• AI
  • Artificial Intelligence
  • Machine learning

How AI and ML Improve Logistics, Sales and Manufacturing

Yura Velichko
18 May 2022
9 min
How AI and ML Improve Logistics, Sales and Manufacturing

Artificial intelligence (AI) powered by machine learning (ML) algorithms has become widespread across the global supply chain. Recent surveys show that the market for AI and ML in manufacturing, distribution, and sales could grow to $16.2 billion by 2027 — ten times the market value of 2020.

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This shows that companies worldwide are on a rocket path toward AI. Despite that, only 4% of supply chain leaders consider their business future-ready. What prevents most companies from unlocking the true value of AI-powered solutions? 

We believe it’s because they don’t understand how to apply these technologies for the right processes in the most effective way. So, this article will tell you about AI’s capabilities and applications, and strategies for implementing AI-based solutions.

AI and ML in supply chain management

Thanks to the ubiquity of artificial intelligence and machine learning, they’ve become buzzwords. To get you up to speed, we’ll start with “AI 101” to clear up things about these technologies in supply chain management.

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In supply chain management, AI encompasses technologies that analyze massive data volumes, support decision-making, and augment human abilities. ML refers to techniques that allow AI to learn. Integrating ML-based algorithms into AI technologies can revolutionize the way companies manage and plan their supply chain.

Modern supply chains leave enormous data trails. However, most companies analyze less than a quarter of that data, leaving many valuable time-sensitive metrics off the radar. 

AI and ML help structure and analyze this data to optimize multiple aspects of the supply chain. Now, let’s look at how they can help your business.

Advantages of using AI and ML in manufacturing, logistics, and business operations

Are you wondering how AL and ML technologies can improve your supply chain? Let’s explore some facts and figures.

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Reduced forecasting errors

ML-enhanced tools learn to predict issues that may disrupt manufacturing and logistics. According to a 2022 McKinsey study on the use of ML in the manufacturing industry, sophisticated forecasting tools improve the accuracy of demand predictions and service-level operations by 13%. More importantly, AI-enhanced tools can eliminate about half of all forecasting errors.

You can forecast your machinery’s wear-and-tear and replace it in a timely manner to prevent disruptions to the supply chain. Advanced planning tools can gather and analyze information about weather, labor shortages, and demand bursts, helping you to fine-tune delivery routes. Plus, AI tools can optimize materials procurement based on future pricing, scheduling, and production planning.

Productivity boosts through automation

Rule-based algorithms can augment skilled labor, streamline operations, and automate tasks. Robotic process automation (RPA), in particular, can increase performance by automating 45% of repetitive tasks. 

ML-enhanced automation platforms predict outcomes and make autonomous real-life recommendations, which can increase factory and equipment efficiency by 5%–16%. In addition, automation rates for some business support functions, like IT service desks, may reach 90%. Experts predict that overall, AI-based tools can enhance labor productivity in developed countries by 40% by 2035.

Safer working environment

AI-based automation and collaborative technologies can automate dangerous tasks in manufacturing environments. For example, context-aware robots can replace humans in labor-intensive and risky industrial operations. You can also boost employee safety with machine-vision algorithms and wearables that ensure safe distancing at manufacturing operations.

Also, AI technologies that enable remote work can eliminate the need for noncritical employees to leave their homes during a pandemic, which enhances employee safety.

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    Fewer operational and business expenses

    ML-enabled tools can diagnose equipment malfunctions and detect product anomalies more effectively than traditional monitoring and maintenance strategies. How so?

    Image recognition algorithms can detect defects with up to a 90% success rate compared to human inspection. Sophisticated systems can analyze variables across machinery and sub-processes to reduce yield detraction by 30%. 

    A 2021 report from McKinsey suggested that AI-enabled supply chain management can improve inventory levels (by 35%) and service levels (by 65%), as well as improve logistic costs-efficiency by 15%. Another McKinsey report showed a 38% long-term cost reduction potential in manufacturing and a 10–14% reduction in administrative spending.

    Statistics are useful, but they merely show trends. It’s too early to talk about the mass adoption of features like fully autonomous robots in factories, so let’s focus on AI-based tools you can use today.

    How companies use AI and ML in supply chains

    Companies rely on digital technologies primarily to automate operations, predict risks, accelerate delivery, and, consequently, reduce their costs. Let’s look at the available solutions to give you a better idea of AI and ML use cases in manufacturing, logistics, and sales.

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    Visual quality control of manufacturing

    Companies can enhance quality assurance with AI-enabled visual quality inspection. One such method is installing cameras with modern computer vision technology at workshops to catch defects. 

    The ML models are fed reference images of good and faulty products taken from different angles. The algorithm then learns about defect types through supervised and semi-supervised learning. Advanced models can abstract through differences in lighting conditions, surface orientation, and background to focus on the products themselves.

    3B-Fibreglass (3B) uses a camera set-up and AI to predict when its fiberglass may break during production and identify common causes for these breaks. Similarly, the Nanotronics platform uses deep learning and computer vision to improve accuracy and reduce the cost of defect detection in production pipelines.

