How AI-Driven Manufacturing Analytics Optimizes Supply Chains in 2025
AI can provide new and impactful insights into optimizing inventory, forecasting demand, and reducing downtime with advanced manufacturing analytics.
Author: Michael Nardacci
Modern supply chains have never faced more difficulties than they do in 2025. Recent years have provided new and expanding obstacles for manufacturers to overcome, from rapid inflation, geopolitical unrest, and COVID-19 to more localized factors such as labor strikes, natural disasters, and regulatory changes. In this challenging environment, organizations that are turning their supply chains into an advantage are doing so by using artificial intelligence and machine learning (AI/ML) applications to optimize processes and leverage their intangible data assets.
Gartner’s 2024 Supply Chain press release highlights how these high-performing organizations are 2-4x as likely to use AI/ML to optimize key supply chain processes.
Gartner’s Rate of High vs. Low Performing Organizations Optimizing Decisions with AI/ML
That said, the ability to extract value from an organization’s supply chain data requires a certain threshold of sophistication in the data governance, architecture, and integrations. RevGen’s Data Modernization service helps organizations establish an ecosystem that can support advanced analytics including AI/ML. With the proper framework in place, models can be deployed to help improve the most common supply chain processes, including maintaining optimal inventory position, anticipating customer demand, and minimizing downtime due to material or component delays.
Deploying Advanced Manufacturing Analytics
To obtain accurate and actionable results from advanced manufacturing analytics, organizations must use their historical supply chain data – from material sourcing to customer consumption – along with external factors identified by the business. Many businesses have the historical information necessary, however lack the specific technical expertise required for normalizing the data and performing feature engineering to prepare the data for ingestion by AI/ML models. Identifying and iterating through external supply chain factors which can be used to enrich the data and corresponding forecast results is a critical step.
Depending on the opportunity for improvement, these factors may be as diverse as market trends or sentiment analysis for demand forecasting, or weather and fuel costs for logistics and distribution. Sophisticated algorithms, including neural networks, decision trees, and clustering techniques can reveal non-linear relationships and interactions between variables, which are key differentiators when compared to traditional analytics.
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To better manage their supply chains, many manufacturers use a Master Production Schedule (MPS), Material Resource Planning (MRP), or Enterprise Resource Planning (ERP) system. These tools provide powerful support to improve the management and productivity of supply chains. However, these systems are heavily reliant on business parameters that must be set and managed by the organization.
For example, variables such as lead time, safety stock levels, and reorder points are critical to maintaining an optimal inventory position. The ideal positions for these variables can be identified on a very granular level through a business-managed AI/ML modeling process.
Advanced supply chain management software systems will include historical demand analysis to predict future demand but will not include the same flexibility to integrate and enrich that data with external factors such as market trends and inflation data that would improve demand forecast accuracy. Similarly, while leading ERP systems have native AI/ML capabilities that can be leveraged for supply chain support, these algorithms are proprietary and organizations don’t have the same ability to manage, refine, and audit as they do with their own algorithms and models. Additionally, the ability to incorporate external data into these models is limited.
When used to augment MPS, MRP, or ERP systems, business managed AI/ML modeling programs have several key benefits:
1. Increased visibility on supply chain challenges as well as the underlying causes. This allows organizations to proactively manage their supply chainbefore issues arise, leading to greater supplier and customer satisfaction and decreased downtime.
2. Greater control of model executions and how frequently resource planning parameters are updated. While a quarterly or monthly refresh may be adequate for demand forecasting, distribution lead time may need to be performed every 10 days to capture any variability due to inclement weather forecasts.
3. Proactive issue resolution through early supply chain disruption warnings. AI models can predict potential supply chain disruptions and suggest alternative solutions, helping to maintain efficiency and reduce downtime.
Optimize Your Manufacturing Analytics Journey
By leveraging AI/ML, businesses can gain a deeper understanding of their supply chain constraints and strengths while making more informed decisions. Through developing their own modeling capabilities, organizations can have direct control over the input data and the ability to enrich that information with any available external factors. The results will enhance the capabilities of the existing systems and processes which are in place, and lead to a more productive supply chain.
RevGen has successfully collaborated with our clients to identify data, perform feature engineering, and build the AI/ML models to meet their manufacturing analytics needs. Our proven track record solving specific challenges such as demand forecasting and working with clients to define their own unique use cases through our AI Accelerator Workshop will help maximize your supply chain productivity and resiliency.
Michael Nardacci is a Sr. Consultant at RevGen Partners where he works on projects related to business current state assessments and data transformation and migration.
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