The manufacturing industry is one of today’s hottest markets for AI applications. With the rise of IoT and telemetry technology, the manufacturing industry has seen an influx of data.
Manufacturing companies are positioned at a critical juncture.
The industry is witnessing an increased demand for infrastructure to improve the collection, management, and action of massive amounts of data. Leveraging design thinking principles and MLOps, manufacturing companies can develop predictive and generative applications tailored to their businesses.
In this post, we aim to shed some light on the applications of AI within manufacturing. We’ll explore some of the most frequent applications for AI in the manufacturing space, highlighting their benefits and providing real-world examples to illustrate their impact.
What Are the Top Applications of AI for Manufacturing?
At phData, we have worked with several leading manufacturing companies to help them better harness AI to make more informed decisions. Based on our experience, manufacturing customers have the most success (in terms of adopting AI) with the following applications:
Demand Forecasting
Whether building better demand forecasts, optimizing logistics, scheduling production, or improving inventory management, ML-driven demand forecasting delivers real predictive value. These solutions use data clustering, historical data, and present-derived features to create a multivariate time-series forecasting framework.
Demand forecasting enhances forecast accuracy by enriching features for both legacy and new products, improving inventory control, reducing costs, avoiding stockouts, and boosting profitability. Additionally, it optimizes logistics by enabling more efficient transportation planning, leading to cost savings across the manufacturing industry.
Check out this dashboard example built in Tableau that provides users with the ability to track key shipment and delivery metrics over time:
Client Example
A startup food manufacturer was utilizing social media data to track trends and find niche markets to develop new products. The marketing team was spending weeks analyzing spreadsheets of TikTok and Twitter data.
phData implemented an end-to-end trend scoring methodology using Natural Language Processing and forecasting techniques that involved human-in-the-loop feedback. After eight short weeks of work, analysis time was reduced to less than two hours. The organization now has the ability to quickly release two new product lines to capitalize on growing food trends.
Six months after the solution was deployed, the company referred to the product as the “backbone of their business.”
Impact:
97.5% decrease in time required by a human to prepare data for analysis
6 new SKUs launched using a data-driven approach
> 40,000 food-related social media hashtags analyzed with each pipeline run
Supply Risk Forecasting
Anticipating demand is the cornerstone of the supply chain. Disruption in demand has upstream and downstream implications, like shifts in procurement to canceled transportation runs. Risk forecasting solutions help businesses understand risk in their supply chain and proactively create risk-mitigating processes.
Minimizing downtime is the key to profitability.
Risk forecasting and reduction in plant disruption allow organizations to mitigate risks and maximize profits. Risk forecasting models can incorporate external data sources, such as weather and location data, to enable organizations to quickly adapt and respond to events.
Client Example
A massive supply chain risk intelligence company was looking to develop an AI strategy that would enable them to better utilize the latest and greatest in ML technology to streamline and expand their business. By including location data, the business developed a strategy that notified retailers of imminent supplier disruptions.
If a disruption is detected, the model directs the retailer toward a supplier in a different region, decreasing downtime and maintaining throughput.
Predictive Maintenance
By leveraging IoT, telemetry, and historical maintenance data, predictive maintenance models help businesses determine the optimal time to service equipment and decrease downtime.
Organizations can maximize profitability through predictive maintenance and tighter downtime management.
Predictive maintenance provides insights for line and shift leaders to better manage downtime, quality issues, and productivity goals. Predictive measures benefit plant leadership, enhancing quality control visibility. Some individuals may be innately reactive to problems. As a result, predictive maintenance solutions can reposition line and shift leaders toward more proactive mindsets.
Client Example
A global food manufacturer needed to lessen downtime, reduce operating costs, create more visibility in their systems, and shift from a reactive to a proactive mindset. The organization partnered with phData to create a standard time series data model of demand, quality, productivity, and safety data, allowing end users to view key metrics in one source of truth location.
Visualizations were built on top of the data model to meet the needs of plant leadership and operational staff around cost reduction, visibility, and a proactive mindset.
Impact:
“Since our engagement with phData, our NPS scores have improved quarter over quarter.”
80% forecast accuracy
Quality Assurance
In manufacturing, fidelity is key. Incomplete or faulty connections and even minor blemishes all impact the end result. Consistency and accuracy in identifying defects are crucial to a successful assembly.
An emerging technology in the computer vision space, LandingAI, tackles these challenges particularly well.
“LandingAI’s LandingLens™ provides an AI/Deep Learning visual inspection development and deployment platform that helps OEMs, system integrators, and distributors to easily evaluate AI/Deep Learning model efficacy for a single application or as part of a hybrid solution combined with traditional 2D/3D machine vision and robotic control solutions.”
LandingAI’s computer vision solution is exceptionally useful in the manufacturing space. The insights gleaned from LandingAI’s proprietary images can aid in:
Assembly Inspection
Quality Control Inspection
Compliance and Regulatory Measures
Electronics Manufacturing Inspection
Textile Quality Control
Equipment Monitoring
To learn more about this powerful technology, check out this blog that shows how to train and deploy an object detection model with 100% accuracy using LandingAI.
Client Example
A construction and engineering organization was interested in developing insights into construction sites where accidents go unnoticed and alerts when an individual is in a certain area or at a particular time of day.
The organization was able to utilize LandingAI’s computer vision models to implement personnel monitoring, detection, and alerting measures at construction sites.
In Conclusion
Manufacturing is benefiting massively from the present AI boom. From predictive maintenance to identifying parts defects and novel product avenues, AI applications are relevant across all aspects of the manufacturing industry.
If any of the AI applications covered in this blog interest you, phData can help your business implement them.
Leading manufacturing companies often leverage phData’s AI expertise to executed AI solutions that strengthen manufacturing plant safety, efficiency, and productivity, driving business growth and increasing profitability. Contact us today to learn more!
FAQs
What are the most common data projects in manufacturing?
Migrations from legacy on-prem systems to cloud data platforms, like Snowflake and Redshift
Creation of dashboards and reports for descriptive analytics, control-tower supply chain reporting, and forecast visibility
Development of AI/ML predictive applications for forecasting and predictive maintenance
When it comes to data modernization, what are the largest challenges facing manufacturing companies?
To remain competitive, manufacturing companies need to leverage data spanning a wide variety of sources. Centralizing and harmonizing that data can be a significant challenge. Once data has been centralized, building reports and analytical applications require a mix of both deep expertise and innovative creativity.
What are the largest areas of opportunity for data-driven modernization in manufacturing?
The modern economy runs on data, and manufacturing organizations have always relied on data within their business. New technologies have opened the doors for groundbreaking transformations, including:
Using data to improve visibility across the entire business or supply chain
Leveraging next-generation AI/ML technology to improve forecasting or marketing
Harmonizing data across various IoT/telemetry sources to understand performance
