Inventory management is often a delicate balancing act. Run out of inventory and risk damaging your customer relationships and possibly your brand. Order too much and you end up paying carrying costs and potential disposal fees for unsold inventory. Supply chain forecasting makes inventory management less of a guessing game. Instead, you base your inventory, production, and procurement strategies on demand.
What Is Supply Chain Forecasting?
Forecasting in supply chain management involves using historical data, market data, and other information to predict future supply, demand, and pricing for products or raw materials. Supply chain forecasting arms you with insights to make business decisions that allow you to meet your goals and satisfy customers at the same time.
Accurate supply chain forecasts consider multiple factors that could impact supply, demand, and pricing. To produce them, you analyze past customer data, industry trends, economic factors, and customer behavior for patterns that affect wants and needs.
Forecasting seasonal demand with AI, combined with other supply chain insights, enables informed decision-making related to inventory management, production schedules, and resource allocation. It’s an important process that lets you plan more effectively to reduce costs and improve operational efficiency while making sure you have enough products on hand to meet customer demand and avoid an inventory stockout.
Why It Matters
This critical process can make your business more competitive. Supply chain forecasting impacts multiple factors that impact profitability and customer service, such as:
- Optimizing Inventory: Accurate demand forecasting helps you manage inventory levels so you have enough product to fill customer orders without paying for storing or disposing of inventory overstock.
- Reducing Costs: Successful inventory management saves you from spending money unnecessarily. Instead of paying extra for rushed shipments or production, or paying to store unused inventory, you base your resources on supply and demand.
- Improving Customer Service: Supply chain visibility and accurate forecasting improve your chances of successfully delivering products to customers on time. This improves their satisfaction and loyalty.
- Mitigating Risk: Doing a risk assessment by analyzing market and competitive data as part of a supply chain forecast helps you identify potential disruptions faster and prepare for them.
- Enhancing Efficiency: Sharing your supply chain forecasts across departments such as sales, logistics, and production eliminates data silos and allows you to coordinate more effectively for streamlined operations.
Supply Chain Forecasting Models
You can build a supply chain model using different types of forecasting in supply chain management. Each method has its own advantages, and you’ll likely use a combination of methods for the most accurate forecast.
Qualitative Forecasting Methods
Qualitative research is descriptive in nature. Instead of analyzing statistics and numerical data, you analyze words, descriptions, and experiences and give them meaning. You often use qualitative forecasting methods for areas in which you don’t have a lot of historical data, such as a new product launch. These methods include:
- The Delphi Method: A process in which you submit questionnaires to a panel of subject matter experts who answer them anonymously in multiple rounds. At the end of each round, a facilitator summarizes the answers, and the panel revises their predictions based on the feedback.
- Market Research: A process of collecting industry data, competitor data, and other information to learn about customer preferences and behaviors. Often involves conducting focus groups, potential customer surveys, or one-on-one interviews.
- Sales Force Composite: A process of making forecasts based on sales insights. Involves interviewing individual members of your sales team to predict demand.
Quantitative Forecasting Methods
Quantitative research deals with numbers, statistics, and other measurable data. These forecasting methods rely on historical data to make predictions. Common quantitative forecasting methods include:
- Time Series Analysis: Assessing historical data collected regularly to identify patterns and trends. For example, you might use quarterly sales data from the past five years to predict seasonal demand fluctuations.
- Causal Models: Assessing various factors that impact supply and demand. For example, new regulations could raise the price of raw materials.
- Regression Analysis: Assessing the relationship between dependent variables and independent variables. For example, you might examine how historical sales data fluctuate based on weather patterns.
- Econometric Models: Similar to a regression analysis, but these models specifically assess how economic and other external factors affect the supply chain.
Challenges of Forecasting in Supply Chain Management
Although supply chain forecasting methods give you valuable data for decision-making, no model is perfect. Supply chain forecasting comes with the following challenges:
- Data Accuracy and Availability: To build a reliable forecast, you need accurate data. Incomplete, inaccurate, and inconsistent data will make your forecasts less useful. Use accuracy metrics to see if you may be working with bad data.
- Market Volatility and Unpredictable Events: External factors can make your previous forecasts inaccurate. Geopolitical shifts, natural disasters, and industry disruptions, such as a new technology, can impact supply and demand in unpredictable ways.
- Data Silos: You can’t create an accurate supply chain forecast unless every department in your company is sharing critical data. You’ve probably seen this in action if your sales team is working with outdated inventory information from warehouse management.
- Rising Costs: When your raw materials costs increase, you often pass them on to the customer, which can impact demand.
- Lack of Real-Time Data: Working with outdated historical data makes forecasts less accurate.
- Supply Chain Disruptions: Forecasting helps you anticipate potential disruptions, but you can’t predict them all.
Technologies Shaping the Future of Forecasting
Improvements in technology, including the future of AI, are making it easier to make accurate supply chain forecasts. Key drivers include:
- AI and Machine Learning: The role of AI in data primarily involves analyzing large datasets in seconds and identifying patterns based on more historical data. AI combines data from multiple sources and finds correlations you might not notice.
- Internet of Things (IoT): IoT technology collects data in real time using sensors on anything from shipping containers to delivery trucks. This information provides more data for better forecasting. IoT sensors can also work with AI to automate predictions, such as predictive machine maintenance.
Master Supply Chain Forecasting With AI
With so many factors impacting supply chain demand, accurate forecasting is a necessity. Surgere’s AI-powered platform offers real-time supply chain visibility and collects valuable data that helps you create accurate forecasts.
Request a demo today to see how we can help you with your supply chain forecasting.
