In my years of untangling supply chain knots, I’ve watched too many organizations treat a single, monthly forecast like the Holy Grail—only to see it turn into a fairy tale at the first disruption. As a result of these flawed forecasts, many decision-makers fall back on gut instinct and Excel spreadsheets, even when making the most high-stakes decisions. It’s a dangerous game we no longer have the luxury to play. Predictive analytics, once the domain of slide rules and Magic 8-Balls, is now going through an incredible transformation. Thanks to massive computing power and new AI models, the mathematics of predictive analytics is being supercharged. What used to take weeks of agonizing number-crunching can now happen on demand.

Without a doubt, we can now move past the illusion of one “perfect” number to dynamic forecasts that actually account for the chaos of the real world. However, AI-powered analytics needs a human in-the-loop. To trust these tools—and to stop flying blind—we need to understand the basics of how these models actually work. You can’t leverage precise probabilities and all possible outcomes if you don’t know what behind these decision tools. If you want to stop reacting to disruptions and start outmaneuvering them, keep reading. In this article, I’ll break down the top 7 predictive analytics models powering today’s advanced software and provide specific, battle-tested supply chain use cases. It’s time to get ahead of the curve.

Understanding Predictive Analytics and Its Growing Significance in the Supply Chain Industry.

Predictive analytics is a branch of advanced analytics that uses historical data, statistical algorithms, and machine learning techniques to anticipate future outcomes. Basically, predictive analytics can be defined as follows:

Predictive Analytics Definition

“Predictive analytics is a form of technology that makes predictions about certain unknowns in the future …”

Investopedia

In the volatile world of supply chains, logistics leaders desperately need timely and effective predictive analytics. By anticipating future outcomes, they can proactively prevent and solve problems. With the exponential growth of computing power, the internet, and AI, there are now more predictive analytics tools available than ever before. These tools are crucial for businesses to predict demand and supply trends. For instance, they enable supply chain leaders to make informed decisions about inventory management, demand forecasting, and operational efficiency.

7 Predictive Analytics Models With Example Supply Chain Use Cases.

Below are the primary predictive analytics models available today. Specifically, these models include regression, classification, time-series, clustering, neural networks, decision tree, and ensemble (use of multiple models). Moreover, each of these models have its own set of strengths and weaknesses that can help supply chains to be both proactive and better predict future outcomes.

1. Regression Modeling: Analytics That Uses Trends To Predict Outcomes.

First, regression models utilize one or more input variables to predict continuous numerical values. For example, analysts can apply these analytics models for maintenance expenses or demand forecasts. Often, these continuous numerical values are depicted as a line. In particular, this is seen in linear regression models where a formula helps extend the line into the future. For example, the following are some supply chain use cases that employ regression modeling.

• Forecasting Demand. Here, regression models can help predict future product demand based on historical data and other independent variables. For instance, these variables can include price, promotional activities, and economic indicators.
• Predicting Fuel Consumption. Also, regression models can predict the fuel consumption based on factors like distance, load, vehicle type, and driving behavior. As a result, this can aid in cost estimation and budgeting.
• Project Maintenance Costs. Regression models can also predict the maintenance costs of vehicles based on variables like vehicle age, mileage, make, and model. As a result, this type of predictive analytics helps to optimize maintenance schedules and budgets.

2. Classification Modeling: Predictive Analytics That Categorizes Data.

Classification models aim to categorize data into two or more groups based on one or more input variables. Specifically, their main purpose is to identify the relationship between input variables and the output variable. As a result, these models effectively classify new data into the appropriate category. Here are some examples of supply chain use cases employing classification modeling.

• Select the Best Suppliers. For instance, analysts use classification models to categorize suppliers into different groups based on their performance metrics. Thus, this leads to more informed supplier selection decisions. 
• Classify Customer by Segment. Classification modeling can also help in classifying customers into different segments based on their purchasing behavior. As a result, this leads to more personalized marketing and service.
• Detect Fraud. In this case, classification models can predict whether a transaction is likely to be fraudulent based on various features of the transaction, helping to secure a ecommerce operations.

3. Time-Series Modeling: Analytics That Forecasts By Time.

These models predict future values by analyzing historical trends and patterns in time-series data. In particular, these types of models are used for stock prices, weather patterns, or website traffic. Typically, these models evaluate inputs at specific frequencies, such as daily, weekly, or monthly intervals. Below are several supply chain use cases that utilize time-series modeling.

• Predict Future Materiel Costs. Here, analysts can apply time series models to predict future costs of raw materials. By analyzing historical price data, companies can forecast future price trends and adjust their sourcing strategies accordingly.
• Forecast Traffic Conditions. In this case, time-series models can forecast traffic conditions on different routes at different times. Thus, this aids in route selection and scheduling.
• Provide Delivery ETAs. For example, time-series modeling can predict the delivery time of shipments for a given zip code based on historical data, improving the accuracy of estimated time of arrivals (ETA).
• Forecast Inventory Demand. Lastly, time-series models can forecast future inventory needs based on past sales trends. Thus, helping to optimize stock levels and reduce stock outs and overstocks.

4. Clustering Modeling: Predictive Analytics That Categorizes.

Clustering models categorize data points based on similarities in their characteristics or behaviors. These models are frequently employed for customer segmentation and market basket analysis. For instance, below are some supply chain use cases involving clustering modeling.

