AI in Supply Chain: Transforming End-to-End Workflows and Experiences

In recent years, the outlook of the global supply chain has drastically changed, but for good. Yet this industry faces many challenges, and there are still some loopholes that need to be addressed. With digitization around the corner, businesses are seeking supply chain solutions that can help them break free from manual processes and traditional workflows. We are explaining here everything about AI in Supply Chain.

Weak supply management systems are impacting businesses a big time. 

Today, businesses want real-time visibility across the supply chain, a unified platform for collaboration, and automatic tasks to improve efficiency, reduce operational costs, and focus on sustainability. Thus they are investing in architecting supply chain operations for the long term by leveraging technologies like AI and GenAI. In 2024, 50% of supply chain organizations will be investing in apps driven by AI and advanced analytics capabilities. 

AI will make significant improvements to optimize the entire supply chain management, opening new opportunities and scope to scale. Talking about so many benefits, let’s understand the role of AI in Developing Resilient Supply Chains.

Let’s first start with supply chain challenges, where businesses lack, and what they want solutions for.

Existing Supply Chain Key Challenges That Need AI Solutions

Currently, the supply chain is the target of several issues like shortages and shipping delays remain top concerns, with 43% supply shortages as a major challenge. But the challenges go beyond storing and shipping products, some of the other challenges are.

Data analytics and reporting Challenges—To better manage distribution and prepare for disruptions, brands must use data to decide what to send where, in what quantities, and at what prices. However, many brands struggle with data spread across different systems, leading to inaccuracies.

• 35% face poor data quality.
• 34% deal with inaccuracies and inconsistencies.
• 30% encounter delays in processing and analyzing data.

Fixing these data problems is crucial for brands to benefit from analytics and improve their supply chain operations fully.

Sustainability Challenges- With a stronger focus on sustainability, businesses need to assess their entire operation for areas needing improvement. They require advanced tools and monitoring processes to prioritize ethical and eco-friendly initiatives.

How can we target these challenges with AI and data analytics? Let’s explore.

Understanding The Role of Data Analytics in Supply Chain

The supply chain is all about how you analyze data and use it to transform operations. Using advanced algorithms and ML techniques, businesses can process large sets of data, extract useful insights, and make smarter decisions. It helps businesses predict demand for products more accurately. By analyzing data, companies can- 

• Optimize inventory levels, 
• Reduce excess stock and minimize shortages. 
• Improves efficiency in shipping and distribution, 
• Ensuring products reach customers on time. 
• Optimize cost
• Identify and mitigate risks
• Enhances decision-making and operational effectiveness in managing supply chains. 

In supply chain software, data is key for making smart decisions. By automatically gathering and analyzing lots of data, analytics software improves how routes are planned and how inventory is managed. Here are some examples of how data analytics helps in supply chain operations.

Predictive analytics

Predictive analytics uses historical data, statistical models, and machine learning algorithms to help businesses forecast future trends and outcomes. Using the data, businesses can predict market trends, identify potential risks, and mitigate them early within the chain to minimize their impact and ensure business continuity.

Descriptive analytics

Descriptive analytics helps businesses analyze past events, large data sets, and trends in the supply chain to identify patterns. It uses historical data to provide insights into what has happened in the past. 

This helps businesses gain visibility to data across supply chain management to make informed decisions. 52% identify visibility as their top priority to control supply chain operations and identify supply shortages. 

Prescriptive analytics

Prescriptive analytics helps businesses understand how changes can affect their results and advises on the best actions to improve. It uses predictions to suggest ways to optimize operations.

For instance, a logistics company can use it to find the cheapest transport routes by predicting traffic and fuel costs. It also helps track supplier performance and ensure compliance, leading to better deals, stronger partnerships, and increased efficiency.

Diagnostic analytics

Using Diagnostic analytics will help businesses identify the root causes of issues or events by analyzing data. It helps understand why certain outcomes occurred, so businesses can make informed decisions to improve the outcomes. 

For instance, a manufacturing company might use diagnostic analytics to analyze production downtime data to identify the reasons for equipment failures or supply shortages.

Cognitive analytics

Cognitive analytics is an advanced analytics technique. It helps businesses quickly process large amounts of data and produce the most accurate answer to improve customer experience and relationships. 

It helps you analyze AI-driven data, to come up with innovative ideas to offer next-level customer experience. If you are not sure which analytics will help your supply chain operations, then our next point of discussion is for you.

Understanding The Role of GenAI in Supply Chain Management 

AI solutions help businesses transform how they manage logistics, handle inventory, and predict demand. This helps them operate more efficiently and stay competitive globally, optimizing end-to-end supply chain operations.

As per McKinsey and Gartner, the benefits of AI in the supply chain management- 

• AI-powered systems reduce supply chain errors by 20% to 50%
• Reduce lost product orders by up to 65%.
• Identifying new information from unrelated data points. 
• 25% of supply chain decisions will be AI-driven.

