For this tutorial, I’m going to cover some high-quality time-related insights directly from your supply chain metrics. You may watch the full video of this tutorial at the bottom of this blog.
This is connected to a tutorial I previously conducted about supply chain management. It involves a technique on managing a particular operational scenario about multiple dates.
I also went through best practice tips on building the right model. In addition to that, I went over how to create inactive relationships between a date table and fact table that has multiple dates. Finally, I covered about how to turn on and off those relationships based on the analysis requirement.
For this tutorial, I will focus on extending your supply chain metric insight by creating additional time-related calculations.
It’s not always enough to get common information from your supply chain. You also have to optimize and compare your supply chain calculations to different time frames. This is where you can apply and layer time intelligence techniques.
Working On The Orders In Progress
Back in the sample data, you can see the Order in Progress by Date table. This table shows the number of orders or transactions that are open between the Order date and the Delivery date.
You can see how many orders are in transition or how many orders there are but haven’t been delivered yet. Moreover, the numbers are dynamic because there’s an influx of orders for a certain date that changes over time.
The best thing about this supply chain metric is the way that I set it up. It’s dynamic so you can look at various warehouses or locations where your inventory is located.
In the Costs by Warehouse Code table, you can see the demands for a specific warehouse.
You can even dive further into your supply chain metric for specific insights. As I have mentioned, it all comes down to two things. Firstly, you need to get your model right and set it up correctly with multiple dates. Secondly, it’s important that you know how to use the correct DAX formula patterns.
By integrating time intelligence calculations, you can analyze your current orders in progress and compare them to a different time frame. The key insight here is to see if there’s a higher, lower, or consistent demand over time.
Consequently, you can use the insight for sales and marketing campaigns to improve the product demand. This is how supply chain metrics and analyses in Power BI are very useful for your business. Once you understand this type of analysis, you can really scale things up within your business and cope with the demands using marketing programs.
Using Time-Related Calculations In Your Supply Chain Metric
If you have already done marketing campaigns multiple times, you can run an analysis to get an average over time. You can check out and compare the data from the time frames.
I will show how to easily overlay time intelligence calculations to your existing supply chain metrics analysis.
If you look at the calculation for Orders in Progress LQ, you can see how straightforward the formula is.
It started with the list of orders in progress, then it branched out into a time intelligence insight. By adding the DATEADD function, you can jump back to a different time frame; for instance, the previous quarter.
When you overlay that measure to the existing one in the visualization, you can come up with a comparison with the current quarter to the last one. The darker blue line shows the last quarter results while the current quarter falls within Q3 2019.
Depending on your sales cycle or supply chain, this time intelligence calculation could be a very relevant insight. Moreover, if you think more broadly, you can still come up with other supply chain metric related insights around multiple dates.
Calculating The Quarterly Order Differences
I have this table for Quarterly Orders Diff. by Date where you can easily compare the difference between the two quarters. I just created the measure to simply branch out from the initial core insight.
Here’s the formula for the order difference. It’s basically just subtracting Orders In Progress LQ from Orders in Progress.
Looking back at the table, you can now have a quick snapshot of how orders are coming in for different time frames.
This is one of the key measure branching methodologies that I want to share with you. I highly recommend developing high-quality scalable reports in Power BI using different branching techniques.
As I already mentioned, these sample calculations are dynamic so you can specifically look into a specific warehouse. There are also various ways to get valuable insights based from this. For instance, you can check the average inventory of a certain warehouse over time or compare different warehouses as well.
This technique that I’m teaching you is a high-quality time related analysis. It would have been very challenging to go about this insight, but because of Power BI you can do it seamlessly using correct DAX formulas and advanced techniques.
Additional Techniques For Supply Chain Metric
On top of everything that I’ve discussed, you can even overlay a product type filter. You can use it to look into how your products or product categories perform over time within your supply chain.
My main example looks at order transactions, but you can also look at inventory costs over time. You might also want to analyze other data such as Costs, Revenue, Transaction, or Order Quantities in any core metric. All of this data can greatly help you measure or forecast your cash requirements.
Additionally, you can find out if your demand during the marketing campaign period is significantly higher than your day-to-day demand. Depending on the results, you can determine whether or not you need to cut your cash requirements or bring in more inventory to fulfill orders.
There are a lot of valuable insights that you can extract from your supply chain metric. Most importantly, these insights can significantly add value to your business.
It all begins by optimizing your supply chain metrics and extracting useful business insights.
It has been amazing to talk about business intelligence in Power BI with you. I’d also appreciate it if you can like the corresponding video of this tutorial.
Good luck exploring our education platform!
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