Accurate demand forecasting allows businesses to meet customer demand without risking stock-outs. If forecasts underestimate demand, businesses risk stock shortages, which can lead to missed sales and dissatisfied customers. On the other hand, overstocking ties up resources and can lead to waste, particularly for perishable or seasonal items.
Consider a company that supplies seasonal products, such as Christmas treats or edible Easter eggs. Incorrect forecasting for such items can result in significant financial losses, particularly when products have a short shelf life.
These challenges become even more nuanced for businesses with multiple locations or warehouses. Inaccurate forecasting for one location can lead to empty shelves in another, and planners who are already too late to move stock before demand declines or products expire.
By improving forecast accuracy, businesses can optimize inventory levels, reduce waste, and enhance customer satisfaction – no matter the number of locations or warehouses they have. Netstock offers advanced forecasting tools that help you streamline operations, boost accuracy, and make more informed supply chain decisions.
Key Takeaways
- Accurate demand forecasting is important to minimize the risk of stock-outs and overstocking, especially across multiple locations and warehouses.
- Different demand forecasting models are applicable to various businesses, but by combining quantitative and qualitative methods, planners can enhance forecast accuracy.
- AI-powered tools manage large amounts of data and rapidly analyze it, using machine learning to detect patterns that traditional forecasting methods (spreadsheets) can easily overlook.
- Multi-location businesses need specialized demand planning tools that deliver location-specific visibility.
Techniques for boosting demand forecasting accuracy
Accurate demand forecasting is essential for optimizing inventory levels. It’s important to note that each location will have its own demand patterns, lead times, and seasonality to consider. For example, multi-location businesses will need to implement a process involving more granular data cleansing, as their supply chains and inventory management ecosystems have numerous moving parts. Incorrect data at one level can cascade throughout the business. Variables like these mean not every technique will work for every business. Most businesses will also need to combine different techniques with technology solutions for the best fit.
Consider these qualitative and quantitative techniques to see what works best for your business:
Quantitative Techniques
- Time series analysis: Predict future demand based on historical data, and identify recurring patterns and trends. It is a valuable forecasting tool in cyclical demand environments.
- ARIMA (AutoRegressive Integrated Moving Average): A popular method for understanding the underlying patterns in time series data. ARIMA is commonly used in stable-demand environments with clear trends.
- Exponential smoothing: This applies decreasing weights to past data, with the most recent data points most heavily weighted. It’s ideal for short-term forecasts where demand fluctuates. Exponential smoothing flattens random variations. Some variants can predict seasonal trends.
- Machine learning models: Advanced machine learning models have revolutionized demand forecasting by processing large, complex datasets that were previously difficult to handle. They can recognize nonlinear relationships and detect patterns that traditional techniques might miss.
- Neural networks: These powerful tools can simulate human learning. They learn from data, identifying complex, hidden patterns and correlations across various inputs. Businesses can use them to make accurate predictions. They are useful when dealing with external variables, like marketing efforts and economic indicators, that traditional models don’t effectively capture.
Note: Businesses managing multiple warehouses, plants, or storefronts often need to calibrate quantitative models per location. Planners will then reconcile them at a higher level.
Qualitative Techniques
Qualitative methods provide valuable insights when historical data is limited, especially for new product launches. Qualitative techniques include:
- Expert opinion: Industry experts or internal teams can provide valuable guidance for new or evolving markets. Experts can offer insights into industry trends, consumer behavior, and external factors, like economic shifts or regulatory changes that could influence demand. Regional managers, sales representatives, and distribution centers are a great resource for gathering location-specific context that impacts model-driven forecasts.
- Market research: Surveys, focus groups, and competitor analysis offer direct feedback from consumers. Depending on the size of your network or footprint, market research may have to be done for each regional location.
Keep in mind that forecasting across multiple markets and locations usually requires a blend of qualitative and quantitative techniques. When dealing with limited historical data, business expansion, and seasonality, technology can be a valuable asset to boost demand forecasting accuracy.
Tools for enhancing demand forecasting accuracy
Two enhancements advancing demand forecasting accuracy include AI-powered software and integrated predictive analytics.
AI-Powered software
AI-powered demand forecasting tools, like Netstock, use powerful automation features to streamline forecasting across all locations. These tools can automatically collect, clean, and organize large datasets, saving time and reducing errors. These systems integrate historical sales data, supplier lead times, and market trends to create comprehensive and current forecasts.
AI tools analyze vast amounts of data, applying advanced algorithms to detect patterns and trends that traditional methods might overlook. AI-powered software continuously learns and adapts as new data is introduced. The result is more precise forecasts, even in volatile markets.
Netstock’s demand and supply planning solutions provide accurate inventory and forecasting insights. Both single- and multi-location businesses can monitor demand fluctuations and dynamically adjust their forecasts. You can forecast by sub-SKU attributes, channel, customer, or region, and work in any unit of measure. Plan on multiple dimensions to generate product and channel-specific forecasts in monthly, weekly, and even daily buckets, creating symmetry between forecasting and planning executives in your business. Netstock uses scenario planning to model demand scenarios, like supply chain disruptions or consumer behavior changes. Real-time scenarios will help you make informed decisions.
