The Role of AI in Enhancing Supply Chain Management: Key Benefits and Future Trends
The most significant task for any industry is ensuring the uninterrupted delivery of goods or services. This is especially the case for fields like fashion or automobile industries, where strategic and on-time delivery will make or break a company. The growing popularity of supply chain management can be attributed to the demand for up-to-date information about a product’s location right from the point of shipment to the time it reaches its final destination. It is an intricate web of operations that relies on different people and processes contributing to its effective execution. As per Deloitte, supply chain management can be broadly defined as ‘a system of organisations, people, activities, information, and resources involved in moving a product or service from supplier to customer.’ While technologies such as Internet of Things and additive manufacturing are changing the face of supply chain management, Artificial Intelligence (AI) and Machine Learning (ML) are increasingly becoming integral to it. This article delves into the rise of AI in enhancing the facets of supply chain management. We take a look at some key AI trends in the sector and how this innovation complements supply chain practices. We also discuss how AI-driven app development is gradually gaining traction towards enhancing supply-chain functions. At the onset, let us answer the question: Why does supply chain management need AI? Let’s find out.
Understanding AI and Machine Learning in Supply Chain Management
AI and Machine Learning technologies use computational algorithms, or human-free computational models to accomplish tasks, traditionally performed by humans, that require intelligence. They have wide application in the area of supply chain management. Supply chain managers can harness the power of AI to perform tasks, such as analysing large sets of data, identifying the hidden patterns within the data, and making predictions, as a way to help maximise returns on investment and optimise operations. An organisation can use these technologies to improve processes like demand forecasting, inventory management, logistics, and handling of risks. AI and ML can provide transformational benefits to supply chain operations.
The Impact of AI on Supply Chain Management
AI is changing the way supply chain functions are being handled, from forecasting demand to optimising deliveries. Here are some of the increasingly important areas where AI is making a major difference.
1. Enhanced Demand Forecasting
Companies can also optimise their demand forecasting using AI as it can mine historical sales data, market trends and ‘exogenous’ data (external information or data), and train a Machine Learning model to predict future demand. The prediction made by AI should be more accurate compared to what companies can do with in-house data and lead to better inventory-tuning decisions, such as avoiding stockouts or overstock situations.
Example:
- Retail Industry: Using artificial intelligence (AI), demand and forecasting can recognise patterns in existing sales data, seasonal trends, and consumers’ behaviour to predict the demand for a given product to ensure the appropriate quantity in the country’s inventory.
2. Optimized Inventory Management
Robots can check for stock and fulfil incoming orders on the factory floor; a bot-based HR system that concentrates on time-off requests and administrative work can easily keep track of all staff holidays. Wherever there’s repetitive work, AI can increase productivity and cut down on errors. But what about inventory management? Whether it’s toothpaste in a corner store, clothes in a closet, farm inputs of cotton seeds in a storage room, or industrial goods like steel in a factory, managing a correct inventory that balances customer demand with stock quality and expiry is far easier said than done. It requires calculating the right number of inventory subject to a specific lead time, and then creating a recommendation for the optimal amount of stock or reorder point that minimises holding cost and avoids stockouts. By training an ML model for a few hundred lines of Python code, AI can find the optimal reorder points with a carefully calculated safety buffer of 15-20 per cent of the requirement.
Example:
- Manufacturing: for example, AI-powered inventory management that enables real-time tracking of inventory levels, reordering new materials automatically as stock levels decrease to below a predetermined level, thus keeping the production line running.
3. Improved Logistics and Transportation
The optimisation of logistics and transportation is another predicament that can be improved upon by AI, helping to optimise routes, reduce fuel consumption, and shorten delivery periods. For example, Machine Learning models can be designed to make better logistical decisions, by analysing data that represents traffic congestion; physical aspects like road structure and weather conditions; and delivery schedules.
Example:
- E-commerce: AI-powered logistics could help companies that sell online to streamline delivery routes, slash costs and delivery times, and improve customer experiences.
