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AdexinBlogArtificial Intelligence in Supply Chain

Artificial Intelligence in Supply Chain

May 9, 2024
8 min

AI in supply chain management has extensive application across all entities connected to it. AI has been adopted in warehouse operations, such as inventory management and transportation planning, where it impacts route planning, and in total logistics operations, where it assists with demand forecasting. 

Today, we have no doubts that it fulfills its role with minimal negative side effects, if any. This indicates that companies will continue to experiment with AI, following the trend of adaptation in each part of the supply chain with an increasing number of AI integrations into ongoing tasks, processes, and operations.

This article shares ten examples of using AI in the supply chain. We reveal a bit about the challenges of AI adaptation.

We as an AI software development company focus on implementing artificial intelligence in supply chain management by using factual numbers and real-life examples. We want to emphasize how important AI is for the future of the supply chain. So, stick with us, and let's look at how AI can enhance your supply chain.

Key roles of AI in supply chain management

It is crucial to outline why the supply chain needs AI solutions. The answer is clear when it comes to data analysis. What should be automated is evident, as no human is fully capable of consuming so much data, as, for example, is reflected in daily e-commerce orders.

What I mean is that e-commerce handles nearly, let's say, 13,000 orders per day. So, we need machine learning and artificial intelligence to help us spot trends and other values in demand forecasting and planning.

The reason seems to be obvious. However, let's think from a wider perspective and observe the supply chain globally. We will see several external factors that have facilitated the need for AI from an outside perspective.

We need to state this clearly: to avoid chaos in the supply chain, we need to adapt technology such as AI. We're talking here about supply chain resilience and a few other subjects.

The roles of AI in SCM
The roles of AI in SCM

Resilience

Artificial intelligence enables supply chain disruption mitigation. That's the way where it is provided real-time visibility and predictive analytics; artificial intelligence helps respond quickly to operational disruptions and implement flexible contingency plans. A resilient supply chain based on artificial intelligence can forecast and anticipate disruptions, often avoiding them altogether.

More accuracy in data management

AI-based supply chains offer end-to-end visibility, resulting in a high degree of accuracy in supply chain operations. Real-time data insights and data analysis facilitate accurate decision-making, optimize production, and ensure cost-effective inventory management.

Efficiency in the supply chain

Artificial intelligence increases the efficiency of warehouse processes. We can see this from receiving and unloading to picking, packing, and shipping. Automation is applied to physical tasks and supply chain planning. It's all about streamlining operations and reducing manual errors.

Optimized production

Artificial intelligence-based supply chain planning includes advanced forecasting methods. AI is integrating internal and external data sources for more accurate demand forecasting. It enables efficient, flexible services with reduced lead times and predictive shipping capabilities.

Flexibility in the supply chain

Artificial intelligence enables real-time planning and continuous adaptation to changing demand or supply scenarios. Supply chains can dynamically adapt to changing conditions. We can now observe new trends in the supply chain supported by new business models, such as Supply Chain as a Service. But this model is still far from perfection.

Microsegmentation 

Artificial intelligence enables easier micro-segmentation and mass personalization across the value chain by managing customers in granular clusters and providing personalized product recommendations. In the long term, artificial intelligence streamlines last-stage delivery strategies and improves customer satisfaction.

What are the challenges of implementing AI in supply chain and logistics?

Large companies have fewer problems with planning transformation journeys when it comes to adapting AI. Nevertheless, they also face some pitfalls, and statistics have shown that nearly 60% of companies have faced delay issues.

Challenges of implementing AI in supply chain and logistics
Challenges of implementing AI in supply chain and logistics

Here are the common problems encountered by supply chain professionals and supply chain managers while implementing AI in the supply chain:

Identify value creation opportunities in global supply chains:

  • Companies need to identify and prioritize value creation across all functions in the supply chain

  • Lack of independent diagnostics at the outset can result in missed opportunities (this refers in general to the areas where AI can be adapted).

  • A clear definition of digital supply chain strategy is essential for better alignment with business strategy (we need to outline where we need AI).

  • A solution-independent assessment helps identify necessary process redesign and opportunities. It is good to use external consultants, such as a software development company that is familiar with AI solutions in the supply chain.

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Target solution design and supplier selection:

  • The complexity of supply chains requires multiple solutions from different suppliers. Not all the providers may be capable of providing you with AI aligned with your business needs, and you may need a custom software development company to integrate it with your supply chain.

