Industry Use cases
Client Background
👉 The transport industry is one of the largest industries globally, covering everything from freight transportation to passenger transportation.
👉 With the increasing demand for fast and efficient transportation, the industry has become more complex and has to deal with many challenges such as route optimization, capacity utilization, and demand forecasting.
👉 In recent years, Machine Learning (ML) has emerged as a powerful tool for addressing these challenges and improving the supply chain of the transport industry.
👉 In this case study, we will discuss a real-world example of how ML was used to improve the supply chain of a transportation company.
Solution
Demand Forecasting :
👉 The ML model used historical data to predict future demand for the company's services. The model took into account various factors such as the time of year, the day of the week, and even the weather.
👉 This helped the company to better forecast demand and allocate resources accordingly. As a result, the company was able to reduce the number of empty trucks on the road, which in turn reduced fuel consumption and transportation costs.
Route Optimization :
👉 The ML model also helped the company to optimize its routes. The model took into account various factors such as the distance between destinations, traffic congestion, and road conditions.
👉 This allowed the company to plan more efficient routes that reduced travel time and improved delivery times.
Capacity Utilization :
👉 The ML model was also used to improve capacity utilization. By analyzing historical data, the model was able to predict the capacity requirements for each route.
👉 This helped the company to optimize the number of trucks on the road and reduce the number of empty trucks. This not only reduced transportation costs but also helped the company to reduce its carbon footprint.
Key Challenges
👉 The transportation company in question had a fleet of trucks that transported goods across the country. The company was facing several challenges, including inefficient route planning, poor capacity utilization, and inaccurate demand forecasting.
👉 To address these challenges, the company decided to implement an ML solution. The first step was to collect and analyze data from various sources such as GPS devices, dispatch systems, and weather reports. The data was then used to train ML models to predict demand, optimize routes, and improve capacity utilization.
Result
👉 The implementation of the ML solution resulted in significant improvements in the company's supply chain.
👉 The company was able to
Reduce transportation costs by 15%
Reduce the number of empty trucks by 20%
Improve delivery times by 10%.
👉 The company also saw a reduction in its carbon footprint, which helped to improve its environmental performance.