There’s probably no company that’s immune from the challenges of making supply meet demand.
However, thanks to the growth of demand forecasting techniques, we’re often able to mathematically predict certain customer behaviors and market factors to a high level of accuracy. With the help of demand forecasting, companies have been able to set correct inventory levels, anticipate price fluctuations, and expand their operations.
The introduction of AI-powered demand forecasting systems has further strengthened this capability. Business managers can now consolidate their materials data in one location and analyze them in real-time. Using predictive demand signals and short-term data that’s just a few hours or days old, managers can forecast demand for a few weeks or months ahead at a time.
In this article, we show you how to improve forecasting accuracy with the help of predictive demand signals.
What is demand forecasting, and why is it important?
Demand forecasting is the process of using historical sales and supply chain data to make predictions about future customer demand.
There are many reasons for demand forecasting. When done well, demand forecasting can help business managers optimize inventory more effectively, create more accurate budgets, and develop sound business growth strategies.
Factors that affect demand forecasting
Experienced managers will often tell you that demand forecasting is more of an art than a science.
Demand constantly fluctuates due to changes in microeconomic factors such as customer preferences, competitor activity, and commodity prices, most of which are beyond a firm’s control.
Firms also have to deal with macroeconomic factors such as international trade disputes and fiscal policies that can dampen or stimulate demand. As a result, demand forecasting can only make limited estimates based on the available data.
Benefits of accurate demand forecasting
But despite these limitations, many firms have managed to excel at demand forecasting.
They understand that although no demand forecasting solution is truly accurate, they can still get quite close to predicting customer demand for the next quarter or even the next year by using data analytics software that studies both historical and real-time data.
These data-driven demand forecast reports can help you better schedule your production, warehousing, and shipping. They can also help you create adequate budgets that can respond to sudden changes in demand. According to Gartner, companies with strong demand forecasting capabilities have 17% better order fulfillment and 60% higher profit margins when compared to their peers.
How to measure how accurate your demand forecast is
It’s not enough for firms to just conduct demand forecasts.
You will also usually need to measure the accuracy of your demand forecasts and make adjustments when necessary. There are three main metrics that you can use: forecast bias, mean average deviation (MAD), and mean absolute percentage error (MAPE).
Forecast bias is often used to identify whether your forecasts tend to over–or under–forecast. MAD, on the other hand, shows you how many units your forecasts deviate from actual demand, making it ideal for comparing the results of two forecasting models for one variable, such as product category. MAPE works similarly to MAD and expresses forecast error as a percentage–rather than units–relative to actual demand.
How can predictive demand signals help improve forecast accuracy?
In recent years, firms have moved away from traditional time series techniques to conduct what is known as demand sensing.
Demand sensing uses detailed short-term data from the recent past to predict the very near future. Depending on how dynamic your industry is, data may be only a few hours or days old, but always close enough to real-time that it allows you to respond quickly.
The real-time data that planners use may include predictive demand signals such as orders for certain product categories over the past 24 hours or 7 days, visits to unique product web pages, and real-time inventory in regional warehouses.
The broad nature of these sensitive predictive demand signals and their timeliness tend to produce more accurate short-term forecasts, primarily because the data reflects ongoing changes in the supply chain. Some estimates suggest that demand sensing can increase inventory accuracy by 20% and reduce forecast error by up to 50%.
Using AI to forecast demand
Supply chain companies today have the benefit of artificial intelligence (AI) and machine learning (ML) technologies to analyze multiple predictive demand signals simultaneously.
AI-enabled demand forecasting approaches help companies learn from past data and current developments while continuously generating new insights that help them refine their demand forecasting processes.
These technologies utilize algorithms that recognize patterns and trends in your supply chain. They can identify peak times of the year where inventory sells out fast and adjust manufacturing output accordingly to meet the demand. They can also help integrate managerial and quantitative estimates to provide demand forecasts that analyze the reasons behind certain customer behaviors and their impact on future buying power.
More accurate forecasts enabled by AI and ML have helped several global companies like Danone reduce lost sales opportunities, increase net Return-on-Investment (ROI), and free up their demand planners to focus on more strategic goal-setting activities.
Leverage AI to gain a competitive advantage
Supply chain firms that leverage AI often achieve better demand forecasting capabilities than their competitors.
