How AI-Enabled Demand Forecasting Boosts Logistics

Daffodil Software
5 min readJan 31, 2022

Out of all the stages of the supply chain, logistics deals with the most unpredictable issues. These include factors like weather patterns, fuel prices, and last-mile delivery charges. The gradual adoption of demand forecasting with Artificial Intelligence (AI) is helping the logistics sector to alleviate some of these uncertainties.

For an industry such as logistics which has traditionally run with pen, paper, and manual control, a shift to modern software tools can be challenging. But by not transforming their workflow with modern predictive tools, organizations dealing with the supply chain are making the operational planning of assets more difficult for themselves.

The problem faced by most logistics operators is the consideration of how many assets their organization needs. Additionally, they must track and ascertain whether those assets are set up and ready to go when needed.

These problems require looking at several independent information streams to come up with appropriate solutions. Logistics companies generate reams of internal operational data and have unfettered access to public data sources as well. Companies can synthesize this data correctly, by combining the expertise and experience of logisticians with the processing capacity of AI.

In this article, we will first discuss how AI-enabled demand forecasting boosts the success rate of logistics firms. We will go on to discuss the benefits that these organizations reap from demand forecasting.

How AI Demand Forecasting Drives the Logistics Sector

AI can help a company with one of two types of demand forecasting models: long-range forecasts and short-range forecasts. Short-range forecasting greatly impacts operational planning and contributes to most of the bottom line.

Long-range forecasting is more strategic in the way it uses the data for budgeting and planning of upcoming assets. Both models are implemented in varying capacities. Following are some of the ways demand forecasting is implemented:

1)Fleet Management:

The best-case scenario for a fleet manager is when all trucks, containers, and spaces are utilized to capacity. For companies that transport to extreme distances, the efficiency level of their fleet repositioning schedule can make or break their businesses.

These companies can apply predictive analytics to their software solutions to cleanse and enrich fleet management data. Tasks like energy management, risk management, and technology management can be automated with AI. The output data can be reprocessed to arrive at best-case scenarios for each of these tasks.

Companies implementing predictive analytics for fleet management can predict asset demand two weeks ahead with over 98% accuracy and six weeks ahead with 95% accuracy. As a result, customers can avail more flexible services and one-way rentals from logistics companies and the companies can expect to increase their revenue bracket.

2)Operation Optimization:

Without modern predictive analytics with AI, logistics organizations stand to secure lower profit margins due to unnecessary costs. Irregular maintenance of machinery and inventory, and half-empty trucks can lead to great losses for these companies.

Logistics operators must ensure proper implementation of demand forecasting models and take actions based on AI insights from the data. This will help companies optimize the supply chain and cut fleet sizes, leasing costs, storage, and maintenance expenditure, etc. The end goal is to make all the operations more efficient for less expenditure by eliminating unnecessary costs.

A real world example of logistics operations optimization is how Daffodil helped Autoload disrupt the Indian logistics market .

3)Intuitive Planning:

Based on data accumulated from past market exposure and knowledge from various sources, AI can make intuitive forecasts. These types of forecasts take into account all kinds of processes within and outside the company and analyze them.

The organization must make key metrics such as demand quota, closed leads, pipeline coverage, and past conversions for the AI algorithm. These can be used to accurately predict future fluctuations in logistics demand.

Intuitive planning led by complex machine learning algorithms can consider several factors which Legacy Supply Chain Management Systems (SCMS) tend to miss. Intuitive forecasting can be leveraged even by new companies with a lack of past logistics data.

4)Managing Extra Assets:

Logistics operators should ideally have a rainy day stock of assets to deal with unexpected shortages. The extra assets left with a company after optimizing their operations based on demand modeling can be sold off too.

Each asset’s costs of failure and criticality are measured by predictive algorithms to produce an asset survival model. The risk measure for the assets can be calculated to produce a prioritized list of assets for maintenance.

Inventory expenses can be greatly reduced with precision demand forecasting. Accurate prediction of asset demand weeks in advance can enable these companies to buy sub-contractors at a much cheaper rate.

5)Employee Analytics:

The most essential employees of the logistics paradigm are the operators and the planners. When granular data about these employee’s performances are fed to predictive AI algorithms, outcomes can be calculated and compared easily.

Demand forecasting methodologies are increasingly being applied to employee appraisal systems to train AI models further. Logistics organizations can hold on to their high-performing employees for assisting in more well-rounded planning processes.

Logisticians can be left in full control of their work and their performance analytics can help improve the AI itself. With AI data-driven decisions, the workforce and the organization can form a mutually beneficial relationship.

ALSO READ: On-Demand Fleet Management Solutions: Empowering Logistics

The Benefits of Demand Forecasting in Logistics

AI demand forecasting tools provide real actionable plans and support decision-making based on extensive data analysis. Implementing these in the logistics workflow helps in the following ways:

  • Controlling Operation Costs: Without strong forecasting models, logistics companies could foot heavy expenses leading to reduced profit margins. This is because fleets operate at nearly half capacity, while operations and maintenance get inefficient in the absence of rich data. Data-driven analytics implemented under the demand forecast models help predict inconsistencies weeks in advance, helping to cut expenses without impacting the workflow. Fleet sizes, leasing costs, maintenance and storage costs, parking, and drivers’ salaries can all become less overwhelming.
  • Dynamic Pricing: Stable Returns on Investment (ROI) can be maintained with good dynamic pricing strategies. With demand forecasting applied to asset management systems, seasonal, weekly, and monthly fluctuations in asset prices can be anticipated. From the carrier partners filling their trucks with freight to the shipment supervisors; everyone benefits from demand forecasting models. Companies can better scope their asset prices with a precise understanding of capacity and inventory.
  • Employee Efficiency: With an efficient employee performance management system integrated with logistics demand forecasting, logisticians can fully realize their potential. The calculation of asset capacities automated by AI leaves operators to dedicate their time to critical planning and scheduling tasks that require human intervention.
  • Improved Scalability: Scaling up operations resources becomes far more flexible due to accurate demand forecasting. Asset demand and supply gaps that come up during scaling can be filled up without wasting time and effort. Manufacturers can reduce or increase factory floor capacity as needed and carriers can decide upon expanding the fleet accordingly.

Leverage Demand Forecasting for your Logistics Needs

There is plenty of data like time taken for a shipment, holding capacity, inventory, driver’s schedules, etc. all across the supply chain. AI helps in enriching this data and derives actionable insights from it for predicting asset demand well in advance.

Logistics operators can quantifiably and accurately anticipate the demand for assets and shipment throughout the supply chain with AI-enabled demand forecasting. You can explore Daffodil’s Fleet Management Solutions for an inside look at what logistics optimization can look like.

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