Integration of predictive analytics
Predictive analytics tools forecast outcomes using historical data, statistical algorithms, and machine learning. You can take your business beyond trend analysis by integrating predictive analytics with your demand forecasting. These tools can help you gain a deeper understanding of the factors driving demand.
For example, a grocery chain can use machine learning models to analyze promotional calendars, past sales numbers, and historic demand records simultaneously to predict that ice cream sales will spike by 30-40% in the next week. This forecast is determined by looking at typical summer holiday sales that correspond with the time period and a heatwave that usually affects their distribution locations in a certain region annually.
Predictive analytics can help your business move from reactive to proactive demand forecasting. Identify potential demand shifts before they happen to make smarter inventory management, production schedules, and supply chain decisions. Predictive models can also assess the likelihood of various outcomes and recommend optimal stock levels, reducing the risk of overstocking or stock-outs.
Key features to look for in demand forecasting tools
Prioritize the following key features to maximize forecast accuracy across your business:
- Accuracy: The cornerstone of effective forecasting, especially for multi-location operations. The best tools should use advanced algorithms for precise predictions. Machine learning algorithms will identify complex patterns, adjust forecasts to changing market conditions, and enhance forecast accuracy over time.
- Scalability and flexibility: As businesses grow, their forecasting tools must adapt. A scalable and flexible solution can handle increasing complexity and data volumes. As a company adds new product lines, expands into new markets, or increases its customer base, the forecasting tool must be able to accommodate this growth without compromise.
- Integration with ERP and CRM: Seamless ERP integration and CRM connectivity ensure that all business systems consistently use the same data.
See Netstock’s flexible demand planning solution in action:
General best practices for optimizing forecasting accuracy
- Collaborative demand planning: Different teams bring unique insights to create more reliable and accurate forecasts. Sales teams provide direct knowledge of customer behavior and upcoming deals, so forecasts are aligned with market realities. Marketing offers information about promotions. Operations can provide capacity and supply chain data.
- Regular data updates and model adjustments: Your forecasts must keep up with changing demand. Real-time sales and external data will enable a quick response to changing sales trends. Automation tools like Netstock ensure real-time data and streamlined processes for more accurate forecasts.
- Customer and supplier collaboration: Customer and supplier feedback can reduce forecast discrepancies. Customers can offer insights into changing demand, and suppliers can warn of supply disruptions and changes in lead times.
- Handling uncertainties and external factors: Use scenario analysis to predict demand under different market conditions, including economic shifts or weather events. Scenario planning can help you make effective contingency plans.
Multi-location demand forecasting
Regardless of the size of your business, the best practices for boosting demand forecasting accuracy remain the same. However, for multi-location businesses, there are a few additional features demand planners should look for when considering new forecasting solutions.
- Multi-location visibility: Solutions that enable planners to include or exclude forecasts and data at a location-specific level are essential for managing businesses that operate in multiple locations.
- Real-time data synchronization: Live data enables planners to quickly see how market changes impact demand and the supply chain across distributed operations, allowing for proactive adjustments.
- Scenario modeling: “What-if” modeling enables best-and worst-case scenario planning. By testing dynamic scenarios at one location, planners can identify where adjustments may be needed at other locations to maintain service levels and prevent stock-outs.
Customer case study
As The Little Potato Company (TLPC), a leading U.S. potato producer, expanded its operations across three production facilities and eight distribution centers in the U.S. and Canada, it faced growing challenges in demand planning. Their multi-location network demanded more advanced forecasting than traditional methods could provide. Their reliance on Excel-based processes became inefficient, especially when managing customer-specific forecasts across the U.S. and Canada. Before implementing an advanced inventory management solution, their processes were both time-consuming and prone to errors.
By integrating Netstock into its Microsoft Dynamics Business Central ERP, TLPC’s forecast accuracy improved significantly. Previously, TLPC had relied on static safety stock levels, but with Netstock’s advanced statistical modeling, they could dynamically adjust safety stock based on historical data. This change resulted in a notable increase in their fill rate, from 90.9% to 98%.
Netstock also helped TLPC address inventory anomalies by smoothing out abnormal demand patterns, preventing excess stock, and reducing waste. The integration streamlined TLPC’s processes, allowing for more efficient forecasting, real-time monitoring of customer trends, and better management of their production facilities and distribution centers.
With Netstock’s scalable solution, TLPC enhanced supply chain efficiency and supported its rapid growth. Looking ahead, TLPC plans to further optimize its operations by adopting Netstock’s capacity planning functionality.
“This will give us a full 360-degree supply chain planning solution and allow us to manage our production plants better. Efficiency is key, especially in the food industry.” – Philip Yu, Senior Sales and Operations Manager
Your journey to forecast accuracy starts with Netstock
In today’s fast-paced marketplaces, businesses need accurate demand forecasting to boost efficiency and minimize costs. Businesses that operate across multiple warehouses, storefronts, regions, or distribution centers face unique challenges, too. From reconciling regional demand patterns to reacting to diverse markets, these multi-location networks require accurate forecasting that doesn’t ignore the nuances of a distributed business.
Netstock’s advanced demand and supply planning solutions are purpose-built, providing improved forecast accuracy, streamlined supply chain processes, and real-time inventory monitoring to keep your operations ahead of the curve.
