Planning
Demand Forecasting
By enhancing track and trace systems with machine learning, businesses can efficiently analyze and process huge amounts of data from various sources like popular online search results, social media posts, website traffic, competitor campaigns, purchase histories, and even GDP growth and inflation rates.
This approach can help companies generate more accurate and dynamic demand forecasts despite the constant market changes.
Pricing Optimization
There are 3 ways AI can help companies with pricing optimization.
Dynamic pricing: AI-powered technology can monitor market conditions, competitor pricing, and consumer behavior in order to help businesses correct their prices in real-time.
Personalized pricing: Companies can determine distinct customer segments by implementing machine learning models to analyze purchasing history, price sensitivity, and demographic data. This analysis can help optimize pricing strategies for each segment and increase revenue that way.
Promo optimization: By using AI, companies can run test drives for different promo strategies and determine the most effective discounts and deals to increase sales. Advanced algorithms help build complex strategies that are tailored to a specific company’s needs.
Sustainability Planning
By implementing AI in their strategies, companies can make data-driven decisions that improve environmental, social, and governance (ESG) performance.
For example, this is how businesses can analyze energy consumption patterns and take action to reduce its usage, minimize overproduction and track the flow of materials to eliminate sources of waste and inefficiency, make sure their design practices are eco-friendly, and team up with suppliers to improve sustainability initiatives.
Source: KPMG, 2024. "Unchain the supply chain. How to break free from supply chain disruptions using integrated AI planning," https://kpmg.com/kpmg-us/content/dam/kpmg/pdf/2024/018035-1a-unchain-the-supply-chain.pdf
Sourcing and Procurement
Supplier Relationship Management
AI-equipped supplier relationship management technology leverages machine learning, natural language processing (NLP), and data analytics to help companies choose and manage the right suppliers for their products and services.
For example, machine learning models can analyze large amounts of data on suppliers and help businesses select partners that fit them best, meanwhile natural language processing techniques are used to scan supplier contracts in order to detect non-compliance with regulations.
Procurement
Procurement allocation processes can be effectively transformed with AI: efficient identification of global supply trends across commodities and products, quick search of relevant data in invoices, automated compliance monitoring, contract data extraction and structurization, and contract lifecycle management.
Spend Analysis and Cost Optimization
Spend analysis tools equipped with AI can quickly categorize and analyze huge amounts of purchasing data across a business. Machine learning and NLP algorithms are used to divide spend data into standardized categories, regardless of how individual vendors or departments may label items. This approach helps organizations identify consolidation options, negotiate better contracts, and uncover abnormal spending.
When it comes to cost optimization, AI systems help analyze historical pricing data, market trends, and supplier performance to develop efficient sourcing strategies. AI models can predict future price changes, determine the best time to make purchases, and automate routine procurement tasks.
Manufacturing
Inventory Management
AI-based inventory management is based on real-time data and predictive analytics. By implementing that, businesses can generate accurate sales forecasts and plan their inventory levels accordingly, determine optimal reorder points and safety stock levels.
In addition, clustering algorithms and machine learning models can categorize customers based on their purchasing patterns, choices, and demographics. This enables companies to tailor their inventory strategies according to different customer groups.
Supply Chain Risk Management
AI in supply chain risk management helps companies enhance capabilities to predict, prevent, and respond to potential disruptions and vulnerabilities in the supply network. Because AI systems can analyze large amounts of data from diverse sources, they can forecast various risks and allow businesses to take action in a timely manner.
Moreover, AI technology can predict potential supply chain disruptions, such as a shortage of materials, or geopolitical factors. It also offers real-time visibility into the movement of products across the supply chain.
Predictive Maintenance
Predictive maintenance systems that are equipped with AI can help companies extract maximum value from their existing infrastructure. The top use cases of AI in predictive maintenance include predictive equipment failure analysis to maximize uptime and efficiency, prescriptive maintenance that involves suggestions for improvement, and asset life extension.
Warehouse Automation
AI use cases in warehouse automation include Autonomous Mobile Robots (AMRs) that can navigate warehouses autonomously and transport products, language and image generation models, computer vision that enables automated item picking and packing as well as NLP-based intelligent warehouse management systems and chatbots for customer support.
Production Planning and Scheduling
Production planning and scheduling help optimize resource utilization, reduce costs, and ensure timely product delivery. With AI, production schedules can be quickly adjusted on demand. It’s also possible to allocate resources depending on equipment or material availability and worker skills.
AI-based production scheduling can be easily integrated with Internet of Things (IoT) devices and automation technologies for increased production efficiency and agility.
Quality Control
AI-powered machine vision can enhance quality inspection by analyzing large datasets of good and bad product samples. This allows the AI to learn a wide range of quality scenarios without the involvement of programming experts. The system can identify complex patterns that human specialists may miss, which definitely helps improve quality control.
Source: Lihui Wang, KTH Royal Institute of Technology, 2019. "From Intelligence Science to Intelligent Manufacturing," https://www.researchgate.net/publication/333567029_From_Intelligence_Science_to_Intelligent_Manufacturing
Supply Chain Automation
Fleet Tracking and Monitoring
AI-equipped telematics can monitor vehicle status as well as its location, speed, and fuel usage, in real-time. In addition, companies can analyze driving habits and detect dangerous behaviors, such as speeding, sudden braking, and lack of attention during driving. After monitoring driver behavior data, AI may also offer coaching and training programs tailored to a specific driver.
Route Optimization
AI technology can provide real-time traffic analysis by processing large amounts of data from GPS, traffic cameras, and mobile apps, which makes it possible to adjust routes and avoid congestion, save time, and reduce fuel consumption.
Machine learning models can also predict problematic routes as well as tailor routes to specific needs, which improves efficiency and customer satisfaction.
Order Fulfillment
Tools that are powered by AI can analyze real-time data on inventory levels, sales trends, and customer demand for an accurate prediction of future requirements.
By implementing AI-based solutions, it’s also possible to automate and streamline key warehouse processes, such as order picking, packing, and shipping.
Supply Chain Visibility
To map a supply chain network for a business, large language models can analyze data from product orders, customs declarations, and freight bookings. AI technology can detect possible supply chain disruptions and changes in consumer demand patterns by analyzing data from point-of-sale systems, social media, reviews, etc.
In addition, AI can simulate different scenarios and determine how efficient certain strategies are and offer suggestions for improvement.
Source: McKinsey & Company, 2021. "Succeeding in the AI supply-chain revolution," https://www.mckinsey.com/industries/metals-and-mining/our-insights/succeeding-in-the-ai-supply-chain-revolution
Conclusion
AI-powered track and trace technology offers significant advantages over traditional methods by increasing operational efficiency, improving decision-making, enhancing security, and providing predictive insights. These benefits are valuable across industries, from logistics and manufacturing to healthcare and public safety.
Digital solutions, such as traceCORE Track and Trace for Governments, are flexible and scalable, which helps take control over key markets and sensitive product categories.
Source: EY, 2022. "Building supply chain sustainability that can drive revenues and reduce operational risks," .png){kind=small}
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