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.
- 1. Descriptive Data Analytics: What Happened?
- 2. Diagnostic Data Analytics: Why Did This Happen?
- 3. Predictive Data Analytics: What Is Most Likely To Happen?
- 4. Prescriptive Data Analytics: What Action Should We Take?
- 5. Real-Time, On-Demand Analytics: What Do I Do Now?
- 6. AI-Powered Analytics: What Questions Did I Not Know to Ask?
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?
Here Ralph uses data to examine, understand, and delineate 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) dashboard that uses key performance indicators (KPI) to help manage shipments. Specifically, the BI system keeps track of shipments, detects delivery exceptions, and provides a dashboard on service performance metrics. As a result, Ralph has situational awareness of shipping activities. Thus, this enables him to monitor shipping, detect anomalies, and identify changes in operational performance. This actionable data enables him to prioritize and task his staff to optimize their shipping operation.
“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?
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. 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.
“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?
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 next identifies the root cause for a high level of delivery exceptions. Namely, select businesses are not open yet for 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.
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?
With the Internet of Things (IoT), mobile communications, global broadband, and cloud computing, Ralph can leverage real-time or on-demand data analytics. Indeed, he can take immediate action based on his timeline. For instance, he can set up customized alerts to quickly receive targeted information about disruptions anywhere in the world. 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 to make time-sensitive decisions. By controlling his own decision cycle and timing through real-time analytics, Ralph can make prompt and informed decisions.
In this case, Ralph can increasingly be more agile by using a full range of real-time analytics and emerging analytics tools such as a Decision Intelligence platform. 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?
Now with recent advances in Artificial Intelligence (AI), Ralph can 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 also 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 my article, Supply Chain Analytics Types: The Way They Work To Better Empower Decision-Making.
- For a detailed explanation of the difference between data analytics and data science, see my 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 my article, Data Analysis Examples To Best Overcome The Challenge Of Supply Chains.
“Without data, you’re just another person with an opinion.”
Edwards Deming
For more information from Supply Chain Tech Insights, see articles on Supply Chain, eCommerce, and Data Analytics.
Greetings! As an independent supply chain tech expert with 30+ years of hands-on experience, I take great pleasure in providing actionable insights and solutions to logistics leaders. My focus is to drive transformation within the logistics industry by leveraging emerging LogTech, applying data-centric solutions, and increasing interoperability within supply chains. I have a wide range of experience to include successfully leading the development of 100s of innovative software solutions across supply chains and delivering business intelligence (BI) solutions to 1,000s of shippers. Click here for more info.