For many logistics companies, digital transformation and AI implementation is not an easy task to accomplish. Companies that do not use logistics demand forecasting find that making the operational planning of assets difficult. The multi-faceted problem requires businesses to consider how many assets they require and whether or not those assets are positioned correctly at any given moment.
This problem is considered one of the major problems to solve, as it requires a large volume of interdependent information. Luckily, logistics companies generate a tremendous amount of data internally and have access to even more data from public sources. Nevertheless, only a few tools are currently available which allow companies to synthesize all of this information and enable data-driven decision-making in conjunction with the experience and instincts of their managers.
But with the help of modern predictive optimization tools, logistics companies can shift to an anticipatory strategy based on accurate demand forecasting.
By creating and implementing their personalized demand forecasting models, companies can more easily achieve accurate data for the forecast. These models can help companies better understand the safety stock they need. Along with this, logistics demand forecasting models can help decrease the number of kilometers spent repositioning assets, increase cargo vehicle capacity utilization, and increase asset utilization for asset owners that include shipping lines, trucking, and intermodal companies.
Overall, the technology helped in reducing the costs of speed to cut the costs by 7-9 times. Their operations will be more efficient with less expenditure. For logistics companies, the profit margins can rise when those unnecessary costs are reduced, and demand forecasting is accurate.
