Mitsubishi Electric's New AI Forecasts Demand for Appliance Repair Parts

Company's first AI for accurate forecasting will help to strengthen maintenance services.

Mitsubishi Electric Corporation (TOKYO: 6503) announced today that it has developed an artificial intelligence (AI) technology that accurately forecasts demand for repair parts. Leveraging the company's Maisart^®* AI, the technology is expected to help avoid over/under-supply of parts needed to service appliances and other equipment and thereby strengthen inventory management and parts availability as well as improve service quality.

Mitsubishi Electric's AI creates the State-of-the- ART in technology Maisart

Product Features
1)More accurate demand forecasting
By incorporating Maisart AI, demand forecasts for individual parts have been improved on average by 25.6% compared to the company's existing production-sales-inventory (PSI) planning and management solution, which is based on factors such as seasonally adjusted 12-month shipment-volume averages. Mitsubishi Electric's new method uses AI learning data on characteristic demand trends for each type of part, such as air filters and controller boards. To forecast demands, the technology clusters trend components, then matches clustered trends to specific repair parts, and finally adjusts the results for seasonal factors.

2)AI optimizes number of clusters
The AI optimizes the number of clusters and classifies characteristics into a maximum 20 patterns using the X-Means method and actual shipment volumes. Clustering normally is performed manually by an analyst, but the X-Means method automates the process with a machine-learning algorithm that classifies data by trends. The optimization process is a challenge since forecast accuracy varies depending on the number of clusters, so Mitsubishi Electric adopted the X-Means method, and incorporated existing know-how, to automate optimization.

3)Also assists wider decision making
The AI also provides information that field forecasters can use to make decisions about shipments of other parts. Conventionally, results produced by AI have been difficult to translate into decisions because evidence used to produce the AI results tended to lack transparency (black box). Mitsubishi Electric's new method, however, indicates the rationale behind its results, allowing forecasters to use the information with confidence.

www.mitsubishielectric.com

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