Supply chain efficiency and AI go hand-in-hand
When many people think of artificial intelligence (AI), their minds race to futuristic images of machines and robots making complex decisions and doing everything from colonizing other planets to world domination. While the possibilities are as vast as our imagination, in the business world, there are already practical applications for the role of AI in the supply chain that logistics leaders are capitalizing on, whether it’s using ChatGPT to ask questions regarding demand forecasting or prompting bots to produce risk assessments. And it’s working. According to a study by McKinsey, early adopters of AI have had impressive results, improving costs by 15%, inventory levels by 35% and service levels by 65% compared to slower-moving competitors.
Whether you believe in the power of AI and logistics to unlock untapped efficiency or have doubts about its cost-effectiveness or complexity, one thing is certain—it’s revolutionizing every industry, and understanding its potential can be your secret weapon for finding efficiencies and staying ahead of the competition.
Read on to discover nine powerful ways that companies are preparing for the future of AI in supply chain and logistics management and using innovations to save costs and find logistics efficiencies.
We cover how you can leverage artificial intelligence in your supply chain for:
- Forecasting inventory levels and demand planning
- Optimizing transport routes
- Providing better customer service
- Making better-informed business decisions
- Managing risk
- Gaining enhanced visibility through transparency
- Incorporating sustainable practices into your supply chain
- Improving warehouse operations
- Leveraging robots, autonomous vehicles and drones
Nine ways that companies are leveraging AI
While insights from AI systems may seem like they’re coming from a crystal ball, behind the scenes, it’s sifting through massive datasets, uncovering hidden patterns, insights and correlations far beyond what humans can process. Whether it’s analyzing customer behaviour, market trends or operational data, AI can delve deep into the data ocean, extracting meaningful information and helping organizations make informed decisions quickly.
Here are just a few of the countless ways that businesses are already taking advantage of this powerful technology.
Forecasting inventory levels and demand planning for a solid supply chain base
Accurately forecasting inventory levels is an initial and crucial step for small businesses to avoid the pitfalls of overstocking or stockouts. Demand forecasting systems with integrated AI capabilities can use real-time tracking to identify slow-moving items, alert you to potential stockouts and even recommend necessary adjustments to your stock levels.
Some specific applications include:
- Near-perfect inventory stocking. By accurately forecasting inventory levels, businesses can avoid having too much or too little stock, which can lead to costly problems.
- Scenario simulation. Digital twins and AI play a crucial role in inventory forecasting for businesses. By creating a virtual replica of physical assets or systems, known as digital twins, companies can gather real-time data on inventory levels, supply chain processes and customer demand.
- Future breakdown predictions. Predictive analytics powered by AI can identify potential breakdowns in assets, allowing businesses to proactively address maintenance needs and minimize disruptions.
- International business decisions. Managing free- and foreign-trade zones requires monitoring continually changing information. AI can assist by analyzing various factors such as trade regulations, costs and logistics to support business decisions related to international trade and inventory keeping.
Optimizing transport routes to improve the last mile of delivery
Let’s imagine a transportation company that wants to optimize its route planning. By using historical data alone, companies are limited to finding the best routes based on past traffic patterns. With predictive analytics and machine learning in transportation, you can factor in real-time traffic data, weather forecasts and even events happening in the area to accurately predict traffic conditions in the future. This allows you to dynamically adjust routes, avoiding potential congestion and delays and ensuring timely deliveries.
Impact of AI technology in transportation:
- Factor in a range of variables. AI can factor in other variables like the size and weight of delivery packages as well as any specific delivery requirements to further optimize transport routes.
- Carbon footprint reduction. Companies concerned about Environmental, Social and Governance (ESG) compliance can use generative AI to reduce their carbon footprint by analyzing their entire product ecosystem to use the most fuel-efficient methods to transport goods.
Providing better customer service
Another area you can optimize is your customer experience. By leveraging technologies that use natural language processing (NLP) and machine learning (ML), you can provide personalized and seamless interactions with your customers, not to mention reduce the workload on your customer service employees.
Examples of how AI can improve the customer experience include:
- Faster and more accurate customer support: Natural Language Processing (NLP) and Machine Learning (ML) approaches present opportunities for intelligent chatbots that can assist your customers with order tracking and comprehensive query resolution at any time of the day and night.
- Real-time updates: AI can provide real-time tracking updates for your customers, allowing them to monitor the progress of their orders. This transparency can help foster trust and set realistic expectations with your customers.
- Personalized marketing: AI algorithms can analyze customer data to identify individual preferences and buying patterns. This opens the door for personalized marketing campaigns, tailored promotions and targeted offers, increasing customer engagement and brand loyalty.
- Reverse logistics: AI can analyze return patterns, identify potential issues and automate the process of handling returns and managing product recalls. This helps ensure efficient resource utilization, reduces delays and enhances customer satisfaction.
- Automated feedback: Instead of tediously combing through shaky and subjective customer feedback, automation allows for the aggregation of objective, value-driven feedback, which helps your logistics team analyze how well your products are being received in the market.
