• Written by Sonali Subhadarsini, Product Manager

Can we really afford to keep using these carriers, or are we sacrificing too much on service to save on costs, is a question that executive leadership teams in logistics often find themselves asking, but struggling to answer, as they wait on a report that may take weeks to compile, and even then, may not provide the clarity they need to make informed decisions.

The executive leadership questions that currently wait on a report

Executive leadership teams in logistics are constantly seeking to balance the trade-off between cost and service, and this requires access to timely and accurate data, however, many of the questions they need to ask, such as what is the current utilization of our fleet, or what are the most common reasons for delays, are often left waiting on a report, which can be a slow and laborious process, involving multiple stakeholders and systems, and even when the report is finally delivered, it may not provide the level of detail or insight that the leadership team needs to make informed decisions.

A shift supervisor, for example, may be able to provide some information on the current state of the fleet, but they may not have access to the broader operational data, such as maintenance schedules, or driver availability, that would be needed to provide a complete picture, and similarly, a category manager may be able to provide some information on carrier performance, but they may not have access to the financial data, such as costs and revenues, that would be needed to fully understand the implications of different carrier choices.

The data behind them: consolidated financials, operational KPIs, sales performance, headcount

The data that executive leadership teams in logistics need to answer their questions is typically scattered across multiple systems, including financial systems, operational systems, and sales systems, and may include consolidated financials, such as profit and loss statements, operational KPIs, such as on-time delivery rates, sales performance data, such as revenue and volume, and headcount data, such as driver availability and utilization, and in order to get a complete picture, this data needs to be brought together and analyzed in a way that provides insight into the trade-offs between cost and service.

For instance, a logistics company may have a financial system that provides data on costs and revenues, an operational system that provides data on fleet utilization and maintenance, and a sales system that provides data on customer demand and sales performance, and in order to understand the impact of different carrier choices on the business, the executive leadership team would need to be able to bring this data together and analyze it in a way that provides insight into the trade-offs between cost and service, and this is where AI for executive leadership in logistics can help, by providing a platform for querying and analyzing this data in real-time.

Sector constraints: carrier performance variance and dwell and detention

One of the key challenges that executive leadership teams in logistics face is the variability in carrier performance, which can make it difficult to predict and plan for deliveries, and this variability can be driven by a range of factors, including traffic and weather conditions, driver availability and training, and equipment maintenance and utilization, and in addition to this variability, logistics companies also have to contend with dwell and detention, which can add significant costs and complexity to the delivery process, and may require specialized systems and processes to manage.

For example, a category manager may need to negotiate with carriers to minimize dwell and detention, while also ensuring that deliveries are made on time and in good condition, and this requires a deep understanding of the carrier's performance and capabilities, as well as the needs and expectations of the customer, and in order to make informed decisions, the category manager would need to have access to timely and accurate data on carrier performance, including data on dwell and detention, and this is where AI for executive leadership in logistics can help, by providing a platform for querying and analyzing this data in real-time.

What improvement looks like: decision latency, actions of the form come back with numbers, definition conflicts resolved

Improvement in this context means being able to make informed decisions quickly, without having to wait on a report, and this requires being able to query and analyze data in real-time, and get answers to questions such as what is the current utilization of our fleet, or what are the most common reasons for delays, and it also requires being able to take actions, such as adjusting carrier choices or modifying delivery schedules, and being able to measure the impact of these actions, and this is where AI for executive leadership in logistics can help, by providing a platform for querying and analyzing data in real-time, and for taking actions and measuring their impact.

Most people get wrong the idea that AI for executive leadership in logistics is about replacing human decision-making with automated systems, but in reality, it is about augmenting human decision-making with timely and accurate data, and providing a platform for querying and analyzing this data in real-time, and this requires a deep understanding of the business and its operations, as well as the needs and expectations of the customer, and it also requires being able to resolve definition conflicts, such as what constitutes on-time delivery, and being able to measure the impact of different actions, such as adjusting carrier choices or modifying delivery schedules.

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

AI for executive leadership in logistics is not a magic bullet, and it is not a replacement for human decision-making, or for the need to understand the business and its operations, and it is also not a solution for all logistics challenges, and there may be cases where other solutions, such as process improvements or system upgrades, are more appropriate, and in order to determine whether AI for executive leadership in logistics is the right choice, logistics companies should carefully consider their needs and goals, and should consult with experts, such as those at Harns Technologies, to determine the best course of action.

For instance, if a logistics company is struggling with basic operational issues, such as fleet utilization or driver availability, then AI for executive leadership in logistics may not be the right solution, and instead, the company may need to focus on process improvements or system upgrades, and similarly, if a logistics company is looking for a way to replace human decision-making with automated systems, then AI for executive leadership in logistics is not the right choice, and instead, the company may need to consider other solutions, such as machine learning or robotic process automation.