    (310101, "Product A", "A00014", "XYZ Co.", "Calcutta", 2023, 850098.00, 756789.00),

    (310101, "Product A", "A00015", "PQR Co.", "Calcutta", 2023, 550098.00, 350100.00),

    (310101, "Product B", "A00012", "ABC Co.", "Delhi", 2022, 350098.00, 456789.00),

    (310101, "Product B", "A00014", "XYZ Co.", "Delhi", 2022, 550098.00, 656789.00),

    (310101, "Product B", "A00015", "PQR Co.", "Delhi", 2022, 850098.00, 756789.00),

    (310101, "Product B", "A00012", "ABC Co.", "Calcutta", 2022, 340098.00, 396789.00),

    (310101, "Product B", "A00014", "XYZ Co.", "Calcutta", 2022, 750098.00, 850098.00),

    (310101, "Product B", "A00015", "PQR Co.", "Calcutta", 2022, 650098.00, 850098.00),

    (310101, "Product B", "A00012", "ABC Co.", "Delhi", 2023, 850098.00, 456789.00),

    (310101, "Product B", "A00014", "XYZ Co.", "Delhi", 2023, 750098.00, 756789.00),

    (310101, "Product B", "A00015", "PQR Co.", "Delhi", 2023, 650768.00, 656789.00),

    (310101, "Product B", "A00012", "ABC Co.", "Calcutta", 2023, 350098.00, 326789.00),

    (310101, "Product B", "A00014", "XYZ Co.", "Calcutta", 2023, 950098.00, 456789.00),

    (310101, "Product B", "A00015", "PQR Co.", "Calcutta", 2023, 450098.00, 650199.00)

]

# Calculate balances and store data

for entry in data:

    account_head, _, _, customer_name, location, year, invoice_amount, amount_received = entry

    balance = invoice_amount - amount_received

    balances.append((balance, invoice_amount, amount_received, account_head, customer_name, location, year))

# Print results

print("Balance Amount\tInvoice Amount\tAmount Received\tAccount Head\tCustomer Name\tLocation\tYear")

for balance_data in balances:

    print("\t".join(map(str, balance_data)))

#Result

Balance Amount   Invoice Amount   Amount Received  Account Head Customer Name Location  Year

93309.0 450098.0   356789.0   310101 ABC Co.  Delhi 2022

193309.0   750098.0   556789.0   310101 XYZ Co.  Delhi 2022

93309.0 750098.0   656789.0   310101 PQR Co.  Delhi 2022

53309.0 350098.0   296789.0   310101 ABC Co.  Calcutta   2022

93309.0 750098.0   656789.0   310101 XYZ Co.  Calcutta   2022

0.0   750098.0   750098.0   310101 PQR Co.  Calcutta   2022

193309.0   750098.0   556789.0   310101 ABC Co.  Delhi 2023

193309.0   650098.0   456789.0   310101 XYZ Co.  Delhi 2023

93309.0 650098.0   556789.0   310101 PQR Co.  Delhi 2023

23309.0 450098.0   426789.0   310101 ABC Co.  Calcutta   2023

93309.0 850098.0   756789.0   310101 XYZ Co.  Calcutta   2023

199998.0   550098.0   350100.0   310101 PQR Co.  Calcutta   2023

-106691.0  350098.0   456789.0   310101 ABC Co.  Delhi 2022

-106691.0  550098.0   656789.0   310101 XYZ Co.  Delhi 2022

93309.0 850098.0   756789.0   310101 PQR Co.  Delhi 2022

-56691.0   340098.0   396789.0   310101 ABC Co.  Calcutta   2022

-100000.0  750098.0   850098.0   310101 XYZ Co.  Calcutta   2022

-200000.0  650098.0   850098.0   310101 PQR Co.  Calcutta   2022

393309.0   850098.0   456789.0   310101 ABC Co.  Delhi 2023

-6691.0 750098.0   756789.0   310101 XYZ Co.  Delhi 2023

-6021.0 650768.0   656789.0   310101 PQR Co.  Delhi 2023

23309.0 350098.0   326789.0   310101 ABC Co.  Calcutta   2023

493309.0   950098.0   456789.0   310101 XYZ Co.  Calcutta   2023

-200101.0  450098.0   650199.0   310101 PQR Co.  Calcutta   2023

==============================================================================================