  • Management should evaluate solutions based on the needs of the business, not on vendor recommendations. It means that your business should focus on what is required for your business, not what they propose to sell you. 

  • Integration of different solutions should be a top priority.

Systems implementation and Integration in supply chain organizations:

  • Many companies are inexperienced in implementing technology across the organization. They take risks that are very costly in adapting AI on their own.

  • Risks include schedule delays, budget overruns, and loss of focus on value creation.

  • A holistic approach to systems implementation and Integration is critical for short-term value delivery and long-term sustainability. The second term is about providing error-less solutions and the same time, maintaining ROI.

Change management, capacity building, and full value capture:

  • Companies must address the essential supporting elements alongside technology solutions. Therefore, what is needed for a specific project must be clearly outlined versus the company's capabilities within the budget frame. Let's be honest: you can do anything if you have a budget.

  • Challenges include organization, change management, and capacity building. This aspect revolves around connecting people, something you need to do alone.

  • Only a small percentage of executives are willing to address skills gaps. It's difficult to acknowledge that you may need to hire people with lower skills, but honesty in this matter is crucial to avoid unforeseen challenges in the process.

  • Change management and capacity-building investments are essential for successfully adopting new solutions. Organizational changes, business process updates, and skill-building efforts are necessary to achieve the expected return on investment from AI-based supply chain solutions.

10 examples of AI in Supply Chain and Logistics

Here we will try to consider in more detail only a few problems, although there might be more, depending on your business specifics. AI in supply chain examples is something important we really need to provide you with.

1. Demand forecasting and planning analytics 

Artificial intelligence-based forecasting engines analyze historical data to generate accurate forecasts, helping companies predict customer demand and optimize inventory levels.

2. Inventory management for supply chain organizations

Artificial intelligence-based inventory management systems optimize inventory levels by analyzing demand patterns and automating replenishment processes. This ensures the efficient use of resources and minimizes stockouts.

3. Route planning

AI systems optimize transportation routes based on road conditions, delivery schedules, and fuel efficiency, enabling companies to reduce operational costs and delivery times.

4. Carrier procurement for better supply management

AI-based platforms help companies select the most suitable carriers by analyzing performance metrics, pricing, and service quality, facilitating efficient procurement processes. Supply chain companies can really benefit from AI.

5. Load planning

AI systems can optimize load distribution in transport vehicles to maximize efficiency and minimize space. This can reduce operational costs and even environmental impact while lowering the number of shipments. Fewer shipments with better-used loading space on containers and trailers are crucial in effective last-mile delivery shipping.

6. Warehouse automation

Robotics and automation technologies based on artificial intelligence improve warehouse operations by automating tasks such as picking, packing, and sorting. This increases productivity and accuracy while reducing labor costs.

7. Manufacturing

AI in manufacturing can help reduce equipment downtime. It is where Artificial Intelligence-based predictive maintenance systems monitor equipment performance in real time. Businesses can predict potential failures and plan maintenance activities to minimize downtime and maximize productivity.

8. Customer service

Artificial intelligence-based chatbots and virtual assistants improve customer service by providing personalized assistance. AI automatically responds to inquiries and resolves issues quickly, increasing customer satisfaction and loyalty.

9. Document management

Artificial intelligence-based document management systems automate document processing tasks such as data extraction, classification, and indexing. A DMS system with a built-in AI plug-in can streamline administrative workflows and reduce manual errors. It can automatically detect where people use certain features, such as OCR (Optical Recognition System) or others.

10. Robotics integration

AI technologies integrate robots across the supply chain. Robotics application goes from manufacturing to warehousing. Businesses can largely improve the efficiency, accuracy, and safety of operations.

Our experience with AI applications in supply chains

From my perspective, I have witnessed numerous instances where companies grapple with handling vast amounts of data from MS Excel using macro and VBA solutions. Individuals often find themselves stationed at computers in the warehouse, extracting data from ERP systems via Crystal Reports in CSV files and utilizing MS Excel to analyze inventory levels, identify discrepancies, and conduct location checks.

This process typically consumes extensive time and yields inconsistent results. Occasional macro breakdowns and script failures exacerbate the situation, compounded by the fact that only specific individuals, usually the authors of the macro scripts, can address these issues. Consequently, this leads to lost working hours, operational halts, and error-prone outcomes.

