Data analytics promises unlimited power to drive informed decision-making, However, in the chaotic reality of supply chains, most organizations focus on gathering data, quickly get overloaded, resulting in few insights for their efforts. The problem with this approach is that simply gathering vast amounts of data is like having a pantry full of gourmet ingredients but no recipe or chef. The truth is supply chain analytics is tough. It consists of dynamic problems and staggering amounts of data from countless systems. For instance, even the data for one shipment is fragmented digitally as well as spread over both time and physical distances, resulting in an analytical nightmare.

That’s why in this article. I’ll help clarify what supply chain analytics truly is, breaking down its six distinct types that actually empower decisions. Further, we’ll step back and look at how a typical supply chain data infrastructure works. Armed with this information on how supply chain analytics can work, you can start to transform your data deluge into actionable intelligence, moving beyond educated guesses. Ready to stop guessing and start knowing? Let’s discover the true power of supply chain analytics. First, let’s start with a definition.

1. Supply Chain Analytics Definition.

Let’s start with a definition for Supply Chain Analytics. See below.

“… [it] is the bridge between data and decision-making. It is the process of analyzing large amounts of data to identify patterns and uncover insights for informed decision-making in supply chain management.”

ThroughPut

Without a doubt, this definition clearly establishes supply chain analytics as the essential method to glean insights, bridging the gap between disparate datasets and effective decision-making. Moreover, it underscores the need for supply chain decision-makers to leverage advanced data analytic software tools and methodologies to make informed decisions to achieve competitive advantage. Crucially, supply chain analytics encompasses far more than just descriptive Business Intelligence (BI) reporting or dashboards. Its true power lies in its ability to “uncover insights for informed decision-making across the supply chain.” Next, we’ll look at the wide variety of data analytics types available to decision-makers.

“supply chain analytics … the essential method to glean insights, bridging the gap between disparate datasets and effective decision-making. “

2. Six Types of Data Analytics As Applied To Supply Chains.

Without a doubt, what truly distinguishes the supply chain industry when it comes to data analytics is the sheer volume of activities and problems unfolding within its dynamic, real-world environment. Indeed, these countless activities, happening in real-time, demand effective management and successful resolution by decision-makers. Moreover, the amount of data involved is staggering, originating from a multitude of disparate sources and systems. Consider, for instance, that even a single shipment can generate hundreds of data elements, flowing from scores of global systems including order fulfillment, warehousing, shipping, carriers’ systems, 3rd-party logistics (3PL) tracking, financial platforms, and Internet of Things (IoT) networks.

Hence, to effectively leverage these immense data sets across the supply chain, decision-makers must utilize the full spectrum of data analytics to rapidly uncover critical insights to make informed decisions. Below, I list the full range of data analytics available to supply chain decision-makers and the specific questions they are designed to answer.

The Full Range of Data Analytics: 6 Types and the Questions They Resolve

A Data Analytics Perspective To Better Empower Supply Chain Managers

1. Descriptive Data Analytics: What Happened? In this case, this analytics enables decision-makers to monitor, detect anomalies, and identify changes across the supply chain. As a result, it is a catalyst for action.
2. Diagnostic Data Analytics: Why Did This Happen? For this analytic type, the decision-maker seeks to understand the why behind what happened – the root cause. As a result, they focus on the right problem.
3. Predictive Data Analytics: What Is Most Likely To Happen? In this case, this analytic type typically relies on historical data, past trends, and assumptions to predict future outcomes. As a result, the best decisions can be made to positively affect future outcomes.
4. Prescriptive Data Analytics: What Action Should We Take? This analytic type identifies feasible actions that would result in achieving future targets or goals. Further, it can recommend a course of action, explain why it is the best, and detail how to implement it.
5. Real-Time, On-Demand Analytics: What Do I Do Now? In this case, this type of analytics rapidly leverages powerful information technologies on-demand to provide decision-makers timely insights at the point of decision. As a result, insights are made available based on the decision-maker’s timeline, not waiting on periodic meetings or a daily update.
6. AI-Powered Analytics: What Questions Did I Not Know to Ask? Lastly, thanks to recent advances in AI, this type of analytics can work rapidly with massive data sets, accessing a wide range of diverse types of structured and unstructured data, learn from new data, and even act autonomously. As a result, this opens more possibilities for decision-makers to include better insights as well as augmenting and automating decision flows.

For a more detail discussion on these distinct types of data analytics, see my article, A Data Analytics Perspective To Better Empower Supply Chain Managers.

“… to effectively leverage these immense data sets across the supply chain, decision-makers must utilize the full spectrum of data analytics to rapidly uncover critical insights to make informed decisions.”

3. Typical Data Infrastructure that Businesses Use for Supply Chain Analytics.

The next question to ask is how can these different types of data analytics work within supply chains? In fact, armed with how data analytics works and its capabilities, supply chain organizations can routinely make rapid, informed decisions to meet customer demands and compete effectively. As an example of how supply chain analytics works, see Qlik’s supply chain analytics diagram below.

So, this supply chain analytics chart above provides a breakdown of its components that span from data sources through actionable insights or the triggering of application events. To detail, see below for a description of each component within a data analytics process.

The Elements of Supply Chain Analytics

1. Operational Data Sources. Typically these data sources are from operational systems that help manage various activities within the supply chain. This includes such functions as procurement, finance, inventory, orders, warehousing, fulfillment, and transportation. Also, this can include data from third-parties such as suppliers, carriers, and merchants.
2. Data Repositories. For instance, data from these sources are then extracted, transformed and loaded (ETL) into a data warehouse or date lake, typically in the cloud.
3. Supply Chain Analytics. Then supply chain analysts and decision-makers use software tools as well as different types of data analytics. For example, this could include using predictive analysis for demand forecasting. Another possible option is for organizations to set up AI agents to act autonomously on analytical tasks.
4. Deliverables: Actionable Insights and Application Events. As a result of these analyses, insights can then be presented in graphs using key performance indicators (KPI) to support decision-making. Also, businesses can elect to fully automate the data analytics. For instance, a system could trigger an application event such as automated replenishment of inventory. Another option is for supply chains to use a Decision Intelligence platform. This type of system enables decision-makers to interact directly with the full spectrum of analytics to make rapid, superior decisions.

“… armed with how data analytics works and its capabilities, supply chain organizations can routinely make rapid, informed decisions to meet customer demands and compete effectively.”

More References.

For a more detail discussion of the components of supply chain analytics and analytics in general, see references below.

• Qlik’s Supply Chain Analytics
• Also for a more comprehensive discussion on Supply Chain Analytics, see ThroughPut’s Supply Chain Analytics: The Complete Guide for Improving Supply Chain KPIs
• Operational Data Sources: Enterprise Systems. Agile Supply Chain Decision-Making: First You Need to Know The Truth About Enterprise Software
• Data Repository Access. The Best Ways To Access Data – Tech Solutions To Unlock Your Data Silos.
• Decision Systems, An Agile Decision Platform to Empower Executives For Superior Supply Chain Performance: Here Are The Best Attributes.
• Supply Chain Analytics Examples. Data Analysis Examples To Best Overcome The Challenge Of Supply Chains

Data Analysis Examples To Best Overcome The Challenge Of Supply Chains

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 launching new analytics-based strategies, proof-of-concepts 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 from SC Tech Insights, see the latest articles on Data, Decision Science, and Supply Chain.