Supply chain effectiveness and material sourcing are critical to Retail operations and is getting more strategic by the day to gain that elusive competitive edge. Let’s say that I’m a store manager for a large retailer with operations across US, and that we’re having some problems with shipping delays. These delays are affecting our business and I’ve been given the job to get to the bottom of it.
So what’s in the data?
I have our Supplier dataset with details of the deliveries along with other pieces of information. I want to focus on the shipment column that tells me whether a delivery was made on-time or delayed.
Let’s see how Watson Analytics can help me with this critical analysis.
After I upload this dataset, I click on it to begin my analysis. Even without my typing a question, I see that Watson Analytics has surfaced these interesting and ‘unbiased’ cognitive starting points for me.
Getting the overall sense –
I click on the first recommendation to look at the spread of opportunity costs across the states. I quickly add Margin and I can see that while Wisconsin has the highest average opportunity costs, Iowa gives us the highest average margins.
With that quick understanding, I open a new tab and this time type my own question in natural language to understand the opportunity cost incurred on delayed vs on-time shipments. I select the most relevant insight and realize the dire impact of lost revenue owing to delayed shipments.
I now want to know who is responsible for this and type my next question to look at the breakdown of opportunity cost by suppliers only for delayed shipments. I can see that the most severe offender here is a supplier called Rich Industries and maybe we should replace them.
Delving a little deeper into our suppliers –
I first need to dig deeper and understand what products they supply. So I add Products as ‘rows’ on top of this visualizations and filter it to show only the top 3 suppliers for each product. I can now starting thinking about replacing Rich Industries with other viable suppliers like Acme and Oscorp with relatively lesser opportunity costs for each of these products.
But before I pull the trigger on this, I decide to delve a little deeper into our Suppliers. I use the dynamic discoveries ribbon on the right for some inspiration and click on the first insight.
I replace the Equipment value with Margins and looks like Rich Industries have the best average Margins and hence getting rid of them might not be such a slam dunk decision.
What really drives shipment delays?
This now brings me to a point where I want to understand the WHY behind my delayed shipments.
I type my next question to know more about what really drives my shipment deliveries and select the most relevant spiral visualization. This easy to understand spiral visualization shows me the factors that drive Shipment and ranks them in order of their predictive strengths. I click to dig deeper into the top factor combination of ‘Weather and Origin State’.
This visualization shows how the interplay between ‘Weather and Origin State’ has a bearing on whether a shipment is delivered on-time or otherwise.
Similarly, I can view the associated detailed insights for each of the other drivers.
Here I first review the statistically derived decision rules for the most likelihood for a Shipment to be ‘Delayed’. I can look at the various profiles to understand the conditions that lead to delayed shipments and then use this knowledge to streamline our approach to minimize such delays going forward. Similarly I can analyze such profiles for other important KPIs as well.
Bottom-line, while it’s great to have a handle on what’s happening, I now know the WHY behind what’s happening.
Wrapping it up in a Display –
Confident and excited about the progress I made, I created this interactive Display based on the insights I gleaned so far.
Creating interactive and multi-tabbed Displays is really easy – I can either create new visualizations from within Display OR I can reuse those that I created earlier by just dragging them in. I filter this Display on Delayed shipments and Weather conditions of Snowstorm and Thunderstorm before sharing it with my boss.
So to recap, I was able to analyze my raw Supplier shipment delay dataset and not only found out more about the Suppliers but also understood the real factors that led to delayed shipments.
Thanks to Watson Analytics, with these findings, I look forward to streamlining our supply chain operations, optimizing our assortment planning and minimizing shipment delays going forward!
You can take a look at the demo video for this usecase here.