As the supply chain processes become more complex and encompass far-reaching tasks, its management is unimaginable without intelligent automation, predictive approaches, and sustainability. All these aspects are achievable nowadays since logistics and supply chains are under the impact of Big Data. In turn, Big Data feeds artificial intelligence (AI), which makes it possible to make supply chain management much more powerful than it is now.

However, many logistics companies have already integrated this technology into their operations or plan to do so shortly. As Statista reports, in 2022, over a third of executives expected their companies to adopt AI on a wide scale. Additionally, the adoption rate of AI in supply chain management and manufacturing is expected to rise by 2025. If you are still skeptical about establishing an AI-driven supply chain, we offer you to check its advantages and challenges.

The Current State of Supply Chain Management

Supply chains depend highly on external factors like changes in regulations, geopolitical events, climate shifts, etc. For example, COVID-19 had a huge impact on global supply chains and forced companies to reshape their approaches to inventory and warehouse management, distribution, collaboration with suppliers, and more. 

Now, artificial intelligence in supply chain management puts many companies at ease by streamlining every stage of operations. Below, we explore how AI helps companies to adapt to global changes.

Policy Changes and Geopolitical Shifts

New regulations regarding product quality standards, allowed ingredients, environmental impact, or labor laws are coming up regularly. They can cause supply chain disruptions while businesses are adapting to new rules. However, with AI-powered predictive analytics, it becomes much easier to collect data on global news, economic reports, and social trends, analyze it, and understand how to adjust logistics operations according to upcoming changes.

Predictive analytics can also help recognize geopolitical tensions, which negatively affect supply chains. For example, the Dun & Bradstreet research stated that over 600,000 enterprises globally depended on suppliers from Russia and Ukraine. The onset of the Russian invasion of Ukraine forced those enterprises to seek alternative suppliers, supply routes, distributors, etc. In cases of such major disturbances, companies can utilize AI for supply chain optimization. AI technology can analyze multiple alternatives that are less affected by geopolitical events.

Serhii Leleko: AI&ML Engineer at SPD Technology

Serhii Leleko

AI&ML Engineer at SPD Technology

“AI leverages advanced machine learning algorithms and Big Data analytics to process vast amounts of unstructured and structured data in real-time. This capability allows it to predict potential disruptions in the supply chain by identifying patterns and correlations that humans might overlook.”

Natural Disasters and Climate Change Effects

72% of responders who took part in the 2022 Gartner Emerging Priorities in Supply Chain Survey said that the environment had a significant impact on their supply chains. This includes both physical risks, such as natural disasters (e.g. floods, droughts, fires), and climate shifts. 

AI in supply chains ensures that processes can be adapted to these changing conditions. Firstly, predictive analytics can enable weather and demand forecasting to anticipate changes in usual consumption patterns. In such a way, supply management can be done by taking into account yield changes, efficient routes, and inventory levels. Secondly, AI algorithms can be trained for scenario planning. It is an approach that helps model different cases of natural disasters allowing companies to adapt their operations accordingly. 

In addition, AI can assist in identifying and putting into effect more environmentally friendly supply chain procedures, such as obtaining goods from suppliers that practice environmental responsibility or optimizing transportation routes to cut carbon emissions.

Evolving Work Landscape

Over recent years, globalization has influenced supply chains to experience drastic changes. Consumers require businesses to produce more personalized, high-quality products within a short period of time to accommodate their growing needs. Now, companies may struggle with complexities in supply chain management. However, AI enables automatization and personalization capabilities to overcome those challenges.

Generative AI, for instance, can help enterprises check for alternative ingredients for their products in order to suit more customer preferences. AI systems can analyze data and pinpoint how to use more organic ingredients and offer hypoallergenic, vegan, and sustainable product formulas.

Additionally, AI and ML in manufacturing help in designing products. These technologies can collect data on targeted audience preferences in terms of packaging or labeling as well as the latest trends in design. Then, companies can use insights received from data analysis to customize item designs and achieve customer satisfaction.

Advantages of AI in Supply Chain Management

Utilizing AI in supply chain and logistics can improve almost every aspect of processes. It has the potential to increase operational efficiency, reduce costs, and even improve worker safety. How exactly? Let’s have a closer look at each link in the chain.

