In supply chain management, demand planning is essential. Understanding what your customers want and when they want it lets you coordinate resources to meet demand without wasting time, money, and other resources. But you need high-quality data and analysis techniques to make reliable forecasts. AI demand planning gives you these resources.
What is AI-Powered Demand Planning?
AI demand planning integrates software powered by artificial intelligence into the traditional process. You still assess historical data, customer behavior, and market trends to make assumptions about future demand. But you are using technology such as AI, large language models, and machine learning to make the process more accurate.
AI-powered demand planning software can analyze large datasets from multiple sources to identify patterns and make connections that might not stand out to a human.
For example, you might prompt your demand planning software to run a social listening analysis. Its AI algorithm would search social media, online forums, reviews, and other sources to see what customers are saying about you online. This data adds a layer to your demand planning. Instead of waiting for sales to reflect shifting consumer sentiment, you can see what people are saying in real time.
Larger datasets give you more accurate insights to inform your operations strategy. Accurate demand planning impacts inventory and production management, pricing strategies, and supply chain management. Forecasts that closely mirror real-world data help you improve efficiency across your supply chain and increase customer satisfaction, which makes you more profitable.
The Benefits of AI for Demand Planning
Using AI-powered demand planning software makes it easier to balance your supply chain resources with customer demand. It also improves traditional planning methods to give you higher-quality data to drive decisions. Consider these benefits.
- Improved Forecast Accuracy: AI analyzes larger datasets from more sources including historical transactions, sales data, warehouse management system data, and external sources. It adapts learning models to shifting market conditions and adjusts predictions. This method of demand planning also reduces human error associated with manual data entry.
- Inventory Optimization: You understand the importance of forecast accuracy. Working with accurate data forecasts helps you maintain the right level of inventory to meet customer demand. You can avoid overstocking and costly stockouts which reduces your operating costs without impacting customer service levels.
- Enhanced Efficiency: AI saves your human analysts from having to enter and analyze data manually. AI will run all the statistical models and consolidate qualitative data to produce reports and data visualizations. Your human analysts can spend time putting insights into action and solving problems.
- Risk Mitigation: Since AI demand planning also involves analyzing real-time industry data, you can look for risk indicators earlier in your supply chain management process. AI can model and predict potential disruptions, including drops in demand and bottlenecks. You can put contingency plans in place to minimize financial impacts to your business.
- Supported Decision-Making: AI lets you adapt to today’s rapidly changing and often volatile market conditions. Instead of spending months making a single forecast, you can run multiple forecast scenarios and strategize for any situation.
How Do AI and Machine Learning Work to Improve Forecasting?
Before jumping into how you use AI in logistics, here is a refresher on AI and machine learning. AI refers to any machine that mimics human intelligence to perform tasks. Machine learning is a component of AI that teaches the machines to learn from data to perform better over time.
AI in supply chain management often uses machine learning algorithms to analyze data, identify patterns, and automate parts of the data forecasting process. Here is how the tools work together in demand planning.
1. Data Collection
The first step in demand planning with AI is data collection. Some AI-powered demand planning software integrates with your systems, including customer relationship management, point of sale, and warehouse management programs. If you’re using AI for warehouse management, you’ve seen this process at work.
In this case, you can write prompts for the demand planning software to gather the data you need. Otherwise, you may need to upload data from various software systems into your demand planning program.
You can also prompt your AI-powered software to run a social listening analysis, scan industry publications, and analyze macroeconomic market data.
Once it extracts the data, AI automatically cleans the data. It identifies missing and duplicate values and standardizes data formats for easier integration and analysis. This makes your final demand forecasts more accurate.
2. Pattern Recognition
After standardizing data, your AI-powered uses machine learning algorithms to look for patterns and relationships within the data. These programs also run statistical analyses on datasets to identify patterns and other relevant information. AI for data analytics is faster and less error prone.
Demand planning AI software is “trained” using sample models and datasets. It learns more from each dataset, meaning the 10th demand forecast you run will likely be more accurate than the first. AI can also identify relationships between seemingly unrelated data. For example, AI might notice that changes in online brand perception, combined with spikes in temperature, accurately predicts demand fluctuations for a certain product.
3. Predictive Analytics
AI uses these patterns to generate a list of predictive analytics that inform your decision-making. These tools are perfect for forecasting seasonal demand, analyzing what your competitors are doing, and identifying customer behaviors that affect your sales.
AI also assesses current factors impacting your supply chain, such as economic conditions and geopolitical conditions.
4. Human Augmentation
Although AI in manufacturing can cut down on the hours needed for demand planning, it can’t replace a human analyst. Your AI-powered demand planning software spits out volumes of analytical data. What you do with that data is up to your human analysts.
Humans are better at interpreting unstructured data such as social media mentions and online reviews. AI for asset management can assess this data and look for patterns, but you need humans to put it in context.
For example, AI may notice that sales dipped at the same time customer reviews got lower. A human analyst can look through your sales data and see that you launched an unsuccessful product and sales and online sentiment recovered shortly after you pulled it.
Driving Smarter Decisions With Surgere
AI makes your data planning activities more meaningful. It enhances accuracy and makes the process more efficient. It also gives you more data for planning. Surgere’s Interius is an AI-powered demand forecasting and supply chain management tool that offers real-time supply chain metrics, reports, and insights that drive supply chain agility.
Contact us today to learn more. ne Picking in Modern Warehouses
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