Demand Forecasting in Logistics: What, Why, and How
This article discusses the best practices for demand forecasting in logistics. Contact us today to learn how we can help you forecast demand accurately!
Did you listen to Dubai’s weather forecast today? What did it sound like?
“You can expect warm weather with plenty of sunshine all morning.”
“Convective clouds may appear from the east by afternoon.”
“It might get windy and hazy at night.”
A logistics demand forecast sounds similar.
“We can expect a busy day with plenty of delivery requests in a certain service area.”
“Demand will more likely peak around midday.”
“In the wake of the upcoming festive occasions, we may receive a lot of shipments from shippers in the coming months.”
Let’s be honest: every business owner longs to be able to see into the future. It’s their deepest desire!
While that’s not humanly possible, we fortunately have the closest thing to a perfect crystal ball—demand forecasting.
It’s a process whereby businesses can predict future demand for products, warehousing, transportation, and services in the supply chain. It requires a thorough analysis of past order histories, delivery data, pricing patterns, and customer interactions during different periods, while factoring in current market trends and other temporal variables to inform predictions regarding growth or decline in demand.
How Demand Forecasting Benefits Your Logistics Business
Accurate predictions of future demand are useful to logistics businesses in several ways. It allows them to react faster to market shifts, save on supply chain costs, meet customer expectations, and base their decisions on concrete data rather than hints and guesses. When done right, demand forecasting can very well be a big competitive advantage for logistics operators.
Improved Inventory Management
One of the most obvious ways this demand forecasting benefits your business is through better inventory management. When you can accurately predict what customers will buy, you can keep your stock levels balanced enough to meet the potential rise in demand without ordering too much or too little inventory.
More Cost Savings
Cost savings are a direct benefit of effective demand prediction. When you overstock, you tie up too much capital in inventory. On the other hand, when you don’t, you save a lot on storage and warehousing fees, and there’s less risk of unsold items becoming obsolete. Plus, with extra capital on hand, you can spend on other key areas of business growth. Better cash flow management and reducing waste naturally lead to huge savings over time.
More Satisfied Customers
This goes without saying: When businesses meet customer expectations without delay, customer satisfaction improves tremendously. Consumers want their purchases delivered quickly and without hassle. When you’re fully prepared to meet fluctuating demand, you avoid long waits and backorders, ensuring that your customers remain loyal and satisfied.
Efficient Production Cycles
Demand forecasting in logistics also has positive ripple effects on the overall production cycle and supply chain. With more accurate insights into upcoming demand, manufacturers can align their production schedules to avoid unnecessary downtime. Supply chains, too, become more efficient when you know in advance where and when certain raw materials or finished products will be needed and make arrangements to expedite the logistics.
Targeted Sales And Marketing Plans
Marketing and sales campaigns benefit greatly from demand forecasting in logistics as well. With a clear understanding of future demand, you can plan promotional campaigns or product launches at the most opportune times. Targeting your marketing activities based on top emerging trends in customer preferences helps optimize your advertising budget and increases the overall returns on investment of campaigns.
Lower Risks
This is a less obvious but nonetheless important benefit of demand forecasting. Predictive data allows you to anticipate market changes, adjust for seasonal fluctuations, and prepare for economic shifts beforehand. The ability to adapt ahead of time means less vulnerability to unexpected market disruptions.
Foresight for Future Planning
Long-term business strategy is another area where demand forecasting proves invaluable. By having a clearer picture of future customer preferences and market trends, businesses can make well-informed decisions regarding product development and potential market expansion. Instead of wasting time in conjectures, demand forecasting in logistics guides strategic business moves that can be backed up by data-driven insights.
Optimal Resource Allocation
Lastly, if you can identify the function or service offering that is expected to be in excessive demand shortly, you can assign personnel, equipment, and other resources to maintain high-quality customer service standards and avoid unnecessary costs of backorders and stockouts.
6 Best Practices for Demand Forecasting in Logistics
The importance of demand forecasting in logistics is undeniable (as we’ve established just now), yet the fact remains that you need a well-rounded approach to planning and predicting future demand in order to continue to deliver greater value to your customers.