Advantages of AI in Supply Chain Management
Advantages of AI in Supply Chain Management

Boosted Warehouse Efficiency

Forbes states warehouses that implement AI solutions can benefit from such enhancements as inventory accuracy reaching up to 99.9%, worker travel time reductions of 20% or more, and order fulfillment speeds increasing by at least 15%. To achieve similar results, supply chain and logistics companies need to consider implementing the following AI-driven solutions:

  • Warehouse Management Systems (WMS): AI features in WMS allow not only to keep all the processes in one system but also generate insights on how to optimize storage space, adjust loading operations, and improve manual processes workflow. 
  • Robotic Process Automation (RPA): RPA development services equip businesses with robots that can completely transform processes that previously required human efforts like picking, packing, sorting, etc. Moreover, they can do it faster and more accurately.
  • Predictive Maintenance: AI can collect the data from sensors on equipment, taking into account various metrics such as vibration, temperature, pressure, etc. This information serves as insights into machinery health and possible maintenance measures.

Lowered Operational Costs

The opportunity to lower operational cost is usually one of the main reasons why businesses start considering AI tools. According to Statista, more than 50% of respondents in a survey said their businesses had seen cost reductions by 10-20% in the supply chain in 2021. The possibilities that AI opens for cost reduction include, but are not limited to:

  • Resource Optimization: Thanks to data analytics powered by AI, supply chain companies can allocate human efforts and machinery to execute work more efficiently. These analytics also help distribute raw materials throughout the supply chain with sustainability in mind.
  • Energy Management: Implementing AI for energy systems can lead to data-driven decision-making when it comes to how much energy is needed for particular tasks in a certain timeframe.
  • Cost-Efficient Procurement: By analyzing data on contracting with vendors, suppliers and distributors, AI helps supply chain organizations negotiate prices and find cost-effective approaches.

Minimization of Errors and Waste

AI and supply chain management make a powerful duo, when it comes to reducing waste and human errors. Automation, data analytics, demand forecasting, and anomaly detection can be used to help companies achieve the lowest level of defects and wastage possible. This is how those can assist:

  • Process Automation: From document processing to logistics operations, AI can help employees minimize efforts for manual tasks and eliminate human error.
  • Quality Control: Anomaly detection using machine learning can be implemented to inspect products for defects, providing real-time data on any anomalies for quick issue resolution.
  • Efficient Resource Allocation: Supply chain solutions with AI can result in data-driven decisions about the amount of resources required for specific tasks, which leads to the minimization of material waste.

Enhanced Inventory Management

McKinsey revealed that implementing AI in supply chain management has enabled organizations to improve inventory levels by 35%. Indeed, intelligent systems equipped with advanced analytics can collect, process, and analyze past data to generate forecasts on how products might be ordered and distributed. To be more precise, AI can do the following:

  • Demand Prediction: Historical sales data and market trends are analyzed by AI algorithms to forecast demand and suggest tips on how to maintain optimal inventory levels.
  • Inventory Tracking: Sensors on IoT devices can monitor the metrics of inventory and send notifications to employees in case some issues with stock levels need to be resolved.
  • Stock Replenishment: AI systems can automate reordering, including calculation of delivery time and negotiation of prices, that ensures stock replenishment is done in a timely manner.

Safer Work Environment and Material Handling

IoT devices connected with AI and computer vision can significantly improve safety conditions in production environments, warehouses, or during transportation. Additionally, employees can rely on robots to perform dangerous tasks with chemicals or heavy machinery, which minimizes injury risks and improves operational efficiency. Those can be achieved by:

  • Hazard Detection: The use of AI in supply chain management, along with cameras and sensors, is the way to detect spills and dangerous lifting techniques in real time and notify employees about possible accidents before they occur. 
  • Automated Handling: AI automates risky jobs (e.g. using large gear in warehouses), which lowers the possibility of worker casualties. 

Faster Deliveries and Better Customer Experience 

In a survey, nearly 17% of US-based customers reported that they have excellent user experience with artificial intelligence. At the same time, almost 18% of the respondents claimed to be fully happy with robotics automation. 

