Demand forecasting is the procedure that utilizes the anticipatory analysis of historical data. The prediction helps to anticipate and forecast the future of customers’ demands for specific products and services. Incorporating demand forecasting helps businesses to make well-informed decisions and predict the total revenue and sales for a period in the future. Predicting demands is a challenging task. Businesses have to be flexible enough to handle the sudden influxes, and also undertake long-term approaches.
When a business considers demand forecasting, there must be a clear objective. The core of forecasting is the prediction of what, when, and how many customers will buy. While setting the demand forecasting objectives, it is necessary to pick the time, specific products and services, and type of target audience. The objective must satisfy the product marketing, logistics, and financial planner in a non-biased manner. Furthermore, businesses must realize that the objectives are for the right planning of demand capacity, allowing better decision-making.
Collecting and recording data
When all the data from various sales sources are integrated, one gets a uniform view of the actual demands for products and services. Additionally, an insight into sales forecasts is also noticeable. Apart from the recording date and time of all orders, the SKU incorporated in every order, and sales origin of sales sources, there are other demand forecasting metrics to monitor.
Inventory Turnover Rate
It refers to the number of times the whole inventory is sold and replaced within a specific timeframe.
This metric indicates the frequency of SKUs picked over a certain timeframe.
Average Order Value (AOV)
It refers to the average amount (in dollars) a customer usually spends every time an order is placed.
It is the frequency at which the SKU is returned.
It refers to the number of times a business sells a specific SKU, exhausting the available SKU units.
By monitoring the above-mentioned inventory metrics, businesses can predict growth and different market trends.
Measuring and analyzing data
A business demands a repeated data analysis process, either via manual, or automation. This needs to compare the predictions made and the actual sales, thereby helping to forecast the next prediction. Measuring data helps to keep track of the demand for different products at distinct timelines.
When the business forecast is below a specific volume, it would fail to have enough inventory for shipping. Additionally, the lack of staff will also disrupt the process timely. On the other hand, when there is over-forecasting, extra capital is spent, taking longer time to yield revenue than anticipated.
Once there is a series of feedback available, businesses can set the next prediction accurately. This helps to update the budget and also allocate funds based on the development goals of the business. Correct demand forecasting helps to curtail inventory-carrying expenses, marketing plan costs, production and inventory demands, and others. Budgeting is the final, but the most important, step in business demand forecasting.
Businesses need to make predictions about everything, starting from optimizing inventory, to catering to the expectations of customers. The inclusion of demand forecasting in a business will never be 100% accurate. With the right steps, businesses can enhance operational efficiencies, increase production lead times, better customer experience, save money, and so on.