Scaling Smart: Strategies to Reduce Logistics Costs When Order Volumes Drop

Businesses often face fluctuations in order volumes due to seasonal trends, economic shifts, or unforeseen disruptions. Supply chain leaders must prepare for more than just rapid growth—they must be equally prepared to scale down operations when the order volume dips. Maintaining high logistics costs during low-demand periods can severely strain profitability. 

This is where smart scaling strategies become critical. The goal of the smart scaling strategies is not just to reduce costs but also to improve operational flexibility and build long-term resilience. In this blog, we’ll explore practical and data-driven strategies logistics and supply chain teams can deploy to reduce costs effectively when order volumes drop.

1. Optimized Transportation Modes: It is necessary to reassess the transportation mix when the order volume drops. Shifting from dedicated to shared truckloads (LTL/FTL optimization) helps consolidate shipment and reduce pre-unit freight costs. Additionally, leveraging multi-modal—combining road, rail, and sea transport—can make long-haul shipments more cost effective. Choosing the right carrier using real-time data helps businesses opt for the most economical shipping options based on their current demand. 
   
   Load optimization can reduce transport costs by 10–20%, especially during low-demand cycles.

2. Right-Size Warehouse Operations: Excess warehouse space during low-demand periods drains capital. Overstocks and underutilized space increase holding costs. Flexible warehousing models, such as shared or on-demand warehousing options, allows businesses to pay only the part of the space the business has used. Moreover, consolidating stock in strategic locations–can further minimize costs for the business. 
   
   According to CBRE, right-sizing warehouse space can reduce logistics costs by 14% annually.

3. Implement Dynamic Route Optimization: Fewer orders typically means that you will have fewer deliveries. But, it can still inflate costs. AI-powered route planning takes into account traffic conditions, delivery windows, and fuel consumption to help reduce cost and determine best routes. Intelligent auto order clubbing of the deliveries in a certain geography, minimize trips and maximize drop density. 
   
   Gartner notes that companies using real-time freight procurement platforms saved 12–18% on average compared to those with static contracts.

4. Real-time visibility:  Leveraging technology to get real time logistics analytics helps in providing critical insights for cost-savings opportunities. Advanced algorithms provide actionable insights into inventory levels and demand patterns by analyzing market trends and historical data, allowing proactive adjustments. AI-powered dashboards consolidate real-time data from multiple sources to provide deep insights into logistics performance. Similarly, analytics on picking times can help managers optimize labor allocation, ensuring that resources are used effectively during uncertain periods.
   
   A Harvard Business Review study found that companies using real-time logistics analytics improved decision-making speed by 37% and cut costs by up to 22%.

5. Hybrid Fleet Strategy: It will be very expensive for the businesses to maintain a full in-house fleet during low demand periods. A hybrid approach—combining owned and rented vehicles—-allows businesses to scale fleet size dynamically based on order volume. Crowdsourced last-mile delivery, partnering with gig economy drivers, offers a flexible and cost-effective solution for urban deliveries.
   
   According to McKinsey, companies that moved to more flexible logistics setups saw a 15–30% reduction in logistics costs during periods of declining demand.

6. Enhancing First Attempt Delivery Success: Each attempted or re-attempted delivery is expensive. Every failed delivery leads to additional trips, leading to higher fuel cost, increased driver hours, and wasted operational efforts. Every mile you ship costs you money. Each time an order is sent out for delivery after a first delivery attempt costs increasingly more money. So for a second delivery attempt shipping costs double, and they triple for a third delivery attempt. Moreover, companies do not charge extra from the client which ultimately is paid by the company, reducing their profit margins and straining logistics resources. Businesses can improve first attempt delivery success by AI-powered dynamic delivery scheduling, automated customer notification, efficient resource allocation, and location intelligence.  
   
   Improving FADR by even 5% can reduce last-mile costs by 8–12%.

Reducing logistics costs when the order volume drops isn’t about cutting corners–it’s about building a smarter, leaner, and more adaptive supply chain. By combining automation, data intelligence, and flexibility, companies can not only survive downturns but thrive in them.