Indeed, AI improves customer service in many ways. The most common are: 

  • Route Optimization: AI-driven deliveries become efficient like never before thanks to finding the most optimal routes that take less time for customers to wait for their orders.
  • Real-Time Tracking: Various sensors installed on delivery vehicles can provide customers with real-time insights into their order statuses.
  • Customer Service: AI chatbots proved to be effective for instant responses to customer queries. This creates a powerful customer experience and long-term connections with brands. 

Moreover, chatbots are extremely helpful for businesses and their tech support teams in terms of streamlining operational processes. For instance, we delivered a chatbot for one of our projects that used pre-trained machine learning models for data embedding and natural language processing to accurately respond to 99% of customer inquiries in less than ten seconds. At the same time, the support team could concentrate on tasks that required more creativity.

Greater Supply Chain Sustainability

Using AI for supply chain management can guide a company towards more environmentally conscious practices. It will be beneficial for our planet as well as for the company’s brand image since consumers tend to adopt eco-friendliness as a lifestyle. The McKinsey survey showed that 43% of respondents from companies that had adopted AI claimed that this technology helped them to enhance sustainability efforts. Part of those efforts can be:

  • Sustainable Sourcing: AI technology can check the data on suppliers to evaluate whether they use sustainable practices and help companies decide if they want to partner with these suppliers.
  • Waste Management: With AI-powered robots, companies can sort their waste faster and more efficiently. Robots can ensure proper disposal and correct recycling processes, which is particularly helpful in the food industry.

Wondering how AI transforms the food industry? 

Learn in our featured article!

Main Use Cases of AI and ML in Supply Chain

With AI, supply chain management becomes data-driven and intelligent. Rather than relying on static forecasts and reacting to disruptions after they occur, companies can now benefit from real-time analytics and predictive modeling, which allows them to anticipate changes in demand, potential bottlenecks, and supplier risks. Below we describe the main AI/ML applications in supply chain operations. 

Main Use Cases of AI and ML in Supply Chain
Main Use Cases of AI and ML in Supply Chain

Simulation-Based Operational Optimization

AI is the main driver in the development of computer models that replicate different scenarios in manufacturing processes, logistics routines, or supply chain operations. With it, businesses can have systems that are set up to find the most practical and efficient methods to optimize their processes, namely:

  • Capacity Planning: Supply chain systems enhanced with AI predict whether logistics can accommodate the demand and look for effective alternative plans in case of unsatisfactory results.
  • Scenario Analysis: Since AI has the ability to predict different scenarios, it can also simulate them to show if there is any possibility of supply chain disruptions, and what impact those disruptions would have on the whole business processes.

Need to know more about AI in logistics?

We have the article fully dedicated to this topic!

Precise Demand Prediction 

A study by Gartner shows a shift in how companies predict what customers will want to purchase: 45% of organizations use AI to forecast demand in 2023, and 43% plan to start using it in the following years. With this being said, we can only highlight the fact that AI is quickly becoming an irreplaceable tool for supply chain planning. Why do companies choose AI for supply management? Here are some of the factors:

  • Predictive Analytics: AI-driven systems have predictive capabilities that analyze historical data and market trends to generate accurate forecasts about what customers will be looking for in stores. 
  • Seasonal Adjustments: AI models can spot seasonal demand fluctuations (for example, during holidays or special events) and suggest marketing strategies like promotions, limited offers, or customized products. 
  • Market Insights: Improving businesses with machine learning and NLP makes it possible to process customer sentiment in reviews, social media trends, or analyze news to predict consumer behavior changes.

Predictive Maintenance of Supply Chain Assets with IoT

To adopt a preventive approach to equipment maintenance instead of repairing machines once they are out of order, supply chain companies can use IoT sensors and ML-powered software  to monitor equipment health. It usually works the following way: IoT devices with sensors are installed on manufacturing machines, conveyor systems, and delivery vehicles, collect real-time performance data (e.g., vibration, temperature, energy consumption), and send them to ML algorithms models for the analysis. This approach to maintenance ensures: 

  • Cost Savings: Companies can act on emerging issues instead of fixed schedules and, therefore, reduce unplanned downtime and lower direct repair costs. 
  • Longer Equipment Lifespan: With scheduled maintenance, companies can reduce strain on components and keep assets running at peak efficiency. Over time, this approach extends machinery’s usable life and minimizes downtime.