4. Enhanced Supplier Management
For supplier management, AI can make things a lot easier – a committee deciding whom to employ does not match the speed needed for global firms that have to make decisions by the hour. For example, a deep learning model can predict supplier behaviour (including lead times, quality and reliability) to create a line-up of suppliers that maximise cost savings.
Example:
- Automative Industry: AI-driven supplier management systems could judge the reliability and cost-effectiveness of different suppliers when sourcing materials and components.
5. Risk Management and Mitigation
MIT Sloan Management ReviewBy leveraging computer performance to ‘learn’ from scenarios, AI can help flag logic flaws in our decision-making and suggest ways to mitigate potential risks. In 2019, machine learning models started being used to detect risks in supply chains and advise on remedial steps. Tools analysed past data on the performance of suppliers, geopolitical events, and natural disasters to systematically spotlook ahead and plan accordingly.
Example:
- Food and Beverage Industry: AI-powered risk-management tools that could keep track of events affecting the supply chain like the weather or issues with suppliers, and help companies identify alternative sources of supply.
The Role of AI Software Development Companies
In order to equip supply chains with AI solutions, expert knowledge of AI and software development is required. This is where an AI software development company comes into play. These companies have the technical know-how and experience to develop custom AI solutions with applications to the supply chain.
Benefits of Partnering with an AI Software Development Company
Specilisation in AI and Supply Chain Expertise: For a start, AI software development companies possess a deep understanding of AI technologies as well as supply chain management requirements. They can develop and deploy solutions target specifically to supply chain issues.
Custom Development – These are companies that can build a custom AI solution for almost any business requirement, from custom demand forecasting tools, inventory management optimisation, to logistic improvements, as well as any other area where algorithms can support the business.
Seamless Integration: an AI softwares development firm that can integrate AI solutions into existing supply chain systems and workflows, so that everything can run smoothly, avoiding disruption, and maximising the benefits of AI implementation.
Continued Support: Access to continued support and maintenance that comes with partnering with an AI software development company ensures that AI solutions stay functional over time as technology improves.
How to Hire App Developers for AI in Supply Chain Management
When you need to hire app developer with artificial intelligence expert skills to build your projects, what should you look for to make sure that the company you join possesses the right skill and attributes for your supply chain project?
1. Technical Proficiency
Look for developers who have a strong AI/ML background and experience developing supply chain applications. They should be comfortable with programming languages including Python, R and frameworks such as TensorFlow and PyTorch.
2. Domain Knowledge
Due to this, developers with supply chain industry knowledge – domain experts in supply chain software – are better positioned to provide solutions that fit the particular demands and nuances of supply chain applications since they already possess the right kind of domain expertise.
3. Problem-Solving Skills
Any successful AI project is going to involve technology development and innovation. Look for developers who have logical thinking and problem-solving skills, and the ability to design and tune algorithms.
4. Collaboration and Communication
A successful AI-enabled SCM operation involves good communication and collaboration. Thus, having developers who learn effectively from supply chain teams, communicate a meaningful message, and clearly understand the scope and requirements of a project are instrumental for success.
5. Ethical Awareness
Ethics plays a central role in supply chain AI, so be sure the developers you choose are committed to balancing the potential benefits and risks considerations, including data privacy, and minimising bias and the black box.
Future Trends in AI and Supply Chain Management
Based on what is being done, we could present the future of AI in supply chain management and elaborate on the major trends that will shape the industry in the coming years:
1. AI-Driven Automation
AI offers a higher level of automation for supply chain management by automating repetitive tasks such as ordering and forecasting, inventory tracking, customers information, cash management and scheduling of shipments. This results in the increase of effective operations and lower operational costs.
Example:
- Warehouse Automation : Artificial intelligence (AI) can have robots take on auto fulfillment tasks in warehouses, such as picking and packing, allowing fulfilling orders quickly and avoiding any inconsistencies.