Here are six best practices that will help you anticipate market demands and optimize your operations better.
1. Collect and Use the Right Data
Data is the foundation of any successful demand forecasting strategy. Start with gathering the right kind of data. Instead of collecting every piece of available data, focus on high-quality, insightful metrics that have a direct impact on your business outcomes. In the warehousing and logistics industry, for example, it’s important to collect data on:
- Inventory levels
- Order cycle time
- Backorder rate
- Product return rate
- Shipping volumes
- Dock-to-stock time
- Customer lifetime value
- Customer churn rate
2. Account for External Variables
Demand forecasting doesn’t happen in isolation. Many external factors can influence demand, and it’s critical to adjust for these variables to avoid inaccuracies. These variables can include seasonal trends, market shifts, economic conditions, and even unexpected events like natural disasters or global supply chain disruptions.
For example, a logistics company must adjust its forecast to account for holidays, seasonal changes in consumer behavior, or market demand spikes for specific products. Similarly, when demand for a product is seasonal or based on emerging trends, you must integrate these factors into the forecasting models to ensure more accurate predictions. Identifying and adjusting for these variables will help you remain agile and prepared at all times!
3. Utilize Real-Time Visibility for Improved Accuracy
Real-time visibility into supply chain activities is essential for making accurate demand forecasts. This involves having a clear, up-to-the-minute view of inventory levels, order statuses, transportation schedules, and other operational factors. Real-time data can help logistics businesses like yours quickly respond to changing market conditions and demand fluctuations.
When you have access to real-time visibility tools, you can continuously monitor your supply chains, detect shifts in demand, and take measures preemptively. These measures can entail proactive adjustments to inventory levels, production schedules, and distribution plans. Real-time insights can also help you optimize your routes, plan multi-stop trips, and prioritize deliveries.
4. Use AI for Demand Forecasting
We live in the digital age today, so this should be a no-brainer. Sure, forecasting in Excel has been the norm for the past few decades. However, modern times require modern solutions. Excel is a good starting point for new businesses, but it’s still manual, slow, and non-scalable, compared to today’s automated delivery management solutions powered by artificial intelligence (AI) and machine learning (ML).
These softwares use advanced AI and ML algorithms to process vast amounts of historical data analytics and real-time zonal and road conditions and identify changing patterns and trends within seconds. They also make predictions with greater accuracy than legacy forecasting systems like Excel and Google Spreadsheet.
5. Monitor and Adjust Forecasts Regularly
We cannot stress this enough: demand forecasting is not a one-time process. It requires ongoing monitoring and adjustment to remain effective for a long period of time. Forecasts should be regularly compared with actual demand to identify any discrepancies or areas that need improvement. By continuously reviewing the forecast’s accuracy and performance, logistics companies can fine-tune their forecasting models and adapt to changes in market conditions, consumer behavior, or external influences.
For example, if actual sales figures differ significantly from the forecasted demand, it’s essential to investigate the cause and update the forecasting model accordingly. This iterative approach ensures that demand forecasting becomes more accurate over time.
6. Align Forecasting with Strategic Goals
Finally, demand forecasting should always align with your company’s broader strategic goals. The purpose of forecasting isn’t just to predict sales or product demand—it’s to inform strategic decisions that drive the business forward. As a logistics business owner, you must set clear goals for your demand forecasting deliverables, whether it’s reducing inventory holding costs, improving delivery timelines, or increasing customer satisfaction.
Also, for logistics operations, demand forecasts should be integrated into broader strategic planning efforts, such as route optimization, warehouse management, and resource allocation. For example, if you forecast a surge in demand during a peak season, you might consider hiring more personnel, expanding warehouse capacity, or even partnering with additional carriers to meet the increased demand efficiently.
Bonus Tip:
Partner With Mile for Accurate Demand Forecasts
Mile’s delivery management software collects the right kind of data, accounts for external variables, offers real-time visibility, leverages advanced AI and ML algorithms, adapts forecasting models to changing conditions, and aligns them with your company’s short-term and long-term goals.
Schedule a demo today to learn how our solution can help you accurately forecast future growth or fluctuations in demand.