What Is Supply Chain Predictive Analytics?
Supply chain predictive analytics use historical and present data and analytics techniques to predict future outcomes. Supply chain managers use sophisticated predictive analysis tools to identify patterns in past data and events to proactively forecast risks and opportunities.
Organizations use predictive analytics to address issues like rising fuel costs, operational inefficiencies, inventory shortages, and carrier capacity constraints.
The 2021 MHI Annual Industry reports that 31% of supply chain managers use predictive and prescriptive analytics to make their processes more efficient.
In this article, we introduce the role of predictive analytics in improving supply chain operations and highlight its benefits for organizations and shippers.
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Predictive Analytics vs. Prescriptive Analytics
Predictive analytics help supply chain managers predict the outcomes of potential scenarios in a given situation using historical data and technologies like AI and machine learning. They monitor performance across various supply chain points to prepare prediction models for supply chain processes such as demand planning, shipping costs, inventory management, and transportation. Predictive models can identify weaknesses in the organization’s processes and areas that need improvement.
Prescriptive analytics takes the insights provided by predictive analytics and offers the best possible steps to mitigate risks and achieve a favorable outcome. For example, prescriptive analysis is used by supply chain managers to optimize inventory levels based on customer demand forecasted by predictive models.
Why Do Supply Chains Use Predictive Analytics?
Predictive analytics enables supply chain professionals to collect and analyze data and helps management make data-driven decisions. It also recommends solutions for issues like shipping delays, carrier constraints, warehouse inefficiencies, and inventory shortages.
Predictive capabilities enable organizations to monitor trends (e.g., customer market, traffic, labor, weather) and get a peek into the future. Supply chain managers utilize technologies like artificial intelligence and machine learning algorithms to recognize and mitigate risks by identifying patterns in yearly, monthly, weekly, and even daily data.
Benefits of Predictive Analytics
Here are the benefits organizations and shippers reap by adopting predictive analytics to optimize supply chain operations.
1. Automated Heavy-lifting of Supply Chain Operations
Supply chain managers routinely apply predictive analytics in business operations to anticipate future events, mitigate supply chain risks, and improve sustainability.
Advancements in cognitive technologies like robotics, process automation, and big data analytics have given predictive models the ability to learn and improve automatically from data experience. Its application in supply chain management is helping companies improve business operations, better understand customer needs, and identify potential revenue opportunities.
Supply chain predictive analytics help businesses:
- Improve productivity: Robotics and automation offer efficient solutions for manufacturers, third-party services, and carriers to handle, store, and transport goods. Human workforces can be focused on more value-added tasks while minimizing human errors caused by negligence. For example, Amazon warehouses use robot-guided vehicles to retrieve products faster without human assistance.
- Improved Procurement: Carrier performance depends on several factors, including the location of distribution zones, shipping volume, and service levels. Predictive analytics allows companies to compare carriers’ performance metrics and outcomes in real-time to accurately forecast carrier options and delivery expectations months in advance.
- Minimizes the need for visual supervision: The digital paper trail created by predictive analytics systems allows warehouse robots to automatically use this data to improve order fulfillment tasks without supervision while communicating information through supply chain software to relevant parties, including supply chain managers and delivery partners.
Coupling supply chain predictive analytics with automation also reduces the need for visual inspection of warehouse tasks. For example, data from cameras equipped with computer vision capabilities are used to develop models that reduce bottlenecks in logistics operations. This approach is particularly useful where the human presence can be difficult to maintain such as in high-temperature locations.
2. Data-driven Supply Chains and Predictive Industry Models
Predictive analytical models can’t see future events. They can only forecast what will likely happen in a particular situation. Supply chain professionals generate multiple forecasting models to identify the one that predicts future outcomes closest to reality. They test the models using known historical data and fine-tune algorithms until the model can predict based on the past with a reasonable degree of certainty.
Linear regression is the most commonly used method by logistics companies to improve inventory management, demand forecasting, risk management, pricing optimization, and predictive maintenance.
Supply chain predictive analytics help businesses:
- Perform Demand Forecasting: Customer demands are volatile. They are affected by seasonality and changes in pricing. Predictive analytics enables businesses to project customer demand months in advance and adapt company output according to those projections. Organizations use predictive technologies like cloud-based inventory management and machine learning algorithms to extract valuable insights from historical datasets (e.g., stock levels and pricing). This information helps them eliminate overstocking, ensure on-time deliveries, and optimize customer service levels.
- Find The Right Product Market Fit: Companies use predictive models to predict future trends by analyzing patterns to ensure that the right product offering is delivered at the right time.
- Perform Predictive Maintenance and Minimize Downtimes: When a piece of warehouse equipment or machine breaks down, companies incur repairing costs and are exposed to disruptions in their supply chain network. Traditionally, organizations reduce these downtimes by storing spare machine parts on-hand. Predictive technologies combined with IoT sensors enable companies to automatically monitor machine performance and warn about expected malfunctions (such as overheating components). As a result, timely replacement of faulty parts can prevent full machine breakdowns.
3. Moving from Reactive to Predictive Supply Chain Transportation
One of the most burning problems in supply chain operations is related to transportation delays. Data-based AI modeling helps businesses proactively identify supply risks and predict their impact on shipping operations.
Effective data collection and processing are essential to make accurate forecasts across the supply chain. Cloud-based analytics tools enable organizations to consolidate various relevant data sources and provide predictions to stakeholders in real-time about traffic, fuel prices, and weather conditions. This allows decision-makers to proactively identify transportation and shipping operations risks and determine the most appropriate actions to take related to sourcing, pricing, and order fulfillment.
Supply chain predictive analytics help businesses:
- Enhance Transportation Management and Optimize Shipping Costs: Most shippers worry about the impact of the ever-changing shipping rate on their profit margins. Implementing predictive analytics into your transportation management system (TMS) allows businesses to analyze carrier options beyond past pricing and general market trends. It uses current pricing data, real-time trends, and carrier capacity to identify the best and most cost-efficient shipping options.
- Increase supply chain visibility: Organizations use predictive analytics to report ETAs (estimated time of arrivals) of ordered goods and factor in any possible delays to avoid negative customer experiences.
Third-party logistics providers use predictive analytics to improve the visibility of shipment location and status. This is achieved by getting data via tracking devices deployed at distribution centers and delivery trucks and historical data (e.g., traffic conditions). As a result, 3PLs can identify the best possible option for fulfilling orders and reliably forecast ETAs.
4. Carrier-level Supply Chain Visibility and Risk Mitigation
Without predictive analytics, shippers are forced to make business decisions based on past data. In contrast, supply chain predictive analytics helps shippers use historical data and real-time trends to prepare models for multiple scenarios and identify possible solutions. This way, businesses know exactly how to respond to issues such as delivery delays, shipping rate spikes, and carrier capacity constraints.
In addition, shipping analytics helps organizations integrate their logistics management systems and data analytics capabilities to identify bottlenecks and inefficiencies and mitigate them to prevent potential delays or shipping breakdowns.
For example, companies can evaluate the impact of carrier rate changes on their profit margins and identify alternate carrier options weeks or even months in advance.
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