Predicting the future is a valuable skill. Demand forecasting provides effective and accurate predictions for companies to meet customer expectations. It is an essential component of supply chain management systems that eliminates upcoming risks before they impact overall business.
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While this type of forecasting is valuable, doing so in real time is even better. Luckily, we have some good news. Real-time demand forecasting is indeed a thing, and it has many benefits to bring to supply chains around the world.
What This Article Covers:
- What Is Demand Forecasting?
- Different Types
- Demand Forecasting vs. Demand Planning
- Benefits
- Traditional Methods
- Real-time Methods
- Needs Analysis
- The Future
What Is Demand Forecasting?
Demand forecasting is a process or technique that predicts your customers’ future desire for your products. Using historical data, it predicts when the peaks and valleys of demand will occur throughout the year. It allows businesses to make intelligent decisions based on insights driven by real-time demand forecasting.
This step is crucial in supply chain planning, as it guides your decision-making processes from production planning to inventory control.
So what is real-time demand forecasting? As you can probably imagine, it happens in real time — no delays, no waiting time and no outdated data.
Types
Before we delve deeper, it may be helpful to get a handle on some other varieties of forecasting:
- Passive:Usually limited to small and local businesses. Uses straightforward projections of historical data.
- Active: Normally used within businesses that are rapidly growing. Actively measure competition, economic environments and the expansion of product portfolios.
- Short-term: Carried out between three and 12 months. It includes seasonal demand patterns and possible decisions that could affect customer demand.
- Medium to Long-term: Carried out between 12 and 24 months. This type of forecasting informs strategy, financial, sales and marketing planning and more.
- External Macro: Deals with broad-strokes market changes. Aids in evaluating strategic planning and large-scale shifts in consumer behaviors.
- Internal Business Forecasting: Focuses on internal operations and how they could affect keeping up with demand. Analyzes sales and financial divisions and includes annual sales forecasts.
Demand Forecasting vs. Demand Planning
Although used interchangeably, demand forecasting and demand planning are two different (albeit related) processes. Demand forecasting is merely one of several components of demand planning.
According to demandplanning.com, “demand planning is defined as using forecasts and experience to estimate demand for various items at various points in the supply chain.”
Demand planning uses forecasts to adjust the supply chain to accommodate high or low demand. Additionally, it assesses forecast accuracy “through ongoing analysis and tracking of the forecast[s].” While demand forecasting gives you the important numbers to work with, demand planning uses those numbers to take action.
Primary Benefits
First and foremost, demand forecasting is critical to formulating many vital strategic planning processes in business. With demand forecasting, budgeting, financial and marketing plans, procurement strategies and capacity planning would be successful. Let’s look at some major benefits.
Improve Inventory Management
Avoid overstocking to ensure the company holds the right amount of stock. Prevent companies from investing a large sum of money in stocks while lowering overall operational costs. Companies can maintain up-to-date forecasts using popular inventory management software like Blue Yonder, NetSuite, Infor SCM and more.
Follow demand trends and adjust inventory forecasts accordingly. Check for weather patterns and upcoming holidays to understand how these factors affect customer purchasing decisions.
Minimize Costs
Prepare budget planning to avoid overspending. It controls production costs by allowing companies to place orders for raw materials ahead of time. It provides efficient financial decisions involving operating costs, cash flow, inventory accounting, profit margins, staffing and more.
Real-time demand forecasting reduces risks by allowing companies to plan their inventory, ensuring they have the right amount of stock.
Increase Efficiency
Keep track of seasonal and sales trends to maintain supply chain schedules such as warehousing, shipping and production. It helps predict future sales volume from time to time by understanding the timing of these market trends.
For instance, if your inventory forecasts predict a demand surge over a period of time, you can start aligning your business strategy by making arrangements with suppliers to ensure you have enough stock.
Reduce Out-of-Stock Situations
Determine stock levels and re-order points by checking current and on-hand order quantities and sales history. It allows companies to plan their stock replenishment cycle and avoid out-of-stock situations.
If you can’t understand how demand trends fluctuate, your company’s agility can suffer greatly. In the event of a rapid unforeseen change in demand, you want to ensure your resources are positioned in the proper areas to keep up.
Dashboard demonstrating real-time demand forecasting.
Traditional Methods
Knowing how to approach something like this is no small task. Luckily, there are some tried and true strategies that can make the process easier:
- Market Research: This qualitative method leverages customer surveys to view demand accurately. Ensure that you keep variables like demographics and location in mind when distributing surveys to customers. You want the information gathered to be relevant enough to formulate a strategy.
