Demand forecasting is an essential part of every business that maintains some kind of inventory. It can assist in restocking the inventory at optimal levels to meet demand timely. Businesses can avoid loss of sales due to stockouts and prevent extra expenses of storage, refrigeration, and heating because of overstocking.
It is very common for the demand for products to suddenly change at any given time. It can be due to seasonality, a dive in the finance market, some promotional activity that got viral, and so on. Thus, to predict demand accurately, it is necessary to include all of this information in the demand forecasting model. Demand forecasting can hugely impact your retail business, and they would know exactly what to produce, in what quantity, and when to reorder for replenishment. With the integration of Artificial Intelligence, it becomes easy to get accurate results and improve product availability.
In the past industry experts used to rely heavily on statistical forecasting for demand planning. However statistical forecasting method might not always yield correct results because it doesn’t consider many external factors that can influence your supply chain. These factors include market indices, adverse weather conditions, public sentiment, commodity prices, and numerous other factors. Demand forecasting integrated with AI and ML algorithms can gather data on these external factors, analyze them and provide accurate forecasting reports.
How to Develop an Efficient Demand Forecasting Process?
For a forecasting process to be efficient, the primary requirement is to have accurate data. A faulty dataset might contribute to inaccurate forecasting that would affect the sales and profitability of the company. Thus, enough emphasis should be placed on having the right data strategy and aligning it with the company’s goals and objectives.
Secondly, the scale is essential, and these days, businesses generate large amounts of data in a short period. Most of this information is unstructured, and some of it is unused. It is always better to do the forecasts at the granular level to match the business goals. It is also essential to adopt new and modern forecasting methods.
Benefits of Demand Forecasting
Demand forecasting assists companies in increasing their sales, improving their supply chain operations and marketing operations, avoiding storing excess amounts of inventory to bear additional storage costs, and also helps to prevent out-of-stock situations. Here are some more benefits listed below:
- Enhanced Accuracy with Time
Machine learning algorithms master the available data and make improved demand forecasts within no time. With historical data, you can get insights into future demand with significant precision. Advanced predictive analytics can assist retailers in making estimations about the future needs of consumers depending on historical data and analytics techniques such as machine learning and statistical modeling. Large datasets of past and current data can predict trends and patterns for particular items.
- Improved Customer Satisfaction
Out-of-stock scenarios degrade customer satisfaction as the order fulfillment cannot be done at the right time, and customers need to wait for things they want immediately. This decreases customer loyalty, and they shift to your competitors. However, with proper demand forecasting, you can quickly fulfill customer orders, and they can receive the delivery on time. Thus, you can improve brand perception and enhance customer loyalty with AI-powered demand forecasting software.
- Better Staff Planning
Demand forecasting for the entire year can help the HR department to make decisions on staff requirements for particular spans i.e during peak seasons. An organization would know the recruitment needs, whether for full-time or part-time staffing needs, optimize expenses, and increase HR effectiveness.
- Enhanced Discount Management
Locked-up revenue is not uncommon for retail businesses. It results in increased inventory expenses for storage, heating, and refrigeration. It can also result in stock wastage as the stock might become obsolete, the expiry date might get over, and the goods may lose value. In such situations, the overstocked products have to be sold with reduced margins i.e on discounts; in order for the business to at least regain the locked-up capital.. With proper demand forecasting, these situations can be lessened and the inventory can be better optimized so that, businesses need not sell out stock at discounted prices.
- Overall Productivity
Accurate demand forecasting lets your teams concentrate on other critical functions such as business growth and strategies instead of managing stocks and employees. It also assists in managing unanticipated surges in demand.
Demand Forecasting Challenges
Organizations may experience failure due to various shortcomings and would not be able to minimize the cost of inventory significantly. Companies can stop these unfortunate outcomes by considering various ways to avoid these pitfalls.
- If the marketing data is not taken into account for demand forecasting, the percentage of inaccuracy in demand forecasting might rise as the marketing activities and demand are directly proportional.
- Newly introduced products in the market do not have enough historical data and thus it is difficult to estimate the demand. In such cases, organizations can rely on the historical sales data of similar products having the same lifecycle.
- When the company’s supply chain network is challenged to take different actions swiftly businesses struggle to get the proper value of demand forecasting. The main aim of demand forecasting is to make modifications to minimize expenses related to the supply chain by optimizing processes.
- Demand forecasts depend highly on the input data, and companies should ensure that significant changes are displayed in real-time in the data sources.
- Demand forecasting is based on predictive analytics, and thus, the shortcomings of analytics also apply to demand to forecast.
About Fountain9
Fountain9 is a company that offers assistance to retail businesses to forecast demand accurately by considering all the external as well as internal factors that influence demand. The AI-powered demand forecasting software utilizes complicated machine learning algorithms to predict demand. It helps avoid overstocking and understocking and keeps the inventory stocked at optimal levels to meet consumer demand at the right time.