Real-time Analytics and AI-Driven Reporting 

Inventory levels, order status, supplier updates, and customer demand signals create continuous data streams. This data can be fed to advanced analytics algorithms, which will transform it into instant insights for making decisions. ML algorithms can find trends and anomalies in this data and create dynamic dashboards and reports to support company employees with a concise summary of changes in demand and potential bottlenecks. With real-time analytics and reporting, companies can get:

  • Proactive Decision-Making: With data analytics and AI, teams gain real-time visibility into potential disruptions or demand fluctuations and can make rapid adjustments to inventory strategies, production schedules, and logistics.
  • Enhanced Visibility: AI-powered dashboards compile multiple performance indicators into a single view with all the bottlenecks and inefficiencies in real time. By taking all this information into account, it becomes possible to incorporate improvements and foster greater alignment among suppliers, manufacturers, and distribution channels.

Preparing Your Supply Chain for AI-Driven Change

Challenges always exist when you are about to transform how your business operates. However, if you know how to approach this transformation, those challenges will not scare you and your employees. Below, we share a few tips for connecting your supply chain and AI.

Steps to Integrate AI into a Supply Chain
Steps to Integrate AI into a Supply Chain

Mapping Current Processes

To perform the AI transformation neatly, it is crucial to start with a detailed visualization of all links in the supply chain. From procurement and production to distribution and delivery, businesses need to take into account not only the processes but also the roles and responsibilities of each employee working in the supply chain. All this information should be collected in the document that will serve as a blueprint for AI implementation.

Serhii Leleko: AI&ML Engineer

Serhii Leleko

AI&ML Engineer

“Do not forget to include risks in your roadmap. It means you and your AI integration vendor should identify what inefficiencies, bottlenecks, or redundancies can be met during the project.”

Evaluating Existing Systems and Data

The integration of the newest technologies is impossible to perform without evaluating the state of the current technological infrastructure first. This assessment needs to cover software, hardware, and network capabilities. Companies must assess if modifications are required for the future incorporation of AI technologies. 

Additionally, supply chain and logistics companies need to take care of the availability and correctness of their data. It is necessary to look at the sources and formats in order to find any gaps. In case the data requires cleansing and standardization, companies need to opt for building EDW systems to power AI models and algorithms with the correct and coherent data for accurate future insights.

Pinpointing AI Integration Opportunities

McKinsey reports that 75% of supply chain leaders feel that their objectives are not aligned with the system integrator’s incentives. To achieve specific business goals with AI integrations, businesses need to identify specific areas where AI can bring the most value: it can be automation, predictive maintenance, or demand prediction. At the same time, processes that require a lot of data, are prone to mistakes, or are extremely repetitive should also be sought out by organizations since they are excellent candidates for AI-driven optimization. 

Furthermore, analyzing operational difficulties and pain areas might highlight areas where AI has the potential to increase productivity or accuracy. In order to guarantee that AI projects are in line with overarching company objectives and target the most influential supply chain segments, this stage calls for a strategic approach.

Prioritizing Use Cases

After identifying potential AI integration opportunities, it is important to prioritize which use cases to focus on first, and which ones can wait till the next iterations. The criteria for prioritization may be a potential return on investment, feasibility, and alignment with strategic objectives. 

Such an approach to planning AI integration into the supply chain systems helps accumulate resources and efforts on projects that offer the greatest benefits while minimizing risks. Considerations include the complexity of implementation, the readiness of existing systems, and the expected impact on operational performance.

Partnering with a Tech Vendor

In order to choose the best vendor, organizations must assess possible collaborators according to their experience, technological prowess, and history of supplying artificial intelligence solutions to the supply chain industry. A reputable tech provider can offer insightful information, cutting-edge resources, and continuous assistance throughout the AI deployment process. 

If you need some assistance, our AI & ML development services are at your disposal. We have experience with both AI/ML integration and supply chain management specifics, and are ready to execute the technical assistance and guidance from initial planning through to full-scale deployment and support.

The Challenges of Developing an AI-Enabled Supply Chain Ecosystem

While AI in supply chain management is capable of transforming the whole niche and increasing operational efficiency, it is not always easy to adopt it. Our clients, working in the logistic and supply management niches, stumbled upon several typical obstacles while enabling AI. We share them for you to stay informed.

