Demand planning is an essential function in any organisation that makes or plans to make products with or without a limited shelf life. The demand for any item changes depending on the season, the weather, new product launches, and many other factors. To be prepared for fluctuations in demand, manufacturers/wholesalers and retailers develop a detailed plan of how much inventory to keep at different times of the year and at different locations. This process is known as demand planning. We are currently seeing an explosive growth in AI adoption across all industries, which means demand planners will have plenty of new tools to leverage while working within their existing processes. These five articles explain how artificial intelligence is upending demand planning. They each explore a specific use case for AI in demand planning and include examples of companies using it effectively today.
How is AI changing the way we forecast demand?
The core functionality of demand planning is forecasting demand for future inventory. Traditionally, demand planners have used sales forecasts, inventory levels, price variations, and competitor data to predict demand. There are several problems with this approach. Firstly, demand forecasting is not an exact science, and these traditional data points aren’t always accurate predictors of future demand. Another problem is that these data points live within separate systems, making it difficult to access them at the same time. Artificial intelligence is designed to address these issues with more accurate forecasting and more streamlined data access. Artificial intelligence has the ability to analyze data across many different systems to find connections and make predictions that human beings simply can’t do. This includes taking data from weather reports, social media feeds, and other sources that might seem unrelated to demand forecasting.
AI for automated demand planning
Artificial intelligence is very good at the repetitive tasks that most humans find boring. Demand planning requires the precision of calculating inventory numbers and the creativity of devising long-term strategies for what to do with that inventory. While these two elements must work together, they are very different skills. AI can help with automated demand planning.A common use case for this type of demand planning AI is inventory optimization. Inventory optimization algorithms calculate the optimal number of items to keep in inventory based on demand, location, and other factors. AI algorithms are particularly good at optimizing inventory because they can learn from historical data and make decisions accordingly. This type of AI can help demand planners make adjustments to inventory quantities in real time based on incoming orders and other factors. This is where AI can really upend Demand Planning.
AI is upending forecasting accuracy
Accuracy is essential in demand forecasting. If a company over-forecasts, it may have to invest in more inventory than it needs and have to find additional space to store it. If a company under-forecasts, it could suffer from stock-outs and lost revenue.AI-powered tools can help increase forecasting accuracy by taking into account various factors that aren’t ideal for a human being to track manually. For example, AI can monitor weather conditions, social media feeds, and other factors that can affect demand.AI technologies can collect, analyze, and store information from a variety of sources to help humans forecast with more accuracy. AI can also be programmed to learn from past forecasting errors to improve forecasting accuracy over time.
AI for supply chain risk identification
Demand planners are responsible for more than just predicting what items customers will buy. They also need to predict where those items will come from and how long they’ll take to arrive. However, forecasting this data can be challenging, especially when trying to account for potential risks. Natural disasters, political upheaval, and other external factors can cause supply chain issues that could lead to stock-outs. AI can be a helpful tool to identify potential risk areas in the supply chain. AI tools can ingest data from a variety of sources and analyze it to determine potential issues in the supply chain.AI software can monitor data from a variety of different sources to help identify potential risk areas in the supply chain. AI can also be programmed to learn from past forecasting errors to improve forecasting accuracy over time.
AI in sales operations
Sales operations are the processes that help sales rep productivity. This includes everything from sales forecast accuracy to customer engagement and lead generation. AI is already transforming sales operations in many ways. AI solutions can help sales teams quickly understand data from various sources to identify new business opportunities. AI solutions can be programmed to analyze data from a variety of sources to understand customer behaviors and patterns. AI can also be programmed to learn from past actions and suggest new ways to engage with customers to generate more sales.
Artificial intelligence offers significant benefits to demand planning. AI can upend Demand Planning over the next few years. This includes more accurate forecasting and streamlined data access. AI has the ability to analyze data across many different systems to find connections and make predictions that human beings simply can’t do. AI can also be programmed to learn from past forecasting errors to improve accuracy over time.AI is currently transforming the way we forecast demand. It has the ability to analyze data from a variety of sources to identify connections and make predictions that human beings simply can’t do. This includes taking data from weather reports, social media feeds, and other sources that might seem unrelated to demand forecasting.AI has the potential to upend demand planning and make it easier for organizations to forecast inventory needs.