- Trend Projections: A quantitative method that requires a sales history of about two years to use effectively. Sales data from the past is analyzed to create a time series that generates a demand projection based on historical sales of a product.
- Sales Force Projection: Another qualitative method that relies on the opinions of your sales teams. Each salesperson should analyze their region and pass on the respective demands of their customers. These data collections are brought together and used to form a realistic projection of demand.
- Barometric: Unlike trend projections, this quantitative method utilizes data from the immediate present. This data creates demand forecasts by analyzing certain economic indicators.
Real-time Methods
Now that we’ve covered the traditional forecasting methods, let’s look at how real-time forecasting works.
Expert Predictions
The first and simplest method we will cover is expert prediction. Although it doesn’t use machine-driven data analysis, this method still has plenty of merit today. For one thing, when experts make their predictions, they’re not just random guesses. Their information comes from years of education, experience and current events.
Although expert predictions aren’t as popular as the other methods, they have one distinct advantage: they take outside factors into account. Since experts are actual people rather than programmed machines, they can combine several factors to make predictions.
They can not only look at your previous demand but also consider real-world events that may affect product demand. For example, they can use this information to make predictions if they see a cultural shift away from your product type.
It’s important to note that experts and forecasting are not mutually exclusive ideas. Even though formulating an expert opinion takes longer than a minute or two, this method still falls within the realm of real-time forecasting.
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Time-series Forecasting
One of the most accurate techniques is the time-series method. This strategy uses historical data gathered either at particular times or during set periods. These forecasts look at the various patterns that occur over these time series and then use that information to predict future trends.
Businesses can use this method when demand shows consistent patterns that will continue into the future. This information helps confirm whether current demand will continue or plateau. It further allows you to make well-informed decisions.
Exponential Smoothing
Similarly to the time-series method, exponential smoothing relies on historical data to make forecasts. However, it weighs each data point differently. In time-series methods, each data point gives equal weight when developing the forecast.
But with exponential smoothing, the most recent data is given extra weight. “If there’s a trend in the data, [the exponential smoothing method will] use the recent observations to make up the bulk of the forecast, and the forecast is more likely to reflect the trend.”
This method is significant for forecasting in real time. These features aim to get a live view of your demand to receive the most up-to-date forecasts. Exponential smoothing showcases the importance of the most recent trend, whether that’s a sharp increase or a slight decrease. With this information in hand, you can get ahead of incoming demand and ramp up or decrease production at a moment’s notice.
Needs Analysis
So you want to start using these tools in your business? We believe they’re some of the most important supply chain tools available today, so we don’t blame you. To start implementing it in your business, gather a group of knowledgeable supply chain and demand experts. They can help you by making predictions and informing your software search.
After gathering your experts, start a search for supply chain management software that includes forecasting capabilities. SCM software has many features that go above and beyond to control your supply chain processes.
Just make sure that all of your various needs, from forecasting to inventory management to reporting, are met by whichever vendor you choose. Selecting the right software that will be viable for a long time ensures that your forecasts will be accurate for years to come.
The Future
If we were to take out a crystal ball and peer into the near future, edge forecasting would appear before us in a mysterious cloud of mist. So, what is edge forecasting, and what does it mean for the future?
For starters, edge forecasting involves the internet of things (IoT) and the myriad of internet-connected devices that make it up.
Most forecasting tools send data back to a data warehouse server for cleaning and dissection. Important data related to demand would then transmit back for fluctuation planning.
Edge analytics and forecasting involve collecting and analyzing data at the touchpoint using sensors and IoT-connected devices. Instead of waiting for data warehouses to scrub and identify useful variables, edge analytics processes data the second it is generated.
It lets users immediately take action on data-based insights gathered by IoT-enabled devices. The immediate turnaround of this type of forecasting is immensely attractive for businesses around the world.
Edge analytics and demand forecasting drastically limit the amount of data while transferring it to the cloud for analysis. If you are looking to cut costs, this benefit should hit home. This method cuts back on bandwidth usage while handling the same amounts of data.
Until recently, the technology needed to make this type of forecasting was hard to come by, but with the rise of predictive analytics tools, this strategy is no longer a dream. Some attribute the sudden rise of this topic to unnecessary hype, but supply chains around the world are still tuning in to see if this method takes off
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Final Thoughts
Real-time forecasting is a valuable tool in the world of supply chain management. Demand is constantly fluctuating, and having the tools available to keep your company ahead of it is important in maintaining a successful supply chain.
How has leveraging forecasting taken your supply chain management to the next level? Let us know with a comment below.