    Audio-based failure prevention

    Combined with ML, an audio analysis can locate, classify, and predict anomalies in gearboxes, engines, and other devices. The sounds and vibrations detected by sensors are sent to the AI system and algorithms use that information to suggest maintenance tasks to workers.

    KONUX uses sensors and cloud-based AI systems to detect anomalies in vibration data. Afterward, the system provides recommendations to optimize maintenance planning. It significantly extends the life cycle of their equipment. Another example is software from Neuron Soundware, which has an algorithm that evaluates sounds to forecast potential mechanical failures.

    Advanced robots

    Enhanced vision with ML algorithms allows deploying context-aware industrial robots in production environments. Unlike traditional machinery, AI-enhanced robots can learn to recognize object types, material properties, empty spaces, and humans. This means they can operate without predefined positions, adjust for disturbances in movement routines, and even interact with your workers.

    Companies already use advanced robots in manufacturing. For example, FANUC has factories where autonomous robots perform complex assemblies, testing, and other continuous operations. And Rethink Robotics designs robots for workplace collaboration with humans.

    Yield enhancement

    AI engines can analyze data from several production tools to identify the causes of reduced quality and yield loss. This is especially important for semiconductor chip manufacturing, where testing, rework, and discarded materials account for 30% of total production costs.

    For example, Qualicent Analytics uses an AI-enhanced algorithm to identify optimal processes and operating conditions to reduce scrap rates. And ML systems from Motivo can identify problematic design elements in microchips and learn to predict the locations of yield detractors.

    Logistics planning

    AI and ML systems can forecast supply chain disruptions, customer demand, labor shortages, and other vital logistics trends and factors. To enhance logistics planning, advanced AI software can even account for extra downtime when it predicts that equipment will need maintenance.

    Look at how a leading German logistics provider, DHL, incorporates AI and ML. DHL uses sophisticated algorithms to detect potential supplier issues by analyzing over eight million online posts every day. Its algorithms consider over 240 million variables to predict global trends up to three months in advance. The company also uses AI tools to optimize routes for its employees in warehouses, which improved the productivity in certain DHL locations by up to 30%.

    Point-of-sale forecasting

    AI-based data modeling helps retailers keep track of their inventory, get real-time data from suppliers and shipment companies, and analyze market demand. This can help you adapt your offerings based on the availability of goods and demand.

    Companies like Alloy offer an analytics platform that uses ML algorithms to forecast unit sales. For example, it can identify phantom inventory, simulate inventory costs, and predict out-of-stock and overstock for certain goods.

    Implementing AI technology in your business is costly and time-consuming, so you need to pick the right aspects of your business to enhance.

    Implementing ML in supply chain management

    Embedding AI and ML in manufacturing, testing, sales, and other processes is an ambitious and challenging undertaking. Over 60% of supply chain companies go over budget or fall behind schedule in their AI transformation journey. Moreover, 25% of leaders feel that the incentives of their technology providers don’t align with the leaders’ business objectives.

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    So, how do you stay on track as you digitize your company? Here’s a strategy we think will help.

    1. Audit your business. Evaluate your internal policies, business functions, and IT infrastructure. This should uncover inefficiencies in your supply management chain and identify the most value-driven processes.
    2. Prioritize investment. Some processes could improve your cost-efficiency more than others. You may want to start with functions that require minimum resources to enhance your business.
    3. Select the right tools. Find the tools that satisfy your technical requirements, security concerns, and budget. Complex supply chains have many elements, so don’t expect one provider to have all the solutions you need. Search for several vendors whose tools support your business strategy.
    4. Collect data. You’ll need datasets to train your algorithm to detect patterns. Low-quality data is pretty much worthless for machine learning. This means you need reliable methods to organize data across your company, including filtering out noise and avoiding redundant data.
    5. Integrate the technology. You must combine tools from different vendors into your systems in a way that doesn’t complicate your architecture. It would help to use cloud computing to improve the system’s performance and stick to agile practices to keep the integration on track. 
    6. Optimize your workforce. ML engineering depends on collaboration between data scientists, analysts, solution architects, and equipment engineers. The best course of action would be to implement MLops in manufacturing — a set of repeatable practices that accelerates the delivery of ML models into your operations.

    As you can see, adopting AI and ML might require reworking the way your organization functions. You’ll need a clear and well-defined strategy and a partner with a great deal of experience. It’s critical to find a reliable IT company that will select the right technologies for your business and streamline your digitalization.

    What’s next?

    AI and ML have never been so accessible to supply chain managers. Analytics platforms, robotic process automation, sensors, and other hardware powered by self-learning algorithms can enhance your manufacturing, reduce operational costs, and improve logistics. However, you need to prioritize problems and select effective solutions. Postindustria can assess your company’s architecture and workflow to identify the best opportunities for improvement. We provide AI and ML solutions for automation, video processing, big data analytics, and predictive maintenance. If you’re interested, make sure to contact our team for more information.

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