Transport & Logistics
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What do you get?
Enhanced visibility and control across the supply chain.
What are we going to do for you?
Optimize the allocation of resources and assets with AI-driven forecasting of activities across the entire logistics value chain.
Challenge
Transport and logistics companies struggle with the unpredictable nature of cost expectations across different modes of delivery, leading to budget overruns and operational inefficiencies. Without accurate forecasting and budgeting data, companies face increased financial risks, supply chain disruptions, and diminished customer satisfaction.
Solution
OmniData's AI-powered platform changes the way cost forecasting and planning is done in the transport and logistics industry. Harnessing machine learning techniques our solution generates accurate predictions of different modes of transport, yielding better insights for data-driven decision making.
Cost forecasting
Forecasting the composition of costs for different modes of transport. Understand the nuances of different carriers and optimized resource allocation.
Budgeting & Planning
Combine the outcomes from expected costs associated with modes and carriers into the budgeting and planning processes using forecasts on the lowest level of granularity for optimal decision making.
Scenario planning
Many factors can have an impact on costs such as exchange rates and other market forces. OmniData's scenario planner will enable impact analysis of different scenarios.
Business Problem
Logistics companies need to manage various large cost lines associated with the shipping and movement of goods. These lines include freight costs, duties & taxes, customs, and warehousing costs amongst others. Various factors play a role in ensuring that the least cost is incurred to move freight. Different factors also affect these cost lines. For example, size, weight, and number of packages will affect the total cost of freight, whereas the country being shipped to will affect things like customs and taxes.
Getting the line items forecasted over time for budgeting and planning purposes provides one layer of intelligence. Extending forecasts to also give a company a view of what the breakdown would be on the lower levels. Freight costs as an example can be broken down into the different modes of transport such as air freight, ocean, truck loads or railroad. Different carriers might have different dynamics as well and understanding how these differ is instrumental.
Solution Approach
OmniData’s budgeting and forecasting engine leverages logistic companies’ financial data, combines it with operational and macro-economic data, and feeds the information through machine learning algorithms and proprietary coding algorithms to provide 15-month forward expenditure forecasts on their General Ledger (GL) Account level items. Essentially, the problem entails the development, training, deployment, and monitoring of hundreds of timeseries-based machine learning algorithms to allow the end user to get to a granular GL Account expense view. As part of the ML modeling techniques, we also provide a confidence bound for the predictions to guide business users on the possible variability in the predictions. Variability can be because of input variable uncertainty. Also, the further the predictions are from the current date, the more uncertain they become.
Value Impact
Enabling the client to have a view of what expenditure levels are expected over time enables intelligent decisioning for budgeting and planning. Having the forecasts also broken down to the low levels of granularity such as modes of transport, carriers and GL levels, enables a new dimension in their analytical capabilities.
This capability allows the company to use data-driven approaches in helping their clients to unlock value by helping them with budgeting and planning.
Technical Implementation: Data Sources
Building a solution of this nature required historical data for different modes of transport and the associated breakdown for example to carriers and GL levels. OmniData ingested this data and created time series values. Challenges presented themselves in the form of different modes and companies having different historical data availability. Removing noise from the historical data was completed to improve the modeling accuracy.
Technical Implementation: Solution Features
- Create time series data per target variable such as number of shipments per mode, expenses related to different carriers, and costs per GL line items.
- Build bespoke machine learning models to forecast the target variables, including multiple ensemble models.
- Apply an optimization layer to the output to ensure the most accurate models are selected per target variable.
- Give the client the ability to perform a what-if analysis for example changing the cost of fuel from the base forecast to a best or worst case scenario, in order to analyze the impact.
Conclusion
The budgeting and planning allows OmniData's client to direct their expenses to suited modes and carriers. It provides a perspective on what the impact might be on financial results over time and allow for better planning.