Shipment Volume Forecasts Powered by AI at Senko Group
Company Size
1,000+
Region
- Asia
Country
- Japan
Product
- H2O Driverless AI
- IBM SPSS
Tech Stack
- Machine Learning
- AI
- R
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Retail
Applicable Functions
- Logistics & Transportation
- Warehouse & Inventory Management
Use Cases
- Fleet Management
- Inventory Management
- Predictive Maintenance
Services
- Data Science Services
About The Customer
Senko Group Holdings is a large integrated logistics service provider that operates logistics business as one of their core businesses focusing on apparel and ecommerce in the Tokyo metropolitan area. The company provides services to their contracted customers and works on improving the efficiency of their operations. One of the key challenges they face is securing workers for operations at warehouses due to the current shortage of workers in Japan. In order to maintain the customer service level, the company has been exploring ways to cover part of these tasks.
The Challenge
Senko Group Holdings, a large integrated logistics service provider, was facing challenges in manpower planning due to the shortage of workers in Japan. The company needed to maintain a high level of customer service despite the inability to cut operational tasks. The logistics staff was burdened with the task of predicting shipment volumes from their warehouses, a task that was time-consuming and complex. The company initially tried using R and IBM SPSS for AI-based shipment volume forecasts, but found them challenging for the logistics staff to use for actual operations. The structure of the model was complicated and required a great deal of effort for creating models as well as for applying feature engineering.
The Solution
Senko Group decided to introduce AI for shipment volume forecasts and chose to use Driverless AI. This AI tool repeatedly performs feature engineering, selects prediction method and performs tuning, delivering highly accurate results without depending on the skill of creating statistical analysis model. The company uses SPSS for data processing and Driverless AI for predictive modelling and prediction output, which help them streamline the operational procedure. The accurate shipment volume forecasts allowed them to plan and allocate workers more efficiently, reducing the workload of logistics staff and improving the efficiency of manpower planning.
Operational Impact
Quantitative Benefit
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