W1_Thuraiya_Leadership Styles analysis Using Tuckman model

W7__Hamda__ Monte Carlo simulation for identified Risks during monthly invoice processing and payment) __

1-     Problem Definition

Under Clients Contract & Interface department there are several number of tasks that some of them must be done in monthly basis according to certain clause in the purchase agreement. One of these tasks is processing the monthly invoice which is submitted in monthly basis from the project company (which is known as a Generator) to the Buyer. Processing the monthly invoices can be considered as a small project under CCI department since it has START and FINISH Date to be completed. The Generator in each month (Belling period) shall submit an Invoice to the Buyer which describes the different charges according to its product (Power/Water). At the date of receiving the invoice, the Contract team shall start processing the invoice and on or before the Due Date the buyer shall pay all the payments to the Generator. If the payment is not payed to the Generator by the Due Date, this will cause a Risk on the Buyer. W7 blog will discuss the analysis and identifying Risk level of completing that project by using Monte Carlo Simulation technique.  

2-     Identify the Feasible Alternative

The Monte Carlo simulation was invented by an atomic nuclear scientist named Stanislaw Ulam in 1940, and it was named Monte Carlo after the town in Monaco which is famous for its casinos. This is a mathematical technique that allows to account for risks in any decision-making process. With the help of this technique, the impact of the identified risks can be determined by running simulations many times, and a range of possible outcomes in different scenarios can be identified.

In order to perform the Monte Carlo simulations, it is important to determine the schedule and we must have the duration estimate for each activity under that project.

3-     Development of the outcome of Alternative

Regarding to the above description of one main task/project under CCI department, the completion of it depends on different tasks such as:

          Task 1: The submitting and receiving Power and Water Outage Confirmations.

          Task 2: Availability of the all data required in processing the invoice.

          Task 3: The preparation, editing, and reviewing the sheet of calculations. 

          Task 4: The process of checking the accuracy of such data received from the Generation.

          Task 5: The process of the plant model (FDM model).

          Task 6: Finalizing the process of monthly invoice.

          Task 7: Reviewing the invoice by the Senior Engineers and approve it.

          Task 8: Reviewing the invoice by the Department Manager and approve it.

          Task 9: Reviewing the invoice by the Department Director and approve it.

          Task 10: Submitting the invoice to Finance Department (FINISH Date).

From all these tasks, I think the main important tasks that processing the Monthly Invoice depends on are the first 4 one (tasks from 3-6), Because completing these four tasks on time which make the other 4 tasks on time.

Task	Optimistic	Most Likely	Pessimistic	PERT
3	2	3	5	3.17
4	3	4	6	4.17
5	4	5	6	5.00
6	2	3	4	3.00
Remaining tasks	1	2	3	2.00
Total	12.00	17.00	24.00	17.33

*The calculations of PERT are by using PERT formula 

From the above table and by using PERT estimate, these activities will be finished in 17.33 days. However, in the best case, the activities will be finished in 12 days, and in the worst case the activities will be finished in 24 days.

4-     Selection Criteria

 Now, if we run the Monte Carlo simulation for these tasks five hundred times by using a special Monte Carlo simulation software, it will show us results like this:

Duration (in days)	Chance of completion
12	2 %
13	8 %
14	15 %
15	23 %
16	30 %
17	45 %
18	56 %
19	60 %
20	68 %
21	75 %
22	86 %
23	95 %
24	100 %

Please note that the above data is for explaining purpose only, and is not taken from an actual Monte Carlo simulation test result.

5-     Analysis and comparison of the Alternative

From table 2, it is seen that:

·       2% chance of completing the project in 12 days

·       8% chance of completing the project in 13 days.

·       15% chance of completing the project in 14 days.

·       23% chance of completing the project in 15 days.

·       30% chance of completing the project in 16 days.

·       45% chance of completing the project in 17 days.

·       56% chance of completing the project in 18 days.

·       60% chance of completing the project in 19 days.

·       68% chance of completing the project in 20 days.

·       75% chance of completing the project in 21 days.

·       86% chance of completing the project in 22 days.

·       95% chance of completing the project in 23 days.

·       100% chance of completing the project in 24 days.

So, by having these number of possible outcomes which are coming from running the Monte Carlo simulation, we can understand that completing that project will be in that specific duration by that specific percentage. And based on that, we can determine in which level and percentage the risk of not complete the project on that specific duration and we can make a better-informed decision to deal with that risk.

     6-     Selection of the preferred Alternative
By identifying the list of activities that are under each project (processing the monthly invoices and the payment of them in the specified and scheduled time), and by specifying the PERT estimates of each activity by using PERT formula, this will help in running the Monte Carlo simulation which will help in determining number of possible outcomes for different scenarios (Risk Level). Then these outcomes will help in making the better decision to deal with that risk.

7-     Performance Monitoring and the Post Evaluation of result

Identifying the Risk level of completing any project, helps in making and determining the preferred and better decision to deal with that risk in the first stage and avoid it in the coming stages. Number of tools and technique can be used in determining and identifying the level of risk, one of these tools is Monte Carlo simulation. The Monte Carlo simulation is a very important tool and technique in the quantitative risk analysis process which helps you make decisions based on an objective data.

   References:

1-     Fahad Usmani (2017), What is a Monte Carlo Simulation? Retrieved 26 December 2017, from https://pmstudycircle.com/2015/02/monte-carlo-simulation/#comment-41717

2-     A Guide to the PROJECT MANAGEMENT BODY OF KNOWLEDGE (PMBOK GUIDE), Fifth Edition. Retrieved 26 December 2017.

3-     S. Kandaswamy, The basics of Monte Carlo simulation, Retrieved 26 December 2017, from https://www.pmi.org/learning/library/monte-carlo-simulation-risk-identification-7856