Fortunately, today's AI adaptation allows companies to seamlessly integrate these solutions with custom software development, which proves more cost-effective compared to earlier years in my tenure in logistics.

Relying on the expertise of personnel involved in software development projects provides invaluable industry-specific experience and know-how. I can surely say that there is less worry about potential issues arising. These solutions effortlessly interface with ERP systems and automate workflows about data and inventory management, significantly streamlining processes.

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What are the benefits of AI in supply chain optimization?

We've looked at the challenges and issues of AI in the supply chain in general, but let's also take a look at the benefits and think about what advantages can be gained from embedding AI into supply chain business processes.

  • Decreasing operating costs with AI in supply chain management. AI streamlines purchasing and production processes and shipping. In many cases, it can forecast demand to minimize waste. It can help in the procurement process and negotiate prices in real time, which results in significant cost reductions.

  • Improving productivity with AI. AI automates repetitive tasks. It can provide insights for workforce optimization and empower employees to focus on high-value activities, enhancing overall productivity and reducing labor costs.

  • Enhancing Stakeholder Relationships with AI. AI-powered platforms facilitate seamless communication and collaboration among supply chain partners. They improve communication, enable proactive decision-making, and foster trust among stakeholders.

  • Ensuring on-time delivery with AI. AI-driven predictive analytics anticipate potential delays. AI can easily optimize delivery routes and schedules and ensure the availability of products at the right time and place. This fact is enhancing customer satisfaction and loyalty.

  • Optimizing transportation routes with AI. AI-based solutions analyze data to identify the most efficient transportation routes. AI-driven solutions take into account factors such as traffic conditions, fuel costs, and vehicle capacity, leading to cost savings and improved delivery times.

  • Managing risks with AI in supply chain management. AI in supply chain risk management is an AI-based system that detects and mitigates potential risks and vulnerabilities in the supply chain. It can affect lower thief negative outcomes. In short, AI ensures business continuity and resilience in the face of disruptions.

  • Empowering data-driven decisions with AI. AI leverages data analytics to provide actionable insights and recommendations for global supply chain management. As was already said, it can help with demand forecasting and enable faster and more informed decision-making across all levels of the organization.

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Final takeaway on AI and supply chain management

To leverage its benefits, you need to address challenges like identifying opportunities. In the second tier, you should select solutions wisely to see where they fit best. But we think that enormous effort is sacrificed when investing time and money in change management.

This area often requires decisions about changing your staff and hiring new ones. It is not often an easy decision, but some great supply chain managers can handle both by keeping personnel in-house while adapting AI solutions.

One example could be diversifying roles and creating additional, cost-effective departments that require manual work with great attention to detail, such as Value-Added Services (VAS). But there can be more examples.

If you are ready to optimize your supply chain with AI software development, you can contact us, and we can discuss where you can enable your business with modern technology. We serve as a custom software developer with hands-on experience in supply chain software development. Contact us to explore solutions tailored to your needs and drive growth. Let's transform your supply chain together.

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FAQ

How does artificial intelligence contribute to optimizing production in supply chains?

Artificial intelligence improves forecasting accuracy by integrating internal and external data sources to forecast demand. This is leading to efficient services with reduced lead times and predictable shipping options.

What role does artificial intelligence play in providing flexibility in supply chains?

Artificial intelligence enables real-time planning and continuous adaptation to changing demand or supply scenarios. You can benefit from it by enabling supply chains to better adapt to changing conditions.

How does artificial intelligence facilitate micro-segmentation and mass personalization across the value chain?

Artificial intelligence manages customers in granular clusters. It provides personalized product recommendations and streamlines last-stage delivery strategies. AI is ultimately increasing customer satisfaction.

What challenges are encountered when implementing artificial intelligence in supply chain and logistics?

Challenges include the identification of value creation opportunities, lack of independent diagnostics at the outset, and clear definition of digital supply chain strategy. AI implementation also solution-independent assessment challenges, and the complexity of target solution design and supplier selection.

How can companies mitigate the challenges of implementing artificial intelligence in supply chain management?

Companies can mitigate challenges by prioritizing value creation across all supply chain functions. They can conduct independent diagnostics, and define a clear digital supply chain strategy aligned with business goals. It is very frequent to work with experienced third-party consultants or software companies specializing in AI solutions for supply chains.

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