1. Wrap-up
Fantastic work! You created a powerful tool to guide make versus buy analysis.
2. Summary
In Chapter 1, we started by developing a quote analysis tool and learning how to calculate extended and full cost for the volumes provided in the quotes.
In Chapter 2, we developed a dynamic scenario analysis tool to calculate full costs over a wider range of potential production volumes.
In Chapter 3, we added analysis of the make option by calculating full cost based on our internal estimates data. In the end, we had a really useful model.
3. A baseline data model
The model is a great baseline for more complex analysis. You can extend the model to include more cost considerations not covered in this case study, such as salaried employees, more facilities, variable overheads, and freight costs.
You can also build in other vital supply chain aspects, such as time and quality.
But it's time to address the elephant in the room.
4. Power BI or spreadsheets
Historically this type of supply chain and financial analysis would occur in spreadsheets. Why are we using Power BI instead?
Power BI offers some huge advantages for analyzing quotes as well as internal manufacturing cost data.
Even if you understand why Power BI might be a superior tool for this type of analysis, this video contains some points that might convince your manager and the rest of your team as well.
5. Consistency and control
After you create a functional data model, you can add more quotes data, and the same logic will be applied. This means your analysis logic will be consistent across projects. In many cases, supply chain teams may instead create a new spreadsheet for every project or even every part of a project!
Power BI also helps control source data. When quote data is entered and manipulated in spreadsheets, it can easily be changed by someone in the analysis process. You don't want your analysis to be based on flawed data. In Power BI, an analyst rarely touches the raw data after it is loaded.
6. Simple, powerful Sharing
Imagine the following scenario - you are presenting to several key decision-makers.
You say, "The best option for Part XYZ is Supplier A".
"For what volume?" one of the leaders interjects.
"10,000 units", you reply.
"Didn't you hear? The estimate is 30,000 units now!" the leader replies.
If you have your analysis in spreadsheets, you would either have to close out your slide deck and open up the spreadsheet, or more likely, you say "We'll have to crunch the numbers and get back to you."
Because you use Power BI, you simply say, "Thanks for the update. Let's see what happens to the analysis when you move the volume parameter."
In moments like this, the investment in building a great data model with clear visuals really pays off.
7. Congratulations!
Congratulations on completing this case study! You have made a useful tool for your organization and improved your abilities as a supply chain analyst.