Making better-informed business decisions
Supply chain resiliency is crucial for businesses to navigate unexpected disruptions and challenges. With the help of AI, businesses can leverage advanced analytics and predictive modeling to make better planning and decisions. AI’s impressive data-analysis capabilities not only let you consider massive datasets, but you can also zoom in and examine intricate details within your data, revealing valuable insights that were previously unseen.
Here are some ways AI can help you make more confident business decisions:
- Procurement logistics planning. When planning your procurement, predictive analytics can be used to evaluate the impact of various factors on the supply chain. You can run different scenarios based on demand forecasts, and assess the effects of changing customer demand, supplier performance, transportation costs and market conditions.
- Marketing and sales. AI, and particularly generative AI, has potential to improve lead identification based on real-time customer trends, A/B testing and marketing optimization, including dynamic and hyper-personalized content on websites, upselling and more.
- Production. AI systems can monitor inventory levels and send notifications for replenishment. Additionally, manufacturers can program AI to detect bottlenecks in the supply chain.
- Pricing strategy. Develop a better pricing strategy by analyzing market trends, customer behaviour and competitor data to determine the optimal price that maximizes profitability and meets customer expectations.
- Predictive maintenance. Analyze sensor data and identify patterns to predict when equipment or machinery is likely to fail, allowing businesses to schedule maintenance proactively and minimize downtime.
- Quality control. Look at large volumes of product data and images to identify defects and anomalies, ensuring consistent quality control and reducing the need for manual inspections.
Managing risk
When you’ve got powerful algorithms at your fingertips, your risk management capabilities can provide more accurate insights. This allows for proactive risk mitigation and contingency preparation. Get a glimpse at examples illustrating how AI can improve risk management:
- Shipping fraud detection. Crunch high volumes of transaction data, customer behaviour and historical patterns to identify potential fraud. For example, AI-integrated risk systems can flag suspicious transactions, like a large purchase in a foreign country, and alert the business.
- Cybersecurity. Detect and prevent malicious attacks by using AI to analyze network traffic, identify anomalies and detect signs of cyber threats in real time. For instance, it can recognize potential breaches, such as an employee accessing sensitive data outside working hours.
- Supply chain disruptions. AI can help you strengthen supply chain risk management by analyzing data like weather forecasts, transport slow downs and supplier performance. You’ll be able to identify potential disruptions and receive recommendations for proactive actions. For example, it predicts severe storms and suggests logistics adjustments to avoid delays.
- Credit risk assessment. Financial institutions might use AI to assess credit risk by analyzing diverse data sources, as it can identify patterns and predict default likelihood. For instance, it can flag potential risks, like a sudden decrease in credit score and changes in spending habits.
Gaining enhanced visibility through transparency
Using AI to enhance the visibility of your supply chain allows you to have a clear and real-time understanding of every step in the process. Ultimately, it empowers you to make smarter decisions, improve efficiency and deliver products to your customers faster and more reliably.
Here are some ways that AI can enhance supply chain visibility:
- Real-time inventory tracking. AI analyzes data from various sources to track inventory in real time, providing businesses with precise visibility into product location and quantity throughout their supply chain.
- Track and trace optimizations. Enhance track and trace capabilities in the business supply chain by analyzing data from various sources, such as RFID tags, barcodes and sensors and quickly identify and address any issues or recalls.
- Supplier performance monitoring. Look at supplier data, such as delivery times and quality metrics, to assess and monitor supplier performance. This helps you identify and mitigate potential bottlenecks or risks in the supply chain.
- Reverse logistics. With AI-powered systems, organizations can have real-time insights into the status and location of returned products, enabling them to efficiently allocate resources and address delays.
Understanding the essential elements for successful supply chain management can help you gain a competitive edge. Explore our Supply Chain Management Playbook to help you improve your logistics efficiency. Download ebook
Incorporating sustainability practices into your supply chain
In the same vein as making smart business decisions, AI can also help you make smart business decisions related to sustainability and reducing your carbon footprint. This might look like analyzing data related to energy consumption, waste generation or carbon emissions. In addition to optimizing transport routes, the technology can also help with other sustainable supply chain optimizations, including:
- Waste reduction: Analyze data to optimize inventory levels and reduce waste and excess production.
- Optimized packaging: AI algorithms can analyze data on product dimensions, weight, fragility and customer preferences to optimize packaging design. By right-sizing packaging, you can reduce material usage and minimize waste. The technology can also identify the most efficient sustainable packaging options that provide adequate protection while using fewer resources.
- Supplier sustainability assessment: Assess the sustainability values of your suppliers by analyzing their environmental practices, social responsibility and ethical sourcing.
Partner with a sustainable shipper Looking for ways to reduce your carbon footprint? Purolator is aiming to achieve net-zero emissions by 2030 and be Canada’s greenest courier. Some of our latest sustainability initiatives include:
- Introducing 3,500 fully electric last-mile delivery vehicles to our fleet.
- Electrifying over 60 terminals across Canada (an ongoing initiative).
