• Written by Sonali Subhadarsini, Product Manager

A logistics operations team often finds itself waiting on a report to answer questions about the current state of its network, from the performance of individual carriers to the status of ongoing projects, and the team's ability to intervene effectively can be hindered by the delay. The shift supervisor may need to know which shipments are running behind schedule, while the category manager may want to compare the performance of different suppliers, but these questions can be difficult to answer in a timely manner without direct access to the relevant data.

The operations questions that currently wait on a report

The operations team in a logistics organisation typically has to deal with a wide range of questions on a daily basis, from the mundane to the critical, and the team's ability to respond quickly and effectively can have a significant impact on the overall performance of the network. For example, a shift supervisor may need to know which shipments are running behind schedule, or which carriers are experiencing delays, in order to make informed decisions about how to allocate resources and intervene where necessary. Similarly, the category manager may want to compare the performance of different suppliers, or to track the status of ongoing projects, in order to identify areas for improvement and optimise the network.

These questions often have to wait on a report, which can be a time-consuming and laborious process, involving multiple stakeholders and systems, and the team may have to rely on manual processes, such as spreadsheet analysis or phone calls to carriers, in order to get the information they need. This can lead to delays and inefficiencies, as well as a lack of visibility and control over the network.

The data behind them: process and exception records, project status, budget consumption, supplier performance

The data that underlies these questions is typically scattered across multiple systems and stakeholders, including process and exception records, project status updates, budget consumption reports, and supplier performance metrics. For example, the transportation management system may hold data on shipment status and carrier performance, while the enterprise resource planning system may hold data on project status and budget consumption. The team may also have to rely on manual data collection, such as surveys or phone calls to suppliers, in order to get a complete picture of the network.

In order to answer the operations team's questions, this data needs to be brought together and made accessible in a way that allows the team to query it directly, without having to wait on a report or rely on manual processes. This can involve integrating multiple systems and data sources, as well as providing the team with the tools and training they need to analyse and interpret the data effectively.

Sector constraints: carrier performance variance and dwell and detention

Logistics operations teams also have to contend with a range of sector-specific constraints, including carrier performance variance and dwell and detention, which can have a significant impact on the overall performance of the network. For example, a carrier that consistently experiences delays or fails to meet its service level agreements can have a major impact on the team's ability to deliver shipments on time, while dwell and detention can result in significant additional costs and inefficiencies.

In order to manage these constraints effectively, the team needs to have access to real-time data and analytics, as well as the ability to model and simulate different scenarios, in order to identify areas for improvement and optimise the network. This can involve using tools such as operations management software, as well as working closely with carriers and other stakeholders to identify and address issues.

What improvement looks like: on-time delivery, exception rate, expediting cost

Improvement in logistics operations can take many forms, but some common metrics include on-time delivery, exception rate, and expediting cost. For example, a team that is able to increase its on-time delivery rate, or reduce its exception rate, can have a significant impact on customer satisfaction and loyalty, while reducing expediting cost can result in major cost savings and efficiencies.

In order to achieve these improvements, the team needs to have access to data and analytics that allow them to identify areas for improvement, as well as the ability to model and simulate different scenarios, in order to identify the most effective interventions. This can involve working closely with stakeholders, such as carriers and suppliers, as well as using tools and techniques, such as lean and six sigma, to identify and address inefficiencies.

By using data and analytics to drive improvement, logistics operations teams can have a major impact on the overall performance of the network, and can help to drive business success and competitiveness. For more information on how to get started with AI for operations in logistics, please contact us.

What this does not do, or when it is the wrong choice

While AI for operations in logistics can be a powerful tool for driving improvement and optimising the network, it is not a panacea, and there are certain situations in which it may not be the best choice. For example, in situations where the data is incomplete or inaccurate, or where the team lacks the skills and training needed to use the tools effectively, AI for operations may not be able to deliver the desired results.

In these situations, it may be necessary to focus on more fundamental issues, such as data quality and team training, before attempting to implement AI for operations. Additionally, AI for operations may not be the best choice for situations where the network is relatively simple, or where the team has a high degree of visibility and control over the operations, as the benefits of using AI may not outweigh the costs and complexities of implementation.