Their intelligent, connected supply chains can harmonize supply chain data across different ERP systems and facilities and provide trusted data-based insights into their materials management processes.
They can automatically adjust resources to meet changing demand and optimize delivery routes to accelerate product deliveries. Managers may even be able to feed external data such as weather forecasts and traffic data into these systems to help them make more accurate delivery and demand forecasts.
Each of these improvements adds up, helping firms reduce inventory costs and automate warehouse operations. And with real-time product intelligence, firms can predict product categories, locations, and prices that will see a rise in consumer demand shortly. This capability can help them take advantage of profit opportunities before their competitors do.
How to implement predictive demand signals to increase the accuracy of your forecasting
If your company has access to vast amounts of customer and supplier data, you can use demand sensing technologies to identify and analyze the most important historical and real-time predictive demand signals.
With better data visibility, your firm can make impactful decisions in real-time and not be surprised by sudden changes in demand. Here are 5 ways you can increase the accuracy of your forecasting using predictive demand signals.
First, identify what data you have available and its impact on demand
Improving your demand forecasting accuracy requires understanding which of your data has the most predictive value over which time frame.
The volume of recent orders, best-selling product categories, and seasonal factors are all great datasets to start with. You could begin experimenting with your forecasting model by focusing on a particular product family or location.
It’s often best to start with data that’s from the last 3 months to 2 years, depending on how deep you want your forecasting to be. Datasets that are less than 12 months old can be used to teach AI algorithms how to identify and analyze short-term trends and insights. Datasets that are older than one year will help fine-tune the demand sensing algorithms.
Use supplier and distributor data to determine short-term customer demand
Because short-term customer demand forecasting usually looks at the next 0 to 3 months, using your most recent distributor data can help you get a sense of how well your products are being sold and in which locations.
Distributors play a crucial role in getting your products to retailers and customers at the right time, so their data can provide valuable insights into how customers respond to different products in the near term.
Equally important is supplier data, which helps you understand how much of the short-term customer demand you’ll be able to meet. Supplier data can show seasonal, labor, and technological factors that affect your short-term operational abilities.
Capture a full range of internal, external, structured, and unstructured data
To help AI-driven demand sensing technologies make the most accurate forecasts, you will want to collect a wide variety of predictive demand signals and input them into the analytics engine.
It’s recommended that you capture internal data such as sales and consumer service data; external data such as social media and the devices people use to purchase your products and services; structured data, which includes weather patterns and inflation rates; and unstructured data from marketing campaigns and emails.
The diversity of these data sources will help teach algorithms to identify trends specific to certain locations and customer segments. Your demand forecasts can then be more insightful and descriptive of your future sales opportunities.
Ensure data visibility across your organization and supply chains
Data visibility offers several advantages for your firm.
With the growth of increasingly complex supply chains, seeing inventory levels and delivery times in real-time and across all ERP systems can help make your company more agile. You can harmonize data from disparate inventory systems and respond to shifts in customer demand quickly.
If you’re thinking of building up this capacity, advanced AI platforms are one of the best tools for consolidating data. They can analyze high volumes of data and deliver very accurate and current supply chain demand forecasts. You’ll likely be able to budget better and optimize your resources to gain a competitive advantage over other firms.
Embrace the power of automation with machine learning and AI-enabled solutions
In today’s world of supply chains, fast, highly accurate, and low-cost deliveries are often key to achieving customer satisfaction.
Unfortunately, many companies still rely on disconnected legacy systems and non-integrated technologies that prevent them from operating a highly efficient supply chain.
Employing powerful cloud-based machine learning and AI-enabled solutions can help you maintain visibility into your supply chain and keep your manufacturing processes up to speed with changing customer demands. They can also help you significantly reduce working capital costs by eliminating guesswork in procurement planning and inventory allocation.
Improve your forecasting with an AI-enabled cloud-based platform
Verusen’s demand forecasting solution can provide you with the ability to search for the right materials and true quantities across your entire enterprise. Using innovative data analytics technology, our AI and machine learning platform studies your inventory and offers suggestions to help you optimize inventory allocation and your procurement needs continually.
With improved visibility into your inventory, your firm can identify the right inventory levels to maintain to respond to changes in demand. Our solution can also help you identify potential duplicates during the procurement process and prevent you from over-purchasing materials for the future.
Try Verusen today to gain the material truth that can help you make better demand forecasts.