Learn how to conduct Cost Analysis using Innoslate's simulation capabilities.
Introduction
Cost analysis is critical in systems engineering as it helps ensure projects are completed within budget constraints while maintaining quality and performance. Analyzing operational costs aids informed decision-making and resource optimization. This is essential since cost overruns can lead to significant financial losses and project delays. Using Innoslate for effective cost analysis contributes to the success, efficiency, and sustainability of complex projects. By using the Action Diagram and Monte Carlo Simulator within Innoslate, a powerful platform is provided for carrying out cost analysis. This platform seamlessly integrates behavior with its allocated Resources, Costs, and Input/Outputs, enhancing the effectiveness of complex project analysis. By leveraging these tools, engineers and project managers can gain valuable insights into the financial aspects of operations, enabling informed decision-making, resource utilization and optimization, and risk management.
To follow along and try out the features mentioned in this walkthrough, click here to instantly download the Cost Analysis Simulation Project .INNO File and import it into a new Innoslate project.
Walkthrough Use Case
Navigate to the Diagrams Dashboard and select the 'AV.1 Research and Development (R&D)' diagram. The 'AV.1 Research and Development (R&D)' model is a decomposed child diagram of the parent Action Diagram 'Autonomous Vehicle Cost Analysis' and focuses on Research and Development activities. As displayed below, this model lays out Actions, Resources, and Input/Output constructs to represent the process.
This Action Diagram for the Research and Development (R&D) phase outlines the structured process for considering and developing an autonomous vehicle project:
- It begins with 'AV.1.1 Literature Review', where the academic database is accessed to gather relevant information and insights. This step involves researching existing studies and technologies to inform the project's initial concept.
- Based on this review, 'AV.1.2 Initial Concept Design' is developed, guided by a design plan derived from the literature review. This involves drafting the preliminary design and requirements for the autonomous vehicle.
- Next, 'AV.1.3 Technical Feasibility' is assessed using simulation tools to evaluate potential challenges and ensure the concept's technical viability. Concurrently, 'AV.1.4 Economic Feasibility' analyzes the cost implications to determine the project's financial viability, ensuring sustainability. Alongside this, 'AV.1.5 Legal Feasibility' ensures compliance with relevant laws and regulations, avoiding legal issues during development and deployment.
- Following the feasibility studies, a detailed cost analysis 'AV.1.6 Cost Analysis' estimates the financial resources required, including material, labor, and other necessary expenditures.
- The budget breakdown derived from this analysis informs 'AV.1.7 Resource Allocation', ensuring all required resources are available for the project's success.
- The R&D phase concludes with 'AV.1.8 Final Review and Approval', where all findings, designs, and plans are reviewed comprehensively. Once approved, the project can move forward to the next phase.
Throughout the process, a variety of external Resources and Inputs/Outputs come into play, including utilizing the academic database for literature reviews, the design plan to guide initial concept designs and technical feasibility assessments, and simulation tools for evaluating technical feasibility. These resources are a part of the incurring costs that need input into the actions to stay within budget.
Now, select an Action construct within the 'AV.1 Research and Development' Diagram and observe in the left sidebar, under 'Active' in the 'Relationships' tab an incurs relationship to 'C.1 Labor.' This relationship indicates when the simulator executes this specific action, it will calculate the total cost incurred over the Action's duration within the model.
As displayed in the image below, connecting the 'Literature Review' Action entity to the 'C.1 Labor' Cost entity with the incurs relationship will tell the Simulator to calculate the costs incurred during the simulation of the 'Literature Review' construct in the model.
From here, select on 'C.1 Labor' to go to its Entity View. This 'C.1 Labor' Cost entity represents a general labor cost and is assuming a uniform rate across the different tasks in the 'AV.1 Research and Development (R&D)' Diagram. The attributes table, as shown in the image below, defines the Cost entity 'C.1 Labor'. For the Cost's attributes, the Amount is set to 40, indicating the labor cost per unit. The Units field specifies that the cost is in dollars ($). The Rate is set to "Per Hour," indicating that the $40 cost is an hourly rate. The Description provides a brief explanation that this cost covers general labor for engineering tasks at the specified hourly rate.
Once an Action Diagram is configured and it's appropriate actions have the incurs relationship to the appropriate Cost entities, we now have the ability to simulate a model in Innoslate and gather useful information from our models to conduct cost analysis.
In the project, navigate to the 'Autonomous Vehicle Cost Analysis' Diagram via the Diagrams Dashboard (or if coming from the 'AV.1 Research and Development' Diagram, select 'Open' on the toolbar and then select 'Parent Diagram'). Next, we will be using the Monte Carlo Simulator with the settings set to 150 iterations of the 'Autonomous Vehicle Cost Analysis' Diagram that includes the 'AV.1 Research and Development' hierarchy decomposed within.
After running the simulation, the image above is the output of the Monte Carlo simulation that Innoslate generated in seconds. The Monte Carlo simulation allows for a realistic analysis of a system or project’s cost, schedule, and performance by running iterations to achieve analytical data, removing inherent uncertainty.
Users may then export their results into an Artifact entity to reference these simulation results within Innoslate later or export these results into other formats.
Monte Carlo Simulation Analysis
The Monte Carlo simulation results offer insights into the cost and duration of creating an AV in a simulated environment. Below we will now dive into specific captures of the Monte Carlo Simulation's output and explain the process and our findings in this particular use case.
In the Status Panel from the Monte Carlo Simulation, it indicates the Simulator ran 150 iterations of the 'AV Autonomous Vehicle Cost Analysis' Diagram achieving a completion rate of 100%. The mean duration for the workflow is 8.41 months, with a standard deviation (SD) of 7.75 days. This variation indicates that while the average workflow time is relatively consistent, there will be fluctuations on time by about 1 week.
We also learned that the Mean cost of providing R&D is $238,947.33, with a Standard Deviation of $7,206.70. Now looking at the Cost Bar Chart widget below we see that the cost distribution shows the highest simulations resulted in costs between $240,000 and $245,000, highlighting the central tendency around the mean cost, and the distribution of what values were generated in the iterations. Outliers suggest some instances where costs were significantly lower or higher, possibly due to variations in resource utilization or project needs.
The Time Bar Chart indicates that most workflows completed within 8.37 to 8.57 months, aligning with the mean duration. Some cases took longer based on the distribution curve, reflecting on complex scenarios that can be unpredictable to reason for.
In conclusion, the cost analysis feature in Innoslate's simulation tool is crucial for systems engineering, offering invaluable insights into resource allocation, process optimization, and risk management. The outcomes of the 'AV Autonomous Vehicle Cost Analysis' simulation underscore the significance of grasping variations in costs and durations to make well-informed decisions. By leveraging these insights, engineers can enhance project efficiency, improve resource management, and streamline production workflows, ultimately enhancing the overall cost-effectiveness, timeline, and performance of their projects.
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