Demand Forecasting for Inventory Management
Any company that carries inventory is forecasting, whether they think they are or not. Many companies claim that they don’t forecast, but if you are stocking raw materials, work-in-process or finished goods in anticipation of fulfilling future demand, you are forecasting.
Often when companies are trying to decide how they can improve their supply chain strategy, the first thing they want to try is to develop an improved demand forecast. Due to seasonality, new products, lead time, or changing customer demand, simply ordering what was sold in the recent past will not work in most cases.
At Supply Velocity we understand demand planning and ultimately how it allows our customers to make informed decisions on how to fulfill future demand. We begin by working with businesses to understand customer demand and the underlying data.
One distribution center (DC) serving 12 branches needs a different demand forecast than trying to figure out the right inventory levels for a single warehouse. Historical data often has issues and understanding which weeks or months will impact the demand forecast is not always clear without a bigger picture perspective.
For example, when one branch has a stockout, in some industries and for some customers, that will lead to a lost sale. In other cases, end customers may be happy to drive to a nearby branch that has the product in stock. Understanding your historical data and what it means for how closely sales reflect demand is a key step in developing an accurate demand forecast using historical sales data.
With accurate input data, there are the parallel efforts of designing the analytics and ensuring the process can be done accurately at a regular cadence. Because of seasonality patterns, it is critical that a demand forecast project include how to automate or simplify the creation of supporting data sets.
With the data accessible and on-hand, our clients are able to make more accurate business decisions than when they consider demand forecasting as a one-time or short-term effort.
With years of experience, Supply Velocity balances sophisticated demand forecasting models that minimize forecast error with the technical sophistication of our clients. This ensures that our approaches are sustainable for the long-term and accurately predict future demand.
Our next step after we have refined the historical data is to run demand forecasting algorithms based on past sales. We run multiple, proven forecast algorithms within a math optimization model to determine the trend projection for each SKU and/or if there is a seasonality factor.
These algorithms are optimized to maximize forecast accuracy. We can then add external factors within a machine learning model to account for additional macro or micro issues that impact the demand forecast. Using these demand forecasting methods we are able to create a highly accurate time-series forecasting model.
We review the aggregate data with our clients to ensure that it aligns with the expectations of the business and will ensure that customer demand will be fulfilled at target service levels.
What about Artificial Intelligence (AI)?
A quick note on using Artificial Intelligence (AI) to support forecasting. As with much of AI, it is a tool to help experts get things done better and faster. If you rely on it without understanding the math and science, it can easily provide bad results that will drive up inventory and still have unacceptably high stockouts. We use AI within our Demand Forecasting & Inventory Optimization service to support the ongoing (usually monthly) update of your forecast and target inventory levels.
Demand Forecasting Methods in Supply Chain Management
At Supply Velocity, we create forecasting frameworks that align with each client’s strategy, data capabilities, and operational goals. Accurate forecasting is essential for supply chain planning, as it allows businesses to anticipate customer needs, balance inventory, and improve profitability. By using a mix of qualitative, quantitative, and hybrid approaches, we help companies predict demand and strengthen overall business performance.
Qualitative Methods
Qualitative forecasting is most effective when historical data is limited or uncertain. These methods rely on expert judgment and market insight to anticipate shifts in customer behavior and effective demand:
- Delphi Method – Gathers input from experts through several rounds of evaluation until a consensus forecast is reached.
- Market Research – Uses customer surveys, interviews, and focus groups to analyze trends and preferences that influence buying behavior.
- Scenario Writing – Develops narratives of potential market conditions, helping organizations plan for different business environments and maintain resilience within supply chain processes.
Quantitative Methods
Quantitative forecasting uses mathematical and statistical modeling to generate accurate, data-driven projections. These approaches convert past data into meaningful predictions that guide an actionable sales forecast:
- Time Series Analysis – Identifies past patterns and seasonality to forecast future demand fluctuations.
- Econometric Modeling – Analyzes relationships between economic drivers and demand levels.
- Regression Analysis – Estimates how variables such as pricing or promotions influence sales outcomes.
- Machine Learning – Applies algorithms that learn from large datasets to continuously improve forecast accuracy.
- Exponential Smoothing – Adjusts forecasts in real time by emphasizing the most recent data points.
At Supply Velocity, we create forecasting frameworks that align with each client’s strategy, data capabilities, and operational goals. Accurate forecasting is essential for supply chain planning, as it allows businesses to anticipate customer needs, balance inventory, and improve profitability. By using a mix of qualitative, quantitative, and hybrid approaches, we help companies predict demand and strengthen overall business performance.
Hybrid Approach
Hybrid models blend statistical methods with business insight, merging qualitative and quantitative forecasting. This ensures alignment across supply planning and operations. Ultimately, demand forecasting helps organizations link strategy and execution, achieving stability and accuracy across supply chain processes.
The approach we take at Supply Velocity allows us to get the maximum benefit from proven supply chain analytics, data science and machine learning techniques. We use regression analysis and other demand forecasting techniques to ensure we support planners and long-term demand needs.
However, unlike data science focused consulting companies, we understand that the value from a demand forecast for a supply chain company comes primarily from how you use it. Most of our clients choose a combined approach, using demand forecasting with inventory optimization.
By taking a more holistic approach our clients go directly from a forecast to actionable recommendations such as reorder points and reorder quantities (or min/max settings).
These recommendations are built on the SKU-level demand forecast, but also rely on techniques like ABC Inventory Classification, Safety Stock, and Service Levels to feed into a profit maximization inventory optimization model. These additional factors are developed using our clients’ expert opinions to ensure they reflect the metrics our clients care about.
For more information on our Inventory Optimization approach, please visit our service page here.
We typically support our clients by starting with an Assessment. This allows us to create a customized plan based on specific needs such as to manage cash flow, capacity planning or setting inventory levels.
Our models support decision making of which products to stock and how to manage market trends while supporting demand planning. Contact us to learn how we can customize our demand forecast and inventory optimization models to your business.