Today’s business environment requires exacting efficiency in the supply chain: providing on-time deliveries, reducing cargo inventory errors, and providing the most efficient shipping routes, just to name a few of the issues. Supply chain resiliency has also become a central concern for many organizations especially in the wake of the COVID-19 pandemic. Advancing efficiency and resiliency in the supply chain requires statistical analysis of historical data, leveraging the latest predictive techniques.
Statistics has been the backbone of supply chain analytics by facilitating demand forecasting, allowing businesses to predict market trends and optimize inventory levels, and aiding in performance measurement, risk assessment, and process optimization. However, while statistical analysis excels in handling structured data and making inferences based on historical patterns, it struggles with today’s complex, multidimensional datasets common in modern supply chains.
Statistical approaches to supply chain analytics, which rely heavily on assumptions, may not adapt well to dynamic and rapidly changing environments, whereas AI, with its machine learning capabilities, can handle diverse data types, detect intricate patterns, and adapt to evolving situations, providing a more flexible and predictive approach in supply chain analysis.
However, predictive analytics can be highly technical and require a depth of expertise in data science and machine learning. These analytics also require a vast amount of data, including real-time data to inform predictive models. The complexity of supply chain networks can also make it challenging to model effectively using predictive techniques. Finally, understanding the reasoning behind model predictions can be just as challenging for decision-makers.
Artificial intelligence (AI) can be used as a tool to close the predictive analytics gap. AI-enabled predictive analytics empowers supply chain managers and procurement and sourcing teams to more effectively use data to make informed decisions.
The power of AI through intuitive natural language search, automated insight generation, and AutoML tools enables supply chain domain experts to ask questions of data and get answers easily. AI analytical tools provide the ability to integrate data from multiple source systems and run analysis in real time with advanced computational engines. These analytical tools also provide in-depth explanations of generated analyses, allowing everyone to get value out of data. AI can be a bridge from data to predictive insights for supply chain teams.
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AI-powered predictive analytics and the supply chain
The benefits of using predictive analytics to improve the supply chain include enhanced operational efficiency, reduced costs, and better decision-making.
More specifically, there’s a range of potential solutions provided by predictive analytics that open avenues for an organization’s improvement. By leveraging historical data, market trends, and other variables, predictive analytics can be used to improve the accuracy of demand forecasting, help organizations optimize inventory levels, enhance supplier management, and much more. Below we’ll provide an overview of some real-world examples of predictive analytics used by supply chain teams.
Demand forecasting plays an absolutely critical role in ensuring supply chains operate efficiently, meet customers’ dynamic needs, and adapt to ever-changing market conditions. With accurate forecasts, supply chain managers can make informed decisions and optimize operations. The benefits of accurate demand forecasting can help to optimize inventory management, reduce costs, increase customer satisfaction, create more efficient production processes, and mitigate supply chain risks.
AI-powered predictive analytics can help enable more accurate forecasting in a number of ways. AI analytical tools help to identify patterns, trends, and seasonality within historical data to better understand past demand fluctuations. AI can also help to select the relevant features or variables (e.g., pricing, marketing campaigns, and weather) to be used as inputs in predictive models.
Supply chain managers can use predictive analytics to segment demand by customer type, product category, region, or other relevant factors to create more targeted forecasting. With AI-powered tools, these features are provided via a graphical point-and-click interface, allowing more of the supply chain team with access to better forecasting capabilities.
Read more: The Ultimate Guide to Supply Chain Analytics & Procurement Analysis
While demand forecasting plays a vital role in managing inventory, there are many other key components in which predictive analytics can add value for an organization.
Inventory management is especially important in today’s environment with extreme fluctuations in customer demand and lead time uncertainty due to a disrupted supplier. Rapid shifts in consumer preferences and market trends can make it difficult to keep inventory aligned with the customer’s needs.
Based on historical data and the likelihood of demand variability, predictive analytics can be used to calculate safety stock levels, classify categories based on their importance, develop dynamic reorder points, and much more.
Predictive analytics platforms can be used to track the health of inventory by providing alerts when inventory falls outside of thresholds determined by forecast models. With models informed by delivery times, order accuracy, and quality control, supply chain managers can make informed decisions related to future supplier performance.
Lead time predictions
Lead time predictions allow organizations to plan and optimize their inventory levels, production schedules, and order placement. With accurate lead time predictions, organizations can prevent stockouts/overstock situations, improve customer service with on-time deliveries, allocate resources more efficiently, and plan for seasonal variations. Lead time predictions also help to inform inventory optimization, demand planning, supplier performance management, and risk mitigation. As such, ensuring accurate lead time predictions is critical to an organization’s supply chain management.
