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 possess 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

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): Robots 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 business 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: AI tools with anomaly detection can be set up to inspect products for defects. They can provide real-time data on any anomaly 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.

Simulation-Based Operational Optimization

AI can assist in the development of computer models that replicate different scenarios in manufacturing processes, logistics routines, or supply chain operations. Businesses can find the most practical and efficient methods to optimize their processes with the following AI capabilities:

  • Scenario Analysis: Since AI has the ability to predict different scenarios, it can also simulate them to show if any supply chain disruptions are possible. Also, AI can suggest what impact those disruptions would have on the supply chain and help craft contingency plans.
  • Capacity Planning: Supply chain solutions can leverage AI models that predict whether logistics can accommodate the demand and look for effective alternative plans in case of unsatisfactory results.

Need to know more about AI in logistics?

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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.

# Data Analytics, # Web development
High-Load Support AI Chatbot for an Online Fashion Store

Highlights Client Our client is a B2C eCommerce company operating in the fashion niche. The organization sells a wide…

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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.

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Precise Demand Prediction 

A study by Gartner shows a shift in how companies predict what customers will want to purchase: 45% of organizations already use AI to forecast demand, and 43% plan to start using it in the following years. This shows how 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 scrutinize seasonal demands (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.

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. Companies report numerous obstacles to such digital transformation. Let’s review some of them. 

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, this entails complex software integrations in order to connect all the devices that transmit data to the entire ecosystem. 

Needless to say, this is a huge part of the work for AI adopters. They need to make all these components communicate with each other, share the same protocols and data formats, and operate with one another seamlessly at the same time. The effort required is daunting, yet it is totally worth it. Once your business sets up this whole ecosystem, you will have impeccable supply chain visibility and an understanding of the entire supply chain network.

Complex ML Models 

To create machine learning models with high accuracy and dependability, one must choose the right algorithms, adjust hyperparameters, and train the models on large datasets. Data scientists and ML engineers are needed for this process since they not only have the knowledge of machine learning techniques but also know how to interpret and apply these techniques for particular supply chain needs. The development process can also be further complicated by the lack of highly qualified AI/ML professionals.

Our team, in turn, developed and trained complex ML models during one of our projects for a transportation construction company. The most challenging part was ensuring people’s safety and traffic analysis because of the nuances of business requirements and creating a universal format that AI could understand. However, we successfully completed the project and delivered ML-powered image processing, report generation, data classification, traffic analysis, and chatbot functionalities.

Potentially High Upfront Costs

Supply chain and AI is a powerful yet expensive combination, especially in the beginning of its adoption. Businesses might need to purchase advanced hardware, including high-performance computing systems and IoT devices. They will require several software licenses and infrastructure investments. However, the most expensive part is the development and integration of customized AI systems specifically for your business requirements. In addition, you will need to allocate additional funds for the support and regular upgrades of your AI-enabled systems.

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 companies may still work on the software and infrastructure that was acquired a few years ago. However, as technologies are evolving exponentially fast, companies always 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. 

Implementing AI systems with legacy platforms will most likely require customization and middleware solutions, take considerable time, and demand specialized expertise. For example, we faced numerous challenges (e.g. rewriting parts of a legacy code, complete redesign, and complex third-party integrations) during our work on a legacy system for our client. However, we overcame these complexities, 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 

It can be difficult to achieve dependable network infrastructure and strong, fast data processing when implementing AI for decision-making in real time. Processing large volumes of data in the quickest time possible is essential for making instant decisions, which draws the need for sophisticated computing capabilities and well-designed data pipelines. 

To deliver timely insights and responses, businesses need to take into account low-latency communication throughout the network. This kind of performance can only be attained with a large investment in advanced hardware and software, together with ongoing system tuning and monitoring.

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

Mapping Current Processes

If you want the AI transformation done neatly, you need to start with a detailed visualization of all links in the supply chain. From procurement and production to distribution and delivery, you 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

It is impossible to integrate the newest technologies without evaluating the state of the current technological infrastructure, including 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. This phase indicates that in order to guarantee that the data can efficiently power AI models and algorithms, it is necessary to perform data enrichment, cleansing, or the adoption of new data management techniques.

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 your business goals with AI integrations, you 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 you want 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 your 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, one 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, we will be happy to help resolve challenges associated with AI deployment. 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.

SPD Technology’s Experience and Expertise 

When it comes to the development of projects for the supply chain sector, we approach it with careful consideration of all business requirements, industry-specific insights, and technical expertise. 

Our team has delivered a business intelligence app for HaulHub, a US-based company that helps connect suppliers and contractors in the country. Our goal was to equip this app with powerful analytics for data-driven decision-making specifically addressing the needs of the transportation industry. To realize this goal, SPD Technology:

  • Enhanced App with Analytics: The functionality was delivered in the form of a visual data analytics tool that provided deeper insights into business needs.
  • Efficient Data Processing: We fine-tuned the system to process millions of analytical data points in less than 2 seconds for swift response time.
  • Zero API Compute and Frontend Infrastructure Cost: We managed to eliminate expenses for frontend infrastructure and API compute costs.
  • Infrastructure Cost Savings Through Optimization: Our engineers contributed to overall operational efficiency by deploying an effective Data Lake solution and carrying out database optimizations, which resulted in infrastructure cost savings.
  • Intuitive UI/UX: Our team created a user-friendly yet industry-trendy user interface that offered a flexible user journey.
# Business Intelligence, # Data Analytics, # Data Lake, # Insights, # Logistics and Sypply Chaine, # OLAP
How HaulHub SupplierCI delivered a Game-Changing Analytical system

Highlights Client HaulHub is a comprehensive platform tailored for heavy construction companies, revolutionizing transportation and digital ticketing processes for…

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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. 

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.