2. Real-Time Visibility and Tracking
It is also enabling real-time visibility and tracking of goods across the supply chain so that businesses can better monitor the status of shipments, track inventory levels and ensure delivery on time.
Example:
- IoT Integration: AI may use IoT sensor data in real time to provide oversight regarding the location and condition of goods when they are in transit, which can help a business track and manage movement through their supply chains.
3. Sustainability and Green Supply Chains
In developing sustainable and environmentally focused supply chains, AI is assisting firms to minimise waste along international supply chains, enabling them to be better, more efficient and fit for purpose. By using machine-learning models, it is possible to calculate the most optimal routes, reducing the amount of wear and tear on vehicles, allowing for a reduced carbon footprint of all operations along a globally networked supply chain.
Example:
- Optimising Routes: Optimising routes for transportation using AI systems can cut fuel usage and CO2 emissions, streamlining supply chain practices to be more sustainable.
4. Collaborative Supply Chain Networks
More collaborative and interconnected supply chain networks are enabled by AI. Through connecting with a wider set of data from other stakeholders in the supply chain, ML models can draw insights, which, in turn, can improve coordination and collaboration.
Example:
- Shared Data Platforms : Shared data platforms, which utilise artificial intelligence, can enable greater collaboration between suppliers, manufacturers and distributors; providing greater visibility and coordination along the supply chain.
5. Advanced Predictive Analytics
Utilisation of AI can significantly improve predictive analytics in supply chain management. Fine-grained predictions present possibilities for more accurate forecasts and other insights. Machine Learning models can use historical data and input from the market to forecast demand as well as inventory levels and disruptions.
Example:
- Demand Sensing: thanks thanks to artificial intelligences, demand sensing programmes that combine machine learning to better analyse wide swathes whole swathes of real-time data, such as social media mentions, macroeconomic factors and weather reports, are now available, letting business owners better understand demand shifts, and allowing for a faster production timeline response to these variations.
Challenges and Considerations
AI can help encapsulate everything that we need to know about supply chains, but overcoming a few barriers is necessary to make this happen:
Data Privacy and Security
While sharing information in the supply chain is essential to ensure the use and application of the data, it is also crucial to ensure that it is kept secure and private through the implementation of adequate security measures. Businesses must take necessary steps to protect information from being compromised by hackers or other nefarious actors.
Regulatory Compliance
AI solutions in the supply chain also have to abide by legal standards: regulatory compliance is key when increasing and leveraging data processing through higher-level AI, as in the case with GDPR and other emerging regulations.
Integration with Existing Systems
One, integrating AI solutions into existing supply chain systems can be difficult. It is important that businesses ensure that AI tools integrate with the existing systems and workflows.
Addressing Bias
Plastic Free An Example from Chapters Five and Six: AI models might replicate any biases present in the training data. Fair and bias-free development of AI algorithms that do not discriminate against any part of the supply chain is necessary.
Conclusion
Improvements in demand forecasting, inventory management, logistics and more will promote efficiencies across all aspects of modern supply chains. The application of AI and Machine Learning to supply chain operations could provide real-time visibility of supply chains, lead-time forecasting, demand forecasting, optimization of inventory levels, and better pricing. The implementation of custom AI solutions for supply chain management will require the collaboration with an AI software development company to define a clear scope and help develop innovative IoT solutions to address the domain-specific use cases.
Technical proficiency, domain knowledge, problem-solving, collaboration and ethics are important qualities to look for when hiring AI-enabled app developers. Armed with this knowledge, companies could effectively transform supply chain management using AI powered app developers, all thanks to AI software development companies.
Now jump ahead as AI continues to innovate. While not all aspects of supply chain management may be directly affected by automated technologies, the more AI-enabled platforms will proliferate and become ubiquitous, the more opportunities there will be for supply chains to benefit from a more efficient, sustainable and resilient business model. By closely tracking those trends as they unfold and also getting clear