How Does AI Optimize Supply Chains Across Departments?

AI unlocks the full potential of supply chain management that businesses seek across departments. From automation to improving supply chains, and reducing costs, AI has other applications too.

1. Vendor management for supplier management efficiency

Businesses can easily select suppliers by evaluating their pricing, historical purchases, and sustainability measures using AI-powered Supplier Relationship Management (SRM) software. Businesses can track and analyze suppliers’ performance to rank them based on their contributions and reliability. 

Examples-

• Sourcing new suppliers. For example, Alibaba used AI algorithms to find suppliers for Taobao and Tmail.
• Contract management. For example, Microsoft uses AI and data science to automate document reviews and improve contract search capabilities.

2. Inventory management to minimize holding costs

Businesses can employ AI software to gather information based on past trends to identify the right stock levels, and slow-selling products, and predict shortages or excess inventory. These insights help businesses improve inventory management, speed up order processing, and reduce storage costs, leading to a more efficient supply chain. 39.5% plan to employ predictive analytics to minimize holding costs. 

Examples-

• Smart reordering. For example, Amazon forecast to identify the exact amount of inventory required to reduce over-purchasing.
• Eliminating obsolete inventory. For example, IKEA has a buyback and resell initiative that allows shoppers to sell back their used furniture. 
• Inventory counting. For example, Zebra has a SmartLens solution that records the location and movement of assets throughout the chain’s stores.

3.  Artificial Intelligence in Logistics and Warehouse Management for faster delivery

Businesses can organize items more efficiently to speed up inventory turnover. AI uses past orders and current demand to place items where they’re needed most, ensuring popular items are easily accessible. It helps businesses place popular items near packing and shipping areas reducing travel time for workers and making operations faster and safer. Smart warehouse management and AI-driven systems will cut down 15% of warehousing costs. 

Examples-

• Organizing warehouse space. For Example, JD Logistics has AI-driven warehouses using robots and conveyors.
• Robotic pick and pack. For example, Amazon Kiva robots for moving, and sorting items.

4. AI bots for seamless Customer Service

Businesses can use chatbots to provide quick updates on order status and delivery times. Chatbots handle inquiries about products, shipping options, and returns, freeing up customer service teams. For example, UPS uses an AI chatbot named UPS Bot to assist with tracking shipments and providing rate quotes.

5. Human Resource Management for effective recruitment

Businesses can employ AI-powered chatbots or virtual assistants for initial job interviews, streamlining the process by asking standard questions. This saves time for HR professionals and ensures consistency in screening candidates.

Key Steps to Implement AI in Supply Chain Management

Implementing end-to-end AI and analytics solutions in the supply chain involves several steps, from establishing goals to scaling and optimizing.

Here are the simple steps to start your AI journey in supply chain management.

Step 1- Define goals and objectives

Define why you want to implement AI solutions in your supply chain. Analyze, how AI can help, what area it will improve, and bring the most value to your business. 

Step 2- Collect and organize data

It is a necessary part where experts gather information from different sources across your supply chain to predict trends and customer preferences. It could be historical sales data, weather data, transportation data, and any other relevant information. AI tools and analytics will help organize and process data at a faster pace for quick decisions.

Step 3- Data Cleaning 

Businesses must not use the raw data to feed the AI due to bias and incorrect responses. You must prepare the data, clean it, and collect it in a unified place. Place ETL process to merge and clean the data for better forecasting.

Step 4- Choose the right AI stack

Based on your challenges, experts will implement the right AI algorithm that will help you improve the workflow. They can use Regression, classification, clustering, or deep learning methods for complicated pattern identification may be used in this case. They can either use prebuilt AI solutions or choose custom solutions based on the project’s complexity. 

Step 5- Data Model development

Create machine learning models using AI technologies like TensorFlow or PyTorch. These models are used in supply chain and logistics for tasks such as predicting demand, optimizing routes, and maintaining equipment. For traditional machine learning, use frameworks like scikit-learn.

Step 6- Integrate with existing supply chain workflow

Then experts can integrate the AI solutions within your existing supply chain management and link them with AI models. 

Step 7- Testing and Validation

Experts rigorously test AI models and linked systems to ensure accuracy and reliability. They compare predictions with real-world results to confirm how well the AI algorithms perform. Based on testing outcomes, experts refine and enhance the models.

Step 8- Pilot Testing and Deployment

It’s recommended that experts conduct pilot testing on a smaller scale before full deployment. This helps identify any issues and allows for fine-tuning of the AI algorithms. Successful pilot testing ensures the AI system is optimized for supply chain automation.

Step 9- Continuous Improvement

Experts continuously evaluate AI’s impact on business processes. They make necessary adjustments to enhance productivity, accuracy, and decision-making. Staying updated on AI advancements helps experts explore further optimization opportunities in supply chain management.

60% of businesses are already using AI-enabled solutions in their supply chain workflow. What’s your excuse? Here are some use cases that will help you improve supply chain operations.

