Technology has transformed into a guiding light for humans and is walking multiple organisations and companies in the supply chain through their daily operations and tasks. The impact of artificial intelligence on the supply chain helps upgrade and optimise supply chain operations by managing and analysing large-scale data. This data is filtered to draw insights to improve demand forecasting, inventory management, customer service, and risk management. Let us learn more about how AI can impact the supply chain operations.
What are supply chain operations?
The global supply chain ensures the seamless flow of goods nationally and internationally to keep the trade industry running and blooming the economy. Raw materials are supplied to manufacturing units from where finished products are made available for the distributors. Although it may seem easy, a line of supply chain operations is involved. The entire process of transfer of goods and services from the supplier to the customer is strategized, planned, and executed. Supply chain operations involve procuring, storing, and transporting goods across the supply chain. It is an endless process, including the shipping, logistics and transportation industry. The supply chain utilises multiple resources such as digital systems, workers, infrastructures, management systems, vehicles, etc. All these resources must be monitored daily to optimise supply chain operations.
How artificial intelligence is used in the supply chain?
Supply chain operations involve numerous tasks requiring decision-making and problem-resolution. Artificial Intelligence (AI) uses computers and machines to perform tasks and mimic human minds for problem-solving or decision-making. It is essential to look for potential failures and inefficiencies in the supply chain to optimise the supply chain operations. Human errors are always a possibility, and there is always a risk if the solution or strategy would work. Human minds may take a significant amount of time and fail to develop result-oriented solutions when human decisions are replaced with AI-dependent decisions, the efficiency of the supply chain increases. Data from the various supply chain tasks is collected and analysed to help AI make well-informed decisions.
Impact of Artificial Intelligence on the Supply Chain Operations
- Demand forecasting– AI can scroll through historical and incoming data to filter from market trends, weather patterns, and industry fluctuations to give accurate information on the influx of demand. By knowing the demand beforehand, distributors can quickly import goods from foreign markets. It helps in preventing last-minute overpriced orders or losses due to insufficient stock.
- Managing inventory– Manufacturers and e-commerce companies must maintain inventory levels to store enough goods before market demand. IoT in the shipping industry helps regulate inventory levels by altering the warehouse authorities of shortage or surplus of goods kept. It helps prevent cargo from sitting in inventories for too long and getting wasted.
- Customer service– AI chatbots help resolve customer queries and give personalised recommendations for products and services. AI can provide quick and customised responses to enrich customers with detailed knowledge of their questions. It is helpful in many levels of the supply chain that require interaction with the end user.
- Improved sustainability– By tracking and monitoring products throughout their transportation in the supply chain, optimising shipping routes, regulating waste management, and giving more power to automated vehicles, AI helps reduce shipping emissions caused throughout the supply chain.
- Risk management– AI has access to a wide range of data, and through AI-powered predictive maintenance, it can look into all potential risks in an organisation. It helps manage and mitigate risks that would have otherwise led to sudden supply chain disruptions.
How can AI optimise supply chain processes?
After studying the impact of artificial intelligence on the supply chain, let us learn more about implementing AI to optimise the supply chain process.
- Managing large-scale data– Supply chain operations revolve around data and information and generate large datasets. Every activity in the supply chain generates data regarding the efficiency of resources, processes, and logistics that can be reviewed later. The collected data also records data of all the shipping parties, including manufacturers, suppliers, distributors, stakeholders, and shippers. AI helps in filtering and systematically organising the data for future use.
- Analysing big data– The datasets collected from various systems are crucial. The role of big data analytics in the supply chain is to help in making sense of the collected data and filter insights to make data-driven decisions. Analysing the supply chain operations makes mitigating the underlying inefficiencies and drawbacks in efficiently carrying out supply chain activities easier. This way, supply chain management can look into the various aspects of the supply chain and optimise supply chain operations.
- Making predictions– By analysing historical data using AI, supply chain demands, bottlenecks and breakdowns can be forecasted to make better decisions. Predictions made by AI help predict future maintenance and repairs that help save money and time and prevent sudden disruptions. Analysing historical data, AI can also offer potential solutions for supply chain operation failures.
- Identifying trends– AI identifies data patterns to look for recurring trends. It helps filter new opportunities and implement them to yield maximum outcomes. Market trends can be identified by analysing consumer behaviour to stay updated and introduce new resources and processes to modify the supply chain operations.
Challenges of using AI in supply chain operations
A few challenges bar the impact of artificial intelligence on the supply chain.
- Ensuring high data quality– AI works efficiently when constantly supplied with accurate, consistent, real-time data to make data-driven decisions by filtering valuable insights. Since the data provided comes from numerous systems, it becomes difficult to ensure data quality. Maintaining data quality while dealing with multiple suppliers, shipping parties, locations, and data formats is challenging.
- Integration costs– Integrating AI into the various supply chain systems is time-consuming and expensive. AI requires updates, maintenance, training, and resources to integrate it successfully and ensure appropriate operations. Although supply chain management is trying to integrate AI, it is an expensive task to be included in every supply chain step.
- Skilled workers– There is a shortage of trained, qualified and experienced workers required to operate AI systems and deduce the filtered insights. It is still challenging to assure people of the reliability of AI-driven decision-making and accept it as a part of the supply chain.
- Data Security– AI collects and processes sensitive data incoming from various supply chain operations, raising concerns about data privacy. Companies and industries must comply with data protection policies and secure their customers’ data to safeguard sensitive information. Data leaks or cyber-crimes can lead to the manipulation of AI-based insights.
The impact of Artificial Intelligence on the supply chain helps to increase productivity, operational efficiency, and profitability in the trade industry.
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