The supply chain industry has become more complex in the past few years. Interconnected and longer physical flows, market instability, and increasing concern for sustainability have all contributed to this complexity. This led companies to focus more on building a resilient supply chain that stays agile despite volatility in market conditions and customer demands.
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Supply chain intelligence platforms support companies in this process through the collection and analysis of supply chain data. These solutions help them to optimize the flow of goods and information across the supply chain, help businesses make informed decisions, and prevent potential risks.
Supply chain forecasting is one of the fundamental steps in this process. It empowers companies to adapt supply chain processes, such as production, procurement, and logistics movements, based on future requirements and disruptions. The advent of AI has further enhanced companies’ forecasting abilities. By applying this AI-powered forecasting, companies can improve stock maintenance abilities, be flexible in the face of interruptions, avoid losses, and realize increased sales.
How AI has Transformed the Forecasting Process
Forecasting in supply chain intelligence is an important part of synchronizing the supply with the demand and getting an appropriate production plan. Past prediction techniques have been based on historical information and manual analysis. These approaches could not adapt to sudden changes in market demand, geopolitical disruptions, and customer trends. As a result, organizations can experience stockouts, excessive accumulation, and loss of customers and revenue.
AI-based prediction has overcome these inefficiencies by merging machine learning, predictive analytics, and real-time data analysis. These approaches enable supply chain intelligence platforms to ingest a variety of data and learn from them to make forecasts. These include historical data, current market trends, weather conditions, and much more.
Through this continuous analysis and learning process, it is possible for companies to get reliable predictions about demand changes. Companies could exploit these data points to change their procurement, production, and distribution approaches.
Supply chain intelligence platforms using AI-based forecasting have the potential to give companies a greater understanding of their operations, reduce operating expenses, and create a robust supply chain network.
Enhancing Supply Chain Intelligence with AI-driven Forecasting
Using AI-based forecasting, supply chain intelligence platforms can become more data-intensive. As a result, they can continuously monitor market developments, changes in demand, and disruptions to supply chain networks and respond with better flexibility.
Supply chain intelligence platforms powered by AI can be used in multiple ways to ensure business continuity, supply chain stability, and revenue growth.
- Achieving Precision in Hyperlocal Demand Forecasting
Supply chain intelligence platforms can perform forecasting at finer levels, like SKUs at every store. Traditional forecasting methods fall short in this aspect as they rely on broad historical data. This can result in incorrect inventory levels at the individual points of sale.
AI employs machine learning algorithms to identify local buying trends, patterns, seasonality, and disruptions. This makes it possible for supply chain intelligence platforms to perform hyperlocal demand forecasting and offer accurate replenishment planning. Such granular forecasting allows the companies to stock the right product at the right place and right time, therefore achieving customer satisfaction and cost savings.
- Preventing Product Cannibalization
If companies use inaccurate forecast methods, then companies may manufacture similar products and face product sales competition among their own varieties. This is a critical issue for the retailers’ ability to effectively manage their inventory, where one item affects the sales of others. Supply chain intelligence platforms that use artificial intelligence solve this problem by predicting trends of demand and balancing sales of different products.
AI-driven forecasting considers various factors to achieve this balance. These include promotions, product availability, and customer preferences. Companies can create accurate replenishment plans and optimize sales distribution to ensure that customers’ demands are met without unintended revenue loss.
- Improving Resilience Against Disruptions
Market noise, political events, and economic disturbances can directly impact supply chain effectiveness. It may lead to inaccuracy in inventory management, delays, and customer dissatisfaction. AI-powered forecasting models with adaptive learning continuously monitor and predict variations in lead times and inventory fill rates.
This enables supply chain intelligence platforms to predict disruptions that may affect the supply chain network. Businesses can use this prediction to adapt their replenishment strategies to changing conditions. They can maintain supply chain resilience against such disruptions and reduce the risk of shortages and delays.
- Optimizing Inventory at Multiple Levels
Modern supply chain networks usually operate with multiple echelons. This includes warehouses, distribution centers, and retail stores. The risk of poor inventory management at one of these stages is significant. Supply chain intelligence platforms powered by AI can help address this issue.
They track and analyze stock movements at different levels of the supply chain to provide accurate demand prediction for each echelon. Therefore, companies can also design distinct replenishment plans, balancing ideal inventory levels and avoiding stocking costs.
- Mitigating Risks with Predictive Alerts
AI-based forecasting enhances the capability of a company to stabilize its supply chain networks and revenue. It drives the supply chain intelligence platforms to generate predictive warnings of low stock, oversupply of goods, and expiry products.
This enables companies to mitigate such risks by taking preventive measures like adjusting replenishment plans and offering discounts. Therefore, it is possible for them to reduce stockouts, reduce waste, and save costs due to overstock.
Bottom Line
AI-based forecasting has brought about substantial improvements to supply chain intelligence. It allows companies to transition away from responding to shifts in demand to accurately anticipating them and protecting against such changes. This transition makes it easier for them to understand and manage the complexity of contemporary supply chain management.
AI’s contribution to the supply chain extends beyond forecasting. They can be used, for example, in optimizations of logistics, automation of operations, or improvement of safety. Supply chain operations usually involve sparse and noisy data. AI can be effectively used to process such data, draw insights, and remove hassles in manual data analysis. They also aid in optimizing routes to avoid geopolitical issues, maintain appropriate inventory levels, and improve collaboration between suppliers.
With the complexity of supply chain management further increasing in recent times, the application of AI to the development of supply chain intelligence platforms is crucial. With these solutions, companies can be assured of timely deliveries, customer satisfaction, and supply chain agility against disruptions.
