Demand Forecasting in Manufacturing Using AI/ML
Mon Aug 21 2023
Introduction
Incorporating AI/ML in demand forecasting helps manufacturers predict product demand accurately and optimize inventory management processes and production planning. The manufacturing company, AI/ML analyst, sales, and marketing team all play significant roles in accomplishing the task, such as providing real-time market data insights, data preprocessing, etc.
Actors
01
Manufacturing Company
The organization seeking to improve demand forecasting capabilities.
02
AI/ML Demand Forecasting System
The intelligent system using AI/ML algorithms to predict future demand.
Preconditions
Historical Data
The manufacturing company must have access to historical sales data for the products.
Data Collection
Relevant data sources, including sales records, external factors, and marketing campaigns, should be available for integration into the AI/ML model.
Training Data
Sufficient and accurate historical data is required to train the AI/ML model.
Post Conditions
Accurate Demand Forecasts
The AI/ML demand forecasting system provides accurate predictions of future demand for various products.
Enhanced Production Planning
The manufacturing company can optimize production schedules based on demand forecasts.
Efficient Inventory Management
Improved demand forecasts enable better inventory management, reducing stockouts and excess inventory.
Increased Customer Satisfaction
Meeting customer demands more efficiently results in higher customer satisfaction.
Cost Optimization
Resource allocation and production planning optimization lead to cost reductions.
Main Flow
Real-time Data Integration
The demand forecasting module seamlessly integrates the company’s existing Manufacturing Management Software Development system and gets updated regularly with real-time data. It ensures accuracy in prediction, allowing production enterprises to leverage the latest trends and technologies and understand consumer behavior.
Model Selection and Training
There are different AI/ML modules like Machine Learning models or time series forecasting models that you can train using preprocessed data. These models learn about historical patterns and relationships available in the data to make predictions.
Production Planning and Inventory Management
Thanks to AI/ML technology, optimizing production planning using demand forecasting has become much simpler and quicker. Manufacturers can adjust production levels to meet specific market demands and nullify unwanted situations, such as overproduction or stockout. It also maximizes inventory levels to meet customer demand.
Forecast Generation and Analysis
You can quickly generate demand forecasts for upcoming weeks and months, leveraging the latest real-time data and metrics. Various vital factors, including market trends, economic indicators, seasonality, etc., are considered to make accurate forecasts and informed decisions on production planning and scheduling.
Supply Chain Optimization
Manufacturing companies can streamline and optimize their supply chain processes and forecast fluctuations in demand to coordinate with suppliers and partners in real time, ensuring the availability of raw materials at all stages of production.
Alternative Flow
- Insufficient Historical Data:
If the company still needs to have the required historical sales data, it must prioritize data collection efforts before proceeding with AI/ML implementation.
- Poor Model Performance
The company may have to refrain from implementing the model or using alternative algorithms if it finds the AI/ML model needs to be revised.
- Market Disruptions
In case of unexpected market disruptions or external events, the demand forecasts may deviate from showing accurate results. The company must deal with such unforeseen conditions through a scalable, flexible production schedule and viable management strategies.
- Changing External Factors
If external factors influence the change in demand, the AI/ML model must be updated to capture the new relationships accurately.
Conclusion
AI/ML-powered demand forecasting digitizes the manufacturing sector, enabling production firms to achieve more accurate forecasts, optimize manufacturing plans and schedules, effectively manage inventory levels, and upscale customer experiences.
Suggested TechStack
This is like a CI/CD model for machine learning, enabling data scientists to test their codes in isolation and run various algorithms needing runtime, such as Python, R, C++, etc. You can scale your production workload by selecting distributed or parallel frameworks. Containers allow gaining flexibility while isolating each algorithm differently, while Kubernetes orchestrate docker containers.
You need to store models, which are serialized versions of calculations. It is recommended to store them in an Object Store, saving their location with other metadata information instead of storing them in a traditional database, like MySQL, due to their blob nature.
APIs power Demand Workbench and descriptive analytics to record transactions like managing forecasts.
You can display customized reports showing the current health condition of the assets and monitoring conditions as an interface or control unit through web or mobile API.
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