#Python Program

# Define data

data = [

    ["Product A", "ABC Co.", "Delhi", 2022, 450098.00, 356789.00, 93309.00],

    ["Product A", "XYZ Co.", "Delhi", 2022, 750098.00, 556789.00, 193309.00],

    ["Product A", "PQR Co.", "Delhi", 2022, 750098.00, 656789.00, 93309.00],

    ["Product A", "ABC Co.", "Calcutta", 2022, 350098.00, 296789.00, 53309.00],

    ["Product A", "XYZ Co.", "Calcutta", 2022, 750098.00, 656789.00, 93309.00],

    ["Product A", "PQR Co.", "Calcutta", 2022, 750098.00, 750098.00, 0.00],

    ["Product A", "ABC Co.", "Delhi", 2023, 750098.00, 556789.00, 193309.00],

    ["Product A", "XYZ Co.", "Delhi", 2023, 650098.00, 456789.00, 193309.00],

    ["Product A", "PQR Co.", "Delhi", 2023, 650098.00, 556789.00, 93309.00],

    ["Product A", "ABC Co.", "Calcutta", 2023, 450098.00, 426789.00, 23309.00],

    ["Product A", "XYZ Co.", "Calcutta", 2023, 850098.00, 756789.00, 93309.00],

    ["Product A", "PQR Co.", "Calcutta", 2023, 550098.00, 350100.00, 199998.00],

    ["Product B", "ABC Co.", "Delhi", 2022, 350098.00, 456789.00, -106691.00],

    ["Product B", "XYZ Co.", "Delhi", 2022, 550098.00, 656789.00, -106691.00],

    ["Product B", "PQR Co.", "Delhi", 2022, 850098.00, 756789.00, 93309.00],

    ["Product B", "ABC Co.", "Calcutta", 2022, 340098.00, 396789.00, -56691.00],

    ["Product B", "XYZ Co.", "Calcutta", 2022, 750098.00, 850098.00, -100000.00],

    ["Product B", "PQR Co.", "Calcutta", 2022, 650098.00, 850098.00, -200000.00],

    ["Product B", "ABC Co.", "Delhi", 2023, 850098.00, 456789.00, 393309.00],

    ["Product B", "XYZ Co.", "Delhi", 2023, 750098.00, 756789.00, -6691.00],

    ["Product B", "PQR Co.", "Delhi", 2023, 650768.00, 656789.00, -6021.00],

    ["Product B", "ABC Co.", "Calcutta", 2023, 350098.00, 326789.00, 23309.00],

    ["Product B", "XYZ Co.", "Calcutta", 2023, 950098.00, 456789.00, 493309.00],

    ["Product B", "PQR Co.", "Calcutta", 2023, 450098.00, 650199.00, -200101.00]

]

# Initialize dictionaries to store totals

invoice_totals = {}

received_totals = {}

balance_totals = {}

# Process data

for item in data:

    product, customer, location, year, invoice_amount, amount_received, balance = item

    if customer not in invoice_totals:

        invoice_totals[customer] = 0

        received_totals[customer] = 0

        balance_totals[customer] = 0

    invoice_totals[customer] += invoice_amount

    received_totals[customer] += amount_received

    balance_totals[customer] += balance

# Print results

print("Customer Name: Total Invoice Amount, Total Amount Received, and Total Balance")

for customer in invoice_totals:

    print(f"{customer}: {invoice_totals[customer]}, {received_totals[customer]}, {balance_totals[customer]}")

#Result

Customer Name: Total Invoice Amount, Total Amount Received, and Total Balance

ABC Co.: 3890784.0, 3274312.0, 616472.0

XYZ Co.: 6000784.0, 5147621.0, 853163.0

PQR Co.: 5301454.0, 5227651.0, 73803.0

===========================================================================

# Define initial data

customer_data = {

    "ABC Co.": {

        "Total Invoice Amount": 3890784.00,

        "Total Amount Received": 3274312.00,

        "Total Balance": 616472.00

    },

    "XYZ Co.": {

        "Total Invoice Amount": 6000784.00,

        "Total Amount Received": 5147621.00,

        "Total Balance": 853163.00

    },

    "PQR Co.": {

        "Total Invoice Amount": 5301454.00,

        "Total Amount Received": 5227651.00,

        "Total Balance": 73803.00

    }

}

# Forecast for each customer

for customer, data in customer_data.items():

    print(f"Forecast for {customer}:")

    invoice_amount = data["Total Invoice Amount"]

    for year in range(2024, 2031):

        invoice_amount *= 1.15  # 15% growth every year

        print(f"Year {year}: {invoice_amount:.2f}")