AI-powered predictive analytics can help improve lead time prediction accuracy in a number of ways. ML models can identify complex and nonlinear relationships between variables. This is crucial for capturing intricate lead time dependencies.
As mentioned earlier with demand forecasting, AI-infused models can automatically detect relevant features and their importance in predicting lead time. By recognizing hidden patterns between billions of variables, AI obviates the need for manual error-prone feature selection. With anomaly detection, AI predictive analytics can identify unusual deviations or disruptions in lead times and allow supply chain teams to respond immediately.
Preventing defects, maintaining a consistent product quality, and meeting customer expectations are top concerns for supply chain teams managing quality control. With effective quality control, analytics can be used to identify defects and quality issues early in the production process, reducing the cost and effort required to address downstream problems. Predictive analytics associated with quality control also enables deeper supplier performance strategies by identifying quality and delivery issues early.
AI-powered predictive analytics improves quality control processes by identifying issues before they become a problem. By leveraging AI-enabled algorithms to analyze data from sensors, cameras, and quality inspection records, supply chain teams can intervene early and take corrective actions to prevent defects from propagating downstream. AI models can optimize manufacturing processes by analyzing data from all sources and identifying variables that may be affecting product quality.
Transportation analytics enables supply chain teams to improve efficiency, cost-effectiveness, and the overall performance of transportation activities. Minimizing empty backhauls, reducing fuel consumption, optimizing routes, and maximizing vehicle capacity utilization can help lower transportation costs. With more optimized transportation, delivery times and service reliability can be improved, leading to higher customer satisfaction.
Predictive analytics powered by AI enables next-level transportation optimization with advanced ML models and more effective data-driven decision-making. ML models can be used to analyze historical data, traffic patterns, weather conditions, and real-time data to optimize transportation routes. With real-time data, dynamic routing can be enabled to make adjustments in response to traffic congestion, accidents, and weather disruptions. AI-powered predictive analytics can be used to identify opportunities from billions of data points for consolidating multiple shipments into a single load and reduce costs associated with less-than-truckload shipments.
Effective risk management helps to safeguard against disruptions, minimize financial losses, and enhance overall supply chain efficiency. Resilience is one of the top issues for supply chain teams with the current climate’s geopolitical issues and growing concerns related to natural disasters. By identifying and mitigating risks, supply chain teams can minimize financial losses related to disruptions, quality problems, and overstock/stockout situations.
AI-powered predictive analytics enables risk mitigation in various aspects of the supply chain. AI-powered models can be used to analyze a wide range of data sources, ranging from historical data to market trends and real-time information to detect early-warning signs of potential risks.
Properly focusing the limited resources of supply chain teams can be one of the most critical tasks for any manager. With the combination of AI and predictive analytics, models can help to assess the severity and likelihood of different risks and help teams prioritize them based on their potential impact.
Supply chain teams play a critical role in ensuring orders are processed, picked, packed, and shipped efficiently. Supply chain teams’ work in order fulfillment includes demand forecasting, inventory management, procurement, warehouse management, order processing, and much more.
Today, analytics is used for things like order prioritization, order routing, warehouse design, process optimization, and returns analysis. However, the value derived from these analytics can be limited by the technical resources available to the supply chain.
One of the key benefits of AI-powered analytics is the ability to unlock organizational bottlenecks associated with highly technical tasks. As previously mentioned, predictive analytics and AI can enable improved demand forecasting and inventory optimization. AI-enabled predictive analytics can also help to improve customer satisfaction by ensuring high-priority orders are processed and shipped quickly. By monitoring order fulfillment in real time, AI predictive analytics can ensure fulfillment processes adhere to established compliance guidelines and standards.
Accelerate your predictive capabilities with AI
AI has the power to fundamentally change how we approach a plethora of different problems. Many aspects of predictive analytics today are off limits except to a handful of data experts, who have been trained with many hours on highly technical toolsets. And while their experience is invaluable for any organization, their productivity and focus may not align with the focus of many supply chain teams.
AI-enabled predictive analytics has the potential to be game-changing for many organizations. By taking away the technical barriers of many challenges associated with predictive analytics, AI can unlock new capabilities for many organizations. Enabling the entire supply chain team with tools to more effectively forecast demand, optimize inventory, make more accurate lead predictions, and much more can reduce organizational costs and increase customer satisfaction.
Learn more about how Tellius enables AI-powered predictive analytics for the supply chain, or sign up for a demo today.