The Challenges of AI in Supply Chain
The Challenges of AI in Supply Chain

Numerous and Complex Integrations 

Since each link of supply chains often relies on machinery work or transportation, it is important to add sensors or other IoT devices for the equipment. However, our clients often have difficulties with complex software integrations while connecting all the devices that transmit data to the entire ecosystem. 

We never underestimate the power of integration and approach it carefully. Firstly, we pay particular attention to make all data the same protocols and formats. For this, we build a data warehouse that ensures that data is homogeneous. Secondly, to make sure all the components communicate with each other, we design APIs or migrate supply chain systems to microservices architectures. This approach ensures we can seamlessly connect new and existing components without incompatibility issues.

Complex ML Models 

To create ML models with high accuracy and dependability, companies must choose the right algorithms, adjust hyperparameters, and train the models on large datasets. For our clients, who have extensive expertise in working with software for supply chain management but not in AI/ML development, this creates a significant problem. Even if our team takes complete control over designing ML models, the development teams from the client’s side need to know how to interpret them. 

In order to facilitate this process, we incorporate Explainable AI techniques and provide clear, user-friendly documentation tailored to our clients’ projects. With Explainable AI frameworks, we ensure that decision drivers, feature importance, and the overall logic of the ML models are transparent and easy to understand.

Potentially High Upfront Costs

Supply chain and AI is a powerful yet expensive combination, especially in the beginning of its adoption. Apart from purchasing high-performance computing systems and IoT devices as well as specific software licenses and infrastructure investments, businesses will need to invest in the development and integration of customized AI systems.

One way to overcome the high initial costs is by adopting a phased implementation strategy. We always recommend beginning with small-scale pilot projects that demonstrate tangible returns before scaling up. In addition to this methodological solution, we also suggest a technological one: leveraging cloud services.  Businesses can significantly reduce upfront capital expenditure if choosing cloud infrastructure with pay-per-use models.

Serhii Leleko: AI&ML Engineer at SPD Technology

Serhii Leleko

AI&ML Engineer at SPD Technology

“The high upfront costs are not limited to technological investments alone. Organizations must also allocate resources for training and upskilling their workforce to effectively utilize AI-driven tools.”

Interoperability with Existing Systems 

Many of our clients relied  on the software and infrastructure that was acquired a few years ago. However, as technologies are evolving exponentially fast, they need to revise their systems. If a business has a legacy system, it is possible that the integration of AI technologies will result in compatibility issues. 

In such cases, we turn to middleware solutions. Middleware bridges this gap by providing standardized interfaces and APIs, enabling different systems to communicate effectively without requiring extensive modifications to existing infrastructure. During the completion of the projects for our clients, it allowed us to handle data transformation, ensuring that information is accurately translated between disparate formats, and manages authentication and security across platforms. 

For example, we faced several challenges with complex third-party integrations during our work on a legacy system for our client. However, we overcame these complexities by using customly designed APIs and delivered a 100% cloud-based solution with top-notch UX, and allowed the business to expand to 20 states in the US.

Real-Time Decision-Making 

Some of our clients require supply chain operations that can handle split-second decisions across multiple touchpoints, from updating supplier lead times to responding swiftly to fluctuating customer demand. However, data often resides in separate systems or is collected too infrequently, leading to delays, inefficiencies, and missed opportunities. This information gap makes it difficult to optimize routing, inventory levels, and production schedules on the fly, increasing the risk of stockouts, costly bottlenecks, and reduced customer satisfaction.

In this case, we suggest benefiting from data analytical services to integrate analytics platforms connected IoT sensors for every stage of the supply chain. With it, businesses gain continuous, granular data on inventory levels, equipment performance, and real-time shipment locations. This data is then processed through AI-driven tools capable of generating instant insights and predictive alerts. As a result, decision-makers can proactively re-route shipments, balance stock between warehouses, or adjust production schedules, all in real time.