Result

Forecast for ABC Co.:

Year 2024: 4474401.60

Year 2025: 5145561.84

Year 2026: 5917396.12

Year 2027: 6805005.53

Year 2028: 7825756.36

Year 2029: 8999619.82

Year 2030: 10349562.79

Forecast for XYZ Co.:

Year 2024: 6900901.60

Year 2025: 7936036.84

Year 2026: 9126442.37

Year 2027: 10495408.72

Year 2028: 12069720.03

Year 2029: 13880178.03

Year 2030: 15962204.74

Forecast for PQR Co.:

Year 2024: 6096672.10

Year 2025: 7011172.91

Year 2026: 8062848.85

Year 2027: 9272276.18

Year 2028: 10663117.61

Year 2029: 12262585.25

Year 2030: 14101973.04

===========================================================================

#Python Program for the Forecast

def create_forecast_table(data):

  """

  Creates a forecast table with grand totals, formatted to two decimal places.

  Args:

      data: A dictionary containing forecasts for different companies.

  Returns:

      A list of lists representing the forecast table.

  """

  # Get company names and forecast years

  company_names = list(data.keys())

  forecast_years = list(data[company_names[0]].keys())

  # Create table header

  table = [["Company Name"] + forecast_years + ["Grand Total"]]

  # Add company data and calculate grand total per year

  grand_total_per_year = [0] * len(forecast_years)

  for company_name, company_data in data.items():

    company_row = [company_name]

    for year, forecast in company_data.items():

      company_row.append("{:.2f}".format(forecast))

      grand_total_per_year[forecast_years.index(year)] += forecast

    table.append(company_row)

  # Add grand total row

  grand_total_row = ["Grand Total"]

  for total in grand_total_per_year:

    grand_total_row.append("{:.2f}".format(total))

  table.append(grand_total_row)

  return table

# Sample forecast data

forecast_data = {

  "ABC Co.": {

    "Year 2024": 4474401.60,

    "Year 2025": 5145561.84,

    "Year 2026": 5917396.12,

    "Year 2027": 6805005.53,

    "Year 2028": 7825756.36,

    "Year 2029": 8999619.82,

    "Year 2030": 10349562.79

  },

  "XYZ Co.": {

    "Year 2024": 6900901.60,

    "Year 2025": 7936036.84,

    "Year 2026": 9126442.37,

    "Year 2027": 10495408.72,

    "Year 2028": 12069720.03,

    "Year 2029": 13880178.03,

    "Year 2030": 15962204.74

  },

  "PQR Co.": {

    "Year 2024": 6096672.10,

    "Year 2025": 7011172.91,

    "Year 2026": 8062848.85,

    "Year 2027": 9272276.18,

    "Year 2028": 10663117.61,

    "Year 2029": 12262585.25,

    "Year 2030": 14101973.04

  }

}

# Generate and print the forecast table

forecast_table = create_forecast_table(forecast_data)

for row in forecast_table:

  print(", ".join(row))

#Result

Company Name, Year 2024, Year 2025, Year 2026, Year 2027, Year 2028, Year 2029, Year 2030, Grand Total

ABC Co., 4474401.60, 5145561.84, 5917396.12, 6805005.53, 7825756.36, 8999619.82, 10349562.79

XYZ Co., 6900901.60, 7936036.84, 9126442.37, 10495408.72, 12069720.03, 13880178.03, 15962204.74

PQR Co., 6096672.10, 7011172.91, 8062848.85, 9272276.18, 10663117.61, 12262585.25, 14101973.04

Grand Total, 17471975.30, 20092771.59, 23106687.34, 26572690.43, 30558594.00, 35142383.10, 40413740.57

======================================================================

#Python Program for Graph

import matplotlib.pyplot as plt

# Company names

companies = ["ABC Co.", "XYZ Co.", "PQR Co."]

