Although global supply chains were negatively impacted during the COVID-19 pandemic, grinding to a halt in many instances, they are no longer affected by the pandemic. However, the logistics industry is still plagued by a slowdown in the global economy. Based on the IMF’s baseline forecast, “growth will slow from last year’s 3.5 percent to 3 percent this year and next.”

Moreover, a significant challenge to supply chains and logistics operations is climate change. In the article “ 3 Key Actions for Supply Chain’s Response to Climate Change,” Gartner reports that the highest climate change impact on supply chains and logistics operations is environmental, caused by increased severe weather events at 72% of all environment issues.

Therefore, to remain in business, Logistics Service Providers (LSPs) must operate on tighter budgets, reducing costs and streamlining operations across all operational touch points while continuing to service their customers, meeting—and exceeding—delivery timelines, and providing the highest quality customer service.

Operating under Severe Budgetary Constraints

It stands to reason that, under the global economic conditions described above, LSPs must revise and streamline their business process and operations to optimize their budgets successfully. However, streamlining an LSP takes careful planning to ensure operational efficiencies are not lost.

Here are several strategies to consider if you are an LSP operating under severe budgetary constraints:

Pivot into an Agile Organization: Pivoting into an Agile organization is a complex but vital operation that requires strategic planning, cultural change, and operational adjustments. However, the benefits are worth it. An Agile organization can adjust to rapidly changing global market conditions, continuing to provide optimal customer service while improving operational efficiencies and remaining within budget.

• Invest in Technology Solutions: Upgrade your current technology stack and software applications like route-optimization software to a cloud native microservices-based architecture where each microservice is packaged in a container, with all these containers orchestrated by a container orchestration platform like Kubernetes, saving costs and improving operational efficiencies.

• Optimize Asset Utilization: Maximize the utilization of existing assets, such as vehicles, by implementing a transport management system and installing IoT devices in all vehicles for real-time tracking and data collection, helping optimize routes and loads, ensuring the vehicles are full when they leave the warehouse, reducing the number of trips required, and saving fuel costs by making sure drivers take the most efficient delivery routes.

• Utilize Data Analytics: Utilize big data analytics to gain insights into operations, providing management with the information to make critical decisions, improve demand forecasting, reduce wastage and financial losses through staff inefficiencies, unplanned deliveries, and an under/over-supply of vehicles, fuel, and so on.

The Imperative of Demand Forecasting

Demand forecasting is a critical part of any organization. Without the ability to forecast—or make informed decisions about—future shipping requests, LSPs cannot operate successfully under the current global conditions.

As highlighted above, one of the best tools available to LSPs is the ability to make decisions based on the data collected during the LSP’s day-to-day operations, as the following points describe:

• Predictive Analytics: Predictive analytics involves integrating historical data, statistical algorithms, and Artificial Intelligence—or machine learning—algorithms to predict future demand based on past trends and patterns.

• Time Frames: Demand forecasting can be short-term—a few weeks to a few months ahead—medium-term, several months to a year ahead, or long-term (years on), depending on what the business requirements are.

• Qualitative and Quantitative Techniques: It is imperative to utilize both qualitative—or informed guesses and expert opinions—and quantitative—or numerical methods and statistical models (such as time series analysis, causal models, and AI-based algorithms)—analytical methods.

• Market Trend Analysis: This involves analyzing market trends and external factors, like economic indicators, market conditions, and seasonal demands.

Moreover, the benefits of adopting a robust demand forecasting model include the following:

• Resource Allocation: Demand forecasting helps in planning for the resources required in the future, such as drivers, vehicles, fuel, packing materials, and logistical support staff, to meet future demand efficiently.

• Risk Management: LSPs can better manage and mitigate the risks associated with having the right resources at any given moment to fulfill their core aim—that is, shipping goods from source to destination.

• Customer Satisfaction: Getting demand forecasting right ensures LSPs can meet customer shipping requirements without delay, resulting in higher customer satisfaction and retention metrics.

Predictive Analytics: Powering Demand Forecasting

While the core focus of this article is utilizing technology—specifically Kubernetes—to collect and analyze the massive amounts of data collected during the LSP’s daily operations, it is also vital to note that successfully leveraging the available data to provide the foundation for demand forecasting means being aware of the following challenges, the most significant being the need to have dynamic capabilities.

