In the age of eCommerce, supply chain leaders often find themselves drowning in a sea of information. Indeed, the real challenge is effectively leveraging this data to streamline processes and make smart decisions. As a supply chain manager, mastering data analytics is crucial for survival. In this article, I’ll walk you through six distinct types of data analytics used in logistics and eCommerce. Furthermore, at the end of this article, I’ll provide additional resources related to real-life data analytics scenarios – covering everything from supply chain planning to customer delivery.

To better illustrate data analytics impact, let’s meet Ralph, a bright supply chain manager. He faces challenges just like those confronted by many real-life managers in the logistics field. Specifically, Ralph’s company is an online furniture store dealing with numerous supply chain issues. Determined to transform these challenges into opportunities, Ralph adopts data analytics as his secret weapon. First, we will witness how Ralph uses six different analytical approaches to optimize his company’s supply chain.

“There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days.”

Eric Schmidt, Executive Chairman at Google

1. Descriptive Data Analytics: What Happened?

Here Ralph uses data to examine, understand, and describe supply chain events that’s already happened. Particularly in supply chain operations, descriptive data analytics and its associated systems provide visibility and act as the “single source of truth”. For example, descriptive analytics can be a Business Intelligence (BI) system that uses key performance indicators (KPI). Specifically, the BI system keeps track of shipments, detects delivery exceptions, and provides a dashboard on service performance metrics. As a result, Ralph has actionable data to prioritize and task his staff to optimize his shipping operation.

2. Diagnostic Data Analytics: Why This Happen?

Here Ralph seeks to understand the why behind what happened. For example, Ralph does a root cause analysis on the high number of delivery exceptions occurring in the New York City area for carrier X. In this case, he is using historical data, the results of descriptive data analysis, to do his root cause analysis. As a result, Ralph’s team identifies that carrier X has unresolvable service issues for select delivery zip codes within New York City. In this case, Ralph can look for alternate carriers to service those destination zip codes.

3. Predictive Data Analytics: What Is Most Likely To Happen?

Here Ralph relies on historical data, past trends, and assumptions to answer questions about what will happen in the future. For example, Ralph works with sales to estimate the impact of future promotions on sales volumes to support inventory management. As a result, Ralph can adjust inventory levels to meet anticipated demand.

4. Prescriptive Data Analytics: What Action Should We Take?  

Internet Of Things Examples – Hidden Technology Automating Logistics

Here Ralph identifies specific actions that should be taken to reach future targets or goals. For example, Ralph identifies that one reason for a high level of delivery exceptions is that select businesses are not open yet for a 8 a.m. delivery. As a result of him analyzing the data, he then changes the delivery commitment for later in the day for those select businesses.

5. Real-Time Analytics: Data Powering The OODA loop (Observe, Orient, Decide, Act).

With Internet Of Things (IoT), mobile communications, and cloud computing. Ralph can get real-time data analytics to enable him to take action immediately. For example, Ralph can set up customized alerts to quickly obtain targeted information about disruptions anywhere in the world. As a result, Ralph can either re-route transportation away from chokepoints or prioritize resources to overcome the disruption.

6. Cognitive Analytics – Leveraging Artificial Intelligence (AI) And Data.

Data Analytics vs Data Science – Know the Most Important Differences

Now with recent advances in Artificial Intelligence (AI), Ralph can use AI coupled with data analytics to do knowledge-based tasks. For example, Ralph prompts an AI chatbot to evaluate a supplier’s data, and then the AI creates a trend chart highlighting issues for Ralph to further evaluate.

To detail further the types of data analytics used in supply chain operations, see TowardsDataScience’s What Is Supply Chain Analytics?. Also, see my article, Data Analytics vs Data Science – Know the Most Important Differences for more an explanation of data analytics and data science.

Supply chains are complex in terms of vast distances, numerous supply chain systems involved, and its tangled processes. Only through data analytics, can supply chain leaders hope to manage these data rich operations spanning from supply chain planning through customer delivery. For additional resources and examples of real-life data analytics scenarios, see my article, Data Analysis Examples To Best Overcome The Challenge Of Supply Chains.

For more information from Supply Chain Tech Insights, see articles on Supply Chain, eCommerce, and Data Analytics.