• Conduct a Risk Assessment. For instance, analysts can use clustering to group suppliers or customers based on their risk profiles. Thus, this helps with formulating risk management and mitigation strategies.
• Analyze Driver Behavior. In this case, clustering can identify groups of drivers with similar driving behaviors. As a result, the model can then recommend optimal driver training and risk management strategies.
• Segment Customers by Buying Behaviors. Also, clustering can identify groups of customers with similar buying behaviors. As a result, a business can develop targeted marketing and personalization strategies.

5. Neural Network Modeling: Emulates The Human Brain To Predict Outcomes.

Neural networks were developed to emulate human brain function as a form of predictive analytics. Specifically, these models utilize machine learning, algorithms, and statistical models to analyze and draw inferences from data patterns. Also, neural networks can handle complex data relationships through artificial intelligence and pattern recognition. Thus, these models are useful for overcoming challenges such as excessive data or lacking a formula to find relationships between inputs and outputs in a dataset. Lastly, they are ideal for making predictions rather than providing explanations. For example, below are some supply chain use cases that employ neural network modeling.

• Optimize Routes. In this case, businesses can use neural networks to optimize routes by learning from historical data and identifying patterns and correlations that might be missed by other methods.
• Predict Demand. Also, neural networks can predict the demand for transportation services based on complex non-linear relationships in the data, guiding capacity planning.
• Act as a Recommendation System. For instance, neural networks can predict what products a customer is likely to buy based on their past behavior and the behavior of similar customers. Thus, powering effective recommendation systems.

6. Decision Tree Modeling: Predictive Analytics Based On Potential Decision Paths.

These models create visual representations of potential outcomes based on different decision paths. Below are some supply chain use cases that employ decision tree modeling.

• Assist with Fleet Management. For instance, decision trees can assist in making complex decisions related to fleet management. Specifically, this can include aiding with vehicle acquisition and disposal, based on factors like cost, utilization, and maintenance. Thus, these models can predict when best to dispose of and acquire equipment.
• Help Guide Customer Service and Self-Help. Decision trees can also model the process of handling customer inquiries and complaints. As a result, these models can guide customer service representatives towards the best course of action based on the specifics of each case.
• Analyze Potential Impacts to a Network. For instance, a business might want to understand the impact on cost and delivery times if they switch from air freight to sea freight, or if they use a different distribution center.

7. Ensemble Modeling: Leveraging Multiple Models For Better Predictions.

Ensemble models boost predictive accuracy and stability by combining multiple models. The underlying principle of ensemble modeling is to reduce the errors and biases present in individual models, leading to enhanced overall performance. Below are several supply chain use cases that employ ensemble modeling.

• Better Predict Traffic Conditions. In this case, ensemble models can provide more accurate and robust predictions of traffic conditions by aggregating the predictions of multiple individual models.
• Help Manage Supply Chain Risk. Also, ensemble models can provide more accurate and robust predictions of supply chain risks by combining the predictions of multiple individual models.
• Improve Demand Forecasting. They can also improve the accuracy of demand forecasts by combining the forecasts from different methods, each of which may capture different  aspects of the demand patterns.
• Enable Proactive Transportation Operations. Lastly, businesses can integrate predictive analytics models with Internet of Things (IoT) devices and sensors to collect real-time data on the location, condition, and status of their shipments. Specifically, this type of real-time visibility helps to proactively address potential delays, damages, or disruptions in the supply chain. For example, it is possible to identify the most efficient routes based on factors such as traffic congestion, road conditions, and delivery deadlines. 

Final Thoughts.

So, which predictive model is best? That depends entirely on your data, your objectives, and the complexity of your problem. Also, the days of chasing a single, monthly forecast are dead. With advanced analytics and massive computing power, businesses are starting to use supercharged predictive analytics where forecasting no longer takes weeks of agonizing number-crunching. Now, supply chain decision-makers can rapidly access forward-thinking analytics for on-demand insights. What’s more, instead of betting the house on one “perfect” forecast number, we can now calculate precise probabilities across every possible outcome. It’s time we stop gambling on a single guess and start managing our supply chains like a resilient investment portfolio.

For more on forward-thinking analytics, see my article, Beyond the Forecast: Forward-Thinking Analytics for Supply Chain Leaders.

Beyond the Forecast: Forward-Thinking Analytics for Supply Chain Leaders

More References.

• Predictive Analytics: Investopedia’s article, Predictive Analytics, Qlik’s article, What is Predictive Analytics, and Throughput Inc’s article, Supply Chain Predictive Analytics: Benefits, Use Cases and Growth Potentials
• Bias: Unvarnished Facts’ article, Bias With Examples – Everything You Need To Know
• Supply Chain Planning: Supply Chain Planning: Data Analytics Advice That Will Result In A Better Way
• Risk Management: Risk Mitigation For Supply Chains: How To Best Identify, Make Assessment, Overcome
• Rapid Impact Assessment: The Best Impact Assessment Approach that will Quickly Orient You for Better Business Decisions
• Supply Chain Analytics Types: Supply Chain Analytics Types and The Way They Work To Better Empower Decision-Making.

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 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.

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