Every supply chain manager I know has the same complaint: “We’re drowning in data with little insights” They’re right. Modern supply chains generate mountains of information – from inventory levels to shipment status to cost variations. But having data isn’t the same as using it wisely. Indeed, nowadays it is crucial that supply chain leaders master data analytics just to survive. So, let me introduce you to Ralph, a savvy supply chain manager who figured out how to turn this flood of information into actual results. By telling his story, I’ll walk you through the six distinct types of data analytics that can solve real operational problems, from predicting delivery delays to optimizing inventory levels.

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.

“We are surrounded by data, but starved for insights.”

Jay Baer

1. Descriptive Data Analytics: What Happened?

Poor Shipping Data – Here Are The 4 Reasons Impeding High Tech Visibility And Actionable Analytics

Here Ralph uses data to examine, understand, and delineate supply chain events that’s already happened or is happening. 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) dashboard that uses key performance indicators (KPI) to help manage shipments. For instance, the BI system keeps track of shipments, detects delivery exceptions, and provides a dashboard on service performance metrics.

As a result of descriptive analytics, Ralph has situational awareness of shipping and supply chain activities. For example, this enables him to monitor shipping, detect anomalies, and identify changes in operational performance. Most importantly, this type of data analytics is a catalyst for action. For instance, Ralph can use this information to prioritize and task his staff to optimize their shipping operation. Also with a new trend or anomaly identified, Ralph and his team can initiate further analysis such as diagnostic analytics to discover the root problem or, possibly, an new opportunity.

“The core advantage of data is that it tells you something about the world that you didn’t know before.”

Hilary Mason

2. Diagnostic Data Analytics: Why Did This Happen?

The Truth About Diagnostic Analytics: A Forgotten Way To Better Business Performance

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. For a more detailed discussion on diagnostics analytics, see my article, The Truth About Diagnostic Analytics: A Forgotten Way To Better Business Performance.

“If you torture the data long enough, it will confess.”

Ronald Coase

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

Here, Ralph relies on historical data, past trends, and assumptions to predict future outcomes. For example, he collaborates with the sales team to estimate the impact of upcoming promotions on sales volumes. Armed with this information, Ralph can better manage his inventory. As a result, Ralph can adjust inventory levels to meet anticipated demand. For more information on predictive analytics, see my article, Predictive Analytics Types: The Best Opportunities For Supply Chains.

Predictive Analytics Types: The Best Opportunities For Supply Chains

“If you do not know where you are going, every road will get you nowhere”

Henry A. Kissinger

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

Prescriptive Analytics in Supply Chains: It Advises What’s the Best Thing to Do, Why, and How to Make It Happen

Here Ralph identifies specific actions that should be taken to reach future targets or goals. Further, he can use analytical tools to recommend a course of action, explain why it is the best, and detail how to implement it. Indeed, prescriptive analytics is a powerful capability for supply chain managers to possess. To continue with Ralph’s analytical journey, he has identified the root cause for a high level of delivery exceptions through diagnostic analytics. For instance, he discovers select businesses are not open yet for 8 a.m. delivery. Based on this diagnosis, he uses prescriptive analytics to identify the appropriate solution. In this case, he decides to change the delivery commitment for later in the day for those select businesses.

For more information on prescriptive analytics, see my article, Prescriptive Analytics in Supply Chains: It Advises What’s the Best Thing to Do, Why, and How to Make It Happen.

“Nothing is more difficult, and therefore more precious, than to be able to decide.”

Napoleon Bonaparte

5. Real-Time, On-Demand Analytics: What Do I Do Now?

Business Agility: The Best Way For Leveraging Digital Tech To Disrupt Competitors, Seize Opportunities, And Overcome Obstacles

With the explosion of information technology, Ralph is realizing that he needs to exploit these digital capabilities to be competitive. In particular, he must leverage real-time and on-demand analytics enabled by innovative tech like the Internet of Things (IoT), mobile communications, global broadband, and cloud computing. Indeed with these emerging technologies, he can now make faster, informed decisions and take immediate action based on his timeline.

As an example, he can set up customized alerts to quickly receive targeted information about disruptions anywhere in the world. As a result, this enables Ralph to either re-route transportation away from choke points or prioritize resources to address the disruption efficiently. Consequently, he no longer has to rely on lengthy analytical processes or wait for his staff in order to make time-sensitive decisions.

In this case, Ralph can increasingly be more agile by using a full range of on-demand analytics and emerging analytics tools such as a Decision Intelligence platforms. For more on Decision Intelligence, see my article, This Is What Decision Intelligence Technology Is And Know What Its Not. Also, for more on business agility, see this article, Business Agility: The Best Way For Leveraging Digital Tech To Disrupt Competitors, Seize Opportunities, And Overcome Obstacles.

“One thing is sure. We have to do something. We have to do the best we know how at the moment…; if it doesn’t turn out right, we can modify it as we go along.”

Franklin Delano Roosevelt

6. AI-Powered Analytics: What Questions Did I Not Know to Ask?

How Data And AI Work Together To Better Empower Analytics

Now with recent advances in Artificial Intelligence (AI), Ralph can now use AI coupled with data analytics and knowledge-based tools to supercharge his analytics capabilities. Specifically, AI empowers data analytics to work with massive data sets. What’s more, AI and knowledge-based technologies can seamlessly access a wide range of diverse types of structured and unstructured data. Further, AI, such as Machine Learning (ML), can learn from new data to increase its knowledge and capabilities. Also, agent-based AI can increasingly act autonomously on data analytics tasks. Hence, AI-powered analytics opens more possibilities for decision-makers to be more agile, working directly with data using advanced capabilities such as Decision Intelligence.

For an example of AI-powered analytics, Ralph can prompt an AI analytical agent to evaluate a supplier’s data. This results in the AI creating a trend chart highlighting issues for Ralph to further evaluate. For more details on how AI can empower analytical tasks, see my article, How Data And AI Work Together To Better Empower Analytics.

“What am I doing that I should not be doing; what am I not doing that I should be doing?”

LTG Hal Moore

More Readings on Data Analytics Types.

For more on types of data analytics used in supply chain operations, see below:

• Samir Saci’s What Is Supply Chain Analytics?
• ThroughPut’s Supply Chain Analytics: The Complete Guide for Improving Supply Chain KPIs
• Also, for more on the data infrastructure that businesses use for data analytics, see this article, Supply Chain Analytics Types: The Way They Work To Better Empower Decision-Making.
• Additionally, for a detailed explanation of the difference between data analytics and data science, see this article, Data Analytics vs Data Science – Know the Most Important Differences.
• Lastly, for additional resources and examples of real-life data analytics scenarios across the supply chain, see this article, Data Analysis Examples To Best Overcome The Challenge Of Supply Chains.

“Without data, you’re just another person with an opinion.”

Edwards Deming

Supply Chain Analytics Types and The Way They Work To Better Empower Decision-Making

Need help with an innovative solution to make your supply chain analytics actionable? I’m Randy McClure, and I’ve spent many years solving data analytics and visibility problems. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers. I specialize in proof-of-concept and operational pilot projects using emerging technologies and methodologies. If you’re ready to supercharge your analytics or if you are a solution provider, let’s talk. To reach me, click here to access my contact form or you can find me on LinkedIn.

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