Jan 23, 2024

A 1% error rate doesn’t seem intimidating at first...until you start handling millions of products. For an automotive factory, a 1% error rate may translate to a ten thousand car shortage for every million units made. To put things into perspective, American car manufacturers typically produce over 8 million vehicles per year, which means an error not only tarnishes a brand’s reputation but also contributes to stockouts and price fluctuations. As businesses seek target errors and blind spots, visibility systems should incorporate accurate demand prediction to monitor inventory levels and avoid damaging stockouts and costly storage costs. Modernized, effective forecasting methods improve visibility, curb errors, and empower better customer experiences through accurate estimates and actionable insights.

But first, what is demand forecasting?

Accurate demand forecasting is a critical aspect of any business that seeks to optimize production, inventory management, and profitability. It involves predicting the future demand for a product or service, which enables teams to plan and allocate resources effectively. On the other hand, inaccurate demand forecasting leads to overproduction, underproduction, stockouts, and lost sales, all of which can negatively impact a business's bottom line. Future-forward demand forecast optimization plans integrate intuitive technologies that connect the entire supply chain so individual businesses may respond quickly to errors before they impair productivity.

4 ways to enhance effective forecasting methods.

For warehouses, transparent forecasting is the difference between a smoothly humming logistics engine and a chaotic symphony of stockouts and overstocked shelves. Therefore, distributors may consider the following tactics when enhancing their systems:

1. Leverage the Power of Data - One of the most effective ways to establish accurate demand forecasting is by using historical data. Historical data provides insight into customer demand patterns, which can help predict future demand. Look at sales data from previous years and identify any patterns or trends. For example, apparel retailers may see sales increase during the back-to-school season, ranging between July and September. This information should then be used to prepare for increased demand during that period. By analyzing historical data with mobile computing, you can identify seasonality and trends to create more accurate predictions.

Furthermore, intelligent statistical models also help analyze collected data and identify trends. These models use mathematical algorithms to identify patterns and predict future demand. There are many statistical models available, including linear regression, exponential smoothing, and ARIMA models. Although these models can help make more accurate predictions, they require some level of automation. By working with a strategic solution partner, businesses can select and deploy intelligent mobile computers, monitoring their performance to ensure seamless workflows, so forecasting teams only need to focus on analyzing collected data within the warehouse management system.

2. Maintain Adaptability to the Ever-Changing Landscape - Markets are dynamic, and so should your forecasts. Continuous monitoring and model adjustments are essential to note key drivers, such as a sudden increase in popularity, and potential blind spots like hidden errors. A few key drivers to consider are economic conditions, population growth, and changes in consumer behavior. By understanding these drivers, you can make more informed predictions about future demand.

Related: See how adaptable technologies maximize transparency in food distribution.

3. Automate Data Processing - The age of manual spreadsheets is over. Advancements in technology have made it easier to predict demand accurately. There are numerous software applications, technology systems, and visibility applications available that can help you analyze historical data, identify key drivers, and predict future demand. These applications use machine learning algorithms to analyze data and make predictions. They can also provide real-time data to help businesses adjust inventory levels and production schedules based on changes in demand.

Artificial intelligence is one of the many technologies revolutionizing demand forecasting as it synthesizes collected data into actionable insights based on historical patterns. Centralized data systems are then able to disperse collected information to all parties involved. Advanced machine learning models such as decision trees and neural networks are also finding their way into mainstream supply chain solutions. However, automated data capture and mobile processing have also offered indisputable benefits by eliminating manual reporting and accelerating data sharing across the supply chain through adaptable connectivity systems. Collaboration with your solution designer can help evaluate technology systems to pair your workflows with a scalable solution that incorporates next-generation technologies without overstepping budgets.  

4. Secure Cross Supply Chain Communication - Forecasting isn't a solo act. Sharing data and insights with suppliers, distributors, and retailers creates a collaborative ecosystem. Moreover, customer feedback also goes a long way in precision demand analysis. For example, seamless proof-of-receipt systems can incorporate customer feedback into an automated system to help minimize future disputes and ensure accuracy. This information can help businesses identify trends and patterns in customer behavior.

Amongst the supply chain, communication has proven indispensable, especially during the holiday peak season. Distributors have long navigated the sudden spikes in transactions during the winter months. Consequently, many know to start increasing stock levels by the summer to stay ahead of the holiday peak season. This requires ongoing communication between suppliers and manufacturers to proactively estimate the amount of products due to ship. 5G cellular and Wi-Fi 6E are a few of the new connectivity systems connecting supply chains to secure business resilience. Equipped with dependable communication, supply chains can adjust inventory levels to brace for high demand without risking stockouts.

Related: Explore the role predictive analytics plays in a successful peak season.

Demand planning excellence can take many forms, but in the end, it is more than just inventory tracking or error mitigation. By wielding data as a weapon, partnering with stakeholders, and constantly adapting, distribution centers can transform workflows into transparent, demand prediction machines. Nevertheless, it is important to remember that demand forecasting is an ongoing process. As a result, businesses should continuously monitor and adjust their predictions based on changes in customer behavior and market conditions. Moreover, forecasting systems should retain a level of adaptability to adjust to new trends and technology advancements. To learn how you can adapt your forecasting systems with the discussed tactics or for more information about next-generation visibility systems, contact our automation team.