Inventory Management in Manufacturing

Industry Challenges & Trends

Organizations in manufacturing, construction, food processing, oil and gas industries utilize heavy machinery and complex assembly lines to transform goods and materials into products. Optimizing a variety of these disjointed production processes is often challenging due to poor facility layout, redundant operations, and worker reluctance to follow the recommendations. Lack of optimization leads to operational inefficiency and equipment idling, which affects performance and slows down production cycles, while process defects result in product rejects and customer penalties.

AI can help businesses achieve accuracy and efficiency on the factory floor. Using AI-driven predictive analytics, they are poised to optimize work-in-progress inventory movement and forklift traffic to eliminate inventory commotion, which is caused by disjointed operations of machinery operators, product pickers, and forklift trucks. Optimization as such accelerates and enhances manufacturing cycles, improves fully productive time, and reduces direct costs of production, thereby improving gross margins and profitability for a competitive edge.

Inventory Management Solution for Process Optimization

The Provectus Inventory Management Solution utilizes AI-based predictive analytics to optimize and automate a wide range of operations in increasingly dynamic manufacturing environments. Using IoT data and industry-specific datasets, it generates inventory and traffic forecasts to adjust product output, product pick-up time, and forklift truck pathing on the factory floor. The solution mitigates technology risks of a full-scale AI/ML adoption across the organization while meeting its unique inventory optimization and management needs.

Real-world Case

The Client, a leading supplier to auto and electric utility industries, experienced inventory management and factory traffic challenges while producing automotive parts on low margins. The difference in production rate of specific manufacturing stages caused large inventory piles and increased forklift traffic on the factory floor, affecting profitability and the number of rush-order shipments to customers. The Provectus Inventory Management Solution allowed to cut the manufacturing cycle of automotive fasteners by 14% and to reduce the number of forklift drivers by 10%, while improving just-in-time delivery of finished goods to customers.

Key Features

• Pre-built machine learning architecture, including infrastructure for model design, training, and deployment in production
• Pre-trained machine learning algorithms that learn, adapt and improve forecast accuracy
• Real-time predictive analytics engine to generate accurate inventory forecasts based on data collected in parallel
• Internal worker alert and truck navigation system to notify managers, operators, and truck drivers on output and traffic using predictive analytics
• Cloud vendor agnostic — The architecture is certified to be used with different cloud providers

How it Works

The proposed solution is a cloud-based platform connected to on-prem infrastructure using VPN and that is hooked up to a local ERP system. Managers and machinery operators use a local application on their PCs to input crucial manufacturing data, which is automatically pushed to the cloud and the ERP system. All the details on orders finished, orders in progress, order status and priority, as well as truckers’ task load are collected and stored in the cloud. The ML-powered predictive analytics engine analyzes these inputs to generate recommendations to forklift truck drivers. Accessible via a mobile app, the recommendations provide guidance on what parts to haul, where to pick them, and what path to follow to avoid congestion and ensure operational efficiency.