- Installing more than 200 new, energy-efficient heating, ventilation and air conditioning (HVAC) units across our network. View 2022 Sustainability Report
Improving warehouse operations
In the era of e-commerce, AI enables companies to streamline logistics flows. By using fulfillment technologies and intelligent systems, businesses can efficiently manage inventory, track consumer preferences and provide real-time delivery tracking updates and status reports. These smart tools enhance the overall customer experience and drive operational efficiency.
Purolator’s National Hub is a prime example of a warehouse that has integrated advanced automation capabilities, allowing it to seamlessly handle large volumes of packages. The intelligent AI systems enable real-time tracking and monitoring of packages, allowing Purolator to provide accurate and up-to-date delivery updates to customers.
Faster fulfillment. AI can optimize warehouse operations by intelligently determining the most efficient picking routes, resulting in faster order fulfillment and improved customer satisfaction.
Challenges of implementing AI in the supply chain and logistics industry
Implementing AI into a business’s value chain can bring numerous benefits, but it also comes with its own set of challenges. According to a study by McKinsey, 47% of business executives surveyed have difficulty integrating cognitive AI processes with existing systems and 40% cite costly technology and expertise as a barrier. Here are some key challenges that businesses may encounter:
- Complexity of implementation: Integrating AI into existing processes and systems can be complex and require significant technical expertise. It often involves restructuring workflows, training employees on new technologies and ensuring compatibility with existing infrastructure.
- Unpredictable external factors: Businesses operate in dynamic environments influenced by various external factors, such as market trends, regulatory changes and customer preferences. These factors can impact the performance and accuracy of AI systems, which could make it challenging to adapt and respond effectively.
- Rapidly changing technology: AI technology is constantly evolving, with new algorithms, models and frameworks emerging regularly. Keeping up with the latest advancements and selecting the right technology for specific business needs can be a challenge. It requires continuous learning, experimentation and staying informed about the latest developments in the AI field.
- Data privacy and security: Implementing AI involves collecting and analyzing large volumes of data. Ensuring the privacy and security of this data is crucial to maintain customer trust and comply with regulations. Businesses must implement robust data protection measures, adhere to data privacy laws and have proper protocols in place to handle sensitive information.
Successfully navigating these challenges requires careful planning, collaboration between different stakeholders and a strategic approach to implementation. By addressing these challenges proactively, businesses can unlock the full potential of AI and drive value across their value chain.
How logistics leaders can leverage AI in logistics
When it comes to harnessing the power of AI in logistics and supply chain management, it’s essential to acknowledge that the technology is only as powerful as the network and systems that are in place. Business leaders must set goals and expectations that leverage deep data and ensure a robust infrastructure to fully reap the benefits.
To achieve this, you should consider flexible shipping and logistics solutions that can adapt to the demands of an AI-driven supply chain. Purolator offers a range of services designed to enhance agility and efficiency.
- Fast and flexible shipping services: Purolator QuickShip™ service provides next-day delivery by 9 p.m., seven days a week, including evenings and weekends in select Canadian markets. With Purolator’s fast shipping services across Canada and cross-border shipping between Canada and the U.S., businesses can benefit from flexible speed-to-market solutions, such as cross-border logistics and best-flight-out/just-in-time delivery.
- Optimal route and shipping methods: Purolator offers less-than-truckload (LTL) and truckload (TL) services to support various shipping volume needs. By leveraging optimal routes and shipping consolidation methods, Purolator ensures efficient transportation within customers’ supply chains.
- Convenient drop off and delivery: Purolator understands the importance of convenience in logistics. With later drop-off times and over 2,500 retail facilities, agents and Mobile Quick Stop locations across Canada, Purolator provides flexibility and accessibility to conveniently reach more customers.
- Supply chain expertise: Purolator’s shipping specialists are equipped with extensive supply chain knowledge and can guide businesses in planning the right shipping solutions. With a wide range of capabilities—including courier and industrial freight, northbound logistics (LTL), offshore inbound logistics, express services and cargo options—Purolator offers comprehensive support for various shipping requirements.
Hear what an expert has to say about the future of supply chain logistics. Learn more
- Ground shipping: For non-urgent shipments where overnight delivery isn’t a priority, Purolator offers an efficient and cost-effective alternative to overnight service. Depending on the destination, a shipment can be delivered in two or more business days, helping to reduce costs while maintaining customer satisfaction.
- Integrating sustainability and technology: Purolator prioritizes sustainability and technology in its operations. The National Hub uses battery-powered forklifts and high-albedo concrete to reduce carbon emissions. Furthermore, Purolator employs the Siemens remote underbelt unload system (RUBUS), automating trailer unloading at an impressive rate of about 12,000 parcels per hour.
Start planning for the future of AI in supply chain management
Planning for the future role of AI in your supply chain is crucial for staying ahead in today’s dynamic business landscape. As the technology continues to evolve and transform industries, integrating AI into your supply chain can unlock new levels of efficiency, productivity and competitiveness. To embrace the potential that accompanies AI, you can start by assessing your current processes and identifying impact areas.
By partnering with Purolator and embracing these flexible shipping and logistics considerations, we can help you ensure your supply chain is nimble enough to take full advantage of the benefits of AI.