Decision Intelligence in
Transport and Logistics

Improving operational efficiency in T&L through better decison making

The challenge to enhance operational efficiency

Transportation and logistics managers are faced with an array of challenges and opportunities that contrast dramatically with those of a decade ago. Factors like lack of truck drivers, lack of container capacity and associated price hikes,…are causing serious disruptions in the supply chain.

Realizing operational efficiency in times of increasing costs and big shortage of skillful people.
Lack of an end-2-end visibility, control and aligned decision making, due to presence of functional silo’s.
Ability to capture data continuously , in real time and to deploy new technology (IoT,..) in the most efficient way.
Ability to act on risks or opportunities more quickly and to make informed decisions.

To deal with unprecedented levels of complexity and uncertainty, organizations have to find ways to make accurate and contextualized decisions more quickly.

Addressing these challenges would require to capture and use data to provide enhanced real-time visibility and in-depth analysis, enabling faster decision making that is aligned with strategic organizational objectives.

Data: Capture data in real time and allow for aggregation and integration of data from different sources across the supply chain. IoT devices could e.g. capture and log temperature to offer a real-time view of all reefer cargo, enabling companies to react in the event of temperature deviations.
Insights: Transform data into actionable insights with increased level of automation. This could be e.g. simulation based price optimization,…where different scenarios can be modeled to provide suitable response.
Recommendations: Make recommendations from acquired information on the best course of action, and in some cases automatically execute them.

Decision Intelligence operationalizes data for efficient decison making, aligned with business strategy.

Process &

Decision modeling

Designing process decision maps, creating and validating decision logic.

Optimizing

decision process

Detecting bias, decision noise,..

From "prediction" to "decision" models that can be implemented effectively

Feedback &

“Continuous Learning”

Data, models, results and feedback are monitored for adjustments

Making changes and quick roll-out

Some typical use cases

Human-centered AI, providing actionable recommendations and driving operational excellence

Managing cold chain logistics

To ensure food safety and quality across the cold chain, awareness and accessibility of product (environment) data from all stages of the cold chain is critical. Variables like temperature, humidity, and vibration can be continuously monitored and translated to support real-time assessment of quality, determination of actual remaining shelf life of products,.... A good understanding of the extent and severity of variations makes it possible to take corrective steps to salvage the product and prevent an insurance claim that can lead to higher premiums.

Container management optimalization

According Boston Consulting Group, every 1 in 3 containers globally is moved empty, which costs the industry up to $20 billion per year. A significant problem influenced by long relocation time, high costs related to safety stock, and unreliable commercial forecasts, often with low accuracy, made by intuition or operational expertise. Accurate demand forecast and optimizing for complex decisions on repositioning empty containers, storage, repair and maintenance, would result in significant operational efficiency improvements.

Price Optimalization

In an industry with complex pricing strategies and susceptibility to market forces beyond their control, pricing is one of the most powerful profit levers available, but only when it’s done right. Effective price management can increase margins by 2 to 7% in as little as 12 months.

Decision Intelligence inTransport and Logistics