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MONOHUETECH MANAGEMEMT SYSTEM

MANAGEMENT INFORMATION SYSTEMS ANALYSIS

Milestones —> Staging —> Development —> Execution —> Assessment —> Invoice —> Payment

Program Management Review and Code:

First let's talk about what we've been doing. Overall, the code here helps to put our project in a more tabular format. As the saying goes, there is often more to something than meets the eye. We’ll break this down to a more granular level shortly. This notebook is coded in, Python which makes it easier to convert them into presentation, data visualization as well as a program documentation, and management review sessions. The review sessions are of particular importance. Today we call these team meetings. With any project there must be room for growth at every level. These are the processing steps we see now may look nothing like what we’re starting with. The primary reason for developing this is to better understand the detail involved at each stage. In baseball terms, we’re still in the dugout, and have yet to get to first base. Reasons matter less than solutions. This is a first step.

Break Down:

Starting with this list of items:

Part I:

  • Start-up
  • 4 Feb 2025
  • Components:
  • AWS
  • Training
  • Certification
  • What is the timeline?
  • Milestones —> Staging —> Development —> Execution —> Assessment —> Invoice —> Payment
  • Value Proposition Management

Part II:

  • Actions:
  • Preparation - contract selection
  • Activation - assignment
  • Execution - tasks or checklist
  • QA/QC -
  • Invoice - Billing (Accounts Payable and Accounts Receivable
  • Payment: Instruments and Tools

Part III:

  • Business Cycle:
  • Length of Time for Completion: Average
  • Invoicing: Who does it? How long from start to payment
  • List Maintenance: When? How often,…?

Part IV:

  • Funding
  • Budget
  • Operating Expense
  • Other Costs
  • Revenue
  • Tax
  • Reconcile Monthly Profit & Loss
import pandas as pd

# DataFrame 1
columns1 = ['Name', 'AWS', 'Training', 'Certification', 'Timeline/Milestones']
df1 = pd.DataFrame(columns=columns1)
df1 = pd.concat([df1, pd.DataFrame([['']*len(columns1)]*10, columns=columns1)], ignore_index=True)
df1.title = 'Start-up: 4 Feb 2025'


# DataFrame 2
columns2 = ['Preparation', 'Activation', 'Execution', 'QA/QC', 'Invoice/Billing', 'Payment']
df2 = pd.DataFrame(columns=columns2)
df2 = pd.concat([df2, pd.DataFrame([['']*len(columns2)]*10, columns=columns2)], ignore_index=True)
df2.title = 'Actions'



# DataFrame 3
columns3 = [
    'Contract Selection',
    'Assignment',
    'Tasks or Checklist',
    'Billing (Accounts Payable and Accounts Receivable)',
    'Payment: Instruments and Tools'
]

df3 = pd.DataFrame(columns=columns3)
df3 = pd.concat([df3, pd.DataFrame([['']*len(columns3)]*10, columns=columns3)], ignore_index=True)
df3.title = ''



# DataFrame 4

columns4 = ["Funding", "Budget", "Operating Expense",
"Other Costs", "Revenue", "Tax", "Reconcile Monthly: Profit &  Loss"]
df4 = pd.DataFrame(columns=columns4)
df4 = pd.concat([df4, pd.DataFrame([['']*len(columns4)]*10, columns=columns4)], ignore_index=True)
df4.title = 'Business Cycle'

display(df1.head())
display(df2.head())
display(df3.head())
display(df4.head())

Part I:

Start-up

4 Feb 2025

  • Components:
  • AWS
  • Training
  • Certification
  • What is the timeline?
  • Milestones —> Staging —> Development —> Execution —> Assessment —> Invoice —> Payment
  • Value Proposition Management

Part I, Start-Up.

This first table is a catch all that summarizes our mission, as well as current status of events. Start up and gives an idea of what the program looks like and look at things we're trying to do. Here you can see that we are primarily interested in working with AWS and partnership program along with that we are training certifying and providing a workforce to deal with AWS projects.

import pandas as pd

# DataFrame 1
columns1 = ['Name', 'AWS', 'Training', 'Certification', 'Timeline/Milestones']
df1 = pd.DataFrame(columns=columns1)
df1 = pd.concat([df1, pd.DataFrame([['']*len(columns1)]*10, columns=columns1)], ignore_index=True)
df1.title = 'Start-up: 4 Feb 2025'
display(df1.head())

Part II:

Actions:

  • Preparation - contract selection
  • Activation - assignment
  • Execution - tasks or checklist
  • QA/QC -
  • Invoice - Billing (Accounts Payable and Accounts Receivable
  • Payment: Instruments and Tools

Actions: Operations

This section actions associated with this project. Initially I started out with a list of pertinent, Actions, Preparation Activation, Execution, QA/QC, Invoice, Payment. After looking at these actions, it became obvious that the organizational structure should then require tasking, assignment, scheduling, invoice, payment methods, etc. These operational aspects are the ones that lead to money. It would be a good idea to outline these steps further, separately. We’ll do that in the next sect

import pandas as pd

# DataFrame 2
columns2 = ['Preparation', 'Activation', 'Execution', 'QA/QC', 'Invoice/Billing', 'Payment']
df2 = pd.DataFrame(columns=columns2)
df2 = pd.concat([df2, pd.DataFrame([['']*len(columns2)]*10, columns=columns2)], ignore_index=True)
df2.title = 'Actions'
display(df2.head())

Part III:

Business Cycle I:

  • Length of Time for Completion: Average
  • Invoicing: Who does it? How long from start to payment
  • List Maintenance: When? How often,…?
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