Managing production and inventory in markets with unpredictable demand is challenging. Stockpiling every possible part in a warehouse is neither practical nor cost-effective. However, Honda’s latest patent outlines a solution: a system that tracks and analyzes numerous factors to more accurately forecast demand for specific car parts. This approach aims to improve efficiency and reduce waste.
The Only Constant Is Change
Constantly in-demand car parts always kept in stock include:
- Service items such as filters and spark plugs
- Wear items such as brake pads and wiper blades
- Consumable such as lubricants and coolant
- The most common crash parts, such as bumpers and fenders
The demand for these parts can be fairly accurately determined most of the time, but static and/or historical prediction models for data collection and processing infrastructure cannot respond to unforeseen changes in time, and cannot adapt to the numerous variables quickly enough.
Some of these variables are:
- Vehicle make and model, and how many are sold
- Shifting dynamics of the market’s vehicle composition, e.g. cars, SUVs, trucks, etc.
- Vehicle-usage patterns
- Economic indicators
- Changing demographic patterns
- Socioeconomic factors
- Seasonal and environmental changes
How Honda’s Patent Is Unique
Honda describes a parts-demand prediction system that adapts in real time to all of these. Some variability is expected, with fluctuating seasonal demand for certain parts, but others are harder to predict, such as evolving trends and consumers’ changing preferences. Honda wants to fine-tune demand by taking into account all the variables listed above to predict which parts will be in demand and when.
It is unique in that it’s not a static model, but continually corrects itself based on the latest data, with the ability to integrate economic indicators and vehicle-usage data for a more accurate approach to demand prediction. The system can compare the variables listed above with industry, production, and GDP indices, or economic indicators, to create a more accurate prediction model.
Advantages of such an intelligent system include:
- Lower inventory levels and streamlined stock management
- Timeous ordering of the correct parts
- Accurately anticipating changing demand
- Dynamically adjusting parts ordering based on real-time data