Succinctly stated, the modern LSP operates in an extremely volatile and competitive environment, where customer demands can change rapidly, breaking all demand forecasting models. Therefore, to remain operational, never mind successful, and maintain a competitive edge, LSPs must develop dynamic capabilities—or integrate, build, and reconfigure operational efficiencies and competencies at short notice to address the rapidly changing environments.

The long—and the short—of this requirement is to become a highly Agile organization, providing the capabilities to be responsive to change, iterate on new operational processes, roll out updates to services and processes incrementally, reducing the time to delivery or respond to customer needs at short notice.

At this juncture, the inferred question is how demand forecasting and predictive analytics, a category of the overarching data analytics paradigm, can facilitate the LSP’s ability to develop dynamic capabilities.

The straightforward answer to this question is the sooner the analyzed data is available, the quicker the LSP can adapt to the rapidly changing environment.

For instance:

Imagine you are the operations executive of a global LSP that has received a request to urgently ship medical supplies—and equipment installed in shipping containers as mobile hospitals—from North America to Southern Turkey, on the Syrian border, where an earthquake like the February 2023 earthquake has caused untold devastation and loss of human life.

Note: This hypothetical scenario has no bearing on current world events.

Your plan is as follows:

• Pack the medical supplies into shipping containers,
• Load these containers, plus the containerized mobile hospitals, onto trucks and deliver to the Savannah Seaport, Georgia, USA,
• Load the containers onto a container ship,
• When the container ship reaches Mersin, Turkey, unload the containers off the ship and onto trucks,
• Drive to the closest airport, load these supplies onto a cargo plane, and fly to the airport closest to the earthquake zone,
• Offload the supplies, equipment, and mobile hospitals from the aircraft onto trucks and drive to the nearest drop-off point, where they will be distributed to local hospitals.

However, because the situation is highly volatile and the ground unstable with the threat of continuing aftershocks, you might have to change this plan at short notice. Canceling the shipment is not an option. Therefore, the question that begs is, where do you find the information you need to pivot to another plan at the last minute?

Enter real-time—or near real-time—data analysis driven by a cloud native, microservices-based, Kubernetes-orchestrated, global hybrid multi-cloud application.

This term might sound like a mouthful, but when decomposed into its components, it is relatively straightforward, as seen in the following text:

• Real-time or Near Real-Time Data Analysis: This concept refers to data analysis techniques that process and analyze data almost immediately after it is generated, allowing for instant insights and decision-making.

• Cloud Native and Microservices-Based Application: Cloud native refers to applications designed to run in the cloud. These applications are developed specifically for the cloud using a containerized microservices architecture and are designed to leverage the scalability and flexibility of cloud computing environments.

• Kubernetes-Orchestrated: Kubernetes, a container orchestration platform, is the de facto standard for deploying, scaling, and managing containerized applications. As an orchestrator, it automates and manages the lifecycle of containerized microservices-based applications.

• Global: This term implies that an application is designed to operate worldwide, handling data and user requests from anywhere.

• Hybrid Multi-Cloud: A hybrid multi-cloud environment combines both public and private clouds, together with on-premises infrastructure, providing flexibility as different components—containerized microservices—of the application are located in the most appropriate environment based on requirements like data sensitivity, regulatory compliance, latency, or cost.

Lastly, in summary, we can see that this concept—that of a cloud-native, microservices-based, Kubernetes-orchestrated, global hybrid multi-cloud application—represents a highly advanced, scalable, flexible data analysis solution, leveraging the latest in cloud native, containerized microservices-based architectures, and data processing technologies.

In Conclusion…

Circling back to our use case cited above, the specific data analytics required to change the logistics plan at short notice include real-time GPS tracking data tracking the containers moving across the world, weather data, specifically severe weather tracking data, and on-site data at the container’s destination. For example, as the ship crosses the Mediterranean Ocean, a severe storm blows over the Turkish coast, preventing the ship from reaching its destination harbor. Consequently, the ship must dock at an Italian port, changing the rest of the logistics operation and plan.

While this logistics operation is in motion, it is imperative that the data collection and analysis microservices are fully operational and the data analytics application does not go down. Kubernetes automates the scaling and monitoring of this app, ensuring that the application stays up by scaling, monitoring, and managing these microservices so that the requisite data is collected and analyzed in near real-time, providing you with the correct information to pivot the plan at short notice, ensuring that the medical supplies, equipment, and mobile hospitals get to the earthquake zone as quickly and efficiently as possible.