Top 5 Use Cases of Analytics and AI-Enabled Supply Chain Management 

It’s time for businesses to adopt advanced AI for supply chain planning and implementation for modern operations. Below are some use cases for end-to-end Supply chain digital transformation.

1. AI for Demand forecasting

Organizations rely on historical data to understand trends and predict future trends. They use AI to analyze historical sales, market trends, and customer preferences to generate real-time demand models. It will help supply chain organizations to manage their warehouse inventory, and schedule production, and distribution plans to meet changing customer demands.

Walmart adjusts its inventory and sales strategies in real-time by analyzing huge datasets, including in-store transactions, and other factors like weather changes.

2. Expediting logistics and transportation for smart route optimization

People demand quick deliveries, putting a lot of pressure on organizations, wanting them to implement AI solutions for faster and more organized deliveries. 

Artificial intelligence analyzes traffic congestion, roadwork, and other variables to build an optimized route for efficient deliveries. This also reduces the fuel consumption. Not only this, integrating AI solutions with IoT will increase your visibility about transportation, reducing delays. 

3. AI for sustainable supply chain solutions

Supply chains have a big impact on the environment through things like emissions from transporting goods, deforestation for raw materials, using too much water, and harming habitats. AI in supply chains helps businesses use resources better, reduce waste, save energy, and choose routes that cause less pollution.

For instance, Nestlé uses AI to predict product usage across different countries, reducing leftover products by 10%.

4. AI for predicting supply chain disruptions

Situations like natural disasters, wars, economic downturns, rules from governments, and pandemics can challenge the already complicated supply chain. For example, if a country has trouble making materials, it can delay making things in another place, or if a rule changes in one area, it might mean products need to be taken back from far away.

IBM says 87% of top supply chain leaders find it difficult to identify and manage risks early. AI can help predict and identify these risks by analyzing data, helping companies save money and avoid getting in trouble.

Challenges of implementing AI in the supply chain

Here are some challenges that you might face while implementing AI within your supply chain operations. You might require expertise to deal with them.

• Data quality and integration issues.
• Lack of skilled AI talent.
• High implementation costs.
• Resistance to change from stakeholders.
• Ensuring AI solutions align with business goals.
• Integrating AI with existing IT infrastructure.
• Addressing ethical and regulatory concerns.
• Managing the complexity of AI algorithms and models.

Future Trends of AI in the Supply Chain to Consider in 2024

AI and data analytics have truly changed supply chain management and improved its end-to-end efficiency. Still, that’s not it. The supply chain is full of many flaws that need attention, and integrating technologies like AI can transform the entire industry.

Some of the top trends that you must read.

• Green and circular supply chain to become carbon neutral and achieve net-zero waste.
• Supply chain data governance helps predict demand, enhance inventory management, and improve the customer experience.
• Agility and resilience allow the supply chain to adapt swiftly to changing circumstances. 
• Collaborative supply chain networks will create a digital ecosystem for organizations to achieve scalability, customization, and agility.
• Digital supply chain twins that help users track and monitor supply-chain trends in real-time.

OnGraph’s Difference: Transform Supply Chain with AI Solutions

AI algorithms have huge potential, but using them effectively requires deep technical skills, experience, and resources. It’s not just about technical know-how; it’s about handling big data, training AI, and constantly improving to find better solutions.

OnGraph understands how AI fits with business goals in the supply chain, avoiding common mistakes and maximizing returns. Here’s why working with an AI software development company is beneficial.

Why Choose OnGraph?

• 15+ years of expertise in digital transformation
• Knowledge of end-to-end supply chain workflow
• Updated with the latest trends
• Agile approach with efficient solutions
• Faster time-to-market
• Speedy support, updates, and maintenance for one month

FAQs

Q. How can AI transform supply chain management?

AI has a profound impact in transforming supply chain management to improve efficiency across workflows and operations.

• Reduced cost- operational, transportation, inventory, and more
• Improved productivity, using robots reducing manual efforts and time
• Smart decision-making, works on real-time data, predicts demand forecast, and market trends
• Personalized customer experience, with AI-enabled software to fulfill instant customer demands.

And more. 

Q. What are the potential challenges of implementing AI in the supply chain?

Well, supply chain management is already complex, any mistake can turn into a disaster. Here are some challenges that you might face.

• Data quality and integration issues.
• Lack of skilled AI talent.
• High implementation costs.
• Resistance to change from stakeholders.
• Ensuring AI solutions align with business goals.
• Integrating AI with existing IT infrastructure.
• Addressing ethical and regulatory concerns.
• Managing the complexity of AI algorithms and models.

Connect with our experts for guidance and smooth AI implementation.

Q. How much time will it take to build AI solutions for supply chain management?

The average cost depends on the type of AI solution you want, the size of the project, and other factors.

Q. Do you provide post-deployment support?

Yes, we offer one month of free support post-deployment for any type of development solution.