Reasons to Partner with a Tech-Savvy Vendor for AI in Supply Chain Integration

AI and ML development require in-depth expertise, and the in-house development teams of supply management and logistics companies may not be able to keep up with this innovation. Luckily, there are technological vendors on the market that learned AI/ML from the onset and polish their skills as this technology evolves. With the help of a tech partner, a business can gain: 

  • Accelerated Time-to-Use: Through established best practices and proven methodologies, experts can rapidly prototype and deploy AI-driven solutions, expediting tangible results for the business.
  • Access to Advanced Expertise and Tools: By partnering with a tech vendor, supply chain management companies can tap into seasoned professionals who leverage the latest AI/ML frameworks and technologies for streamlined supply chain processes.
  • Ensured Optimal Use of Data Infrastructure: Skilled AI/ML specialists create robust data pipelines and integrations, guaranteeing reliable data flow and efficient analysis at every stage of the supply chain.
  • Enhanced Security and Compliance: Tech partners know how to embed strict security measures and regulatory protocols to safeguard sensitive information and maintain compliance across global supply chain operations.

SPD Technology’s Experience and Expertise in AI for Supply Chain Management

When it comes to the development of projects for the supply chain sector, we approach them with careful consideration of all business requirements, industry-specific insights, and technical expertise. By partnering with us, you can count on: 

  • Strategic Alignment with Business Goals: We thoroughly analyze your operational priorities to ensure AI-driven solutions we develop deliver measurable outcomes and support overarching objectives.
  • Experience with Integration of AI and IoT Ecosystems: Our cross-functional teams seamlessly connect diverse devices and platforms, providing real-time analytics and automation across the entire supply chain.
  • Focus on Security and Compliance: We implement stringent security measures and adhere to global regulations (e.g. GDPR, FDA and REACH regulations), protecting sensitive data and maintaining enterprise-grade standards.
  • Scalable and Future-Ready Solutions: Our team builds systems with modular architecture, which enables solutions that evolve with market demands and technological advancements.

HaulHub and SPD Technology Partnership Success Story

One of the notable projects we developed for the supply chain management niche is the all-in-one platform for HaulHub, which integrates and optimizes the entire transportation construction ecosystem.

Business Challenge

The client needed a full-fledged platform that would combine vendors, contractors, suppliers, and agencies operating in transportation construction to make it easier for all parties engaged in construction to collaborate. We were tasked with the development of such a platform and overhauling existing parts of the system as well as creating an automated reporting system, which needed AI/ML development. Additionally, our AI/ML skills were also required for image processing, traffic analysis, and customer support features.

SPD Technology’s Approach

By leveraging our AI/ML skills, we completed several functionalities for HaulHub’s platform:

  • We used AI and ML for automated reporting that analyzed daily work data and generated targeted insights. 
  • Our team developed and trained AI models for traffic analysis and worker safety, translating nuanced requirements into AI-friendly formats.
  • We implemented image processing and traffic analysis tools and thus ensured safety compliance, identified areas for improvement, and provided insights into the impact of construction on traffic. 
  • Our AI also parsed large datasets to inform more efficient decision-making. 
  • We built intelligent chatbots to facilitate seamless data access and timely responses. 

Value Delivered

Through solutions we built, our AI/ML development team successfully delivered key features and optimized HaulHub’s transportation construction ecosystem, driving efficiency, safety, and collaboration across all stakeholders.

Conclusion

Supply chain management becomes much more powerful with AI. When natural disasters, geopolitical changes, or evolving work landscapes pose challenges for logistics and supply chains, AI comes to help. This technology allows for exceptional inventory and warehouse efficiency, minimizes human errors with automation, creates safer work environments, enables real-time decision-making, and provides the possibility of forecasting demand.

However, when AI helps to overcome market complexities, its adoption may also have obstacles for individual companies. Those can be numerous integrations, development of intricate ML models, high costs, or connection with legacy systems. However, with the right vendor, you can navigate those bottlenecks by creating a comprehensive plan for adoption, evaluating your current system and elaborating strategies for addressing the most demanding areas of your business first. If you need any assistance with your AI-related project for the supply chain, do not hesitate to ask our team for expert advice.

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FAQ

  • What Is the Future of AI in Supply Chain Management?

    AI is expected to revolutionize supply chains with accurate forecasting of demand, seamless automation of routine processes, powerful robots freeing up human time and eliminating errors, and vehicles taking optimal routes. These opportunities brought by AI translate to lower costs, faster deliveries, and a smoother supply chain.