In order for a business to have longevity, management should utilize forecasting analysis to optimize inventory and prepare for customer demand.
By integrating information from point-of-sale (POS), demand forecasting, and supply chain management systems, businesses can enhance inventory control. By using real-time information, management can make data-based decisions to optimally restock their shelves and accurately track their products.
Companies that are able to monitor stock levels, especially by automating business processes, can also improve their inventory management to reduce costs and boost profits.
What is Inventory Forecasting?
Inventory forecasting is the procedure of collaborating information from historical sales data and transactions with current demand planning and production reports to project an ideal future product inventory level. This allows organizations to prepare for a dip or boost in customer demand by manipulating reorders and production to minimize inventory costs.
Forecasting inventory varies depending on the business. An inventory planner can set the forecast demand parameters based on item level, category level, item location, category location, or any customized requirement. While the forecasting process is typically used to analyze finished goods, raw materials and unfinished products can also be monitored.
Accurate demand planning can optimize inventory forecasts, allowing businesses to regulate purchase orders and replenishment strategies to avoid stockouts and drive revenue. Therefore, management should understand the key components for successful inventory forecasting, such as-
Historical Sales Data
Historical Purchasing Data
Purchasing Lead Time
Manufacturing Lead Time
Distribution Lead Time
With an advanced system integrator, all of this information can be pulled from existing POS, inventory control, and stock ordering systems for automatic data input into a demand forecasting software. This streamlines the data exchange and planning process while reducing the risk of human error from manual inputs.
Management should also be aware of the internal and external factors that can cause discrepancies between projected and actual inventory levels, including-
Change in Regulations
Inventory Demand Forecasting Best Practices
An accurate inventory forecast relies on quantitative and qualitative data analysis best practices, such as-
1. Forecasting by Inventory Demand Types Reviewing historical sales data shows the difference in demand between various items, allowing management to monitor trends and project future sales. For example, some products may consistently sell throughout the year, while another experiences spikes of high demand due to seasonal popularity.
Identifying these demand types is essential for determining the appropriate forecasting algorithm for each item. These algorithms determine the base demand, ensuring production can start fulfilling minimum inventory levels. However, it is up to the management to identify the different safety stock levels, which are extra inventory stored in case of an unexpected drive in sales.
2. Forecasting by Inventory Demand Trends As fads come and go and new technology is introduced, inventory demand rates continue to fluctuate. However, products also follow basic demand trends, including the new, growth, maturity, and decline phases, which identify what stage an item is in and where it will be in the near future. For example, after a new item line gains popularity, it exits the new and enters the growth phase where demand and sales increase.
These trends allow analysts to cross-examine the inventory demand types to ensure the forecasting is accurate. Solely forecasting using base demand data negates the valuable estimations that can be calculated following item trends.
3. Understand Seasonal Demand Fluctuations Nearly every manufacturer and retailer experience demand fluctuation from busy and slow seasons. These seasons can be based on school schedules, weather patterns, holidays, and traditions. Understanding the annual demand patterns gives insight into customers' habits and allows management to create a plan of action to take advantage of demand peaks.
4. Utilize Qualitative Forecasting While sales data provides quantitative information for analysts to calculate, qualitative forecasting accounts for external elements that can affect future demand. For example, factors within the market and competitor efforts can hinder or promote average sales by attracting customers. Therefore, considering both quantitative and qualitative information can provide well-rounded data for accurate demand forecasting and inventory control.
5. Identify Demand Forecasting Outliers Sometimes unusual demand outliers can occur due to purposeful actions, such as promotions, unique orders, or unpredictable events. Management should take the time to analyze data to define all outliers and determine if there are any preventative measures to maintain demand trends. It is also essential to decide whether the skewed data should be entered into the forecasting calculations or disregarded.
The Importance of Inventory Optimization
Demand forecasting is essential for optimizing inventory levels, allowing businesses to reduce ordering, handling, and stockout costs. However, in order to produce effective forecasts, companies need access to accurate data.
By using business process automation, organizations can ensure tasks are completed efficiently and data is automatically collected. Information exchange can be further enhanced with a system integrator that collaborates data from all existing software to streamline communication and optimize inventory.
Optimizing inventory provides companies with many benefits, such as reduced stock costs and increased profits. However, to achieve inventory optimization, businesses should implement advanced demand forecasting tools to-
Boost Turnover Rate - Through advanced demand forecasting and inventory monitoring, businesses can determine what products are slow-moving and implement marketing strategies to boost sales where necessary. Increasing transactions for goods with low turnover rates increases revenue and opens up space in the warehouse for high-profit items.
Understand How Price Effects Sales - Management should understand how promotions affect sales, such as discounting an item to increase its turnover rate. This information allows machine learning to forecast demand based on different prices and marketing techniques so businesses can anticipate customer purchases.
Develop Effective Promotions - Promotions tend to disrupt the supply chain by causing three effects- halo, cannibalization, and post-promotion dip. Halo refers to the increase in demand for non-promoted items that are related to a promoted product. Cannibalization is the dip in demand for products that are similar to advertised items. Post-promotion dip refers to the decrease in sales after a promotion has ended since customers have already purchased the item at the discounted price. Machine learning forecast software can predict these demand fluctuations, allowing marketing teams to develop high-performing promotions with realistic expectations.
Predict Demand by Location - Through automation tools, such as barcodes, scanners, and location-based forecasting, businesses can track inventory movement throughout all warehouse and store locations. This also gives insight into how items perform at different shops, allowing forecasting software to predict future demand specific to each store. The respective warehouse can then increase or decrease their stock levels to meet demand, minimizing inventory costs, and driving profits.
Demand forecasting not only promotes a company's bottom line by adequately preparing them for future sales but ensures inventory optimization as well. This advanced solution gives insights on upcoming demand trends, so businesses can reduce or increase item production to maintain optimal stock levels.