# Years

years = ["2024", "2025", "2026", "2027", "2028", "2029", "2030"]

# Profit data (in millions)

profits = [

    [44.74, 51.45, 59.17, 68.05, 78.25, 89.99, 103.49],

    [69.00, 79.36, 91.26, 104.95, 120.69, 138.80, 159.62],

    [60.96, 70.11, 80.62, 92.72, 106.63, 122.62, 141.02],

]

# Create a line chart

plt.figure(figsize=(10, 6))

for i, company in enumerate(companies):

    plt.plot(years, profits[i], label=company)

# Set labels and title

plt.xlabel("Year")

plt.ylabel("Profit (in Millions)")

plt.title("Company Profits from 2024 to 2030")

# Add legend

plt.legend()

# Rotate x-axis labels for better readability

plt.xticks(rotation=45)

# Add grid lines

plt.grid(True)

# Show the plot

plt.tight_layout()

plt.show()

 ======================================================

#Python  Program

# Function to calculate the invoice amount for the year 2024 with 15% sales growth

def calculate_invoice_amount(previous_year_amount):

    growth_rate = 0.15

    growth_amount = previous_year_amount * growth_rate

    invoice_amount_2024 = previous_year_amount + growth_amount

    return invoice_amount_2024

# Example data

previous_year_invoice_amount = 650098.00

# Calculate the invoice amount for the year 2024

invoice_amount_2024 = calculate_invoice_amount(previous_year_invoice_amount)

# Print the result rounded to two decimal places

print("Invoice amount for the year 2024:", round(invoice_amount_2024, 2), "INR")

Result:

The invoice amount for the year 2024: is 747612.7 INR

The AI Engine Result Testing: #Result Audit

=======================================================================================

#Python

#Python

import pandas as pd

import matplotlib.pyplot as plt

# Create a DataFrame from the provided data

data = {

    "Expense Head": ["vehicle repair and maintenance", "vehicle repair and maintenance", "vehicle repair and maintenance"],

    "Vehicle No": [3211, 3343, 4989],

    "2021": [45000.00, 36000.00, 27000.00],

    "2022": [33987.00, 43456.00, 65342.00],

    "2023": [29878.00, 65789.00, 76443.00],

    "2024 (Forecast)": [32865.80, 72367.90, 84087.30],

    "2025 (Forecast)": [33463.36, 73683.68, 85616.16],

}

df = pd.DataFrame(data)

# Define colors for each year

colors = ['blue', 'green', 'red', 'purple', 'orange']

# Create a bar chart

plt.figure(figsize=(10, 6))

for i, row in df.iterrows():

    plt.bar(df.columns[2:], row[2:], label=row['Expense Head'] + ' (' + str(row['Vehicle No']) + ')', color=colors[i])

# Set labels and title

plt.xlabel('Year')

plt.ylabel('Expense')

plt.title('Vehicle Repair and Maintenance Expenses by Year')

# Rotate x-axis labels for better readability

plt.xticks(rotation=45)

# Add legend

plt.legend()

# Show the plot

plt.tight_layout()

plt.show()

============================================================================================

#From CoPilot

#Python Program

import pandas as pd

import matplotlib.pyplot as plt

# Create a DataFrame from the provided data

data = {

    "Expense Head": ["vehicle repair and maintenance", "vehicle repair and maintenance", "vehicle repair and maintenance"],

    "Vehicle No": [3211, 3343, 4989],

    "2021": [45000.00, 36000.00, 27000.00],

    "2022": [33987.00, 43456.00, 65342.00],

    "2023": [29878.00, 65789.00, 76443.00],

    "2024 (Forecast)": [32865.80, 72367.90, 84087.30],

    "2025 (Forecast)": [33463.36, 73683.68, 85616.16],

}

df = pd.DataFrame(data)

# Define colors for each year

colors = ['blue', 'green', 'red', 'purple', 'orange']

# Create a bar chart

plt.figure(figsize=(10, 6))

for i, row in df.iterrows():

    plt.bar(df.columns[2:], row[2:], label=row['Expense Head'] + ' (' + str(row['Vehicle No']) + ')', color=colors[i])

# Set labels and title

plt.xlabel('Year')

plt.ylabel('Expense')

plt.title('Vehicle Repair and Maintenance Expenses by Year')

# Rotate x-axis labels for better readability

plt.xticks(rotation=45)

# Add legend

plt.legend()

# Show the plot

plt.tight_layout()

plt.show()

 =============================================================